Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
Event streaming fundamentals with Apache Kafka
Search
Keith Resar
February 24, 2022
Technology
1
470
Event streaming fundamentals with Apache Kafka
Keith Resar
February 24, 2022
Tweet
Share
More Decks by Keith Resar
See All by Keith Resar
Real-Time Data Transformation by Example
keithresar
0
55
Exactly-Once Semantics and Transactions in Kafka
keithresar
0
120
Implementing Strangler pattern for microservices migrations
keithresar
0
360
Stream processing with ksqlDB and Apache Kafka
keithresar
1
410
How Nagios is leveraging Ansible Network Automation
keithresar
1
80
Automating Satellite Installation and Configuration With the Ansible Foreman Modules
keithresar
1
690
Writing your first Ansible operator for OpenShift
keithresar
1
210
Intro to CI/CD in GitLab and Anatomy of a Pipeline
keithresar
2
370
Ansible Ecosystem Future Directions
keithresar
0
160
Other Decks in Technology
See All in Technology
OCI Oracle Database Services新機能アップデート(2025/06-2025/08)
oracle4engineer
PRO
0
160
Evolución del razonamiento matemático de GPT-4.1 a GPT-5 - Data Aventura Summit 2025 & VSCode DevDays
lauchacarro
0
210
DevIO2025_継続的なサービス開発のための技術的意思決定のポイント / how-to-tech-decision-makaing-devio2025
nologyance
1
400
react-callを使ってダイヤログをいろんなとこで再利用しよう!
shinaps
1
240
テストを軸にした生き残り術
kworkdev
PRO
0
210
AWSで始める実践Dagster入門
kitagawaz
1
620
「全員プロダクトマネージャー」を実現する、Cursorによる仕様検討の自動運転
applism118
21
11k
品質視点から考える組織デザイン/Organizational Design from Quality
mii3king
0
210
【初心者向け】ローカルLLMの色々な動かし方まとめ
aratako
7
3.5k
EncryptedSharedPreferences が deprecated になっちゃった!どうしよう! / Oh no! EncryptedSharedPreferences has been deprecated! What should I do?
yanzm
0
390
共有と分離 - Compose Multiplatform "本番導入" の設計指針
error96num
2
580
DDD集約とサービスコンテキスト境界との関係性
pandayumi
3
280
Featured
See All Featured
VelocityConf: Rendering Performance Case Studies
addyosmani
332
24k
It's Worth the Effort
3n
187
28k
Become a Pro
speakerdeck
PRO
29
5.5k
Building Adaptive Systems
keathley
43
2.7k
Optimising Largest Contentful Paint
csswizardry
37
3.4k
Building Flexible Design Systems
yeseniaperezcruz
328
39k
Code Reviewing Like a Champion
maltzj
525
40k
The Cost Of JavaScript in 2023
addyosmani
53
8.9k
Facilitating Awesome Meetings
lara
55
6.5k
XXLCSS - How to scale CSS and keep your sanity
sugarenia
248
1.3M
Why Our Code Smells
bkeepers
PRO
339
57k
Intergalactic Javascript Robots from Outer Space
tanoku
272
27k
Transcript
Event Streaming Fundamentals with Apache Kafka Keith Resar Sr. Kafka
Developer @KeithResar
Data-Driven Operations
Data-Driven Operations
Data-Driven Operations
None
@KeithResar
@KeithResar
The Rise of Event Streaming 2010 Apache Kafka created at
LinkedIn 2022 Most fortune 100 companies trust and use Kafka
A company is built on _DATA FLOWS_ but all we
have are _DATA STORES_
Example Application Architecture Serving Layer (Microservices, Elastic, etc.) Java Apps
with Kafka Streams or ksqlDB Continuous Computation High-Throughput Event Streaming Platform API-Based Clustering @KeithResar
Apache Kafka is an Event Streaming Platform 1. Storage 2.
Pub / Sub 3. Processing @KeithResar
Storage 12 @KeithResar
Core Abstractions @KeithResar • DB → table • Hadoop →
file • Kafka - ?
LOG
Immutable Event Log New Messages are added at the end
of the log Old @KeithResar
Messages are KV Bytes key: byte[] value: byte[] Headers =>
[Header] @KeithResar
Messages Inside Topics Clicks Orders Customers Topics are similar to
database tables @KeithResar
Topics divide into Partitions Messages are guaranteed to be strictly
ordered within a partition @KeithResar P 0 Clicks P 1 P 2
None
Pub / Sub 20 @KeithResar
Producing Data New Messages are added at the end of
the log Old @KeithResar
Consuming Data New Consume via sequential data access starting from
a specific offset. Old @KeithResar Read to offset & scan
Distinct Consumer Positions New Old @KeithResar Sally offset 12 Fred
offset 3 Rick offset 9
None
Messages are KV Bytes key: byte[] value: byte[] Headers =>
[Header] @KeithResar
Producing to Kafka - No Key @KeithResar P 0 P
1 P 2 P 3 Messages will be produced in a round robin fashion
Producing to Kafka - No Key @KeithResar P 0 P
1 P 2 P 3 Messages will be produced in a round robin fashion
Producing to Kafka - With Key @KeithResar P 0 P
1 P 2 P 3 hash(key) % numPartitions = N
Producing to Kafka - With Key @KeithResar P 0 P
1 P 2 P 3 hash(key) % numPartitions = N
Consumer from Kafka - Single @KeithResar P 0 P 1
P 2 P 3 Single consumer reads from all partitions
Consumer from Kafka - Multiple @KeithResar P 0 P 1
P 2 P 3 Consumers can be split into multiple groups each of which operate in isolation
CONSUMER GROUP COORDINATOR CONSUMERS CONSUMER GROUP
Consumer from Kafka - Multiple @KeithResar P 0 P 1
P 2 P 3 Consumers can be split into multiple groups each of which operate in isolation
Consumer from Kafka - Multiple @KeithResar P 0 P 1
P 2 P 3 Consumers can be split into multiple groups each of which operate in isolation
Grouped Consumers @KeithResar P 0 P 1 P 2 P
3 Consumers can be split into multiple groups each of which operate in isolation
Grouped Consumers @KeithResar P 0 P 1 P 2 P
3 Consumers can be split into multiple groups each of which operate in isolation X
None
Linearly Scalable Architecture @KeithResar Producers • Many producers machines •
Many consumer machines • Many Broker machines Consumers Single topic, No Bottleneck!
Replicate for Fault Tolerance @KeithResar Broker A Broker B Message
✓ Leader Replicate
Partition Leadership / Replication @KeithResar Broker 1 Broker 2 Broker
3 Broker 4 P 0 P 1 P 2 P 3 Partition 0 Partition 2 Partition 3 Partition 0 Partition 1 Partition 3 Partition 0 Partition 1 Partition 2 Partition 1 Partition 2 Partition 3 Follower Leader
Replication Provides Resiliency @KeithResar Producers Consumers Replica followers become leaders
on machine failure X X X X X
Partition Leadership / Replication @KeithResar Broker 1 Broker 2 Broker
3 Broker 4 P 0 P 1 P 2 P 3 Partition 0 Partition 2 Partition 3 Partition 0 Partition 1 Partition 3 Partition 0 Partition 1 Partition 2 Partition 1 Partition 2 Partition 3 Follower Leader
Partition Leadership / Replication @KeithResar Broker 1 Broker 2 Broker
3 Broker 4 P 0 P 1 P 2 P 3 Partition 0 Partition 2 Partition 3 Partition 0 Partition 1 Partition 3 Partition 0 Partition 1 Partition 2 Partition 1 Partition 2 Partition 3 Follower Leader
Partition Leadership / Replication @KeithResar Broker 1 Broker 2 Broker
3 Broker 4 P 0 P 1 P 2 P 3 Partition 0 Partition 2 Partition 3 Partition 0 Partition 1 Partition 3 Partition 0 Partition 1 Partition 2 Partition 1 Partition 2 Partition 3 Follower Leader
Partition Leadership / Replication @KeithResar Broker 1 Broker 2 Broker
3 Broker 4 P 0 P 1 P 2 P 3 Partition 0 Partition 2 Partition 3 Partition 0 Partition 1 Partition 3 Partition 0 Partition 1 Partition 2 Partition 1 Partition 2 Partition 3 Follower Leader Partition 2 Partition 1 Partition 3
Partition Leadership / Replication @KeithResar Broker 1 Broker 2 Broker
3 Broker 4 P 0 P 1 P 2 P 3 Partition 0 Partition 2 Partition 3 Partition 0 Partition 1 Partition 3 Partition 0 Partition 1 Partition 2 Follower Leader Partition 2 Partition 1 Partition 3
None
The log is a type of durable messaging system @KeithResar
Similar to a traditional messaging system (ActiveMQ, Rabbit, etc.) but with: • Far better scalability • Built-in fault tolerance/HA • Storage
None
Origins in Stream Processing Serving Layer (Microservices, Elastic, etc.) Java
Apps with Kafka Streams or ksqlDB Continuous Computation High-Throughput Event Streaming Platform API-Based Clustering
Processing 51 @KeithResar
Streaming is the toolset for working with events as they
move! @KeithResar
What is stream processing? @KeithResar auth attempts possible fraud
What is stream processing? @KeithResar User Population Coding Sophistication Core
developers who use Java/Scala Core developers who don’t use Java/Scala Data engineers, architects, DevOps/SRE BI analysts streams
Standing on the Shoulders of Streaming Giants Producer, Consumer APIs
Kafka Streams ksqlDB Ease of use Flexibility ksqlDB UDFs Powered by Powered by
What is stream processing? @KeithResar CREATE STREAM possible_fraud AS SELECT
card_number, count(*) FROM authorization_attempts WINDOW TUMBLING (SIZE 5 MINUTE) GROUP BY card_number HAVING count(*) > 3;
What is stream processing? @KeithResar CREATE STREAM possible_fraud AS SELECT
card_number, count(*) FROM authorization_attempts WINDOW TUMBLING (SIZE 5 MINUTE) GROUP BY card_number HAVING count(*) > 3;
What is stream processing? @KeithResar CREATE STREAM possible_fraud AS SELECT
card_number, count(*) FROM authorization_attempts WINDOW TUMBLING (SIZE 5 MINUTE) GROUP BY card_number HAVING count(*) > 3;
What is stream processing? @KeithResar CREATE STREAM possible_fraud AS SELECT
card_number, count(*) FROM authorization_attempts WINDOW TUMBLING (SIZE 5 MINUTE) GROUP BY card_number HAVING count(*) > 3;
What is stream processing? @KeithResar CREATE STREAM possible_fraud AS SELECT
card_number, count(*) FROM authorization_attempts WINDOW TUMBLING (SIZE 5 MINUTE) GROUP BY card_number HAVING count(*) > 3;
What is stream processing? @KeithResar CREATE STREAM possible_fraud AS SELECT
card_number, count(*) FROM authorization_attempts WINDOW TUMBLING (SIZE 5 MINUTE) GROUP BY card_number HAVING count(*) > 3;
What is stream processing? @KeithResar CREATE STREAM possible_fraud AS SELECT
card_number, count(*) FROM authorization_attempts WINDOW TUMBLING (SIZE 5 MINUTE) GROUP BY card_number HAVING count(*) > 3;
None
Wrap Up 64 @KeithResar
developer.confluent.io Learn Kafka. Start building with Apache Kafka at Confluent
Developer.
Free eBooks Designing Event-Driven Systems Ben Stopford Kafka: The Definitive
Guide Neha Narkhede, Gwen Shapira, Todd Palino Making Sense of Stream Processing Martin Kleppmann I ❤ Logs Jay Kreps http://cnfl.io/book-bundle
None
Thank You @KeithResar Kafka Developer confluent.io