The document provides an overview of windowing options in Kafka Streams. It defines different window types (tumbling, hopping, sliding, session), explains how they work, and provides examples. It also discusses how windowing is implemented in RocksDB and changelog topics. The presentation aims to help understand windowing use cases and considerations like window boundaries, suppression, and joining windows. It provides resources for learning more about Kafka Streams and windowing.

1. Kafka Streams Windowing
Behind the Curtain
Neil Buesing
Principal Solutions Architect, Rill
Confluent Meetup
July 15th, 2021

2. • Operational intelligence for data in motion
• Easy In - Easy Up - Easy Out
• Work with customers to build & modernize their pipelines
What Does Rill Do?

3. • Principal Solutions Architect
• Help customers with pipelines leveraging Apache Druid, Apache Kafka, Kafka
Streams, Apache Beam, and other technologies.
• Data Modeling and Governance
• Rill Data / Apache Druid
What Do I Do?

4. • Overview of the Windowing Options within Kafka Streams
• Windowing Use-Cases
• Examples of Aggregate Windowing
• What each windowing options does within RocksDB and the -changelog topics
• The key serialization of the -changeling topics
• Developer Tools & Ideas
Takeaways

5. • Stream / Table Duality
• Compacted Topics need stateful front-end
• Stateful Operations
• Finite datasets — tables
• Boundaries for unbounded data — windows
Why Kafka Streams

6. Windowing Options
Window Type Time boundary Examples
# records for key
@ point in time
Fixed Size
Tumbling Epoch
[8:00, 8:30)
[8:30, 9:00)
single
Yes
Hopping Epoch
[8:00, 8:30)
[8:15, 8:45)
[8:30, 8:45)
[8:45, 9:00)
constant
Yes
Sliding Record
[8:02, 8:32]
[8:20, 8:50]
[8:21, 8:51]
variable
Yes
Session Record
[8:02, 8:02]
[8:02, 8:10]
[9:10, 12:56]
single
(by tombstoning)
No

7. 001 / 12:00
002 / 12:00
003 / 12:00
001 / 12:30
002 / 12:30
003 / 12:30
12:00 12:15 12:30 12:45 1:00
01
5
Tumbling Time Windows
01
3
02
9
03
5
01
4
5 8 12
5
9
03
11
02
3
02
6
02
5
02
1
03
2
03
7
01
7
01
5
01
3
16 7 9
6 11 12
3 8 15
12

8. Hopping Time Windows
001/ 12:00
002 / 12:00
003 / 12:00
001 / 12:30
002 / 12:30
003 / 12:30
12:00 12:15 12:30 12:45 1:00
01
5
01
3
02
9
03
5
01
4
5 8 12
5
9
03
11
02
3
02
6
02
5
02
1
03
2
03
7
01
7
01
5
01
3
16 7 9
6 11 12
3 8 15
001 / 11:45
5 8 12
001 / 12:45
002 / 11:45 002 / 12:15 002 / 12:45
003 / 11:45 003 / 12:45
003 / 12:15
3 8 15
1
18 20
11
5
9
12
3 9 14

9. Sliding Time Windows
001 / 11:31:00.000
12:00 12:15 12:30 12:45 1:00
01
5
01
3
01
4
5
01
5
01
3
001 / 11:33:00.000
8
001 / 11:47.00.000
12
001 / 12:31:00.001
7
001 / 11:33.00:001
4
001 / 11:45:00.000
3
12:01
12:03
12:12
12:48
12:55
001 / 12:31:00.001
3
001 / 11:55:00.000
001 / 11:45:00.001
8
5

10. 12:00 12:15 12:30 12:45 1:00
01
5
Session Windows
01
3
02
9
03
5
01
4
03
11
02
3
02
6
02
5
02
1
03
2
03
7
01
7
01
5
01
3
5 8 12 3 8 15
5
9
16 23 25
18 23 24
12

11. Windowing Options
Window Type Time boundary Examples
# records for key
@ point in time
Fixed Size
Tumbling Epoch
[8:00, 8:30)
[8:30, 9:00)
single
Yes
Hopping Epoch
[8:00, 8:30)
[8:15, 8:45)
[8:30, 8:45)
[8:45, 9:00)
constant
Yes
Sliding Record
[8:02, 8:32]
[8:20, 8:50]
[8:21, 8:51]
variable
Yes
Session Record
[8:02, 8:02]
[8:02, 8:10]
[9:10, 12:56]
single
(by tombstoning)
No

12. • Good
• Web Visitors
• Products Purchased
• Inventory Management
• IoT Sensors
• Ad Impressions*
• Bad
• Fraud Detection
• User Interactions
• Composition*
Tumbling Time Windows
* event timestamp & grace period

13. • Good
• Web Visitors
• Products Purchased
• Fraud Detection
• IoT Sensors
• Bad
• User Interactions
• Inventory Management
• Composition
• Ad Impressions
Hopping Time Windows

14. • Good
• User Interactions
• Fraud Detection
• Usage Changes
• Bad
• Composition
• IoT Sensors
Sliding Time Windows

15. • Good
• User Interactions / Click Stream
• User Behavior Analysis
• IoT device - session oriented
(running)
• Bad
• Data Analytics (Generalizations)
• IoT sensors - always on
(pacemaker)
Session Windows

16. • Good
• Composition*
• Finite Datasets
• Bad
• Fraud Detection
• Monitoring
• Unbounded data*
No Windows
* manual tombstoning

17. Order Processing
Order Analytics
Demo Applications
orders-purchase orders-pickup
repartition
attach
user
& store
attach
line item
pricing
assemble
product
analytics
pickup-order-handler-purchase-order-join-product-repartition
product-repartition product-stats
State

18. Materialized!<> store = Materialized.as("po").withCachingDisabled();
builder.<String, PurchaseOrder>stream(opt.getTopic())
.groupByKey(Grouped.as("groupByKey"))
.windowedBy(TimeWindows.of(Duration.ofSeconds(opt.getWindowSize()))
.grace(Duration.ofSeconds(opt.getGracePeriod())))
.aggregate(Streams!::initialize,
Streams!::aggregator,
Named.as("aggregate"),
store)
.toStream(Named.as("toStream"))
.selectKey((k, v) !-> k.key() + " " + toStr(k.window()) + "," + ")")
.mapValues(Streams!::minimize)
.to(opt.topic(), Produced.as("to"));

19. Materialized!<> store = Materialized.as("po").withCachingDisabled();
builder.<String, PurchaseOrder>stream(opt.getTopic())
.groupByKey(Grouped.as("groupByKey"))
.windowedBy(TimeWindows.of(Duration.ofSeconds(opt.getWindowSize()))
.grace(Duration.ofSeconds(opt.getGracePeriod())))
.aggregate(Streams!::initialize,
Streams!::aggregator,
Named.as("aggregate"),
store)
.toStream(Named.as("toStream"))
.selectKey((k, v) !-> k.key() + " " + toStr(k.window()) + "," + ")")
.mapValues(Streams!::minimize)
.to(opt.topic(), Produced.as("to"));
TimeWindows.of(Duration.ofSeconds(opt.getWindowSize()))
.advanceBy(Duration.ofSeconds(opt.getWindowSize() / 2))
.grace(Duration.ofSeconds(opt.getGracePeriod())
SlidingWindows.withTimeDifferenceAndGrace(
Duration.ofSeconds(opt.getWindowSize()),
Duration.ofSeconds(opt.getGracePeriod()))
SessionWindows.with(Duration.ofSeconds(opt.getWindowSize()))

20. Demo “Time”
product
analytics
product-repartition product-stats
rocksdb_ldb
console-
consumer
console-
consumer
Metrics
-changelog
* Caching disabled

21. • Deserializers. % ln -s {jar} /usr/local/confluent/share/java/kafka
• WindowDeserializer
• SessionDeserializer
• RocksDB
• RocksDB’s rocksdb_ldb % brew install rocksdb
• Scripts
• rocksdb_key_parser
• rocksdb_window_parser
• rocksdb_session_parser
Demo Tools

22. DEMO

23. • Emitting Results
• Suppression
• Commit Time
• Window Boundaries
• Epoch vs. Event
• Long Windows*
• Join Windowing
• RocksDB Tuning
• RocksDB state store instances…
What Next

24. • Overview of the Windowing Options within Kafka Streams
• Windowing Use-Cases
• Examples of Aggregate Windowing
• What each windowing options does within RocksDB and the -changelog topics
• The key serialization of the -changeling topics
• Advance Considerations
Takeaways

25. • Demo Application
https://github.com/nbuesing/kafka-streams-dashboards
• Kafka Summit Europe 2021
https://www.confluent.io/events/kafka-summit-europe-2021/what-is-the-state-of-
my-kafka-streams-application-unleashing-metrics/
Resources

26. • Nick Dearden’s
https://www.confluent.io/kafka-summit-ny19/zen-and-the-art-of-streaming-joins/
• Anna McDonald’s
https://www.confluent.io/kafka-summit-san-francisco-2019/using-kafka-to-discover-events-hidden-in-
your-database/
• Matthias Sax’s
https://www.confluent.io/kafka-summit-san-francisco-2019/whats-the-time-and-why/
https://www.confluent.io/resources/kafka-summit-2020/the-flux-capacitor-of-kafka-streams-and-ksqldb/
Additional Resources

27. Blooper Reel