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Posted to user-zh@flink.apache.org by lxk <lx...@163.com> on 2022/06/10 02:28:40 UTC
Flink 使用interval join数据丢失疑问
flink 版本:1.14.4
目前在使用flink interval join进行数据关联，在测试的时候发现一个问题，就是使用interval join完之后数据会丢失，但是使用sql api，直接进行join，数据是正常的，没有丢失。
水印是直接使用kafka 自带的时间戳生成watermark
以下是代码 ---interval join
SingleOutputStreamOperator<HeaderFull> headerFullStream = headerFilterStream.keyBy(data -> data.getId())
.intervalJoin(filterItemStream.keyBy(data -> data.getOrder_id()))
.between(Time.seconds(-10), Time.seconds(20))
.process(new ProcessJoinFunction<OrderHeader, OrderItem, HeaderFull>() {
@Override
public void processElement(OrderHeader left, OrderItem right, Context context, Collector<HeaderFull> collector) throws Exception {
HeaderFull headerFull = new HeaderFull();
BeanUtilsBean beanUtilsBean = new BeanUtilsBean();
beanUtilsBean.copyProperties(headerFull, left);
beanUtilsBean.copyProperties(headerFull, right);
String event_date = left.getOrder_at().substring(0, 10);
headerFull.setEvent_date(event_date);
headerFull.setItem_id(right.getId());
collector.collect(headerFull);
}
}
使用sql 进行join
Configuration conf = new Configuration();
conf.setString("table.exec.mini-batch.enabled","true");
conf.setString("table.exec.mini-batch.allow-latency","15 s");
conf.setString("table.exec.mini-batch.size","100");
conf.setString("table.exec.state.ttl","20 s");
env.configure(conf);
Table headerTable = streamTableEnvironment.fromDataStream(headerFilterStream);
Table itemTable = streamTableEnvironment.fromDataStream(filterItemStream);
streamTableEnvironment.createTemporaryView("header",headerTable);
streamTableEnvironment.createTemporaryView("item",itemTable);
Table result = streamTableEnvironment.sqlQuery("select header.customer_id" +
",item.goods_id" +
",header.id" +
",header.order_status" +
",header.shop_id" +
",header.parent_order_id" +
",header.order_at" +
",header.pay_at" +
",header.channel_id" +
",header.root_order_id" +
",item.id" +
",item.row_num" +
",item.p_sp_sub_amt" +
",item.display_qty" +
",item.qty" +
",item.bom_type" +
" from header JOIN item on header.id = item.order_id");
DataStream<Row> rowDataStream = streamTableEnvironment.toChangelogStream(result);
不太理解为什么使用interval join会丢这么多数据，按照我的理解使用sql join，底层应该也是用的类似interval join，为啥两者最终关联上的结果差异这么大。
Re:Re: Re:Re: Re: Flink 使用interval join数据丢失疑问
Posted by lxk <lx...@163.com>.
我大致理解了，数据其实是在关联之前就丢掉了。之前了解的最多的是interval join，目前来看我这种场景其实使用inner join比较合适，这个水印确实感觉挺难很合理的去设置。
在 2022-06-15 12:06:56，"Zhiwen Sun" <pe...@gmail.com> 写道：
>我猜测是 watermark 的问题， 看楼主的设置， watermark 是 -2s ，也就是说， order header 流，有数据晚了 2s
>，就会被丢弃。
>
>楼主之前看的也是 订单明细比订单主表晚几秒， 这只是同一个订单的数据生成时间差异。 如果是这样的话，使用一般的 inner join + ttl
>就可以满足需求了。
>
>BTW： watermark 我觉得很难使用好，实际使用场景非常有限。
>
>
>
>Zhiwen Sun
>
>
>
>On Wed, Jun 15, 2022 at 11:43 AM Shengkai Fang <fs...@gmail.com> wrote:
>
>> > 我在sql inner join里设置的状态ttl为20s,为什么20s的状态保留数据要比interval join开分钟级别的数据还要准确
>>
>> 不合理的 watermark 设置在 interval join 就会导致丢数据。设置 ttl 情况下，如果某个 key
>> 的数据频繁访问情况下，那么这个数据就不会过期。
>>
>> > 我的做法是排查了tm和jm的日志，没有看见写的表的配置的日志。
>>
>> 我记得日志是会打印相关的日志。能提一些相关的日志吗？
>>
>> best,
>> Shengkai
>>
>> lxk <lx...@163.com> 于2022年6月14日周二 20:04写道：
>>
>> > Hi,
>> > 我目前使用sql interval join,窗口的上下界增加到分钟级别，分别是-2 minute 和 +4 minute
>> > 目前来看数据量和使用inner join要差不多了。以下是代码
>> > Table headerTable =
>> > streamTableEnvironment.fromDataStream(headerFilterStream,
>> > Schema.newBuilder()
>> > .columnByExpression("rowtime", "CAST(substring(last_updated_at,0,19) AS
>> > TIMESTAMP_LTZ(3))")
>> > .watermark("rowtime", "rowtime + INTERVAL '2' SECOND")
>> > .build());
>> > Table itemTable = streamTableEnvironment.fromDataStream(filterItemStream,
>> > Schema.newBuilder()
>> > .columnByExpression("rowtime", "CAST(substring(last_updated_at,0,19) AS
>> > TIMESTAMP_LTZ(3))")
>> > .watermark("rowtime", "rowtime + INTERVAL '2' SECOND")
>> > .build());
>> >
>> >
>> > streamTableEnvironment.createTemporaryView("header",headerTable);
>> > streamTableEnvironment.createTemporaryView("item",itemTable);
>> > Table result = streamTableEnvironment.sqlQuery("select
>> header.customer_id"
>> > +
>> > ",item.goods_id" +
>> > ",header.id" +
>> > ",header.order_status" +
>> > ",header.shop_id" +
>> > ",header.parent_order_id" +
>> > ",header.order_at" +
>> > ",header.pay_at" +
>> > ",header.channel_id" +
>> > ",header.root_order_id" +
>> > ",item.id" +
>> > ",item.row_num" +
>> > ",item.p_sp_sub_amt" +
>> > ",item.display_qty" +
>> > ",item.qty" +
>> > ",item.bom_type" +
>> > " from item JOIN header on header.id = item.order_id and item.rowtime
>> > BETWEEN header.rowtime - INTERVAL '2' MINUTE AND header.rowtime +
>> INTERVAL
>> > '4' MINUTE");
>> >
>> > 对此，我又有新的疑问了，我在sql inner join里设置的状态ttl为20s,为什么20s的状态保留数据要比interval
>> > join开分钟级别的数据还要准确？针对这个问题，不知道大家有什么看法和思路？
>> > 我的一个猜测是我设置的表的ttl没有生效，inner join一直使用的是全量的数据，所以结果准确度要比interval
>> > join高，我的做法是排查了tm和jm的日志，没有看见写的表的配置的日志，我的配置如下：
>> > Configuration conf = new Configuration();
>> > conf.setString("table.exec.mini-batch.enabled","true");
>> > conf.setString("table.exec.mini-batch.allow-latency","15 s");
>> > conf.setString("table.exec.mini-batch.size","100");
>> > conf.setString("table.exec.state.ttl","20 s");
>> > env.configure(conf);
>> > StreamTableEnvironment streamTableEnvironment =
>> > StreamTableEnvironment.create(env,
>> > EnvironmentSettings.fromConfiguration(conf));
>> >
>> >
>> > 我想了解下，从tm和jm日志是否能正确反应我的配置生效？如果不行，那我要使用什么方法才能知道我的这个配置是否生效？
>> >
>> >
>> >
>> >
>> >
>> >
>> >
>> >
>> >
>> >
>> >
>> >
>> >
>> >
>> >
>> >
>> >
>> >
>> >
>> >
>> >
>> > 在 2022-06-13 21:12:48，"Xuyang" <xy...@163.com> 写道：
>> > >Hi,
>> > > 1、理论上来说inner join关联的数据量应该比interval
>> > join更大吧。关于左右两边流速度不一致的情况，理论上应该问题不大，因为需要等到两边的watermark都到齐之后才会触发状态里过期数据的清除。
>> > > 2、inner
>> >
>> join没有水印的情况下，就是到了就发，完全根据这条数据进入这个算子的时间来算，也就是“处理时间”。默认数据是不会过期的，会存全量数据。如果定义了ttl，得看join两侧的表的pk和join
>> >
>> key，分别用不同的state数据格式来存数据（ListState、MapState等），原则上是多长时间没更新数据之后，就清空过期数据，是按照的“处理时间”来处理的。
>> > >
>> > >
>> > >如果我有不对的地方，请指正我哈。
>> > >
>> > >
>> > >
>> > >
>> > >--
>> > >
>> > > Best！
>> > > Xuyang
>> > >
>> > >
>> > >
>> > >
>> > >
>> > >在 2022-06-12 14:39:39，"lxk7491@163.com" <lx...@163.com> 写道：
>> > >>非常感谢回复
>> > >>1.针对watermark,我会再去进行测试。同时还会测试使用处理时间，interval join会不会丢失数据
>> > >>2.针对interval jon，我个人的理解是它能关联到的数据范围要比inner
>> >
>> join大，所以数据应该更准确，但是从结果上看却是数据丢失，当时非常震惊，有点颠覆我的认知了。同时我自己还有一个新的猜测，就是两个流的数据量不一样，可能也会造成数据丢失。目前左流是订单粒度数据，右流是订单-商品粒度数据，数据量要大很多。我个人理解，在处理右流的时候，应该会慢一点，所以导致两边的时间进展可能不一致。但是这又引发了一个新的疑问?inner
>> > join应该也会受这样的影响
>> > >>3.还有一个问题可能是我没有阐述清楚，我在sql里使用inner
>> > join，没有注册水印，那么两个流的join应该是以处理时间来定义的？那么表的state的过期是否也是以处理时间来定义？
>> > >>
>> > >>
>> > >>
>> > >>lxk7491@163.com
>> > >>
>> > >>发件人： Shengkai Fang
>> > >>发送时间： 2022-06-11 20:35
>> > >>收件人： user-zh
>> > >>主题： Re: Re: Flink 使用interval join数据丢失疑问
>> > >>hi,
>> > >>
>> > >>对于第一点，丢数据的情况有很多。首先，要确认是不是 JOIN 算子丢数据（SINK 的使用不当也会丢数据）。如果明确了是 join
>> > >>算子丢的数据，建议明确下丢的数据是咋样的，是不是 watermark 设置不合理，导致数据被误认为是晚到数据从而被丢了。例如，这里的是
>> > `event
>> > >>time` = `rowtime` - 2s，是不是不合适，我咋记得一般都是 +2 s 呢？
>> > >>
>> > >>对于第二点，interval join 我个人初步的理解是 state 的清理是根据两边的 event time，也就是说，如果右流的
>> event
>> > >>time 的更新会影响左流的数据清理。比如说右流的时间点到了 12：00，join 条件要求左流的时间不会晚于右流的时间 1h，那么左流
>> > >>11:00之前的数据都可以被清理了。
>> > >>
>> > >>对于第三点，我觉得是不能的。目前的 inner join + state 清理无法覆盖 event time 的window join 的。
>> > >>
>> > >>best,
>> > >>Shengkai
>> > >>
>> > >>lxk7491@163.com <lx...@163.com> 于2022年6月10日周五 23:03写道：
>> > >>
>> > >>> 对于这个问题，我还是有很大的疑问，再把我这个场景描述一下：
>> > >>>
>> > >>>
>> >
>> 目前是使用flink进行双流join,两个流的数据，一个流是订单主表，另一个流是订单明细表。我们探查了离线的数据，订单明细表一般会在订单主表生成后晚几秒内生成，这个差异在秒级别。
>> > >>>
>> > 我们做了以下几轮测试，并对比了另一个实时落的表数据量。(这个表就是基准参照数据，只是简单落表，没做任何处理，两边的数据源一致，对比的口径一致。)
>> > >>> 1.使用datastream api,使用kafka自带的时间戳做水印，使用interval join。对比完结果，数据少。
>> > >>> 2.使用流转表，sql inner join,没有设置watermark。对比完结果数据正常。
>> > >>> 3.使用流转表，sql interval join,从数据中的事件时间提取水印，对比完结果数据，数据少。
>> > >>> 从结果上看，我不太明白为什么sql里inner join能保证数据准确，而interval
>> > >>> join不行？有什么好的方式或者思路能让我更好的去尝试了解这个问题产生的原因
>> > >>>
>> > >>>
>> >
>> 针对第二种方式，我的疑问是，sql里没有设置水印，那么表的state过期是以处理时间来计算吗？针对这种设置了表state过期时间的join，我能理解为这个inner
>> > >>> join其实是一个window join吗？
>> > >>>
>> > >>>
>> > >>>
>> > >>> lxk7491@163.com
>> > >>>
>> > >>> 发件人： lxk
>> > >>> 发送时间： 2022-06-10 18:18
>> > >>> 收件人： user-zh
>> > >>> 主题： Re:Re:Re:Re:Re:Re:Re: Flink 使用interval join数据丢失疑问
>> > >>>
>> > >>>
>> > >>>
>> > >>> 现在改成了sql interval join,代码和执行计划如下，其他配置没变，数据量还是少，使用inner join就没问题
>> > >>>
>> > >>>
>> > >>>
>> > >>>
>> > >>> Table headerTable =
>> > >>> streamTableEnvironment.fromDataStream(headerFilterStream,
>> > >>> Schema.newBuilder()
>> > >>> .columnByExpression("rowtime",
>> > >>> "CAST(substring(last_updated_at,0,19) AS TIMESTAMP_LTZ(3))")
>> > >>> .watermark("rowtime", "rowtime - INTERVAL '2'
>> SECOND")
>> > >>> .build());
>> > >>> Table itemTable =
>> > >>> streamTableEnvironment.fromDataStream(filterItemStream,
>> > Schema.newBuilder()
>> > >>> .columnByExpression("rowtime",
>> > >>> "CAST(substring(last_updated_at,0,19) AS TIMESTAMP_LTZ(3))")
>> > >>> .watermark("rowtime", "rowtime - INTERVAL '2'
>> SECOND")
>> > >>> .build());
>> > >>>
>> > >>>
>> > >>>
>> > >>>
>> > >>>
>> > streamTableEnvironment.createTemporaryView("header",headerTable);
>> > >>> streamTableEnvironment.createTemporaryView("item",itemTable);
>> > >>>
>> > >>>
>> > >>>
>> > >>>
>> > >>>
>> > >>>
>> > >>> Table result = streamTableEnvironment.sqlQuery("select
>> > >>> header.customer_id" +
>> > >>> ",item.goods_id" +
>> > >>> ",header.id" +
>> > >>> ",header.order_status" +
>> > >>> ",header.shop_id" +
>> > >>> ",header.parent_order_id" +
>> > >>> ",header.order_at" +
>> > >>> ",header.pay_at" +
>> > >>> ",header.channel_id" +
>> > >>> ",header.root_order_id" +
>> > >>> ",item.id" +
>> > >>> ",item.row_num" +
>> > >>> ",item.p_sp_sub_amt" +
>> > >>> ",item.display_qty" +
>> > >>> ",item.qty" +
>> > >>> ",item.bom_type" +
>> > >>> " from header JOIN item on header.id = item.order_id
>> > and
>> > >>> item.rowtime BETWEEN header.rowtime - INTERVAL '10' SECOND AND
>> > >>> header.rowtime + INTERVAL '20' SECOND");
>> > >>>
>> > >>>
>> > >>>
>> > >>>
>> > >>> String intervalJoin =
>> streamTableEnvironment.explainSql("select
>> > >>> header.customer_id" +
>> > >>> ",item.goods_id" +
>> > >>> ",header.id" +
>> > >>> ",header.order_status" +
>> > >>> ",header.shop_id" +
>> > >>> ",header.parent_order_id" +
>> > >>> ",header.order_at" +
>> > >>> ",header.pay_at" +
>> > >>> ",header.channel_id" +
>> > >>> ",header.root_order_id" +
>> > >>> ",item.id" +
>> > >>> ",item.row_num" +
>> > >>> ",item.p_sp_sub_amt" +
>> > >>> ",item.display_qty" +
>> > >>> ",item.qty" +
>> > >>> ",item.bom_type" +
>> > >>> " from header JOIN item on header.id = item.order_id
>> > and
>> > >>> item.rowtime BETWEEN header.rowtime - INTERVAL '10' SECOND AND
>> > >>> header.rowtime + INTERVAL '20' SECOND");
>> > >>>
>> > >>>
>> > >>> System.out.println(intervalJoin);
>> > >>>
>> > >>>
>> > >>> DataStream<Row> rowDataStream =
>> > >>> streamTableEnvironment.toChangelogStream(result);
>> > >>>
>> > >>>
>> > >>>
>> > >>>
>> > >>>
>> > >>>
>> > >>> 执行计划:
>> > >>> == Abstract Syntax Tree ==
>> > >>> LogicalProject(customer_id=[$2], goods_id=[$16], id=[$0],
>> > >>> order_status=[$1], shop_id=[$3], parent_order_id=[$4], order_at=[$5],
>> > >>> pay_at=[$6], channel_id=[$7], root_order_id=[$8], id0=[$13],
>> > row_num=[$15],
>> > >>> p_sp_sub_amt=[$20], display_qty=[$23], qty=[$18], bom_type=[$21])
>> > >>> +- LogicalJoin(condition=[AND(=($0, $14), >=($25, -($12,
>> 10000:INTERVAL
>> > >>> SECOND)), <=($25, +($12, 20000:INTERVAL SECOND)))], joinType=[inner])
>> > >>> :- LogicalWatermarkAssigner(rowtime=[rowtime], watermark=[-($12,
>> > >>> 2000:INTERVAL SECOND)])
>> > >>> : +- LogicalProject(id=[$0], order_status=[$1], customer_id=[$2],
>> > >>> shop_id=[$3], parent_order_id=[$4], order_at=[$5], pay_at=[$6],
>> > >>> channel_id=[$7], root_order_id=[$8], last_updated_at=[$9],
>> > >>> business_flag=[$10], mysql_op_type=[$11], rowtime=[CAST(SUBSTRING($9,
>> > 0,
>> > >>> 19)):TIMESTAMP_WITH_LOCAL_TIME_ZONE(3)])
>> > >>> : +- LogicalTableScan(table=[[default_catalog,
>> default_database,
>> > >>> Unregistered_DataStream_Source_5]])
>> > >>> +- LogicalWatermarkAssigner(rowtime=[rowtime], watermark=[-($12,
>> > >>> 2000:INTERVAL SECOND)])
>> > >>> +- LogicalProject(id=[$0], order_id=[$1], row_num=[$2],
>> > >>> goods_id=[$3], s_sku_code=[$4], qty=[$5], p_paid_sub_amt=[$6],
>> > >>> p_sp_sub_amt=[$7], bom_type=[$8], last_updated_at=[$9],
>> > display_qty=[$10],
>> > >>> is_first_flag=[$11], rowtime=[CAST(SUBSTRING($9, 0,
>> > >>> 19)):TIMESTAMP_WITH_LOCAL_TIME_ZONE(3)])
>> > >>> +- LogicalTableScan(table=[[default_catalog,
>> default_database,
>> > >>> Unregistered_DataStream_Source_8]])
>> > >>>
>> > >>>
>> > >>> == Optimized Physical Plan ==
>> > >>> Calc(select=[customer_id, goods_id, id, order_status, shop_id,
>> > >>> parent_order_id, order_at, pay_at, channel_id, root_order_id, id0,
>> > row_num,
>> > >>> p_sp_sub_amt, display_qty, qty, bom_type])
>> > >>> +- IntervalJoin(joinType=[InnerJoin], windowBounds=[isRowTime=true,
>> > >>> leftLowerBound=-20000, leftUpperBound=10000, leftTimeIndex=9,
>> > >>> rightTimeIndex=8], where=[AND(=(id, order_id), >=(rowtime0,
>> -(rowtime,
>> > >>> 10000:INTERVAL SECOND)), <=(rowtime0, +(rowtime, 20000:INTERVAL
>> > SECOND)))],
>> > >>> select=[id, order_status, customer_id, shop_id, parent_order_id,
>> > order_at,
>> > >>> pay_at, channel_id, root_order_id, rowtime, id0, order_id, row_num,
>> > >>> goods_id, qty, p_sp_sub_amt, bom_type, display_qty, rowtime0])
>> > >>> :- Exchange(distribution=[hash[id]])
>> > >>> : +- WatermarkAssigner(rowtime=[rowtime], watermark=[-(rowtime,
>> > >>> 2000:INTERVAL SECOND)])
>> > >>> : +- Calc(select=[id, order_status, customer_id, shop_id,
>> > >>> parent_order_id, order_at, pay_at, channel_id, root_order_id,
>> > >>> CAST(SUBSTRING(last_updated_at, 0, 19)) AS rowtime])
>> > >>> : +- TableSourceScan(table=[[default_catalog,
>> > default_database,
>> > >>> Unregistered_DataStream_Source_5]], fields=[id, order_status,
>> > customer_id,
>> > >>> shop_id, parent_order_id, order_at, pay_at, channel_id,
>> root_order_id,
>> > >>> last_updated_at, business_flag, mysql_op_type])
>> > >>> +- Exchange(distribution=[hash[order_id]])
>> > >>> +- WatermarkAssigner(rowtime=[rowtime], watermark=[-(rowtime,
>> > >>> 2000:INTERVAL SECOND)])
>> > >>> +- Calc(select=[id, order_id, row_num, goods_id, qty,
>> > >>> p_sp_sub_amt, bom_type, display_qty, CAST(SUBSTRING(last_updated_at,
>> 0,
>> > >>> 19)) AS rowtime])
>> > >>> +- TableSourceScan(table=[[default_catalog,
>> > default_database,
>> > >>> Unregistered_DataStream_Source_8]], fields=[id, order_id, row_num,
>> > >>> goods_id, s_sku_code, qty, p_paid_sub_amt, p_sp_sub_amt, bom_type,
>> > >>> last_updated_at, display_qty, is_first_flag])
>> > >>>
>> > >>>
>> > >>> == Optimized Execution Plan ==
>> > >>> Calc(select=[customer_id, goods_id, id, order_status, shop_id,
>> > >>> parent_order_id, order_at, pay_at, channel_id, root_order_id, id0,
>> > row_num,
>> > >>> p_sp_sub_amt, display_qty, qty, bom_type])
>> > >>> +- IntervalJoin(joinType=[InnerJoin], windowBounds=[isRowTime=true,
>> > >>> leftLowerBound=-20000, leftUpperBound=10000, leftTimeIndex=9,
>> > >>> rightTimeIndex=8], where=[((id = order_id) AND (rowtime0 >= (rowtime
>> -
>> > >>> 10000:INTERVAL SECOND)) AND (rowtime0 <= (rowtime + 20000:INTERVAL
>> > >>> SECOND)))], select=[id, order_status, customer_id, shop_id,
>> > >>> parent_order_id, order_at, pay_at, channel_id, root_order_id,
>> rowtime,
>> > id0,
>> > >>> order_id, row_num, goods_id, qty, p_sp_sub_amt, bom_type,
>> display_qty,
>> > >>> rowtime0])
>> > >>> :- Exchange(distribution=[hash[id]])
>> > >>> : +- WatermarkAssigner(rowtime=[rowtime], watermark=[(rowtime -
>> > >>> 2000:INTERVAL SECOND)])
>> > >>> : +- Calc(select=[id, order_status, customer_id, shop_id,
>> > >>> parent_order_id, order_at, pay_at, channel_id, root_order_id,
>> > >>> CAST(SUBSTRING(last_updated_at, 0, 19)) AS rowtime])
>> > >>> : +- TableSourceScan(table=[[default_catalog,
>> > default_database,
>> > >>> Unregistered_DataStream_Source_5]], fields=[id, order_status,
>> > customer_id,
>> > >>> shop_id, parent_order_id, order_at, pay_at, channel_id,
>> root_order_id,
>> > >>> last_updated_at, business_flag, mysql_op_type])
>> > >>> +- Exchange(distribution=[hash[order_id]])
>> > >>> +- WatermarkAssigner(rowtime=[rowtime], watermark=[(rowtime -
>> > >>> 2000:INTERVAL SECOND)])
>> > >>> +- Calc(select=[id, order_id, row_num, goods_id, qty,
>> > >>> p_sp_sub_amt, bom_type, display_qty, CAST(SUBSTRING(last_updated_at,
>> 0,
>> > >>> 19)) AS rowtime])
>> > >>> +- TableSourceScan(table=[[default_catalog,
>> > default_database,
>> > >>> Unregistered_DataStream_Source_8]], fields=[id, order_id, row_num,
>> > >>> goods_id, s_sku_code, qty, p_paid_sub_amt, p_sp_sub_amt, bom_type,
>> > >>> last_updated_at, display_qty, is_first_flag])
>> > >>>
>> > >>>
>> > >>>
>> > >>>
>> > >>>
>> > >>>
>> > >>>
>> > >>>
>> > >>>
>> > >>>
>> > >>>
>> > >>>
>> > >>>
>> > >>>
>> > >>>
>> > >>>
>> > >>> 在 2022-06-10 17:16:31，"lxk" <lx...@163.com> 写道：
>> > >>> >使用sql 进行interval
>> > >>> join，我目前的问题是感觉时间转换这块不太友好，我目前流里面的事件时间字段是string类型，数据样式是2022-06-10
>> > >>> 13:08:55,但是我使用TO_TIMESTAMP这个函数进行转换一直报错
>> > >>> >
>> > >>> >
>> > >>> >
>> > >>> >
>> > >>> >
>> > >>> >
>> > >>> >
>> > >>> >
>> > >>> >
>> > >>> >
>> > >>> >
>> > >>> >
>> > >>> >
>> > >>> >
>> > >>> >
>> > >>> >
>> > >>> >
>> > >>> >在 2022-06-10 15:04:31，"Xuyang" <xy...@163.com> 写道：
>> > >>> >>Hi, datastream的这个interval join的api应该对标的是sql中的interval
>> > >>> join。但是你目前写的这个sql，是普通join。普通join和interval
>> > >>> join在业务含义和实现上都是有区别的。所以你直接拿datastream api的interval
>> > >>> join和sql上的普通join结果对比，其实是有问题的。所以我之前的建议是让你试下让sql也使用interval
>> > join，这样双方才有可比性。
>> > >>> >>
>> > >>> >>
>> > >>>
>> > >>>
>> >
>> >>另外sql中设置的table.exec.state.ttl这个参数，只是代表的state会20s清空过期数据，但我看你要比较的时间窗口是-10s和20s，貌似也不大一样。
>> > >>> >>
>> > >>> >>
>> > >>> >>
>> > >>> >>
>> > >>> >>--
>> > >>> >>
>> > >>> >> Best！
>> > >>> >> Xuyang
>> > >>> >>
>> > >>> >>
>> > >>> >>
>> > >>> >>
>> > >>> >>
>> > >>> >>在 2022-06-10 14:33:37，"lxk" <lx...@163.com> 写道：
>> > >>> >>>
>> > >>> >>>
>> > >>> >>>
>> > >>> >>>我不理解的点在于，我interval join开的时间窗口比我sql中设置的状态时间都要长，窗口的上下界别是-10s 和
>> > >>> 20s，为什么会丢数据？
>> > >>> >>>
>> > >>> >>>sql中我设置这个table.exec.state.ttl参数
>> > >>> 为20s，照理来说两个流应该也是保留20s的数据在状态中进行join。不知道我的理解是否有问题，希望能够得到解答。
>> > >>> >>>
>> > >>> >>>
>> > >>> >>>
>> > >>> >>>
>> > >>> >>>
>> > >>> >>>
>> > >>> >>>
>> > >>> >>>
>> > >>> >>>
>> > >>> >>>
>> > >>> >>>
>> > >>> >>>
>> > >>> >>>
>> > >>> >>>
>> > >>> >>>在 2022-06-10 14:15:29，"Xuyang" <xy...@163.com> 写道：
>> > >>> >>>>Hi, 你的这条SQL 并不是interval join，是普通join。
>> > >>> >>>>interval join的使用文档可以参考文档[1]。可以试下使用SQL interval
>> > >>> join会不会丢数据（注意设置state的ttl），从而判断是数据的问题还是datastream api的问题。
>> > >>> >>>>
>> > >>> >>>>
>> > >>> >>>>
>> > >>> >>>>
>> > >>> >>>>[1]
>> > >>>
>> >
>> https://nightlies.apache.org/flink/flink-docs-master/docs/dev/table/sql/queries/joins/#interval-joins
>> > >>> >>>>
>> > >>> >>>>
>> > >>> >>>>
>> > >>> >>>>
>> > >>> >>>>
>> > >>> >>>>
>> > >>> >>>>
>> > >>> >>>>--
>> > >>> >>>>
>> > >>> >>>> Best！
>> > >>> >>>> Xuyang
>> > >>> >>>>
>> > >>> >>>>
>> > >>> >>>>
>> > >>> >>>>
>> > >>> >>>>
>> > >>> >>>>在 2022-06-10 11:26:33，"lxk" <lx...@163.com> 写道：
>> > >>> >>>>>我用的是以下代码:
>> > >>> >>>>>String s = streamTableEnvironment.explainSql("select
>> > >>> header.customer_id" +
>> > >>> >>>>>",item.goods_id" +
>> > >>> >>>>>",header.id" +
>> > >>> >>>>>",header.order_status" +
>> > >>> >>>>>",header.shop_id" +
>> > >>> >>>>>",header.parent_order_id" +
>> > >>> >>>>>",header.order_at" +
>> > >>> >>>>>",header.pay_at" +
>> > >>> >>>>>",header.channel_id" +
>> > >>> >>>>>",header.root_order_id" +
>> > >>> >>>>>",item.id" +
>> > >>> >>>>>",item.row_num" +
>> > >>> >>>>>",item.p_sp_sub_amt" +
>> > >>> >>>>>",item.display_qty" +
>> > >>> >>>>>",item.qty" +
>> > >>> >>>>>",item.bom_type" +
>> > >>> >>>>>" from header JOIN item on header.id = item.order_id");
>> > >>> >>>>>
>> > >>> >>>>>System.out.println("explain:" + s);
>> > >>> >>>>>
>> > >>> >>>>>
>> > >>> >>>>>
>> > >>> >>>>>
>> > >>> >>>>>plan信息为:
>> > >>> >>>>>explain:== Abstract Syntax Tree ==
>> > >>> >>>>>LogicalProject(customer_id=[$2], goods_id=[$15], id=[$0],
>> > >>> order_status=[$1], shop_id=[$3], parent_order_id=[$4], order_at=[$5],
>> > >>> pay_at=[$6], channel_id=[$7], root_order_id=[$8], id0=[$12],
>> > row_num=[$14],
>> > >>> p_sp_sub_amt=[$19], display_qty=[$22], qty=[$17], bom_type=[$20])
>> > >>> >>>>>+- LogicalJoin(condition=[=($0, $13)], joinType=[inner])
>> > >>> >>>>> :- LogicalTableScan(table=[[default_catalog,
>> default_database,
>> > >>> Unregistered_DataStream_Source_5]])
>> > >>> >>>>> +- LogicalTableScan(table=[[default_catalog,
>> default_database,
>> > >>> Unregistered_DataStream_Source_8]])
>> > >>> >>>>>
>> > >>> >>>>>
>> > >>> >>>>>== Optimized Physical Plan ==
>> > >>> >>>>>Calc(select=[customer_id, goods_id, id, order_status, shop_id,
>> > >>> parent_order_id, order_at, pay_at, channel_id, root_order_id, id0,
>> > row_num,
>> > >>> p_sp_sub_amt, display_qty, qty, bom_type])
>> > >>> >>>>>+- Join(joinType=[InnerJoin], where=[=(id, order_id)],
>> select=[id,
>> > >>> order_status, customer_id, shop_id, parent_order_id, order_at,
>> pay_at,
>> > >>> channel_id, root_order_id, id0, order_id, row_num, goods_id, qty,
>> > >>> p_sp_sub_amt, bom_type, display_qty], leftInputSpec=[NoUniqueKey],
>> > >>> rightInputSpec=[NoUniqueKey])
>> > >>> >>>>> :- Exchange(distribution=[hash[id]])
>> > >>> >>>>> : +- Calc(select=[id, order_status, customer_id, shop_id,
>> > >>> parent_order_id, order_at, pay_at, channel_id, root_order_id])
>> > >>> >>>>> : +- TableSourceScan(table=[[default_catalog,
>> > >>> default_database, Unregistered_DataStream_Source_5]], fields=[id,
>> > >>> order_status, customer_id, shop_id, parent_order_id, order_at,
>> pay_at,
>> > >>> channel_id, root_order_id, last_updated_at, business_flag,
>> > mysql_op_type])
>> > >>> >>>>> +- Exchange(distribution=[hash[order_id]])
>> > >>> >>>>> +- Calc(select=[id, order_id, row_num, goods_id, qty,
>> > >>> p_sp_sub_amt, bom_type, display_qty])
>> > >>> >>>>> +- TableSourceScan(table=[[default_catalog,
>> > >>> default_database, Unregistered_DataStream_Source_8]], fields=[id,
>> > order_id,
>> > >>> row_num, goods_id, s_sku_code, qty, p_paid_sub_amt, p_sp_sub_amt,
>> > bom_type,
>> > >>> last_updated_at, display_qty, is_first_flag])
>> > >>> >>>>>
>> > >>> >>>>>
>> > >>> >>>>>== Optimized Execution Plan ==
>> > >>> >>>>>Calc(select=[customer_id, goods_id, id, order_status, shop_id,
>> > >>> parent_order_id, order_at, pay_at, channel_id, root_order_id, id0,
>> > row_num,
>> > >>> p_sp_sub_amt, display_qty, qty, bom_type])
>> > >>> >>>>>+- Join(joinType=[InnerJoin], where=[(id = order_id)],
>> select=[id,
>> > >>> order_status, customer_id, shop_id, parent_order_id, order_at,
>> pay_at,
>> > >>> channel_id, root_order_id, id0, order_id, row_num, goods_id, qty,
>> > >>> p_sp_sub_amt, bom_type, display_qty], leftInputSpec=[NoUniqueKey],
>> > >>> rightInputSpec=[NoUniqueKey])
>> > >>> >>>>> :- Exchange(distribution=[hash[id]])
>> > >>> >>>>> : +- Calc(select=[id, order_status, customer_id, shop_id,
>> > >>> parent_order_id, order_at, pay_at, channel_id, root_order_id])
>> > >>> >>>>> : +- TableSourceScan(table=[[default_catalog,
>> > >>> default_database, Unregistered_DataStream_Source_5]], fields=[id,
>> > >>> order_status, customer_id, shop_id, parent_order_id, order_at,
>> pay_at,
>> > >>> channel_id, root_order_id, last_updated_at, business_flag,
>> > mysql_op_type])
>> > >>> >>>>> +- Exchange(distribution=[hash[order_id]])
>> > >>> >>>>> +- Calc(select=[id, order_id, row_num, goods_id, qty,
>> > >>> p_sp_sub_amt, bom_type, display_qty])
>> > >>> >>>>> +- TableSourceScan(table=[[default_catalog,
>> > >>> default_database, Unregistered_DataStream_Source_8]], fields=[id,
>> > order_id,
>> > >>> row_num, goods_id, s_sku_code, qty, p_paid_sub_amt, p_sp_sub_amt,
>> > bom_type,
>> > >>> last_updated_at, display_qty, is_first_flag])
>> > >>> >>>>>
>> > >>> >>>>>
>> > >>> >>>>>
>> > >>> >>>>>
>> > >>> >>>>>
>> > >>> >>>>>
>> > >>> >>>>>
>> > >>> >>>>>
>> > >>> >>>>>
>> > >>> >>>>>
>> > >>> >>>>>
>> > >>> >>>>>
>> > >>> >>>>>
>> > >>> >>>>>在 2022-06-10 11:02:56，"Shengkai Fang" <fs...@gmail.com> 写道：
>> > >>> >>>>>>你好，能提供下具体的 plan 供大家查看下吗？
>> > >>> >>>>>>
>> > >>> >>>>>>你可以直接 使用 tEnv.executeSql("Explain JSON_EXECUTION_PLAN
>> > >>> >>>>>><YOUR_QUERY>").print() 打印下相关的信息。
>> > >>> >>>>>>
>> > >>> >>>>>>Best,
>> > >>> >>>>>>Shengkai
>> > >>> >>>>>>
>> > >>> >>>>>>lxk <lx...@163.com> 于2022年6月10日周五 10:29写道：
>> > >>> >>>>>>
>> > >>> >>>>>>> flink 版本:1.14.4
>> > >>> >>>>>>> 目前在使用flink interval join进行数据关联，在测试的时候发现一个问题，就是使用interval
>> > >>> >>>>>>> join完之后数据会丢失，但是使用sql api，直接进行join，数据是正常的，没有丢失。
>> > >>> >>>>>>> 水印是直接使用kafka 自带的时间戳生成watermark
>> > >>> >>>>>>>
>> > >>> >>>>>>>
>> > >>> >>>>>>> 以下是代码 ---interval join
>> > >>> >>>>>>>
>> > >>> >>>>>>> SingleOutputStreamOperator<HeaderFull> headerFullStream =
>> > >>> >>>>>>> headerFilterStream.keyBy(data -> data.getId())
>> > >>> >>>>>>> .intervalJoin(filterItemStream.keyBy(data ->
>> > data.getOrder_id()))
>> > >>> >>>>>>> .between(Time.seconds(-10), Time.seconds(20))
>> > >>> >>>>>>> .process(new ProcessJoinFunction<OrderHeader, OrderItem,
>> > >>> HeaderFull>() {
>> > >>> >>>>>>> @Override
>> > >>> >>>>>>> public void processElement(OrderHeader left, OrderItem right,
>> > >>> Context
>> > >>> >>>>>>> context, Collector<HeaderFull> collector) throws Exception {
>> > >>> >>>>>>> HeaderFull headerFull = new HeaderFull();
>> > >>> >>>>>>> BeanUtilsBean beanUtilsBean = new BeanUtilsBean();
>> > >>> >>>>>>> beanUtilsBean.copyProperties(headerFull, left);
>> > >>> >>>>>>> beanUtilsBean.copyProperties(headerFull, right);
>> > >>> >>>>>>> String event_date = left.getOrder_at().substring(0, 10);
>> > >>> >>>>>>> headerFull.setEvent_date(event_date);
>> > >>> >>>>>>> headerFull.setItem_id(right.getId());
>> > >>> >>>>>>> collector.collect(headerFull);
>> > >>> >>>>>>> }
>> > >>> >>>>>>> }
>> > >>> >>>>>>> 使用sql 进行join
>> > >>> >>>>>>> Configuration conf = new Configuration();
>> > >>> >>>>>>> conf.setString("table.exec.mini-batch.enabled","true");
>> > >>> >>>>>>> conf.setString("table.exec.mini-batch.allow-latency","15 s");
>> > >>> >>>>>>> conf.setString("table.exec.mini-batch.size","100");
>> > >>> >>>>>>> conf.setString("table.exec.state.ttl","20 s");
>> > >>> >>>>>>> env.configure(conf);
>> > >>> >>>>>>> Table headerTable =
>> > >>> >>>>>>> streamTableEnvironment.fromDataStream(headerFilterStream);
>> > >>> >>>>>>> Table itemTable =
>> > >>> streamTableEnvironment.fromDataStream(filterItemStream);
>> > >>> >>>>>>>
>> > >>> >>>>>>>
>> > >>> >>>>>>>
>> > streamTableEnvironment.createTemporaryView("header",headerTable);
>> > >>> >>>>>>> streamTableEnvironment.createTemporaryView("item",itemTable);
>> > >>> >>>>>>>
>> > >>> >>>>>>> Table result = streamTableEnvironment.sqlQuery("select
>> > >>> header.customer_id"
>> > >>> >>>>>>> +
>> > >>> >>>>>>> ",item.goods_id" +
>> > >>> >>>>>>> ",header.id" +
>> > >>> >>>>>>> ",header.order_status" +
>> > >>> >>>>>>> ",header.shop_id" +
>> > >>> >>>>>>> ",header.parent_order_id" +
>> > >>> >>>>>>> ",header.order_at" +
>> > >>> >>>>>>> ",header.pay_at" +
>> > >>> >>>>>>> ",header.channel_id" +
>> > >>> >>>>>>> ",header.root_order_id" +
>> > >>> >>>>>>> ",item.id" +
>> > >>> >>>>>>> ",item.row_num" +
>> > >>> >>>>>>> ",item.p_sp_sub_amt" +
>> > >>> >>>>>>> ",item.display_qty" +
>> > >>> >>>>>>> ",item.qty" +
>> > >>> >>>>>>> ",item.bom_type" +
>> > >>> >>>>>>> " from header JOIN item on header.id = item.order_id");
>> > >>> >>>>>>>
>> > >>> >>>>>>>
>> > >>> >>>>>>> DataStream<Row> rowDataStream =
>> > >>> >>>>>>> streamTableEnvironment.toChangelogStream(result);
>> > >>> >>>>>>> 不太理解为什么使用interval join会丢这么多数据，按照我的理解使用sql
>> > join，底层应该也是用的类似interval
>> > >>> >>>>>>> join，为啥两者最终关联上的结果差异这么大。
>> > >>> >>>>>>>
>> > >>> >>>>>>>
>> > >>> >>>>>>>
>> > >>> >>>>>>>
>> > >>> >>>>>>>
>> > >>> >>>>>>>
>> > >>> >>>>>>>
>> > >>> >>>>>>>
>> > >>> >>>>>>>
>> > >>> >>>>>>>
>> > >>> >>>>>>>
>> > >>>
>> >
>>
Re: Re:Re: Re: Flink 使用interval join数据丢失疑问
Posted by Zhiwen Sun <pe...@gmail.com>.
我猜测是 watermark 的问题， 看楼主的设置， watermark 是 -2s ，也就是说， order header 流，有数据晚了 2s
，就会被丢弃。
楼主之前看的也是 订单明细比订单主表晚几秒， 这只是同一个订单的数据生成时间差异。 如果是这样的话，使用一般的 inner join + ttl
就可以满足需求了。
BTW： watermark 我觉得很难使用好，实际使用场景非常有限。
Zhiwen Sun
On Wed, Jun 15, 2022 at 11:43 AM Shengkai Fang <fs...@gmail.com> wrote:
> > 我在sql inner join里设置的状态ttl为20s,为什么20s的状态保留数据要比interval join开分钟级别的数据还要准确
>
> 不合理的 watermark 设置在 interval join 就会导致丢数据。设置 ttl 情况下，如果某个 key
> 的数据频繁访问情况下，那么这个数据就不会过期。
>
> > 我的做法是排查了tm和jm的日志，没有看见写的表的配置的日志。
>
> 我记得日志是会打印相关的日志。能提一些相关的日志吗？
>
> best,
> Shengkai
>
> lxk <lx...@163.com> 于2022年6月14日周二 20:04写道：
>
> > Hi,
> > 我目前使用sql interval join,窗口的上下界增加到分钟级别，分别是-2 minute 和 +4 minute
> > 目前来看数据量和使用inner join要差不多了。以下是代码
> > Table headerTable =
> > streamTableEnvironment.fromDataStream(headerFilterStream,
> > Schema.newBuilder()
> > .columnByExpression("rowtime", "CAST(substring(last_updated_at,0,19) AS
> > TIMESTAMP_LTZ(3))")
> > .watermark("rowtime", "rowtime + INTERVAL '2' SECOND")
> > .build());
> > Table itemTable = streamTableEnvironment.fromDataStream(filterItemStream,
> > Schema.newBuilder()
> > .columnByExpression("rowtime", "CAST(substring(last_updated_at,0,19) AS
> > TIMESTAMP_LTZ(3))")
> > .watermark("rowtime", "rowtime + INTERVAL '2' SECOND")
> > .build());
> >
> >
> > streamTableEnvironment.createTemporaryView("header",headerTable);
> > streamTableEnvironment.createTemporaryView("item",itemTable);
> > Table result = streamTableEnvironment.sqlQuery("select
> header.customer_id"
> > +
> > ",item.goods_id" +
> > ",header.id" +
> > ",header.order_status" +
> > ",header.shop_id" +
> > ",header.parent_order_id" +
> > ",header.order_at" +
> > ",header.pay_at" +
> > ",header.channel_id" +
> > ",header.root_order_id" +
> > ",item.id" +
> > ",item.row_num" +
> > ",item.p_sp_sub_amt" +
> > ",item.display_qty" +
> > ",item.qty" +
> > ",item.bom_type" +
> > " from item JOIN header on header.id = item.order_id and item.rowtime
> > BETWEEN header.rowtime - INTERVAL '2' MINUTE AND header.rowtime +
> INTERVAL
> > '4' MINUTE");
> >
> > 对此，我又有新的疑问了，我在sql inner join里设置的状态ttl为20s,为什么20s的状态保留数据要比interval
> > join开分钟级别的数据还要准确？针对这个问题，不知道大家有什么看法和思路？
> > 我的一个猜测是我设置的表的ttl没有生效，inner join一直使用的是全量的数据，所以结果准确度要比interval
> > join高，我的做法是排查了tm和jm的日志，没有看见写的表的配置的日志，我的配置如下：
> > Configuration conf = new Configuration();
> > conf.setString("table.exec.mini-batch.enabled","true");
> > conf.setString("table.exec.mini-batch.allow-latency","15 s");
> > conf.setString("table.exec.mini-batch.size","100");
> > conf.setString("table.exec.state.ttl","20 s");
> > env.configure(conf);
> > StreamTableEnvironment streamTableEnvironment =
> > StreamTableEnvironment.create(env,
> > EnvironmentSettings.fromConfiguration(conf));
> >
> >
> > 我想了解下，从tm和jm日志是否能正确反应我的配置生效？如果不行，那我要使用什么方法才能知道我的这个配置是否生效？
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >
> > 在 2022-06-13 21:12:48，"Xuyang" <xy...@163.com> 写道：
> > >Hi,
> > > 1、理论上来说inner join关联的数据量应该比interval
> > join更大吧。关于左右两边流速度不一致的情况，理论上应该问题不大，因为需要等到两边的watermark都到齐之后才会触发状态里过期数据的清除。
> > > 2、inner
> >
> join没有水印的情况下，就是到了就发，完全根据这条数据进入这个算子的时间来算，也就是“处理时间”。默认数据是不会过期的，会存全量数据。如果定义了ttl，得看join两侧的表的pk和join
> >
> key，分别用不同的state数据格式来存数据（ListState、MapState等），原则上是多长时间没更新数据之后，就清空过期数据，是按照的“处理时间”来处理的。
> > >
> > >
> > >如果我有不对的地方，请指正我哈。
> > >
> > >
> > >
> > >
> > >--
> > >
> > > Best！
> > > Xuyang
> > >
> > >
> > >
> > >
> > >
> > >在 2022-06-12 14:39:39，"lxk7491@163.com" <lx...@163.com> 写道：
> > >>非常感谢回复
> > >>1.针对watermark,我会再去进行测试。同时还会测试使用处理时间，interval join会不会丢失数据
> > >>2.针对interval jon，我个人的理解是它能关联到的数据范围要比inner
> >
> join大，所以数据应该更准确，但是从结果上看却是数据丢失，当时非常震惊，有点颠覆我的认知了。同时我自己还有一个新的猜测，就是两个流的数据量不一样，可能也会造成数据丢失。目前左流是订单粒度数据，右流是订单-商品粒度数据，数据量要大很多。我个人理解，在处理右流的时候，应该会慢一点，所以导致两边的时间进展可能不一致。但是这又引发了一个新的疑问?inner
> > join应该也会受这样的影响
> > >>3.还有一个问题可能是我没有阐述清楚，我在sql里使用inner
> > join，没有注册水印，那么两个流的join应该是以处理时间来定义的？那么表的state的过期是否也是以处理时间来定义？
> > >>
> > >>
> > >>
> > >>lxk7491@163.com
> > >>
> > >>发件人： Shengkai Fang
> > >>发送时间： 2022-06-11 20:35
> > >>收件人： user-zh
> > >>主题： Re: Re: Flink 使用interval join数据丢失疑问
> > >>hi,
> > >>
> > >>对于第一点，丢数据的情况有很多。首先，要确认是不是 JOIN 算子丢数据（SINK 的使用不当也会丢数据）。如果明确了是 join
> > >>算子丢的数据，建议明确下丢的数据是咋样的，是不是 watermark 设置不合理，导致数据被误认为是晚到数据从而被丢了。例如，这里的是
> > `event
> > >>time` = `rowtime` - 2s，是不是不合适，我咋记得一般都是 +2 s 呢？
> > >>
> > >>对于第二点，interval join 我个人初步的理解是 state 的清理是根据两边的 event time，也就是说，如果右流的
> event
> > >>time 的更新会影响左流的数据清理。比如说右流的时间点到了 12：00，join 条件要求左流的时间不会晚于右流的时间 1h，那么左流
> > >>11:00之前的数据都可以被清理了。
> > >>
> > >>对于第三点，我觉得是不能的。目前的 inner join + state 清理无法覆盖 event time 的window join 的。
> > >>
> > >>best,
> > >>Shengkai
> > >>
> > >>lxk7491@163.com <lx...@163.com> 于2022年6月10日周五 23:03写道：
> > >>
> > >>> 对于这个问题，我还是有很大的疑问，再把我这个场景描述一下：
> > >>>
> > >>>
> >
> 目前是使用flink进行双流join,两个流的数据，一个流是订单主表，另一个流是订单明细表。我们探查了离线的数据，订单明细表一般会在订单主表生成后晚几秒内生成，这个差异在秒级别。
> > >>>
> > 我们做了以下几轮测试，并对比了另一个实时落的表数据量。(这个表就是基准参照数据，只是简单落表，没做任何处理，两边的数据源一致，对比的口径一致。)
> > >>> 1.使用datastream api,使用kafka自带的时间戳做水印，使用interval join。对比完结果，数据少。
> > >>> 2.使用流转表，sql inner join,没有设置watermark。对比完结果数据正常。
> > >>> 3.使用流转表，sql interval join,从数据中的事件时间提取水印，对比完结果数据，数据少。
> > >>> 从结果上看，我不太明白为什么sql里inner join能保证数据准确，而interval
> > >>> join不行？有什么好的方式或者思路能让我更好的去尝试了解这个问题产生的原因
> > >>>
> > >>>
> >
> 针对第二种方式，我的疑问是，sql里没有设置水印，那么表的state过期是以处理时间来计算吗？针对这种设置了表state过期时间的join，我能理解为这个inner
> > >>> join其实是一个window join吗？
> > >>>
> > >>>
> > >>>
> > >>> lxk7491@163.com
> > >>>
> > >>> 发件人： lxk
> > >>> 发送时间： 2022-06-10 18:18
> > >>> 收件人： user-zh
> > >>> 主题： Re:Re:Re:Re:Re:Re:Re: Flink 使用interval join数据丢失疑问
> > >>>
> > >>>
> > >>>
> > >>> 现在改成了sql interval join,代码和执行计划如下，其他配置没变，数据量还是少，使用inner join就没问题
> > >>>
> > >>>
> > >>>
> > >>>
> > >>> Table headerTable =
> > >>> streamTableEnvironment.fromDataStream(headerFilterStream,
> > >>> Schema.newBuilder()
> > >>> .columnByExpression("rowtime",
> > >>> "CAST(substring(last_updated_at,0,19) AS TIMESTAMP_LTZ(3))")
> > >>> .watermark("rowtime", "rowtime - INTERVAL '2'
> SECOND")
> > >>> .build());
> > >>> Table itemTable =
> > >>> streamTableEnvironment.fromDataStream(filterItemStream,
> > Schema.newBuilder()
> > >>> .columnByExpression("rowtime",
> > >>> "CAST(substring(last_updated_at,0,19) AS TIMESTAMP_LTZ(3))")
> > >>> .watermark("rowtime", "rowtime - INTERVAL '2'
> SECOND")
> > >>> .build());
> > >>>
> > >>>
> > >>>
> > >>>
> > >>>
> > streamTableEnvironment.createTemporaryView("header",headerTable);
> > >>> streamTableEnvironment.createTemporaryView("item",itemTable);
> > >>>
> > >>>
> > >>>
> > >>>
> > >>>
> > >>>
> > >>> Table result = streamTableEnvironment.sqlQuery("select
> > >>> header.customer_id" +
> > >>> ",item.goods_id" +
> > >>> ",header.id" +
> > >>> ",header.order_status" +
> > >>> ",header.shop_id" +
> > >>> ",header.parent_order_id" +
> > >>> ",header.order_at" +
> > >>> ",header.pay_at" +
> > >>> ",header.channel_id" +
> > >>> ",header.root_order_id" +
> > >>> ",item.id" +
> > >>> ",item.row_num" +
> > >>> ",item.p_sp_sub_amt" +
> > >>> ",item.display_qty" +
> > >>> ",item.qty" +
> > >>> ",item.bom_type" +
> > >>> " from header JOIN item on header.id = item.order_id
> > and
> > >>> item.rowtime BETWEEN header.rowtime - INTERVAL '10' SECOND AND
> > >>> header.rowtime + INTERVAL '20' SECOND");
> > >>>
> > >>>
> > >>>
> > >>>
> > >>> String intervalJoin =
> streamTableEnvironment.explainSql("select
> > >>> header.customer_id" +
> > >>> ",item.goods_id" +
> > >>> ",header.id" +
> > >>> ",header.order_status" +
> > >>> ",header.shop_id" +
> > >>> ",header.parent_order_id" +
> > >>> ",header.order_at" +
> > >>> ",header.pay_at" +
> > >>> ",header.channel_id" +
> > >>> ",header.root_order_id" +
> > >>> ",item.id" +
> > >>> ",item.row_num" +
> > >>> ",item.p_sp_sub_amt" +
> > >>> ",item.display_qty" +
> > >>> ",item.qty" +
> > >>> ",item.bom_type" +
> > >>> " from header JOIN item on header.id = item.order_id
> > and
> > >>> item.rowtime BETWEEN header.rowtime - INTERVAL '10' SECOND AND
> > >>> header.rowtime + INTERVAL '20' SECOND");
> > >>>
> > >>>
> > >>> System.out.println(intervalJoin);
> > >>>
> > >>>
> > >>> DataStream<Row> rowDataStream =
> > >>> streamTableEnvironment.toChangelogStream(result);
> > >>>
> > >>>
> > >>>
> > >>>
> > >>>
> > >>>
> > >>> 执行计划:
> > >>> == Abstract Syntax Tree ==
> > >>> LogicalProject(customer_id=[$2], goods_id=[$16], id=[$0],
> > >>> order_status=[$1], shop_id=[$3], parent_order_id=[$4], order_at=[$5],
> > >>> pay_at=[$6], channel_id=[$7], root_order_id=[$8], id0=[$13],
> > row_num=[$15],
> > >>> p_sp_sub_amt=[$20], display_qty=[$23], qty=[$18], bom_type=[$21])
> > >>> +- LogicalJoin(condition=[AND(=($0, $14), >=($25, -($12,
> 10000:INTERVAL
> > >>> SECOND)), <=($25, +($12, 20000:INTERVAL SECOND)))], joinType=[inner])
> > >>> :- LogicalWatermarkAssigner(rowtime=[rowtime], watermark=[-($12,
> > >>> 2000:INTERVAL SECOND)])
> > >>> : +- LogicalProject(id=[$0], order_status=[$1], customer_id=[$2],
> > >>> shop_id=[$3], parent_order_id=[$4], order_at=[$5], pay_at=[$6],
> > >>> channel_id=[$7], root_order_id=[$8], last_updated_at=[$9],
> > >>> business_flag=[$10], mysql_op_type=[$11], rowtime=[CAST(SUBSTRING($9,
> > 0,
> > >>> 19)):TIMESTAMP_WITH_LOCAL_TIME_ZONE(3)])
> > >>> : +- LogicalTableScan(table=[[default_catalog,
> default_database,
> > >>> Unregistered_DataStream_Source_5]])
> > >>> +- LogicalWatermarkAssigner(rowtime=[rowtime], watermark=[-($12,
> > >>> 2000:INTERVAL SECOND)])
> > >>> +- LogicalProject(id=[$0], order_id=[$1], row_num=[$2],
> > >>> goods_id=[$3], s_sku_code=[$4], qty=[$5], p_paid_sub_amt=[$6],
> > >>> p_sp_sub_amt=[$7], bom_type=[$8], last_updated_at=[$9],
> > display_qty=[$10],
> > >>> is_first_flag=[$11], rowtime=[CAST(SUBSTRING($9, 0,
> > >>> 19)):TIMESTAMP_WITH_LOCAL_TIME_ZONE(3)])
> > >>> +- LogicalTableScan(table=[[default_catalog,
> default_database,
> > >>> Unregistered_DataStream_Source_8]])
> > >>>
> > >>>
> > >>> == Optimized Physical Plan ==
> > >>> Calc(select=[customer_id, goods_id, id, order_status, shop_id,
> > >>> parent_order_id, order_at, pay_at, channel_id, root_order_id, id0,
> > row_num,
> > >>> p_sp_sub_amt, display_qty, qty, bom_type])
> > >>> +- IntervalJoin(joinType=[InnerJoin], windowBounds=[isRowTime=true,
> > >>> leftLowerBound=-20000, leftUpperBound=10000, leftTimeIndex=9,
> > >>> rightTimeIndex=8], where=[AND(=(id, order_id), >=(rowtime0,
> -(rowtime,
> > >>> 10000:INTERVAL SECOND)), <=(rowtime0, +(rowtime, 20000:INTERVAL
> > SECOND)))],
> > >>> select=[id, order_status, customer_id, shop_id, parent_order_id,
> > order_at,
> > >>> pay_at, channel_id, root_order_id, rowtime, id0, order_id, row_num,
> > >>> goods_id, qty, p_sp_sub_amt, bom_type, display_qty, rowtime0])
> > >>> :- Exchange(distribution=[hash[id]])
> > >>> : +- WatermarkAssigner(rowtime=[rowtime], watermark=[-(rowtime,
> > >>> 2000:INTERVAL SECOND)])
> > >>> : +- Calc(select=[id, order_status, customer_id, shop_id,
> > >>> parent_order_id, order_at, pay_at, channel_id, root_order_id,
> > >>> CAST(SUBSTRING(last_updated_at, 0, 19)) AS rowtime])
> > >>> : +- TableSourceScan(table=[[default_catalog,
> > default_database,
> > >>> Unregistered_DataStream_Source_5]], fields=[id, order_status,
> > customer_id,
> > >>> shop_id, parent_order_id, order_at, pay_at, channel_id,
> root_order_id,
> > >>> last_updated_at, business_flag, mysql_op_type])
> > >>> +- Exchange(distribution=[hash[order_id]])
> > >>> +- WatermarkAssigner(rowtime=[rowtime], watermark=[-(rowtime,
> > >>> 2000:INTERVAL SECOND)])
> > >>> +- Calc(select=[id, order_id, row_num, goods_id, qty,
> > >>> p_sp_sub_amt, bom_type, display_qty, CAST(SUBSTRING(last_updated_at,
> 0,
> > >>> 19)) AS rowtime])
> > >>> +- TableSourceScan(table=[[default_catalog,
> > default_database,
> > >>> Unregistered_DataStream_Source_8]], fields=[id, order_id, row_num,
> > >>> goods_id, s_sku_code, qty, p_paid_sub_amt, p_sp_sub_amt, bom_type,
> > >>> last_updated_at, display_qty, is_first_flag])
> > >>>
> > >>>
> > >>> == Optimized Execution Plan ==
> > >>> Calc(select=[customer_id, goods_id, id, order_status, shop_id,
> > >>> parent_order_id, order_at, pay_at, channel_id, root_order_id, id0,
> > row_num,
> > >>> p_sp_sub_amt, display_qty, qty, bom_type])
> > >>> +- IntervalJoin(joinType=[InnerJoin], windowBounds=[isRowTime=true,
> > >>> leftLowerBound=-20000, leftUpperBound=10000, leftTimeIndex=9,
> > >>> rightTimeIndex=8], where=[((id = order_id) AND (rowtime0 >= (rowtime
> -
> > >>> 10000:INTERVAL SECOND)) AND (rowtime0 <= (rowtime + 20000:INTERVAL
> > >>> SECOND)))], select=[id, order_status, customer_id, shop_id,
> > >>> parent_order_id, order_at, pay_at, channel_id, root_order_id,
> rowtime,
> > id0,
> > >>> order_id, row_num, goods_id, qty, p_sp_sub_amt, bom_type,
> display_qty,
> > >>> rowtime0])
> > >>> :- Exchange(distribution=[hash[id]])
> > >>> : +- WatermarkAssigner(rowtime=[rowtime], watermark=[(rowtime -
> > >>> 2000:INTERVAL SECOND)])
> > >>> : +- Calc(select=[id, order_status, customer_id, shop_id,
> > >>> parent_order_id, order_at, pay_at, channel_id, root_order_id,
> > >>> CAST(SUBSTRING(last_updated_at, 0, 19)) AS rowtime])
> > >>> : +- TableSourceScan(table=[[default_catalog,
> > default_database,
> > >>> Unregistered_DataStream_Source_5]], fields=[id, order_status,
> > customer_id,
> > >>> shop_id, parent_order_id, order_at, pay_at, channel_id,
> root_order_id,
> > >>> last_updated_at, business_flag, mysql_op_type])
> > >>> +- Exchange(distribution=[hash[order_id]])
> > >>> +- WatermarkAssigner(rowtime=[rowtime], watermark=[(rowtime -
> > >>> 2000:INTERVAL SECOND)])
> > >>> +- Calc(select=[id, order_id, row_num, goods_id, qty,
> > >>> p_sp_sub_amt, bom_type, display_qty, CAST(SUBSTRING(last_updated_at,
> 0,
> > >>> 19)) AS rowtime])
> > >>> +- TableSourceScan(table=[[default_catalog,
> > default_database,
> > >>> Unregistered_DataStream_Source_8]], fields=[id, order_id, row_num,
> > >>> goods_id, s_sku_code, qty, p_paid_sub_amt, p_sp_sub_amt, bom_type,
> > >>> last_updated_at, display_qty, is_first_flag])
> > >>>
> > >>>
> > >>>
> > >>>
> > >>>
> > >>>
> > >>>
> > >>>
> > >>>
> > >>>
> > >>>
> > >>>
> > >>>
> > >>>
> > >>>
> > >>>
> > >>> 在 2022-06-10 17:16:31，"lxk" <lx...@163.com> 写道：
> > >>> >使用sql 进行interval
> > >>> join，我目前的问题是感觉时间转换这块不太友好，我目前流里面的事件时间字段是string类型，数据样式是2022-06-10
> > >>> 13:08:55,但是我使用TO_TIMESTAMP这个函数进行转换一直报错
> > >>> >
> > >>> >
> > >>> >
> > >>> >
> > >>> >
> > >>> >
> > >>> >
> > >>> >
> > >>> >
> > >>> >
> > >>> >
> > >>> >
> > >>> >
> > >>> >
> > >>> >
> > >>> >
> > >>> >
> > >>> >在 2022-06-10 15:04:31，"Xuyang" <xy...@163.com> 写道：
> > >>> >>Hi, datastream的这个interval join的api应该对标的是sql中的interval
> > >>> join。但是你目前写的这个sql，是普通join。普通join和interval
> > >>> join在业务含义和实现上都是有区别的。所以你直接拿datastream api的interval
> > >>> join和sql上的普通join结果对比，其实是有问题的。所以我之前的建议是让你试下让sql也使用interval
> > join，这样双方才有可比性。
> > >>> >>
> > >>> >>
> > >>>
> > >>>
> >
> >>另外sql中设置的table.exec.state.ttl这个参数，只是代表的state会20s清空过期数据，但我看你要比较的时间窗口是-10s和20s，貌似也不大一样。
> > >>> >>
> > >>> >>
> > >>> >>
> > >>> >>
> > >>> >>--
> > >>> >>
> > >>> >> Best！
> > >>> >> Xuyang
> > >>> >>
> > >>> >>
> > >>> >>
> > >>> >>
> > >>> >>
> > >>> >>在 2022-06-10 14:33:37，"lxk" <lx...@163.com> 写道：
> > >>> >>>
> > >>> >>>
> > >>> >>>
> > >>> >>>我不理解的点在于，我interval join开的时间窗口比我sql中设置的状态时间都要长，窗口的上下界别是-10s 和
> > >>> 20s，为什么会丢数据？
> > >>> >>>
> > >>> >>>sql中我设置这个table.exec.state.ttl参数
> > >>> 为20s，照理来说两个流应该也是保留20s的数据在状态中进行join。不知道我的理解是否有问题，希望能够得到解答。
> > >>> >>>
> > >>> >>>
> > >>> >>>
> > >>> >>>
> > >>> >>>
> > >>> >>>
> > >>> >>>
> > >>> >>>
> > >>> >>>
> > >>> >>>
> > >>> >>>
> > >>> >>>
> > >>> >>>
> > >>> >>>
> > >>> >>>在 2022-06-10 14:15:29，"Xuyang" <xy...@163.com> 写道：
> > >>> >>>>Hi, 你的这条SQL 并不是interval join，是普通join。
> > >>> >>>>interval join的使用文档可以参考文档[1]。可以试下使用SQL interval
> > >>> join会不会丢数据（注意设置state的ttl），从而判断是数据的问题还是datastream api的问题。
> > >>> >>>>
> > >>> >>>>
> > >>> >>>>
> > >>> >>>>
> > >>> >>>>[1]
> > >>>
> >
> https://nightlies.apache.org/flink/flink-docs-master/docs/dev/table/sql/queries/joins/#interval-joins
> > >>> >>>>
> > >>> >>>>
> > >>> >>>>
> > >>> >>>>
> > >>> >>>>
> > >>> >>>>
> > >>> >>>>
> > >>> >>>>--
> > >>> >>>>
> > >>> >>>> Best！
> > >>> >>>> Xuyang
> > >>> >>>>
> > >>> >>>>
> > >>> >>>>
> > >>> >>>>
> > >>> >>>>
> > >>> >>>>在 2022-06-10 11:26:33，"lxk" <lx...@163.com> 写道：
> > >>> >>>>>我用的是以下代码:
> > >>> >>>>>String s = streamTableEnvironment.explainSql("select
> > >>> header.customer_id" +
> > >>> >>>>>",item.goods_id" +
> > >>> >>>>>",header.id" +
> > >>> >>>>>",header.order_status" +
> > >>> >>>>>",header.shop_id" +
> > >>> >>>>>",header.parent_order_id" +
> > >>> >>>>>",header.order_at" +
> > >>> >>>>>",header.pay_at" +
> > >>> >>>>>",header.channel_id" +
> > >>> >>>>>",header.root_order_id" +
> > >>> >>>>>",item.id" +
> > >>> >>>>>",item.row_num" +
> > >>> >>>>>",item.p_sp_sub_amt" +
> > >>> >>>>>",item.display_qty" +
> > >>> >>>>>",item.qty" +
> > >>> >>>>>",item.bom_type" +
> > >>> >>>>>" from header JOIN item on header.id = item.order_id");
> > >>> >>>>>
> > >>> >>>>>System.out.println("explain:" + s);
> > >>> >>>>>
> > >>> >>>>>
> > >>> >>>>>
> > >>> >>>>>
> > >>> >>>>>plan信息为:
> > >>> >>>>>explain:== Abstract Syntax Tree ==
> > >>> >>>>>LogicalProject(customer_id=[$2], goods_id=[$15], id=[$0],
> > >>> order_status=[$1], shop_id=[$3], parent_order_id=[$4], order_at=[$5],
> > >>> pay_at=[$6], channel_id=[$7], root_order_id=[$8], id0=[$12],
> > row_num=[$14],
> > >>> p_sp_sub_amt=[$19], display_qty=[$22], qty=[$17], bom_type=[$20])
> > >>> >>>>>+- LogicalJoin(condition=[=($0, $13)], joinType=[inner])
> > >>> >>>>> :- LogicalTableScan(table=[[default_catalog,
> default_database,
> > >>> Unregistered_DataStream_Source_5]])
> > >>> >>>>> +- LogicalTableScan(table=[[default_catalog,
> default_database,
> > >>> Unregistered_DataStream_Source_8]])
> > >>> >>>>>
> > >>> >>>>>
> > >>> >>>>>== Optimized Physical Plan ==
> > >>> >>>>>Calc(select=[customer_id, goods_id, id, order_status, shop_id,
> > >>> parent_order_id, order_at, pay_at, channel_id, root_order_id, id0,
> > row_num,
> > >>> p_sp_sub_amt, display_qty, qty, bom_type])
> > >>> >>>>>+- Join(joinType=[InnerJoin], where=[=(id, order_id)],
> select=[id,
> > >>> order_status, customer_id, shop_id, parent_order_id, order_at,
> pay_at,
> > >>> channel_id, root_order_id, id0, order_id, row_num, goods_id, qty,
> > >>> p_sp_sub_amt, bom_type, display_qty], leftInputSpec=[NoUniqueKey],
> > >>> rightInputSpec=[NoUniqueKey])
> > >>> >>>>> :- Exchange(distribution=[hash[id]])
> > >>> >>>>> : +- Calc(select=[id, order_status, customer_id, shop_id,
> > >>> parent_order_id, order_at, pay_at, channel_id, root_order_id])
> > >>> >>>>> : +- TableSourceScan(table=[[default_catalog,
> > >>> default_database, Unregistered_DataStream_Source_5]], fields=[id,
> > >>> order_status, customer_id, shop_id, parent_order_id, order_at,
> pay_at,
> > >>> channel_id, root_order_id, last_updated_at, business_flag,
> > mysql_op_type])
> > >>> >>>>> +- Exchange(distribution=[hash[order_id]])
> > >>> >>>>> +- Calc(select=[id, order_id, row_num, goods_id, qty,
> > >>> p_sp_sub_amt, bom_type, display_qty])
> > >>> >>>>> +- TableSourceScan(table=[[default_catalog,
> > >>> default_database, Unregistered_DataStream_Source_8]], fields=[id,
> > order_id,
> > >>> row_num, goods_id, s_sku_code, qty, p_paid_sub_amt, p_sp_sub_amt,
> > bom_type,
> > >>> last_updated_at, display_qty, is_first_flag])
> > >>> >>>>>
> > >>> >>>>>
> > >>> >>>>>== Optimized Execution Plan ==
> > >>> >>>>>Calc(select=[customer_id, goods_id, id, order_status, shop_id,
> > >>> parent_order_id, order_at, pay_at, channel_id, root_order_id, id0,
> > row_num,
> > >>> p_sp_sub_amt, display_qty, qty, bom_type])
> > >>> >>>>>+- Join(joinType=[InnerJoin], where=[(id = order_id)],
> select=[id,
> > >>> order_status, customer_id, shop_id, parent_order_id, order_at,
> pay_at,
> > >>> channel_id, root_order_id, id0, order_id, row_num, goods_id, qty,
> > >>> p_sp_sub_amt, bom_type, display_qty], leftInputSpec=[NoUniqueKey],
> > >>> rightInputSpec=[NoUniqueKey])
> > >>> >>>>> :- Exchange(distribution=[hash[id]])
> > >>> >>>>> : +- Calc(select=[id, order_status, customer_id, shop_id,
> > >>> parent_order_id, order_at, pay_at, channel_id, root_order_id])
> > >>> >>>>> : +- TableSourceScan(table=[[default_catalog,
> > >>> default_database, Unregistered_DataStream_Source_5]], fields=[id,
> > >>> order_status, customer_id, shop_id, parent_order_id, order_at,
> pay_at,
> > >>> channel_id, root_order_id, last_updated_at, business_flag,
> > mysql_op_type])
> > >>> >>>>> +- Exchange(distribution=[hash[order_id]])
> > >>> >>>>> +- Calc(select=[id, order_id, row_num, goods_id, qty,
> > >>> p_sp_sub_amt, bom_type, display_qty])
> > >>> >>>>> +- TableSourceScan(table=[[default_catalog,
> > >>> default_database, Unregistered_DataStream_Source_8]], fields=[id,
> > order_id,
> > >>> row_num, goods_id, s_sku_code, qty, p_paid_sub_amt, p_sp_sub_amt,
> > bom_type,
> > >>> last_updated_at, display_qty, is_first_flag])
> > >>> >>>>>
> > >>> >>>>>
> > >>> >>>>>
> > >>> >>>>>
> > >>> >>>>>
> > >>> >>>>>
> > >>> >>>>>
> > >>> >>>>>
> > >>> >>>>>
> > >>> >>>>>
> > >>> >>>>>
> > >>> >>>>>
> > >>> >>>>>
> > >>> >>>>>在 2022-06-10 11:02:56，"Shengkai Fang" <fs...@gmail.com> 写道：
> > >>> >>>>>>你好，能提供下具体的 plan 供大家查看下吗？
> > >>> >>>>>>
> > >>> >>>>>>你可以直接 使用 tEnv.executeSql("Explain JSON_EXECUTION_PLAN
> > >>> >>>>>><YOUR_QUERY>").print() 打印下相关的信息。
> > >>> >>>>>>
> > >>> >>>>>>Best,
> > >>> >>>>>>Shengkai
> > >>> >>>>>>
> > >>> >>>>>>lxk <lx...@163.com> 于2022年6月10日周五 10:29写道：
> > >>> >>>>>>
> > >>> >>>>>>> flink 版本:1.14.4
> > >>> >>>>>>> 目前在使用flink interval join进行数据关联，在测试的时候发现一个问题，就是使用interval
> > >>> >>>>>>> join完之后数据会丢失，但是使用sql api，直接进行join，数据是正常的，没有丢失。
> > >>> >>>>>>> 水印是直接使用kafka 自带的时间戳生成watermark
> > >>> >>>>>>>
> > >>> >>>>>>>
> > >>> >>>>>>> 以下是代码 ---interval join
> > >>> >>>>>>>
> > >>> >>>>>>> SingleOutputStreamOperator<HeaderFull> headerFullStream =
> > >>> >>>>>>> headerFilterStream.keyBy(data -> data.getId())
> > >>> >>>>>>> .intervalJoin(filterItemStream.keyBy(data ->
> > data.getOrder_id()))
> > >>> >>>>>>> .between(Time.seconds(-10), Time.seconds(20))
> > >>> >>>>>>> .process(new ProcessJoinFunction<OrderHeader, OrderItem,
> > >>> HeaderFull>() {
> > >>> >>>>>>> @Override
> > >>> >>>>>>> public void processElement(OrderHeader left, OrderItem right,
> > >>> Context
> > >>> >>>>>>> context, Collector<HeaderFull> collector) throws Exception {
> > >>> >>>>>>> HeaderFull headerFull = new HeaderFull();
> > >>> >>>>>>> BeanUtilsBean beanUtilsBean = new BeanUtilsBean();
> > >>> >>>>>>> beanUtilsBean.copyProperties(headerFull, left);
> > >>> >>>>>>> beanUtilsBean.copyProperties(headerFull, right);
> > >>> >>>>>>> String event_date = left.getOrder_at().substring(0, 10);
> > >>> >>>>>>> headerFull.setEvent_date(event_date);
> > >>> >>>>>>> headerFull.setItem_id(right.getId());
> > >>> >>>>>>> collector.collect(headerFull);
> > >>> >>>>>>> }
> > >>> >>>>>>> }
> > >>> >>>>>>> 使用sql 进行join
> > >>> >>>>>>> Configuration conf = new Configuration();
> > >>> >>>>>>> conf.setString("table.exec.mini-batch.enabled","true");
> > >>> >>>>>>> conf.setString("table.exec.mini-batch.allow-latency","15 s");
> > >>> >>>>>>> conf.setString("table.exec.mini-batch.size","100");
> > >>> >>>>>>> conf.setString("table.exec.state.ttl","20 s");
> > >>> >>>>>>> env.configure(conf);
> > >>> >>>>>>> Table headerTable =
> > >>> >>>>>>> streamTableEnvironment.fromDataStream(headerFilterStream);
> > >>> >>>>>>> Table itemTable =
> > >>> streamTableEnvironment.fromDataStream(filterItemStream);
> > >>> >>>>>>>
> > >>> >>>>>>>
> > >>> >>>>>>>
> > streamTableEnvironment.createTemporaryView("header",headerTable);
> > >>> >>>>>>> streamTableEnvironment.createTemporaryView("item",itemTable);
> > >>> >>>>>>>
> > >>> >>>>>>> Table result = streamTableEnvironment.sqlQuery("select
> > >>> header.customer_id"
> > >>> >>>>>>> +
> > >>> >>>>>>> ",item.goods_id" +
> > >>> >>>>>>> ",header.id" +
> > >>> >>>>>>> ",header.order_status" +
> > >>> >>>>>>> ",header.shop_id" +
> > >>> >>>>>>> ",header.parent_order_id" +
> > >>> >>>>>>> ",header.order_at" +
> > >>> >>>>>>> ",header.pay_at" +
> > >>> >>>>>>> ",header.channel_id" +
> > >>> >>>>>>> ",header.root_order_id" +
> > >>> >>>>>>> ",item.id" +
> > >>> >>>>>>> ",item.row_num" +
> > >>> >>>>>>> ",item.p_sp_sub_amt" +
> > >>> >>>>>>> ",item.display_qty" +
> > >>> >>>>>>> ",item.qty" +
> > >>> >>>>>>> ",item.bom_type" +
> > >>> >>>>>>> " from header JOIN item on header.id = item.order_id");
> > >>> >>>>>>>
> > >>> >>>>>>>
> > >>> >>>>>>> DataStream<Row> rowDataStream =
> > >>> >>>>>>> streamTableEnvironment.toChangelogStream(result);
> > >>> >>>>>>> 不太理解为什么使用interval join会丢这么多数据，按照我的理解使用sql
> > join，底层应该也是用的类似interval
> > >>> >>>>>>> join，为啥两者最终关联上的结果差异这么大。
> > >>> >>>>>>>
> > >>> >>>>>>>
> > >>> >>>>>>>
> > >>> >>>>>>>
> > >>> >>>>>>>
> > >>> >>>>>>>
> > >>> >>>>>>>
> > >>> >>>>>>>
> > >>> >>>>>>>
> > >>> >>>>>>>
> > >>> >>>>>>>
> > >>>
> >
>
Re:Re: Re: Re:Re: Re: Flink 使用interval join数据丢失疑问
Posted by lxk <lx...@163.com>.
我猜测使用api配置可能不会生效，因为目前程序一直报oom,我会换成conf文件配置再试一下。
java.lang.OutOfMemoryError: GC overhead limit exceeded
at java.util.Arrays.copyOfRange(Arrays.java:3664)
at java.lang.String.<init>(String.java:207)
at org.apache.flink.types.StringValue.readString(StringValue.java:795)
at org.apache.flink.api.common.typeutils.base.StringSerializer.deserialize(StringSerializer.java:73)
at org.apache.flink.api.common.typeutils.base.StringSerializer.deserialize(StringSerializer.java:31)
at org.apache.flink.streaming.runtime.streamrecord.StreamElementSerializer.deserialize(StreamElementSerializer.java:193)
at org.apache.flink.streaming.runtime.streamrecord.StreamElementSerializer.deserialize(StreamElementSerializer.java:46)
at org.apache.flink.runtime.plugable.NonReusingDeserializationDelegate.read(NonReusingDeserializationDelegate.java:53)
at org.apache.flink.runtime.io.network.api.serialization.NonSpanningWrapper.readInto(NonSpanningWrapper.java:337)
at org.apache.flink.runtime.io.network.api.serialization.SpillingAdaptiveSpanningRecordDeserializer.readNonSpanningRecord(SpillingAdaptiveSpanningRecordDeserializer.java:128)
at org.apache.flink.runtime.io.network.api.serialization.SpillingAdaptiveSpanningRecordDeserializer.readNextRecord(SpillingAdaptiveSpanningRecordDeserializer.java:103)
at org.apache.flink.runtime.io.network.api.serialization.SpillingAdaptiveSpanningRecordDeserializer.getNextRecord(SpillingAdaptiveSpanningRecordDeserializer.java:93)
at org.apache.flink.streaming.runtime.io.AbstractStreamTaskNetworkInput.emitNext(AbstractStreamTaskNetworkInput.java:95)
at org.apache.flink.streaming.runtime.io.StreamOneInputProcessor.processInput(StreamOneInputProcessor.java:65)
at org.apache.flink.streaming.runtime.tasks.StreamTask.processInput(StreamTask.java:496)
at org.apache.flink.streaming.runtime.tasks.StreamTask$$Lambda$615/715764777.runDefaultAction(Unknown Source)
at org.apache.flink.streaming.runtime.tasks.mailbox.MailboxProcessor.runMailboxLoop(MailboxProcessor.java:203)
at org.apache.flink.streaming.runtime.tasks.StreamTask.runMailboxLoop(StreamTask.java:809)
at org.apache.flink.streaming.runtime.tasks.StreamTask.invoke(StreamTask.java:761)
at org.apache.flink.runtime.taskmanager.Task$$Lambda$1435/1094361063.run(Unknown Source)
at org.apache.flink.runtime.taskmanager.Task.runWithSystemExitMonitoring(Task.java:958)
at org.apache.flink.runtime.taskmanager.Task.restoreAndInvoke(Task.java:937)
at org.apache.flink.runtime.taskmanager.Task.doRun(Task.java:766)
at org.apache.flink.runtime.taskmanager.Task.run(Task.java:575)
at java.lang.Thread.run(Thread.java:748)
第二点是我昨天使用侧输出流进行测试，发现数据在interval join之前是没有丢的。那主要原因还是watermark 设置不合理导致interval join丢数据。
针对这一点，我想具体了解什么是不合理的watermark?什么是合理的watarmark? 我测试过使用kafka 自带的时间戳为水印，进行interval join还是会丢数据，只有把窗口调整为足够大的时候，数据准确度才会高。这与我观察到的两条流的先后时间差异有点不太一样，两条流的时间差异在秒级内。
在 2022-06-16 09:49:50，"Shengkai Fang" <fs...@gmail.com> 写道：
>hi,
>
>我问了下 如果配置在 flink-conf 情况下，则是会在jm 中打印相关的参数。如果是 api 配置的话，目前 log 中是不会打印相关信息的。
>
>Best,
>Shengkai
>
>lxk <lx...@163.com> 于2022年6月15日周三 16:10写道：
>
>> 我大致理解为，watermark设置不合理，导致延迟的数据就丢失了，这块我会再去从测输出流去验证一下数据。
>> 频繁更新应该不太有可能，因为程序在流转表之前已经做了一道过滤，订单header只取了一个支付状态的数据，订单item也进行了去重处理。
>> 然后这是我的jm和tm日志，目前好像没看见表的ttl相关日志。
>>
>>
>>
>>
>>
>>
>>
>> 在 2022-06-15 11:42:19，"Shengkai Fang" <fs...@gmail.com> 写道：
>> >> 我在sql inner join里设置的状态ttl为20s,为什么20s的状态保留数据要比interval join开分钟级别的数据还要准确
>> >
>> >不合理的 watermark 设置在 interval join 就会导致丢数据。设置 ttl 情况下，如果某个 key
>> >的数据频繁访问情况下，那么这个数据就不会过期。
>> >
>> >> 我的做法是排查了tm和jm的日志，没有看见写的表的配置的日志。
>> >
>> >我记得日志是会打印相关的日志。能提一些相关的日志吗？
>> >
>> >best,
>> >Shengkai
>> >
>> >lxk <lx...@163.com> 于2022年6月14日周二 20:04写道：
>> >
>> >> Hi,
>> >> 我目前使用sql interval join,窗口的上下界增加到分钟级别，分别是-2 minute 和 +4 minute
>> >> 目前来看数据量和使用inner join要差不多了。以下是代码
>> >> Table headerTable =
>> >> streamTableEnvironment.fromDataStream(headerFilterStream,
>> >> Schema.newBuilder()
>> >> .columnByExpression("rowtime", "CAST(substring(last_updated_at,0,19) AS
>> >> TIMESTAMP_LTZ(3))")
>> >> .watermark("rowtime", "rowtime + INTERVAL '2' SECOND")
>> >> .build());
>> >> Table itemTable = streamTableEnvironment.fromDataStream(filterItemStream,
>> >> Schema.newBuilder()
>> >> .columnByExpression("rowtime", "CAST(substring(last_updated_at,0,19) AS
>> >> TIMESTAMP_LTZ(3))")
>> >> .watermark("rowtime", "rowtime + INTERVAL '2' SECOND")
>> >> .build());
>> >>
>> >>
>> >> streamTableEnvironment.createTemporaryView("header",headerTable);
>> >> streamTableEnvironment.createTemporaryView("item",itemTable);
>> >> Table result = streamTableEnvironment.sqlQuery("select header.customer_id"
>> >> +
>> >> ",item.goods_id" +
>> >> ",header.id" +
>> >> ",header.order_status" +
>> >> ",header.shop_id" +
>> >> ",header.parent_order_id" +
>> >> ",header.order_at" +
>> >> ",header.pay_at" +
>> >> ",header.channel_id" +
>> >> ",header.root_order_id" +
>> >> ",item.id" +
>> >> ",item.row_num" +
>> >> ",item.p_sp_sub_amt" +
>> >> ",item.display_qty" +
>> >> ",item.qty" +
>> >> ",item.bom_type" +
>> >> " from item JOIN header on header.id = item.order_id and item.rowtime
>> >> BETWEEN header.rowtime - INTERVAL '2' MINUTE AND header.rowtime + INTERVAL
>> >> '4' MINUTE");
>> >>
>> >> 对此，我又有新的疑问了，我在sql inner join里设置的状态ttl为20s,为什么20s的状态保留数据要比interval
>> >> join开分钟级别的数据还要准确？针对这个问题，不知道大家有什么看法和思路？
>> >> 我的一个猜测是我设置的表的ttl没有生效，inner join一直使用的是全量的数据，所以结果准确度要比interval
>> >> join高，我的做法是排查了tm和jm的日志，没有看见写的表的配置的日志，我的配置如下：
>> >> Configuration conf = new Configuration();
>> >> conf.setString("table.exec.mini-batch.enabled","true");
>> >> conf.setString("table.exec.mini-batch.allow-latency","15 s");
>> >> conf.setString("table.exec.mini-batch.size","100");
>> >> conf.setString("table.exec.state.ttl","20 s");
>> >> env.configure(conf);
>> >> StreamTableEnvironment streamTableEnvironment =
>> >> StreamTableEnvironment.create(env,
>> >> EnvironmentSettings.fromConfiguration(conf));
>> >>
>> >>
>> >> 我想了解下，从tm和jm日志是否能正确反应我的配置生效？如果不行，那我要使用什么方法才能知道我的这个配置是否生效？
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >> 在 2022-06-13 21:12:48，"Xuyang" <xy...@163.com> 写道：
>> >> >Hi,
>> >> > 1、理论上来说inner join关联的数据量应该比interval
>> >> join更大吧。关于左右两边流速度不一致的情况，理论上应该问题不大，因为需要等到两边的watermark都到齐之后才会触发状态里过期数据的清除。
>> >> > 2、inner
>> >> join没有水印的情况下，就是到了就发，完全根据这条数据进入这个算子的时间来算，也就是“处理时间”。默认数据是不会过期的，会存全量数据。如果定义了ttl，得看join两侧的表的pk和join
>> >> key，分别用不同的state数据格式来存数据（ListState、MapState等），原则上是多长时间没更新数据之后，就清空过期数据，是按照的“处理时间”来处理的。
>> >> >
>> >> >
>> >> >如果我有不对的地方，请指正我哈。
>> >> >
>> >> >
>> >> >
>> >> >
>> >> >--
>> >> >
>> >> > Best！
>> >> > Xuyang
>> >> >
>> >> >
>> >> >
>> >> >
>> >> >
>> >> >在 2022-06-12 14:39:39，"lxk7491@163.com" <lx...@163.com> 写道：
>> >> >>非常感谢回复
>> >> >>1.针对watermark,我会再去进行测试。同时还会测试使用处理时间，interval join会不会丢失数据
>> >> >>2.针对interval jon，我个人的理解是它能关联到的数据范围要比inner
>> >> join大，所以数据应该更准确，但是从结果上看却是数据丢失，当时非常震惊，有点颠覆我的认知了。同时我自己还有一个新的猜测，就是两个流的数据量不一样，可能也会造成数据丢失。目前左流是订单粒度数据，右流是订单-商品粒度数据，数据量要大很多。我个人理解，在处理右流的时候，应该会慢一点，所以导致两边的时间进展可能不一致。但是这又引发了一个新的疑问?inner
>> >> join应该也会受这样的影响
>> >> >>3.还有一个问题可能是我没有阐述清楚，我在sql里使用inner
>> >> join，没有注册水印，那么两个流的join应该是以处理时间来定义的？那么表的state的过期是否也是以处理时间来定义？
>> >> >>
>> >> >>
>> >> >>
>> >> >>lxk7491@163.com
>> >> >>
>> >> >>发件人： Shengkai Fang
>> >> >>发送时间： 2022-06-11 20:35
>> >> >>收件人： user-zh
>> >> >>主题： Re: Re: Flink 使用interval join数据丢失疑问
>> >> >>hi,
>> >> >>
>> >> >>对于第一点，丢数据的情况有很多。首先，要确认是不是 JOIN 算子丢数据（SINK 的使用不当也会丢数据）。如果明确了是 join
>> >> >>算子丢的数据，建议明确下丢的数据是咋样的，是不是 watermark 设置不合理，导致数据被误认为是晚到数据从而被丢了。例如，这里的是
>> >> `event
>> >> >>time` = `rowtime` - 2s，是不是不合适，我咋记得一般都是 +2 s 呢？
>> >> >>
>> >> >>对于第二点，interval join 我个人初步的理解是 state 的清理是根据两边的 event time，也就是说，如果右流的 event
>> >> >>time 的更新会影响左流的数据清理。比如说右流的时间点到了 12：00，join 条件要求左流的时间不会晚于右流的时间 1h，那么左流
>> >> >>11:00之前的数据都可以被清理了。
>> >> >>
>> >> >>对于第三点，我觉得是不能的。目前的 inner join + state 清理无法覆盖 event time 的window join 的。
>> >> >>
>> >> >>best,
>> >> >>Shengkai
>> >> >>
>> >> >>lxk7491@163.com <lx...@163.com> 于2022年6月10日周五 23:03写道：
>> >> >>
>> >> >>> 对于这个问题，我还是有很大的疑问，再把我这个场景描述一下：
>> >> >>>
>> >> >>>
>> >> 目前是使用flink进行双流join,两个流的数据，一个流是订单主表，另一个流是订单明细表。我们探查了离线的数据，订单明细表一般会在订单主表生成后晚几秒内生成，这个差异在秒级别。
>> >> >>>
>> >> 我们做了以下几轮测试，并对比了另一个实时落的表数据量。(这个表就是基准参照数据，只是简单落表，没做任何处理，两边的数据源一致，对比的口径一致。)
>> >> >>> 1.使用datastream api,使用kafka自带的时间戳做水印，使用interval join。对比完结果，数据少。
>> >> >>> 2.使用流转表，sql inner join,没有设置watermark。对比完结果数据正常。
>> >> >>> 3.使用流转表，sql interval join,从数据中的事件时间提取水印，对比完结果数据，数据少。
>> >> >>> 从结果上看，我不太明白为什么sql里inner join能保证数据准确，而interval
>> >> >>> join不行？有什么好的方式或者思路能让我更好的去尝试了解这个问题产生的原因
>> >> >>>
>> >> >>>
>> >> 针对第二种方式，我的疑问是，sql里没有设置水印，那么表的state过期是以处理时间来计算吗？针对这种设置了表state过期时间的join，我能理解为这个inner
>> >> >>> join其实是一个window join吗？
>> >> >>>
>> >> >>>
>> >> >>>
>> >> >>> lxk7491@163.com
>> >> >>>
>> >> >>> 发件人： lxk
>> >> >>> 发送时间： 2022-06-10 18:18
>> >> >>> 收件人： user-zh
>> >> >>> 主题： Re:Re:Re:Re:Re:Re:Re: Flink 使用interval join数据丢失疑问
>> >> >>>
>> >> >>>
>> >> >>>
>> >> >>> 现在改成了sql interval join,代码和执行计划如下，其他配置没变，数据量还是少，使用inner join就没问题
>> >> >>>
>> >> >>>
>> >> >>>
>> >> >>>
>> >> >>> Table headerTable =
>> >> >>> streamTableEnvironment.fromDataStream(headerFilterStream,
>> >> >>> Schema.newBuilder()
>> >> >>> .columnByExpression("rowtime",
>> >> >>> "CAST(substring(last_updated_at,0,19) AS TIMESTAMP_LTZ(3))")
>> >> >>> .watermark("rowtime", "rowtime - INTERVAL '2' SECOND")
>> >> >>> .build());
>> >> >>> Table itemTable =
>> >> >>> streamTableEnvironment.fromDataStream(filterItemStream,
>> >> Schema.newBuilder()
>> >> >>> .columnByExpression("rowtime",
>> >> >>> "CAST(substring(last_updated_at,0,19) AS TIMESTAMP_LTZ(3))")
>> >> >>> .watermark("rowtime", "rowtime - INTERVAL '2' SECOND")
>> >> >>> .build());
>> >> >>>
>> >> >>>
>> >> >>>
>> >> >>>
>> >> >>>
>> >> streamTableEnvironment.createTemporaryView("header",headerTable);
>> >> >>> streamTableEnvironment.createTemporaryView("item",itemTable);
>> >> >>>
>> >> >>>
>> >> >>>
>> >> >>>
>> >> >>>
>> >> >>>
>> >> >>> Table result = streamTableEnvironment.sqlQuery("select
>> >> >>> header.customer_id" +
>> >> >>> ",item.goods_id" +
>> >> >>> ",header.id" +
>> >> >>> ",header.order_status" +
>> >> >>> ",header.shop_id" +
>> >> >>> ",header.parent_order_id" +
>> >> >>> ",header.order_at" +
>> >> >>> ",header.pay_at" +
>> >> >>> ",header.channel_id" +
>> >> >>> ",header.root_order_id" +
>> >> >>> ",item.id" +
>> >> >>> ",item.row_num" +
>> >> >>> ",item.p_sp_sub_amt" +
>> >> >>> ",item.display_qty" +
>> >> >>> ",item.qty" +
>> >> >>> ",item.bom_type" +
>> >> >>> " from header JOIN item on header.id = item.order_id
>> >> and
>> >> >>> item.rowtime BETWEEN header.rowtime - INTERVAL '10' SECOND AND
>> >> >>> header.rowtime + INTERVAL '20' SECOND");
>> >> >>>
>> >> >>>
>> >> >>>
>> >> >>>
>> >> >>> String intervalJoin = streamTableEnvironment.explainSql("select
>> >> >>> header.customer_id" +
>> >> >>> ",item.goods_id" +
>> >> >>> ",header.id" +
>> >> >>> ",header.order_status" +
>> >> >>> ",header.shop_id" +
>> >> >>> ",header.parent_order_id" +
>> >> >>> ",header.order_at" +
>> >> >>> ",header.pay_at" +
>> >> >>> ",header.channel_id" +
>> >> >>> ",header.root_order_id" +
>> >> >>> ",item.id" +
>> >> >>> ",item.row_num" +
>> >> >>> ",item.p_sp_sub_amt" +
>> >> >>> ",item.display_qty" +
>> >> >>> ",item.qty" +
>> >> >>> ",item.bom_type" +
>> >> >>> " from header JOIN item on header.id = item.order_id
>> >> and
>> >> >>> item.rowtime BETWEEN header.rowtime - INTERVAL '10' SECOND AND
>> >> >>> header.rowtime + INTERVAL '20' SECOND");
>> >> >>>
>> >> >>>
>> >> >>> System.out.println(intervalJoin);
>> >> >>>
>> >> >>>
>> >> >>> DataStream<Row> rowDataStream =
>> >> >>> streamTableEnvironment.toChangelogStream(result);
>> >> >>>
>> >> >>>
>> >> >>>
>> >> >>>
>> >> >>>
>> >> >>>
>> >> >>> 执行计划:
>> >> >>> == Abstract Syntax Tree ==
>> >> >>> LogicalProject(customer_id=[$2], goods_id=[$16], id=[$0],
>> >> >>> order_status=[$1], shop_id=[$3], parent_order_id=[$4], order_at=[$5],
>> >> >>> pay_at=[$6], channel_id=[$7], root_order_id=[$8], id0=[$13],
>> >> row_num=[$15],
>> >> >>> p_sp_sub_amt=[$20], display_qty=[$23], qty=[$18], bom_type=[$21])
>> >> >>> +- LogicalJoin(condition=[AND(=($0, $14), >=($25, -($12, 10000:INTERVAL
>> >> >>> SECOND)), <=($25, +($12, 20000:INTERVAL SECOND)))], joinType=[inner])
>> >> >>> :- LogicalWatermarkAssigner(rowtime=[rowtime], watermark=[-($12,
>> >> >>> 2000:INTERVAL SECOND)])
>> >> >>> : +- LogicalProject(id=[$0], order_status=[$1], customer_id=[$2],
>> >> >>> shop_id=[$3], parent_order_id=[$4], order_at=[$5], pay_at=[$6],
>> >> >>> channel_id=[$7], root_order_id=[$8], last_updated_at=[$9],
>> >> >>> business_flag=[$10], mysql_op_type=[$11], rowtime=[CAST(SUBSTRING($9,
>> >> 0,
>> >> >>> 19)):TIMESTAMP_WITH_LOCAL_TIME_ZONE(3)])
>> >> >>> : +- LogicalTableScan(table=[[default_catalog, default_database,
>> >> >>> Unregistered_DataStream_Source_5]])
>> >> >>> +- LogicalWatermarkAssigner(rowtime=[rowtime], watermark=[-($12,
>> >> >>> 2000:INTERVAL SECOND)])
>> >> >>> +- LogicalProject(id=[$0], order_id=[$1], row_num=[$2],
>> >> >>> goods_id=[$3], s_sku_code=[$4], qty=[$5], p_paid_sub_amt=[$6],
>> >> >>> p_sp_sub_amt=[$7], bom_type=[$8], last_updated_at=[$9],
>> >> display_qty=[$10],
>> >> >>> is_first_flag=[$11], rowtime=[CAST(SUBSTRING($9, 0,
>> >> >>> 19)):TIMESTAMP_WITH_LOCAL_TIME_ZONE(3)])
>> >> >>> +- LogicalTableScan(table=[[default_catalog, default_database,
>> >> >>> Unregistered_DataStream_Source_8]])
>> >> >>>
>> >> >>>
>> >> >>> == Optimized Physical Plan ==
>> >> >>> Calc(select=[customer_id, goods_id, id, order_status, shop_id,
>> >> >>> parent_order_id, order_at, pay_at, channel_id, root_order_id, id0,
>> >> row_num,
>> >> >>> p_sp_sub_amt, display_qty, qty, bom_type])
>> >> >>> +- IntervalJoin(joinType=[InnerJoin], windowBounds=[isRowTime=true,
>> >> >>> leftLowerBound=-20000, leftUpperBound=10000, leftTimeIndex=9,
>> >> >>> rightTimeIndex=8], where=[AND(=(id, order_id), >=(rowtime0, -(rowtime,
>> >> >>> 10000:INTERVAL SECOND)), <=(rowtime0, +(rowtime, 20000:INTERVAL
>> >> SECOND)))],
>> >> >>> select=[id, order_status, customer_id, shop_id, parent_order_id,
>> >> order_at,
>> >> >>> pay_at, channel_id, root_order_id, rowtime, id0, order_id, row_num,
>> >> >>> goods_id, qty, p_sp_sub_amt, bom_type, display_qty, rowtime0])
>> >> >>> :- Exchange(distribution=[hash[id]])
>> >> >>> : +- WatermarkAssigner(rowtime=[rowtime], watermark=[-(rowtime,
>> >> >>> 2000:INTERVAL SECOND)])
>> >> >>> : +- Calc(select=[id, order_status, customer_id, shop_id,
>> >> >>> parent_order_id, order_at, pay_at, channel_id, root_order_id,
>> >> >>> CAST(SUBSTRING(last_updated_at, 0, 19)) AS rowtime])
>> >> >>> : +- TableSourceScan(table=[[default_catalog,
>> >> default_database,
>> >> >>> Unregistered_DataStream_Source_5]], fields=[id, order_status,
>> >> customer_id,
>> >> >>> shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id,
>> >> >>> last_updated_at, business_flag, mysql_op_type])
>> >> >>> +- Exchange(distribution=[hash[order_id]])
>> >> >>> +- WatermarkAssigner(rowtime=[rowtime], watermark=[-(rowtime,
>> >> >>> 2000:INTERVAL SECOND)])
>> >> >>> +- Calc(select=[id, order_id, row_num, goods_id, qty,
>> >> >>> p_sp_sub_amt, bom_type, display_qty, CAST(SUBSTRING(last_updated_at, 0,
>> >> >>> 19)) AS rowtime])
>> >> >>> +- TableSourceScan(table=[[default_catalog,
>> >> default_database,
>> >> >>> Unregistered_DataStream_Source_8]], fields=[id, order_id, row_num,
>> >> >>> goods_id, s_sku_code, qty, p_paid_sub_amt, p_sp_sub_amt, bom_type,
>> >> >>> last_updated_at, display_qty, is_first_flag])
>> >> >>>
>> >> >>>
>> >> >>> == Optimized Execution Plan ==
>> >> >>> Calc(select=[customer_id, goods_id, id, order_status, shop_id,
>> >> >>> parent_order_id, order_at, pay_at, channel_id, root_order_id, id0,
>> >> row_num,
>> >> >>> p_sp_sub_amt, display_qty, qty, bom_type])
>> >> >>> +- IntervalJoin(joinType=[InnerJoin], windowBounds=[isRowTime=true,
>> >> >>> leftLowerBound=-20000, leftUpperBound=10000, leftTimeIndex=9,
>> >> >>> rightTimeIndex=8], where=[((id = order_id) AND (rowtime0 >= (rowtime -
>> >> >>> 10000:INTERVAL SECOND)) AND (rowtime0 <= (rowtime + 20000:INTERVAL
>> >> >>> SECOND)))], select=[id, order_status, customer_id, shop_id,
>> >> >>> parent_order_id, order_at, pay_at, channel_id, root_order_id, rowtime,
>> >> id0,
>> >> >>> order_id, row_num, goods_id, qty, p_sp_sub_amt, bom_type, display_qty,
>> >> >>> rowtime0])
>> >> >>> :- Exchange(distribution=[hash[id]])
>> >> >>> : +- WatermarkAssigner(rowtime=[rowtime], watermark=[(rowtime -
>> >> >>> 2000:INTERVAL SECOND)])
>> >> >>> : +- Calc(select=[id, order_status, customer_id, shop_id,
>> >> >>> parent_order_id, order_at, pay_at, channel_id, root_order_id,
>> >> >>> CAST(SUBSTRING(last_updated_at, 0, 19)) AS rowtime])
>> >> >>> : +- TableSourceScan(table=[[default_catalog,
>> >> default_database,
>> >> >>> Unregistered_DataStream_Source_5]], fields=[id, order_status,
>> >> customer_id,
>> >> >>> shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id,
>> >> >>> last_updated_at, business_flag, mysql_op_type])
>> >> >>> +- Exchange(distribution=[hash[order_id]])
>> >> >>> +- WatermarkAssigner(rowtime=[rowtime], watermark=[(rowtime -
>> >> >>> 2000:INTERVAL SECOND)])
>> >> >>> +- Calc(select=[id, order_id, row_num, goods_id, qty,
>> >> >>> p_sp_sub_amt, bom_type, display_qty, CAST(SUBSTRING(last_updated_at, 0,
>> >> >>> 19)) AS rowtime])
>> >> >>> +- TableSourceScan(table=[[default_catalog,
>> >> default_database,
>> >> >>> Unregistered_DataStream_Source_8]], fields=[id, order_id, row_num,
>> >> >>> goods_id, s_sku_code, qty, p_paid_sub_amt, p_sp_sub_amt, bom_type,
>> >> >>> last_updated_at, display_qty, is_first_flag])
>> >> >>>
>> >> >>>
>> >> >>>
>> >> >>>
>> >> >>>
>> >> >>>
>> >> >>>
>> >> >>>
>> >> >>>
>> >> >>>
>> >> >>>
>> >> >>>
>> >> >>>
>> >> >>>
>> >> >>>
>> >> >>>
>> >> >>> 在 2022-06-10 17:16:31，"lxk" <lx...@163.com> 写道：
>> >> >>> >使用sql 进行interval
>> >> >>> join，我目前的问题是感觉时间转换这块不太友好，我目前流里面的事件时间字段是string类型，数据样式是2022-06-10
>> >> >>> 13:08:55,但是我使用TO_TIMESTAMP这个函数进行转换一直报错
>> >> >>> >
>> >> >>> >
>> >> >>> >
>> >> >>> >
>> >> >>> >
>> >> >>> >
>> >> >>> >
>> >> >>> >
>> >> >>> >
>> >> >>> >
>> >> >>> >
>> >> >>> >
>> >> >>> >
>> >> >>> >
>> >> >>> >
>> >> >>> >
>> >> >>> >
>> >> >>> >在 2022-06-10 15:04:31，"Xuyang" <xy...@163.com> 写道：
>> >> >>> >>Hi, datastream的这个interval join的api应该对标的是sql中的interval
>> >> >>> join。但是你目前写的这个sql，是普通join。普通join和interval
>> >> >>> join在业务含义和实现上都是有区别的。所以你直接拿datastream api的interval
>> >> >>> join和sql上的普通join结果对比，其实是有问题的。所以我之前的建议是让你试下让sql也使用interval
>> >> join，这样双方才有可比性。
>> >> >>> >>
>> >> >>> >>
>> >> >>>
>> >> >>>
>> >> >>另外sql中设置的table.exec.state.ttl这个参数，只是代表的state会20s清空过期数据，但我看你要比较的时间窗口是-10s和20s，貌似也不大一样。
>> >> >>> >>
>> >> >>> >>
>> >> >>> >>
>> >> >>> >>
>> >> >>> >>--
>> >> >>> >>
>> >> >>> >> Best！
>> >> >>> >> Xuyang
>> >> >>> >>
>> >> >>> >>
>> >> >>> >>
>> >> >>> >>
>> >> >>> >>
>> >> >>> >>在 2022-06-10 14:33:37，"lxk" <lx...@163.com> 写道：
>> >> >>> >>>
>> >> >>> >>>
>> >> >>> >>>
>> >> >>> >>>我不理解的点在于，我interval join开的时间窗口比我sql中设置的状态时间都要长，窗口的上下界别是-10s 和
>> >> >>> 20s，为什么会丢数据？
>> >> >>> >>>
>> >> >>> >>>sql中我设置这个table.exec.state.ttl参数
>> >> >>> 为20s，照理来说两个流应该也是保留20s的数据在状态中进行join。不知道我的理解是否有问题，希望能够得到解答。
>> >> >>> >>>
>> >> >>> >>>
>> >> >>> >>>
>> >> >>> >>>
>> >> >>> >>>
>> >> >>> >>>
>> >> >>> >>>
>> >> >>> >>>
>> >> >>> >>>
>> >> >>> >>>
>> >> >>> >>>
>> >> >>> >>>
>> >> >>> >>>
>> >> >>> >>>
>> >> >>> >>>在 2022-06-10 14:15:29，"Xuyang" <xy...@163.com> 写道：
>> >> >>> >>>>Hi, 你的这条SQL 并不是interval join，是普通join。
>> >> >>> >>>>interval join的使用文档可以参考文档[1]。可以试下使用SQL interval
>> >> >>> join会不会丢数据（注意设置state的ttl），从而判断是数据的问题还是datastream api的问题。
>> >> >>> >>>>
>> >> >>> >>>>
>> >> >>> >>>>
>> >> >>> >>>>
>> >> >>> >>>>[1]
>> >> >>>
>> >> https://nightlies.apache.org/flink/flink-docs-master/docs/dev/table/sql/queries/joins/#interval-joins
>> >> >>> >>>>
>> >> >>> >>>>
>> >> >>> >>>>
>> >> >>> >>>>
>> >> >>> >>>>
>> >> >>> >>>>
>> >> >>> >>>>
>> >> >>> >>>>--
>> >> >>> >>>>
>> >> >>> >>>> Best！
>> >> >>> >>>> Xuyang
>> >> >>> >>>>
>> >> >>> >>>>
>> >> >>> >>>>
>> >> >>> >>>>
>> >> >>> >>>>
>> >> >>> >>>>在 2022-06-10 11:26:33，"lxk" <lx...@163.com> 写道：
>> >> >>> >>>>>我用的是以下代码:
>> >> >>> >>>>>String s = streamTableEnvironment.explainSql("select
>> >> >>> header.customer_id" +
>> >> >>> >>>>>",item.goods_id" +
>> >> >>> >>>>>",header.id" +
>> >> >>> >>>>>",header.order_status" +
>> >> >>> >>>>>",header.shop_id" +
>> >> >>> >>>>>",header.parent_order_id" +
>> >> >>> >>>>>",header.order_at" +
>> >> >>> >>>>>",header.pay_at" +
>> >> >>> >>>>>",header.channel_id" +
>> >> >>> >>>>>",header.root_order_id" +
>> >> >>> >>>>>",item.id" +
>> >> >>> >>>>>",item.row_num" +
>> >> >>> >>>>>",item.p_sp_sub_amt" +
>> >> >>> >>>>>",item.display_qty" +
>> >> >>> >>>>>",item.qty" +
>> >> >>> >>>>>",item.bom_type" +
>> >> >>> >>>>>" from header JOIN item on header.id = item.order_id");
>> >> >>> >>>>>
>> >> >>> >>>>>System.out.println("explain:" + s);
>> >> >>> >>>>>
>> >> >>> >>>>>
>> >> >>> >>>>>
>> >> >>> >>>>>
>> >> >>> >>>>>plan信息为:
>> >> >>> >>>>>explain:== Abstract Syntax Tree ==
>> >> >>> >>>>>LogicalProject(customer_id=[$2], goods_id=[$15], id=[$0],
>> >> >>> order_status=[$1], shop_id=[$3], parent_order_id=[$4], order_at=[$5],
>> >> >>> pay_at=[$6], channel_id=[$7], root_order_id=[$8], id0=[$12],
>> >> row_num=[$14],
>> >> >>> p_sp_sub_amt=[$19], display_qty=[$22], qty=[$17], bom_type=[$20])
>> >> >>> >>>>>+- LogicalJoin(condition=[=($0, $13)], joinType=[inner])
>> >> >>> >>>>> :- LogicalTableScan(table=[[default_catalog, default_database,
>> >> >>> Unregistered_DataStream_Source_5]])
>> >> >>> >>>>> +- LogicalTableScan(table=[[default_catalog, default_database,
>> >> >>> Unregistered_DataStream_Source_8]])
>> >> >>> >>>>>
>> >> >>> >>>>>
>> >> >>> >>>>>== Optimized Physical Plan ==
>> >> >>> >>>>>Calc(select=[customer_id, goods_id, id, order_status, shop_id,
>> >> >>> parent_order_id, order_at, pay_at, channel_id, root_order_id, id0,
>> >> row_num,
>> >> >>> p_sp_sub_amt, display_qty, qty, bom_type])
>> >> >>> >>>>>+- Join(joinType=[InnerJoin], where=[=(id, order_id)], select=[id,
>> >> >>> order_status, customer_id, shop_id, parent_order_id, order_at, pay_at,
>> >> >>> channel_id, root_order_id, id0, order_id, row_num, goods_id, qty,
>> >> >>> p_sp_sub_amt, bom_type, display_qty], leftInputSpec=[NoUniqueKey],
>> >> >>> rightInputSpec=[NoUniqueKey])
>> >> >>> >>>>> :- Exchange(distribution=[hash[id]])
>> >> >>> >>>>> : +- Calc(select=[id, order_status, customer_id, shop_id,
>> >> >>> parent_order_id, order_at, pay_at, channel_id, root_order_id])
>> >> >>> >>>>> : +- TableSourceScan(table=[[default_catalog,
>> >> >>> default_database, Unregistered_DataStream_Source_5]], fields=[id,
>> >> >>> order_status, customer_id, shop_id, parent_order_id, order_at, pay_at,
>> >> >>> channel_id, root_order_id, last_updated_at, business_flag,
>> >> mysql_op_type])
>> >> >>> >>>>> +- Exchange(distribution=[hash[order_id]])
>> >> >>> >>>>> +- Calc(select=[id, order_id, row_num, goods_id, qty,
>> >> >>> p_sp_sub_amt, bom_type, display_qty])
>> >> >>> >>>>> +- TableSourceScan(table=[[default_catalog,
>> >> >>> default_database, Unregistered_DataStream_Source_8]], fields=[id,
>> >> order_id,
>> >> >>> row_num, goods_id, s_sku_code, qty, p_paid_sub_amt, p_sp_sub_amt,
>> >> bom_type,
>> >> >>> last_updated_at, display_qty, is_first_flag])
>> >> >>> >>>>>
>> >> >>> >>>>>
>> >> >>> >>>>>== Optimized Execution Plan ==
>> >> >>> >>>>>Calc(select=[customer_id, goods_id, id, order_status, shop_id,
>> >> >>> parent_order_id, order_at, pay_at, channel_id, root_order_id, id0,
>> >> row_num,
>> >> >>> p_sp_sub_amt, display_qty, qty, bom_type])
>> >> >>> >>>>>+- Join(joinType=[InnerJoin], where=[(id = order_id)], select=[id,
>> >> >>> order_status, customer_id, shop_id, parent_order_id, order_at, pay_at,
>> >> >>> channel_id, root_order_id, id0, order_id, row_num, goods_id, qty,
>> >> >>> p_sp_sub_amt, bom_type, display_qty], leftInputSpec=[NoUniqueKey],
>> >> >>> rightInputSpec=[NoUniqueKey])
>> >> >>> >>>>> :- Exchange(distribution=[hash[id]])
>> >> >>> >>>>> : +- Calc(select=[id, order_status, customer_id, shop_id,
>> >> >>> parent_order_id, order_at, pay_at, channel_id, root_order_id])
>> >> >>> >>>>> : +- TableSourceScan(table=[[default_catalog,
>> >> >>> default_database, Unregistered_DataStream_Source_5]], fields=[id,
>> >> >>> order_status, customer_id, shop_id, parent_order_id, order_at, pay_at,
>> >> >>> channel_id, root_order_id, last_updated_at, business_flag,
>> >> mysql_op_type])
>> >> >>> >>>>> +- Exchange(distribution=[hash[order_id]])
>> >> >>> >>>>> +- Calc(select=[id, order_id, row_num, goods_id, qty,
>> >> >>> p_sp_sub_amt, bom_type, display_qty])
>> >> >>> >>>>> +- TableSourceScan(table=[[default_catalog,
>> >> >>> default_database, Unregistered_DataStream_Source_8]], fields=[id,
>> >> order_id,
>> >> >>> row_num, goods_id, s_sku_code, qty, p_paid_sub_amt, p_sp_sub_amt,
>> >> bom_type,
>> >> >>> last_updated_at, display_qty, is_first_flag])
>> >> >>> >>>>>
>> >> >>> >>>>>
>> >> >>> >>>>>
>> >> >>> >>>>>
>> >> >>> >>>>>
>> >> >>> >>>>>
>> >> >>> >>>>>
>> >> >>> >>>>>
>> >> >>> >>>>>
>> >> >>> >>>>>
>> >> >>> >>>>>
>> >> >>> >>>>>
>> >> >>> >>>>>
>> >> >>> >>>>>在 2022-06-10 11:02:56，"Shengkai Fang" <fs...@gmail.com> 写道：
>> >> >>> >>>>>>你好，能提供下具体的 plan 供大家查看下吗？
>> >> >>> >>>>>>
>> >> >>> >>>>>>你可以直接 使用 tEnv.executeSql("Explain JSON_EXECUTION_PLAN
>> >> >>> >>>>>><YOUR_QUERY>").print() 打印下相关的信息。
>> >> >>> >>>>>>
>> >> >>> >>>>>>Best,
>> >> >>> >>>>>>Shengkai
>> >> >>> >>>>>>
>> >> >>> >>>>>>lxk <lx...@163.com> 于2022年6月10日周五 10:29写道：
>> >> >>> >>>>>>
>> >> >>> >>>>>>> flink 版本:1.14.4
>> >> >>> >>>>>>> 目前在使用flink interval join进行数据关联，在测试的时候发现一个问题，就是使用interval
>> >> >>> >>>>>>> join完之后数据会丢失，但是使用sql api，直接进行join，数据是正常的，没有丢失。
>> >> >>> >>>>>>> 水印是直接使用kafka 自带的时间戳生成watermark
>> >> >>> >>>>>>>
>> >> >>> >>>>>>>
>> >> >>> >>>>>>> 以下是代码 ---interval join
>> >> >>> >>>>>>>
>> >> >>> >>>>>>> SingleOutputStreamOperator<HeaderFull> headerFullStream =
>> >> >>> >>>>>>> headerFilterStream.keyBy(data -> data.getId())
>> >> >>> >>>>>>> .intervalJoin(filterItemStream.keyBy(data ->
>> >> data.getOrder_id()))
>> >> >>> >>>>>>> .between(Time.seconds(-10), Time.seconds(20))
>> >> >>> >>>>>>> .process(new ProcessJoinFunction<OrderHeader, OrderItem,
>> >> >>> HeaderFull>() {
>> >> >>> >>>>>>> @Override
>> >> >>> >>>>>>> public void processElement(OrderHeader left, OrderItem right,
>> >> >>> Context
>> >> >>> >>>>>>> context, Collector<HeaderFull> collector) throws Exception {
>> >> >>> >>>>>>> HeaderFull headerFull = new HeaderFull();
>> >> >>> >>>>>>> BeanUtilsBean beanUtilsBean = new BeanUtilsBean();
>> >> >>> >>>>>>> beanUtilsBean.copyProperties(headerFull, left);
>> >> >>> >>>>>>> beanUtilsBean.copyProperties(headerFull, right);
>> >> >>> >>>>>>> String event_date = left.getOrder_at().substring(0, 10);
>> >> >>> >>>>>>> headerFull.setEvent_date(event_date);
>> >> >>> >>>>>>> headerFull.setItem_id(right.getId());
>> >> >>> >>>>>>> collector.collect(headerFull);
>> >> >>> >>>>>>> }
>> >> >>> >>>>>>> }
>> >> >>> >>>>>>> 使用sql 进行join
>> >> >>> >>>>>>> Configuration conf = new Configuration();
>> >> >>> >>>>>>> conf.setString("table.exec.mini-batch.enabled","true");
>> >> >>> >>>>>>> conf.setString("table.exec.mini-batch.allow-latency","15 s");
>> >> >>> >>>>>>> conf.setString("table.exec.mini-batch.size","100");
>> >> >>> >>>>>>> conf.setString("table.exec.state.ttl","20 s");
>> >> >>> >>>>>>> env.configure(conf);
>> >> >>> >>>>>>> Table headerTable =
>> >> >>> >>>>>>> streamTableEnvironment.fromDataStream(headerFilterStream);
>> >> >>> >>>>>>> Table itemTable =
>> >> >>> streamTableEnvironment.fromDataStream(filterItemStream);
>> >> >>> >>>>>>>
>> >> >>> >>>>>>>
>> >> >>> >>>>>>>
>> >> streamTableEnvironment.createTemporaryView("header",headerTable);
>> >> >>> >>>>>>> streamTableEnvironment.createTemporaryView("item",itemTable);
>> >> >>> >>>>>>>
>> >> >>> >>>>>>> Table result = streamTableEnvironment.sqlQuery("select
>> >> >>> header.customer_id"
>> >> >>> >>>>>>> +
>> >> >>> >>>>>>> ",item.goods_id" +
>> >> >>> >>>>>>> ",header.id" +
>> >> >>> >>>>>>> ",header.order_status" +
>> >> >>> >>>>>>> ",header.shop_id" +
>> >> >>> >>>>>>> ",header.parent_order_id" +
>> >> >>> >>>>>>> ",header.order_at" +
>> >> >>> >>>>>>> ",header.pay_at" +
>> >> >>> >>>>>>> ",header.channel_id" +
>> >> >>> >>>>>>> ",header.root_order_id" +
>> >> >>> >>>>>>> ",item.id" +
>> >> >>> >>>>>>> ",item.row_num" +
>> >> >>> >>>>>>> ",item.p_sp_sub_amt" +
>> >> >>> >>>>>>> ",item.display_qty" +
>> >> >>> >>>>>>> ",item.qty" +
>> >> >>> >>>>>>> ",item.bom_type" +
>> >> >>> >>>>>>> " from header JOIN item on header.id = item.order_id");
>> >> >>> >>>>>>>
>> >> >>> >>>>>>>
>> >> >>> >>>>>>> DataStream<Row> rowDataStream =
>> >> >>> >>>>>>> streamTableEnvironment.toChangelogStream(result);
>> >> >>> >>>>>>> 不太理解为什么使用interval join会丢这么多数据，按照我的理解使用sql
>> >> join，底层应该也是用的类似interval
>> >> >>> >>>>>>> join，为啥两者最终关联上的结果差异这么大。
>> >> >>> >>>>>>>
>> >> >>> >>>>>>>
>> >> >>> >>>>>>>
>> >> >>> >>>>>>>
>> >> >>> >>>>>>>
>> >> >>> >>>>>>>
>> >> >>> >>>>>>>
>> >> >>> >>>>>>>
>> >> >>> >>>>>>>
>> >> >>> >>>>>>>
>> >> >>> >>>>>>>
>> >> >>>
>> >>
>>
>>
Re: Re: Re:Re: Re: Flink 使用interval join数据丢失疑问
Posted by Shengkai Fang <fs...@gmail.com>.
hi,
我问了下 如果配置在 flink-conf 情况下，则是会在jm 中打印相关的参数。如果是 api 配置的话，目前 log 中是不会打印相关信息的。
Best,
Shengkai
lxk <lx...@163.com> 于2022年6月15日周三 16:10写道：
> 我大致理解为，watermark设置不合理，导致延迟的数据就丢失了，这块我会再去从测输出流去验证一下数据。
> 频繁更新应该不太有可能，因为程序在流转表之前已经做了一道过滤，订单header只取了一个支付状态的数据，订单item也进行了去重处理。
> 然后这是我的jm和tm日志，目前好像没看见表的ttl相关日志。
>
>
>
>
>
>
>
> 在 2022-06-15 11:42:19，"Shengkai Fang" <fs...@gmail.com> 写道：
> >> 我在sql inner join里设置的状态ttl为20s,为什么20s的状态保留数据要比interval join开分钟级别的数据还要准确
> >
> >不合理的 watermark 设置在 interval join 就会导致丢数据。设置 ttl 情况下，如果某个 key
> >的数据频繁访问情况下，那么这个数据就不会过期。
> >
> >> 我的做法是排查了tm和jm的日志，没有看见写的表的配置的日志。
> >
> >我记得日志是会打印相关的日志。能提一些相关的日志吗？
> >
> >best,
> >Shengkai
> >
> >lxk <lx...@163.com> 于2022年6月14日周二 20:04写道：
> >
> >> Hi,
> >> 我目前使用sql interval join,窗口的上下界增加到分钟级别，分别是-2 minute 和 +4 minute
> >> 目前来看数据量和使用inner join要差不多了。以下是代码
> >> Table headerTable =
> >> streamTableEnvironment.fromDataStream(headerFilterStream,
> >> Schema.newBuilder()
> >> .columnByExpression("rowtime", "CAST(substring(last_updated_at,0,19) AS
> >> TIMESTAMP_LTZ(3))")
> >> .watermark("rowtime", "rowtime + INTERVAL '2' SECOND")
> >> .build());
> >> Table itemTable = streamTableEnvironment.fromDataStream(filterItemStream,
> >> Schema.newBuilder()
> >> .columnByExpression("rowtime", "CAST(substring(last_updated_at,0,19) AS
> >> TIMESTAMP_LTZ(3))")
> >> .watermark("rowtime", "rowtime + INTERVAL '2' SECOND")
> >> .build());
> >>
> >>
> >> streamTableEnvironment.createTemporaryView("header",headerTable);
> >> streamTableEnvironment.createTemporaryView("item",itemTable);
> >> Table result = streamTableEnvironment.sqlQuery("select header.customer_id"
> >> +
> >> ",item.goods_id" +
> >> ",header.id" +
> >> ",header.order_status" +
> >> ",header.shop_id" +
> >> ",header.parent_order_id" +
> >> ",header.order_at" +
> >> ",header.pay_at" +
> >> ",header.channel_id" +
> >> ",header.root_order_id" +
> >> ",item.id" +
> >> ",item.row_num" +
> >> ",item.p_sp_sub_amt" +
> >> ",item.display_qty" +
> >> ",item.qty" +
> >> ",item.bom_type" +
> >> " from item JOIN header on header.id = item.order_id and item.rowtime
> >> BETWEEN header.rowtime - INTERVAL '2' MINUTE AND header.rowtime + INTERVAL
> >> '4' MINUTE");
> >>
> >> 对此，我又有新的疑问了，我在sql inner join里设置的状态ttl为20s,为什么20s的状态保留数据要比interval
> >> join开分钟级别的数据还要准确？针对这个问题，不知道大家有什么看法和思路？
> >> 我的一个猜测是我设置的表的ttl没有生效，inner join一直使用的是全量的数据，所以结果准确度要比interval
> >> join高，我的做法是排查了tm和jm的日志，没有看见写的表的配置的日志，我的配置如下：
> >> Configuration conf = new Configuration();
> >> conf.setString("table.exec.mini-batch.enabled","true");
> >> conf.setString("table.exec.mini-batch.allow-latency","15 s");
> >> conf.setString("table.exec.mini-batch.size","100");
> >> conf.setString("table.exec.state.ttl","20 s");
> >> env.configure(conf);
> >> StreamTableEnvironment streamTableEnvironment =
> >> StreamTableEnvironment.create(env,
> >> EnvironmentSettings.fromConfiguration(conf));
> >>
> >>
> >> 我想了解下，从tm和jm日志是否能正确反应我的配置生效？如果不行，那我要使用什么方法才能知道我的这个配置是否生效？
> >>
> >>
> >>
> >>
> >>
> >>
> >>
> >>
> >>
> >>
> >>
> >>
> >>
> >>
> >>
> >>
> >>
> >>
> >>
> >>
> >>
> >> 在 2022-06-13 21:12:48，"Xuyang" <xy...@163.com> 写道：
> >> >Hi,
> >> > 1、理论上来说inner join关联的数据量应该比interval
> >> join更大吧。关于左右两边流速度不一致的情况，理论上应该问题不大，因为需要等到两边的watermark都到齐之后才会触发状态里过期数据的清除。
> >> > 2、inner
> >> join没有水印的情况下，就是到了就发，完全根据这条数据进入这个算子的时间来算，也就是“处理时间”。默认数据是不会过期的，会存全量数据。如果定义了ttl，得看join两侧的表的pk和join
> >> key，分别用不同的state数据格式来存数据（ListState、MapState等），原则上是多长时间没更新数据之后，就清空过期数据，是按照的“处理时间”来处理的。
> >> >
> >> >
> >> >如果我有不对的地方，请指正我哈。
> >> >
> >> >
> >> >
> >> >
> >> >--
> >> >
> >> > Best！
> >> > Xuyang
> >> >
> >> >
> >> >
> >> >
> >> >
> >> >在 2022-06-12 14:39:39，"lxk7491@163.com" <lx...@163.com> 写道：
> >> >>非常感谢回复
> >> >>1.针对watermark,我会再去进行测试。同时还会测试使用处理时间，interval join会不会丢失数据
> >> >>2.针对interval jon，我个人的理解是它能关联到的数据范围要比inner
> >> join大，所以数据应该更准确，但是从结果上看却是数据丢失，当时非常震惊，有点颠覆我的认知了。同时我自己还有一个新的猜测，就是两个流的数据量不一样，可能也会造成数据丢失。目前左流是订单粒度数据，右流是订单-商品粒度数据，数据量要大很多。我个人理解，在处理右流的时候，应该会慢一点，所以导致两边的时间进展可能不一致。但是这又引发了一个新的疑问?inner
> >> join应该也会受这样的影响
> >> >>3.还有一个问题可能是我没有阐述清楚，我在sql里使用inner
> >> join，没有注册水印，那么两个流的join应该是以处理时间来定义的？那么表的state的过期是否也是以处理时间来定义？
> >> >>
> >> >>
> >> >>
> >> >>lxk7491@163.com
> >> >>
> >> >>发件人： Shengkai Fang
> >> >>发送时间： 2022-06-11 20:35
> >> >>收件人： user-zh
> >> >>主题： Re: Re: Flink 使用interval join数据丢失疑问
> >> >>hi,
> >> >>
> >> >>对于第一点，丢数据的情况有很多。首先，要确认是不是 JOIN 算子丢数据（SINK 的使用不当也会丢数据）。如果明确了是 join
> >> >>算子丢的数据，建议明确下丢的数据是咋样的，是不是 watermark 设置不合理，导致数据被误认为是晚到数据从而被丢了。例如，这里的是
> >> `event
> >> >>time` = `rowtime` - 2s，是不是不合适，我咋记得一般都是 +2 s 呢？
> >> >>
> >> >>对于第二点，interval join 我个人初步的理解是 state 的清理是根据两边的 event time，也就是说，如果右流的 event
> >> >>time 的更新会影响左流的数据清理。比如说右流的时间点到了 12：00，join 条件要求左流的时间不会晚于右流的时间 1h，那么左流
> >> >>11:00之前的数据都可以被清理了。
> >> >>
> >> >>对于第三点，我觉得是不能的。目前的 inner join + state 清理无法覆盖 event time 的window join 的。
> >> >>
> >> >>best,
> >> >>Shengkai
> >> >>
> >> >>lxk7491@163.com <lx...@163.com> 于2022年6月10日周五 23:03写道：
> >> >>
> >> >>> 对于这个问题，我还是有很大的疑问，再把我这个场景描述一下：
> >> >>>
> >> >>>
> >> 目前是使用flink进行双流join,两个流的数据，一个流是订单主表，另一个流是订单明细表。我们探查了离线的数据，订单明细表一般会在订单主表生成后晚几秒内生成，这个差异在秒级别。
> >> >>>
> >> 我们做了以下几轮测试，并对比了另一个实时落的表数据量。(这个表就是基准参照数据，只是简单落表，没做任何处理，两边的数据源一致，对比的口径一致。)
> >> >>> 1.使用datastream api,使用kafka自带的时间戳做水印，使用interval join。对比完结果，数据少。
> >> >>> 2.使用流转表，sql inner join,没有设置watermark。对比完结果数据正常。
> >> >>> 3.使用流转表，sql interval join,从数据中的事件时间提取水印，对比完结果数据，数据少。
> >> >>> 从结果上看，我不太明白为什么sql里inner join能保证数据准确，而interval
> >> >>> join不行？有什么好的方式或者思路能让我更好的去尝试了解这个问题产生的原因
> >> >>>
> >> >>>
> >> 针对第二种方式，我的疑问是，sql里没有设置水印，那么表的state过期是以处理时间来计算吗？针对这种设置了表state过期时间的join，我能理解为这个inner
> >> >>> join其实是一个window join吗？
> >> >>>
> >> >>>
> >> >>>
> >> >>> lxk7491@163.com
> >> >>>
> >> >>> 发件人： lxk
> >> >>> 发送时间： 2022-06-10 18:18
> >> >>> 收件人： user-zh
> >> >>> 主题： Re:Re:Re:Re:Re:Re:Re: Flink 使用interval join数据丢失疑问
> >> >>>
> >> >>>
> >> >>>
> >> >>> 现在改成了sql interval join,代码和执行计划如下，其他配置没变，数据量还是少，使用inner join就没问题
> >> >>>
> >> >>>
> >> >>>
> >> >>>
> >> >>> Table headerTable =
> >> >>> streamTableEnvironment.fromDataStream(headerFilterStream,
> >> >>> Schema.newBuilder()
> >> >>> .columnByExpression("rowtime",
> >> >>> "CAST(substring(last_updated_at,0,19) AS TIMESTAMP_LTZ(3))")
> >> >>> .watermark("rowtime", "rowtime - INTERVAL '2' SECOND")
> >> >>> .build());
> >> >>> Table itemTable =
> >> >>> streamTableEnvironment.fromDataStream(filterItemStream,
> >> Schema.newBuilder()
> >> >>> .columnByExpression("rowtime",
> >> >>> "CAST(substring(last_updated_at,0,19) AS TIMESTAMP_LTZ(3))")
> >> >>> .watermark("rowtime", "rowtime - INTERVAL '2' SECOND")
> >> >>> .build());
> >> >>>
> >> >>>
> >> >>>
> >> >>>
> >> >>>
> >> streamTableEnvironment.createTemporaryView("header",headerTable);
> >> >>> streamTableEnvironment.createTemporaryView("item",itemTable);
> >> >>>
> >> >>>
> >> >>>
> >> >>>
> >> >>>
> >> >>>
> >> >>> Table result = streamTableEnvironment.sqlQuery("select
> >> >>> header.customer_id" +
> >> >>> ",item.goods_id" +
> >> >>> ",header.id" +
> >> >>> ",header.order_status" +
> >> >>> ",header.shop_id" +
> >> >>> ",header.parent_order_id" +
> >> >>> ",header.order_at" +
> >> >>> ",header.pay_at" +
> >> >>> ",header.channel_id" +
> >> >>> ",header.root_order_id" +
> >> >>> ",item.id" +
> >> >>> ",item.row_num" +
> >> >>> ",item.p_sp_sub_amt" +
> >> >>> ",item.display_qty" +
> >> >>> ",item.qty" +
> >> >>> ",item.bom_type" +
> >> >>> " from header JOIN item on header.id = item.order_id
> >> and
> >> >>> item.rowtime BETWEEN header.rowtime - INTERVAL '10' SECOND AND
> >> >>> header.rowtime + INTERVAL '20' SECOND");
> >> >>>
> >> >>>
> >> >>>
> >> >>>
> >> >>> String intervalJoin = streamTableEnvironment.explainSql("select
> >> >>> header.customer_id" +
> >> >>> ",item.goods_id" +
> >> >>> ",header.id" +
> >> >>> ",header.order_status" +
> >> >>> ",header.shop_id" +
> >> >>> ",header.parent_order_id" +
> >> >>> ",header.order_at" +
> >> >>> ",header.pay_at" +
> >> >>> ",header.channel_id" +
> >> >>> ",header.root_order_id" +
> >> >>> ",item.id" +
> >> >>> ",item.row_num" +
> >> >>> ",item.p_sp_sub_amt" +
> >> >>> ",item.display_qty" +
> >> >>> ",item.qty" +
> >> >>> ",item.bom_type" +
> >> >>> " from header JOIN item on header.id = item.order_id
> >> and
> >> >>> item.rowtime BETWEEN header.rowtime - INTERVAL '10' SECOND AND
> >> >>> header.rowtime + INTERVAL '20' SECOND");
> >> >>>
> >> >>>
> >> >>> System.out.println(intervalJoin);
> >> >>>
> >> >>>
> >> >>> DataStream<Row> rowDataStream =
> >> >>> streamTableEnvironment.toChangelogStream(result);
> >> >>>
> >> >>>
> >> >>>
> >> >>>
> >> >>>
> >> >>>
> >> >>> 执行计划:
> >> >>> == Abstract Syntax Tree ==
> >> >>> LogicalProject(customer_id=[$2], goods_id=[$16], id=[$0],
> >> >>> order_status=[$1], shop_id=[$3], parent_order_id=[$4], order_at=[$5],
> >> >>> pay_at=[$6], channel_id=[$7], root_order_id=[$8], id0=[$13],
> >> row_num=[$15],
> >> >>> p_sp_sub_amt=[$20], display_qty=[$23], qty=[$18], bom_type=[$21])
> >> >>> +- LogicalJoin(condition=[AND(=($0, $14), >=($25, -($12, 10000:INTERVAL
> >> >>> SECOND)), <=($25, +($12, 20000:INTERVAL SECOND)))], joinType=[inner])
> >> >>> :- LogicalWatermarkAssigner(rowtime=[rowtime], watermark=[-($12,
> >> >>> 2000:INTERVAL SECOND)])
> >> >>> : +- LogicalProject(id=[$0], order_status=[$1], customer_id=[$2],
> >> >>> shop_id=[$3], parent_order_id=[$4], order_at=[$5], pay_at=[$6],
> >> >>> channel_id=[$7], root_order_id=[$8], last_updated_at=[$9],
> >> >>> business_flag=[$10], mysql_op_type=[$11], rowtime=[CAST(SUBSTRING($9,
> >> 0,
> >> >>> 19)):TIMESTAMP_WITH_LOCAL_TIME_ZONE(3)])
> >> >>> : +- LogicalTableScan(table=[[default_catalog, default_database,
> >> >>> Unregistered_DataStream_Source_5]])
> >> >>> +- LogicalWatermarkAssigner(rowtime=[rowtime], watermark=[-($12,
> >> >>> 2000:INTERVAL SECOND)])
> >> >>> +- LogicalProject(id=[$0], order_id=[$1], row_num=[$2],
> >> >>> goods_id=[$3], s_sku_code=[$4], qty=[$5], p_paid_sub_amt=[$6],
> >> >>> p_sp_sub_amt=[$7], bom_type=[$8], last_updated_at=[$9],
> >> display_qty=[$10],
> >> >>> is_first_flag=[$11], rowtime=[CAST(SUBSTRING($9, 0,
> >> >>> 19)):TIMESTAMP_WITH_LOCAL_TIME_ZONE(3)])
> >> >>> +- LogicalTableScan(table=[[default_catalog, default_database,
> >> >>> Unregistered_DataStream_Source_8]])
> >> >>>
> >> >>>
> >> >>> == Optimized Physical Plan ==
> >> >>> Calc(select=[customer_id, goods_id, id, order_status, shop_id,
> >> >>> parent_order_id, order_at, pay_at, channel_id, root_order_id, id0,
> >> row_num,
> >> >>> p_sp_sub_amt, display_qty, qty, bom_type])
> >> >>> +- IntervalJoin(joinType=[InnerJoin], windowBounds=[isRowTime=true,
> >> >>> leftLowerBound=-20000, leftUpperBound=10000, leftTimeIndex=9,
> >> >>> rightTimeIndex=8], where=[AND(=(id, order_id), >=(rowtime0, -(rowtime,
> >> >>> 10000:INTERVAL SECOND)), <=(rowtime0, +(rowtime, 20000:INTERVAL
> >> SECOND)))],
> >> >>> select=[id, order_status, customer_id, shop_id, parent_order_id,
> >> order_at,
> >> >>> pay_at, channel_id, root_order_id, rowtime, id0, order_id, row_num,
> >> >>> goods_id, qty, p_sp_sub_amt, bom_type, display_qty, rowtime0])
> >> >>> :- Exchange(distribution=[hash[id]])
> >> >>> : +- WatermarkAssigner(rowtime=[rowtime], watermark=[-(rowtime,
> >> >>> 2000:INTERVAL SECOND)])
> >> >>> : +- Calc(select=[id, order_status, customer_id, shop_id,
> >> >>> parent_order_id, order_at, pay_at, channel_id, root_order_id,
> >> >>> CAST(SUBSTRING(last_updated_at, 0, 19)) AS rowtime])
> >> >>> : +- TableSourceScan(table=[[default_catalog,
> >> default_database,
> >> >>> Unregistered_DataStream_Source_5]], fields=[id, order_status,
> >> customer_id,
> >> >>> shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id,
> >> >>> last_updated_at, business_flag, mysql_op_type])
> >> >>> +- Exchange(distribution=[hash[order_id]])
> >> >>> +- WatermarkAssigner(rowtime=[rowtime], watermark=[-(rowtime,
> >> >>> 2000:INTERVAL SECOND)])
> >> >>> +- Calc(select=[id, order_id, row_num, goods_id, qty,
> >> >>> p_sp_sub_amt, bom_type, display_qty, CAST(SUBSTRING(last_updated_at, 0,
> >> >>> 19)) AS rowtime])
> >> >>> +- TableSourceScan(table=[[default_catalog,
> >> default_database,
> >> >>> Unregistered_DataStream_Source_8]], fields=[id, order_id, row_num,
> >> >>> goods_id, s_sku_code, qty, p_paid_sub_amt, p_sp_sub_amt, bom_type,
> >> >>> last_updated_at, display_qty, is_first_flag])
> >> >>>
> >> >>>
> >> >>> == Optimized Execution Plan ==
> >> >>> Calc(select=[customer_id, goods_id, id, order_status, shop_id,
> >> >>> parent_order_id, order_at, pay_at, channel_id, root_order_id, id0,
> >> row_num,
> >> >>> p_sp_sub_amt, display_qty, qty, bom_type])
> >> >>> +- IntervalJoin(joinType=[InnerJoin], windowBounds=[isRowTime=true,
> >> >>> leftLowerBound=-20000, leftUpperBound=10000, leftTimeIndex=9,
> >> >>> rightTimeIndex=8], where=[((id = order_id) AND (rowtime0 >= (rowtime -
> >> >>> 10000:INTERVAL SECOND)) AND (rowtime0 <= (rowtime + 20000:INTERVAL
> >> >>> SECOND)))], select=[id, order_status, customer_id, shop_id,
> >> >>> parent_order_id, order_at, pay_at, channel_id, root_order_id, rowtime,
> >> id0,
> >> >>> order_id, row_num, goods_id, qty, p_sp_sub_amt, bom_type, display_qty,
> >> >>> rowtime0])
> >> >>> :- Exchange(distribution=[hash[id]])
> >> >>> : +- WatermarkAssigner(rowtime=[rowtime], watermark=[(rowtime -
> >> >>> 2000:INTERVAL SECOND)])
> >> >>> : +- Calc(select=[id, order_status, customer_id, shop_id,
> >> >>> parent_order_id, order_at, pay_at, channel_id, root_order_id,
> >> >>> CAST(SUBSTRING(last_updated_at, 0, 19)) AS rowtime])
> >> >>> : +- TableSourceScan(table=[[default_catalog,
> >> default_database,
> >> >>> Unregistered_DataStream_Source_5]], fields=[id, order_status,
> >> customer_id,
> >> >>> shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id,
> >> >>> last_updated_at, business_flag, mysql_op_type])
> >> >>> +- Exchange(distribution=[hash[order_id]])
> >> >>> +- WatermarkAssigner(rowtime=[rowtime], watermark=[(rowtime -
> >> >>> 2000:INTERVAL SECOND)])
> >> >>> +- Calc(select=[id, order_id, row_num, goods_id, qty,
> >> >>> p_sp_sub_amt, bom_type, display_qty, CAST(SUBSTRING(last_updated_at, 0,
> >> >>> 19)) AS rowtime])
> >> >>> +- TableSourceScan(table=[[default_catalog,
> >> default_database,
> >> >>> Unregistered_DataStream_Source_8]], fields=[id, order_id, row_num,
> >> >>> goods_id, s_sku_code, qty, p_paid_sub_amt, p_sp_sub_amt, bom_type,
> >> >>> last_updated_at, display_qty, is_first_flag])
> >> >>>
> >> >>>
> >> >>>
> >> >>>
> >> >>>
> >> >>>
> >> >>>
> >> >>>
> >> >>>
> >> >>>
> >> >>>
> >> >>>
> >> >>>
> >> >>>
> >> >>>
> >> >>>
> >> >>> 在 2022-06-10 17:16:31，"lxk" <lx...@163.com> 写道：
> >> >>> >使用sql 进行interval
> >> >>> join，我目前的问题是感觉时间转换这块不太友好，我目前流里面的事件时间字段是string类型，数据样式是2022-06-10
> >> >>> 13:08:55,但是我使用TO_TIMESTAMP这个函数进行转换一直报错
> >> >>> >
> >> >>> >
> >> >>> >
> >> >>> >
> >> >>> >
> >> >>> >
> >> >>> >
> >> >>> >
> >> >>> >
> >> >>> >
> >> >>> >
> >> >>> >
> >> >>> >
> >> >>> >
> >> >>> >
> >> >>> >
> >> >>> >
> >> >>> >在 2022-06-10 15:04:31，"Xuyang" <xy...@163.com> 写道：
> >> >>> >>Hi, datastream的这个interval join的api应该对标的是sql中的interval
> >> >>> join。但是你目前写的这个sql，是普通join。普通join和interval
> >> >>> join在业务含义和实现上都是有区别的。所以你直接拿datastream api的interval
> >> >>> join和sql上的普通join结果对比，其实是有问题的。所以我之前的建议是让你试下让sql也使用interval
> >> join，这样双方才有可比性。
> >> >>> >>
> >> >>> >>
> >> >>>
> >> >>>
> >> >>另外sql中设置的table.exec.state.ttl这个参数，只是代表的state会20s清空过期数据，但我看你要比较的时间窗口是-10s和20s，貌似也不大一样。
> >> >>> >>
> >> >>> >>
> >> >>> >>
> >> >>> >>
> >> >>> >>--
> >> >>> >>
> >> >>> >> Best！
> >> >>> >> Xuyang
> >> >>> >>
> >> >>> >>
> >> >>> >>
> >> >>> >>
> >> >>> >>
> >> >>> >>在 2022-06-10 14:33:37，"lxk" <lx...@163.com> 写道：
> >> >>> >>>
> >> >>> >>>
> >> >>> >>>
> >> >>> >>>我不理解的点在于，我interval join开的时间窗口比我sql中设置的状态时间都要长，窗口的上下界别是-10s 和
> >> >>> 20s，为什么会丢数据？
> >> >>> >>>
> >> >>> >>>sql中我设置这个table.exec.state.ttl参数
> >> >>> 为20s，照理来说两个流应该也是保留20s的数据在状态中进行join。不知道我的理解是否有问题，希望能够得到解答。
> >> >>> >>>
> >> >>> >>>
> >> >>> >>>
> >> >>> >>>
> >> >>> >>>
> >> >>> >>>
> >> >>> >>>
> >> >>> >>>
> >> >>> >>>
> >> >>> >>>
> >> >>> >>>
> >> >>> >>>
> >> >>> >>>
> >> >>> >>>
> >> >>> >>>在 2022-06-10 14:15:29，"Xuyang" <xy...@163.com> 写道：
> >> >>> >>>>Hi, 你的这条SQL 并不是interval join，是普通join。
> >> >>> >>>>interval join的使用文档可以参考文档[1]。可以试下使用SQL interval
> >> >>> join会不会丢数据（注意设置state的ttl），从而判断是数据的问题还是datastream api的问题。
> >> >>> >>>>
> >> >>> >>>>
> >> >>> >>>>
> >> >>> >>>>
> >> >>> >>>>[1]
> >> >>>
> >> https://nightlies.apache.org/flink/flink-docs-master/docs/dev/table/sql/queries/joins/#interval-joins
> >> >>> >>>>
> >> >>> >>>>
> >> >>> >>>>
> >> >>> >>>>
> >> >>> >>>>
> >> >>> >>>>
> >> >>> >>>>
> >> >>> >>>>--
> >> >>> >>>>
> >> >>> >>>> Best！
> >> >>> >>>> Xuyang
> >> >>> >>>>
> >> >>> >>>>
> >> >>> >>>>
> >> >>> >>>>
> >> >>> >>>>
> >> >>> >>>>在 2022-06-10 11:26:33，"lxk" <lx...@163.com> 写道：
> >> >>> >>>>>我用的是以下代码:
> >> >>> >>>>>String s = streamTableEnvironment.explainSql("select
> >> >>> header.customer_id" +
> >> >>> >>>>>",item.goods_id" +
> >> >>> >>>>>",header.id" +
> >> >>> >>>>>",header.order_status" +
> >> >>> >>>>>",header.shop_id" +
> >> >>> >>>>>",header.parent_order_id" +
> >> >>> >>>>>",header.order_at" +
> >> >>> >>>>>",header.pay_at" +
> >> >>> >>>>>",header.channel_id" +
> >> >>> >>>>>",header.root_order_id" +
> >> >>> >>>>>",item.id" +
> >> >>> >>>>>",item.row_num" +
> >> >>> >>>>>",item.p_sp_sub_amt" +
> >> >>> >>>>>",item.display_qty" +
> >> >>> >>>>>",item.qty" +
> >> >>> >>>>>",item.bom_type" +
> >> >>> >>>>>" from header JOIN item on header.id = item.order_id");
> >> >>> >>>>>
> >> >>> >>>>>System.out.println("explain:" + s);
> >> >>> >>>>>
> >> >>> >>>>>
> >> >>> >>>>>
> >> >>> >>>>>
> >> >>> >>>>>plan信息为:
> >> >>> >>>>>explain:== Abstract Syntax Tree ==
> >> >>> >>>>>LogicalProject(customer_id=[$2], goods_id=[$15], id=[$0],
> >> >>> order_status=[$1], shop_id=[$3], parent_order_id=[$4], order_at=[$5],
> >> >>> pay_at=[$6], channel_id=[$7], root_order_id=[$8], id0=[$12],
> >> row_num=[$14],
> >> >>> p_sp_sub_amt=[$19], display_qty=[$22], qty=[$17], bom_type=[$20])
> >> >>> >>>>>+- LogicalJoin(condition=[=($0, $13)], joinType=[inner])
> >> >>> >>>>> :- LogicalTableScan(table=[[default_catalog, default_database,
> >> >>> Unregistered_DataStream_Source_5]])
> >> >>> >>>>> +- LogicalTableScan(table=[[default_catalog, default_database,
> >> >>> Unregistered_DataStream_Source_8]])
> >> >>> >>>>>
> >> >>> >>>>>
> >> >>> >>>>>== Optimized Physical Plan ==
> >> >>> >>>>>Calc(select=[customer_id, goods_id, id, order_status, shop_id,
> >> >>> parent_order_id, order_at, pay_at, channel_id, root_order_id, id0,
> >> row_num,
> >> >>> p_sp_sub_amt, display_qty, qty, bom_type])
> >> >>> >>>>>+- Join(joinType=[InnerJoin], where=[=(id, order_id)], select=[id,
> >> >>> order_status, customer_id, shop_id, parent_order_id, order_at, pay_at,
> >> >>> channel_id, root_order_id, id0, order_id, row_num, goods_id, qty,
> >> >>> p_sp_sub_amt, bom_type, display_qty], leftInputSpec=[NoUniqueKey],
> >> >>> rightInputSpec=[NoUniqueKey])
> >> >>> >>>>> :- Exchange(distribution=[hash[id]])
> >> >>> >>>>> : +- Calc(select=[id, order_status, customer_id, shop_id,
> >> >>> parent_order_id, order_at, pay_at, channel_id, root_order_id])
> >> >>> >>>>> : +- TableSourceScan(table=[[default_catalog,
> >> >>> default_database, Unregistered_DataStream_Source_5]], fields=[id,
> >> >>> order_status, customer_id, shop_id, parent_order_id, order_at, pay_at,
> >> >>> channel_id, root_order_id, last_updated_at, business_flag,
> >> mysql_op_type])
> >> >>> >>>>> +- Exchange(distribution=[hash[order_id]])
> >> >>> >>>>> +- Calc(select=[id, order_id, row_num, goods_id, qty,
> >> >>> p_sp_sub_amt, bom_type, display_qty])
> >> >>> >>>>> +- TableSourceScan(table=[[default_catalog,
> >> >>> default_database, Unregistered_DataStream_Source_8]], fields=[id,
> >> order_id,
> >> >>> row_num, goods_id, s_sku_code, qty, p_paid_sub_amt, p_sp_sub_amt,
> >> bom_type,
> >> >>> last_updated_at, display_qty, is_first_flag])
> >> >>> >>>>>
> >> >>> >>>>>
> >> >>> >>>>>== Optimized Execution Plan ==
> >> >>> >>>>>Calc(select=[customer_id, goods_id, id, order_status, shop_id,
> >> >>> parent_order_id, order_at, pay_at, channel_id, root_order_id, id0,
> >> row_num,
> >> >>> p_sp_sub_amt, display_qty, qty, bom_type])
> >> >>> >>>>>+- Join(joinType=[InnerJoin], where=[(id = order_id)], select=[id,
> >> >>> order_status, customer_id, shop_id, parent_order_id, order_at, pay_at,
> >> >>> channel_id, root_order_id, id0, order_id, row_num, goods_id, qty,
> >> >>> p_sp_sub_amt, bom_type, display_qty], leftInputSpec=[NoUniqueKey],
> >> >>> rightInputSpec=[NoUniqueKey])
> >> >>> >>>>> :- Exchange(distribution=[hash[id]])
> >> >>> >>>>> : +- Calc(select=[id, order_status, customer_id, shop_id,
> >> >>> parent_order_id, order_at, pay_at, channel_id, root_order_id])
> >> >>> >>>>> : +- TableSourceScan(table=[[default_catalog,
> >> >>> default_database, Unregistered_DataStream_Source_5]], fields=[id,
> >> >>> order_status, customer_id, shop_id, parent_order_id, order_at, pay_at,
> >> >>> channel_id, root_order_id, last_updated_at, business_flag,
> >> mysql_op_type])
> >> >>> >>>>> +- Exchange(distribution=[hash[order_id]])
> >> >>> >>>>> +- Calc(select=[id, order_id, row_num, goods_id, qty,
> >> >>> p_sp_sub_amt, bom_type, display_qty])
> >> >>> >>>>> +- TableSourceScan(table=[[default_catalog,
> >> >>> default_database, Unregistered_DataStream_Source_8]], fields=[id,
> >> order_id,
> >> >>> row_num, goods_id, s_sku_code, qty, p_paid_sub_amt, p_sp_sub_amt,
> >> bom_type,
> >> >>> last_updated_at, display_qty, is_first_flag])
> >> >>> >>>>>
> >> >>> >>>>>
> >> >>> >>>>>
> >> >>> >>>>>
> >> >>> >>>>>
> >> >>> >>>>>
> >> >>> >>>>>
> >> >>> >>>>>
> >> >>> >>>>>
> >> >>> >>>>>
> >> >>> >>>>>
> >> >>> >>>>>
> >> >>> >>>>>
> >> >>> >>>>>在 2022-06-10 11:02:56，"Shengkai Fang" <fs...@gmail.com> 写道：
> >> >>> >>>>>>你好，能提供下具体的 plan 供大家查看下吗？
> >> >>> >>>>>>
> >> >>> >>>>>>你可以直接 使用 tEnv.executeSql("Explain JSON_EXECUTION_PLAN
> >> >>> >>>>>><YOUR_QUERY>").print() 打印下相关的信息。
> >> >>> >>>>>>
> >> >>> >>>>>>Best,
> >> >>> >>>>>>Shengkai
> >> >>> >>>>>>
> >> >>> >>>>>>lxk <lx...@163.com> 于2022年6月10日周五 10:29写道：
> >> >>> >>>>>>
> >> >>> >>>>>>> flink 版本:1.14.4
> >> >>> >>>>>>> 目前在使用flink interval join进行数据关联，在测试的时候发现一个问题，就是使用interval
> >> >>> >>>>>>> join完之后数据会丢失，但是使用sql api，直接进行join，数据是正常的，没有丢失。
> >> >>> >>>>>>> 水印是直接使用kafka 自带的时间戳生成watermark
> >> >>> >>>>>>>
> >> >>> >>>>>>>
> >> >>> >>>>>>> 以下是代码 ---interval join
> >> >>> >>>>>>>
> >> >>> >>>>>>> SingleOutputStreamOperator<HeaderFull> headerFullStream =
> >> >>> >>>>>>> headerFilterStream.keyBy(data -> data.getId())
> >> >>> >>>>>>> .intervalJoin(filterItemStream.keyBy(data ->
> >> data.getOrder_id()))
> >> >>> >>>>>>> .between(Time.seconds(-10), Time.seconds(20))
> >> >>> >>>>>>> .process(new ProcessJoinFunction<OrderHeader, OrderItem,
> >> >>> HeaderFull>() {
> >> >>> >>>>>>> @Override
> >> >>> >>>>>>> public void processElement(OrderHeader left, OrderItem right,
> >> >>> Context
> >> >>> >>>>>>> context, Collector<HeaderFull> collector) throws Exception {
> >> >>> >>>>>>> HeaderFull headerFull = new HeaderFull();
> >> >>> >>>>>>> BeanUtilsBean beanUtilsBean = new BeanUtilsBean();
> >> >>> >>>>>>> beanUtilsBean.copyProperties(headerFull, left);
> >> >>> >>>>>>> beanUtilsBean.copyProperties(headerFull, right);
> >> >>> >>>>>>> String event_date = left.getOrder_at().substring(0, 10);
> >> >>> >>>>>>> headerFull.setEvent_date(event_date);
> >> >>> >>>>>>> headerFull.setItem_id(right.getId());
> >> >>> >>>>>>> collector.collect(headerFull);
> >> >>> >>>>>>> }
> >> >>> >>>>>>> }
> >> >>> >>>>>>> 使用sql 进行join
> >> >>> >>>>>>> Configuration conf = new Configuration();
> >> >>> >>>>>>> conf.setString("table.exec.mini-batch.enabled","true");
> >> >>> >>>>>>> conf.setString("table.exec.mini-batch.allow-latency","15 s");
> >> >>> >>>>>>> conf.setString("table.exec.mini-batch.size","100");
> >> >>> >>>>>>> conf.setString("table.exec.state.ttl","20 s");
> >> >>> >>>>>>> env.configure(conf);
> >> >>> >>>>>>> Table headerTable =
> >> >>> >>>>>>> streamTableEnvironment.fromDataStream(headerFilterStream);
> >> >>> >>>>>>> Table itemTable =
> >> >>> streamTableEnvironment.fromDataStream(filterItemStream);
> >> >>> >>>>>>>
> >> >>> >>>>>>>
> >> >>> >>>>>>>
> >> streamTableEnvironment.createTemporaryView("header",headerTable);
> >> >>> >>>>>>> streamTableEnvironment.createTemporaryView("item",itemTable);
> >> >>> >>>>>>>
> >> >>> >>>>>>> Table result = streamTableEnvironment.sqlQuery("select
> >> >>> header.customer_id"
> >> >>> >>>>>>> +
> >> >>> >>>>>>> ",item.goods_id" +
> >> >>> >>>>>>> ",header.id" +
> >> >>> >>>>>>> ",header.order_status" +
> >> >>> >>>>>>> ",header.shop_id" +
> >> >>> >>>>>>> ",header.parent_order_id" +
> >> >>> >>>>>>> ",header.order_at" +
> >> >>> >>>>>>> ",header.pay_at" +
> >> >>> >>>>>>> ",header.channel_id" +
> >> >>> >>>>>>> ",header.root_order_id" +
> >> >>> >>>>>>> ",item.id" +
> >> >>> >>>>>>> ",item.row_num" +
> >> >>> >>>>>>> ",item.p_sp_sub_amt" +
> >> >>> >>>>>>> ",item.display_qty" +
> >> >>> >>>>>>> ",item.qty" +
> >> >>> >>>>>>> ",item.bom_type" +
> >> >>> >>>>>>> " from header JOIN item on header.id = item.order_id");
> >> >>> >>>>>>>
> >> >>> >>>>>>>
> >> >>> >>>>>>> DataStream<Row> rowDataStream =
> >> >>> >>>>>>> streamTableEnvironment.toChangelogStream(result);
> >> >>> >>>>>>> 不太理解为什么使用interval join会丢这么多数据，按照我的理解使用sql
> >> join，底层应该也是用的类似interval
> >> >>> >>>>>>> join，为啥两者最终关联上的结果差异这么大。
> >> >>> >>>>>>>
> >> >>> >>>>>>>
> >> >>> >>>>>>>
> >> >>> >>>>>>>
> >> >>> >>>>>>>
> >> >>> >>>>>>>
> >> >>> >>>>>>>
> >> >>> >>>>>>>
> >> >>> >>>>>>>
> >> >>> >>>>>>>
> >> >>> >>>>>>>
> >> >>>
> >>
>
>
Re:Re: Re:Re: Re: Flink 使用interval join数据丢失疑问
Posted by lxk <lx...@163.com>.
我大致理解为，watermark设置不合理，导致延迟的数据就丢失了，这块我会再去从测输出流去验证一下数据。
频繁更新应该不太有可能，因为程序在流转表之前已经做了一道过滤，订单header只取了一个支付状态的数据，订单item也进行了去重处理。
然后这是我的jm和tm日志，目前好像没看见表的ttl相关日志。
在 2022-06-15 11:42:19，"Shengkai Fang" <fs...@gmail.com> 写道：
>> 我在sql inner join里设置的状态ttl为20s,为什么20s的状态保留数据要比interval join开分钟级别的数据还要准确
>
>不合理的 watermark 设置在 interval join 就会导致丢数据。设置 ttl 情况下，如果某个 key
>的数据频繁访问情况下，那么这个数据就不会过期。
>
>> 我的做法是排查了tm和jm的日志，没有看见写的表的配置的日志。
>
>我记得日志是会打印相关的日志。能提一些相关的日志吗？
>
>best,
>Shengkai
>
>lxk <lx...@163.com> 于2022年6月14日周二 20:04写道：
>
>> Hi,
>> 我目前使用sql interval join,窗口的上下界增加到分钟级别，分别是-2 minute 和 +4 minute
>> 目前来看数据量和使用inner join要差不多了。以下是代码
>> Table headerTable =
>> streamTableEnvironment.fromDataStream(headerFilterStream,
>> Schema.newBuilder()
>> .columnByExpression("rowtime", "CAST(substring(last_updated_at,0,19) AS
>> TIMESTAMP_LTZ(3))")
>> .watermark("rowtime", "rowtime + INTERVAL '2' SECOND")
>> .build());
>> Table itemTable = streamTableEnvironment.fromDataStream(filterItemStream,
>> Schema.newBuilder()
>> .columnByExpression("rowtime", "CAST(substring(last_updated_at,0,19) AS
>> TIMESTAMP_LTZ(3))")
>> .watermark("rowtime", "rowtime + INTERVAL '2' SECOND")
>> .build());
>>
>>
>> streamTableEnvironment.createTemporaryView("header",headerTable);
>> streamTableEnvironment.createTemporaryView("item",itemTable);
>> Table result = streamTableEnvironment.sqlQuery("select header.customer_id"
>> +
>> ",item.goods_id" +
>> ",header.id" +
>> ",header.order_status" +
>> ",header.shop_id" +
>> ",header.parent_order_id" +
>> ",header.order_at" +
>> ",header.pay_at" +
>> ",header.channel_id" +
>> ",header.root_order_id" +
>> ",item.id" +
>> ",item.row_num" +
>> ",item.p_sp_sub_amt" +
>> ",item.display_qty" +
>> ",item.qty" +
>> ",item.bom_type" +
>> " from item JOIN header on header.id = item.order_id and item.rowtime
>> BETWEEN header.rowtime - INTERVAL '2' MINUTE AND header.rowtime + INTERVAL
>> '4' MINUTE");
>>
>> 对此，我又有新的疑问了，我在sql inner join里设置的状态ttl为20s,为什么20s的状态保留数据要比interval
>> join开分钟级别的数据还要准确？针对这个问题，不知道大家有什么看法和思路？
>> 我的一个猜测是我设置的表的ttl没有生效，inner join一直使用的是全量的数据，所以结果准确度要比interval
>> join高，我的做法是排查了tm和jm的日志，没有看见写的表的配置的日志，我的配置如下：
>> Configuration conf = new Configuration();
>> conf.setString("table.exec.mini-batch.enabled","true");
>> conf.setString("table.exec.mini-batch.allow-latency","15 s");
>> conf.setString("table.exec.mini-batch.size","100");
>> conf.setString("table.exec.state.ttl","20 s");
>> env.configure(conf);
>> StreamTableEnvironment streamTableEnvironment =
>> StreamTableEnvironment.create(env,
>> EnvironmentSettings.fromConfiguration(conf));
>>
>>
>> 我想了解下，从tm和jm日志是否能正确反应我的配置生效？如果不行，那我要使用什么方法才能知道我的这个配置是否生效？
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>> 在 2022-06-13 21:12:48，"Xuyang" <xy...@163.com> 写道：
>> >Hi,
>> > 1、理论上来说inner join关联的数据量应该比interval
>> join更大吧。关于左右两边流速度不一致的情况，理论上应该问题不大，因为需要等到两边的watermark都到齐之后才会触发状态里过期数据的清除。
>> > 2、inner
>> join没有水印的情况下，就是到了就发，完全根据这条数据进入这个算子的时间来算，也就是“处理时间”。默认数据是不会过期的，会存全量数据。如果定义了ttl，得看join两侧的表的pk和join
>> key，分别用不同的state数据格式来存数据（ListState、MapState等），原则上是多长时间没更新数据之后，就清空过期数据，是按照的“处理时间”来处理的。
>> >
>> >
>> >如果我有不对的地方，请指正我哈。
>> >
>> >
>> >
>> >
>> >--
>> >
>> > Best！
>> > Xuyang
>> >
>> >
>> >
>> >
>> >
>> >在 2022-06-12 14:39:39，"lxk7491@163.com" <lx...@163.com> 写道：
>> >>非常感谢回复
>> >>1.针对watermark,我会再去进行测试。同时还会测试使用处理时间，interval join会不会丢失数据
>> >>2.针对interval jon，我个人的理解是它能关联到的数据范围要比inner
>> join大，所以数据应该更准确，但是从结果上看却是数据丢失，当时非常震惊，有点颠覆我的认知了。同时我自己还有一个新的猜测，就是两个流的数据量不一样，可能也会造成数据丢失。目前左流是订单粒度数据，右流是订单-商品粒度数据，数据量要大很多。我个人理解，在处理右流的时候，应该会慢一点，所以导致两边的时间进展可能不一致。但是这又引发了一个新的疑问?inner
>> join应该也会受这样的影响
>> >>3.还有一个问题可能是我没有阐述清楚，我在sql里使用inner
>> join，没有注册水印，那么两个流的join应该是以处理时间来定义的？那么表的state的过期是否也是以处理时间来定义？
>> >>
>> >>
>> >>
>> >>lxk7491@163.com
>> >>
>> >>发件人： Shengkai Fang
>> >>发送时间： 2022-06-11 20:35
>> >>收件人： user-zh
>> >>主题： Re: Re: Flink 使用interval join数据丢失疑问
>> >>hi,
>> >>
>> >>对于第一点，丢数据的情况有很多。首先，要确认是不是 JOIN 算子丢数据（SINK 的使用不当也会丢数据）。如果明确了是 join
>> >>算子丢的数据，建议明确下丢的数据是咋样的，是不是 watermark 设置不合理，导致数据被误认为是晚到数据从而被丢了。例如，这里的是
>> `event
>> >>time` = `rowtime` - 2s，是不是不合适，我咋记得一般都是 +2 s 呢？
>> >>
>> >>对于第二点，interval join 我个人初步的理解是 state 的清理是根据两边的 event time，也就是说，如果右流的 event
>> >>time 的更新会影响左流的数据清理。比如说右流的时间点到了 12：00，join 条件要求左流的时间不会晚于右流的时间 1h，那么左流
>> >>11:00之前的数据都可以被清理了。
>> >>
>> >>对于第三点，我觉得是不能的。目前的 inner join + state 清理无法覆盖 event time 的window join 的。
>> >>
>> >>best,
>> >>Shengkai
>> >>
>> >>lxk7491@163.com <lx...@163.com> 于2022年6月10日周五 23:03写道：
>> >>
>> >>> 对于这个问题，我还是有很大的疑问，再把我这个场景描述一下：
>> >>>
>> >>>
>> 目前是使用flink进行双流join,两个流的数据，一个流是订单主表，另一个流是订单明细表。我们探查了离线的数据，订单明细表一般会在订单主表生成后晚几秒内生成，这个差异在秒级别。
>> >>>
>> 我们做了以下几轮测试，并对比了另一个实时落的表数据量。(这个表就是基准参照数据，只是简单落表，没做任何处理，两边的数据源一致，对比的口径一致。)
>> >>> 1.使用datastream api,使用kafka自带的时间戳做水印，使用interval join。对比完结果，数据少。
>> >>> 2.使用流转表，sql inner join,没有设置watermark。对比完结果数据正常。
>> >>> 3.使用流转表，sql interval join,从数据中的事件时间提取水印，对比完结果数据，数据少。
>> >>> 从结果上看，我不太明白为什么sql里inner join能保证数据准确，而interval
>> >>> join不行？有什么好的方式或者思路能让我更好的去尝试了解这个问题产生的原因
>> >>>
>> >>>
>> 针对第二种方式，我的疑问是，sql里没有设置水印，那么表的state过期是以处理时间来计算吗？针对这种设置了表state过期时间的join，我能理解为这个inner
>> >>> join其实是一个window join吗？
>> >>>
>> >>>
>> >>>
>> >>> lxk7491@163.com
>> >>>
>> >>> 发件人： lxk
>> >>> 发送时间： 2022-06-10 18:18
>> >>> 收件人： user-zh
>> >>> 主题： Re:Re:Re:Re:Re:Re:Re: Flink 使用interval join数据丢失疑问
>> >>>
>> >>>
>> >>>
>> >>> 现在改成了sql interval join,代码和执行计划如下，其他配置没变，数据量还是少，使用inner join就没问题
>> >>>
>> >>>
>> >>>
>> >>>
>> >>> Table headerTable =
>> >>> streamTableEnvironment.fromDataStream(headerFilterStream,
>> >>> Schema.newBuilder()
>> >>> .columnByExpression("rowtime",
>> >>> "CAST(substring(last_updated_at,0,19) AS TIMESTAMP_LTZ(3))")
>> >>> .watermark("rowtime", "rowtime - INTERVAL '2' SECOND")
>> >>> .build());
>> >>> Table itemTable =
>> >>> streamTableEnvironment.fromDataStream(filterItemStream,
>> Schema.newBuilder()
>> >>> .columnByExpression("rowtime",
>> >>> "CAST(substring(last_updated_at,0,19) AS TIMESTAMP_LTZ(3))")
>> >>> .watermark("rowtime", "rowtime - INTERVAL '2' SECOND")
>> >>> .build());
>> >>>
>> >>>
>> >>>
>> >>>
>> >>>
>> streamTableEnvironment.createTemporaryView("header",headerTable);
>> >>> streamTableEnvironment.createTemporaryView("item",itemTable);
>> >>>
>> >>>
>> >>>
>> >>>
>> >>>
>> >>>
>> >>> Table result = streamTableEnvironment.sqlQuery("select
>> >>> header.customer_id" +
>> >>> ",item.goods_id" +
>> >>> ",header.id" +
>> >>> ",header.order_status" +
>> >>> ",header.shop_id" +
>> >>> ",header.parent_order_id" +
>> >>> ",header.order_at" +
>> >>> ",header.pay_at" +
>> >>> ",header.channel_id" +
>> >>> ",header.root_order_id" +
>> >>> ",item.id" +
>> >>> ",item.row_num" +
>> >>> ",item.p_sp_sub_amt" +
>> >>> ",item.display_qty" +
>> >>> ",item.qty" +
>> >>> ",item.bom_type" +
>> >>> " from header JOIN item on header.id = item.order_id
>> and
>> >>> item.rowtime BETWEEN header.rowtime - INTERVAL '10' SECOND AND
>> >>> header.rowtime + INTERVAL '20' SECOND");
>> >>>
>> >>>
>> >>>
>> >>>
>> >>> String intervalJoin = streamTableEnvironment.explainSql("select
>> >>> header.customer_id" +
>> >>> ",item.goods_id" +
>> >>> ",header.id" +
>> >>> ",header.order_status" +
>> >>> ",header.shop_id" +
>> >>> ",header.parent_order_id" +
>> >>> ",header.order_at" +
>> >>> ",header.pay_at" +
>> >>> ",header.channel_id" +
>> >>> ",header.root_order_id" +
>> >>> ",item.id" +
>> >>> ",item.row_num" +
>> >>> ",item.p_sp_sub_amt" +
>> >>> ",item.display_qty" +
>> >>> ",item.qty" +
>> >>> ",item.bom_type" +
>> >>> " from header JOIN item on header.id = item.order_id
>> and
>> >>> item.rowtime BETWEEN header.rowtime - INTERVAL '10' SECOND AND
>> >>> header.rowtime + INTERVAL '20' SECOND");
>> >>>
>> >>>
>> >>> System.out.println(intervalJoin);
>> >>>
>> >>>
>> >>> DataStream<Row> rowDataStream =
>> >>> streamTableEnvironment.toChangelogStream(result);
>> >>>
>> >>>
>> >>>
>> >>>
>> >>>
>> >>>
>> >>> 执行计划:
>> >>> == Abstract Syntax Tree ==
>> >>> LogicalProject(customer_id=[$2], goods_id=[$16], id=[$0],
>> >>> order_status=[$1], shop_id=[$3], parent_order_id=[$4], order_at=[$5],
>> >>> pay_at=[$6], channel_id=[$7], root_order_id=[$8], id0=[$13],
>> row_num=[$15],
>> >>> p_sp_sub_amt=[$20], display_qty=[$23], qty=[$18], bom_type=[$21])
>> >>> +- LogicalJoin(condition=[AND(=($0, $14), >=($25, -($12, 10000:INTERVAL
>> >>> SECOND)), <=($25, +($12, 20000:INTERVAL SECOND)))], joinType=[inner])
>> >>> :- LogicalWatermarkAssigner(rowtime=[rowtime], watermark=[-($12,
>> >>> 2000:INTERVAL SECOND)])
>> >>> : +- LogicalProject(id=[$0], order_status=[$1], customer_id=[$2],
>> >>> shop_id=[$3], parent_order_id=[$4], order_at=[$5], pay_at=[$6],
>> >>> channel_id=[$7], root_order_id=[$8], last_updated_at=[$9],
>> >>> business_flag=[$10], mysql_op_type=[$11], rowtime=[CAST(SUBSTRING($9,
>> 0,
>> >>> 19)):TIMESTAMP_WITH_LOCAL_TIME_ZONE(3)])
>> >>> : +- LogicalTableScan(table=[[default_catalog, default_database,
>> >>> Unregistered_DataStream_Source_5]])
>> >>> +- LogicalWatermarkAssigner(rowtime=[rowtime], watermark=[-($12,
>> >>> 2000:INTERVAL SECOND)])
>> >>> +- LogicalProject(id=[$0], order_id=[$1], row_num=[$2],
>> >>> goods_id=[$3], s_sku_code=[$4], qty=[$5], p_paid_sub_amt=[$6],
>> >>> p_sp_sub_amt=[$7], bom_type=[$8], last_updated_at=[$9],
>> display_qty=[$10],
>> >>> is_first_flag=[$11], rowtime=[CAST(SUBSTRING($9, 0,
>> >>> 19)):TIMESTAMP_WITH_LOCAL_TIME_ZONE(3)])
>> >>> +- LogicalTableScan(table=[[default_catalog, default_database,
>> >>> Unregistered_DataStream_Source_8]])
>> >>>
>> >>>
>> >>> == Optimized Physical Plan ==
>> >>> Calc(select=[customer_id, goods_id, id, order_status, shop_id,
>> >>> parent_order_id, order_at, pay_at, channel_id, root_order_id, id0,
>> row_num,
>> >>> p_sp_sub_amt, display_qty, qty, bom_type])
>> >>> +- IntervalJoin(joinType=[InnerJoin], windowBounds=[isRowTime=true,
>> >>> leftLowerBound=-20000, leftUpperBound=10000, leftTimeIndex=9,
>> >>> rightTimeIndex=8], where=[AND(=(id, order_id), >=(rowtime0, -(rowtime,
>> >>> 10000:INTERVAL SECOND)), <=(rowtime0, +(rowtime, 20000:INTERVAL
>> SECOND)))],
>> >>> select=[id, order_status, customer_id, shop_id, parent_order_id,
>> order_at,
>> >>> pay_at, channel_id, root_order_id, rowtime, id0, order_id, row_num,
>> >>> goods_id, qty, p_sp_sub_amt, bom_type, display_qty, rowtime0])
>> >>> :- Exchange(distribution=[hash[id]])
>> >>> : +- WatermarkAssigner(rowtime=[rowtime], watermark=[-(rowtime,
>> >>> 2000:INTERVAL SECOND)])
>> >>> : +- Calc(select=[id, order_status, customer_id, shop_id,
>> >>> parent_order_id, order_at, pay_at, channel_id, root_order_id,
>> >>> CAST(SUBSTRING(last_updated_at, 0, 19)) AS rowtime])
>> >>> : +- TableSourceScan(table=[[default_catalog,
>> default_database,
>> >>> Unregistered_DataStream_Source_5]], fields=[id, order_status,
>> customer_id,
>> >>> shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id,
>> >>> last_updated_at, business_flag, mysql_op_type])
>> >>> +- Exchange(distribution=[hash[order_id]])
>> >>> +- WatermarkAssigner(rowtime=[rowtime], watermark=[-(rowtime,
>> >>> 2000:INTERVAL SECOND)])
>> >>> +- Calc(select=[id, order_id, row_num, goods_id, qty,
>> >>> p_sp_sub_amt, bom_type, display_qty, CAST(SUBSTRING(last_updated_at, 0,
>> >>> 19)) AS rowtime])
>> >>> +- TableSourceScan(table=[[default_catalog,
>> default_database,
>> >>> Unregistered_DataStream_Source_8]], fields=[id, order_id, row_num,
>> >>> goods_id, s_sku_code, qty, p_paid_sub_amt, p_sp_sub_amt, bom_type,
>> >>> last_updated_at, display_qty, is_first_flag])
>> >>>
>> >>>
>> >>> == Optimized Execution Plan ==
>> >>> Calc(select=[customer_id, goods_id, id, order_status, shop_id,
>> >>> parent_order_id, order_at, pay_at, channel_id, root_order_id, id0,
>> row_num,
>> >>> p_sp_sub_amt, display_qty, qty, bom_type])
>> >>> +- IntervalJoin(joinType=[InnerJoin], windowBounds=[isRowTime=true,
>> >>> leftLowerBound=-20000, leftUpperBound=10000, leftTimeIndex=9,
>> >>> rightTimeIndex=8], where=[((id = order_id) AND (rowtime0 >= (rowtime -
>> >>> 10000:INTERVAL SECOND)) AND (rowtime0 <= (rowtime + 20000:INTERVAL
>> >>> SECOND)))], select=[id, order_status, customer_id, shop_id,
>> >>> parent_order_id, order_at, pay_at, channel_id, root_order_id, rowtime,
>> id0,
>> >>> order_id, row_num, goods_id, qty, p_sp_sub_amt, bom_type, display_qty,
>> >>> rowtime0])
>> >>> :- Exchange(distribution=[hash[id]])
>> >>> : +- WatermarkAssigner(rowtime=[rowtime], watermark=[(rowtime -
>> >>> 2000:INTERVAL SECOND)])
>> >>> : +- Calc(select=[id, order_status, customer_id, shop_id,
>> >>> parent_order_id, order_at, pay_at, channel_id, root_order_id,
>> >>> CAST(SUBSTRING(last_updated_at, 0, 19)) AS rowtime])
>> >>> : +- TableSourceScan(table=[[default_catalog,
>> default_database,
>> >>> Unregistered_DataStream_Source_5]], fields=[id, order_status,
>> customer_id,
>> >>> shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id,
>> >>> last_updated_at, business_flag, mysql_op_type])
>> >>> +- Exchange(distribution=[hash[order_id]])
>> >>> +- WatermarkAssigner(rowtime=[rowtime], watermark=[(rowtime -
>> >>> 2000:INTERVAL SECOND)])
>> >>> +- Calc(select=[id, order_id, row_num, goods_id, qty,
>> >>> p_sp_sub_amt, bom_type, display_qty, CAST(SUBSTRING(last_updated_at, 0,
>> >>> 19)) AS rowtime])
>> >>> +- TableSourceScan(table=[[default_catalog,
>> default_database,
>> >>> Unregistered_DataStream_Source_8]], fields=[id, order_id, row_num,
>> >>> goods_id, s_sku_code, qty, p_paid_sub_amt, p_sp_sub_amt, bom_type,
>> >>> last_updated_at, display_qty, is_first_flag])
>> >>>
>> >>>
>> >>>
>> >>>
>> >>>
>> >>>
>> >>>
>> >>>
>> >>>
>> >>>
>> >>>
>> >>>
>> >>>
>> >>>
>> >>>
>> >>>
>> >>> 在 2022-06-10 17:16:31，"lxk" <lx...@163.com> 写道：
>> >>> >使用sql 进行interval
>> >>> join，我目前的问题是感觉时间转换这块不太友好，我目前流里面的事件时间字段是string类型，数据样式是2022-06-10
>> >>> 13:08:55,但是我使用TO_TIMESTAMP这个函数进行转换一直报错
>> >>> >
>> >>> >
>> >>> >
>> >>> >
>> >>> >
>> >>> >
>> >>> >
>> >>> >
>> >>> >
>> >>> >
>> >>> >
>> >>> >
>> >>> >
>> >>> >
>> >>> >
>> >>> >
>> >>> >
>> >>> >在 2022-06-10 15:04:31，"Xuyang" <xy...@163.com> 写道：
>> >>> >>Hi, datastream的这个interval join的api应该对标的是sql中的interval
>> >>> join。但是你目前写的这个sql，是普通join。普通join和interval
>> >>> join在业务含义和实现上都是有区别的。所以你直接拿datastream api的interval
>> >>> join和sql上的普通join结果对比，其实是有问题的。所以我之前的建议是让你试下让sql也使用interval
>> join，这样双方才有可比性。
>> >>> >>
>> >>> >>
>> >>>
>> >>>
>> >>另外sql中设置的table.exec.state.ttl这个参数，只是代表的state会20s清空过期数据，但我看你要比较的时间窗口是-10s和20s，貌似也不大一样。
>> >>> >>
>> >>> >>
>> >>> >>
>> >>> >>
>> >>> >>--
>> >>> >>
>> >>> >> Best！
>> >>> >> Xuyang
>> >>> >>
>> >>> >>
>> >>> >>
>> >>> >>
>> >>> >>
>> >>> >>在 2022-06-10 14:33:37，"lxk" <lx...@163.com> 写道：
>> >>> >>>
>> >>> >>>
>> >>> >>>
>> >>> >>>我不理解的点在于，我interval join开的时间窗口比我sql中设置的状态时间都要长，窗口的上下界别是-10s 和
>> >>> 20s，为什么会丢数据？
>> >>> >>>
>> >>> >>>sql中我设置这个table.exec.state.ttl参数
>> >>> 为20s，照理来说两个流应该也是保留20s的数据在状态中进行join。不知道我的理解是否有问题，希望能够得到解答。
>> >>> >>>
>> >>> >>>
>> >>> >>>
>> >>> >>>
>> >>> >>>
>> >>> >>>
>> >>> >>>
>> >>> >>>
>> >>> >>>
>> >>> >>>
>> >>> >>>
>> >>> >>>
>> >>> >>>
>> >>> >>>
>> >>> >>>在 2022-06-10 14:15:29，"Xuyang" <xy...@163.com> 写道：
>> >>> >>>>Hi, 你的这条SQL 并不是interval join，是普通join。
>> >>> >>>>interval join的使用文档可以参考文档[1]。可以试下使用SQL interval
>> >>> join会不会丢数据（注意设置state的ttl），从而判断是数据的问题还是datastream api的问题。
>> >>> >>>>
>> >>> >>>>
>> >>> >>>>
>> >>> >>>>
>> >>> >>>>[1]
>> >>>
>> https://nightlies.apache.org/flink/flink-docs-master/docs/dev/table/sql/queries/joins/#interval-joins
>> >>> >>>>
>> >>> >>>>
>> >>> >>>>
>> >>> >>>>
>> >>> >>>>
>> >>> >>>>
>> >>> >>>>
>> >>> >>>>--
>> >>> >>>>
>> >>> >>>> Best！
>> >>> >>>> Xuyang
>> >>> >>>>
>> >>> >>>>
>> >>> >>>>
>> >>> >>>>
>> >>> >>>>
>> >>> >>>>在 2022-06-10 11:26:33，"lxk" <lx...@163.com> 写道：
>> >>> >>>>>我用的是以下代码:
>> >>> >>>>>String s = streamTableEnvironment.explainSql("select
>> >>> header.customer_id" +
>> >>> >>>>>",item.goods_id" +
>> >>> >>>>>",header.id" +
>> >>> >>>>>",header.order_status" +
>> >>> >>>>>",header.shop_id" +
>> >>> >>>>>",header.parent_order_id" +
>> >>> >>>>>",header.order_at" +
>> >>> >>>>>",header.pay_at" +
>> >>> >>>>>",header.channel_id" +
>> >>> >>>>>",header.root_order_id" +
>> >>> >>>>>",item.id" +
>> >>> >>>>>",item.row_num" +
>> >>> >>>>>",item.p_sp_sub_amt" +
>> >>> >>>>>",item.display_qty" +
>> >>> >>>>>",item.qty" +
>> >>> >>>>>",item.bom_type" +
>> >>> >>>>>" from header JOIN item on header.id = item.order_id");
>> >>> >>>>>
>> >>> >>>>>System.out.println("explain:" + s);
>> >>> >>>>>
>> >>> >>>>>
>> >>> >>>>>
>> >>> >>>>>
>> >>> >>>>>plan信息为:
>> >>> >>>>>explain:== Abstract Syntax Tree ==
>> >>> >>>>>LogicalProject(customer_id=[$2], goods_id=[$15], id=[$0],
>> >>> order_status=[$1], shop_id=[$3], parent_order_id=[$4], order_at=[$5],
>> >>> pay_at=[$6], channel_id=[$7], root_order_id=[$8], id0=[$12],
>> row_num=[$14],
>> >>> p_sp_sub_amt=[$19], display_qty=[$22], qty=[$17], bom_type=[$20])
>> >>> >>>>>+- LogicalJoin(condition=[=($0, $13)], joinType=[inner])
>> >>> >>>>> :- LogicalTableScan(table=[[default_catalog, default_database,
>> >>> Unregistered_DataStream_Source_5]])
>> >>> >>>>> +- LogicalTableScan(table=[[default_catalog, default_database,
>> >>> Unregistered_DataStream_Source_8]])
>> >>> >>>>>
>> >>> >>>>>
>> >>> >>>>>== Optimized Physical Plan ==
>> >>> >>>>>Calc(select=[customer_id, goods_id, id, order_status, shop_id,
>> >>> parent_order_id, order_at, pay_at, channel_id, root_order_id, id0,
>> row_num,
>> >>> p_sp_sub_amt, display_qty, qty, bom_type])
>> >>> >>>>>+- Join(joinType=[InnerJoin], where=[=(id, order_id)], select=[id,
>> >>> order_status, customer_id, shop_id, parent_order_id, order_at, pay_at,
>> >>> channel_id, root_order_id, id0, order_id, row_num, goods_id, qty,
>> >>> p_sp_sub_amt, bom_type, display_qty], leftInputSpec=[NoUniqueKey],
>> >>> rightInputSpec=[NoUniqueKey])
>> >>> >>>>> :- Exchange(distribution=[hash[id]])
>> >>> >>>>> : +- Calc(select=[id, order_status, customer_id, shop_id,
>> >>> parent_order_id, order_at, pay_at, channel_id, root_order_id])
>> >>> >>>>> : +- TableSourceScan(table=[[default_catalog,
>> >>> default_database, Unregistered_DataStream_Source_5]], fields=[id,
>> >>> order_status, customer_id, shop_id, parent_order_id, order_at, pay_at,
>> >>> channel_id, root_order_id, last_updated_at, business_flag,
>> mysql_op_type])
>> >>> >>>>> +- Exchange(distribution=[hash[order_id]])
>> >>> >>>>> +- Calc(select=[id, order_id, row_num, goods_id, qty,
>> >>> p_sp_sub_amt, bom_type, display_qty])
>> >>> >>>>> +- TableSourceScan(table=[[default_catalog,
>> >>> default_database, Unregistered_DataStream_Source_8]], fields=[id,
>> order_id,
>> >>> row_num, goods_id, s_sku_code, qty, p_paid_sub_amt, p_sp_sub_amt,
>> bom_type,
>> >>> last_updated_at, display_qty, is_first_flag])
>> >>> >>>>>
>> >>> >>>>>
>> >>> >>>>>== Optimized Execution Plan ==
>> >>> >>>>>Calc(select=[customer_id, goods_id, id, order_status, shop_id,
>> >>> parent_order_id, order_at, pay_at, channel_id, root_order_id, id0,
>> row_num,
>> >>> p_sp_sub_amt, display_qty, qty, bom_type])
>> >>> >>>>>+- Join(joinType=[InnerJoin], where=[(id = order_id)], select=[id,
>> >>> order_status, customer_id, shop_id, parent_order_id, order_at, pay_at,
>> >>> channel_id, root_order_id, id0, order_id, row_num, goods_id, qty,
>> >>> p_sp_sub_amt, bom_type, display_qty], leftInputSpec=[NoUniqueKey],
>> >>> rightInputSpec=[NoUniqueKey])
>> >>> >>>>> :- Exchange(distribution=[hash[id]])
>> >>> >>>>> : +- Calc(select=[id, order_status, customer_id, shop_id,
>> >>> parent_order_id, order_at, pay_at, channel_id, root_order_id])
>> >>> >>>>> : +- TableSourceScan(table=[[default_catalog,
>> >>> default_database, Unregistered_DataStream_Source_5]], fields=[id,
>> >>> order_status, customer_id, shop_id, parent_order_id, order_at, pay_at,
>> >>> channel_id, root_order_id, last_updated_at, business_flag,
>> mysql_op_type])
>> >>> >>>>> +- Exchange(distribution=[hash[order_id]])
>> >>> >>>>> +- Calc(select=[id, order_id, row_num, goods_id, qty,
>> >>> p_sp_sub_amt, bom_type, display_qty])
>> >>> >>>>> +- TableSourceScan(table=[[default_catalog,
>> >>> default_database, Unregistered_DataStream_Source_8]], fields=[id,
>> order_id,
>> >>> row_num, goods_id, s_sku_code, qty, p_paid_sub_amt, p_sp_sub_amt,
>> bom_type,
>> >>> last_updated_at, display_qty, is_first_flag])
>> >>> >>>>>
>> >>> >>>>>
>> >>> >>>>>
>> >>> >>>>>
>> >>> >>>>>
>> >>> >>>>>
>> >>> >>>>>
>> >>> >>>>>
>> >>> >>>>>
>> >>> >>>>>
>> >>> >>>>>
>> >>> >>>>>
>> >>> >>>>>
>> >>> >>>>>在 2022-06-10 11:02:56，"Shengkai Fang" <fs...@gmail.com> 写道：
>> >>> >>>>>>你好，能提供下具体的 plan 供大家查看下吗？
>> >>> >>>>>>
>> >>> >>>>>>你可以直接 使用 tEnv.executeSql("Explain JSON_EXECUTION_PLAN
>> >>> >>>>>><YOUR_QUERY>").print() 打印下相关的信息。
>> >>> >>>>>>
>> >>> >>>>>>Best,
>> >>> >>>>>>Shengkai
>> >>> >>>>>>
>> >>> >>>>>>lxk <lx...@163.com> 于2022年6月10日周五 10:29写道：
>> >>> >>>>>>
>> >>> >>>>>>> flink 版本:1.14.4
>> >>> >>>>>>> 目前在使用flink interval join进行数据关联，在测试的时候发现一个问题，就是使用interval
>> >>> >>>>>>> join完之后数据会丢失，但是使用sql api，直接进行join，数据是正常的，没有丢失。
>> >>> >>>>>>> 水印是直接使用kafka 自带的时间戳生成watermark
>> >>> >>>>>>>
>> >>> >>>>>>>
>> >>> >>>>>>> 以下是代码 ---interval join
>> >>> >>>>>>>
>> >>> >>>>>>> SingleOutputStreamOperator<HeaderFull> headerFullStream =
>> >>> >>>>>>> headerFilterStream.keyBy(data -> data.getId())
>> >>> >>>>>>> .intervalJoin(filterItemStream.keyBy(data ->
>> data.getOrder_id()))
>> >>> >>>>>>> .between(Time.seconds(-10), Time.seconds(20))
>> >>> >>>>>>> .process(new ProcessJoinFunction<OrderHeader, OrderItem,
>> >>> HeaderFull>() {
>> >>> >>>>>>> @Override
>> >>> >>>>>>> public void processElement(OrderHeader left, OrderItem right,
>> >>> Context
>> >>> >>>>>>> context, Collector<HeaderFull> collector) throws Exception {
>> >>> >>>>>>> HeaderFull headerFull = new HeaderFull();
>> >>> >>>>>>> BeanUtilsBean beanUtilsBean = new BeanUtilsBean();
>> >>> >>>>>>> beanUtilsBean.copyProperties(headerFull, left);
>> >>> >>>>>>> beanUtilsBean.copyProperties(headerFull, right);
>> >>> >>>>>>> String event_date = left.getOrder_at().substring(0, 10);
>> >>> >>>>>>> headerFull.setEvent_date(event_date);
>> >>> >>>>>>> headerFull.setItem_id(right.getId());
>> >>> >>>>>>> collector.collect(headerFull);
>> >>> >>>>>>> }
>> >>> >>>>>>> }
>> >>> >>>>>>> 使用sql 进行join
>> >>> >>>>>>> Configuration conf = new Configuration();
>> >>> >>>>>>> conf.setString("table.exec.mini-batch.enabled","true");
>> >>> >>>>>>> conf.setString("table.exec.mini-batch.allow-latency","15 s");
>> >>> >>>>>>> conf.setString("table.exec.mini-batch.size","100");
>> >>> >>>>>>> conf.setString("table.exec.state.ttl","20 s");
>> >>> >>>>>>> env.configure(conf);
>> >>> >>>>>>> Table headerTable =
>> >>> >>>>>>> streamTableEnvironment.fromDataStream(headerFilterStream);
>> >>> >>>>>>> Table itemTable =
>> >>> streamTableEnvironment.fromDataStream(filterItemStream);
>> >>> >>>>>>>
>> >>> >>>>>>>
>> >>> >>>>>>>
>> streamTableEnvironment.createTemporaryView("header",headerTable);
>> >>> >>>>>>> streamTableEnvironment.createTemporaryView("item",itemTable);
>> >>> >>>>>>>
>> >>> >>>>>>> Table result = streamTableEnvironment.sqlQuery("select
>> >>> header.customer_id"
>> >>> >>>>>>> +
>> >>> >>>>>>> ",item.goods_id" +
>> >>> >>>>>>> ",header.id" +
>> >>> >>>>>>> ",header.order_status" +
>> >>> >>>>>>> ",header.shop_id" +
>> >>> >>>>>>> ",header.parent_order_id" +
>> >>> >>>>>>> ",header.order_at" +
>> >>> >>>>>>> ",header.pay_at" +
>> >>> >>>>>>> ",header.channel_id" +
>> >>> >>>>>>> ",header.root_order_id" +
>> >>> >>>>>>> ",item.id" +
>> >>> >>>>>>> ",item.row_num" +
>> >>> >>>>>>> ",item.p_sp_sub_amt" +
>> >>> >>>>>>> ",item.display_qty" +
>> >>> >>>>>>> ",item.qty" +
>> >>> >>>>>>> ",item.bom_type" +
>> >>> >>>>>>> " from header JOIN item on header.id = item.order_id");
>> >>> >>>>>>>
>> >>> >>>>>>>
>> >>> >>>>>>> DataStream<Row> rowDataStream =
>> >>> >>>>>>> streamTableEnvironment.toChangelogStream(result);
>> >>> >>>>>>> 不太理解为什么使用interval join会丢这么多数据，按照我的理解使用sql
>> join，底层应该也是用的类似interval
>> >>> >>>>>>> join，为啥两者最终关联上的结果差异这么大。
>> >>> >>>>>>>
>> >>> >>>>>>>
>> >>> >>>>>>>
>> >>> >>>>>>>
>> >>> >>>>>>>
>> >>> >>>>>>>
>> >>> >>>>>>>
>> >>> >>>>>>>
>> >>> >>>>>>>
>> >>> >>>>>>>
>> >>> >>>>>>>
>> >>>
>>
Re: Re:Re: Re: Flink 使用interval join数据丢失疑问
Posted by Shengkai Fang <fs...@gmail.com>.
> 我在sql inner join里设置的状态ttl为20s,为什么20s的状态保留数据要比interval join开分钟级别的数据还要准确
不合理的 watermark 设置在 interval join 就会导致丢数据。设置 ttl 情况下，如果某个 key
的数据频繁访问情况下，那么这个数据就不会过期。
> 我的做法是排查了tm和jm的日志，没有看见写的表的配置的日志。
我记得日志是会打印相关的日志。能提一些相关的日志吗？
best,
Shengkai
lxk <lx...@163.com> 于2022年6月14日周二 20:04写道：
> Hi,
> 我目前使用sql interval join,窗口的上下界增加到分钟级别，分别是-2 minute 和 +4 minute
> 目前来看数据量和使用inner join要差不多了。以下是代码
> Table headerTable =
> streamTableEnvironment.fromDataStream(headerFilterStream,
> Schema.newBuilder()
> .columnByExpression("rowtime", "CAST(substring(last_updated_at,0,19) AS
> TIMESTAMP_LTZ(3))")
> .watermark("rowtime", "rowtime + INTERVAL '2' SECOND")
> .build());
> Table itemTable = streamTableEnvironment.fromDataStream(filterItemStream,
> Schema.newBuilder()
> .columnByExpression("rowtime", "CAST(substring(last_updated_at,0,19) AS
> TIMESTAMP_LTZ(3))")
> .watermark("rowtime", "rowtime + INTERVAL '2' SECOND")
> .build());
>
>
> streamTableEnvironment.createTemporaryView("header",headerTable);
> streamTableEnvironment.createTemporaryView("item",itemTable);
> Table result = streamTableEnvironment.sqlQuery("select header.customer_id"
> +
> ",item.goods_id" +
> ",header.id" +
> ",header.order_status" +
> ",header.shop_id" +
> ",header.parent_order_id" +
> ",header.order_at" +
> ",header.pay_at" +
> ",header.channel_id" +
> ",header.root_order_id" +
> ",item.id" +
> ",item.row_num" +
> ",item.p_sp_sub_amt" +
> ",item.display_qty" +
> ",item.qty" +
> ",item.bom_type" +
> " from item JOIN header on header.id = item.order_id and item.rowtime
> BETWEEN header.rowtime - INTERVAL '2' MINUTE AND header.rowtime + INTERVAL
> '4' MINUTE");
>
> 对此，我又有新的疑问了，我在sql inner join里设置的状态ttl为20s,为什么20s的状态保留数据要比interval
> join开分钟级别的数据还要准确？针对这个问题，不知道大家有什么看法和思路？
> 我的一个猜测是我设置的表的ttl没有生效，inner join一直使用的是全量的数据，所以结果准确度要比interval
> join高，我的做法是排查了tm和jm的日志，没有看见写的表的配置的日志，我的配置如下：
> Configuration conf = new Configuration();
> conf.setString("table.exec.mini-batch.enabled","true");
> conf.setString("table.exec.mini-batch.allow-latency","15 s");
> conf.setString("table.exec.mini-batch.size","100");
> conf.setString("table.exec.state.ttl","20 s");
> env.configure(conf);
> StreamTableEnvironment streamTableEnvironment =
> StreamTableEnvironment.create(env,
> EnvironmentSettings.fromConfiguration(conf));
>
>
> 我想了解下，从tm和jm日志是否能正确反应我的配置生效？如果不行，那我要使用什么方法才能知道我的这个配置是否生效？
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
> 在 2022-06-13 21:12:48，"Xuyang" <xy...@163.com> 写道：
> >Hi,
> > 1、理论上来说inner join关联的数据量应该比interval
> join更大吧。关于左右两边流速度不一致的情况，理论上应该问题不大，因为需要等到两边的watermark都到齐之后才会触发状态里过期数据的清除。
> > 2、inner
> join没有水印的情况下，就是到了就发，完全根据这条数据进入这个算子的时间来算，也就是“处理时间”。默认数据是不会过期的，会存全量数据。如果定义了ttl，得看join两侧的表的pk和join
> key，分别用不同的state数据格式来存数据（ListState、MapState等），原则上是多长时间没更新数据之后，就清空过期数据，是按照的“处理时间”来处理的。
> >
> >
> >如果我有不对的地方，请指正我哈。
> >
> >
> >
> >
> >--
> >
> > Best！
> > Xuyang
> >
> >
> >
> >
> >
> >在 2022-06-12 14:39:39，"lxk7491@163.com" <lx...@163.com> 写道：
> >>非常感谢回复
> >>1.针对watermark,我会再去进行测试。同时还会测试使用处理时间，interval join会不会丢失数据
> >>2.针对interval jon，我个人的理解是它能关联到的数据范围要比inner
> join大，所以数据应该更准确，但是从结果上看却是数据丢失，当时非常震惊，有点颠覆我的认知了。同时我自己还有一个新的猜测，就是两个流的数据量不一样，可能也会造成数据丢失。目前左流是订单粒度数据，右流是订单-商品粒度数据，数据量要大很多。我个人理解，在处理右流的时候，应该会慢一点，所以导致两边的时间进展可能不一致。但是这又引发了一个新的疑问?inner
> join应该也会受这样的影响
> >>3.还有一个问题可能是我没有阐述清楚，我在sql里使用inner
> join，没有注册水印，那么两个流的join应该是以处理时间来定义的？那么表的state的过期是否也是以处理时间来定义？
> >>
> >>
> >>
> >>lxk7491@163.com
> >>
> >>发件人： Shengkai Fang
> >>发送时间： 2022-06-11 20:35
> >>收件人： user-zh
> >>主题： Re: Re: Flink 使用interval join数据丢失疑问
> >>hi,
> >>
> >>对于第一点，丢数据的情况有很多。首先，要确认是不是 JOIN 算子丢数据（SINK 的使用不当也会丢数据）。如果明确了是 join
> >>算子丢的数据，建议明确下丢的数据是咋样的，是不是 watermark 设置不合理，导致数据被误认为是晚到数据从而被丢了。例如，这里的是
> `event
> >>time` = `rowtime` - 2s，是不是不合适，我咋记得一般都是 +2 s 呢？
> >>
> >>对于第二点，interval join 我个人初步的理解是 state 的清理是根据两边的 event time，也就是说，如果右流的 event
> >>time 的更新会影响左流的数据清理。比如说右流的时间点到了 12：00，join 条件要求左流的时间不会晚于右流的时间 1h，那么左流
> >>11:00之前的数据都可以被清理了。
> >>
> >>对于第三点，我觉得是不能的。目前的 inner join + state 清理无法覆盖 event time 的window join 的。
> >>
> >>best,
> >>Shengkai
> >>
> >>lxk7491@163.com <lx...@163.com> 于2022年6月10日周五 23:03写道：
> >>
> >>> 对于这个问题，我还是有很大的疑问，再把我这个场景描述一下：
> >>>
> >>>
> 目前是使用flink进行双流join,两个流的数据，一个流是订单主表，另一个流是订单明细表。我们探查了离线的数据，订单明细表一般会在订单主表生成后晚几秒内生成，这个差异在秒级别。
> >>>
> 我们做了以下几轮测试，并对比了另一个实时落的表数据量。(这个表就是基准参照数据，只是简单落表，没做任何处理，两边的数据源一致，对比的口径一致。)
> >>> 1.使用datastream api,使用kafka自带的时间戳做水印，使用interval join。对比完结果，数据少。
> >>> 2.使用流转表，sql inner join,没有设置watermark。对比完结果数据正常。
> >>> 3.使用流转表，sql interval join,从数据中的事件时间提取水印，对比完结果数据，数据少。
> >>> 从结果上看，我不太明白为什么sql里inner join能保证数据准确，而interval
> >>> join不行？有什么好的方式或者思路能让我更好的去尝试了解这个问题产生的原因
> >>>
> >>>
> 针对第二种方式，我的疑问是，sql里没有设置水印，那么表的state过期是以处理时间来计算吗？针对这种设置了表state过期时间的join，我能理解为这个inner
> >>> join其实是一个window join吗？
> >>>
> >>>
> >>>
> >>> lxk7491@163.com
> >>>
> >>> 发件人： lxk
> >>> 发送时间： 2022-06-10 18:18
> >>> 收件人： user-zh
> >>> 主题： Re:Re:Re:Re:Re:Re:Re: Flink 使用interval join数据丢失疑问
> >>>
> >>>
> >>>
> >>> 现在改成了sql interval join,代码和执行计划如下，其他配置没变，数据量还是少，使用inner join就没问题
> >>>
> >>>
> >>>
> >>>
> >>> Table headerTable =
> >>> streamTableEnvironment.fromDataStream(headerFilterStream,
> >>> Schema.newBuilder()
> >>> .columnByExpression("rowtime",
> >>> "CAST(substring(last_updated_at,0,19) AS TIMESTAMP_LTZ(3))")
> >>> .watermark("rowtime", "rowtime - INTERVAL '2' SECOND")
> >>> .build());
> >>> Table itemTable =
> >>> streamTableEnvironment.fromDataStream(filterItemStream,
> Schema.newBuilder()
> >>> .columnByExpression("rowtime",
> >>> "CAST(substring(last_updated_at,0,19) AS TIMESTAMP_LTZ(3))")
> >>> .watermark("rowtime", "rowtime - INTERVAL '2' SECOND")
> >>> .build());
> >>>
> >>>
> >>>
> >>>
> >>>
> streamTableEnvironment.createTemporaryView("header",headerTable);
> >>> streamTableEnvironment.createTemporaryView("item",itemTable);
> >>>
> >>>
> >>>
> >>>
> >>>
> >>>
> >>> Table result = streamTableEnvironment.sqlQuery("select
> >>> header.customer_id" +
> >>> ",item.goods_id" +
> >>> ",header.id" +
> >>> ",header.order_status" +
> >>> ",header.shop_id" +
> >>> ",header.parent_order_id" +
> >>> ",header.order_at" +
> >>> ",header.pay_at" +
> >>> ",header.channel_id" +
> >>> ",header.root_order_id" +
> >>> ",item.id" +
> >>> ",item.row_num" +
> >>> ",item.p_sp_sub_amt" +
> >>> ",item.display_qty" +
> >>> ",item.qty" +
> >>> ",item.bom_type" +
> >>> " from header JOIN item on header.id = item.order_id
> and
> >>> item.rowtime BETWEEN header.rowtime - INTERVAL '10' SECOND AND
> >>> header.rowtime + INTERVAL '20' SECOND");
> >>>
> >>>
> >>>
> >>>
> >>> String intervalJoin = streamTableEnvironment.explainSql("select
> >>> header.customer_id" +
> >>> ",item.goods_id" +
> >>> ",header.id" +
> >>> ",header.order_status" +
> >>> ",header.shop_id" +
> >>> ",header.parent_order_id" +
> >>> ",header.order_at" +
> >>> ",header.pay_at" +
> >>> ",header.channel_id" +
> >>> ",header.root_order_id" +
> >>> ",item.id" +
> >>> ",item.row_num" +
> >>> ",item.p_sp_sub_amt" +
> >>> ",item.display_qty" +
> >>> ",item.qty" +
> >>> ",item.bom_type" +
> >>> " from header JOIN item on header.id = item.order_id
> and
> >>> item.rowtime BETWEEN header.rowtime - INTERVAL '10' SECOND AND
> >>> header.rowtime + INTERVAL '20' SECOND");
> >>>
> >>>
> >>> System.out.println(intervalJoin);
> >>>
> >>>
> >>> DataStream<Row> rowDataStream =
> >>> streamTableEnvironment.toChangelogStream(result);
> >>>
> >>>
> >>>
> >>>
> >>>
> >>>
> >>> 执行计划:
> >>> == Abstract Syntax Tree ==
> >>> LogicalProject(customer_id=[$2], goods_id=[$16], id=[$0],
> >>> order_status=[$1], shop_id=[$3], parent_order_id=[$4], order_at=[$5],
> >>> pay_at=[$6], channel_id=[$7], root_order_id=[$8], id0=[$13],
> row_num=[$15],
> >>> p_sp_sub_amt=[$20], display_qty=[$23], qty=[$18], bom_type=[$21])
> >>> +- LogicalJoin(condition=[AND(=($0, $14), >=($25, -($12, 10000:INTERVAL
> >>> SECOND)), <=($25, +($12, 20000:INTERVAL SECOND)))], joinType=[inner])
> >>> :- LogicalWatermarkAssigner(rowtime=[rowtime], watermark=[-($12,
> >>> 2000:INTERVAL SECOND)])
> >>> : +- LogicalProject(id=[$0], order_status=[$1], customer_id=[$2],
> >>> shop_id=[$3], parent_order_id=[$4], order_at=[$5], pay_at=[$6],
> >>> channel_id=[$7], root_order_id=[$8], last_updated_at=[$9],
> >>> business_flag=[$10], mysql_op_type=[$11], rowtime=[CAST(SUBSTRING($9,
> 0,
> >>> 19)):TIMESTAMP_WITH_LOCAL_TIME_ZONE(3)])
> >>> : +- LogicalTableScan(table=[[default_catalog, default_database,
> >>> Unregistered_DataStream_Source_5]])
> >>> +- LogicalWatermarkAssigner(rowtime=[rowtime], watermark=[-($12,
> >>> 2000:INTERVAL SECOND)])
> >>> +- LogicalProject(id=[$0], order_id=[$1], row_num=[$2],
> >>> goods_id=[$3], s_sku_code=[$4], qty=[$5], p_paid_sub_amt=[$6],
> >>> p_sp_sub_amt=[$7], bom_type=[$8], last_updated_at=[$9],
> display_qty=[$10],
> >>> is_first_flag=[$11], rowtime=[CAST(SUBSTRING($9, 0,
> >>> 19)):TIMESTAMP_WITH_LOCAL_TIME_ZONE(3)])
> >>> +- LogicalTableScan(table=[[default_catalog, default_database,
> >>> Unregistered_DataStream_Source_8]])
> >>>
> >>>
> >>> == Optimized Physical Plan ==
> >>> Calc(select=[customer_id, goods_id, id, order_status, shop_id,
> >>> parent_order_id, order_at, pay_at, channel_id, root_order_id, id0,
> row_num,
> >>> p_sp_sub_amt, display_qty, qty, bom_type])
> >>> +- IntervalJoin(joinType=[InnerJoin], windowBounds=[isRowTime=true,
> >>> leftLowerBound=-20000, leftUpperBound=10000, leftTimeIndex=9,
> >>> rightTimeIndex=8], where=[AND(=(id, order_id), >=(rowtime0, -(rowtime,
> >>> 10000:INTERVAL SECOND)), <=(rowtime0, +(rowtime, 20000:INTERVAL
> SECOND)))],
> >>> select=[id, order_status, customer_id, shop_id, parent_order_id,
> order_at,
> >>> pay_at, channel_id, root_order_id, rowtime, id0, order_id, row_num,
> >>> goods_id, qty, p_sp_sub_amt, bom_type, display_qty, rowtime0])
> >>> :- Exchange(distribution=[hash[id]])
> >>> : +- WatermarkAssigner(rowtime=[rowtime], watermark=[-(rowtime,
> >>> 2000:INTERVAL SECOND)])
> >>> : +- Calc(select=[id, order_status, customer_id, shop_id,
> >>> parent_order_id, order_at, pay_at, channel_id, root_order_id,
> >>> CAST(SUBSTRING(last_updated_at, 0, 19)) AS rowtime])
> >>> : +- TableSourceScan(table=[[default_catalog,
> default_database,
> >>> Unregistered_DataStream_Source_5]], fields=[id, order_status,
> customer_id,
> >>> shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id,
> >>> last_updated_at, business_flag, mysql_op_type])
> >>> +- Exchange(distribution=[hash[order_id]])
> >>> +- WatermarkAssigner(rowtime=[rowtime], watermark=[-(rowtime,
> >>> 2000:INTERVAL SECOND)])
> >>> +- Calc(select=[id, order_id, row_num, goods_id, qty,
> >>> p_sp_sub_amt, bom_type, display_qty, CAST(SUBSTRING(last_updated_at, 0,
> >>> 19)) AS rowtime])
> >>> +- TableSourceScan(table=[[default_catalog,
> default_database,
> >>> Unregistered_DataStream_Source_8]], fields=[id, order_id, row_num,
> >>> goods_id, s_sku_code, qty, p_paid_sub_amt, p_sp_sub_amt, bom_type,
> >>> last_updated_at, display_qty, is_first_flag])
> >>>
> >>>
> >>> == Optimized Execution Plan ==
> >>> Calc(select=[customer_id, goods_id, id, order_status, shop_id,
> >>> parent_order_id, order_at, pay_at, channel_id, root_order_id, id0,
> row_num,
> >>> p_sp_sub_amt, display_qty, qty, bom_type])
> >>> +- IntervalJoin(joinType=[InnerJoin], windowBounds=[isRowTime=true,
> >>> leftLowerBound=-20000, leftUpperBound=10000, leftTimeIndex=9,
> >>> rightTimeIndex=8], where=[((id = order_id) AND (rowtime0 >= (rowtime -
> >>> 10000:INTERVAL SECOND)) AND (rowtime0 <= (rowtime + 20000:INTERVAL
> >>> SECOND)))], select=[id, order_status, customer_id, shop_id,
> >>> parent_order_id, order_at, pay_at, channel_id, root_order_id, rowtime,
> id0,
> >>> order_id, row_num, goods_id, qty, p_sp_sub_amt, bom_type, display_qty,
> >>> rowtime0])
> >>> :- Exchange(distribution=[hash[id]])
> >>> : +- WatermarkAssigner(rowtime=[rowtime], watermark=[(rowtime -
> >>> 2000:INTERVAL SECOND)])
> >>> : +- Calc(select=[id, order_status, customer_id, shop_id,
> >>> parent_order_id, order_at, pay_at, channel_id, root_order_id,
> >>> CAST(SUBSTRING(last_updated_at, 0, 19)) AS rowtime])
> >>> : +- TableSourceScan(table=[[default_catalog,
> default_database,
> >>> Unregistered_DataStream_Source_5]], fields=[id, order_status,
> customer_id,
> >>> shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id,
> >>> last_updated_at, business_flag, mysql_op_type])
> >>> +- Exchange(distribution=[hash[order_id]])
> >>> +- WatermarkAssigner(rowtime=[rowtime], watermark=[(rowtime -
> >>> 2000:INTERVAL SECOND)])
> >>> +- Calc(select=[id, order_id, row_num, goods_id, qty,
> >>> p_sp_sub_amt, bom_type, display_qty, CAST(SUBSTRING(last_updated_at, 0,
> >>> 19)) AS rowtime])
> >>> +- TableSourceScan(table=[[default_catalog,
> default_database,
> >>> Unregistered_DataStream_Source_8]], fields=[id, order_id, row_num,
> >>> goods_id, s_sku_code, qty, p_paid_sub_amt, p_sp_sub_amt, bom_type,
> >>> last_updated_at, display_qty, is_first_flag])
> >>>
> >>>
> >>>
> >>>
> >>>
> >>>
> >>>
> >>>
> >>>
> >>>
> >>>
> >>>
> >>>
> >>>
> >>>
> >>>
> >>> 在 2022-06-10 17:16:31，"lxk" <lx...@163.com> 写道：
> >>> >使用sql 进行interval
> >>> join，我目前的问题是感觉时间转换这块不太友好，我目前流里面的事件时间字段是string类型，数据样式是2022-06-10
> >>> 13:08:55,但是我使用TO_TIMESTAMP这个函数进行转换一直报错
> >>> >
> >>> >
> >>> >
> >>> >
> >>> >
> >>> >
> >>> >
> >>> >
> >>> >
> >>> >
> >>> >
> >>> >
> >>> >
> >>> >
> >>> >
> >>> >
> >>> >
> >>> >在 2022-06-10 15:04:31，"Xuyang" <xy...@163.com> 写道：
> >>> >>Hi, datastream的这个interval join的api应该对标的是sql中的interval
> >>> join。但是你目前写的这个sql，是普通join。普通join和interval
> >>> join在业务含义和实现上都是有区别的。所以你直接拿datastream api的interval
> >>> join和sql上的普通join结果对比，其实是有问题的。所以我之前的建议是让你试下让sql也使用interval
> join，这样双方才有可比性。
> >>> >>
> >>> >>
> >>>
> >>>
> >>另外sql中设置的table.exec.state.ttl这个参数，只是代表的state会20s清空过期数据，但我看你要比较的时间窗口是-10s和20s，貌似也不大一样。
> >>> >>
> >>> >>
> >>> >>
> >>> >>
> >>> >>--
> >>> >>
> >>> >> Best！
> >>> >> Xuyang
> >>> >>
> >>> >>
> >>> >>
> >>> >>
> >>> >>
> >>> >>在 2022-06-10 14:33:37，"lxk" <lx...@163.com> 写道：
> >>> >>>
> >>> >>>
> >>> >>>
> >>> >>>我不理解的点在于，我interval join开的时间窗口比我sql中设置的状态时间都要长，窗口的上下界别是-10s 和
> >>> 20s，为什么会丢数据？
> >>> >>>
> >>> >>>sql中我设置这个table.exec.state.ttl参数
> >>> 为20s，照理来说两个流应该也是保留20s的数据在状态中进行join。不知道我的理解是否有问题，希望能够得到解答。
> >>> >>>
> >>> >>>
> >>> >>>
> >>> >>>
> >>> >>>
> >>> >>>
> >>> >>>
> >>> >>>
> >>> >>>
> >>> >>>
> >>> >>>
> >>> >>>
> >>> >>>
> >>> >>>
> >>> >>>在 2022-06-10 14:15:29，"Xuyang" <xy...@163.com> 写道：
> >>> >>>>Hi, 你的这条SQL 并不是interval join，是普通join。
> >>> >>>>interval join的使用文档可以参考文档[1]。可以试下使用SQL interval
> >>> join会不会丢数据（注意设置state的ttl），从而判断是数据的问题还是datastream api的问题。
> >>> >>>>
> >>> >>>>
> >>> >>>>
> >>> >>>>
> >>> >>>>[1]
> >>>
> https://nightlies.apache.org/flink/flink-docs-master/docs/dev/table/sql/queries/joins/#interval-joins
> >>> >>>>
> >>> >>>>
> >>> >>>>
> >>> >>>>
> >>> >>>>
> >>> >>>>
> >>> >>>>
> >>> >>>>--
> >>> >>>>
> >>> >>>> Best！
> >>> >>>> Xuyang
> >>> >>>>
> >>> >>>>
> >>> >>>>
> >>> >>>>
> >>> >>>>
> >>> >>>>在 2022-06-10 11:26:33，"lxk" <lx...@163.com> 写道：
> >>> >>>>>我用的是以下代码:
> >>> >>>>>String s = streamTableEnvironment.explainSql("select
> >>> header.customer_id" +
> >>> >>>>>",item.goods_id" +
> >>> >>>>>",header.id" +
> >>> >>>>>",header.order_status" +
> >>> >>>>>",header.shop_id" +
> >>> >>>>>",header.parent_order_id" +
> >>> >>>>>",header.order_at" +
> >>> >>>>>",header.pay_at" +
> >>> >>>>>",header.channel_id" +
> >>> >>>>>",header.root_order_id" +
> >>> >>>>>",item.id" +
> >>> >>>>>",item.row_num" +
> >>> >>>>>",item.p_sp_sub_amt" +
> >>> >>>>>",item.display_qty" +
> >>> >>>>>",item.qty" +
> >>> >>>>>",item.bom_type" +
> >>> >>>>>" from header JOIN item on header.id = item.order_id");
> >>> >>>>>
> >>> >>>>>System.out.println("explain:" + s);
> >>> >>>>>
> >>> >>>>>
> >>> >>>>>
> >>> >>>>>
> >>> >>>>>plan信息为:
> >>> >>>>>explain:== Abstract Syntax Tree ==
> >>> >>>>>LogicalProject(customer_id=[$2], goods_id=[$15], id=[$0],
> >>> order_status=[$1], shop_id=[$3], parent_order_id=[$4], order_at=[$5],
> >>> pay_at=[$6], channel_id=[$7], root_order_id=[$8], id0=[$12],
> row_num=[$14],
> >>> p_sp_sub_amt=[$19], display_qty=[$22], qty=[$17], bom_type=[$20])
> >>> >>>>>+- LogicalJoin(condition=[=($0, $13)], joinType=[inner])
> >>> >>>>> :- LogicalTableScan(table=[[default_catalog, default_database,
> >>> Unregistered_DataStream_Source_5]])
> >>> >>>>> +- LogicalTableScan(table=[[default_catalog, default_database,
> >>> Unregistered_DataStream_Source_8]])
> >>> >>>>>
> >>> >>>>>
> >>> >>>>>== Optimized Physical Plan ==
> >>> >>>>>Calc(select=[customer_id, goods_id, id, order_status, shop_id,
> >>> parent_order_id, order_at, pay_at, channel_id, root_order_id, id0,
> row_num,
> >>> p_sp_sub_amt, display_qty, qty, bom_type])
> >>> >>>>>+- Join(joinType=[InnerJoin], where=[=(id, order_id)], select=[id,
> >>> order_status, customer_id, shop_id, parent_order_id, order_at, pay_at,
> >>> channel_id, root_order_id, id0, order_id, row_num, goods_id, qty,
> >>> p_sp_sub_amt, bom_type, display_qty], leftInputSpec=[NoUniqueKey],
> >>> rightInputSpec=[NoUniqueKey])
> >>> >>>>> :- Exchange(distribution=[hash[id]])
> >>> >>>>> : +- Calc(select=[id, order_status, customer_id, shop_id,
> >>> parent_order_id, order_at, pay_at, channel_id, root_order_id])
> >>> >>>>> : +- TableSourceScan(table=[[default_catalog,
> >>> default_database, Unregistered_DataStream_Source_5]], fields=[id,
> >>> order_status, customer_id, shop_id, parent_order_id, order_at, pay_at,
> >>> channel_id, root_order_id, last_updated_at, business_flag,
> mysql_op_type])
> >>> >>>>> +- Exchange(distribution=[hash[order_id]])
> >>> >>>>> +- Calc(select=[id, order_id, row_num, goods_id, qty,
> >>> p_sp_sub_amt, bom_type, display_qty])
> >>> >>>>> +- TableSourceScan(table=[[default_catalog,
> >>> default_database, Unregistered_DataStream_Source_8]], fields=[id,
> order_id,
> >>> row_num, goods_id, s_sku_code, qty, p_paid_sub_amt, p_sp_sub_amt,
> bom_type,
> >>> last_updated_at, display_qty, is_first_flag])
> >>> >>>>>
> >>> >>>>>
> >>> >>>>>== Optimized Execution Plan ==
> >>> >>>>>Calc(select=[customer_id, goods_id, id, order_status, shop_id,
> >>> parent_order_id, order_at, pay_at, channel_id, root_order_id, id0,
> row_num,
> >>> p_sp_sub_amt, display_qty, qty, bom_type])
> >>> >>>>>+- Join(joinType=[InnerJoin], where=[(id = order_id)], select=[id,
> >>> order_status, customer_id, shop_id, parent_order_id, order_at, pay_at,
> >>> channel_id, root_order_id, id0, order_id, row_num, goods_id, qty,
> >>> p_sp_sub_amt, bom_type, display_qty], leftInputSpec=[NoUniqueKey],
> >>> rightInputSpec=[NoUniqueKey])
> >>> >>>>> :- Exchange(distribution=[hash[id]])
> >>> >>>>> : +- Calc(select=[id, order_status, customer_id, shop_id,
> >>> parent_order_id, order_at, pay_at, channel_id, root_order_id])
> >>> >>>>> : +- TableSourceScan(table=[[default_catalog,
> >>> default_database, Unregistered_DataStream_Source_5]], fields=[id,
> >>> order_status, customer_id, shop_id, parent_order_id, order_at, pay_at,
> >>> channel_id, root_order_id, last_updated_at, business_flag,
> mysql_op_type])
> >>> >>>>> +- Exchange(distribution=[hash[order_id]])
> >>> >>>>> +- Calc(select=[id, order_id, row_num, goods_id, qty,
> >>> p_sp_sub_amt, bom_type, display_qty])
> >>> >>>>> +- TableSourceScan(table=[[default_catalog,
> >>> default_database, Unregistered_DataStream_Source_8]], fields=[id,
> order_id,
> >>> row_num, goods_id, s_sku_code, qty, p_paid_sub_amt, p_sp_sub_amt,
> bom_type,
> >>> last_updated_at, display_qty, is_first_flag])
> >>> >>>>>
> >>> >>>>>
> >>> >>>>>
> >>> >>>>>
> >>> >>>>>
> >>> >>>>>
> >>> >>>>>
> >>> >>>>>
> >>> >>>>>
> >>> >>>>>
> >>> >>>>>
> >>> >>>>>
> >>> >>>>>
> >>> >>>>>在 2022-06-10 11:02:56，"Shengkai Fang" <fs...@gmail.com> 写道：
> >>> >>>>>>你好，能提供下具体的 plan 供大家查看下吗？
> >>> >>>>>>
> >>> >>>>>>你可以直接 使用 tEnv.executeSql("Explain JSON_EXECUTION_PLAN
> >>> >>>>>><YOUR_QUERY>").print() 打印下相关的信息。
> >>> >>>>>>
> >>> >>>>>>Best,
> >>> >>>>>>Shengkai
> >>> >>>>>>
> >>> >>>>>>lxk <lx...@163.com> 于2022年6月10日周五 10:29写道：
> >>> >>>>>>
> >>> >>>>>>> flink 版本:1.14.4
> >>> >>>>>>> 目前在使用flink interval join进行数据关联，在测试的时候发现一个问题，就是使用interval
> >>> >>>>>>> join完之后数据会丢失，但是使用sql api，直接进行join，数据是正常的，没有丢失。
> >>> >>>>>>> 水印是直接使用kafka 自带的时间戳生成watermark
> >>> >>>>>>>
> >>> >>>>>>>
> >>> >>>>>>> 以下是代码 ---interval join
> >>> >>>>>>>
> >>> >>>>>>> SingleOutputStreamOperator<HeaderFull> headerFullStream =
> >>> >>>>>>> headerFilterStream.keyBy(data -> data.getId())
> >>> >>>>>>> .intervalJoin(filterItemStream.keyBy(data ->
> data.getOrder_id()))
> >>> >>>>>>> .between(Time.seconds(-10), Time.seconds(20))
> >>> >>>>>>> .process(new ProcessJoinFunction<OrderHeader, OrderItem,
> >>> HeaderFull>() {
> >>> >>>>>>> @Override
> >>> >>>>>>> public void processElement(OrderHeader left, OrderItem right,
> >>> Context
> >>> >>>>>>> context, Collector<HeaderFull> collector) throws Exception {
> >>> >>>>>>> HeaderFull headerFull = new HeaderFull();
> >>> >>>>>>> BeanUtilsBean beanUtilsBean = new BeanUtilsBean();
> >>> >>>>>>> beanUtilsBean.copyProperties(headerFull, left);
> >>> >>>>>>> beanUtilsBean.copyProperties(headerFull, right);
> >>> >>>>>>> String event_date = left.getOrder_at().substring(0, 10);
> >>> >>>>>>> headerFull.setEvent_date(event_date);
> >>> >>>>>>> headerFull.setItem_id(right.getId());
> >>> >>>>>>> collector.collect(headerFull);
> >>> >>>>>>> }
> >>> >>>>>>> }
> >>> >>>>>>> 使用sql 进行join
> >>> >>>>>>> Configuration conf = new Configuration();
> >>> >>>>>>> conf.setString("table.exec.mini-batch.enabled","true");
> >>> >>>>>>> conf.setString("table.exec.mini-batch.allow-latency","15 s");
> >>> >>>>>>> conf.setString("table.exec.mini-batch.size","100");
> >>> >>>>>>> conf.setString("table.exec.state.ttl","20 s");
> >>> >>>>>>> env.configure(conf);
> >>> >>>>>>> Table headerTable =
> >>> >>>>>>> streamTableEnvironment.fromDataStream(headerFilterStream);
> >>> >>>>>>> Table itemTable =
> >>> streamTableEnvironment.fromDataStream(filterItemStream);
> >>> >>>>>>>
> >>> >>>>>>>
> >>> >>>>>>>
> streamTableEnvironment.createTemporaryView("header",headerTable);
> >>> >>>>>>> streamTableEnvironment.createTemporaryView("item",itemTable);
> >>> >>>>>>>
> >>> >>>>>>> Table result = streamTableEnvironment.sqlQuery("select
> >>> header.customer_id"
> >>> >>>>>>> +
> >>> >>>>>>> ",item.goods_id" +
> >>> >>>>>>> ",header.id" +
> >>> >>>>>>> ",header.order_status" +
> >>> >>>>>>> ",header.shop_id" +
> >>> >>>>>>> ",header.parent_order_id" +
> >>> >>>>>>> ",header.order_at" +
> >>> >>>>>>> ",header.pay_at" +
> >>> >>>>>>> ",header.channel_id" +
> >>> >>>>>>> ",header.root_order_id" +
> >>> >>>>>>> ",item.id" +
> >>> >>>>>>> ",item.row_num" +
> >>> >>>>>>> ",item.p_sp_sub_amt" +
> >>> >>>>>>> ",item.display_qty" +
> >>> >>>>>>> ",item.qty" +
> >>> >>>>>>> ",item.bom_type" +
> >>> >>>>>>> " from header JOIN item on header.id = item.order_id");
> >>> >>>>>>>
> >>> >>>>>>>
> >>> >>>>>>> DataStream<Row> rowDataStream =
> >>> >>>>>>> streamTableEnvironment.toChangelogStream(result);
> >>> >>>>>>> 不太理解为什么使用interval join会丢这么多数据，按照我的理解使用sql
> join，底层应该也是用的类似interval
> >>> >>>>>>> join，为啥两者最终关联上的结果差异这么大。
> >>> >>>>>>>
> >>> >>>>>>>
> >>> >>>>>>>
> >>> >>>>>>>
> >>> >>>>>>>
> >>> >>>>>>>
> >>> >>>>>>>
> >>> >>>>>>>
> >>> >>>>>>>
> >>> >>>>>>>
> >>> >>>>>>>
> >>>
>
Re:Re:Re: Re: Flink 使用interval join数据丢失疑问
Posted by lxk <lx...@163.com>.
Hi,
我目前使用sql interval join,窗口的上下界增加到分钟级别，分别是-2 minute 和 +4 minute 目前来看数据量和使用inner join要差不多了。以下是代码
Table headerTable = streamTableEnvironment.fromDataStream(headerFilterStream, Schema.newBuilder()
.columnByExpression("rowtime", "CAST(substring(last_updated_at,0,19) AS TIMESTAMP_LTZ(3))")
.watermark("rowtime", "rowtime + INTERVAL '2' SECOND")
.build());
Table itemTable = streamTableEnvironment.fromDataStream(filterItemStream, Schema.newBuilder()
.columnByExpression("rowtime", "CAST(substring(last_updated_at,0,19) AS TIMESTAMP_LTZ(3))")
.watermark("rowtime", "rowtime + INTERVAL '2' SECOND")
.build());
streamTableEnvironment.createTemporaryView("header",headerTable);
streamTableEnvironment.createTemporaryView("item",itemTable);
Table result = streamTableEnvironment.sqlQuery("select header.customer_id" +
",item.goods_id" +
",header.id" +
",header.order_status" +
",header.shop_id" +
",header.parent_order_id" +
",header.order_at" +
",header.pay_at" +
",header.channel_id" +
",header.root_order_id" +
",item.id" +
",item.row_num" +
",item.p_sp_sub_amt" +
",item.display_qty" +
",item.qty" +
",item.bom_type" +
" from item JOIN header on header.id = item.order_id and item.rowtime BETWEEN header.rowtime - INTERVAL '2' MINUTE AND header.rowtime + INTERVAL '4' MINUTE");
对此，我又有新的疑问了，我在sql inner join里设置的状态ttl为20s,为什么20s的状态保留数据要比interval join开分钟级别的数据还要准确？针对这个问题，不知道大家有什么看法和思路？
我的一个猜测是我设置的表的ttl没有生效，inner join一直使用的是全量的数据，所以结果准确度要比interval join高，我的做法是排查了tm和jm的日志，没有看见写的表的配置的日志，我的配置如下：
Configuration conf = new Configuration();
conf.setString("table.exec.mini-batch.enabled","true");
conf.setString("table.exec.mini-batch.allow-latency","15 s");
conf.setString("table.exec.mini-batch.size","100");
conf.setString("table.exec.state.ttl","20 s");
env.configure(conf);
StreamTableEnvironment streamTableEnvironment = StreamTableEnvironment.create(env, EnvironmentSettings.fromConfiguration(conf));
我想了解下，从tm和jm日志是否能正确反应我的配置生效？如果不行，那我要使用什么方法才能知道我的这个配置是否生效？
在 2022-06-13 21:12:48，"Xuyang" <xy...@163.com> 写道：
>Hi,
> 1、理论上来说inner join关联的数据量应该比interval join更大吧。关于左右两边流速度不一致的情况，理论上应该问题不大，因为需要等到两边的watermark都到齐之后才会触发状态里过期数据的清除。
> 2、inner join没有水印的情况下，就是到了就发，完全根据这条数据进入这个算子的时间来算，也就是“处理时间”。默认数据是不会过期的，会存全量数据。如果定义了ttl，得看join两侧的表的pk和join key，分别用不同的state数据格式来存数据（ListState、MapState等），原则上是多长时间没更新数据之后，就清空过期数据，是按照的“处理时间”来处理的。
>
>
>如果我有不对的地方，请指正我哈。
>
>
>
>
>--
>
> Best！
> Xuyang
>
>
>
>
>
>在 2022-06-12 14:39:39，"lxk7491@163.com" <lx...@163.com> 写道：
>>非常感谢回复
>>1.针对watermark,我会再去进行测试。同时还会测试使用处理时间，interval join会不会丢失数据
>>2.针对interval jon，我个人的理解是它能关联到的数据范围要比inner join大，所以数据应该更准确，但是从结果上看却是数据丢失，当时非常震惊，有点颠覆我的认知了。同时我自己还有一个新的猜测，就是两个流的数据量不一样，可能也会造成数据丢失。目前左流是订单粒度数据，右流是订单-商品粒度数据，数据量要大很多。我个人理解，在处理右流的时候，应该会慢一点，所以导致两边的时间进展可能不一致。但是这又引发了一个新的疑问?inner join应该也会受这样的影响
>>3.还有一个问题可能是我没有阐述清楚，我在sql里使用inner join，没有注册水印，那么两个流的join应该是以处理时间来定义的？那么表的state的过期是否也是以处理时间来定义？
>>
>>
>>
>>lxk7491@163.com
>>
>>发件人： Shengkai Fang
>>发送时间： 2022-06-11 20:35
>>收件人： user-zh
>>主题： Re: Re: Flink 使用interval join数据丢失疑问
>>hi,
>>
>>对于第一点，丢数据的情况有很多。首先，要确认是不是 JOIN 算子丢数据（SINK 的使用不当也会丢数据）。如果明确了是 join
>>算子丢的数据，建议明确下丢的数据是咋样的，是不是 watermark 设置不合理，导致数据被误认为是晚到数据从而被丢了。例如，这里的是 `event
>>time` = `rowtime` - 2s，是不是不合适，我咋记得一般都是 +2 s 呢？
>>
>>对于第二点，interval join 我个人初步的理解是 state 的清理是根据两边的 event time，也就是说，如果右流的 event
>>time 的更新会影响左流的数据清理。比如说右流的时间点到了 12：00，join 条件要求左流的时间不会晚于右流的时间 1h，那么左流
>>11:00之前的数据都可以被清理了。
>>
>>对于第三点，我觉得是不能的。目前的 inner join + state 清理无法覆盖 event time 的window join 的。
>>
>>best,
>>Shengkai
>>
>>lxk7491@163.com <lx...@163.com> 于2022年6月10日周五 23:03写道：
>>
>>> 对于这个问题，我还是有很大的疑问，再把我这个场景描述一下：
>>>
>>> 目前是使用flink进行双流join,两个流的数据，一个流是订单主表，另一个流是订单明细表。我们探查了离线的数据，订单明细表一般会在订单主表生成后晚几秒内生成，这个差异在秒级别。
>>> 我们做了以下几轮测试，并对比了另一个实时落的表数据量。(这个表就是基准参照数据，只是简单落表，没做任何处理，两边的数据源一致，对比的口径一致。)
>>> 1.使用datastream api,使用kafka自带的时间戳做水印，使用interval join。对比完结果，数据少。
>>> 2.使用流转表，sql inner join,没有设置watermark。对比完结果数据正常。
>>> 3.使用流转表，sql interval join,从数据中的事件时间提取水印，对比完结果数据，数据少。
>>> 从结果上看，我不太明白为什么sql里inner join能保证数据准确，而interval
>>> join不行？有什么好的方式或者思路能让我更好的去尝试了解这个问题产生的原因
>>>
>>> 针对第二种方式，我的疑问是，sql里没有设置水印，那么表的state过期是以处理时间来计算吗？针对这种设置了表state过期时间的join，我能理解为这个inner
>>> join其实是一个window join吗？
>>>
>>>
>>>
>>> lxk7491@163.com
>>>
>>> 发件人： lxk
>>> 发送时间： 2022-06-10 18:18
>>> 收件人： user-zh
>>> 主题： Re:Re:Re:Re:Re:Re:Re: Flink 使用interval join数据丢失疑问
>>>
>>>
>>>
>>> 现在改成了sql interval join,代码和执行计划如下，其他配置没变，数据量还是少，使用inner join就没问题
>>>
>>>
>>>
>>>
>>> Table headerTable =
>>> streamTableEnvironment.fromDataStream(headerFilterStream,
>>> Schema.newBuilder()
>>> .columnByExpression("rowtime",
>>> "CAST(substring(last_updated_at,0,19) AS TIMESTAMP_LTZ(3))")
>>> .watermark("rowtime", "rowtime - INTERVAL '2' SECOND")
>>> .build());
>>> Table itemTable =
>>> streamTableEnvironment.fromDataStream(filterItemStream, Schema.newBuilder()
>>> .columnByExpression("rowtime",
>>> "CAST(substring(last_updated_at,0,19) AS TIMESTAMP_LTZ(3))")
>>> .watermark("rowtime", "rowtime - INTERVAL '2' SECOND")
>>> .build());
>>>
>>>
>>>
>>>
>>> streamTableEnvironment.createTemporaryView("header",headerTable);
>>> streamTableEnvironment.createTemporaryView("item",itemTable);
>>>
>>>
>>>
>>>
>>>
>>>
>>> Table result = streamTableEnvironment.sqlQuery("select
>>> header.customer_id" +
>>> ",item.goods_id" +
>>> ",header.id" +
>>> ",header.order_status" +
>>> ",header.shop_id" +
>>> ",header.parent_order_id" +
>>> ",header.order_at" +
>>> ",header.pay_at" +
>>> ",header.channel_id" +
>>> ",header.root_order_id" +
>>> ",item.id" +
>>> ",item.row_num" +
>>> ",item.p_sp_sub_amt" +
>>> ",item.display_qty" +
>>> ",item.qty" +
>>> ",item.bom_type" +
>>> " from header JOIN item on header.id = item.order_id and
>>> item.rowtime BETWEEN header.rowtime - INTERVAL '10' SECOND AND
>>> header.rowtime + INTERVAL '20' SECOND");
>>>
>>>
>>>
>>>
>>> String intervalJoin = streamTableEnvironment.explainSql("select
>>> header.customer_id" +
>>> ",item.goods_id" +
>>> ",header.id" +
>>> ",header.order_status" +
>>> ",header.shop_id" +
>>> ",header.parent_order_id" +
>>> ",header.order_at" +
>>> ",header.pay_at" +
>>> ",header.channel_id" +
>>> ",header.root_order_id" +
>>> ",item.id" +
>>> ",item.row_num" +
>>> ",item.p_sp_sub_amt" +
>>> ",item.display_qty" +
>>> ",item.qty" +
>>> ",item.bom_type" +
>>> " from header JOIN item on header.id = item.order_id and
>>> item.rowtime BETWEEN header.rowtime - INTERVAL '10' SECOND AND
>>> header.rowtime + INTERVAL '20' SECOND");
>>>
>>>
>>> System.out.println(intervalJoin);
>>>
>>>
>>> DataStream<Row> rowDataStream =
>>> streamTableEnvironment.toChangelogStream(result);
>>>
>>>
>>>
>>>
>>>
>>>
>>> 执行计划:
>>> == Abstract Syntax Tree ==
>>> LogicalProject(customer_id=[$2], goods_id=[$16], id=[$0],
>>> order_status=[$1], shop_id=[$3], parent_order_id=[$4], order_at=[$5],
>>> pay_at=[$6], channel_id=[$7], root_order_id=[$8], id0=[$13], row_num=[$15],
>>> p_sp_sub_amt=[$20], display_qty=[$23], qty=[$18], bom_type=[$21])
>>> +- LogicalJoin(condition=[AND(=($0, $14), >=($25, -($12, 10000:INTERVAL
>>> SECOND)), <=($25, +($12, 20000:INTERVAL SECOND)))], joinType=[inner])
>>> :- LogicalWatermarkAssigner(rowtime=[rowtime], watermark=[-($12,
>>> 2000:INTERVAL SECOND)])
>>> : +- LogicalProject(id=[$0], order_status=[$1], customer_id=[$2],
>>> shop_id=[$3], parent_order_id=[$4], order_at=[$5], pay_at=[$6],
>>> channel_id=[$7], root_order_id=[$8], last_updated_at=[$9],
>>> business_flag=[$10], mysql_op_type=[$11], rowtime=[CAST(SUBSTRING($9, 0,
>>> 19)):TIMESTAMP_WITH_LOCAL_TIME_ZONE(3)])
>>> : +- LogicalTableScan(table=[[default_catalog, default_database,
>>> Unregistered_DataStream_Source_5]])
>>> +- LogicalWatermarkAssigner(rowtime=[rowtime], watermark=[-($12,
>>> 2000:INTERVAL SECOND)])
>>> +- LogicalProject(id=[$0], order_id=[$1], row_num=[$2],
>>> goods_id=[$3], s_sku_code=[$4], qty=[$5], p_paid_sub_amt=[$6],
>>> p_sp_sub_amt=[$7], bom_type=[$8], last_updated_at=[$9], display_qty=[$10],
>>> is_first_flag=[$11], rowtime=[CAST(SUBSTRING($9, 0,
>>> 19)):TIMESTAMP_WITH_LOCAL_TIME_ZONE(3)])
>>> +- LogicalTableScan(table=[[default_catalog, default_database,
>>> Unregistered_DataStream_Source_8]])
>>>
>>>
>>> == Optimized Physical Plan ==
>>> Calc(select=[customer_id, goods_id, id, order_status, shop_id,
>>> parent_order_id, order_at, pay_at, channel_id, root_order_id, id0, row_num,
>>> p_sp_sub_amt, display_qty, qty, bom_type])
>>> +- IntervalJoin(joinType=[InnerJoin], windowBounds=[isRowTime=true,
>>> leftLowerBound=-20000, leftUpperBound=10000, leftTimeIndex=9,
>>> rightTimeIndex=8], where=[AND(=(id, order_id), >=(rowtime0, -(rowtime,
>>> 10000:INTERVAL SECOND)), <=(rowtime0, +(rowtime, 20000:INTERVAL SECOND)))],
>>> select=[id, order_status, customer_id, shop_id, parent_order_id, order_at,
>>> pay_at, channel_id, root_order_id, rowtime, id0, order_id, row_num,
>>> goods_id, qty, p_sp_sub_amt, bom_type, display_qty, rowtime0])
>>> :- Exchange(distribution=[hash[id]])
>>> : +- WatermarkAssigner(rowtime=[rowtime], watermark=[-(rowtime,
>>> 2000:INTERVAL SECOND)])
>>> : +- Calc(select=[id, order_status, customer_id, shop_id,
>>> parent_order_id, order_at, pay_at, channel_id, root_order_id,
>>> CAST(SUBSTRING(last_updated_at, 0, 19)) AS rowtime])
>>> : +- TableSourceScan(table=[[default_catalog, default_database,
>>> Unregistered_DataStream_Source_5]], fields=[id, order_status, customer_id,
>>> shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id,
>>> last_updated_at, business_flag, mysql_op_type])
>>> +- Exchange(distribution=[hash[order_id]])
>>> +- WatermarkAssigner(rowtime=[rowtime], watermark=[-(rowtime,
>>> 2000:INTERVAL SECOND)])
>>> +- Calc(select=[id, order_id, row_num, goods_id, qty,
>>> p_sp_sub_amt, bom_type, display_qty, CAST(SUBSTRING(last_updated_at, 0,
>>> 19)) AS rowtime])
>>> +- TableSourceScan(table=[[default_catalog, default_database,
>>> Unregistered_DataStream_Source_8]], fields=[id, order_id, row_num,
>>> goods_id, s_sku_code, qty, p_paid_sub_amt, p_sp_sub_amt, bom_type,
>>> last_updated_at, display_qty, is_first_flag])
>>>
>>>
>>> == Optimized Execution Plan ==
>>> Calc(select=[customer_id, goods_id, id, order_status, shop_id,
>>> parent_order_id, order_at, pay_at, channel_id, root_order_id, id0, row_num,
>>> p_sp_sub_amt, display_qty, qty, bom_type])
>>> +- IntervalJoin(joinType=[InnerJoin], windowBounds=[isRowTime=true,
>>> leftLowerBound=-20000, leftUpperBound=10000, leftTimeIndex=9,
>>> rightTimeIndex=8], where=[((id = order_id) AND (rowtime0 >= (rowtime -
>>> 10000:INTERVAL SECOND)) AND (rowtime0 <= (rowtime + 20000:INTERVAL
>>> SECOND)))], select=[id, order_status, customer_id, shop_id,
>>> parent_order_id, order_at, pay_at, channel_id, root_order_id, rowtime, id0,
>>> order_id, row_num, goods_id, qty, p_sp_sub_amt, bom_type, display_qty,
>>> rowtime0])
>>> :- Exchange(distribution=[hash[id]])
>>> : +- WatermarkAssigner(rowtime=[rowtime], watermark=[(rowtime -
>>> 2000:INTERVAL SECOND)])
>>> : +- Calc(select=[id, order_status, customer_id, shop_id,
>>> parent_order_id, order_at, pay_at, channel_id, root_order_id,
>>> CAST(SUBSTRING(last_updated_at, 0, 19)) AS rowtime])
>>> : +- TableSourceScan(table=[[default_catalog, default_database,
>>> Unregistered_DataStream_Source_5]], fields=[id, order_status, customer_id,
>>> shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id,
>>> last_updated_at, business_flag, mysql_op_type])
>>> +- Exchange(distribution=[hash[order_id]])
>>> +- WatermarkAssigner(rowtime=[rowtime], watermark=[(rowtime -
>>> 2000:INTERVAL SECOND)])
>>> +- Calc(select=[id, order_id, row_num, goods_id, qty,
>>> p_sp_sub_amt, bom_type, display_qty, CAST(SUBSTRING(last_updated_at, 0,
>>> 19)) AS rowtime])
>>> +- TableSourceScan(table=[[default_catalog, default_database,
>>> Unregistered_DataStream_Source_8]], fields=[id, order_id, row_num,
>>> goods_id, s_sku_code, qty, p_paid_sub_amt, p_sp_sub_amt, bom_type,
>>> last_updated_at, display_qty, is_first_flag])
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>> 在 2022-06-10 17:16:31，"lxk" <lx...@163.com> 写道：
>>> >使用sql 进行interval
>>> join，我目前的问题是感觉时间转换这块不太友好，我目前流里面的事件时间字段是string类型，数据样式是2022-06-10
>>> 13:08:55,但是我使用TO_TIMESTAMP这个函数进行转换一直报错
>>> >
>>> >
>>> >
>>> >
>>> >
>>> >
>>> >
>>> >
>>> >
>>> >
>>> >
>>> >
>>> >
>>> >
>>> >
>>> >
>>> >
>>> >在 2022-06-10 15:04:31，"Xuyang" <xy...@163.com> 写道：
>>> >>Hi, datastream的这个interval join的api应该对标的是sql中的interval
>>> join。但是你目前写的这个sql，是普通join。普通join和interval
>>> join在业务含义和实现上都是有区别的。所以你直接拿datastream api的interval
>>> join和sql上的普通join结果对比，其实是有问题的。所以我之前的建议是让你试下让sql也使用interval join，这样双方才有可比性。
>>> >>
>>> >>
>>>
>>> >>另外sql中设置的table.exec.state.ttl这个参数，只是代表的state会20s清空过期数据，但我看你要比较的时间窗口是-10s和20s，貌似也不大一样。
>>> >>
>>> >>
>>> >>
>>> >>
>>> >>--
>>> >>
>>> >> Best！
>>> >> Xuyang
>>> >>
>>> >>
>>> >>
>>> >>
>>> >>
>>> >>在 2022-06-10 14:33:37，"lxk" <lx...@163.com> 写道：
>>> >>>
>>> >>>
>>> >>>
>>> >>>我不理解的点在于，我interval join开的时间窗口比我sql中设置的状态时间都要长，窗口的上下界别是-10s 和
>>> 20s，为什么会丢数据？
>>> >>>
>>> >>>sql中我设置这个table.exec.state.ttl参数
>>> 为20s，照理来说两个流应该也是保留20s的数据在状态中进行join。不知道我的理解是否有问题，希望能够得到解答。
>>> >>>
>>> >>>
>>> >>>
>>> >>>
>>> >>>
>>> >>>
>>> >>>
>>> >>>
>>> >>>
>>> >>>
>>> >>>
>>> >>>
>>> >>>
>>> >>>
>>> >>>在 2022-06-10 14:15:29，"Xuyang" <xy...@163.com> 写道：
>>> >>>>Hi, 你的这条SQL 并不是interval join，是普通join。
>>> >>>>interval join的使用文档可以参考文档[1]。可以试下使用SQL interval
>>> join会不会丢数据（注意设置state的ttl），从而判断是数据的问题还是datastream api的问题。
>>> >>>>
>>> >>>>
>>> >>>>
>>> >>>>
>>> >>>>[1]
>>> https://nightlies.apache.org/flink/flink-docs-master/docs/dev/table/sql/queries/joins/#interval-joins
>>> >>>>
>>> >>>>
>>> >>>>
>>> >>>>
>>> >>>>
>>> >>>>
>>> >>>>
>>> >>>>--
>>> >>>>
>>> >>>> Best！
>>> >>>> Xuyang
>>> >>>>
>>> >>>>
>>> >>>>
>>> >>>>
>>> >>>>
>>> >>>>在 2022-06-10 11:26:33，"lxk" <lx...@163.com> 写道：
>>> >>>>>我用的是以下代码:
>>> >>>>>String s = streamTableEnvironment.explainSql("select
>>> header.customer_id" +
>>> >>>>>",item.goods_id" +
>>> >>>>>",header.id" +
>>> >>>>>",header.order_status" +
>>> >>>>>",header.shop_id" +
>>> >>>>>",header.parent_order_id" +
>>> >>>>>",header.order_at" +
>>> >>>>>",header.pay_at" +
>>> >>>>>",header.channel_id" +
>>> >>>>>",header.root_order_id" +
>>> >>>>>",item.id" +
>>> >>>>>",item.row_num" +
>>> >>>>>",item.p_sp_sub_amt" +
>>> >>>>>",item.display_qty" +
>>> >>>>>",item.qty" +
>>> >>>>>",item.bom_type" +
>>> >>>>>" from header JOIN item on header.id = item.order_id");
>>> >>>>>
>>> >>>>>System.out.println("explain:" + s);
>>> >>>>>
>>> >>>>>
>>> >>>>>
>>> >>>>>
>>> >>>>>plan信息为:
>>> >>>>>explain:== Abstract Syntax Tree ==
>>> >>>>>LogicalProject(customer_id=[$2], goods_id=[$15], id=[$0],
>>> order_status=[$1], shop_id=[$3], parent_order_id=[$4], order_at=[$5],
>>> pay_at=[$6], channel_id=[$7], root_order_id=[$8], id0=[$12], row_num=[$14],
>>> p_sp_sub_amt=[$19], display_qty=[$22], qty=[$17], bom_type=[$20])
>>> >>>>>+- LogicalJoin(condition=[=($0, $13)], joinType=[inner])
>>> >>>>> :- LogicalTableScan(table=[[default_catalog, default_database,
>>> Unregistered_DataStream_Source_5]])
>>> >>>>> +- LogicalTableScan(table=[[default_catalog, default_database,
>>> Unregistered_DataStream_Source_8]])
>>> >>>>>
>>> >>>>>
>>> >>>>>== Optimized Physical Plan ==
>>> >>>>>Calc(select=[customer_id, goods_id, id, order_status, shop_id,
>>> parent_order_id, order_at, pay_at, channel_id, root_order_id, id0, row_num,
>>> p_sp_sub_amt, display_qty, qty, bom_type])
>>> >>>>>+- Join(joinType=[InnerJoin], where=[=(id, order_id)], select=[id,
>>> order_status, customer_id, shop_id, parent_order_id, order_at, pay_at,
>>> channel_id, root_order_id, id0, order_id, row_num, goods_id, qty,
>>> p_sp_sub_amt, bom_type, display_qty], leftInputSpec=[NoUniqueKey],
>>> rightInputSpec=[NoUniqueKey])
>>> >>>>> :- Exchange(distribution=[hash[id]])
>>> >>>>> : +- Calc(select=[id, order_status, customer_id, shop_id,
>>> parent_order_id, order_at, pay_at, channel_id, root_order_id])
>>> >>>>> : +- TableSourceScan(table=[[default_catalog,
>>> default_database, Unregistered_DataStream_Source_5]], fields=[id,
>>> order_status, customer_id, shop_id, parent_order_id, order_at, pay_at,
>>> channel_id, root_order_id, last_updated_at, business_flag, mysql_op_type])
>>> >>>>> +- Exchange(distribution=[hash[order_id]])
>>> >>>>> +- Calc(select=[id, order_id, row_num, goods_id, qty,
>>> p_sp_sub_amt, bom_type, display_qty])
>>> >>>>> +- TableSourceScan(table=[[default_catalog,
>>> default_database, Unregistered_DataStream_Source_8]], fields=[id, order_id,
>>> row_num, goods_id, s_sku_code, qty, p_paid_sub_amt, p_sp_sub_amt, bom_type,
>>> last_updated_at, display_qty, is_first_flag])
>>> >>>>>
>>> >>>>>
>>> >>>>>== Optimized Execution Plan ==
>>> >>>>>Calc(select=[customer_id, goods_id, id, order_status, shop_id,
>>> parent_order_id, order_at, pay_at, channel_id, root_order_id, id0, row_num,
>>> p_sp_sub_amt, display_qty, qty, bom_type])
>>> >>>>>+- Join(joinType=[InnerJoin], where=[(id = order_id)], select=[id,
>>> order_status, customer_id, shop_id, parent_order_id, order_at, pay_at,
>>> channel_id, root_order_id, id0, order_id, row_num, goods_id, qty,
>>> p_sp_sub_amt, bom_type, display_qty], leftInputSpec=[NoUniqueKey],
>>> rightInputSpec=[NoUniqueKey])
>>> >>>>> :- Exchange(distribution=[hash[id]])
>>> >>>>> : +- Calc(select=[id, order_status, customer_id, shop_id,
>>> parent_order_id, order_at, pay_at, channel_id, root_order_id])
>>> >>>>> : +- TableSourceScan(table=[[default_catalog,
>>> default_database, Unregistered_DataStream_Source_5]], fields=[id,
>>> order_status, customer_id, shop_id, parent_order_id, order_at, pay_at,
>>> channel_id, root_order_id, last_updated_at, business_flag, mysql_op_type])
>>> >>>>> +- Exchange(distribution=[hash[order_id]])
>>> >>>>> +- Calc(select=[id, order_id, row_num, goods_id, qty,
>>> p_sp_sub_amt, bom_type, display_qty])
>>> >>>>> +- TableSourceScan(table=[[default_catalog,
>>> default_database, Unregistered_DataStream_Source_8]], fields=[id, order_id,
>>> row_num, goods_id, s_sku_code, qty, p_paid_sub_amt, p_sp_sub_amt, bom_type,
>>> last_updated_at, display_qty, is_first_flag])
>>> >>>>>
>>> >>>>>
>>> >>>>>
>>> >>>>>
>>> >>>>>
>>> >>>>>
>>> >>>>>
>>> >>>>>
>>> >>>>>
>>> >>>>>
>>> >>>>>
>>> >>>>>
>>> >>>>>
>>> >>>>>在 2022-06-10 11:02:56，"Shengkai Fang" <fs...@gmail.com> 写道：
>>> >>>>>>你好，能提供下具体的 plan 供大家查看下吗？
>>> >>>>>>
>>> >>>>>>你可以直接 使用 tEnv.executeSql("Explain JSON_EXECUTION_PLAN
>>> >>>>>><YOUR_QUERY>").print() 打印下相关的信息。
>>> >>>>>>
>>> >>>>>>Best,
>>> >>>>>>Shengkai
>>> >>>>>>
>>> >>>>>>lxk <lx...@163.com> 于2022年6月10日周五 10:29写道：
>>> >>>>>>
>>> >>>>>>> flink 版本:1.14.4
>>> >>>>>>> 目前在使用flink interval join进行数据关联，在测试的时候发现一个问题，就是使用interval
>>> >>>>>>> join完之后数据会丢失，但是使用sql api，直接进行join，数据是正常的，没有丢失。
>>> >>>>>>> 水印是直接使用kafka 自带的时间戳生成watermark
>>> >>>>>>>
>>> >>>>>>>
>>> >>>>>>> 以下是代码 ---interval join
>>> >>>>>>>
>>> >>>>>>> SingleOutputStreamOperator<HeaderFull> headerFullStream =
>>> >>>>>>> headerFilterStream.keyBy(data -> data.getId())
>>> >>>>>>> .intervalJoin(filterItemStream.keyBy(data -> data.getOrder_id()))
>>> >>>>>>> .between(Time.seconds(-10), Time.seconds(20))
>>> >>>>>>> .process(new ProcessJoinFunction<OrderHeader, OrderItem,
>>> HeaderFull>() {
>>> >>>>>>> @Override
>>> >>>>>>> public void processElement(OrderHeader left, OrderItem right,
>>> Context
>>> >>>>>>> context, Collector<HeaderFull> collector) throws Exception {
>>> >>>>>>> HeaderFull headerFull = new HeaderFull();
>>> >>>>>>> BeanUtilsBean beanUtilsBean = new BeanUtilsBean();
>>> >>>>>>> beanUtilsBean.copyProperties(headerFull, left);
>>> >>>>>>> beanUtilsBean.copyProperties(headerFull, right);
>>> >>>>>>> String event_date = left.getOrder_at().substring(0, 10);
>>> >>>>>>> headerFull.setEvent_date(event_date);
>>> >>>>>>> headerFull.setItem_id(right.getId());
>>> >>>>>>> collector.collect(headerFull);
>>> >>>>>>> }
>>> >>>>>>> }
>>> >>>>>>> 使用sql 进行join
>>> >>>>>>> Configuration conf = new Configuration();
>>> >>>>>>> conf.setString("table.exec.mini-batch.enabled","true");
>>> >>>>>>> conf.setString("table.exec.mini-batch.allow-latency","15 s");
>>> >>>>>>> conf.setString("table.exec.mini-batch.size","100");
>>> >>>>>>> conf.setString("table.exec.state.ttl","20 s");
>>> >>>>>>> env.configure(conf);
>>> >>>>>>> Table headerTable =
>>> >>>>>>> streamTableEnvironment.fromDataStream(headerFilterStream);
>>> >>>>>>> Table itemTable =
>>> streamTableEnvironment.fromDataStream(filterItemStream);
>>> >>>>>>>
>>> >>>>>>>
>>> >>>>>>> streamTableEnvironment.createTemporaryView("header",headerTable);
>>> >>>>>>> streamTableEnvironment.createTemporaryView("item",itemTable);
>>> >>>>>>>
>>> >>>>>>> Table result = streamTableEnvironment.sqlQuery("select
>>> header.customer_id"
>>> >>>>>>> +
>>> >>>>>>> ",item.goods_id" +
>>> >>>>>>> ",header.id" +
>>> >>>>>>> ",header.order_status" +
>>> >>>>>>> ",header.shop_id" +
>>> >>>>>>> ",header.parent_order_id" +
>>> >>>>>>> ",header.order_at" +
>>> >>>>>>> ",header.pay_at" +
>>> >>>>>>> ",header.channel_id" +
>>> >>>>>>> ",header.root_order_id" +
>>> >>>>>>> ",item.id" +
>>> >>>>>>> ",item.row_num" +
>>> >>>>>>> ",item.p_sp_sub_amt" +
>>> >>>>>>> ",item.display_qty" +
>>> >>>>>>> ",item.qty" +
>>> >>>>>>> ",item.bom_type" +
>>> >>>>>>> " from header JOIN item on header.id = item.order_id");
>>> >>>>>>>
>>> >>>>>>>
>>> >>>>>>> DataStream<Row> rowDataStream =
>>> >>>>>>> streamTableEnvironment.toChangelogStream(result);
>>> >>>>>>> 不太理解为什么使用interval join会丢这么多数据，按照我的理解使用sql join，底层应该也是用的类似interval
>>> >>>>>>> join，为啥两者最终关联上的结果差异这么大。
>>> >>>>>>>
>>> >>>>>>>
>>> >>>>>>>
>>> >>>>>>>
>>> >>>>>>>
>>> >>>>>>>
>>> >>>>>>>
>>> >>>>>>>
>>> >>>>>>>
>>> >>>>>>>
>>> >>>>>>>
>>>
Re:Re: Re: Flink 使用interval join数据丢失疑问
Posted by Xuyang <xy...@163.com>.
Hi,
1、理论上来说inner join关联的数据量应该比interval join更大吧。关于左右两边流速度不一致的情况，理论上应该问题不大，因为需要等到两边的watermark都到齐之后才会触发状态里过期数据的清除。
2、inner join没有水印的情况下，就是到了就发，完全根据这条数据进入这个算子的时间来算，也就是“处理时间”。默认数据是不会过期的，会存全量数据。如果定义了ttl，得看join两侧的表的pk和join key，分别用不同的state数据格式来存数据（ListState、MapState等），原则上是多长时间没更新数据之后，就清空过期数据，是按照的“处理时间”来处理的。
如果我有不对的地方，请指正我哈。
--
Best！
Xuyang
在 2022-06-12 14:39:39，"lxk7491@163.com" <lx...@163.com> 写道：
>非常感谢回复
>1.针对watermark,我会再去进行测试。同时还会测试使用处理时间，interval join会不会丢失数据
>2.针对interval jon，我个人的理解是它能关联到的数据范围要比inner join大，所以数据应该更准确，但是从结果上看却是数据丢失，当时非常震惊，有点颠覆我的认知了。同时我自己还有一个新的猜测，就是两个流的数据量不一样，可能也会造成数据丢失。目前左流是订单粒度数据，右流是订单-商品粒度数据，数据量要大很多。我个人理解，在处理右流的时候，应该会慢一点，所以导致两边的时间进展可能不一致。但是这又引发了一个新的疑问?inner join应该也会受这样的影响
>3.还有一个问题可能是我没有阐述清楚，我在sql里使用inner join，没有注册水印，那么两个流的join应该是以处理时间来定义的？那么表的state的过期是否也是以处理时间来定义？
>
>
>
>lxk7491@163.com
>
>发件人： Shengkai Fang
>发送时间： 2022-06-11 20:35
>收件人： user-zh
>主题： Re: Re: Flink 使用interval join数据丢失疑问
>hi,
>
>对于第一点，丢数据的情况有很多。首先，要确认是不是 JOIN 算子丢数据（SINK 的使用不当也会丢数据）。如果明确了是 join
>算子丢的数据，建议明确下丢的数据是咋样的，是不是 watermark 设置不合理，导致数据被误认为是晚到数据从而被丢了。例如，这里的是 `event
>time` = `rowtime` - 2s，是不是不合适，我咋记得一般都是 +2 s 呢？
>
>对于第二点，interval join 我个人初步的理解是 state 的清理是根据两边的 event time，也就是说，如果右流的 event
>time 的更新会影响左流的数据清理。比如说右流的时间点到了 12：00，join 条件要求左流的时间不会晚于右流的时间 1h，那么左流
>11:00之前的数据都可以被清理了。
>
>对于第三点，我觉得是不能的。目前的 inner join + state 清理无法覆盖 event time 的window join 的。
>
>best,
>Shengkai
>
>lxk7491@163.com <lx...@163.com> 于2022年6月10日周五 23:03写道：
>
>> 对于这个问题，我还是有很大的疑问，再把我这个场景描述一下：
>>
>> 目前是使用flink进行双流join,两个流的数据，一个流是订单主表，另一个流是订单明细表。我们探查了离线的数据，订单明细表一般会在订单主表生成后晚几秒内生成，这个差异在秒级别。
>> 我们做了以下几轮测试，并对比了另一个实时落的表数据量。(这个表就是基准参照数据，只是简单落表，没做任何处理，两边的数据源一致，对比的口径一致。)
>> 1.使用datastream api,使用kafka自带的时间戳做水印，使用interval join。对比完结果，数据少。
>> 2.使用流转表，sql inner join,没有设置watermark。对比完结果数据正常。
>> 3.使用流转表，sql interval join,从数据中的事件时间提取水印，对比完结果数据，数据少。
>> 从结果上看，我不太明白为什么sql里inner join能保证数据准确，而interval
>> join不行？有什么好的方式或者思路能让我更好的去尝试了解这个问题产生的原因
>>
>> 针对第二种方式，我的疑问是，sql里没有设置水印，那么表的state过期是以处理时间来计算吗？针对这种设置了表state过期时间的join，我能理解为这个inner
>> join其实是一个window join吗？
>>
>>
>>
>> lxk7491@163.com
>>
>> 发件人： lxk
>> 发送时间： 2022-06-10 18:18
>> 收件人： user-zh
>> 主题： Re:Re:Re:Re:Re:Re:Re: Flink 使用interval join数据丢失疑问
>>
>>
>>
>> 现在改成了sql interval join,代码和执行计划如下，其他配置没变，数据量还是少，使用inner join就没问题
>>
>>
>>
>>
>> Table headerTable =
>> streamTableEnvironment.fromDataStream(headerFilterStream,
>> Schema.newBuilder()
>> .columnByExpression("rowtime",
>> "CAST(substring(last_updated_at,0,19) AS TIMESTAMP_LTZ(3))")
>> .watermark("rowtime", "rowtime - INTERVAL '2' SECOND")
>> .build());
>> Table itemTable =
>> streamTableEnvironment.fromDataStream(filterItemStream, Schema.newBuilder()
>> .columnByExpression("rowtime",
>> "CAST(substring(last_updated_at,0,19) AS TIMESTAMP_LTZ(3))")
>> .watermark("rowtime", "rowtime - INTERVAL '2' SECOND")
>> .build());
>>
>>
>>
>>
>> streamTableEnvironment.createTemporaryView("header",headerTable);
>> streamTableEnvironment.createTemporaryView("item",itemTable);
>>
>>
>>
>>
>>
>>
>> Table result = streamTableEnvironment.sqlQuery("select
>> header.customer_id" +
>> ",item.goods_id" +
>> ",header.id" +
>> ",header.order_status" +
>> ",header.shop_id" +
>> ",header.parent_order_id" +
>> ",header.order_at" +
>> ",header.pay_at" +
>> ",header.channel_id" +
>> ",header.root_order_id" +
>> ",item.id" +
>> ",item.row_num" +
>> ",item.p_sp_sub_amt" +
>> ",item.display_qty" +
>> ",item.qty" +
>> ",item.bom_type" +
>> " from header JOIN item on header.id = item.order_id and
>> item.rowtime BETWEEN header.rowtime - INTERVAL '10' SECOND AND
>> header.rowtime + INTERVAL '20' SECOND");
>>
>>
>>
>>
>> String intervalJoin = streamTableEnvironment.explainSql("select
>> header.customer_id" +
>> ",item.goods_id" +
>> ",header.id" +
>> ",header.order_status" +
>> ",header.shop_id" +
>> ",header.parent_order_id" +
>> ",header.order_at" +
>> ",header.pay_at" +
>> ",header.channel_id" +
>> ",header.root_order_id" +
>> ",item.id" +
>> ",item.row_num" +
>> ",item.p_sp_sub_amt" +
>> ",item.display_qty" +
>> ",item.qty" +
>> ",item.bom_type" +
>> " from header JOIN item on header.id = item.order_id and
>> item.rowtime BETWEEN header.rowtime - INTERVAL '10' SECOND AND
>> header.rowtime + INTERVAL '20' SECOND");
>>
>>
>> System.out.println(intervalJoin);
>>
>>
>> DataStream<Row> rowDataStream =
>> streamTableEnvironment.toChangelogStream(result);
>>
>>
>>
>>
>>
>>
>> 执行计划:
>> == Abstract Syntax Tree ==
>> LogicalProject(customer_id=[$2], goods_id=[$16], id=[$0],
>> order_status=[$1], shop_id=[$3], parent_order_id=[$4], order_at=[$5],
>> pay_at=[$6], channel_id=[$7], root_order_id=[$8], id0=[$13], row_num=[$15],
>> p_sp_sub_amt=[$20], display_qty=[$23], qty=[$18], bom_type=[$21])
>> +- LogicalJoin(condition=[AND(=($0, $14), >=($25, -($12, 10000:INTERVAL
>> SECOND)), <=($25, +($12, 20000:INTERVAL SECOND)))], joinType=[inner])
>> :- LogicalWatermarkAssigner(rowtime=[rowtime], watermark=[-($12,
>> 2000:INTERVAL SECOND)])
>> : +- LogicalProject(id=[$0], order_status=[$1], customer_id=[$2],
>> shop_id=[$3], parent_order_id=[$4], order_at=[$5], pay_at=[$6],
>> channel_id=[$7], root_order_id=[$8], last_updated_at=[$9],
>> business_flag=[$10], mysql_op_type=[$11], rowtime=[CAST(SUBSTRING($9, 0,
>> 19)):TIMESTAMP_WITH_LOCAL_TIME_ZONE(3)])
>> : +- LogicalTableScan(table=[[default_catalog, default_database,
>> Unregistered_DataStream_Source_5]])
>> +- LogicalWatermarkAssigner(rowtime=[rowtime], watermark=[-($12,
>> 2000:INTERVAL SECOND)])
>> +- LogicalProject(id=[$0], order_id=[$1], row_num=[$2],
>> goods_id=[$3], s_sku_code=[$4], qty=[$5], p_paid_sub_amt=[$6],
>> p_sp_sub_amt=[$7], bom_type=[$8], last_updated_at=[$9], display_qty=[$10],
>> is_first_flag=[$11], rowtime=[CAST(SUBSTRING($9, 0,
>> 19)):TIMESTAMP_WITH_LOCAL_TIME_ZONE(3)])
>> +- LogicalTableScan(table=[[default_catalog, default_database,
>> Unregistered_DataStream_Source_8]])
>>
>>
>> == Optimized Physical Plan ==
>> Calc(select=[customer_id, goods_id, id, order_status, shop_id,
>> parent_order_id, order_at, pay_at, channel_id, root_order_id, id0, row_num,
>> p_sp_sub_amt, display_qty, qty, bom_type])
>> +- IntervalJoin(joinType=[InnerJoin], windowBounds=[isRowTime=true,
>> leftLowerBound=-20000, leftUpperBound=10000, leftTimeIndex=9,
>> rightTimeIndex=8], where=[AND(=(id, order_id), >=(rowtime0, -(rowtime,
>> 10000:INTERVAL SECOND)), <=(rowtime0, +(rowtime, 20000:INTERVAL SECOND)))],
>> select=[id, order_status, customer_id, shop_id, parent_order_id, order_at,
>> pay_at, channel_id, root_order_id, rowtime, id0, order_id, row_num,
>> goods_id, qty, p_sp_sub_amt, bom_type, display_qty, rowtime0])
>> :- Exchange(distribution=[hash[id]])
>> : +- WatermarkAssigner(rowtime=[rowtime], watermark=[-(rowtime,
>> 2000:INTERVAL SECOND)])
>> : +- Calc(select=[id, order_status, customer_id, shop_id,
>> parent_order_id, order_at, pay_at, channel_id, root_order_id,
>> CAST(SUBSTRING(last_updated_at, 0, 19)) AS rowtime])
>> : +- TableSourceScan(table=[[default_catalog, default_database,
>> Unregistered_DataStream_Source_5]], fields=[id, order_status, customer_id,
>> shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id,
>> last_updated_at, business_flag, mysql_op_type])
>> +- Exchange(distribution=[hash[order_id]])
>> +- WatermarkAssigner(rowtime=[rowtime], watermark=[-(rowtime,
>> 2000:INTERVAL SECOND)])
>> +- Calc(select=[id, order_id, row_num, goods_id, qty,
>> p_sp_sub_amt, bom_type, display_qty, CAST(SUBSTRING(last_updated_at, 0,
>> 19)) AS rowtime])
>> +- TableSourceScan(table=[[default_catalog, default_database,
>> Unregistered_DataStream_Source_8]], fields=[id, order_id, row_num,
>> goods_id, s_sku_code, qty, p_paid_sub_amt, p_sp_sub_amt, bom_type,
>> last_updated_at, display_qty, is_first_flag])
>>
>>
>> == Optimized Execution Plan ==
>> Calc(select=[customer_id, goods_id, id, order_status, shop_id,
>> parent_order_id, order_at, pay_at, channel_id, root_order_id, id0, row_num,
>> p_sp_sub_amt, display_qty, qty, bom_type])
>> +- IntervalJoin(joinType=[InnerJoin], windowBounds=[isRowTime=true,
>> leftLowerBound=-20000, leftUpperBound=10000, leftTimeIndex=9,
>> rightTimeIndex=8], where=[((id = order_id) AND (rowtime0 >= (rowtime -
>> 10000:INTERVAL SECOND)) AND (rowtime0 <= (rowtime + 20000:INTERVAL
>> SECOND)))], select=[id, order_status, customer_id, shop_id,
>> parent_order_id, order_at, pay_at, channel_id, root_order_id, rowtime, id0,
>> order_id, row_num, goods_id, qty, p_sp_sub_amt, bom_type, display_qty,
>> rowtime0])
>> :- Exchange(distribution=[hash[id]])
>> : +- WatermarkAssigner(rowtime=[rowtime], watermark=[(rowtime -
>> 2000:INTERVAL SECOND)])
>> : +- Calc(select=[id, order_status, customer_id, shop_id,
>> parent_order_id, order_at, pay_at, channel_id, root_order_id,
>> CAST(SUBSTRING(last_updated_at, 0, 19)) AS rowtime])
>> : +- TableSourceScan(table=[[default_catalog, default_database,
>> Unregistered_DataStream_Source_5]], fields=[id, order_status, customer_id,
>> shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id,
>> last_updated_at, business_flag, mysql_op_type])
>> +- Exchange(distribution=[hash[order_id]])
>> +- WatermarkAssigner(rowtime=[rowtime], watermark=[(rowtime -
>> 2000:INTERVAL SECOND)])
>> +- Calc(select=[id, order_id, row_num, goods_id, qty,
>> p_sp_sub_amt, bom_type, display_qty, CAST(SUBSTRING(last_updated_at, 0,
>> 19)) AS rowtime])
>> +- TableSourceScan(table=[[default_catalog, default_database,
>> Unregistered_DataStream_Source_8]], fields=[id, order_id, row_num,
>> goods_id, s_sku_code, qty, p_paid_sub_amt, p_sp_sub_amt, bom_type,
>> last_updated_at, display_qty, is_first_flag])
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>> 在 2022-06-10 17:16:31，"lxk" <lx...@163.com> 写道：
>> >使用sql 进行interval
>> join，我目前的问题是感觉时间转换这块不太友好，我目前流里面的事件时间字段是string类型，数据样式是2022-06-10
>> 13:08:55,但是我使用TO_TIMESTAMP这个函数进行转换一直报错
>> >
>> >
>> >
>> >
>> >
>> >
>> >
>> >
>> >
>> >
>> >
>> >
>> >
>> >
>> >
>> >
>> >
>> >在 2022-06-10 15:04:31，"Xuyang" <xy...@163.com> 写道：
>> >>Hi, datastream的这个interval join的api应该对标的是sql中的interval
>> join。但是你目前写的这个sql，是普通join。普通join和interval
>> join在业务含义和实现上都是有区别的。所以你直接拿datastream api的interval
>> join和sql上的普通join结果对比，其实是有问题的。所以我之前的建议是让你试下让sql也使用interval join，这样双方才有可比性。
>> >>
>> >>
>>
>> >>另外sql中设置的table.exec.state.ttl这个参数，只是代表的state会20s清空过期数据，但我看你要比较的时间窗口是-10s和20s，貌似也不大一样。
>> >>
>> >>
>> >>
>> >>
>> >>--
>> >>
>> >> Best！
>> >> Xuyang
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>在 2022-06-10 14:33:37，"lxk" <lx...@163.com> 写道：
>> >>>
>> >>>
>> >>>
>> >>>我不理解的点在于，我interval join开的时间窗口比我sql中设置的状态时间都要长，窗口的上下界别是-10s 和
>> 20s，为什么会丢数据？
>> >>>
>> >>>sql中我设置这个table.exec.state.ttl参数
>> 为20s，照理来说两个流应该也是保留20s的数据在状态中进行join。不知道我的理解是否有问题，希望能够得到解答。
>> >>>
>> >>>
>> >>>
>> >>>
>> >>>
>> >>>
>> >>>
>> >>>
>> >>>
>> >>>
>> >>>
>> >>>
>> >>>
>> >>>
>> >>>在 2022-06-10 14:15:29，"Xuyang" <xy...@163.com> 写道：
>> >>>>Hi, 你的这条SQL 并不是interval join，是普通join。
>> >>>>interval join的使用文档可以参考文档[1]。可以试下使用SQL interval
>> join会不会丢数据（注意设置state的ttl），从而判断是数据的问题还是datastream api的问题。
>> >>>>
>> >>>>
>> >>>>
>> >>>>
>> >>>>[1]
>> https://nightlies.apache.org/flink/flink-docs-master/docs/dev/table/sql/queries/joins/#interval-joins
>> >>>>
>> >>>>
>> >>>>
>> >>>>
>> >>>>
>> >>>>
>> >>>>
>> >>>>--
>> >>>>
>> >>>> Best！
>> >>>> Xuyang
>> >>>>
>> >>>>
>> >>>>
>> >>>>
>> >>>>
>> >>>>在 2022-06-10 11:26:33，"lxk" <lx...@163.com> 写道：
>> >>>>>我用的是以下代码:
>> >>>>>String s = streamTableEnvironment.explainSql("select
>> header.customer_id" +
>> >>>>>",item.goods_id" +
>> >>>>>",header.id" +
>> >>>>>",header.order_status" +
>> >>>>>",header.shop_id" +
>> >>>>>",header.parent_order_id" +
>> >>>>>",header.order_at" +
>> >>>>>",header.pay_at" +
>> >>>>>",header.channel_id" +
>> >>>>>",header.root_order_id" +
>> >>>>>",item.id" +
>> >>>>>",item.row_num" +
>> >>>>>",item.p_sp_sub_amt" +
>> >>>>>",item.display_qty" +
>> >>>>>",item.qty" +
>> >>>>>",item.bom_type" +
>> >>>>>" from header JOIN item on header.id = item.order_id");
>> >>>>>
>> >>>>>System.out.println("explain:" + s);
>> >>>>>
>> >>>>>
>> >>>>>
>> >>>>>
>> >>>>>plan信息为:
>> >>>>>explain:== Abstract Syntax Tree ==
>> >>>>>LogicalProject(customer_id=[$2], goods_id=[$15], id=[$0],
>> order_status=[$1], shop_id=[$3], parent_order_id=[$4], order_at=[$5],
>> pay_at=[$6], channel_id=[$7], root_order_id=[$8], id0=[$12], row_num=[$14],
>> p_sp_sub_amt=[$19], display_qty=[$22], qty=[$17], bom_type=[$20])
>> >>>>>+- LogicalJoin(condition=[=($0, $13)], joinType=[inner])
>> >>>>> :- LogicalTableScan(table=[[default_catalog, default_database,
>> Unregistered_DataStream_Source_5]])
>> >>>>> +- LogicalTableScan(table=[[default_catalog, default_database,
>> Unregistered_DataStream_Source_8]])
>> >>>>>
>> >>>>>
>> >>>>>== Optimized Physical Plan ==
>> >>>>>Calc(select=[customer_id, goods_id, id, order_status, shop_id,
>> parent_order_id, order_at, pay_at, channel_id, root_order_id, id0, row_num,
>> p_sp_sub_amt, display_qty, qty, bom_type])
>> >>>>>+- Join(joinType=[InnerJoin], where=[=(id, order_id)], select=[id,
>> order_status, customer_id, shop_id, parent_order_id, order_at, pay_at,
>> channel_id, root_order_id, id0, order_id, row_num, goods_id, qty,
>> p_sp_sub_amt, bom_type, display_qty], leftInputSpec=[NoUniqueKey],
>> rightInputSpec=[NoUniqueKey])
>> >>>>> :- Exchange(distribution=[hash[id]])
>> >>>>> : +- Calc(select=[id, order_status, customer_id, shop_id,
>> parent_order_id, order_at, pay_at, channel_id, root_order_id])
>> >>>>> : +- TableSourceScan(table=[[default_catalog,
>> default_database, Unregistered_DataStream_Source_5]], fields=[id,
>> order_status, customer_id, shop_id, parent_order_id, order_at, pay_at,
>> channel_id, root_order_id, last_updated_at, business_flag, mysql_op_type])
>> >>>>> +- Exchange(distribution=[hash[order_id]])
>> >>>>> +- Calc(select=[id, order_id, row_num, goods_id, qty,
>> p_sp_sub_amt, bom_type, display_qty])
>> >>>>> +- TableSourceScan(table=[[default_catalog,
>> default_database, Unregistered_DataStream_Source_8]], fields=[id, order_id,
>> row_num, goods_id, s_sku_code, qty, p_paid_sub_amt, p_sp_sub_amt, bom_type,
>> last_updated_at, display_qty, is_first_flag])
>> >>>>>
>> >>>>>
>> >>>>>== Optimized Execution Plan ==
>> >>>>>Calc(select=[customer_id, goods_id, id, order_status, shop_id,
>> parent_order_id, order_at, pay_at, channel_id, root_order_id, id0, row_num,
>> p_sp_sub_amt, display_qty, qty, bom_type])
>> >>>>>+- Join(joinType=[InnerJoin], where=[(id = order_id)], select=[id,
>> order_status, customer_id, shop_id, parent_order_id, order_at, pay_at,
>> channel_id, root_order_id, id0, order_id, row_num, goods_id, qty,
>> p_sp_sub_amt, bom_type, display_qty], leftInputSpec=[NoUniqueKey],
>> rightInputSpec=[NoUniqueKey])
>> >>>>> :- Exchange(distribution=[hash[id]])
>> >>>>> : +- Calc(select=[id, order_status, customer_id, shop_id,
>> parent_order_id, order_at, pay_at, channel_id, root_order_id])
>> >>>>> : +- TableSourceScan(table=[[default_catalog,
>> default_database, Unregistered_DataStream_Source_5]], fields=[id,
>> order_status, customer_id, shop_id, parent_order_id, order_at, pay_at,
>> channel_id, root_order_id, last_updated_at, business_flag, mysql_op_type])
>> >>>>> +- Exchange(distribution=[hash[order_id]])
>> >>>>> +- Calc(select=[id, order_id, row_num, goods_id, qty,
>> p_sp_sub_amt, bom_type, display_qty])
>> >>>>> +- TableSourceScan(table=[[default_catalog,
>> default_database, Unregistered_DataStream_Source_8]], fields=[id, order_id,
>> row_num, goods_id, s_sku_code, qty, p_paid_sub_amt, p_sp_sub_amt, bom_type,
>> last_updated_at, display_qty, is_first_flag])
>> >>>>>
>> >>>>>
>> >>>>>
>> >>>>>
>> >>>>>
>> >>>>>
>> >>>>>
>> >>>>>
>> >>>>>
>> >>>>>
>> >>>>>
>> >>>>>
>> >>>>>
>> >>>>>在 2022-06-10 11:02:56，"Shengkai Fang" <fs...@gmail.com> 写道：
>> >>>>>>你好，能提供下具体的 plan 供大家查看下吗？
>> >>>>>>
>> >>>>>>你可以直接 使用 tEnv.executeSql("Explain JSON_EXECUTION_PLAN
>> >>>>>><YOUR_QUERY>").print() 打印下相关的信息。
>> >>>>>>
>> >>>>>>Best,
>> >>>>>>Shengkai
>> >>>>>>
>> >>>>>>lxk <lx...@163.com> 于2022年6月10日周五 10:29写道：
>> >>>>>>
>> >>>>>>> flink 版本:1.14.4
>> >>>>>>> 目前在使用flink interval join进行数据关联，在测试的时候发现一个问题，就是使用interval
>> >>>>>>> join完之后数据会丢失，但是使用sql api，直接进行join，数据是正常的，没有丢失。
>> >>>>>>> 水印是直接使用kafka 自带的时间戳生成watermark
>> >>>>>>>
>> >>>>>>>
>> >>>>>>> 以下是代码 ---interval join
>> >>>>>>>
>> >>>>>>> SingleOutputStreamOperator<HeaderFull> headerFullStream =
>> >>>>>>> headerFilterStream.keyBy(data -> data.getId())
>> >>>>>>> .intervalJoin(filterItemStream.keyBy(data -> data.getOrder_id()))
>> >>>>>>> .between(Time.seconds(-10), Time.seconds(20))
>> >>>>>>> .process(new ProcessJoinFunction<OrderHeader, OrderItem,
>> HeaderFull>() {
>> >>>>>>> @Override
>> >>>>>>> public void processElement(OrderHeader left, OrderItem right,
>> Context
>> >>>>>>> context, Collector<HeaderFull> collector) throws Exception {
>> >>>>>>> HeaderFull headerFull = new HeaderFull();
>> >>>>>>> BeanUtilsBean beanUtilsBean = new BeanUtilsBean();
>> >>>>>>> beanUtilsBean.copyProperties(headerFull, left);
>> >>>>>>> beanUtilsBean.copyProperties(headerFull, right);
>> >>>>>>> String event_date = left.getOrder_at().substring(0, 10);
>> >>>>>>> headerFull.setEvent_date(event_date);
>> >>>>>>> headerFull.setItem_id(right.getId());
>> >>>>>>> collector.collect(headerFull);
>> >>>>>>> }
>> >>>>>>> }
>> >>>>>>> 使用sql 进行join
>> >>>>>>> Configuration conf = new Configuration();
>> >>>>>>> conf.setString("table.exec.mini-batch.enabled","true");
>> >>>>>>> conf.setString("table.exec.mini-batch.allow-latency","15 s");
>> >>>>>>> conf.setString("table.exec.mini-batch.size","100");
>> >>>>>>> conf.setString("table.exec.state.ttl","20 s");
>> >>>>>>> env.configure(conf);
>> >>>>>>> Table headerTable =
>> >>>>>>> streamTableEnvironment.fromDataStream(headerFilterStream);
>> >>>>>>> Table itemTable =
>> streamTableEnvironment.fromDataStream(filterItemStream);
>> >>>>>>>
>> >>>>>>>
>> >>>>>>> streamTableEnvironment.createTemporaryView("header",headerTable);
>> >>>>>>> streamTableEnvironment.createTemporaryView("item",itemTable);
>> >>>>>>>
>> >>>>>>> Table result = streamTableEnvironment.sqlQuery("select
>> header.customer_id"
>> >>>>>>> +
>> >>>>>>> ",item.goods_id" +
>> >>>>>>> ",header.id" +
>> >>>>>>> ",header.order_status" +
>> >>>>>>> ",header.shop_id" +
>> >>>>>>> ",header.parent_order_id" +
>> >>>>>>> ",header.order_at" +
>> >>>>>>> ",header.pay_at" +
>> >>>>>>> ",header.channel_id" +
>> >>>>>>> ",header.root_order_id" +
>> >>>>>>> ",item.id" +
>> >>>>>>> ",item.row_num" +
>> >>>>>>> ",item.p_sp_sub_amt" +
>> >>>>>>> ",item.display_qty" +
>> >>>>>>> ",item.qty" +
>> >>>>>>> ",item.bom_type" +
>> >>>>>>> " from header JOIN item on header.id = item.order_id");
>> >>>>>>>
>> >>>>>>>
>> >>>>>>> DataStream<Row> rowDataStream =
>> >>>>>>> streamTableEnvironment.toChangelogStream(result);
>> >>>>>>> 不太理解为什么使用interval join会丢这么多数据，按照我的理解使用sql join，底层应该也是用的类似interval
>> >>>>>>> join，为啥两者最终关联上的结果差异这么大。
>> >>>>>>>
>> >>>>>>>
>> >>>>>>>
>> >>>>>>>
>> >>>>>>>
>> >>>>>>>
>> >>>>>>>
>> >>>>>>>
>> >>>>>>>
>> >>>>>>>
>> >>>>>>>
>>
Re: Re: Flink 使用interval join数据丢失疑问
Posted by "lxk7491@163.com" <lx...@163.com>.
非常感谢回复
1.针对watermark,我会再去进行测试。同时还会测试使用处理时间，interval join会不会丢失数据
2.针对interval jon，我个人的理解是它能关联到的数据范围要比inner join大，所以数据应该更准确，但是从结果上看却是数据丢失，当时非常震惊，有点颠覆我的认知了。同时我自己还有一个新的猜测，就是两个流的数据量不一样，可能也会造成数据丢失。目前左流是订单粒度数据，右流是订单-商品粒度数据，数据量要大很多。我个人理解，在处理右流的时候，应该会慢一点，所以导致两边的时间进展可能不一致。但是这又引发了一个新的疑问?inner join应该也会受这样的影响
3.还有一个问题可能是我没有阐述清楚，我在sql里使用inner join，没有注册水印，那么两个流的join应该是以处理时间来定义的？那么表的state的过期是否也是以处理时间来定义？
lxk7491@163.com
发件人： Shengkai Fang
发送时间： 2022-06-11 20:35
收件人： user-zh
主题： Re: Re: Flink 使用interval join数据丢失疑问
hi,
对于第一点，丢数据的情况有很多。首先，要确认是不是 JOIN 算子丢数据（SINK 的使用不当也会丢数据）。如果明确了是 join
算子丢的数据，建议明确下丢的数据是咋样的，是不是 watermark 设置不合理，导致数据被误认为是晚到数据从而被丢了。例如，这里的是 `event
time` = `rowtime` - 2s，是不是不合适，我咋记得一般都是 +2 s 呢？
对于第二点，interval join 我个人初步的理解是 state 的清理是根据两边的 event time，也就是说，如果右流的 event
time 的更新会影响左流的数据清理。比如说右流的时间点到了 12：00，join 条件要求左流的时间不会晚于右流的时间 1h，那么左流
11:00之前的数据都可以被清理了。
对于第三点，我觉得是不能的。目前的 inner join + state 清理无法覆盖 event time 的window join 的。
best,
Shengkai
lxk7491@163.com <lx...@163.com> 于2022年6月10日周五 23:03写道：
> 对于这个问题，我还是有很大的疑问，再把我这个场景描述一下：
>
> 目前是使用flink进行双流join,两个流的数据，一个流是订单主表，另一个流是订单明细表。我们探查了离线的数据，订单明细表一般会在订单主表生成后晚几秒内生成，这个差异在秒级别。
> 我们做了以下几轮测试，并对比了另一个实时落的表数据量。(这个表就是基准参照数据，只是简单落表，没做任何处理，两边的数据源一致，对比的口径一致。)
> 1.使用datastream api,使用kafka自带的时间戳做水印，使用interval join。对比完结果，数据少。
> 2.使用流转表，sql inner join,没有设置watermark。对比完结果数据正常。
> 3.使用流转表，sql interval join,从数据中的事件时间提取水印，对比完结果数据，数据少。
> 从结果上看，我不太明白为什么sql里inner join能保证数据准确，而interval
> join不行？有什么好的方式或者思路能让我更好的去尝试了解这个问题产生的原因
>
> 针对第二种方式，我的疑问是，sql里没有设置水印，那么表的state过期是以处理时间来计算吗？针对这种设置了表state过期时间的join，我能理解为这个inner
> join其实是一个window join吗？
>
>
>
> lxk7491@163.com
>
> 发件人： lxk
> 发送时间： 2022-06-10 18:18
> 收件人： user-zh
> 主题： Re:Re:Re:Re:Re:Re:Re: Flink 使用interval join数据丢失疑问
>
>
>
> 现在改成了sql interval join,代码和执行计划如下，其他配置没变，数据量还是少，使用inner join就没问题
>
>
>
>
> Table headerTable =
> streamTableEnvironment.fromDataStream(headerFilterStream,
> Schema.newBuilder()
> .columnByExpression("rowtime",
> "CAST(substring(last_updated_at,0,19) AS TIMESTAMP_LTZ(3))")
> .watermark("rowtime", "rowtime - INTERVAL '2' SECOND")
> .build());
> Table itemTable =
> streamTableEnvironment.fromDataStream(filterItemStream, Schema.newBuilder()
> .columnByExpression("rowtime",
> "CAST(substring(last_updated_at,0,19) AS TIMESTAMP_LTZ(3))")
> .watermark("rowtime", "rowtime - INTERVAL '2' SECOND")
> .build());
>
>
>
>
> streamTableEnvironment.createTemporaryView("header",headerTable);
> streamTableEnvironment.createTemporaryView("item",itemTable);
>
>
>
>
>
>
> Table result = streamTableEnvironment.sqlQuery("select
> header.customer_id" +
> ",item.goods_id" +
> ",header.id" +
> ",header.order_status" +
> ",header.shop_id" +
> ",header.parent_order_id" +
> ",header.order_at" +
> ",header.pay_at" +
> ",header.channel_id" +
> ",header.root_order_id" +
> ",item.id" +
> ",item.row_num" +
> ",item.p_sp_sub_amt" +
> ",item.display_qty" +
> ",item.qty" +
> ",item.bom_type" +
> " from header JOIN item on header.id = item.order_id and
> item.rowtime BETWEEN header.rowtime - INTERVAL '10' SECOND AND
> header.rowtime + INTERVAL '20' SECOND");
>
>
>
>
> String intervalJoin = streamTableEnvironment.explainSql("select
> header.customer_id" +
> ",item.goods_id" +
> ",header.id" +
> ",header.order_status" +
> ",header.shop_id" +
> ",header.parent_order_id" +
> ",header.order_at" +
> ",header.pay_at" +
> ",header.channel_id" +
> ",header.root_order_id" +
> ",item.id" +
> ",item.row_num" +
> ",item.p_sp_sub_amt" +
> ",item.display_qty" +
> ",item.qty" +
> ",item.bom_type" +
> " from header JOIN item on header.id = item.order_id and
> item.rowtime BETWEEN header.rowtime - INTERVAL '10' SECOND AND
> header.rowtime + INTERVAL '20' SECOND");
>
>
> System.out.println(intervalJoin);
>
>
> DataStream<Row> rowDataStream =
> streamTableEnvironment.toChangelogStream(result);
>
>
>
>
>
>
> 执行计划:
> == Abstract Syntax Tree ==
> LogicalProject(customer_id=[$2], goods_id=[$16], id=[$0],
> order_status=[$1], shop_id=[$3], parent_order_id=[$4], order_at=[$5],
> pay_at=[$6], channel_id=[$7], root_order_id=[$8], id0=[$13], row_num=[$15],
> p_sp_sub_amt=[$20], display_qty=[$23], qty=[$18], bom_type=[$21])
> +- LogicalJoin(condition=[AND(=($0, $14), >=($25, -($12, 10000:INTERVAL
> SECOND)), <=($25, +($12, 20000:INTERVAL SECOND)))], joinType=[inner])
> :- LogicalWatermarkAssigner(rowtime=[rowtime], watermark=[-($12,
> 2000:INTERVAL SECOND)])
> : +- LogicalProject(id=[$0], order_status=[$1], customer_id=[$2],
> shop_id=[$3], parent_order_id=[$4], order_at=[$5], pay_at=[$6],
> channel_id=[$7], root_order_id=[$8], last_updated_at=[$9],
> business_flag=[$10], mysql_op_type=[$11], rowtime=[CAST(SUBSTRING($9, 0,
> 19)):TIMESTAMP_WITH_LOCAL_TIME_ZONE(3)])
> : +- LogicalTableScan(table=[[default_catalog, default_database,
> Unregistered_DataStream_Source_5]])
> +- LogicalWatermarkAssigner(rowtime=[rowtime], watermark=[-($12,
> 2000:INTERVAL SECOND)])
> +- LogicalProject(id=[$0], order_id=[$1], row_num=[$2],
> goods_id=[$3], s_sku_code=[$4], qty=[$5], p_paid_sub_amt=[$6],
> p_sp_sub_amt=[$7], bom_type=[$8], last_updated_at=[$9], display_qty=[$10],
> is_first_flag=[$11], rowtime=[CAST(SUBSTRING($9, 0,
> 19)):TIMESTAMP_WITH_LOCAL_TIME_ZONE(3)])
> +- LogicalTableScan(table=[[default_catalog, default_database,
> Unregistered_DataStream_Source_8]])
>
>
> == Optimized Physical Plan ==
> Calc(select=[customer_id, goods_id, id, order_status, shop_id,
> parent_order_id, order_at, pay_at, channel_id, root_order_id, id0, row_num,
> p_sp_sub_amt, display_qty, qty, bom_type])
> +- IntervalJoin(joinType=[InnerJoin], windowBounds=[isRowTime=true,
> leftLowerBound=-20000, leftUpperBound=10000, leftTimeIndex=9,
> rightTimeIndex=8], where=[AND(=(id, order_id), >=(rowtime0, -(rowtime,
> 10000:INTERVAL SECOND)), <=(rowtime0, +(rowtime, 20000:INTERVAL SECOND)))],
> select=[id, order_status, customer_id, shop_id, parent_order_id, order_at,
> pay_at, channel_id, root_order_id, rowtime, id0, order_id, row_num,
> goods_id, qty, p_sp_sub_amt, bom_type, display_qty, rowtime0])
> :- Exchange(distribution=[hash[id]])
> : +- WatermarkAssigner(rowtime=[rowtime], watermark=[-(rowtime,
> 2000:INTERVAL SECOND)])
> : +- Calc(select=[id, order_status, customer_id, shop_id,
> parent_order_id, order_at, pay_at, channel_id, root_order_id,
> CAST(SUBSTRING(last_updated_at, 0, 19)) AS rowtime])
> : +- TableSourceScan(table=[[default_catalog, default_database,
> Unregistered_DataStream_Source_5]], fields=[id, order_status, customer_id,
> shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id,
> last_updated_at, business_flag, mysql_op_type])
> +- Exchange(distribution=[hash[order_id]])
> +- WatermarkAssigner(rowtime=[rowtime], watermark=[-(rowtime,
> 2000:INTERVAL SECOND)])
> +- Calc(select=[id, order_id, row_num, goods_id, qty,
> p_sp_sub_amt, bom_type, display_qty, CAST(SUBSTRING(last_updated_at, 0,
> 19)) AS rowtime])
> +- TableSourceScan(table=[[default_catalog, default_database,
> Unregistered_DataStream_Source_8]], fields=[id, order_id, row_num,
> goods_id, s_sku_code, qty, p_paid_sub_amt, p_sp_sub_amt, bom_type,
> last_updated_at, display_qty, is_first_flag])
>
>
> == Optimized Execution Plan ==
> Calc(select=[customer_id, goods_id, id, order_status, shop_id,
> parent_order_id, order_at, pay_at, channel_id, root_order_id, id0, row_num,
> p_sp_sub_amt, display_qty, qty, bom_type])
> +- IntervalJoin(joinType=[InnerJoin], windowBounds=[isRowTime=true,
> leftLowerBound=-20000, leftUpperBound=10000, leftTimeIndex=9,
> rightTimeIndex=8], where=[((id = order_id) AND (rowtime0 >= (rowtime -
> 10000:INTERVAL SECOND)) AND (rowtime0 <= (rowtime + 20000:INTERVAL
> SECOND)))], select=[id, order_status, customer_id, shop_id,
> parent_order_id, order_at, pay_at, channel_id, root_order_id, rowtime, id0,
> order_id, row_num, goods_id, qty, p_sp_sub_amt, bom_type, display_qty,
> rowtime0])
> :- Exchange(distribution=[hash[id]])
> : +- WatermarkAssigner(rowtime=[rowtime], watermark=[(rowtime -
> 2000:INTERVAL SECOND)])
> : +- Calc(select=[id, order_status, customer_id, shop_id,
> parent_order_id, order_at, pay_at, channel_id, root_order_id,
> CAST(SUBSTRING(last_updated_at, 0, 19)) AS rowtime])
> : +- TableSourceScan(table=[[default_catalog, default_database,
> Unregistered_DataStream_Source_5]], fields=[id, order_status, customer_id,
> shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id,
> last_updated_at, business_flag, mysql_op_type])
> +- Exchange(distribution=[hash[order_id]])
> +- WatermarkAssigner(rowtime=[rowtime], watermark=[(rowtime -
> 2000:INTERVAL SECOND)])
> +- Calc(select=[id, order_id, row_num, goods_id, qty,
> p_sp_sub_amt, bom_type, display_qty, CAST(SUBSTRING(last_updated_at, 0,
> 19)) AS rowtime])
> +- TableSourceScan(table=[[default_catalog, default_database,
> Unregistered_DataStream_Source_8]], fields=[id, order_id, row_num,
> goods_id, s_sku_code, qty, p_paid_sub_amt, p_sp_sub_amt, bom_type,
> last_updated_at, display_qty, is_first_flag])
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
> 在 2022-06-10 17:16:31，"lxk" <lx...@163.com> 写道：
> >使用sql 进行interval
> join，我目前的问题是感觉时间转换这块不太友好，我目前流里面的事件时间字段是string类型，数据样式是2022-06-10
> 13:08:55,但是我使用TO_TIMESTAMP这个函数进行转换一直报错
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >在 2022-06-10 15:04:31，"Xuyang" <xy...@163.com> 写道：
> >>Hi, datastream的这个interval join的api应该对标的是sql中的interval
> join。但是你目前写的这个sql，是普通join。普通join和interval
> join在业务含义和实现上都是有区别的。所以你直接拿datastream api的interval
> join和sql上的普通join结果对比，其实是有问题的。所以我之前的建议是让你试下让sql也使用interval join，这样双方才有可比性。
> >>
> >>
>
> >>另外sql中设置的table.exec.state.ttl这个参数，只是代表的state会20s清空过期数据，但我看你要比较的时间窗口是-10s和20s，貌似也不大一样。
> >>
> >>
> >>
> >>
> >>--
> >>
> >> Best！
> >> Xuyang
> >>
> >>
> >>
> >>
> >>
> >>在 2022-06-10 14:33:37，"lxk" <lx...@163.com> 写道：
> >>>
> >>>
> >>>
> >>>我不理解的点在于，我interval join开的时间窗口比我sql中设置的状态时间都要长，窗口的上下界别是-10s 和
> 20s，为什么会丢数据？
> >>>
> >>>sql中我设置这个table.exec.state.ttl参数
> 为20s，照理来说两个流应该也是保留20s的数据在状态中进行join。不知道我的理解是否有问题，希望能够得到解答。
> >>>
> >>>
> >>>
> >>>
> >>>
> >>>
> >>>
> >>>
> >>>
> >>>
> >>>
> >>>
> >>>
> >>>
> >>>在 2022-06-10 14:15:29，"Xuyang" <xy...@163.com> 写道：
> >>>>Hi, 你的这条SQL 并不是interval join，是普通join。
> >>>>interval join的使用文档可以参考文档[1]。可以试下使用SQL interval
> join会不会丢数据（注意设置state的ttl），从而判断是数据的问题还是datastream api的问题。
> >>>>
> >>>>
> >>>>
> >>>>
> >>>>[1]
> https://nightlies.apache.org/flink/flink-docs-master/docs/dev/table/sql/queries/joins/#interval-joins
> >>>>
> >>>>
> >>>>
> >>>>
> >>>>
> >>>>
> >>>>
> >>>>--
> >>>>
> >>>> Best！
> >>>> Xuyang
> >>>>
> >>>>
> >>>>
> >>>>
> >>>>
> >>>>在 2022-06-10 11:26:33，"lxk" <lx...@163.com> 写道：
> >>>>>我用的是以下代码:
> >>>>>String s = streamTableEnvironment.explainSql("select
> header.customer_id" +
> >>>>>",item.goods_id" +
> >>>>>",header.id" +
> >>>>>",header.order_status" +
> >>>>>",header.shop_id" +
> >>>>>",header.parent_order_id" +
> >>>>>",header.order_at" +
> >>>>>",header.pay_at" +
> >>>>>",header.channel_id" +
> >>>>>",header.root_order_id" +
> >>>>>",item.id" +
> >>>>>",item.row_num" +
> >>>>>",item.p_sp_sub_amt" +
> >>>>>",item.display_qty" +
> >>>>>",item.qty" +
> >>>>>",item.bom_type" +
> >>>>>" from header JOIN item on header.id = item.order_id");
> >>>>>
> >>>>>System.out.println("explain:" + s);
> >>>>>
> >>>>>
> >>>>>
> >>>>>
> >>>>>plan信息为:
> >>>>>explain:== Abstract Syntax Tree ==
> >>>>>LogicalProject(customer_id=[$2], goods_id=[$15], id=[$0],
> order_status=[$1], shop_id=[$3], parent_order_id=[$4], order_at=[$5],
> pay_at=[$6], channel_id=[$7], root_order_id=[$8], id0=[$12], row_num=[$14],
> p_sp_sub_amt=[$19], display_qty=[$22], qty=[$17], bom_type=[$20])
> >>>>>+- LogicalJoin(condition=[=($0, $13)], joinType=[inner])
> >>>>> :- LogicalTableScan(table=[[default_catalog, default_database,
> Unregistered_DataStream_Source_5]])
> >>>>> +- LogicalTableScan(table=[[default_catalog, default_database,
> Unregistered_DataStream_Source_8]])
> >>>>>
> >>>>>
> >>>>>== Optimized Physical Plan ==
> >>>>>Calc(select=[customer_id, goods_id, id, order_status, shop_id,
> parent_order_id, order_at, pay_at, channel_id, root_order_id, id0, row_num,
> p_sp_sub_amt, display_qty, qty, bom_type])
> >>>>>+- Join(joinType=[InnerJoin], where=[=(id, order_id)], select=[id,
> order_status, customer_id, shop_id, parent_order_id, order_at, pay_at,
> channel_id, root_order_id, id0, order_id, row_num, goods_id, qty,
> p_sp_sub_amt, bom_type, display_qty], leftInputSpec=[NoUniqueKey],
> rightInputSpec=[NoUniqueKey])
> >>>>> :- Exchange(distribution=[hash[id]])
> >>>>> : +- Calc(select=[id, order_status, customer_id, shop_id,
> parent_order_id, order_at, pay_at, channel_id, root_order_id])
> >>>>> : +- TableSourceScan(table=[[default_catalog,
> default_database, Unregistered_DataStream_Source_5]], fields=[id,
> order_status, customer_id, shop_id, parent_order_id, order_at, pay_at,
> channel_id, root_order_id, last_updated_at, business_flag, mysql_op_type])
> >>>>> +- Exchange(distribution=[hash[order_id]])
> >>>>> +- Calc(select=[id, order_id, row_num, goods_id, qty,
> p_sp_sub_amt, bom_type, display_qty])
> >>>>> +- TableSourceScan(table=[[default_catalog,
> default_database, Unregistered_DataStream_Source_8]], fields=[id, order_id,
> row_num, goods_id, s_sku_code, qty, p_paid_sub_amt, p_sp_sub_amt, bom_type,
> last_updated_at, display_qty, is_first_flag])
> >>>>>
> >>>>>
> >>>>>== Optimized Execution Plan ==
> >>>>>Calc(select=[customer_id, goods_id, id, order_status, shop_id,
> parent_order_id, order_at, pay_at, channel_id, root_order_id, id0, row_num,
> p_sp_sub_amt, display_qty, qty, bom_type])
> >>>>>+- Join(joinType=[InnerJoin], where=[(id = order_id)], select=[id,
> order_status, customer_id, shop_id, parent_order_id, order_at, pay_at,
> channel_id, root_order_id, id0, order_id, row_num, goods_id, qty,
> p_sp_sub_amt, bom_type, display_qty], leftInputSpec=[NoUniqueKey],
> rightInputSpec=[NoUniqueKey])
> >>>>> :- Exchange(distribution=[hash[id]])
> >>>>> : +- Calc(select=[id, order_status, customer_id, shop_id,
> parent_order_id, order_at, pay_at, channel_id, root_order_id])
> >>>>> : +- TableSourceScan(table=[[default_catalog,
> default_database, Unregistered_DataStream_Source_5]], fields=[id,
> order_status, customer_id, shop_id, parent_order_id, order_at, pay_at,
> channel_id, root_order_id, last_updated_at, business_flag, mysql_op_type])
> >>>>> +- Exchange(distribution=[hash[order_id]])
> >>>>> +- Calc(select=[id, order_id, row_num, goods_id, qty,
> p_sp_sub_amt, bom_type, display_qty])
> >>>>> +- TableSourceScan(table=[[default_catalog,
> default_database, Unregistered_DataStream_Source_8]], fields=[id, order_id,
> row_num, goods_id, s_sku_code, qty, p_paid_sub_amt, p_sp_sub_amt, bom_type,
> last_updated_at, display_qty, is_first_flag])
> >>>>>
> >>>>>
> >>>>>
> >>>>>
> >>>>>
> >>>>>
> >>>>>
> >>>>>
> >>>>>
> >>>>>
> >>>>>
> >>>>>
> >>>>>
> >>>>>在 2022-06-10 11:02:56，"Shengkai Fang" <fs...@gmail.com> 写道：
> >>>>>>你好，能提供下具体的 plan 供大家查看下吗？
> >>>>>>
> >>>>>>你可以直接 使用 tEnv.executeSql("Explain JSON_EXECUTION_PLAN
> >>>>>><YOUR_QUERY>").print() 打印下相关的信息。
> >>>>>>
> >>>>>>Best,
> >>>>>>Shengkai
> >>>>>>
> >>>>>>lxk <lx...@163.com> 于2022年6月10日周五 10:29写道：
> >>>>>>
> >>>>>>> flink 版本:1.14.4
> >>>>>>> 目前在使用flink interval join进行数据关联，在测试的时候发现一个问题，就是使用interval
> >>>>>>> join完之后数据会丢失，但是使用sql api，直接进行join，数据是正常的，没有丢失。
> >>>>>>> 水印是直接使用kafka 自带的时间戳生成watermark
> >>>>>>>
> >>>>>>>
> >>>>>>> 以下是代码 ---interval join
> >>>>>>>
> >>>>>>> SingleOutputStreamOperator<HeaderFull> headerFullStream =
> >>>>>>> headerFilterStream.keyBy(data -> data.getId())
> >>>>>>> .intervalJoin(filterItemStream.keyBy(data -> data.getOrder_id()))
> >>>>>>> .between(Time.seconds(-10), Time.seconds(20))
> >>>>>>> .process(new ProcessJoinFunction<OrderHeader, OrderItem,
> HeaderFull>() {
> >>>>>>> @Override
> >>>>>>> public void processElement(OrderHeader left, OrderItem right,
> Context
> >>>>>>> context, Collector<HeaderFull> collector) throws Exception {
> >>>>>>> HeaderFull headerFull = new HeaderFull();
> >>>>>>> BeanUtilsBean beanUtilsBean = new BeanUtilsBean();
> >>>>>>> beanUtilsBean.copyProperties(headerFull, left);
> >>>>>>> beanUtilsBean.copyProperties(headerFull, right);
> >>>>>>> String event_date = left.getOrder_at().substring(0, 10);
> >>>>>>> headerFull.setEvent_date(event_date);
> >>>>>>> headerFull.setItem_id(right.getId());
> >>>>>>> collector.collect(headerFull);
> >>>>>>> }
> >>>>>>> }
> >>>>>>> 使用sql 进行join
> >>>>>>> Configuration conf = new Configuration();
> >>>>>>> conf.setString("table.exec.mini-batch.enabled","true");
> >>>>>>> conf.setString("table.exec.mini-batch.allow-latency","15 s");
> >>>>>>> conf.setString("table.exec.mini-batch.size","100");
> >>>>>>> conf.setString("table.exec.state.ttl","20 s");
> >>>>>>> env.configure(conf);
> >>>>>>> Table headerTable =
> >>>>>>> streamTableEnvironment.fromDataStream(headerFilterStream);
> >>>>>>> Table itemTable =
> streamTableEnvironment.fromDataStream(filterItemStream);
> >>>>>>>
> >>>>>>>
> >>>>>>> streamTableEnvironment.createTemporaryView("header",headerTable);
> >>>>>>> streamTableEnvironment.createTemporaryView("item",itemTable);
> >>>>>>>
> >>>>>>> Table result = streamTableEnvironment.sqlQuery("select
> header.customer_id"
> >>>>>>> +
> >>>>>>> ",item.goods_id" +
> >>>>>>> ",header.id" +
> >>>>>>> ",header.order_status" +
> >>>>>>> ",header.shop_id" +
> >>>>>>> ",header.parent_order_id" +
> >>>>>>> ",header.order_at" +
> >>>>>>> ",header.pay_at" +
> >>>>>>> ",header.channel_id" +
> >>>>>>> ",header.root_order_id" +
> >>>>>>> ",item.id" +
> >>>>>>> ",item.row_num" +
> >>>>>>> ",item.p_sp_sub_amt" +
> >>>>>>> ",item.display_qty" +
> >>>>>>> ",item.qty" +
> >>>>>>> ",item.bom_type" +
> >>>>>>> " from header JOIN item on header.id = item.order_id");
> >>>>>>>
> >>>>>>>
> >>>>>>> DataStream<Row> rowDataStream =
> >>>>>>> streamTableEnvironment.toChangelogStream(result);
> >>>>>>> 不太理解为什么使用interval join会丢这么多数据，按照我的理解使用sql join，底层应该也是用的类似interval
> >>>>>>> join，为啥两者最终关联上的结果差异这么大。
> >>>>>>>
> >>>>>>>
> >>>>>>>
> >>>>>>>
> >>>>>>>
> >>>>>>>
> >>>>>>>
> >>>>>>>
> >>>>>>>
> >>>>>>>
> >>>>>>>
>
Re: Re: Flink 使用interval join数据丢失疑问
Posted by Shengkai Fang <fs...@gmail.com>.
hi,
对于第一点，丢数据的情况有很多。首先，要确认是不是 JOIN 算子丢数据（SINK 的使用不当也会丢数据）。如果明确了是 join
算子丢的数据，建议明确下丢的数据是咋样的，是不是 watermark 设置不合理，导致数据被误认为是晚到数据从而被丢了。例如，这里的是 `event
time` = `rowtime` - 2s，是不是不合适，我咋记得一般都是 +2 s 呢？
对于第二点，interval join 我个人初步的理解是 state 的清理是根据两边的 event time，也就是说，如果右流的 event
time 的更新会影响左流的数据清理。比如说右流的时间点到了 12：00，join 条件要求左流的时间不会晚于右流的时间 1h，那么左流
11:00之前的数据都可以被清理了。
对于第三点，我觉得是不能的。目前的 inner join + state 清理无法覆盖 event time 的window join 的。
best,
Shengkai
lxk7491@163.com <lx...@163.com> 于2022年6月10日周五 23:03写道：
> 对于这个问题，我还是有很大的疑问，再把我这个场景描述一下：
>
> 目前是使用flink进行双流join,两个流的数据，一个流是订单主表，另一个流是订单明细表。我们探查了离线的数据，订单明细表一般会在订单主表生成后晚几秒内生成，这个差异在秒级别。
> 我们做了以下几轮测试，并对比了另一个实时落的表数据量。(这个表就是基准参照数据，只是简单落表，没做任何处理，两边的数据源一致，对比的口径一致。)
> 1.使用datastream api,使用kafka自带的时间戳做水印，使用interval join。对比完结果，数据少。
> 2.使用流转表，sql inner join,没有设置watermark。对比完结果数据正常。
> 3.使用流转表，sql interval join,从数据中的事件时间提取水印，对比完结果数据，数据少。
> 从结果上看，我不太明白为什么sql里inner join能保证数据准确，而interval
> join不行？有什么好的方式或者思路能让我更好的去尝试了解这个问题产生的原因
>
> 针对第二种方式，我的疑问是，sql里没有设置水印，那么表的state过期是以处理时间来计算吗？针对这种设置了表state过期时间的join，我能理解为这个inner
> join其实是一个window join吗？
>
>
>
> lxk7491@163.com
>
> 发件人： lxk
> 发送时间： 2022-06-10 18:18
> 收件人： user-zh
> 主题： Re:Re:Re:Re:Re:Re:Re: Flink 使用interval join数据丢失疑问
>
>
>
> 现在改成了sql interval join,代码和执行计划如下，其他配置没变，数据量还是少，使用inner join就没问题
>
>
>
>
> Table headerTable =
> streamTableEnvironment.fromDataStream(headerFilterStream,
> Schema.newBuilder()
> .columnByExpression("rowtime",
> "CAST(substring(last_updated_at,0,19) AS TIMESTAMP_LTZ(3))")
> .watermark("rowtime", "rowtime - INTERVAL '2' SECOND")
> .build());
> Table itemTable =
> streamTableEnvironment.fromDataStream(filterItemStream, Schema.newBuilder()
> .columnByExpression("rowtime",
> "CAST(substring(last_updated_at,0,19) AS TIMESTAMP_LTZ(3))")
> .watermark("rowtime", "rowtime - INTERVAL '2' SECOND")
> .build());
>
>
>
>
> streamTableEnvironment.createTemporaryView("header",headerTable);
> streamTableEnvironment.createTemporaryView("item",itemTable);
>
>
>
>
>
>
> Table result = streamTableEnvironment.sqlQuery("select
> header.customer_id" +
> ",item.goods_id" +
> ",header.id" +
> ",header.order_status" +
> ",header.shop_id" +
> ",header.parent_order_id" +
> ",header.order_at" +
> ",header.pay_at" +
> ",header.channel_id" +
> ",header.root_order_id" +
> ",item.id" +
> ",item.row_num" +
> ",item.p_sp_sub_amt" +
> ",item.display_qty" +
> ",item.qty" +
> ",item.bom_type" +
> " from header JOIN item on header.id = item.order_id and
> item.rowtime BETWEEN header.rowtime - INTERVAL '10' SECOND AND
> header.rowtime + INTERVAL '20' SECOND");
>
>
>
>
> String intervalJoin = streamTableEnvironment.explainSql("select
> header.customer_id" +
> ",item.goods_id" +
> ",header.id" +
> ",header.order_status" +
> ",header.shop_id" +
> ",header.parent_order_id" +
> ",header.order_at" +
> ",header.pay_at" +
> ",header.channel_id" +
> ",header.root_order_id" +
> ",item.id" +
> ",item.row_num" +
> ",item.p_sp_sub_amt" +
> ",item.display_qty" +
> ",item.qty" +
> ",item.bom_type" +
> " from header JOIN item on header.id = item.order_id and
> item.rowtime BETWEEN header.rowtime - INTERVAL '10' SECOND AND
> header.rowtime + INTERVAL '20' SECOND");
>
>
> System.out.println(intervalJoin);
>
>
> DataStream<Row> rowDataStream =
> streamTableEnvironment.toChangelogStream(result);
>
>
>
>
>
>
> 执行计划:
> == Abstract Syntax Tree ==
> LogicalProject(customer_id=[$2], goods_id=[$16], id=[$0],
> order_status=[$1], shop_id=[$3], parent_order_id=[$4], order_at=[$5],
> pay_at=[$6], channel_id=[$7], root_order_id=[$8], id0=[$13], row_num=[$15],
> p_sp_sub_amt=[$20], display_qty=[$23], qty=[$18], bom_type=[$21])
> +- LogicalJoin(condition=[AND(=($0, $14), >=($25, -($12, 10000:INTERVAL
> SECOND)), <=($25, +($12, 20000:INTERVAL SECOND)))], joinType=[inner])
> :- LogicalWatermarkAssigner(rowtime=[rowtime], watermark=[-($12,
> 2000:INTERVAL SECOND)])
> : +- LogicalProject(id=[$0], order_status=[$1], customer_id=[$2],
> shop_id=[$3], parent_order_id=[$4], order_at=[$5], pay_at=[$6],
> channel_id=[$7], root_order_id=[$8], last_updated_at=[$9],
> business_flag=[$10], mysql_op_type=[$11], rowtime=[CAST(SUBSTRING($9, 0,
> 19)):TIMESTAMP_WITH_LOCAL_TIME_ZONE(3)])
> : +- LogicalTableScan(table=[[default_catalog, default_database,
> Unregistered_DataStream_Source_5]])
> +- LogicalWatermarkAssigner(rowtime=[rowtime], watermark=[-($12,
> 2000:INTERVAL SECOND)])
> +- LogicalProject(id=[$0], order_id=[$1], row_num=[$2],
> goods_id=[$3], s_sku_code=[$4], qty=[$5], p_paid_sub_amt=[$6],
> p_sp_sub_amt=[$7], bom_type=[$8], last_updated_at=[$9], display_qty=[$10],
> is_first_flag=[$11], rowtime=[CAST(SUBSTRING($9, 0,
> 19)):TIMESTAMP_WITH_LOCAL_TIME_ZONE(3)])
> +- LogicalTableScan(table=[[default_catalog, default_database,
> Unregistered_DataStream_Source_8]])
>
>
> == Optimized Physical Plan ==
> Calc(select=[customer_id, goods_id, id, order_status, shop_id,
> parent_order_id, order_at, pay_at, channel_id, root_order_id, id0, row_num,
> p_sp_sub_amt, display_qty, qty, bom_type])
> +- IntervalJoin(joinType=[InnerJoin], windowBounds=[isRowTime=true,
> leftLowerBound=-20000, leftUpperBound=10000, leftTimeIndex=9,
> rightTimeIndex=8], where=[AND(=(id, order_id), >=(rowtime0, -(rowtime,
> 10000:INTERVAL SECOND)), <=(rowtime0, +(rowtime, 20000:INTERVAL SECOND)))],
> select=[id, order_status, customer_id, shop_id, parent_order_id, order_at,
> pay_at, channel_id, root_order_id, rowtime, id0, order_id, row_num,
> goods_id, qty, p_sp_sub_amt, bom_type, display_qty, rowtime0])
> :- Exchange(distribution=[hash[id]])
> : +- WatermarkAssigner(rowtime=[rowtime], watermark=[-(rowtime,
> 2000:INTERVAL SECOND)])
> : +- Calc(select=[id, order_status, customer_id, shop_id,
> parent_order_id, order_at, pay_at, channel_id, root_order_id,
> CAST(SUBSTRING(last_updated_at, 0, 19)) AS rowtime])
> : +- TableSourceScan(table=[[default_catalog, default_database,
> Unregistered_DataStream_Source_5]], fields=[id, order_status, customer_id,
> shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id,
> last_updated_at, business_flag, mysql_op_type])
> +- Exchange(distribution=[hash[order_id]])
> +- WatermarkAssigner(rowtime=[rowtime], watermark=[-(rowtime,
> 2000:INTERVAL SECOND)])
> +- Calc(select=[id, order_id, row_num, goods_id, qty,
> p_sp_sub_amt, bom_type, display_qty, CAST(SUBSTRING(last_updated_at, 0,
> 19)) AS rowtime])
> +- TableSourceScan(table=[[default_catalog, default_database,
> Unregistered_DataStream_Source_8]], fields=[id, order_id, row_num,
> goods_id, s_sku_code, qty, p_paid_sub_amt, p_sp_sub_amt, bom_type,
> last_updated_at, display_qty, is_first_flag])
>
>
> == Optimized Execution Plan ==
> Calc(select=[customer_id, goods_id, id, order_status, shop_id,
> parent_order_id, order_at, pay_at, channel_id, root_order_id, id0, row_num,
> p_sp_sub_amt, display_qty, qty, bom_type])
> +- IntervalJoin(joinType=[InnerJoin], windowBounds=[isRowTime=true,
> leftLowerBound=-20000, leftUpperBound=10000, leftTimeIndex=9,
> rightTimeIndex=8], where=[((id = order_id) AND (rowtime0 >= (rowtime -
> 10000:INTERVAL SECOND)) AND (rowtime0 <= (rowtime + 20000:INTERVAL
> SECOND)))], select=[id, order_status, customer_id, shop_id,
> parent_order_id, order_at, pay_at, channel_id, root_order_id, rowtime, id0,
> order_id, row_num, goods_id, qty, p_sp_sub_amt, bom_type, display_qty,
> rowtime0])
> :- Exchange(distribution=[hash[id]])
> : +- WatermarkAssigner(rowtime=[rowtime], watermark=[(rowtime -
> 2000:INTERVAL SECOND)])
> : +- Calc(select=[id, order_status, customer_id, shop_id,
> parent_order_id, order_at, pay_at, channel_id, root_order_id,
> CAST(SUBSTRING(last_updated_at, 0, 19)) AS rowtime])
> : +- TableSourceScan(table=[[default_catalog, default_database,
> Unregistered_DataStream_Source_5]], fields=[id, order_status, customer_id,
> shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id,
> last_updated_at, business_flag, mysql_op_type])
> +- Exchange(distribution=[hash[order_id]])
> +- WatermarkAssigner(rowtime=[rowtime], watermark=[(rowtime -
> 2000:INTERVAL SECOND)])
> +- Calc(select=[id, order_id, row_num, goods_id, qty,
> p_sp_sub_amt, bom_type, display_qty, CAST(SUBSTRING(last_updated_at, 0,
> 19)) AS rowtime])
> +- TableSourceScan(table=[[default_catalog, default_database,
> Unregistered_DataStream_Source_8]], fields=[id, order_id, row_num,
> goods_id, s_sku_code, qty, p_paid_sub_amt, p_sp_sub_amt, bom_type,
> last_updated_at, display_qty, is_first_flag])
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
> 在 2022-06-10 17:16:31，"lxk" <lx...@163.com> 写道：
> >使用sql 进行interval
> join，我目前的问题是感觉时间转换这块不太友好，我目前流里面的事件时间字段是string类型，数据样式是2022-06-10
> 13:08:55,但是我使用TO_TIMESTAMP这个函数进行转换一直报错
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >在 2022-06-10 15:04:31，"Xuyang" <xy...@163.com> 写道：
> >>Hi, datastream的这个interval join的api应该对标的是sql中的interval
> join。但是你目前写的这个sql，是普通join。普通join和interval
> join在业务含义和实现上都是有区别的。所以你直接拿datastream api的interval
> join和sql上的普通join结果对比，其实是有问题的。所以我之前的建议是让你试下让sql也使用interval join，这样双方才有可比性。
> >>
> >>
>
> >>另外sql中设置的table.exec.state.ttl这个参数，只是代表的state会20s清空过期数据，但我看你要比较的时间窗口是-10s和20s，貌似也不大一样。
> >>
> >>
> >>
> >>
> >>--
> >>
> >> Best！
> >> Xuyang
> >>
> >>
> >>
> >>
> >>
> >>在 2022-06-10 14:33:37，"lxk" <lx...@163.com> 写道：
> >>>
> >>>
> >>>
> >>>我不理解的点在于，我interval join开的时间窗口比我sql中设置的状态时间都要长，窗口的上下界别是-10s 和
> 20s，为什么会丢数据？
> >>>
> >>>sql中我设置这个table.exec.state.ttl参数
> 为20s，照理来说两个流应该也是保留20s的数据在状态中进行join。不知道我的理解是否有问题，希望能够得到解答。
> >>>
> >>>
> >>>
> >>>
> >>>
> >>>
> >>>
> >>>
> >>>
> >>>
> >>>
> >>>
> >>>
> >>>
> >>>在 2022-06-10 14:15:29，"Xuyang" <xy...@163.com> 写道：
> >>>>Hi, 你的这条SQL 并不是interval join，是普通join。
> >>>>interval join的使用文档可以参考文档[1]。可以试下使用SQL interval
> join会不会丢数据（注意设置state的ttl），从而判断是数据的问题还是datastream api的问题。
> >>>>
> >>>>
> >>>>
> >>>>
> >>>>[1]
> https://nightlies.apache.org/flink/flink-docs-master/docs/dev/table/sql/queries/joins/#interval-joins
> >>>>
> >>>>
> >>>>
> >>>>
> >>>>
> >>>>
> >>>>
> >>>>--
> >>>>
> >>>> Best！
> >>>> Xuyang
> >>>>
> >>>>
> >>>>
> >>>>
> >>>>
> >>>>在 2022-06-10 11:26:33，"lxk" <lx...@163.com> 写道：
> >>>>>我用的是以下代码:
> >>>>>String s = streamTableEnvironment.explainSql("select
> header.customer_id" +
> >>>>>",item.goods_id" +
> >>>>>",header.id" +
> >>>>>",header.order_status" +
> >>>>>",header.shop_id" +
> >>>>>",header.parent_order_id" +
> >>>>>",header.order_at" +
> >>>>>",header.pay_at" +
> >>>>>",header.channel_id" +
> >>>>>",header.root_order_id" +
> >>>>>",item.id" +
> >>>>>",item.row_num" +
> >>>>>",item.p_sp_sub_amt" +
> >>>>>",item.display_qty" +
> >>>>>",item.qty" +
> >>>>>",item.bom_type" +
> >>>>>" from header JOIN item on header.id = item.order_id");
> >>>>>
> >>>>>System.out.println("explain:" + s);
> >>>>>
> >>>>>
> >>>>>
> >>>>>
> >>>>>plan信息为:
> >>>>>explain:== Abstract Syntax Tree ==
> >>>>>LogicalProject(customer_id=[$2], goods_id=[$15], id=[$0],
> order_status=[$1], shop_id=[$3], parent_order_id=[$4], order_at=[$5],
> pay_at=[$6], channel_id=[$7], root_order_id=[$8], id0=[$12], row_num=[$14],
> p_sp_sub_amt=[$19], display_qty=[$22], qty=[$17], bom_type=[$20])
> >>>>>+- LogicalJoin(condition=[=($0, $13)], joinType=[inner])
> >>>>> :- LogicalTableScan(table=[[default_catalog, default_database,
> Unregistered_DataStream_Source_5]])
> >>>>> +- LogicalTableScan(table=[[default_catalog, default_database,
> Unregistered_DataStream_Source_8]])
> >>>>>
> >>>>>
> >>>>>== Optimized Physical Plan ==
> >>>>>Calc(select=[customer_id, goods_id, id, order_status, shop_id,
> parent_order_id, order_at, pay_at, channel_id, root_order_id, id0, row_num,
> p_sp_sub_amt, display_qty, qty, bom_type])
> >>>>>+- Join(joinType=[InnerJoin], where=[=(id, order_id)], select=[id,
> order_status, customer_id, shop_id, parent_order_id, order_at, pay_at,
> channel_id, root_order_id, id0, order_id, row_num, goods_id, qty,
> p_sp_sub_amt, bom_type, display_qty], leftInputSpec=[NoUniqueKey],
> rightInputSpec=[NoUniqueKey])
> >>>>> :- Exchange(distribution=[hash[id]])
> >>>>> : +- Calc(select=[id, order_status, customer_id, shop_id,
> parent_order_id, order_at, pay_at, channel_id, root_order_id])
> >>>>> : +- TableSourceScan(table=[[default_catalog,
> default_database, Unregistered_DataStream_Source_5]], fields=[id,
> order_status, customer_id, shop_id, parent_order_id, order_at, pay_at,
> channel_id, root_order_id, last_updated_at, business_flag, mysql_op_type])
> >>>>> +- Exchange(distribution=[hash[order_id]])
> >>>>> +- Calc(select=[id, order_id, row_num, goods_id, qty,
> p_sp_sub_amt, bom_type, display_qty])
> >>>>> +- TableSourceScan(table=[[default_catalog,
> default_database, Unregistered_DataStream_Source_8]], fields=[id, order_id,
> row_num, goods_id, s_sku_code, qty, p_paid_sub_amt, p_sp_sub_amt, bom_type,
> last_updated_at, display_qty, is_first_flag])
> >>>>>
> >>>>>
> >>>>>== Optimized Execution Plan ==
> >>>>>Calc(select=[customer_id, goods_id, id, order_status, shop_id,
> parent_order_id, order_at, pay_at, channel_id, root_order_id, id0, row_num,
> p_sp_sub_amt, display_qty, qty, bom_type])
> >>>>>+- Join(joinType=[InnerJoin], where=[(id = order_id)], select=[id,
> order_status, customer_id, shop_id, parent_order_id, order_at, pay_at,
> channel_id, root_order_id, id0, order_id, row_num, goods_id, qty,
> p_sp_sub_amt, bom_type, display_qty], leftInputSpec=[NoUniqueKey],
> rightInputSpec=[NoUniqueKey])
> >>>>> :- Exchange(distribution=[hash[id]])
> >>>>> : +- Calc(select=[id, order_status, customer_id, shop_id,
> parent_order_id, order_at, pay_at, channel_id, root_order_id])
> >>>>> : +- TableSourceScan(table=[[default_catalog,
> default_database, Unregistered_DataStream_Source_5]], fields=[id,
> order_status, customer_id, shop_id, parent_order_id, order_at, pay_at,
> channel_id, root_order_id, last_updated_at, business_flag, mysql_op_type])
> >>>>> +- Exchange(distribution=[hash[order_id]])
> >>>>> +- Calc(select=[id, order_id, row_num, goods_id, qty,
> p_sp_sub_amt, bom_type, display_qty])
> >>>>> +- TableSourceScan(table=[[default_catalog,
> default_database, Unregistered_DataStream_Source_8]], fields=[id, order_id,
> row_num, goods_id, s_sku_code, qty, p_paid_sub_amt, p_sp_sub_amt, bom_type,
> last_updated_at, display_qty, is_first_flag])
> >>>>>
> >>>>>
> >>>>>
> >>>>>
> >>>>>
> >>>>>
> >>>>>
> >>>>>
> >>>>>
> >>>>>
> >>>>>
> >>>>>
> >>>>>
> >>>>>在 2022-06-10 11:02:56，"Shengkai Fang" <fs...@gmail.com> 写道：
> >>>>>>你好，能提供下具体的 plan 供大家查看下吗？
> >>>>>>
> >>>>>>你可以直接 使用 tEnv.executeSql("Explain JSON_EXECUTION_PLAN
> >>>>>><YOUR_QUERY>").print() 打印下相关的信息。
> >>>>>>
> >>>>>>Best,
> >>>>>>Shengkai
> >>>>>>
> >>>>>>lxk <lx...@163.com> 于2022年6月10日周五 10:29写道：
> >>>>>>
> >>>>>>> flink 版本:1.14.4
> >>>>>>> 目前在使用flink interval join进行数据关联，在测试的时候发现一个问题，就是使用interval
> >>>>>>> join完之后数据会丢失，但是使用sql api，直接进行join，数据是正常的，没有丢失。
> >>>>>>> 水印是直接使用kafka 自带的时间戳生成watermark
> >>>>>>>
> >>>>>>>
> >>>>>>> 以下是代码 ---interval join
> >>>>>>>
> >>>>>>> SingleOutputStreamOperator<HeaderFull> headerFullStream =
> >>>>>>> headerFilterStream.keyBy(data -> data.getId())
> >>>>>>> .intervalJoin(filterItemStream.keyBy(data -> data.getOrder_id()))
> >>>>>>> .between(Time.seconds(-10), Time.seconds(20))
> >>>>>>> .process(new ProcessJoinFunction<OrderHeader, OrderItem,
> HeaderFull>() {
> >>>>>>> @Override
> >>>>>>> public void processElement(OrderHeader left, OrderItem right,
> Context
> >>>>>>> context, Collector<HeaderFull> collector) throws Exception {
> >>>>>>> HeaderFull headerFull = new HeaderFull();
> >>>>>>> BeanUtilsBean beanUtilsBean = new BeanUtilsBean();
> >>>>>>> beanUtilsBean.copyProperties(headerFull, left);
> >>>>>>> beanUtilsBean.copyProperties(headerFull, right);
> >>>>>>> String event_date = left.getOrder_at().substring(0, 10);
> >>>>>>> headerFull.setEvent_date(event_date);
> >>>>>>> headerFull.setItem_id(right.getId());
> >>>>>>> collector.collect(headerFull);
> >>>>>>> }
> >>>>>>> }
> >>>>>>> 使用sql 进行join
> >>>>>>> Configuration conf = new Configuration();
> >>>>>>> conf.setString("table.exec.mini-batch.enabled","true");
> >>>>>>> conf.setString("table.exec.mini-batch.allow-latency","15 s");
> >>>>>>> conf.setString("table.exec.mini-batch.size","100");
> >>>>>>> conf.setString("table.exec.state.ttl","20 s");
> >>>>>>> env.configure(conf);
> >>>>>>> Table headerTable =
> >>>>>>> streamTableEnvironment.fromDataStream(headerFilterStream);
> >>>>>>> Table itemTable =
> streamTableEnvironment.fromDataStream(filterItemStream);
> >>>>>>>
> >>>>>>>
> >>>>>>> streamTableEnvironment.createTemporaryView("header",headerTable);
> >>>>>>> streamTableEnvironment.createTemporaryView("item",itemTable);
> >>>>>>>
> >>>>>>> Table result = streamTableEnvironment.sqlQuery("select
> header.customer_id"
> >>>>>>> +
> >>>>>>> ",item.goods_id" +
> >>>>>>> ",header.id" +
> >>>>>>> ",header.order_status" +
> >>>>>>> ",header.shop_id" +
> >>>>>>> ",header.parent_order_id" +
> >>>>>>> ",header.order_at" +
> >>>>>>> ",header.pay_at" +
> >>>>>>> ",header.channel_id" +
> >>>>>>> ",header.root_order_id" +
> >>>>>>> ",item.id" +
> >>>>>>> ",item.row_num" +
> >>>>>>> ",item.p_sp_sub_amt" +
> >>>>>>> ",item.display_qty" +
> >>>>>>> ",item.qty" +
> >>>>>>> ",item.bom_type" +
> >>>>>>> " from header JOIN item on header.id = item.order_id");
> >>>>>>>
> >>>>>>>
> >>>>>>> DataStream<Row> rowDataStream =
> >>>>>>> streamTableEnvironment.toChangelogStream(result);
> >>>>>>> 不太理解为什么使用interval join会丢这么多数据，按照我的理解使用sql join，底层应该也是用的类似interval
> >>>>>>> join，为啥两者最终关联上的结果差异这么大。
> >>>>>>>
> >>>>>>>
> >>>>>>>
> >>>>>>>
> >>>>>>>
> >>>>>>>
> >>>>>>>
> >>>>>>>
> >>>>>>>
> >>>>>>>
> >>>>>>>
>
回复: Re: Flink 使用interval join数据丢失疑问
Posted by "lxk7491@163.com" <lx...@163.com>.
对于这个问题，我还是有很大的疑问，再把我这个场景描述一下：
目前是使用flink进行双流join,两个流的数据，一个流是订单主表，另一个流是订单明细表。我们探查了离线的数据，订单明细表一般会在订单主表生成后晚几秒内生成，这个差异在秒级别。
我们做了以下几轮测试，并对比了另一个实时落的表数据量。(这个表就是基准参照数据，只是简单落表，没做任何处理，两边的数据源一致，对比的口径一致。)
1.使用datastream api,使用kafka自带的时间戳做水印，使用interval join。对比完结果，数据少。
2.使用流转表，sql inner join,没有设置watermark。对比完结果数据正常。
3.使用流转表，sql interval join,从数据中的事件时间提取水印，对比完结果数据，数据少。
从结果上看，我不太明白为什么sql里inner join能保证数据准确，而interval join不行？有什么好的方式或者思路能让我更好的去尝试了解这个问题产生的原因
针对第二种方式，我的疑问是，sql里没有设置水印，那么表的state过期是以处理时间来计算吗？针对这种设置了表state过期时间的join，我能理解为这个inner join其实是一个window join吗？
lxk7491@163.com
发件人： lxk
发送时间： 2022-06-10 18:18
收件人： user-zh
主题： Re:Re:Re:Re:Re:Re:Re: Flink 使用interval join数据丢失疑问
现在改成了sql interval join,代码和执行计划如下，其他配置没变，数据量还是少，使用inner join就没问题
Table headerTable = streamTableEnvironment.fromDataStream(headerFilterStream, Schema.newBuilder()
.columnByExpression("rowtime", "CAST(substring(last_updated_at,0,19) AS TIMESTAMP_LTZ(3))")
.watermark("rowtime", "rowtime - INTERVAL '2' SECOND")
.build());
Table itemTable = streamTableEnvironment.fromDataStream(filterItemStream, Schema.newBuilder()
.columnByExpression("rowtime", "CAST(substring(last_updated_at,0,19) AS TIMESTAMP_LTZ(3))")
.watermark("rowtime", "rowtime - INTERVAL '2' SECOND")
.build());
streamTableEnvironment.createTemporaryView("header",headerTable);
streamTableEnvironment.createTemporaryView("item",itemTable);
Table result = streamTableEnvironment.sqlQuery("select header.customer_id" +
",item.goods_id" +
",header.id" +
",header.order_status" +
",header.shop_id" +
",header.parent_order_id" +
",header.order_at" +
",header.pay_at" +
",header.channel_id" +
",header.root_order_id" +
",item.id" +
",item.row_num" +
",item.p_sp_sub_amt" +
",item.display_qty" +
",item.qty" +
",item.bom_type" +
" from header JOIN item on header.id = item.order_id and item.rowtime BETWEEN header.rowtime - INTERVAL '10' SECOND AND header.rowtime + INTERVAL '20' SECOND");
String intervalJoin = streamTableEnvironment.explainSql("select header.customer_id" +
",item.goods_id" +
",header.id" +
",header.order_status" +
",header.shop_id" +
",header.parent_order_id" +
",header.order_at" +
",header.pay_at" +
",header.channel_id" +
",header.root_order_id" +
",item.id" +
",item.row_num" +
",item.p_sp_sub_amt" +
",item.display_qty" +
",item.qty" +
",item.bom_type" +
" from header JOIN item on header.id = item.order_id and item.rowtime BETWEEN header.rowtime - INTERVAL '10' SECOND AND header.rowtime + INTERVAL '20' SECOND");
System.out.println(intervalJoin);
DataStream<Row> rowDataStream = streamTableEnvironment.toChangelogStream(result);
执行计划:
== Abstract Syntax Tree ==
LogicalProject(customer_id=[$2], goods_id=[$16], id=[$0], order_status=[$1], shop_id=[$3], parent_order_id=[$4], order_at=[$5], pay_at=[$6], channel_id=[$7], root_order_id=[$8], id0=[$13], row_num=[$15], p_sp_sub_amt=[$20], display_qty=[$23], qty=[$18], bom_type=[$21])
+- LogicalJoin(condition=[AND(=($0, $14), >=($25, -($12, 10000:INTERVAL SECOND)), <=($25, +($12, 20000:INTERVAL SECOND)))], joinType=[inner])
:- LogicalWatermarkAssigner(rowtime=[rowtime], watermark=[-($12, 2000:INTERVAL SECOND)])
: +- LogicalProject(id=[$0], order_status=[$1], customer_id=[$2], shop_id=[$3], parent_order_id=[$4], order_at=[$5], pay_at=[$6], channel_id=[$7], root_order_id=[$8], last_updated_at=[$9], business_flag=[$10], mysql_op_type=[$11], rowtime=[CAST(SUBSTRING($9, 0, 19)):TIMESTAMP_WITH_LOCAL_TIME_ZONE(3)])
: +- LogicalTableScan(table=[[default_catalog, default_database, Unregistered_DataStream_Source_5]])
+- LogicalWatermarkAssigner(rowtime=[rowtime], watermark=[-($12, 2000:INTERVAL SECOND)])
+- LogicalProject(id=[$0], order_id=[$1], row_num=[$2], goods_id=[$3], s_sku_code=[$4], qty=[$5], p_paid_sub_amt=[$6], p_sp_sub_amt=[$7], bom_type=[$8], last_updated_at=[$9], display_qty=[$10], is_first_flag=[$11], rowtime=[CAST(SUBSTRING($9, 0, 19)):TIMESTAMP_WITH_LOCAL_TIME_ZONE(3)])
+- LogicalTableScan(table=[[default_catalog, default_database, Unregistered_DataStream_Source_8]])
== Optimized Physical Plan ==
Calc(select=[customer_id, goods_id, id, order_status, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id, id0, row_num, p_sp_sub_amt, display_qty, qty, bom_type])
+- IntervalJoin(joinType=[InnerJoin], windowBounds=[isRowTime=true, leftLowerBound=-20000, leftUpperBound=10000, leftTimeIndex=9, rightTimeIndex=8], where=[AND(=(id, order_id), >=(rowtime0, -(rowtime, 10000:INTERVAL SECOND)), <=(rowtime0, +(rowtime, 20000:INTERVAL SECOND)))], select=[id, order_status, customer_id, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id, rowtime, id0, order_id, row_num, goods_id, qty, p_sp_sub_amt, bom_type, display_qty, rowtime0])
:- Exchange(distribution=[hash[id]])
: +- WatermarkAssigner(rowtime=[rowtime], watermark=[-(rowtime, 2000:INTERVAL SECOND)])
: +- Calc(select=[id, order_status, customer_id, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id, CAST(SUBSTRING(last_updated_at, 0, 19)) AS rowtime])
: +- TableSourceScan(table=[[default_catalog, default_database, Unregistered_DataStream_Source_5]], fields=[id, order_status, customer_id, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id, last_updated_at, business_flag, mysql_op_type])
+- Exchange(distribution=[hash[order_id]])
+- WatermarkAssigner(rowtime=[rowtime], watermark=[-(rowtime, 2000:INTERVAL SECOND)])
+- Calc(select=[id, order_id, row_num, goods_id, qty, p_sp_sub_amt, bom_type, display_qty, CAST(SUBSTRING(last_updated_at, 0, 19)) AS rowtime])
+- TableSourceScan(table=[[default_catalog, default_database, Unregistered_DataStream_Source_8]], fields=[id, order_id, row_num, goods_id, s_sku_code, qty, p_paid_sub_amt, p_sp_sub_amt, bom_type, last_updated_at, display_qty, is_first_flag])
== Optimized Execution Plan ==
Calc(select=[customer_id, goods_id, id, order_status, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id, id0, row_num, p_sp_sub_amt, display_qty, qty, bom_type])
+- IntervalJoin(joinType=[InnerJoin], windowBounds=[isRowTime=true, leftLowerBound=-20000, leftUpperBound=10000, leftTimeIndex=9, rightTimeIndex=8], where=[((id = order_id) AND (rowtime0 >= (rowtime - 10000:INTERVAL SECOND)) AND (rowtime0 <= (rowtime + 20000:INTERVAL SECOND)))], select=[id, order_status, customer_id, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id, rowtime, id0, order_id, row_num, goods_id, qty, p_sp_sub_amt, bom_type, display_qty, rowtime0])
:- Exchange(distribution=[hash[id]])
: +- WatermarkAssigner(rowtime=[rowtime], watermark=[(rowtime - 2000:INTERVAL SECOND)])
: +- Calc(select=[id, order_status, customer_id, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id, CAST(SUBSTRING(last_updated_at, 0, 19)) AS rowtime])
: +- TableSourceScan(table=[[default_catalog, default_database, Unregistered_DataStream_Source_5]], fields=[id, order_status, customer_id, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id, last_updated_at, business_flag, mysql_op_type])
+- Exchange(distribution=[hash[order_id]])
+- WatermarkAssigner(rowtime=[rowtime], watermark=[(rowtime - 2000:INTERVAL SECOND)])
+- Calc(select=[id, order_id, row_num, goods_id, qty, p_sp_sub_amt, bom_type, display_qty, CAST(SUBSTRING(last_updated_at, 0, 19)) AS rowtime])
+- TableSourceScan(table=[[default_catalog, default_database, Unregistered_DataStream_Source_8]], fields=[id, order_id, row_num, goods_id, s_sku_code, qty, p_paid_sub_amt, p_sp_sub_amt, bom_type, last_updated_at, display_qty, is_first_flag])
在 2022-06-10 17:16:31，"lxk" <lx...@163.com> 写道：
>使用sql 进行interval join，我目前的问题是感觉时间转换这块不太友好，我目前流里面的事件时间字段是string类型，数据样式是2022-06-10 13:08:55,但是我使用TO_TIMESTAMP这个函数进行转换一直报错
>
>
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>
>在 2022-06-10 15:04:31，"Xuyang" <xy...@163.com> 写道：
>>Hi, datastream的这个interval join的api应该对标的是sql中的interval join。但是你目前写的这个sql，是普通join。普通join和interval join在业务含义和实现上都是有区别的。所以你直接拿datastream api的interval join和sql上的普通join结果对比，其实是有问题的。所以我之前的建议是让你试下让sql也使用interval join，这样双方才有可比性。
>>
>>
>>另外sql中设置的table.exec.state.ttl这个参数，只是代表的state会20s清空过期数据，但我看你要比较的时间窗口是-10s和20s，貌似也不大一样。
>>
>>
>>
>>
>>--
>>
>> Best！
>> Xuyang
>>
>>
>>
>>
>>
>>在 2022-06-10 14:33:37，"lxk" <lx...@163.com> 写道：
>>>
>>>
>>>
>>>我不理解的点在于，我interval join开的时间窗口比我sql中设置的状态时间都要长，窗口的上下界别是-10s 和 20s，为什么会丢数据？
>>>
>>>sql中我设置这个table.exec.state.ttl参数 为20s，照理来说两个流应该也是保留20s的数据在状态中进行join。不知道我的理解是否有问题，希望能够得到解答。
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>在 2022-06-10 14:15:29，"Xuyang" <xy...@163.com> 写道：
>>>>Hi, 你的这条SQL 并不是interval join，是普通join。
>>>>interval join的使用文档可以参考文档[1]。可以试下使用SQL interval join会不会丢数据（注意设置state的ttl），从而判断是数据的问题还是datastream api的问题。
>>>>
>>>>
>>>>
>>>>
>>>>[1] https://nightlies.apache.org/flink/flink-docs-master/docs/dev/table/sql/queries/joins/#interval-joins
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>--
>>>>
>>>> Best！
>>>> Xuyang
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>在 2022-06-10 11:26:33，"lxk" <lx...@163.com> 写道：
>>>>>我用的是以下代码:
>>>>>String s = streamTableEnvironment.explainSql("select header.customer_id" +
>>>>>",item.goods_id" +
>>>>>",header.id" +
>>>>>",header.order_status" +
>>>>>",header.shop_id" +
>>>>>",header.parent_order_id" +
>>>>>",header.order_at" +
>>>>>",header.pay_at" +
>>>>>",header.channel_id" +
>>>>>",header.root_order_id" +
>>>>>",item.id" +
>>>>>",item.row_num" +
>>>>>",item.p_sp_sub_amt" +
>>>>>",item.display_qty" +
>>>>>",item.qty" +
>>>>>",item.bom_type" +
>>>>>" from header JOIN item on header.id = item.order_id");
>>>>>
>>>>>System.out.println("explain:" + s);
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>plan信息为:
>>>>>explain:== Abstract Syntax Tree ==
>>>>>LogicalProject(customer_id=[$2], goods_id=[$15], id=[$0], order_status=[$1], shop_id=[$3], parent_order_id=[$4], order_at=[$5], pay_at=[$6], channel_id=[$7], root_order_id=[$8], id0=[$12], row_num=[$14], p_sp_sub_amt=[$19], display_qty=[$22], qty=[$17], bom_type=[$20])
>>>>>+- LogicalJoin(condition=[=($0, $13)], joinType=[inner])
>>>>> :- LogicalTableScan(table=[[default_catalog, default_database, Unregistered_DataStream_Source_5]])
>>>>> +- LogicalTableScan(table=[[default_catalog, default_database, Unregistered_DataStream_Source_8]])
>>>>>
>>>>>
>>>>>== Optimized Physical Plan ==
>>>>>Calc(select=[customer_id, goods_id, id, order_status, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id, id0, row_num, p_sp_sub_amt, display_qty, qty, bom_type])
>>>>>+- Join(joinType=[InnerJoin], where=[=(id, order_id)], select=[id, order_status, customer_id, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id, id0, order_id, row_num, goods_id, qty, p_sp_sub_amt, bom_type, display_qty], leftInputSpec=[NoUniqueKey], rightInputSpec=[NoUniqueKey])
>>>>> :- Exchange(distribution=[hash[id]])
>>>>> : +- Calc(select=[id, order_status, customer_id, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id])
>>>>> : +- TableSourceScan(table=[[default_catalog, default_database, Unregistered_DataStream_Source_5]], fields=[id, order_status, customer_id, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id, last_updated_at, business_flag, mysql_op_type])
>>>>> +- Exchange(distribution=[hash[order_id]])
>>>>> +- Calc(select=[id, order_id, row_num, goods_id, qty, p_sp_sub_amt, bom_type, display_qty])
>>>>> +- TableSourceScan(table=[[default_catalog, default_database, Unregistered_DataStream_Source_8]], fields=[id, order_id, row_num, goods_id, s_sku_code, qty, p_paid_sub_amt, p_sp_sub_amt, bom_type, last_updated_at, display_qty, is_first_flag])
>>>>>
>>>>>
>>>>>== Optimized Execution Plan ==
>>>>>Calc(select=[customer_id, goods_id, id, order_status, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id, id0, row_num, p_sp_sub_amt, display_qty, qty, bom_type])
>>>>>+- Join(joinType=[InnerJoin], where=[(id = order_id)], select=[id, order_status, customer_id, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id, id0, order_id, row_num, goods_id, qty, p_sp_sub_amt, bom_type, display_qty], leftInputSpec=[NoUniqueKey], rightInputSpec=[NoUniqueKey])
>>>>> :- Exchange(distribution=[hash[id]])
>>>>> : +- Calc(select=[id, order_status, customer_id, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id])
>>>>> : +- TableSourceScan(table=[[default_catalog, default_database, Unregistered_DataStream_Source_5]], fields=[id, order_status, customer_id, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id, last_updated_at, business_flag, mysql_op_type])
>>>>> +- Exchange(distribution=[hash[order_id]])
>>>>> +- Calc(select=[id, order_id, row_num, goods_id, qty, p_sp_sub_amt, bom_type, display_qty])
>>>>> +- TableSourceScan(table=[[default_catalog, default_database, Unregistered_DataStream_Source_8]], fields=[id, order_id, row_num, goods_id, s_sku_code, qty, p_paid_sub_amt, p_sp_sub_amt, bom_type, last_updated_at, display_qty, is_first_flag])
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>在 2022-06-10 11:02:56，"Shengkai Fang" <fs...@gmail.com> 写道：
>>>>>>你好，能提供下具体的 plan 供大家查看下吗？
>>>>>>
>>>>>>你可以直接 使用 tEnv.executeSql("Explain JSON_EXECUTION_PLAN
>>>>>><YOUR_QUERY>").print() 打印下相关的信息。
>>>>>>
>>>>>>Best,
>>>>>>Shengkai
>>>>>>
>>>>>>lxk <lx...@163.com> 于2022年6月10日周五 10:29写道：
>>>>>>
>>>>>>> flink 版本:1.14.4
>>>>>>> 目前在使用flink interval join进行数据关联，在测试的时候发现一个问题，就是使用interval
>>>>>>> join完之后数据会丢失，但是使用sql api，直接进行join，数据是正常的，没有丢失。
>>>>>>> 水印是直接使用kafka 自带的时间戳生成watermark
>>>>>>>
>>>>>>>
>>>>>>> 以下是代码 ---interval join
>>>>>>>
>>>>>>> SingleOutputStreamOperator<HeaderFull> headerFullStream =
>>>>>>> headerFilterStream.keyBy(data -> data.getId())
>>>>>>> .intervalJoin(filterItemStream.keyBy(data -> data.getOrder_id()))
>>>>>>> .between(Time.seconds(-10), Time.seconds(20))
>>>>>>> .process(new ProcessJoinFunction<OrderHeader, OrderItem, HeaderFull>() {
>>>>>>> @Override
>>>>>>> public void processElement(OrderHeader left, OrderItem right, Context
>>>>>>> context, Collector<HeaderFull> collector) throws Exception {
>>>>>>> HeaderFull headerFull = new HeaderFull();
>>>>>>> BeanUtilsBean beanUtilsBean = new BeanUtilsBean();
>>>>>>> beanUtilsBean.copyProperties(headerFull, left);
>>>>>>> beanUtilsBean.copyProperties(headerFull, right);
>>>>>>> String event_date = left.getOrder_at().substring(0, 10);
>>>>>>> headerFull.setEvent_date(event_date);
>>>>>>> headerFull.setItem_id(right.getId());
>>>>>>> collector.collect(headerFull);
>>>>>>> }
>>>>>>> }
>>>>>>> 使用sql 进行join
>>>>>>> Configuration conf = new Configuration();
>>>>>>> conf.setString("table.exec.mini-batch.enabled","true");
>>>>>>> conf.setString("table.exec.mini-batch.allow-latency","15 s");
>>>>>>> conf.setString("table.exec.mini-batch.size","100");
>>>>>>> conf.setString("table.exec.state.ttl","20 s");
>>>>>>> env.configure(conf);
>>>>>>> Table headerTable =
>>>>>>> streamTableEnvironment.fromDataStream(headerFilterStream);
>>>>>>> Table itemTable = streamTableEnvironment.fromDataStream(filterItemStream);
>>>>>>>
>>>>>>>
>>>>>>> streamTableEnvironment.createTemporaryView("header",headerTable);
>>>>>>> streamTableEnvironment.createTemporaryView("item",itemTable);
>>>>>>>
>>>>>>> Table result = streamTableEnvironment.sqlQuery("select header.customer_id"
>>>>>>> +
>>>>>>> ",item.goods_id" +
>>>>>>> ",header.id" +
>>>>>>> ",header.order_status" +
>>>>>>> ",header.shop_id" +
>>>>>>> ",header.parent_order_id" +
>>>>>>> ",header.order_at" +
>>>>>>> ",header.pay_at" +
>>>>>>> ",header.channel_id" +
>>>>>>> ",header.root_order_id" +
>>>>>>> ",item.id" +
>>>>>>> ",item.row_num" +
>>>>>>> ",item.p_sp_sub_amt" +
>>>>>>> ",item.display_qty" +
>>>>>>> ",item.qty" +
>>>>>>> ",item.bom_type" +
>>>>>>> " from header JOIN item on header.id = item.order_id");
>>>>>>>
>>>>>>>
>>>>>>> DataStream<Row> rowDataStream =
>>>>>>> streamTableEnvironment.toChangelogStream(result);
>>>>>>> 不太理解为什么使用interval join会丢这么多数据，按照我的理解使用sql join，底层应该也是用的类似interval
>>>>>>> join，为啥两者最终关联上的结果差异这么大。
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
Re:Re:Re:Re:Re:Re:Re: Flink 使用interval join数据丢失疑问
Posted by lxk <lx...@163.com>.
现在改成了sql interval join,代码和执行计划如下，其他配置没变，数据量还是少，使用inner join就没问题
Table headerTable = streamTableEnvironment.fromDataStream(headerFilterStream, Schema.newBuilder()
.columnByExpression("rowtime", "CAST(substring(last_updated_at,0,19) AS TIMESTAMP_LTZ(3))")
.watermark("rowtime", "rowtime - INTERVAL '2' SECOND")
.build());
Table itemTable = streamTableEnvironment.fromDataStream(filterItemStream, Schema.newBuilder()
.columnByExpression("rowtime", "CAST(substring(last_updated_at,0,19) AS TIMESTAMP_LTZ(3))")
.watermark("rowtime", "rowtime - INTERVAL '2' SECOND")
.build());
streamTableEnvironment.createTemporaryView("header",headerTable);
streamTableEnvironment.createTemporaryView("item",itemTable);
Table result = streamTableEnvironment.sqlQuery("select header.customer_id" +
",item.goods_id" +
",header.id" +
",header.order_status" +
",header.shop_id" +
",header.parent_order_id" +
",header.order_at" +
",header.pay_at" +
",header.channel_id" +
",header.root_order_id" +
",item.id" +
",item.row_num" +
",item.p_sp_sub_amt" +
",item.display_qty" +
",item.qty" +
",item.bom_type" +
" from header JOIN item on header.id = item.order_id and item.rowtime BETWEEN header.rowtime - INTERVAL '10' SECOND AND header.rowtime + INTERVAL '20' SECOND");
String intervalJoin = streamTableEnvironment.explainSql("select header.customer_id" +
",item.goods_id" +
",header.id" +
",header.order_status" +
",header.shop_id" +
",header.parent_order_id" +
",header.order_at" +
",header.pay_at" +
",header.channel_id" +
",header.root_order_id" +
",item.id" +
",item.row_num" +
",item.p_sp_sub_amt" +
",item.display_qty" +
",item.qty" +
",item.bom_type" +
" from header JOIN item on header.id = item.order_id and item.rowtime BETWEEN header.rowtime - INTERVAL '10' SECOND AND header.rowtime + INTERVAL '20' SECOND");
System.out.println(intervalJoin);
DataStream<Row> rowDataStream = streamTableEnvironment.toChangelogStream(result);
执行计划:
== Abstract Syntax Tree ==
LogicalProject(customer_id=[$2], goods_id=[$16], id=[$0], order_status=[$1], shop_id=[$3], parent_order_id=[$4], order_at=[$5], pay_at=[$6], channel_id=[$7], root_order_id=[$8], id0=[$13], row_num=[$15], p_sp_sub_amt=[$20], display_qty=[$23], qty=[$18], bom_type=[$21])
+- LogicalJoin(condition=[AND(=($0, $14), >=($25, -($12, 10000:INTERVAL SECOND)), <=($25, +($12, 20000:INTERVAL SECOND)))], joinType=[inner])
:- LogicalWatermarkAssigner(rowtime=[rowtime], watermark=[-($12, 2000:INTERVAL SECOND)])
: +- LogicalProject(id=[$0], order_status=[$1], customer_id=[$2], shop_id=[$3], parent_order_id=[$4], order_at=[$5], pay_at=[$6], channel_id=[$7], root_order_id=[$8], last_updated_at=[$9], business_flag=[$10], mysql_op_type=[$11], rowtime=[CAST(SUBSTRING($9, 0, 19)):TIMESTAMP_WITH_LOCAL_TIME_ZONE(3)])
: +- LogicalTableScan(table=[[default_catalog, default_database, Unregistered_DataStream_Source_5]])
+- LogicalWatermarkAssigner(rowtime=[rowtime], watermark=[-($12, 2000:INTERVAL SECOND)])
+- LogicalProject(id=[$0], order_id=[$1], row_num=[$2], goods_id=[$3], s_sku_code=[$4], qty=[$5], p_paid_sub_amt=[$6], p_sp_sub_amt=[$7], bom_type=[$8], last_updated_at=[$9], display_qty=[$10], is_first_flag=[$11], rowtime=[CAST(SUBSTRING($9, 0, 19)):TIMESTAMP_WITH_LOCAL_TIME_ZONE(3)])
+- LogicalTableScan(table=[[default_catalog, default_database, Unregistered_DataStream_Source_8]])
== Optimized Physical Plan ==
Calc(select=[customer_id, goods_id, id, order_status, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id, id0, row_num, p_sp_sub_amt, display_qty, qty, bom_type])
+- IntervalJoin(joinType=[InnerJoin], windowBounds=[isRowTime=true, leftLowerBound=-20000, leftUpperBound=10000, leftTimeIndex=9, rightTimeIndex=8], where=[AND(=(id, order_id), >=(rowtime0, -(rowtime, 10000:INTERVAL SECOND)), <=(rowtime0, +(rowtime, 20000:INTERVAL SECOND)))], select=[id, order_status, customer_id, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id, rowtime, id0, order_id, row_num, goods_id, qty, p_sp_sub_amt, bom_type, display_qty, rowtime0])
:- Exchange(distribution=[hash[id]])
: +- WatermarkAssigner(rowtime=[rowtime], watermark=[-(rowtime, 2000:INTERVAL SECOND)])
: +- Calc(select=[id, order_status, customer_id, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id, CAST(SUBSTRING(last_updated_at, 0, 19)) AS rowtime])
: +- TableSourceScan(table=[[default_catalog, default_database, Unregistered_DataStream_Source_5]], fields=[id, order_status, customer_id, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id, last_updated_at, business_flag, mysql_op_type])
+- Exchange(distribution=[hash[order_id]])
+- WatermarkAssigner(rowtime=[rowtime], watermark=[-(rowtime, 2000:INTERVAL SECOND)])
+- Calc(select=[id, order_id, row_num, goods_id, qty, p_sp_sub_amt, bom_type, display_qty, CAST(SUBSTRING(last_updated_at, 0, 19)) AS rowtime])
+- TableSourceScan(table=[[default_catalog, default_database, Unregistered_DataStream_Source_8]], fields=[id, order_id, row_num, goods_id, s_sku_code, qty, p_paid_sub_amt, p_sp_sub_amt, bom_type, last_updated_at, display_qty, is_first_flag])
== Optimized Execution Plan ==
Calc(select=[customer_id, goods_id, id, order_status, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id, id0, row_num, p_sp_sub_amt, display_qty, qty, bom_type])
+- IntervalJoin(joinType=[InnerJoin], windowBounds=[isRowTime=true, leftLowerBound=-20000, leftUpperBound=10000, leftTimeIndex=9, rightTimeIndex=8], where=[((id = order_id) AND (rowtime0 >= (rowtime - 10000:INTERVAL SECOND)) AND (rowtime0 <= (rowtime + 20000:INTERVAL SECOND)))], select=[id, order_status, customer_id, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id, rowtime, id0, order_id, row_num, goods_id, qty, p_sp_sub_amt, bom_type, display_qty, rowtime0])
:- Exchange(distribution=[hash[id]])
: +- WatermarkAssigner(rowtime=[rowtime], watermark=[(rowtime - 2000:INTERVAL SECOND)])
: +- Calc(select=[id, order_status, customer_id, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id, CAST(SUBSTRING(last_updated_at, 0, 19)) AS rowtime])
: +- TableSourceScan(table=[[default_catalog, default_database, Unregistered_DataStream_Source_5]], fields=[id, order_status, customer_id, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id, last_updated_at, business_flag, mysql_op_type])
+- Exchange(distribution=[hash[order_id]])
+- WatermarkAssigner(rowtime=[rowtime], watermark=[(rowtime - 2000:INTERVAL SECOND)])
+- Calc(select=[id, order_id, row_num, goods_id, qty, p_sp_sub_amt, bom_type, display_qty, CAST(SUBSTRING(last_updated_at, 0, 19)) AS rowtime])
+- TableSourceScan(table=[[default_catalog, default_database, Unregistered_DataStream_Source_8]], fields=[id, order_id, row_num, goods_id, s_sku_code, qty, p_paid_sub_amt, p_sp_sub_amt, bom_type, last_updated_at, display_qty, is_first_flag])
在 2022-06-10 17:16:31，"lxk" <lx...@163.com> 写道：
>使用sql 进行interval join，我目前的问题是感觉时间转换这块不太友好，我目前流里面的事件时间字段是string类型，数据样式是2022-06-10 13:08:55,但是我使用TO_TIMESTAMP这个函数进行转换一直报错
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>在 2022-06-10 15:04:31，"Xuyang" <xy...@163.com> 写道：
>>Hi, datastream的这个interval join的api应该对标的是sql中的interval join。但是你目前写的这个sql，是普通join。普通join和interval join在业务含义和实现上都是有区别的。所以你直接拿datastream api的interval join和sql上的普通join结果对比，其实是有问题的。所以我之前的建议是让你试下让sql也使用interval join，这样双方才有可比性。
>>
>>
>>另外sql中设置的table.exec.state.ttl这个参数，只是代表的state会20s清空过期数据，但我看你要比较的时间窗口是-10s和20s，貌似也不大一样。
>>
>>
>>
>>
>>--
>>
>> Best！
>> Xuyang
>>
>>
>>
>>
>>
>>在 2022-06-10 14:33:37，"lxk" <lx...@163.com> 写道：
>>>
>>>
>>>
>>>我不理解的点在于，我interval join开的时间窗口比我sql中设置的状态时间都要长，窗口的上下界别是-10s 和 20s，为什么会丢数据？
>>>
>>>sql中我设置这个table.exec.state.ttl参数 为20s，照理来说两个流应该也是保留20s的数据在状态中进行join。不知道我的理解是否有问题，希望能够得到解答。
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>在 2022-06-10 14:15:29，"Xuyang" <xy...@163.com> 写道：
>>>>Hi, 你的这条SQL 并不是interval join，是普通join。
>>>>interval join的使用文档可以参考文档[1]。可以试下使用SQL interval join会不会丢数据（注意设置state的ttl），从而判断是数据的问题还是datastream api的问题。
>>>>
>>>>
>>>>
>>>>
>>>>[1] https://nightlies.apache.org/flink/flink-docs-master/docs/dev/table/sql/queries/joins/#interval-joins
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>--
>>>>
>>>> Best！
>>>> Xuyang
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>在 2022-06-10 11:26:33，"lxk" <lx...@163.com> 写道：
>>>>>我用的是以下代码:
>>>>>String s = streamTableEnvironment.explainSql("select header.customer_id" +
>>>>>",item.goods_id" +
>>>>>",header.id" +
>>>>>",header.order_status" +
>>>>>",header.shop_id" +
>>>>>",header.parent_order_id" +
>>>>>",header.order_at" +
>>>>>",header.pay_at" +
>>>>>",header.channel_id" +
>>>>>",header.root_order_id" +
>>>>>",item.id" +
>>>>>",item.row_num" +
>>>>>",item.p_sp_sub_amt" +
>>>>>",item.display_qty" +
>>>>>",item.qty" +
>>>>>",item.bom_type" +
>>>>>" from header JOIN item on header.id = item.order_id");
>>>>>
>>>>>System.out.println("explain:" + s);
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>plan信息为:
>>>>>explain:== Abstract Syntax Tree ==
>>>>>LogicalProject(customer_id=[$2], goods_id=[$15], id=[$0], order_status=[$1], shop_id=[$3], parent_order_id=[$4], order_at=[$5], pay_at=[$6], channel_id=[$7], root_order_id=[$8], id0=[$12], row_num=[$14], p_sp_sub_amt=[$19], display_qty=[$22], qty=[$17], bom_type=[$20])
>>>>>+- LogicalJoin(condition=[=($0, $13)], joinType=[inner])
>>>>> :- LogicalTableScan(table=[[default_catalog, default_database, Unregistered_DataStream_Source_5]])
>>>>> +- LogicalTableScan(table=[[default_catalog, default_database, Unregistered_DataStream_Source_8]])
>>>>>
>>>>>
>>>>>== Optimized Physical Plan ==
>>>>>Calc(select=[customer_id, goods_id, id, order_status, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id, id0, row_num, p_sp_sub_amt, display_qty, qty, bom_type])
>>>>>+- Join(joinType=[InnerJoin], where=[=(id, order_id)], select=[id, order_status, customer_id, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id, id0, order_id, row_num, goods_id, qty, p_sp_sub_amt, bom_type, display_qty], leftInputSpec=[NoUniqueKey], rightInputSpec=[NoUniqueKey])
>>>>> :- Exchange(distribution=[hash[id]])
>>>>> : +- Calc(select=[id, order_status, customer_id, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id])
>>>>> : +- TableSourceScan(table=[[default_catalog, default_database, Unregistered_DataStream_Source_5]], fields=[id, order_status, customer_id, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id, last_updated_at, business_flag, mysql_op_type])
>>>>> +- Exchange(distribution=[hash[order_id]])
>>>>> +- Calc(select=[id, order_id, row_num, goods_id, qty, p_sp_sub_amt, bom_type, display_qty])
>>>>> +- TableSourceScan(table=[[default_catalog, default_database, Unregistered_DataStream_Source_8]], fields=[id, order_id, row_num, goods_id, s_sku_code, qty, p_paid_sub_amt, p_sp_sub_amt, bom_type, last_updated_at, display_qty, is_first_flag])
>>>>>
>>>>>
>>>>>== Optimized Execution Plan ==
>>>>>Calc(select=[customer_id, goods_id, id, order_status, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id, id0, row_num, p_sp_sub_amt, display_qty, qty, bom_type])
>>>>>+- Join(joinType=[InnerJoin], where=[(id = order_id)], select=[id, order_status, customer_id, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id, id0, order_id, row_num, goods_id, qty, p_sp_sub_amt, bom_type, display_qty], leftInputSpec=[NoUniqueKey], rightInputSpec=[NoUniqueKey])
>>>>> :- Exchange(distribution=[hash[id]])
>>>>> : +- Calc(select=[id, order_status, customer_id, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id])
>>>>> : +- TableSourceScan(table=[[default_catalog, default_database, Unregistered_DataStream_Source_5]], fields=[id, order_status, customer_id, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id, last_updated_at, business_flag, mysql_op_type])
>>>>> +- Exchange(distribution=[hash[order_id]])
>>>>> +- Calc(select=[id, order_id, row_num, goods_id, qty, p_sp_sub_amt, bom_type, display_qty])
>>>>> +- TableSourceScan(table=[[default_catalog, default_database, Unregistered_DataStream_Source_8]], fields=[id, order_id, row_num, goods_id, s_sku_code, qty, p_paid_sub_amt, p_sp_sub_amt, bom_type, last_updated_at, display_qty, is_first_flag])
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>在 2022-06-10 11:02:56，"Shengkai Fang" <fs...@gmail.com> 写道：
>>>>>>你好，能提供下具体的 plan 供大家查看下吗？
>>>>>>
>>>>>>你可以直接 使用 tEnv.executeSql("Explain JSON_EXECUTION_PLAN
>>>>>><YOUR_QUERY>").print() 打印下相关的信息。
>>>>>>
>>>>>>Best,
>>>>>>Shengkai
>>>>>>
>>>>>>lxk <lx...@163.com> 于2022年6月10日周五 10:29写道：
>>>>>>
>>>>>>> flink 版本:1.14.4
>>>>>>> 目前在使用flink interval join进行数据关联，在测试的时候发现一个问题，就是使用interval
>>>>>>> join完之后数据会丢失，但是使用sql api，直接进行join，数据是正常的，没有丢失。
>>>>>>> 水印是直接使用kafka 自带的时间戳生成watermark
>>>>>>>
>>>>>>>
>>>>>>> 以下是代码 ---interval join
>>>>>>>
>>>>>>> SingleOutputStreamOperator<HeaderFull> headerFullStream =
>>>>>>> headerFilterStream.keyBy(data -> data.getId())
>>>>>>> .intervalJoin(filterItemStream.keyBy(data -> data.getOrder_id()))
>>>>>>> .between(Time.seconds(-10), Time.seconds(20))
>>>>>>> .process(new ProcessJoinFunction<OrderHeader, OrderItem, HeaderFull>() {
>>>>>>> @Override
>>>>>>> public void processElement(OrderHeader left, OrderItem right, Context
>>>>>>> context, Collector<HeaderFull> collector) throws Exception {
>>>>>>> HeaderFull headerFull = new HeaderFull();
>>>>>>> BeanUtilsBean beanUtilsBean = new BeanUtilsBean();
>>>>>>> beanUtilsBean.copyProperties(headerFull, left);
>>>>>>> beanUtilsBean.copyProperties(headerFull, right);
>>>>>>> String event_date = left.getOrder_at().substring(0, 10);
>>>>>>> headerFull.setEvent_date(event_date);
>>>>>>> headerFull.setItem_id(right.getId());
>>>>>>> collector.collect(headerFull);
>>>>>>> }
>>>>>>> }
>>>>>>> 使用sql 进行join
>>>>>>> Configuration conf = new Configuration();
>>>>>>> conf.setString("table.exec.mini-batch.enabled","true");
>>>>>>> conf.setString("table.exec.mini-batch.allow-latency","15 s");
>>>>>>> conf.setString("table.exec.mini-batch.size","100");
>>>>>>> conf.setString("table.exec.state.ttl","20 s");
>>>>>>> env.configure(conf);
>>>>>>> Table headerTable =
>>>>>>> streamTableEnvironment.fromDataStream(headerFilterStream);
>>>>>>> Table itemTable = streamTableEnvironment.fromDataStream(filterItemStream);
>>>>>>>
>>>>>>>
>>>>>>> streamTableEnvironment.createTemporaryView("header",headerTable);
>>>>>>> streamTableEnvironment.createTemporaryView("item",itemTable);
>>>>>>>
>>>>>>> Table result = streamTableEnvironment.sqlQuery("select header.customer_id"
>>>>>>> +
>>>>>>> ",item.goods_id" +
>>>>>>> ",header.id" +
>>>>>>> ",header.order_status" +
>>>>>>> ",header.shop_id" +
>>>>>>> ",header.parent_order_id" +
>>>>>>> ",header.order_at" +
>>>>>>> ",header.pay_at" +
>>>>>>> ",header.channel_id" +
>>>>>>> ",header.root_order_id" +
>>>>>>> ",item.id" +
>>>>>>> ",item.row_num" +
>>>>>>> ",item.p_sp_sub_amt" +
>>>>>>> ",item.display_qty" +
>>>>>>> ",item.qty" +
>>>>>>> ",item.bom_type" +
>>>>>>> " from header JOIN item on header.id = item.order_id");
>>>>>>>
>>>>>>>
>>>>>>> DataStream<Row> rowDataStream =
>>>>>>> streamTableEnvironment.toChangelogStream(result);
>>>>>>> 不太理解为什么使用interval join会丢这么多数据，按照我的理解使用sql join，底层应该也是用的类似interval
>>>>>>> join，为啥两者最终关联上的结果差异这么大。
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
Re:Re:Re:Re:Re:Re: Flink 使用interval join数据丢失疑问
Posted by lxk <lx...@163.com>.
使用sql 进行interval join，我目前的问题是感觉时间转换这块不太友好，我目前流里面的事件时间字段是string类型，数据样式是2022-06-10 13:08:55,但是我使用TO_TIMESTAMP这个函数进行转换一直报错
在 2022-06-10 15:04:31，"Xuyang" <xy...@163.com> 写道：
>Hi, datastream的这个interval join的api应该对标的是sql中的interval join。但是你目前写的这个sql，是普通join。普通join和interval join在业务含义和实现上都是有区别的。所以你直接拿datastream api的interval join和sql上的普通join结果对比，其实是有问题的。所以我之前的建议是让你试下让sql也使用interval join，这样双方才有可比性。
>
>
>另外sql中设置的table.exec.state.ttl这个参数，只是代表的state会20s清空过期数据，但我看你要比较的时间窗口是-10s和20s，貌似也不大一样。
>
>
>
>
>--
>
> Best！
> Xuyang
>
>
>
>
>
>在 2022-06-10 14:33:37，"lxk" <lx...@163.com> 写道：
>>
>>
>>
>>我不理解的点在于，我interval join开的时间窗口比我sql中设置的状态时间都要长，窗口的上下界别是-10s 和 20s，为什么会丢数据？
>>
>>sql中我设置这个table.exec.state.ttl参数 为20s，照理来说两个流应该也是保留20s的数据在状态中进行join。不知道我的理解是否有问题，希望能够得到解答。
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>在 2022-06-10 14:15:29，"Xuyang" <xy...@163.com> 写道：
>>>Hi, 你的这条SQL 并不是interval join，是普通join。
>>>interval join的使用文档可以参考文档[1]。可以试下使用SQL interval join会不会丢数据（注意设置state的ttl），从而判断是数据的问题还是datastream api的问题。
>>>
>>>
>>>
>>>
>>>[1] https://nightlies.apache.org/flink/flink-docs-master/docs/dev/table/sql/queries/joins/#interval-joins
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>--
>>>
>>> Best！
>>> Xuyang
>>>
>>>
>>>
>>>
>>>
>>>在 2022-06-10 11:26:33，"lxk" <lx...@163.com> 写道：
>>>>我用的是以下代码:
>>>>String s = streamTableEnvironment.explainSql("select header.customer_id" +
>>>>",item.goods_id" +
>>>>",header.id" +
>>>>",header.order_status" +
>>>>",header.shop_id" +
>>>>",header.parent_order_id" +
>>>>",header.order_at" +
>>>>",header.pay_at" +
>>>>",header.channel_id" +
>>>>",header.root_order_id" +
>>>>",item.id" +
>>>>",item.row_num" +
>>>>",item.p_sp_sub_amt" +
>>>>",item.display_qty" +
>>>>",item.qty" +
>>>>",item.bom_type" +
>>>>" from header JOIN item on header.id = item.order_id");
>>>>
>>>>System.out.println("explain:" + s);
>>>>
>>>>
>>>>
>>>>
>>>>plan信息为:
>>>>explain:== Abstract Syntax Tree ==
>>>>LogicalProject(customer_id=[$2], goods_id=[$15], id=[$0], order_status=[$1], shop_id=[$3], parent_order_id=[$4], order_at=[$5], pay_at=[$6], channel_id=[$7], root_order_id=[$8], id0=[$12], row_num=[$14], p_sp_sub_amt=[$19], display_qty=[$22], qty=[$17], bom_type=[$20])
>>>>+- LogicalJoin(condition=[=($0, $13)], joinType=[inner])
>>>> :- LogicalTableScan(table=[[default_catalog, default_database, Unregistered_DataStream_Source_5]])
>>>> +- LogicalTableScan(table=[[default_catalog, default_database, Unregistered_DataStream_Source_8]])
>>>>
>>>>
>>>>== Optimized Physical Plan ==
>>>>Calc(select=[customer_id, goods_id, id, order_status, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id, id0, row_num, p_sp_sub_amt, display_qty, qty, bom_type])
>>>>+- Join(joinType=[InnerJoin], where=[=(id, order_id)], select=[id, order_status, customer_id, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id, id0, order_id, row_num, goods_id, qty, p_sp_sub_amt, bom_type, display_qty], leftInputSpec=[NoUniqueKey], rightInputSpec=[NoUniqueKey])
>>>> :- Exchange(distribution=[hash[id]])
>>>> : +- Calc(select=[id, order_status, customer_id, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id])
>>>> : +- TableSourceScan(table=[[default_catalog, default_database, Unregistered_DataStream_Source_5]], fields=[id, order_status, customer_id, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id, last_updated_at, business_flag, mysql_op_type])
>>>> +- Exchange(distribution=[hash[order_id]])
>>>> +- Calc(select=[id, order_id, row_num, goods_id, qty, p_sp_sub_amt, bom_type, display_qty])
>>>> +- TableSourceScan(table=[[default_catalog, default_database, Unregistered_DataStream_Source_8]], fields=[id, order_id, row_num, goods_id, s_sku_code, qty, p_paid_sub_amt, p_sp_sub_amt, bom_type, last_updated_at, display_qty, is_first_flag])
>>>>
>>>>
>>>>== Optimized Execution Plan ==
>>>>Calc(select=[customer_id, goods_id, id, order_status, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id, id0, row_num, p_sp_sub_amt, display_qty, qty, bom_type])
>>>>+- Join(joinType=[InnerJoin], where=[(id = order_id)], select=[id, order_status, customer_id, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id, id0, order_id, row_num, goods_id, qty, p_sp_sub_amt, bom_type, display_qty], leftInputSpec=[NoUniqueKey], rightInputSpec=[NoUniqueKey])
>>>> :- Exchange(distribution=[hash[id]])
>>>> : +- Calc(select=[id, order_status, customer_id, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id])
>>>> : +- TableSourceScan(table=[[default_catalog, default_database, Unregistered_DataStream_Source_5]], fields=[id, order_status, customer_id, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id, last_updated_at, business_flag, mysql_op_type])
>>>> +- Exchange(distribution=[hash[order_id]])
>>>> +- Calc(select=[id, order_id, row_num, goods_id, qty, p_sp_sub_amt, bom_type, display_qty])
>>>> +- TableSourceScan(table=[[default_catalog, default_database, Unregistered_DataStream_Source_8]], fields=[id, order_id, row_num, goods_id, s_sku_code, qty, p_paid_sub_amt, p_sp_sub_amt, bom_type, last_updated_at, display_qty, is_first_flag])
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>在 2022-06-10 11:02:56，"Shengkai Fang" <fs...@gmail.com> 写道：
>>>>>你好，能提供下具体的 plan 供大家查看下吗？
>>>>>
>>>>>你可以直接 使用 tEnv.executeSql("Explain JSON_EXECUTION_PLAN
>>>>><YOUR_QUERY>").print() 打印下相关的信息。
>>>>>
>>>>>Best,
>>>>>Shengkai
>>>>>
>>>>>lxk <lx...@163.com> 于2022年6月10日周五 10:29写道：
>>>>>
>>>>>> flink 版本:1.14.4
>>>>>> 目前在使用flink interval join进行数据关联，在测试的时候发现一个问题，就是使用interval
>>>>>> join完之后数据会丢失，但是使用sql api，直接进行join，数据是正常的，没有丢失。
>>>>>> 水印是直接使用kafka 自带的时间戳生成watermark
>>>>>>
>>>>>>
>>>>>> 以下是代码 ---interval join
>>>>>>
>>>>>> SingleOutputStreamOperator<HeaderFull> headerFullStream =
>>>>>> headerFilterStream.keyBy(data -> data.getId())
>>>>>> .intervalJoin(filterItemStream.keyBy(data -> data.getOrder_id()))
>>>>>> .between(Time.seconds(-10), Time.seconds(20))
>>>>>> .process(new ProcessJoinFunction<OrderHeader, OrderItem, HeaderFull>() {
>>>>>> @Override
>>>>>> public void processElement(OrderHeader left, OrderItem right, Context
>>>>>> context, Collector<HeaderFull> collector) throws Exception {
>>>>>> HeaderFull headerFull = new HeaderFull();
>>>>>> BeanUtilsBean beanUtilsBean = new BeanUtilsBean();
>>>>>> beanUtilsBean.copyProperties(headerFull, left);
>>>>>> beanUtilsBean.copyProperties(headerFull, right);
>>>>>> String event_date = left.getOrder_at().substring(0, 10);
>>>>>> headerFull.setEvent_date(event_date);
>>>>>> headerFull.setItem_id(right.getId());
>>>>>> collector.collect(headerFull);
>>>>>> }
>>>>>> }
>>>>>> 使用sql 进行join
>>>>>> Configuration conf = new Configuration();
>>>>>> conf.setString("table.exec.mini-batch.enabled","true");
>>>>>> conf.setString("table.exec.mini-batch.allow-latency","15 s");
>>>>>> conf.setString("table.exec.mini-batch.size","100");
>>>>>> conf.setString("table.exec.state.ttl","20 s");
>>>>>> env.configure(conf);
>>>>>> Table headerTable =
>>>>>> streamTableEnvironment.fromDataStream(headerFilterStream);
>>>>>> Table itemTable = streamTableEnvironment.fromDataStream(filterItemStream);
>>>>>>
>>>>>>
>>>>>> streamTableEnvironment.createTemporaryView("header",headerTable);
>>>>>> streamTableEnvironment.createTemporaryView("item",itemTable);
>>>>>>
>>>>>> Table result = streamTableEnvironment.sqlQuery("select header.customer_id"
>>>>>> +
>>>>>> ",item.goods_id" +
>>>>>> ",header.id" +
>>>>>> ",header.order_status" +
>>>>>> ",header.shop_id" +
>>>>>> ",header.parent_order_id" +
>>>>>> ",header.order_at" +
>>>>>> ",header.pay_at" +
>>>>>> ",header.channel_id" +
>>>>>> ",header.root_order_id" +
>>>>>> ",item.id" +
>>>>>> ",item.row_num" +
>>>>>> ",item.p_sp_sub_amt" +
>>>>>> ",item.display_qty" +
>>>>>> ",item.qty" +
>>>>>> ",item.bom_type" +
>>>>>> " from header JOIN item on header.id = item.order_id");
>>>>>>
>>>>>>
>>>>>> DataStream<Row> rowDataStream =
>>>>>> streamTableEnvironment.toChangelogStream(result);
>>>>>> 不太理解为什么使用interval join会丢这么多数据，按照我的理解使用sql join，底层应该也是用的类似interval
>>>>>> join，为啥两者最终关联上的结果差异这么大。
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
Re:Re:Re:Re:Re: Flink 使用interval join数据丢失疑问
Posted by Xuyang <xy...@163.com>.
Hi, datastream的这个interval join的api应该对标的是sql中的interval join。但是你目前写的这个sql，是普通join。普通join和interval join在业务含义和实现上都是有区别的。所以你直接拿datastream api的interval join和sql上的普通join结果对比，其实是有问题的。所以我之前的建议是让你试下让sql也使用interval join，这样双方才有可比性。
另外sql中设置的table.exec.state.ttl这个参数，只是代表的state会20s清空过期数据，但我看你要比较的时间窗口是-10s和20s，貌似也不大一样。
--
Best！
Xuyang
在 2022-06-10 14:33:37，"lxk" <lx...@163.com> 写道：
>
>
>
>我不理解的点在于，我interval join开的时间窗口比我sql中设置的状态时间都要长，窗口的上下界别是-10s 和 20s，为什么会丢数据？
>
>sql中我设置这个table.exec.state.ttl参数 为20s，照理来说两个流应该也是保留20s的数据在状态中进行join。不知道我的理解是否有问题，希望能够得到解答。
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>在 2022-06-10 14:15:29，"Xuyang" <xy...@163.com> 写道：
>>Hi, 你的这条SQL 并不是interval join，是普通join。
>>interval join的使用文档可以参考文档[1]。可以试下使用SQL interval join会不会丢数据（注意设置state的ttl），从而判断是数据的问题还是datastream api的问题。
>>
>>
>>
>>
>>[1] https://nightlies.apache.org/flink/flink-docs-master/docs/dev/table/sql/queries/joins/#interval-joins
>>
>>
>>
>>
>>
>>
>>
>>--
>>
>> Best！
>> Xuyang
>>
>>
>>
>>
>>
>>在 2022-06-10 11:26:33，"lxk" <lx...@163.com> 写道：
>>>我用的是以下代码:
>>>String s = streamTableEnvironment.explainSql("select header.customer_id" +
>>>",item.goods_id" +
>>>",header.id" +
>>>",header.order_status" +
>>>",header.shop_id" +
>>>",header.parent_order_id" +
>>>",header.order_at" +
>>>",header.pay_at" +
>>>",header.channel_id" +
>>>",header.root_order_id" +
>>>",item.id" +
>>>",item.row_num" +
>>>",item.p_sp_sub_amt" +
>>>",item.display_qty" +
>>>",item.qty" +
>>>",item.bom_type" +
>>>" from header JOIN item on header.id = item.order_id");
>>>
>>>System.out.println("explain:" + s);
>>>
>>>
>>>
>>>
>>>plan信息为:
>>>explain:== Abstract Syntax Tree ==
>>>LogicalProject(customer_id=[$2], goods_id=[$15], id=[$0], order_status=[$1], shop_id=[$3], parent_order_id=[$4], order_at=[$5], pay_at=[$6], channel_id=[$7], root_order_id=[$8], id0=[$12], row_num=[$14], p_sp_sub_amt=[$19], display_qty=[$22], qty=[$17], bom_type=[$20])
>>>+- LogicalJoin(condition=[=($0, $13)], joinType=[inner])
>>> :- LogicalTableScan(table=[[default_catalog, default_database, Unregistered_DataStream_Source_5]])
>>> +- LogicalTableScan(table=[[default_catalog, default_database, Unregistered_DataStream_Source_8]])
>>>
>>>
>>>== Optimized Physical Plan ==
>>>Calc(select=[customer_id, goods_id, id, order_status, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id, id0, row_num, p_sp_sub_amt, display_qty, qty, bom_type])
>>>+- Join(joinType=[InnerJoin], where=[=(id, order_id)], select=[id, order_status, customer_id, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id, id0, order_id, row_num, goods_id, qty, p_sp_sub_amt, bom_type, display_qty], leftInputSpec=[NoUniqueKey], rightInputSpec=[NoUniqueKey])
>>> :- Exchange(distribution=[hash[id]])
>>> : +- Calc(select=[id, order_status, customer_id, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id])
>>> : +- TableSourceScan(table=[[default_catalog, default_database, Unregistered_DataStream_Source_5]], fields=[id, order_status, customer_id, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id, last_updated_at, business_flag, mysql_op_type])
>>> +- Exchange(distribution=[hash[order_id]])
>>> +- Calc(select=[id, order_id, row_num, goods_id, qty, p_sp_sub_amt, bom_type, display_qty])
>>> +- TableSourceScan(table=[[default_catalog, default_database, Unregistered_DataStream_Source_8]], fields=[id, order_id, row_num, goods_id, s_sku_code, qty, p_paid_sub_amt, p_sp_sub_amt, bom_type, last_updated_at, display_qty, is_first_flag])
>>>
>>>
>>>== Optimized Execution Plan ==
>>>Calc(select=[customer_id, goods_id, id, order_status, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id, id0, row_num, p_sp_sub_amt, display_qty, qty, bom_type])
>>>+- Join(joinType=[InnerJoin], where=[(id = order_id)], select=[id, order_status, customer_id, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id, id0, order_id, row_num, goods_id, qty, p_sp_sub_amt, bom_type, display_qty], leftInputSpec=[NoUniqueKey], rightInputSpec=[NoUniqueKey])
>>> :- Exchange(distribution=[hash[id]])
>>> : +- Calc(select=[id, order_status, customer_id, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id])
>>> : +- TableSourceScan(table=[[default_catalog, default_database, Unregistered_DataStream_Source_5]], fields=[id, order_status, customer_id, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id, last_updated_at, business_flag, mysql_op_type])
>>> +- Exchange(distribution=[hash[order_id]])
>>> +- Calc(select=[id, order_id, row_num, goods_id, qty, p_sp_sub_amt, bom_type, display_qty])
>>> +- TableSourceScan(table=[[default_catalog, default_database, Unregistered_DataStream_Source_8]], fields=[id, order_id, row_num, goods_id, s_sku_code, qty, p_paid_sub_amt, p_sp_sub_amt, bom_type, last_updated_at, display_qty, is_first_flag])
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>在 2022-06-10 11:02:56，"Shengkai Fang" <fs...@gmail.com> 写道：
>>>>你好，能提供下具体的 plan 供大家查看下吗？
>>>>
>>>>你可以直接 使用 tEnv.executeSql("Explain JSON_EXECUTION_PLAN
>>>><YOUR_QUERY>").print() 打印下相关的信息。
>>>>
>>>>Best,
>>>>Shengkai
>>>>
>>>>lxk <lx...@163.com> 于2022年6月10日周五 10:29写道：
>>>>
>>>>> flink 版本:1.14.4
>>>>> 目前在使用flink interval join进行数据关联，在测试的时候发现一个问题，就是使用interval
>>>>> join完之后数据会丢失，但是使用sql api，直接进行join，数据是正常的，没有丢失。
>>>>> 水印是直接使用kafka 自带的时间戳生成watermark
>>>>>
>>>>>
>>>>> 以下是代码 ---interval join
>>>>>
>>>>> SingleOutputStreamOperator<HeaderFull> headerFullStream =
>>>>> headerFilterStream.keyBy(data -> data.getId())
>>>>> .intervalJoin(filterItemStream.keyBy(data -> data.getOrder_id()))
>>>>> .between(Time.seconds(-10), Time.seconds(20))
>>>>> .process(new ProcessJoinFunction<OrderHeader, OrderItem, HeaderFull>() {
>>>>> @Override
>>>>> public void processElement(OrderHeader left, OrderItem right, Context
>>>>> context, Collector<HeaderFull> collector) throws Exception {
>>>>> HeaderFull headerFull = new HeaderFull();
>>>>> BeanUtilsBean beanUtilsBean = new BeanUtilsBean();
>>>>> beanUtilsBean.copyProperties(headerFull, left);
>>>>> beanUtilsBean.copyProperties(headerFull, right);
>>>>> String event_date = left.getOrder_at().substring(0, 10);
>>>>> headerFull.setEvent_date(event_date);
>>>>> headerFull.setItem_id(right.getId());
>>>>> collector.collect(headerFull);
>>>>> }
>>>>> }
>>>>> 使用sql 进行join
>>>>> Configuration conf = new Configuration();
>>>>> conf.setString("table.exec.mini-batch.enabled","true");
>>>>> conf.setString("table.exec.mini-batch.allow-latency","15 s");
>>>>> conf.setString("table.exec.mini-batch.size","100");
>>>>> conf.setString("table.exec.state.ttl","20 s");
>>>>> env.configure(conf);
>>>>> Table headerTable =
>>>>> streamTableEnvironment.fromDataStream(headerFilterStream);
>>>>> Table itemTable = streamTableEnvironment.fromDataStream(filterItemStream);
>>>>>
>>>>>
>>>>> streamTableEnvironment.createTemporaryView("header",headerTable);
>>>>> streamTableEnvironment.createTemporaryView("item",itemTable);
>>>>>
>>>>> Table result = streamTableEnvironment.sqlQuery("select header.customer_id"
>>>>> +
>>>>> ",item.goods_id" +
>>>>> ",header.id" +
>>>>> ",header.order_status" +
>>>>> ",header.shop_id" +
>>>>> ",header.parent_order_id" +
>>>>> ",header.order_at" +
>>>>> ",header.pay_at" +
>>>>> ",header.channel_id" +
>>>>> ",header.root_order_id" +
>>>>> ",item.id" +
>>>>> ",item.row_num" +
>>>>> ",item.p_sp_sub_amt" +
>>>>> ",item.display_qty" +
>>>>> ",item.qty" +
>>>>> ",item.bom_type" +
>>>>> " from header JOIN item on header.id = item.order_id");
>>>>>
>>>>>
>>>>> DataStream<Row> rowDataStream =
>>>>> streamTableEnvironment.toChangelogStream(result);
>>>>> 不太理解为什么使用interval join会丢这么多数据，按照我的理解使用sql join，底层应该也是用的类似interval
>>>>> join，为啥两者最终关联上的结果差异这么大。
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
Re:Re:Re:Re: Flink 使用interval join数据丢失疑问
Posted by lxk <lx...@163.com>.
我不理解的点在于，我interval join开的时间窗口比我sql中设置的状态时间都要长，窗口的上下界别是-10s 和 20s，为什么会丢数据？
sql中我设置这个table.exec.state.ttl参数 为20s，照理来说两个流应该也是保留20s的数据在状态中进行join。不知道我的理解是否有问题，希望能够得到解答。
在 2022-06-10 14:15:29，"Xuyang" <xy...@163.com> 写道：
>Hi, 你的这条SQL 并不是interval join，是普通join。
>interval join的使用文档可以参考文档[1]。可以试下使用SQL interval join会不会丢数据（注意设置state的ttl），从而判断是数据的问题还是datastream api的问题。
>
>
>
>
>[1] https://nightlies.apache.org/flink/flink-docs-master/docs/dev/table/sql/queries/joins/#interval-joins
>
>
>
>
>
>
>
>--
>
> Best！
> Xuyang
>
>
>
>
>
>在 2022-06-10 11:26:33，"lxk" <lx...@163.com> 写道：
>>我用的是以下代码:
>>String s = streamTableEnvironment.explainSql("select header.customer_id" +
>>",item.goods_id" +
>>",header.id" +
>>",header.order_status" +
>>",header.shop_id" +
>>",header.parent_order_id" +
>>",header.order_at" +
>>",header.pay_at" +
>>",header.channel_id" +
>>",header.root_order_id" +
>>",item.id" +
>>",item.row_num" +
>>",item.p_sp_sub_amt" +
>>",item.display_qty" +
>>",item.qty" +
>>",item.bom_type" +
>>" from header JOIN item on header.id = item.order_id");
>>
>>System.out.println("explain:" + s);
>>
>>
>>
>>
>>plan信息为:
>>explain:== Abstract Syntax Tree ==
>>LogicalProject(customer_id=[$2], goods_id=[$15], id=[$0], order_status=[$1], shop_id=[$3], parent_order_id=[$4], order_at=[$5], pay_at=[$6], channel_id=[$7], root_order_id=[$8], id0=[$12], row_num=[$14], p_sp_sub_amt=[$19], display_qty=[$22], qty=[$17], bom_type=[$20])
>>+- LogicalJoin(condition=[=($0, $13)], joinType=[inner])
>> :- LogicalTableScan(table=[[default_catalog, default_database, Unregistered_DataStream_Source_5]])
>> +- LogicalTableScan(table=[[default_catalog, default_database, Unregistered_DataStream_Source_8]])
>>
>>
>>== Optimized Physical Plan ==
>>Calc(select=[customer_id, goods_id, id, order_status, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id, id0, row_num, p_sp_sub_amt, display_qty, qty, bom_type])
>>+- Join(joinType=[InnerJoin], where=[=(id, order_id)], select=[id, order_status, customer_id, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id, id0, order_id, row_num, goods_id, qty, p_sp_sub_amt, bom_type, display_qty], leftInputSpec=[NoUniqueKey], rightInputSpec=[NoUniqueKey])
>> :- Exchange(distribution=[hash[id]])
>> : +- Calc(select=[id, order_status, customer_id, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id])
>> : +- TableSourceScan(table=[[default_catalog, default_database, Unregistered_DataStream_Source_5]], fields=[id, order_status, customer_id, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id, last_updated_at, business_flag, mysql_op_type])
>> +- Exchange(distribution=[hash[order_id]])
>> +- Calc(select=[id, order_id, row_num, goods_id, qty, p_sp_sub_amt, bom_type, display_qty])
>> +- TableSourceScan(table=[[default_catalog, default_database, Unregistered_DataStream_Source_8]], fields=[id, order_id, row_num, goods_id, s_sku_code, qty, p_paid_sub_amt, p_sp_sub_amt, bom_type, last_updated_at, display_qty, is_first_flag])
>>
>>
>>== Optimized Execution Plan ==
>>Calc(select=[customer_id, goods_id, id, order_status, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id, id0, row_num, p_sp_sub_amt, display_qty, qty, bom_type])
>>+- Join(joinType=[InnerJoin], where=[(id = order_id)], select=[id, order_status, customer_id, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id, id0, order_id, row_num, goods_id, qty, p_sp_sub_amt, bom_type, display_qty], leftInputSpec=[NoUniqueKey], rightInputSpec=[NoUniqueKey])
>> :- Exchange(distribution=[hash[id]])
>> : +- Calc(select=[id, order_status, customer_id, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id])
>> : +- TableSourceScan(table=[[default_catalog, default_database, Unregistered_DataStream_Source_5]], fields=[id, order_status, customer_id, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id, last_updated_at, business_flag, mysql_op_type])
>> +- Exchange(distribution=[hash[order_id]])
>> +- Calc(select=[id, order_id, row_num, goods_id, qty, p_sp_sub_amt, bom_type, display_qty])
>> +- TableSourceScan(table=[[default_catalog, default_database, Unregistered_DataStream_Source_8]], fields=[id, order_id, row_num, goods_id, s_sku_code, qty, p_paid_sub_amt, p_sp_sub_amt, bom_type, last_updated_at, display_qty, is_first_flag])
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>在 2022-06-10 11:02:56，"Shengkai Fang" <fs...@gmail.com> 写道：
>>>你好，能提供下具体的 plan 供大家查看下吗？
>>>
>>>你可以直接 使用 tEnv.executeSql("Explain JSON_EXECUTION_PLAN
>>><YOUR_QUERY>").print() 打印下相关的信息。
>>>
>>>Best,
>>>Shengkai
>>>
>>>lxk <lx...@163.com> 于2022年6月10日周五 10:29写道：
>>>
>>>> flink 版本:1.14.4
>>>> 目前在使用flink interval join进行数据关联，在测试的时候发现一个问题，就是使用interval
>>>> join完之后数据会丢失，但是使用sql api，直接进行join，数据是正常的，没有丢失。
>>>> 水印是直接使用kafka 自带的时间戳生成watermark
>>>>
>>>>
>>>> 以下是代码 ---interval join
>>>>
>>>> SingleOutputStreamOperator<HeaderFull> headerFullStream =
>>>> headerFilterStream.keyBy(data -> data.getId())
>>>> .intervalJoin(filterItemStream.keyBy(data -> data.getOrder_id()))
>>>> .between(Time.seconds(-10), Time.seconds(20))
>>>> .process(new ProcessJoinFunction<OrderHeader, OrderItem, HeaderFull>() {
>>>> @Override
>>>> public void processElement(OrderHeader left, OrderItem right, Context
>>>> context, Collector<HeaderFull> collector) throws Exception {
>>>> HeaderFull headerFull = new HeaderFull();
>>>> BeanUtilsBean beanUtilsBean = new BeanUtilsBean();
>>>> beanUtilsBean.copyProperties(headerFull, left);
>>>> beanUtilsBean.copyProperties(headerFull, right);
>>>> String event_date = left.getOrder_at().substring(0, 10);
>>>> headerFull.setEvent_date(event_date);
>>>> headerFull.setItem_id(right.getId());
>>>> collector.collect(headerFull);
>>>> }
>>>> }
>>>> 使用sql 进行join
>>>> Configuration conf = new Configuration();
>>>> conf.setString("table.exec.mini-batch.enabled","true");
>>>> conf.setString("table.exec.mini-batch.allow-latency","15 s");
>>>> conf.setString("table.exec.mini-batch.size","100");
>>>> conf.setString("table.exec.state.ttl","20 s");
>>>> env.configure(conf);
>>>> Table headerTable =
>>>> streamTableEnvironment.fromDataStream(headerFilterStream);
>>>> Table itemTable = streamTableEnvironment.fromDataStream(filterItemStream);
>>>>
>>>>
>>>> streamTableEnvironment.createTemporaryView("header",headerTable);
>>>> streamTableEnvironment.createTemporaryView("item",itemTable);
>>>>
>>>> Table result = streamTableEnvironment.sqlQuery("select header.customer_id"
>>>> +
>>>> ",item.goods_id" +
>>>> ",header.id" +
>>>> ",header.order_status" +
>>>> ",header.shop_id" +
>>>> ",header.parent_order_id" +
>>>> ",header.order_at" +
>>>> ",header.pay_at" +
>>>> ",header.channel_id" +
>>>> ",header.root_order_id" +
>>>> ",item.id" +
>>>> ",item.row_num" +
>>>> ",item.p_sp_sub_amt" +
>>>> ",item.display_qty" +
>>>> ",item.qty" +
>>>> ",item.bom_type" +
>>>> " from header JOIN item on header.id = item.order_id");
>>>>
>>>>
>>>> DataStream<Row> rowDataStream =
>>>> streamTableEnvironment.toChangelogStream(result);
>>>> 不太理解为什么使用interval join会丢这么多数据，按照我的理解使用sql join，底层应该也是用的类似interval
>>>> join，为啥两者最终关联上的结果差异这么大。
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
Re:Re:Re: Flink 使用interval join数据丢失疑问
Posted by Xuyang <xy...@163.com>.
Hi, 你的这条SQL 并不是interval join，是普通join。
interval join的使用文档可以参考文档[1]。可以试下使用SQL interval join会不会丢数据（注意设置state的ttl），从而判断是数据的问题还是datastream api的问题。
[1] https://nightlies.apache.org/flink/flink-docs-master/docs/dev/table/sql/queries/joins/#interval-joins
--
Best！
Xuyang
在 2022-06-10 11:26:33，"lxk" <lx...@163.com> 写道：
>我用的是以下代码:
>String s = streamTableEnvironment.explainSql("select header.customer_id" +
>",item.goods_id" +
>",header.id" +
>",header.order_status" +
>",header.shop_id" +
>",header.parent_order_id" +
>",header.order_at" +
>",header.pay_at" +
>",header.channel_id" +
>",header.root_order_id" +
>",item.id" +
>",item.row_num" +
>",item.p_sp_sub_amt" +
>",item.display_qty" +
>",item.qty" +
>",item.bom_type" +
>" from header JOIN item on header.id = item.order_id");
>
>System.out.println("explain:" + s);
>
>
>
>
>plan信息为:
>explain:== Abstract Syntax Tree ==
>LogicalProject(customer_id=[$2], goods_id=[$15], id=[$0], order_status=[$1], shop_id=[$3], parent_order_id=[$4], order_at=[$5], pay_at=[$6], channel_id=[$7], root_order_id=[$8], id0=[$12], row_num=[$14], p_sp_sub_amt=[$19], display_qty=[$22], qty=[$17], bom_type=[$20])
>+- LogicalJoin(condition=[=($0, $13)], joinType=[inner])
> :- LogicalTableScan(table=[[default_catalog, default_database, Unregistered_DataStream_Source_5]])
> +- LogicalTableScan(table=[[default_catalog, default_database, Unregistered_DataStream_Source_8]])
>
>
>== Optimized Physical Plan ==
>Calc(select=[customer_id, goods_id, id, order_status, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id, id0, row_num, p_sp_sub_amt, display_qty, qty, bom_type])
>+- Join(joinType=[InnerJoin], where=[=(id, order_id)], select=[id, order_status, customer_id, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id, id0, order_id, row_num, goods_id, qty, p_sp_sub_amt, bom_type, display_qty], leftInputSpec=[NoUniqueKey], rightInputSpec=[NoUniqueKey])
> :- Exchange(distribution=[hash[id]])
> : +- Calc(select=[id, order_status, customer_id, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id])
> : +- TableSourceScan(table=[[default_catalog, default_database, Unregistered_DataStream_Source_5]], fields=[id, order_status, customer_id, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id, last_updated_at, business_flag, mysql_op_type])
> +- Exchange(distribution=[hash[order_id]])
> +- Calc(select=[id, order_id, row_num, goods_id, qty, p_sp_sub_amt, bom_type, display_qty])
> +- TableSourceScan(table=[[default_catalog, default_database, Unregistered_DataStream_Source_8]], fields=[id, order_id, row_num, goods_id, s_sku_code, qty, p_paid_sub_amt, p_sp_sub_amt, bom_type, last_updated_at, display_qty, is_first_flag])
>
>
>== Optimized Execution Plan ==
>Calc(select=[customer_id, goods_id, id, order_status, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id, id0, row_num, p_sp_sub_amt, display_qty, qty, bom_type])
>+- Join(joinType=[InnerJoin], where=[(id = order_id)], select=[id, order_status, customer_id, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id, id0, order_id, row_num, goods_id, qty, p_sp_sub_amt, bom_type, display_qty], leftInputSpec=[NoUniqueKey], rightInputSpec=[NoUniqueKey])
> :- Exchange(distribution=[hash[id]])
> : +- Calc(select=[id, order_status, customer_id, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id])
> : +- TableSourceScan(table=[[default_catalog, default_database, Unregistered_DataStream_Source_5]], fields=[id, order_status, customer_id, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id, last_updated_at, business_flag, mysql_op_type])
> +- Exchange(distribution=[hash[order_id]])
> +- Calc(select=[id, order_id, row_num, goods_id, qty, p_sp_sub_amt, bom_type, display_qty])
> +- TableSourceScan(table=[[default_catalog, default_database, Unregistered_DataStream_Source_8]], fields=[id, order_id, row_num, goods_id, s_sku_code, qty, p_paid_sub_amt, p_sp_sub_amt, bom_type, last_updated_at, display_qty, is_first_flag])
>
>
>
>
>
>
>
>
>
>
>
>
>
>在 2022-06-10 11:02:56，"Shengkai Fang" <fs...@gmail.com> 写道：
>>你好，能提供下具体的 plan 供大家查看下吗？
>>
>>你可以直接 使用 tEnv.executeSql("Explain JSON_EXECUTION_PLAN
>><YOUR_QUERY>").print() 打印下相关的信息。
>>
>>Best,
>>Shengkai
>>
>>lxk <lx...@163.com> 于2022年6月10日周五 10:29写道：
>>
>>> flink 版本:1.14.4
>>> 目前在使用flink interval join进行数据关联，在测试的时候发现一个问题，就是使用interval
>>> join完之后数据会丢失，但是使用sql api，直接进行join，数据是正常的，没有丢失。
>>> 水印是直接使用kafka 自带的时间戳生成watermark
>>>
>>>
>>> 以下是代码 ---interval join
>>>
>>> SingleOutputStreamOperator<HeaderFull> headerFullStream =
>>> headerFilterStream.keyBy(data -> data.getId())
>>> .intervalJoin(filterItemStream.keyBy(data -> data.getOrder_id()))
>>> .between(Time.seconds(-10), Time.seconds(20))
>>> .process(new ProcessJoinFunction<OrderHeader, OrderItem, HeaderFull>() {
>>> @Override
>>> public void processElement(OrderHeader left, OrderItem right, Context
>>> context, Collector<HeaderFull> collector) throws Exception {
>>> HeaderFull headerFull = new HeaderFull();
>>> BeanUtilsBean beanUtilsBean = new BeanUtilsBean();
>>> beanUtilsBean.copyProperties(headerFull, left);
>>> beanUtilsBean.copyProperties(headerFull, right);
>>> String event_date = left.getOrder_at().substring(0, 10);
>>> headerFull.setEvent_date(event_date);
>>> headerFull.setItem_id(right.getId());
>>> collector.collect(headerFull);
>>> }
>>> }
>>> 使用sql 进行join
>>> Configuration conf = new Configuration();
>>> conf.setString("table.exec.mini-batch.enabled","true");
>>> conf.setString("table.exec.mini-batch.allow-latency","15 s");
>>> conf.setString("table.exec.mini-batch.size","100");
>>> conf.setString("table.exec.state.ttl","20 s");
>>> env.configure(conf);
>>> Table headerTable =
>>> streamTableEnvironment.fromDataStream(headerFilterStream);
>>> Table itemTable = streamTableEnvironment.fromDataStream(filterItemStream);
>>>
>>>
>>> streamTableEnvironment.createTemporaryView("header",headerTable);
>>> streamTableEnvironment.createTemporaryView("item",itemTable);
>>>
>>> Table result = streamTableEnvironment.sqlQuery("select header.customer_id"
>>> +
>>> ",item.goods_id" +
>>> ",header.id" +
>>> ",header.order_status" +
>>> ",header.shop_id" +
>>> ",header.parent_order_id" +
>>> ",header.order_at" +
>>> ",header.pay_at" +
>>> ",header.channel_id" +
>>> ",header.root_order_id" +
>>> ",item.id" +
>>> ",item.row_num" +
>>> ",item.p_sp_sub_amt" +
>>> ",item.display_qty" +
>>> ",item.qty" +
>>> ",item.bom_type" +
>>> " from header JOIN item on header.id = item.order_id");
>>>
>>>
>>> DataStream<Row> rowDataStream =
>>> streamTableEnvironment.toChangelogStream(result);
>>> 不太理解为什么使用interval join会丢这么多数据，按照我的理解使用sql join，底层应该也是用的类似interval
>>> join，为啥两者最终关联上的结果差异这么大。
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
Re:Re: Flink 使用interval join数据丢失疑问
Posted by lxk <lx...@163.com>.
我用的是以下代码:
String s = streamTableEnvironment.explainSql("select header.customer_id" +
",item.goods_id" +
",header.id" +
",header.order_status" +
",header.shop_id" +
",header.parent_order_id" +
",header.order_at" +
",header.pay_at" +
",header.channel_id" +
",header.root_order_id" +
",item.id" +
",item.row_num" +
",item.p_sp_sub_amt" +
",item.display_qty" +
",item.qty" +
",item.bom_type" +
" from header JOIN item on header.id = item.order_id");
System.out.println("explain:" + s);
plan信息为:
explain:== Abstract Syntax Tree ==
LogicalProject(customer_id=[$2], goods_id=[$15], id=[$0], order_status=[$1], shop_id=[$3], parent_order_id=[$4], order_at=[$5], pay_at=[$6], channel_id=[$7], root_order_id=[$8], id0=[$12], row_num=[$14], p_sp_sub_amt=[$19], display_qty=[$22], qty=[$17], bom_type=[$20])
+- LogicalJoin(condition=[=($0, $13)], joinType=[inner])
:- LogicalTableScan(table=[[default_catalog, default_database, Unregistered_DataStream_Source_5]])
+- LogicalTableScan(table=[[default_catalog, default_database, Unregistered_DataStream_Source_8]])
== Optimized Physical Plan ==
Calc(select=[customer_id, goods_id, id, order_status, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id, id0, row_num, p_sp_sub_amt, display_qty, qty, bom_type])
+- Join(joinType=[InnerJoin], where=[=(id, order_id)], select=[id, order_status, customer_id, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id, id0, order_id, row_num, goods_id, qty, p_sp_sub_amt, bom_type, display_qty], leftInputSpec=[NoUniqueKey], rightInputSpec=[NoUniqueKey])
:- Exchange(distribution=[hash[id]])
: +- Calc(select=[id, order_status, customer_id, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id])
: +- TableSourceScan(table=[[default_catalog, default_database, Unregistered_DataStream_Source_5]], fields=[id, order_status, customer_id, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id, last_updated_at, business_flag, mysql_op_type])
+- Exchange(distribution=[hash[order_id]])
+- Calc(select=[id, order_id, row_num, goods_id, qty, p_sp_sub_amt, bom_type, display_qty])
+- TableSourceScan(table=[[default_catalog, default_database, Unregistered_DataStream_Source_8]], fields=[id, order_id, row_num, goods_id, s_sku_code, qty, p_paid_sub_amt, p_sp_sub_amt, bom_type, last_updated_at, display_qty, is_first_flag])
== Optimized Execution Plan ==
Calc(select=[customer_id, goods_id, id, order_status, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id, id0, row_num, p_sp_sub_amt, display_qty, qty, bom_type])
+- Join(joinType=[InnerJoin], where=[(id = order_id)], select=[id, order_status, customer_id, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id, id0, order_id, row_num, goods_id, qty, p_sp_sub_amt, bom_type, display_qty], leftInputSpec=[NoUniqueKey], rightInputSpec=[NoUniqueKey])
:- Exchange(distribution=[hash[id]])
: +- Calc(select=[id, order_status, customer_id, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id])
: +- TableSourceScan(table=[[default_catalog, default_database, Unregistered_DataStream_Source_5]], fields=[id, order_status, customer_id, shop_id, parent_order_id, order_at, pay_at, channel_id, root_order_id, last_updated_at, business_flag, mysql_op_type])
+- Exchange(distribution=[hash[order_id]])
+- Calc(select=[id, order_id, row_num, goods_id, qty, p_sp_sub_amt, bom_type, display_qty])
+- TableSourceScan(table=[[default_catalog, default_database, Unregistered_DataStream_Source_8]], fields=[id, order_id, row_num, goods_id, s_sku_code, qty, p_paid_sub_amt, p_sp_sub_amt, bom_type, last_updated_at, display_qty, is_first_flag])
在 2022-06-10 11:02:56，"Shengkai Fang" <fs...@gmail.com> 写道：
>你好，能提供下具体的 plan 供大家查看下吗？
>
>你可以直接 使用 tEnv.executeSql("Explain JSON_EXECUTION_PLAN
><YOUR_QUERY>").print() 打印下相关的信息。
>
>Best,
>Shengkai
>
>lxk <lx...@163.com> 于2022年6月10日周五 10:29写道：
>
>> flink 版本:1.14.4
>> 目前在使用flink interval join进行数据关联，在测试的时候发现一个问题，就是使用interval
>> join完之后数据会丢失，但是使用sql api，直接进行join，数据是正常的，没有丢失。
>> 水印是直接使用kafka 自带的时间戳生成watermark
>>
>>
>> 以下是代码 ---interval join
>>
>> SingleOutputStreamOperator<HeaderFull> headerFullStream =
>> headerFilterStream.keyBy(data -> data.getId())
>> .intervalJoin(filterItemStream.keyBy(data -> data.getOrder_id()))
>> .between(Time.seconds(-10), Time.seconds(20))
>> .process(new ProcessJoinFunction<OrderHeader, OrderItem, HeaderFull>() {
>> @Override
>> public void processElement(OrderHeader left, OrderItem right, Context
>> context, Collector<HeaderFull> collector) throws Exception {
>> HeaderFull headerFull = new HeaderFull();
>> BeanUtilsBean beanUtilsBean = new BeanUtilsBean();
>> beanUtilsBean.copyProperties(headerFull, left);
>> beanUtilsBean.copyProperties(headerFull, right);
>> String event_date = left.getOrder_at().substring(0, 10);
>> headerFull.setEvent_date(event_date);
>> headerFull.setItem_id(right.getId());
>> collector.collect(headerFull);
>> }
>> }
>> 使用sql 进行join
>> Configuration conf = new Configuration();
>> conf.setString("table.exec.mini-batch.enabled","true");
>> conf.setString("table.exec.mini-batch.allow-latency","15 s");
>> conf.setString("table.exec.mini-batch.size","100");
>> conf.setString("table.exec.state.ttl","20 s");
>> env.configure(conf);
>> Table headerTable =
>> streamTableEnvironment.fromDataStream(headerFilterStream);
>> Table itemTable = streamTableEnvironment.fromDataStream(filterItemStream);
>>
>>
>> streamTableEnvironment.createTemporaryView("header",headerTable);
>> streamTableEnvironment.createTemporaryView("item",itemTable);
>>
>> Table result = streamTableEnvironment.sqlQuery("select header.customer_id"
>> +
>> ",item.goods_id" +
>> ",header.id" +
>> ",header.order_status" +
>> ",header.shop_id" +
>> ",header.parent_order_id" +
>> ",header.order_at" +
>> ",header.pay_at" +
>> ",header.channel_id" +
>> ",header.root_order_id" +
>> ",item.id" +
>> ",item.row_num" +
>> ",item.p_sp_sub_amt" +
>> ",item.display_qty" +
>> ",item.qty" +
>> ",item.bom_type" +
>> " from header JOIN item on header.id = item.order_id");
>>
>>
>> DataStream<Row> rowDataStream =
>> streamTableEnvironment.toChangelogStream(result);
>> 不太理解为什么使用interval join会丢这么多数据，按照我的理解使用sql join，底层应该也是用的类似interval
>> join，为啥两者最终关联上的结果差异这么大。
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
Re: Flink 使用interval join数据丢失疑问
Posted by Shengkai Fang <fs...@gmail.com>.
你好，能提供下具体的 plan 供大家查看下吗？
你可以直接 使用 tEnv.executeSql("Explain JSON_EXECUTION_PLAN
<YOUR_QUERY>").print() 打印下相关的信息。
Best,
Shengkai
lxk <lx...@163.com> 于2022年6月10日周五 10:29写道：
> flink 版本:1.14.4
> 目前在使用flink interval join进行数据关联，在测试的时候发现一个问题，就是使用interval
> join完之后数据会丢失，但是使用sql api，直接进行join，数据是正常的，没有丢失。
> 水印是直接使用kafka 自带的时间戳生成watermark
>
>
> 以下是代码 ---interval join
>
> SingleOutputStreamOperator<HeaderFull> headerFullStream =
> headerFilterStream.keyBy(data -> data.getId())
> .intervalJoin(filterItemStream.keyBy(data -> data.getOrder_id()))
> .between(Time.seconds(-10), Time.seconds(20))
> .process(new ProcessJoinFunction<OrderHeader, OrderItem, HeaderFull>() {
> @Override
> public void processElement(OrderHeader left, OrderItem right, Context
> context, Collector<HeaderFull> collector) throws Exception {
> HeaderFull headerFull = new HeaderFull();
> BeanUtilsBean beanUtilsBean = new BeanUtilsBean();
> beanUtilsBean.copyProperties(headerFull, left);
> beanUtilsBean.copyProperties(headerFull, right);
> String event_date = left.getOrder_at().substring(0, 10);
> headerFull.setEvent_date(event_date);
> headerFull.setItem_id(right.getId());
> collector.collect(headerFull);
> }
> }
> 使用sql 进行join
> Configuration conf = new Configuration();
> conf.setString("table.exec.mini-batch.enabled","true");
> conf.setString("table.exec.mini-batch.allow-latency","15 s");
> conf.setString("table.exec.mini-batch.size","100");
> conf.setString("table.exec.state.ttl","20 s");
> env.configure(conf);
> Table headerTable =
> streamTableEnvironment.fromDataStream(headerFilterStream);
> Table itemTable = streamTableEnvironment.fromDataStream(filterItemStream);
>
>
> streamTableEnvironment.createTemporaryView("header",headerTable);
> streamTableEnvironment.createTemporaryView("item",itemTable);
>
> Table result = streamTableEnvironment.sqlQuery("select header.customer_id"
> +
> ",item.goods_id" +
> ",header.id" +
> ",header.order_status" +
> ",header.shop_id" +
> ",header.parent_order_id" +
> ",header.order_at" +
> ",header.pay_at" +
> ",header.channel_id" +
> ",header.root_order_id" +
> ",item.id" +
> ",item.row_num" +
> ",item.p_sp_sub_amt" +
> ",item.display_qty" +
> ",item.qty" +
> ",item.bom_type" +
> " from header JOIN item on header.id = item.order_id");
>
>
> DataStream<Row> rowDataStream =
> streamTableEnvironment.toChangelogStream(result);
> 不太理解为什么使用interval join会丢这么多数据，按照我的理解使用sql join，底层应该也是用的类似interval
> join，为啥两者最终关联上的结果差异这么大。
>
>
>
>
>
>
>
>
>
>
>