THIS IS A TEST INSTANCE. ALL YOUR CHANGES WILL BE LOST!!!!
...
Pay attention, that both streams are use as examples for KStream (ie, record stream) and KTable (ie, changelog stream) with different semantics. For KTable, so-called tombstone records with format key:null are of special interest, as they delete a key (those records are shown as null in all examples to highlight tombstone semantics). Last but not least, in Kafka Streams each join is "customized" by the user with a ValueJoiner function that compute the actual result. Hence, we show output records as "X - Y" with X and Y being the left and right value, respectively, given to the value joiner. If the output is shown as null (ie, tombstone message), ValueJoiner will not be called because a result record will be deleted.
...
| Anchor | ||||
|---|---|---|---|---|
|
Improved Left/Outer Stream-Stream Join (v3.0.x and newer)
| Warning |
|---|
This section describes the new join semantics as of version |
(See KIP-77: Improve Kafka Streams Join Semantics)
Kafka Streams offers the follow join operators (operators in bold font were added in current trunk, compared to 0.10.1.x and older):
...
Prior to version |
(See
| Jira | ||||||
|---|---|---|---|---|---|---|
|
KStream-KStream Join
This is a sliding window join, ie, all tuples that are "close" to each other with regard to time (ie, time difference up to window size) are joined. The result is a KStream. The table below shows the output (for each processed input record) for all three join variantsleft and outer join only (as inner joins are not subject to spurious join results). Pay attention, that some input records do not produce output records., and that left/outer output record are emitted with some "delay" (ie, only emitted after grace-period passed). Also note, that the new behavior effectively requires to set a gracePeriod in the window definition to specify when left/outer join result should be emitted via JoinWindows.of(...).grace(...).
In contrast to the later examples, we assume a window size of 10, and a grace period of 5.
ts | STREAM_1 (left) | STREAM_2 (right) | leftJoin | outerJoin |
1 | null | |||
2 | null | |||
3 | A | |||
4 | a | A - a | A - a | |
5 | B |
ts
STREAM_1 (left)
STREAM_2 (right)
innerJoin
leftJoin
outerJoin
1
null
2
null
3
A
A - null
A - null
4
a
A - a
A - a
5
B
B - a | B - a | |||
6 | b | A - b B - b | A - b B |
- b |
B - b
7 | null | |||
8 | null | |||
9 | C | C - a C - b | C - a C - b |
C - b
10 | c | A - c B - c C - c | A - c B - c C - c |
A - c
B - c
C - c
11
11 | null | |||
12 | null | |||
13 | null | |||
14 |
d | B - d C - d |
A - d
B - d C |
- d |
B - d
C - d
15 |
D |
D - b D - c D - d |
D - b D - c D - d |
D - a
D - b
D - c
D - d
KStream-KTable Join
This is an asymmetric non-window join. The basic semantics is a KTable lookup for each KStream record (while each KTable input record updates the current KTable view but does never yield any result record). The result is a KStream. Pay attention, that the KTable lookup is done on the current KTable state, and thus, out-of-order records can yield non-deterministic result. Furthermore, in practice Kafka Streams does not guarantee that all records will be processed in timestamp order (even if processing records in timestamp order is the goal, it is only best effort). The table below shows the output (for each processed input record) for both offered join variants. Pay attention, that some input records do not produce output records.
...
ts
...
STREAM_1 (left)
...
STREAM_2 (right)
...
1
...
null
...
2
...
null
...
3
...
A
...
4
...
a
...
5
...
B
...
| ... | ||||
| 30 | E | |||
| ... | ||||
| 45 | F | E - null | E - null | |
| ... | ||||
| 60 | f | F - null | F - null | |
| ... | ||||
| 75 | G | null - f |
| Anchor | ||||
|---|---|---|---|---|
|
New Join Semantics (v0.10.2.x and newer)
| Warning |
|---|
This section describes the new join semantics as of version |
(See KIP-77: Improve Kafka Streams Join Semantics)
Kafka Streams offers the follow join operators (operators in bold font were added in current trunk, compared to 0.10.1.x and older):
| inner join | left join | outer join | |
|---|---|---|---|
| KStream-KStream | yes | yes | yes |
| KStream-KTable | yes | yes | no |
| KTable-KTable | yes | yes | yes |
KStream-KStream Join
This is a sliding window join, ie, all tuples that are "close" to each other with regard to time (ie, time difference up to window size) are joined. The result is a KStream. The table below shows the output (for each processed input record) for all three join variants. Pay attention, that some input records do not produce output records.
The table below marks so called "spurious" left/outer join results, that are in the result in version 0.10.2.x to 2.8.x, in bold face. Compare Improved left/outer stream-stream join above for version 3.0.x that avoids spurious results.
ts | STREAM_1 (left) | STREAM_2 (right) |
...
6
...
b
...
7
...
null
...
8
...
null
...
9
...
C
...
10
...
c
...
11
...
null
...
12
...
null
...
13
...
null
...
14
...
d
...
15
...
D
...
KTable-KTable Join
This is a symmetric non-window join. The basic semantics is a KTable lookup in the "other" stream for each KTable update. The result is a (continuously updating) KTable (ie, a changelog stream that can contain tombstone message with format <key:null>; those tombstone are shown as null in the result in contrast to results "X - null" indicating a valid join result with only one join partner). Pay attention, that the KTable lookup is done on the current KTable state, and thus, out-of-order records can yield non-deterministic result. Furthermore, in practice Kafka Streams does not guarantee that all records will be processed in timestamp order (even if processing records in timestamp order is the goal, it is only best effort).
| Warning | ||
|---|---|---|
| ||
If you want to observe the below described behavior, you will most likely need to disable |
ts | left | right | innerJoin | leftJoin | outerJoin | ||||
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 | null | null | null - b | |||||
8 | null | null | |||||||
9 | C | C - nulla C - null10b | c | C - ca C - cb | C - a C - b | ||||
10 | c | A - c B - c | 11 | null | null | C - c | A - c B - c C - c | A - c B - cC - null C - nullc | |
1211 | nullnull | ||||||||
12 | null | ||||||||
13 | null | ||||||||
14 | d | null A - d | 15 | D | B - d C - d | A D - dD B - d D C - d | 16 | A - d B - d C - d | |
15 | D | D - a D - b D - c D - d | D - a D - b D - c | 17 | d | D - dD - d | D - a D - b D - c D - d |
...
KStream-KTable
...
Join
This is a symmetric an asymmetric non-window join. There are two streams involved in this join, the left stream and the right stream, each of which are usually keyed on different key types. The left stream is keyed on the primary key, whereas the right stream is keyed on the foreign key. Each element in the left stream has a foreign-key extractor function applied to it, which extracts the foreign key. The resultant left-event is then joined with the right-event keyed on the corresponding foreign-key. Updates made to the right-event will also trigger joins with the left-events containing that foreign-key. It can be helpful to think of the left-hand materialized stream as events containing a foreign key, and the right-hand materialized stream as entities keyed on the foreign key.
KTable lookups are done on the current KTable state, and thus, out-of-order records can yield non-deterministic result. Furthermore, in practice Kafka Streams does not guarantee that all records will be processed in timestamp order (even if processing records in timestamp order is the goal, it is only best effort).The basic semantics is a KTable lookup for each KStream record (while each KTable input record updates the current KTable view but does never yield any result record). The result is a KStream. Pay attention, that the KTable lookup is done on the current KTable state, and thus, out-of-order records can yield non-deterministic result. Furthermore, in older version of Kafka Streams there is no guarantee that all records will be processed in timestamp order (even if processing records in timestamp order is the goal, it is only best effort). The table below shows the output (for each processed input record) for both offered join variants. Pay attention, that some input records do not produce output records.
In newer versions, Kafka Streams improved timestamp synchronization significantly:
2.1.xand newer: improvements in processing order and introducingmax.task.idle.msconfig to allow for partial blocking if one input is empty (cf. KIP-353: Improve Kafka Streams Timestamp Synchronization)2.8.xand newer: stronger synchronization guarantees to avoid race conditions due to unpredictable consumer fetch behavior (cf. KIP-695: Further Improve Kafka Streams Timestamp Synchronization)
ts | STREAM_1 (left) | STREAM_2 (right) | leftJoin | innerJoin |
1 | null | |||
2 | null | |||
3 | A | A - null | ||
4 | a | |||
5 | B | B - a | B - a | |
6 | b | |||
7 | null | |||
8 | null | |||
9 | C | C - null | ||
10 | c | |||
11 | null | |||
12 | null | |||
13 | null | |||
14 | d | |||
15 | D | D - d | D - d |
KTable-KTable Join
This is a symmetric non-window join. The basic semantics is a KTable lookup in the "other" stream for each KTable update. The result is a (continuously updating) KTable (ie, a changelog stream that can contain tombstone message with format <key:null>; those tombstone are shown as null in the result in contrast to results "X - null" indicating a valid join result with only one join partner). Pay attention, that the KTable lookup is done on the current KTable state, and thus, out-of-order records can yield non-deterministic result. Furthermore, in older versions of Kafka Streams there is no guarantee that all records will be processed in timestamp order (even if processing records in timestamp order is the goal, it is only best effort).
In newer versions, Kafka Streams improved timestamp synchronization significantly:
2.1.xand newer: improvements in processing order and introducingmax.task.idle.msconfig to allow for partial blocking if one input is empty (cf. KIP-353: Improve Kafka Streams Timestamp Synchronization)2.8.xand newer: stronger synchronization guarantees to avoid race conditions due to unpredictable consumer fetch behavior (cf. KIP-695: Further Improve Kafka Streams Timestamp Synchronization)
| Warning | ||
|---|---|---|
| ||
If you want to observe the below described behavior, you will most likely need to disable |
ts | left | right | innerJoin | leftJoin | outerJoin |
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 | B - b | B - b | B - b | |
7 | null | null | null | null - b | |
8 | null | null | |||
9 | C | C - null | C - null | ||
10 | c | C - c | C - c | C - c | |
11 | null | null | C - null | C - null | |
12 | null | null | null | ||
13 | null | ||||
14 | d | null - d | |||
15 | D | D - d | D - d | D - d | |
| 16 | |||||
| 17 | d | D - d | D - d | D - d |
KTable-KTable Foreign-Key Join
This is a symmetric non-window join. There are two streams involved in this join, the left stream and the right stream, each of which are usually keyed on different key types. The left stream is keyed on the primary key, whereas the right stream is keyed on the foreign key. Each element in the left stream has a foreign-key extractor function applied to it, which extracts the foreign key. The resultant left-event is then joined with the right-event keyed on the corresponding foreign-key. Updates made to the right-event will also trigger joins with the left-events containing that foreign-key. It can be helpful to think of the left-hand materialized stream as events containing a foreign key, and the right-hand materialized stream as entities keyed on the foreign key.
KTable lookups are done on the current KTable state, and thus, out-of-order records can yield non-deterministic result. Furthermore, in older versions of Kafka Streams there is no guarantee that all records will be processed in timestamp order (even if processing records in timestamp order is the goal, it is only best effort).
In newer versions, Kafka Streams improved timestamp synchronization significantly:
2.1.xand newer: improvements in processing order and introducingmax.task.idle.msconfig to allow for partial blocking if one input is empty (cf. KIP-353: Improve Kafka Streams Timestamp Synchronization)2.8.xand newer: stronger synchronization guarantees to avoid race conditions due to unpredictable consumer fetch behavior (cf. KIP-695: Further Improve Kafka Streams Timestamp Synchronization)
The workflow of LHS-generated changes to outputs is shown below. Each step is cumulative with the previous step. Only LEFT and INNER joins are supported, and their outputs are shown below.
...
| inner join | left join | outer join | |
|---|---|---|---|
| KStream-KStream | yes | yes | yes |
| KStream-KTable | no | yes | no |
| KTable-KTable | yes | yes | yes |
KStream-KStream Join
This is a sliding window join, ie, all tuples that are "close" to each other with regard to time (ie, time difference up to window size) are joined. The result is a KStream. The table below shows the output (for each processed input record) for all three join variants. Pay attention, that some input records do not produce output records.
...