Kafka Connect on practice

1. Kafka Connect
Oleg Kuznetsov
Big Data Engineer

2. Intro

3. What is Kafka?
3

4. What is Kafka?
4

5. What is Kafka?
5

6. Kafka Connect
6

7. Kafka Connect
7
〉Focusing on data ingestion in / out Kafka topics
〉KafkaConnect - a standalone app, not a library
〉Distributed mode

8. Source Connector

9. Topic => Topic
9

10. External storage => Topic
10

11. External storage => Topic
11

12. 12
Storage Kafka
Entity “Virtual” topic Topic
Partition
Logical partition
- file name
- table name
Physical partition
file on disk
Offset in
partition
Logical offset
- line number in file
- ID value in table
Record number within partition
offset
External storage ≈ Kafka topic

13. Components
13
SourceConnector
- defines parallelism level
- work distribution
- starts on leader node
- rebalancing job
Rebalancing job
- applying new connector config (REST-API)
- changes in structure of ingested data (new table, files, partitions, etc.)
SourceTask
data ingestion

14. Architecture
14

15. Architecture
15

16. Architecture
16

17. Architecture
17

18. Methods: SourceConnector
18
〉void start(Map<String, String> props)
〉List< Map<String, String> > taskConfigs(int maxTasks)
〉void stop()

19. FileSourceConnector
19

20. FileSourceConnector (rebalancing)
20

21. Architecture
21

22. Architecture
22

23. Architecture
23

24. Methods: SourceTask
24
〉void start(Map<String, String> props)
〉Collection<SourceRecord> poll()
〉void stop()

25. FileSourceTask
25

26. FileSourceTask
26

27. FileSourceTask
27

28. Architecture
28

29. FileSourceTask (offset filtering)
29

30. Architecture
30

31. Architecture
31

32. Sink Connector

33. Architecture
33

34. FileSinkConnector
34

35. Methods: SinkTask
35
〉void start(Map<String, String> props)
〉void put(Collection<SinkRecord>)
〉void flush(Map<TopicPatition, OffsetAndMetadata> currOffsets)
〉void stop()

36. Storing in put()
36
〉put() should be quick (there is an internal timeout)
〉A limited number of records are passed in put()
〉Automatic offset management (consumer)

37. Storing in flush()
37
〉put() stores in temp file / memory
〉flush() uploads optimal data amount in storage
〉Manual offset management (uploading index-files)

38. Resume reading using offsets
38

39. Run

40. Dockerfile
40

41. Starting connector
41

42. Facing reality

43. Global rebalancing
43
〉JVM with KafkaConnect can host multiple connectors
〉Rebalancing one of them initiates the rebalancing of the rest
Solution: run 1 connector per 1 JVM

44. Writing offsets without sending source record
44
〉Ingesting file without records (e.g. it is empty)
Solutions:
1) send marker SourceRecord with offset
2) get offsetStorageWriter by reflection and write offset directly

45. Controlling ingestion speed (backpressure)
45
〉Source
- no control of ingestion speed for writes to Kafka
- solution: sleep() in poll() + producer tuning
〉Sink
- no control of speed of storing data in external storage
- solution: sleep() + throw new RetryableException in put()

46. Exactly once delivery
46
〉not supported
〉Source
- data and offsets are stored separately => duplicates are possible
- there is technical capability, but it has not been implemented
Solution:
- extra deduplication process (for instance, KafkaStreams)
- compacted data topic
〉Sink
- idempotence: loading index-file with data files + consistent file naming

47. Conclusion

48. Conclusion
48
〉Simple and fast
〉Control how to ingest data
〉Mature
〉Cluster less
〉Lots of free connectors (Debezium, S3, FTP, ElasticSearch, etc.)

49. Questions?