Window Deduplication

Function

Window Deduplication is a special Deduplication which removes rows that duplicate over a set of columns, keeping the first one or the last one for each window and partitioned keys.

For streaming queries, unlike regular Deduplicate on continuous tables, Window Deduplication does not emit intermediate results but only a final result at the end of the window. Moreover, window Deduplication purges all intermediate state when no longer needed. Therefore, Window Deduplication queries have better performance if users do not need results updated per record. Usually, Window Deduplication is used with Windowing TVF directly. Besides, Window Deduplication could be used with other operations based on Windowing TVF, such as Window Aggregation, Window TopN and Window Join.

Window Top-N can be defined in the same syntax as regular Top-N, see Top-N documentation for more information. Besides that, Window Deduplication requires the PARTITION BY clause contains window_start and window_end columns of the relation. Otherwise, the optimizer will not be able to translate the query.

Flink uses ROW_NUMBER() to remove duplicates, just like the way of Window Top-N query. In theory, Window Deduplication is a special case of Window Top-N in which the N is one and order by the processing time or event time.

For more information, see Window Deduplication.

Syntax

SELECT [column_list]
FROM (
   SELECT [column_list],
     ROW_NUMBER() OVER (PARTITION BY window_start, window_end [, col_key1...]
       ORDER BY time_attr [asc|desc]) AS rownum
   FROM table_name) -- relation applied windowing TVF
WHERE (rownum = 1 | rownum <=1 | rownum < 2) [AND conditions]

Caveats

• Flink can only perform window deduplication on window table value functions that are based on tumble, hop, or cumulate windows.
• Window deduplication is only supported when sorting based on the event time attribute.

Example

The following example shows how to keep last record for every 10 minutes tumbling window.

-- tables must have time attribute, e.g. `bidtime` in this table
Flink SQL> DESC Bid;
+-------------+------------------------+------+-----+--------+---------------------------------+
|        name |                   type | null | key | extras |                       watermark |
+-------------+------------------------+------+-----+--------+---------------------------------+
|     bidtime | TIMESTAMP(3) *ROWTIME* | true |     |        | `bidtime` - INTERVAL '1' SECOND |
|       price |         DECIMAL(10, 2) | true |     |        |                                 |
|        item |                 STRING | true |     |        |                                 |
+-------------+------------------------+------+-----+--------+---------------------------------+

Flink SQL> SELECT * FROM Bid;
+------------------+-------+------+
|          bidtime | price | item |
+------------------+-------+------+
| 2020-04-15 08:05 |  4.00 | C    |
| 2020-04-15 08:07 |  2.00 | A    |
| 2020-04-15 08:09 |  5.00 | D    |
| 2020-04-15 08:11 |  3.00 | B    |
| 2020-04-15 08:13 |  1.00 | E    |
| 2020-04-15 08:17 |  6.00 | F    |
+------------------+-------+------+

Flink SQL> SELECT *
  FROM (
    SELECT bidtime, price, item, supplier_id, window_start, window_end, 
      ROW_NUMBER() OVER (PARTITION BY window_start, window_end ORDER BY bidtime DESC) AS rownum
    FROM TABLE(
               TUMBLE(TABLE Bid, DESCRIPTOR(bidtime), INTERVAL '10' MINUTES))
  ) WHERE rownum <= 1;
+------------------+-------+------+-------------+------------------+------------------+--------+
|          bidtime | price | item | supplier_id |     window_start |       window_end | rownum |
+------------------+-------+------+-------------+------------------+------------------+--------+
| 2020-04-15 08:09 |  5.00 |    D |   supplier4 | 2020-04-15 08:00 | 2020-04-15 08:10 |      1 |
| 2020-04-15 08:17 |  6.00 |    F |   supplier5 | 2020-04-15 08:10 | 2020-04-15 08:20 |      1 |
+------------------+-------+------+-------------+------------------+------------------+--------+