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Posted to user-zh@flink.apache.org by Robin Zhang <vi...@outlook.com> on 2020/09/29 06:03:56 UTC
如何在流式数据源上使用分析函数LAG和EAD函数
环境: flink 1.10,使用flinkSQL
kafka输入数据如:
{"t":"2020-04-01T05:00:00Z", "id":"1", "speed":1.0}
{"t":"2020-04-01T05:05:00Z", "id":"1", "speed":2.0}
{"t":"2020-04-01T05:10:00Z", "id":"1", "speed":3.0}
{"t":"2020-04-01T05:15:00Z", "id":"1", "speed":4.0}
{"t":"2020-04-01T05:20:00Z", "id":"1", "speed":5.0}
{"t":"2020-04-01T05:25:00Z", "id":"1", "speed":6.0}
sql如下:
INSERT INTO topic_sink
SELECT
t,
id,
speed,
LAG(speed, 1) OVER w AS speed_1,
LAG(speed, 2) OVER w AS speed_2
FROM topic_source
WINDOW w AS (
PARTITION BY id
ORDER BY t
)
我期望得到的结果数据是
{"t":"2020-04-01T05:00:00Z", "id":"1", "speed":1.0, "speed_1":null,
"speed_2":null}
{"t":"2020-04-01T05:05:00Z", "id":"1", "speed":2.0,"speed_1":1.0,
"speed_2":null}
{"t":"2020-04-01T05:10:00Z", "id":"1", "speed":3.0,"speed_1":2.0,
"speed_2":1.0}
{"t":"2020-04-01T05:15:00Z", "id":"1", "speed":4.0,"speed_1":3.0,
"speed_2":2.0}
{"t":"2020-04-01T05:20:00Z", "id":"1", "speed":5.0,"speed_1":4.0,
"speed_2":3.0}
{"t":"2020-04-01T05:25:00Z", "id":"1", "speed":6.0",speed_1":5.0,
"speed_2":4.0}
实际得到的结果数据是:
{"t":"2020-04-01T05:00:00Z", "id":"1", "speed":1.0, "speed_1":1.0,
"speed_2":1.0}
{"t":"2020-04-01T05:05:00Z", "id":"1", "speed":2.0,"speed_1":2.0,
"speed_2":2.0}
{"t":"2020-04-01T05:10:00Z", "id":"1", "speed":3.0,"speed_1":3.0,
"speed_2":3.0}
{"t":"2020-04-01T05:15:00Z", "id":"1", "speed":4.0,"speed_1":4.0,
"speed_2":4.0}
{"t":"2020-04-01T05:20:00Z", "id":"1", "speed":5.0,"speed_1":5.0,
"speed_2":5.0}
{"t":"2020-04-01T05:25:00Z", "id":"1", "speed":6.0",speed_1":6.0,
"speed_2":6.0}
想问一下flink sql里的LAG函数能完成我期望的计算吗?如果可以sql该如何写?
--
Sent from: http://apache-flink.147419.n8.nabble.com/
Re: 如何在流式数据源上使用分析函数LAG和EAD函数
Posted by Robin Zhang <vi...@outlook.com>.
Hi Benchao,
感谢回复,解决了我最近的疑惑。
Best,
Robin
Benchao Li-2 wrote
> Hi Robin,
>
> 目前LAG/LEAD函数在流式场景下的实现的确是有bug的,那个实现只能在批式场景下work,
> 是线上其实没有考虑流式的场景。所以你看到的结果应该是它只能返回当前数据。
> 这个问题我也是最近才发现的,刚刚建了一个issue[1] 来跟踪这个问题。
> 当前如果你想实现类似功能,可以先自己写一个udaf来做。
>
> [1] https://issues.apache.org/jira/browse/FLINK-19449
>
> Robin Zhang <
> vincent2015qdlg@
> > 于2020年9月29日周二 下午2:04写道:
>
>> 环境: flink 1.10,使用flinkSQL
>>
>> kafka输入数据如:
>> {"t":"2020-04-01T05:00:00Z", "id":"1", "speed":1.0}
>> {"t":"2020-04-01T05:05:00Z", "id":"1", "speed":2.0}
>> {"t":"2020-04-01T05:10:00Z", "id":"1", "speed":3.0}
>> {"t":"2020-04-01T05:15:00Z", "id":"1", "speed":4.0}
>> {"t":"2020-04-01T05:20:00Z", "id":"1", "speed":5.0}
>> {"t":"2020-04-01T05:25:00Z", "id":"1", "speed":6.0}
>>
>> sql如下:
>>
>> INSERT INTO topic_sink
>> SELECT
>> t,
>> id,
>> speed,
>> LAG(speed, 1) OVER w AS speed_1,
>> LAG(speed, 2) OVER w AS speed_2
>> FROM topic_source
>> WINDOW w AS (
>> PARTITION BY id
>> ORDER BY t
>> )
>> 我期望得到的结果数据是
>> {"t":"2020-04-01T05:00:00Z", "id":"1", "speed":1.0, "speed_1":null,
>> "speed_2":null}
>> {"t":"2020-04-01T05:05:00Z", "id":"1", "speed":2.0,"speed_1":1.0,
>> "speed_2":null}
>> {"t":"2020-04-01T05:10:00Z", "id":"1", "speed":3.0,"speed_1":2.0,
>> "speed_2":1.0}
>> {"t":"2020-04-01T05:15:00Z", "id":"1", "speed":4.0,"speed_1":3.0,
>> "speed_2":2.0}
>> {"t":"2020-04-01T05:20:00Z", "id":"1", "speed":5.0,"speed_1":4.0,
>> "speed_2":3.0}
>> {"t":"2020-04-01T05:25:00Z", "id":"1", "speed":6.0",speed_1":5.0,
>> "speed_2":4.0}
>>
>> 实际得到的结果数据是:
>> {"t":"2020-04-01T05:00:00Z", "id":"1", "speed":1.0, "speed_1":1.0,
>> "speed_2":1.0}
>> {"t":"2020-04-01T05:05:00Z", "id":"1", "speed":2.0,"speed_1":2.0,
>> "speed_2":2.0}
>> {"t":"2020-04-01T05:10:00Z", "id":"1", "speed":3.0,"speed_1":3.0,
>> "speed_2":3.0}
>> {"t":"2020-04-01T05:15:00Z", "id":"1", "speed":4.0,"speed_1":4.0,
>> "speed_2":4.0}
>> {"t":"2020-04-01T05:20:00Z", "id":"1", "speed":5.0,"speed_1":5.0,
>> "speed_2":5.0}
>> {"t":"2020-04-01T05:25:00Z", "id":"1", "speed":6.0",speed_1":6.0,
>> "speed_2":6.0}
>>
>> 想问一下flink sql里的LAG函数能完成我期望的计算吗?如果可以sql该如何写?
>>
>>
>>
>> --
>> Sent from: http://apache-flink.147419.n8.nabble.com/
>>
>
>
> --
>
> Best,
> Benchao Li
--
Sent from: http://apache-flink.147419.n8.nabble.com/
Re: 如何在流式数据源上使用分析函数LAG和EAD函数
Posted by Benchao Li <li...@apache.org>.
Hi Robin,
目前LAG/LEAD函数在流式场景下的实现的确是有bug的,那个实现只能在批式场景下work,
是线上其实没有考虑流式的场景。所以你看到的结果应该是它只能返回当前数据。
这个问题我也是最近才发现的,刚刚建了一个issue[1] 来跟踪这个问题。
当前如果你想实现类似功能,可以先自己写一个udaf来做。
[1] https://issues.apache.org/jira/browse/FLINK-19449
Robin Zhang <vi...@outlook.com> 于2020年9月29日周二 下午2:04写道:
> 环境: flink 1.10,使用flinkSQL
>
> kafka输入数据如:
> {"t":"2020-04-01T05:00:00Z", "id":"1", "speed":1.0}
> {"t":"2020-04-01T05:05:00Z", "id":"1", "speed":2.0}
> {"t":"2020-04-01T05:10:00Z", "id":"1", "speed":3.0}
> {"t":"2020-04-01T05:15:00Z", "id":"1", "speed":4.0}
> {"t":"2020-04-01T05:20:00Z", "id":"1", "speed":5.0}
> {"t":"2020-04-01T05:25:00Z", "id":"1", "speed":6.0}
>
> sql如下:
>
> INSERT INTO topic_sink
> SELECT
> t,
> id,
> speed,
> LAG(speed, 1) OVER w AS speed_1,
> LAG(speed, 2) OVER w AS speed_2
> FROM topic_source
> WINDOW w AS (
> PARTITION BY id
> ORDER BY t
> )
> 我期望得到的结果数据是
> {"t":"2020-04-01T05:00:00Z", "id":"1", "speed":1.0, "speed_1":null,
> "speed_2":null}
> {"t":"2020-04-01T05:05:00Z", "id":"1", "speed":2.0,"speed_1":1.0,
> "speed_2":null}
> {"t":"2020-04-01T05:10:00Z", "id":"1", "speed":3.0,"speed_1":2.0,
> "speed_2":1.0}
> {"t":"2020-04-01T05:15:00Z", "id":"1", "speed":4.0,"speed_1":3.0,
> "speed_2":2.0}
> {"t":"2020-04-01T05:20:00Z", "id":"1", "speed":5.0,"speed_1":4.0,
> "speed_2":3.0}
> {"t":"2020-04-01T05:25:00Z", "id":"1", "speed":6.0",speed_1":5.0,
> "speed_2":4.0}
>
> 实际得到的结果数据是:
> {"t":"2020-04-01T05:00:00Z", "id":"1", "speed":1.0, "speed_1":1.0,
> "speed_2":1.0}
> {"t":"2020-04-01T05:05:00Z", "id":"1", "speed":2.0,"speed_1":2.0,
> "speed_2":2.0}
> {"t":"2020-04-01T05:10:00Z", "id":"1", "speed":3.0,"speed_1":3.0,
> "speed_2":3.0}
> {"t":"2020-04-01T05:15:00Z", "id":"1", "speed":4.0,"speed_1":4.0,
> "speed_2":4.0}
> {"t":"2020-04-01T05:20:00Z", "id":"1", "speed":5.0,"speed_1":5.0,
> "speed_2":5.0}
> {"t":"2020-04-01T05:25:00Z", "id":"1", "speed":6.0",speed_1":6.0,
> "speed_2":6.0}
>
> 想问一下flink sql里的LAG函数能完成我期望的计算吗?如果可以sql该如何写?
>
>
>
> --
> Sent from: http://apache-flink.147419.n8.nabble.com/
>
--
Best,
Benchao Li