For many industries the need to group together related events based on a period of activity or inactivity is key. Advertising businesses, content producers are just a few examples of where session windows can be used to better understand user behavior. While such sessionization has been possible in Apache Kafka up to this point, implementing it has been rather complex and required leveraging low-level APIs. In the most recent release of Kafka, however, new capabilities have been added making session windows much easier to implement. In this online talk, we’ll introduce the concept of a session window, talk about common use cases, and walk through how Apache Kafka can be used for session-oriented use cases.

1. 1
User behavior analysis with
Session Windows and Apache
Kafka’s Streams API
Michael G. Noll
Product Manager

2. 2
Attend the whole series!
Simplify Governance for Streaming Data in Apache Kafka
Date: Thursday, April 6, 2017
Time: 9:30 am - 10:00 am PT | 12:30 pm - 1:00 pm ET
Speaker: Gwen Shapira, Product Manager, Confluent
Using Apache Kafka to Analyze Session Windows
Date: Thursday, March 30, 2017
Time: 9:30 am - 10:00 am PT | 12:30 pm - 1:00 pm ET
Speaker: Michael Noll, Product Manager, Confluent
Monitoring and Alerting Apache Kafka with Confluent Control
Center
Date: Thursday, March 16, 2017
Time: 9:30 am - 10:00 am PT | 12:30 pm - 1:00 pm ET
Speaker: Nick Dearden, Director, Engineering and Product
Data Pipelines Made Simple with Apache Kafka
Date: Thursday, March 23, 2017
Time: 9:30 am - 10:00 am PT | 12:30 pm - 1:00 pm ET
Speaker: Ewen Cheslack-Postava, Engineer, Confluent
https://www.confluent.io/online-talk/online-talk-series-five-steps-to-production-with-apache-kafka/
What’s New in Apache Kafka 0.10.2 and Confluent 3.2
Date: Thursday, March 9, 2017
Time: 9:30 am - 10:00 am PT | 12:30 pm - 1:00 pm ET
Speaker: Clarke Patterson, Senior Director, Product Marketing

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Kafka Streams API: to build real-time apps that power your core business
Key benefits
• Makes your Java apps highly scalable,
elastic, fault-tolerant, stateful,
distributed
• No additional cluster
• Easy to run as a service
• Supports large aggregations and joins
• Security and permissions fully
integrated from Kafka
Example Use Cases
• Microservices
• Reactive applications
• Continuous queries
• Continuous transformations
• Event-triggered processes
Streams
API
App Instance 1
Kafka
Cluster
Streams
API
App Instance N
Your
App ...

4. 4
Use case examples
Industry Use case examples
Travel Build applications with the Kafka Streams API to make real-time decisions to find
best suitable pricing for individual customers, to cross-sell additional services,
and to process bookings and reservations
Finance Build applications to aggregate data sources for real-time views of potential
exposures and for detecting and minimizing fraudulent transactions
Logistics Build applications to track shipments fast, reliably, and in real-time
Retail Build applications to decide in real-time on next best offers, personalized
promotions, pricing, and inventory management
Automotive,
Manufacturing
Build applications to ensure their production lines perform optimally, to gain real-
time insights into supply chains, and to monitor telemetry data from connected
cars to decide if an inspection is needed
And many more …

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Some public use cases in the wild
• Why Kafka Streams: towards a real-time streaming architecture, by Sky Betting and Gaming
• http://engineering.skybettingandgaming.com/2017/01/23/streaming-architectures/
• Applying Kafka’s Streams API for social messaging at LINE Corp.
• http://developers.linecorp.com/blog/?p=3960
• Production pipeline at LINE, a social platform based in Japan with 220+ million users
• Microservices and Reactive Applications at Capital One
• https://speakerdeck.com/bobbycalderwood/commander-decoupled-immutable-rest-apis-with-kafka-streams
• Containerized Kafka Streams applications in Scala, by Hive Streaming
• https://www.madewithtea.com/processing-tweets-with-kafka-streams.html
• Geo-spatial data analysis
• http://www.infolace.com/blog/2016/07/14/simple-spatial-windowing-with-kafka-streams/
• Language classification with machine learning
• https://dzone.com/articles/machine-learning-with-kafka-streams

6. 6
Kafka Summit NYC, May 09
Here, the community will share
latest Kafka Streams use cases.
http://kafka-summit.org/

7. 7
Agenda
• Why are session windows so important?
• Recap: What is windowing?
• Session windows – example use case
• Session windows – how they work
• Session windows – API

8. 8
Why are session windows so important?
• We want to analyze user behavior, which is a very common use case area
• To analyze user behavior on newspapers, social platforms, video sharing sites, booking sites, etc.
• AND tailor the analysis to the individual user
• Specifically, analyses of the type “how many X in one go?” – how many movies watched in one go?
• Achieved through a per-user sessionization step on the input data.
• AND this tailoring must be convenient and scalable
• Achieved through automating the sessionization step, i.e. auto-discovery of sessions
• Session-based analyses can range from simple metrics (e.g. count of user visits on a news
website or social platform) to more complex metrics (e.g. customer conversion funnel and event
flows).

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What is windowing?
• Aggregations such as “counting things” are key-based operations
• Before you can aggregate your input data, it must first be grouped by key
event-time8 AM7 AM6 AM event-time
Alice
Bob
Dave
8 AM7 AM6 AM

10. 10
What is windowing?
• Aggregations such as “counting things” are key-based operations
Alice: 10 movies
Bob: 11 movies
Dave: 8 movies
“Let me COUNT how many movies each user has watched (IN TOTAL)”
event-time
Alice
Bob
Dave
Feb 7Feb 6Feb 5

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What is windowing?
• Windowing allows you to further “sub-group” the input data for each user
event-time
Alice
Bob
Dave
“Let me COUNT how many movies each user has watched PER DAY”
Alice: 4 movies
Bob: 3 movies
Dave: 2 movies
Feb 5
Feb 7Feb 6Feb 5

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What is windowing?
• Windowing allows you to further “sub-group” the input data for each user
event-time
Alice
Bob
Dave
Alice: 1 movie
Bob: 2 movies
Dave: 4 movies
Feb 6
Feb 7Feb 6Feb 5
“Let me COUNT how many movies each user has watched PER DAY”

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What is windowing?
• Windowing allows you to further “sub-group” the input data for each user
event-time
Alice
Bob
Dave
Alice: 4 movies
Bob: 4 movies
Dave: 1 movie
Feb 7
Feb 7Feb 6Feb 5
“Let me COUNT how many movies each user has watched PER DAY”

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Session windows: use case
• Session windows allow for “how many X in one go?” analyses, tailored to each key
• Sessions are auto-discovered from the input data (we see how later)
event-time
Alice
Bob
Dave
Alice: 1, 4, 1, 4 movies
(4 sessions)
Bob: 4, 6 movies
(2 sessions)
Dave: 3, 5 movies
(2 sessions)
Feb 7Feb 6Feb 5
“Let me COUNT how many movies each user has watched PER SESSION”

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Comparing results
• Let’s compare how results differ
Alice
Bob
Dave
IN TOTAL
10
11
8
PER DAY
3.0 (avg)
3.0 (avg)
2.3 (avg)
time windows
PER SESSION
2.5 (avg)
5.0 (avg)
4.0 (avg)
session windowsno windows

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Comparing results
• Let’s compare how results differ if we our task was to rank the top users
Alice
Bob
Dave
IN TOTAL
#2
#1
#3
PER DAY
#1
#1
#3
time windows
PER SESSION
#3
#1
#2
session windowsno windows

17. 17Confidential
Session windows: how they work

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Session windows: how they work
• Definition of a session in Kafka Streams API is based on a configurable period of inactivity
• Example: “If Alice hasn’t watched another movie in the past 3 hours, then next movie = new
session!”
Inactivity period

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Auto-discovering sessions, per user
event-time
Alice
Bob
Dave
… …
… …
… …

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Auto-discovering sessions, per user
event-time
Alice
Bob
Dave
… …
… …
… …
Example: How many movies does Alice watch on average per session?”
Inactivity period (e.g. 3 hours)

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Auto-discovering sessions, per user
event-time
Alice
Bob
Dave
… …
… …
… …
Example: How many movies does Alice watch on average per session?”

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Late-arriving data is handled transparently
• Handling of late-arriving data is important because, in practice, a lot of data arrives late

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Late-arriving data: example
Users with mobile phones enter
airplane, lose Internet connectivity
Emails are being written
during the 8h flight
Internet connectivity is restored,
phones will send queued emails now,
though with an 8h delay
Bob writes Alice an
email at 2 P.M.
Bob’s email is finally
being sent at 10 P.M.

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Late-arriving data is handled transparently
• Handling of late-arriving data is important because, in practice, a lot of data arrives late
• Good news: late-arriving data is handled transparently and efficiently for you
• Also, in your applications, you can define a grace period after which late-arriving data will be
discarded (default: 1 day), and you can define this granularly per windowed operation
• Example: “I want to sessionize the input data based on 15-min inactivity periods, and late-arriving
data should be discarded if it is more than 12 hours late”

25. 25
Late-arriving data is handled transparently
event-time
Alice
Bob
Dave
… …
… …
… …
• Late-arriving data may (1) create new sessions or (2) merge existing sessions

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Sessions potentially merge as new events arrive
Session Window

27. 27
Sessions potentially merge as new events arrive
Session Window

28. 28
Late-arriving data is handled transparently
event-time
Alice
Bob
Dave
… …
… …
… …

29. 29
Late-arriving data is handled transparently
event-time
Alice
Bob
Dave
… …
… …
… …

30. 30Confidential
Session windows: API

31. 31Confidential
Session windows: API in Confluent 3.2 / Apache Kafka 0.10.2
// A session window with an inactivity gap of 3h; discard data that is 12h late
SessionWindows.with(TimeUnit.HOURS.toMillis(3)).until(TimeUnit.HOURS.toMillis(12));
Defining a session window
// Key (String) is user, value (Avro record) is the movie view event for that user.
KStream<String, GenericRecord> movieViews = ...;
// Count movie views per session, per user
KTable<Windowed<String>, Long> sessionizedMovieCounts =
movieViews
.groupByKey(Serdes.String(), genericAvroSerde)
.count(SessionWindows.with(TimeUnit.HOURS.toMillis(3)), "views-­‐per-­‐session");
Full example: aggregating with session windows
More details with documentation and examples at:
http://docs.confluent.io/current/streams/developer-guide.html#session-windows
https://github.com/confluentinc/examples

32. 32Confidential
Attend the whole series!
Simplify Governance for Streaming Data in Apache Kafka
Date: Thursday, April 6, 2017
Time: 9:30 am - 10:00 am PT | 12:30 pm - 1:00 pm ET
Speaker: Gwen Shapira, Product Manager, Confluent
Using Apache Kafka to Analyze Session Windows
Date: Thursday, March 30, 2017
Time: 9:30 am - 10:00 am PT | 12:30 pm - 1:00 pm ET
Speaker: Michael Noll, Product Manager, Confluent
Monitoring and Alerting Apache Kafka with Confluent Control
Center
Date: Thursday, March 16, 2017
Time: 9:30 am - 10:00 am PT | 12:30 pm - 1:00 pm ET
Speaker: Nick Dearden, Director, Engineering and Product
Data Pipelines Made Simple with Apache Kafka
Date: Thursday, March 23, 2017
Time: 9:30 am - 10:00 am PT | 12:30 pm - 1:00 pm ET
Speaker: Ewen Cheslack-Postava, Engineer, Confluent
https://www.confluent.io/online-talk/online-talk-series-five-steps-to-production-with-apache-kafka/
What’s New in Apache Kafka 0.10.2 and Confluent 3.2
Date: Thursday, March 9, 2017
Time: 9:30 am - 10:00 am PT | 12:30 pm - 1:00 pm ET
Speaker: Clarke Patterson, Senior Director, Product Marketing
UP
NEXT

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34. 34
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35. 35
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