Important
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Join Event Streams with KSQL¶
You can use KSQL to merge streams of data in real time by using a SQL-like join syntax. A KSQL join and a relational database join are similar in that they both combine data from two sources based on common values. The result of a KSQL join is a new stream or table that’s populated with the column values that you specify in a SELECT statement.
With KSQL, you don’t need to write the low-level logic around joining streams and tables, so you can focus on the business logic for combining your streaming data.
You can join streams and tables in these ways:
- Join two streams to create a new stream.
- Join two tables to create a new table.
- Join a stream and a table to create a new stream.
JOIN Clause¶
The KSQL JOIN clause has the familiar syntax of a SQL JOIN clause.
The following example creates a pageviews_enriched stream, which is a
combination of a pageviews stream and a users table:
CREATE STREAM pageviews_enriched AS
SELECT users.userid AS userid, pageid, regionid, gender FROM pageviews
LEFT JOIN users ON pageviews.userid = users.userid;
For the full code example, see Writing Streaming Queries Against Apache Kafka® Using KSQL (Docker).
When you join two streams, you must specify a WITHIN clause for matching records that both occur within a specified time interval. For valid time units, see KSQL Time Units.
Here’s an example stream-stream join that combines a shipments stream with
an orders stream. The resulting shipped_orders stream contains all
orders shipped within two hours of when the order was placed.
CREATE STREAM shipped_orders AS
SELECT o.orderid, o.itemid, s.shipmentid
FROM orders o
INNER JOIN shipments s WITHIN 2 HOURS
ON s.orderid = o.orderid;
Joins and Windows¶
KSQL enables grouping records that have the same key for stateful operations, like joins, into windows. You specify a retention period for the window, and this retention period controls how long KSQL will wait for out-of-order and late-arriving records. If a record arrives after the window’s retention period has passed, the record is discarded and isn’t processed in that window.
Only stream-stream joins are windowed.
Windows are tracked per record key. In join operations, KSQL uses a windowing state store to store all of the records received so far within the defined window boundary. Old records in the state store are purged after the specified window retention period.
For more information on windows, see Windows in KSQL Queries.
Join Requirements¶
Your KSQL applications must meet specific requirements for joins to be successful.
- Co-partitioned data
- Input data must be co-partitioned when joining. This ensures that input records with the same key, from both sides of the join, are delivered to the same stream task during processing. It’s your responsibility to ensure data co-partitioning when joining. For more information, see Partition Data to Enable Joins.
- KEY property
If you set the KEY property when you create a table, ensure that both of the following conditions are true:
- For every record, the contents of the message key of the Apache Kafka® message itself must be the same as the contents of the column set in KEY.
- The KEY property must be set to a column of type VARCHAR or STRING.
For more information, see Key Requirements.
Join Capabilities¶
KSQL supports a large set of join operations for streams and tables, including INNER, LEFT OUTER, and FULL OUTER. Frequently, LEFT OUTER is shortened to LEFT JOIN, and FULL OUTER is shortened to OUTER JOIN.
Note
RIGHT OUTER JOIN isn’t supported. Instead, swap the operands and use LEFT JOIN.
The following table shows the supported combinations.
| Type | INNER | LEFT OUTER | FULL OUTER | |
|---|---|---|---|---|
| Stream-Stream | Windowed | Supported | Supported | Supported |
| Table-Table | Non-windowed | Supported | Supported | Supported |
| Stream-Table | Non-windowed | Supported | Supported | Not supported |
Stream-Stream Joins¶
KSQL supports INNER, LEFT OUTER, and FULL OUTER joins between streams.
All of these operations support out-of-order records.
Joins between streams are always windowed joins. A new input record on one side produces a join output for each matching record on the other side, and there can be multiple such matching records within a join window.
Joins cause data re-partitioning of a stream only if the stream was marked for re-partitioning. If both streams are marked, both are re-partitioned.
LEFT OUTER joins will contain leftRecord-NULL records in the result stream, which means that the join contains NULL values for fields selected from the right-hand stream where no match is made.
FULL OUTER joins will contain leftRecord-NULL or NULL-rightRecord records in the result stream, which means that the join contains NULL values for fields coming from a stream where no match is made.
Semantics of Stream-Stream Joins¶
The semantics of the various stream-stream join variants are shown in the following table. In the table, each row represents a new incoming record. The following assumptions apply:
- All records have the same key.
- All records belong to a single join window.
- All records are processed in timestamp order.
When new input is received, the join is triggered under the conditions listed in the table. Input records with a NULL key or a NULL value are ignored and don’t trigger the join.
| Timestamp | Left Stream | Right Stream | INNER JOIN | LEFT JOIN | OUTER JOIN |
|---|---|---|---|---|---|
| 1 | null | ||||
| 2 | null | ||||
| 3 | A | [A, null] | [A, null] | ||
| 4 | a | [A, a] | [A, a] | [A, a] | |
| 5 | B | [B, a] | [B, a] | [B, a] | |
| 6 | b | [A, b], [B, b] | [A, b], [B, b] | [A, b], [B, b] | |
| 7 | null | ||||
| 8 | null | ||||
| 9 | C | [C, a], [C, b] | [C, a], [C, b] | [C, a], [C, b] | |
| 10 | c | [A, c], [B, c], [C, c] | [A, c], [B, c], [C, c] | [A, c], [B, c], [C, c] | |
| 11 | null | ||||
| 12 | null | ||||
| 13 | null | ||||
| 14 | d | [A, d], [B, d], [C, d] | [A, d], [B, d], [C, d] | [A, d], [B, d], [C, d] | |
| 15 | D | [D, a], [D, b], [D, c], [D, d] | [D, a], [D, b], [D, c], [D, d] | [D, a], [D, b], [D, c], [D, d] |
Stream-Table Joins¶
KSQL only supports INNER and LEFT joins between a stream and a table.
Stream-table joins are always non-windowed joins. You can perform table lookups against a table when a new record arrives on the stream. Only events arriving on the stream side trigger downstream updates and produce join output. Updates on the table side don’t produce updated join output.
Out-of-order records aren’t supported, which means that KSQL processes all records in offset order and doesn’t check for out-of-order records.
Stream-table joins cause data re-partitioning of the stream only if the stream was marked for re-partitioning.
Important
KSQL currently provides best-effort on time synchronization, but there are no guarantees, which can cause missing results or leftRecord-NULL results.
Semantics of Stream-Table Joins¶
The semantics of the various stream-table join variants are shown in the following table. In the table, each row represents a new incoming record. The following assumptions apply:
- All records have the same key.
- All records are processed in timestamp order.
Input records for the stream with a NULL key or a NULL value are ignored and don’t trigger the join.
Only input records for the left-side stream trigger the join. Input records for the right-side table update only the internal right-side join state.
Input records for the table with a NULL value are interpreted as tombstones for the corresponding key, which indicate the deletion of the key from the table. Tombstones don’t trigger the join.
| Timestamp | Left Stream | Right Table | INNER JOIN | LEFT JOIN |
|---|---|---|---|---|
| 1 | null | |||
| 2 | null (tombstone) | |||
| 3 | A | [A, null] | ||
| 4 | a | |||
| 5 | B | [B, a] | [B, a] | |
| 6 | b | |||
| 7 | null | |||
| 8 | null (tombstone) | |||
| 9 | C | [C, null] | ||
| 10 | c | |||
| 11 | null | |||
| 12 | null | |||
| 13 | null | |||
| 14 | d | |||
| 15 | D | [D, d] | [D, d] |
For stream-table joins, KSQL assumes that the joining stream and table follow the event-time ordering exactly. Follow these steps to ensure that joins are synchronized:
- Start the query, which starts consumers.
- Populate the table completely. This ensures that the table items exist when the stream events come in to trigger the join.
- Populate the stream completely.
Table-Table Joins¶
KSQL supports INNER, LEFT OUTER, and FULL OUTER joins between tables. Joins matching multiple records (one-to-many) aren’t supported.
Table-table joins are always non-windowed joins.
Out-of-order records are not supported, which means that KSQL processes all records in offset order and does not check for out-of-order records.
Table-table joins are eventually consistent.
Important
KSQL currently provides best-effort on time synchronization, but there are no guarantees, which can cause missing results or leftRecord-NULL results.
Table-table joins can be joined only on the KEY field, and one-to-many
(1:N) joins aren’t supported.
Semantics of Table-Table Joins¶
The semantics of the various table-table join variants are shown in the following table. In the table, each row represents a new incoming record. The following assumptions apply:
- All records have the same key.
- All records are processed in timestamp order.
Input records with a NULL value are interpreted as tombstones for the corresponding key, which indicate the deletion of the key from the table. Tombstones don’t trigger the join. When an input tombstone is received, an output tombstone is forwarded directly to the join result table, if the corresponding key exists already in the join result table.
| Timestamp | Left Table | Right Table | INNER JOIN | LEFT JOIN | OUTER JOIN |
|---|---|---|---|---|---|
| 1 | null (tombstone) | ||||
| 2 | null (tombstone) | ||||
| 3 | A | [A, null] | [A, null] | ||
| 4 | a | [A, a] | [A, a] | [A, a] | |
| 5 | B | [B, a] | [B, a] | [B, a] | |
| 6 | b | [B, b] | [B, b] | [B, b] | |
| 7 | null (tombstone) | null (tombstone) | null (tombstone) | [null, b] | |
| 8 | null (tombstone) | null (tombstone) | |||
| 9 | C | [C, null] | [C, null] | ||
| 10 | c | [C, c] | [C, c] | [C, c] | |
| 11 | null (tombstone) | null (tombstone) | [C, null] | [C, null] | |
| 12 | null (tombstone) | null (tombstone) | null (tombstone) | ||
| 13 | null (tombstone) | ||||
| 14 | d | [null, d] | |||
| 15 | D | [D, d] | [D, d] | [D, d] |