[GitHub] [flink] JingsongLi commented on a change in pull request #9511: [FLINK-13356][table][docs] Add documentation for TopN and Deduplication in blink planner

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JingsongLi commented on a change in pull request #9511: [FLINK-13356][table][docs] Add documentation for TopN and Deduplication in blink planner
URL: https://github.com/apache/flink/pull/9511#discussion_r316672158
 
 

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 File path: docs/dev/table/sql.md
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 @@ -813,6 +813,222 @@ LIMIT 3
 
 {% top %}
 
+### Top-N
+
+TopN is used to calculate the maximum/minimum N records in a stream. It can be flexibly completed based on OVER window aggregation. The grammar is shown as below:
+
+Top-N queries ask for the N smallest or largest values ordered by columns. Both smallest and largest values sets are considered Top-N queries. Top-N queries are useful in cases where the need is to display only the N bottom-most or the N top-
+most records from batch/stream table on a condition. This result set can be used for further analysis.
+
+Flink uses the combination of a OVER window clause and a filter condition to express a Top-N query. With the power of OVER window `PARTITION BY` clause, Flink also supports per group Top-N. For example, the top five products per category that have the maximum sales in realtime. Top-N queries are supported for SQL on batch and streaming tables.
+
+The following shows the syntax of the TOP-N statement:
+
+{% highlight sql %}
+SELECT [column_list]
+FROM (
+   SELECT [column_list],
+     ROW_NUMBER() OVER ([PARTITION BY col1[, col2...]]
+       ORDER BY col1 [asc|desc][, col2 [asc|desc]...]) AS rownum
+   FROM table_name)
+WHERE rownum <= N [AND conditions]
+{% endhighlight %}
+
+**Parameter Specification:**
+
+- `ROW_NUMBER()`: Assigns an unique, sequential number to each row, starting with one, according to the ordering of rows within the partition. Currently, we only support `ROW_NUMBER` as the over window function. In the future, we will support `RANK()` and `DENSE_RANK()`.
+- `PARTITION BY col1[, col2...]`: Specifies the partition columns. Each partition will have a Top-N result.
+- `ORDER BY col1 [asc|desc][, col2 [asc|desc]...]`: Specifies the ordering columns. The ordering directions can be different on different columns.
+- `WHERE rownum <= N`: The `rownum <= N` is required for Flink to recognize this query is a Top-N query. The N represents the N smallest or largest records will be retained.
+- `[AND conditions]`: It is free to add other conditions in the where clause, but the other conditions can only be combined with `rownum <= N` using `AND` conjunction.
+
+<span class="label label-danger">Attention</span> Flink SQL will sort the input data stream according to the order key, so if the top N records have been changed, the changed ones will be sent as retraction/update records to downstream. In addition, if the top N records need to be stored in external storage, the result table must have the same primary key with the Top-N query. By default, the primary key of Top-N query is the combination of partition columns + rownum column.
+
+The following examples show how to specify SQL queries with Top-N on streaming tables. This is an example to get "the top five products per category that have the maximum sales in realtime" we mentioned above.
+
+<div class="codetabs" markdown="1">
+<div data-lang="java" markdown="1">
+{% highlight java %}
+StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
+StreamTableEnvironment tableEnv = TableEnvironment.getTableEnvironment(env);
+
+// ingest a DataStream from an external source
+DataStream<Tuple3<String, String, String, Long>> ds = env.addSource(...);
+// register the DataStream as table "ShopSales"
+tableEnv.registerDataStream("ShopSales", ds, "product_id, category, product_name, sales");
+
+// select top-5 products per category which have the maximum sales.
+Table result1 = tableEnv.sqlQuery(
+  "SELECT * " +
+  "FROM (" +
+  "   SELECT *," +
+  "       ROW_NUMBER() OVER (PARTITION BY category ORDER BY sales DESC) as row_num" +
+  "   FROM ShopSales)" +
+  "WHERE row_num <= 5");
+{% endhighlight %}
+</div>
+
+<div data-lang="scala" markdown="1">
+{% highlight scala %}
+val env = StreamExecutionEnvironment.getExecutionEnvironment
+val tableEnv = TableEnvironment.getTableEnvironment(env)
+
+// read a DataStream from an external source
+val ds: DataStream[(String, String, String, Long)] = env.addSource(...)
+// register the DataStream under the name "ShopSales"
+tableEnv.registerDataStream("ShopSales", ds, 'product_id, 'category, 'product_name, 'sales)
+
+
+// select top-5 products per category which have the maximum sales.
+val result1 = tableEnv.sqlQuery(
+    """
+      |SELECT *
+      |FROM (
+      |   SELECT *,
+      |       ROW_NUMBER() OVER (PARTITION BY category ORDER BY sales DESC) as row_num
+      |   FROM ShopSales)
+      |WHERE row_num <= 5
+    """.stripMargin)
+{% endhighlight %}
+</div>
+</div>
+
+#### No Ranking Output Optimization
+
+As described above, the `rownum` field will be written into the result table as one field of the primary key, which may lead to a lot of records being written to the result table. For example, when the record (say `product-1001`) of ranking 9 is updated and its rank is upgraded to 1, all the records from ranking 1 ~ 9 will be output to the result table as update messages. If the result table receives too many data, it will become the bottleneck of the SQL job.
+
+The optimization way is omitting rownum field in the outer SELECT clause of the Top-N query. This is reasonable because the number of the top N records is usually not large, thus the consumers can sort the records themselves quickly. Without rownum field, in the example above, only the changed record (`product-1001`) needs to be sent to downstream, which can reduce much IO to the result table.
+
+The following example shows how to optimize the above Top-N example in this way:
+
+<div class="codetabs" markdown="1">
+<div data-lang="java" markdown="1">
+{% highlight java %}
+StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
+StreamTableEnvironment tableEnv = TableEnvironment.getTableEnvironment(env);
+
+// ingest a DataStream from an external source
+DataStream<Tuple3<String, String, String, Long>> ds = env.addSource(...);
+// register the DataStream as table "ShopSales"
+tableEnv.registerDataStream("ShopSales", ds, "product_id, category, product_name, sales");
+
+// select top-5 products per category which have the maximum sales.
+Table result1 = tableEnv.sqlQuery(
+  "SELECT product_id, category, product_name, sales " + // omit row_num field in the output
+  "FROM (" +
+  "   SELECT *," +
+  "       ROW_NUMBER() OVER (PARTITION BY category ORDER BY sales DESC) as row_num" +
+  "   FROM ShopSales)" +
+  "WHERE row_num <= 5");
+{% endhighlight %}
+</div>
+
+<div data-lang="scala" markdown="1">
+{% highlight scala %}
+val env = StreamExecutionEnvironment.getExecutionEnvironment
+val tableEnv = TableEnvironment.getTableEnvironment(env)
+
+// read a DataStream from an external source
+val ds: DataStream[(String, String, String, Long)] = env.addSource(...)
+// register the DataStream under the name "ShopSales"
+tableEnv.registerDataStream("ShopSales", ds, 'product_id, 'category, 'product_name, 'sales)
+
+
+// select top-5 products per category which have the maximum sales.
+val result1 = tableEnv.sqlQuery(
+    """
+      |SELECT product_id, category, product_name, sales  -- omit row_num field in the output
+      |FROM (
+      |   SELECT *,
+      |       ROW_NUMBER() OVER (PARTITION BY category ORDER BY sales DESC) as row_num
+      |   FROM ShopSales)
+      |WHERE row_num <= 5
+    """.stripMargin)
+{% endhighlight %}
+</div>
+</div>
+
+<span class="label label-danger">Attention</span> In order to output the above query to an external storage and have a correct result, the external storage must have the same primary key with the Top-N query. In the above example query, if the `product_id` is the primary key of the query, then the external table should also has `product_id` as the primary key.
 
 Review comment:
   `if the top N records need to be stored in external storage, the result table must have the same primary key with the Top-N query. By default, the primary key of Top-N query is the combination of partition columns + rownum column.` above.
   Confuse here, so what is the real pks of top-n query?

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