Slides from my talk at Berlin Buzzwords, 27 May 2014. Unfortunately Slideshare has screwed up the fonts. See https://speakerdeck.com/ept/samza-at-linkedin-taking-stream-processing-to-the-next-level for a version of the deck with correct fonts. Stream processing is an essential part of real-time data systems, such as news feeds, live search indexes, real-time analytics, metrics and monitoring. But writing stream processes is still hard, especially when you're dealing with so much data that you have to distribute it across multiple machines. How can you keep the system running smoothly, even when machines fail and bugs occur? Apache Samza is a new framework for writing scalable stream processing jobs. Like Hadoop and MapReduce for batch processing, it takes care of the hard parts of running your message-processing code on a distributed infrastructure, so that you can concentrate on writing your application using simple APIs. It is in production use at LinkedIn. This talk will introduce Samza, and show how to use it to solve a range of different problems. Samza has some unique features that make it especially interesting for large deployments, and in this talk we will dig into how they work under the hood. In particular: • Samza is built to support many different jobs written by different teams. Isolation between jobs ensures that a single badly behaved job doesn't affect other jobs. It is robust by design. • Samza can handle jobs that require large amounts of state, for example joining multiple streams, augmenting a stream with data from a database, or aggregating data over long time windows. This makes it a very powerful tool for applications.

1. Apache Samza:
Taking stream processing
to the next level

2. Martin Kleppmann
 Co-founded two startups, Rapportive ⇒ LinkedIn
 Committer on Avro & Samza ⇒ Apache
 Writing book on data-intensive apps ⇒ O’Reilly
 martinkl.com | @martinkl

3. Apache Kafka Apache Samza

4. Apache Kafka Apache Samza
Credit: Jason Walsh on Flickr
https://www.flickr.com/photos/22882695@N00/2477241427/
Credit: Lucas Richarz on Flickr
https://www.flickr.com/photos/22057420@N00/5690285549

5. Things we would like to do
(better)

6. Provide timely, relevant updates to your newsfeed

7. Update search results with new information as it
appears

8. “Real-time” analysis of logs and metrics

9. Tools?
Response latency
Kafka & Samza
Milliseconds to minutes
Loosely coupled
REST
Synchronous
Closely coupled
Hours to days
Loosely

10. Service 1
Kafka
events/messages
Analytics
Cache
maintenance
Notifications
subscribe subscribe subscribe
publish publish
Service 2

11. Publish / subscribe
 Event / message = “something happened”
– Tracking: User x clicked y at time z
– Data change: Key x, old value y, set to new value z
– Logging: Service x threw exception y in
request z
– Metrics: Machine x had free memory y at time
z
 Many independent consumers

12. Kafka at LinkedIn
 350+ Kafka brokers
 8,000+ topics
 140,000+ Partitions
 278 Billion messages/day
 49 TB/day in
 176 TB/day out
 Peak Load
– 4.4 Million messages per second
– 6 Gigabits/sec Inbound
– 21 Gigabits/sec Outbound

13. public interface StreamTask {
void process( IncomingMessageEnvelope
envelope,
MessageCollector collector,
TaskCoordinator coordinator);
}
Samza API: processing messages
getKey(), getMsg()
commit(), shutdown()
sendMsg(topic, key, val

14. Familiar ideas from
MR/Pig/Cascading/…
 Filter records matching condition
 Map record ⇒ func(record)
 Join two/more datasets by key
 Group records with the same value in field
 Aggregate records within the same group
 Pipe job 1’s output ⇒ job 2’s input
 MapReduce assumes fixed dataset.
Can we adapt this to unbounded streams?

15. Operations on streams
 Filter records matching condition✔ easy
 Map record ⇒ func(record) ✔ easy
 Join two/more datasets by key
…within time window, need buffer
 Group records with the same value in field
…within time window, need buffer
 Aggregate records within the same group
✔ ok… when do you emit result?
 Pipe job 1’s output ⇒ job 2’s input
✔ ok… but what about faults?

16. Stateful stream processing
(join, group, aggregate)

17. Joining streams requires state
 User goes to lunch ⇒ click long after impression
 Queue backlog ⇒ click before impression
 “Window join”
Join and aggregate
Click-through rate
Key-value
store
Ad impressionsAd clicks

18. Remote state or local state?
Samza job partition 0 Samza job partition 1
e.g. Cassandra, MongoDB,
…
100-500k msg/sec/node 100-500k msg/sec/node
1-5k queries/sec??

19. Remote state or local state?
Samza job partition 0 Samza job partition 1
Local
LevelDB/
RocksDB
Local
LevelDB/
RocksDB

20. Another example: Newsfeed &
following
 User 138 followed user 582
 User 463 followed user 536
 User 582 posted: “I’m at Berlin Buzzwords and it rocks”
 User 507 unfollowed user 115
 User 536 posted: “Nice weather today, going for a walk”
 User 981 followed user 575
 Expected output: “inbox” (newsfeed) for each user

21. Newsfeed & following
Fan out messages to
followers
Delivered messages
582 => [
138, 721,
…
]
Follow/unfollow
events
Posted messages
User 582 posted: “I’m at Berlin
Buzzwords and it rocks” User 138 followed user 582
Notify user 138: {User 582 posted:
“I’m at Berlin Buzzwords and it rocks”}Push notifications etc.

22. Local state:
Bring computation and data
together in one place

23. Fault tolerance

24. Kafka Kafka
YARN NodeManager YARN NodeManager
YARN
RM Samza Container
Samza Container
Samza Container
Samza Container
Machine 1 Machine 2
Task Task
Task Task
Task Task
Task Task
BOOM

25. Kafka Kafka
YARN NodeManager YARN NodeManager
YARN
RM Samza Container
Samza Container
Samza Container
Samza Container
Machine 1 Machine 2
Task Task
Task Task
Task Task
Task Task
BOOM

26. YARN NodeManager
Samza Container
Samza Container
Kafka
YARN NodeManager
Samza Container
Samza Container
Machine 2
Task Task
Task Task
Kafka
Machine 3
Task Task
Task Task


27. Fault-tolerant local state
Samza job partition 0 Samza job partition 1
Local
LevelDB/
RocksDB
Local
LevelDB/
RocksDBDurable changelog
replicate writes

28. YARN NodeManager
Samza Container
Samza Container
Kafka
YARN NodeManager
Samza Container
Samza Container
Machine 2
Task Task
Task Task
Machine 3
Task Task
Task Task

Kafka

29. Samza’s fault-tolerant local state
 Embedded key-value: very fast
 Machine dies ⇒ local key-value store is lost
 Solution: replicate all writes to Kafka!
 Machine dies ⇒ restart on another machine
 Restore key-value store from changelog
 Changelog compaction in the background (Kafka 0.8.1)

30. When things go slow…

31. Owned by
Team X
Team Y Team Z
Cascades of jobs
Job 1
Stream BStream A
Job 2 Job 3
Job 4 Job 5

32. Consumer goes slow
Backpressur
e
Queue upDrop data
Other jobs grind
to a halt 
Run out of
memory 
Spill to disk
Oh wait… Kafka does this anyway!
No thanks 

33. Job 1
Stream BStream A
Job 2 Job 3
Job 4 Job 5
Job 1 output
Job 2 output Job 3 output

34. Samza always writes
job output to Kafka
MapReduce always writes
job output to HDFS

35. Every job output is a named stream
 Open: Anyone can consume it
 Robust: If a consumer goes slow, nobody else is
affected
 Durable: Tolerates machine failure
 Debuggable: Just look at it
 Scalable: Clean interface between teams
 Clean: loose coupling between jobs

36. Problem Solution
Need to buffer job output
for downstream consumers
Write it to Kafka!
Need to make local state
store fault-tolerant
Write it to Kafka!
Need to checkpoint job
state for recovery
Write it to Kafka!
Recap

37. Apache Kafka Apache Samza
kafka.apache.org samza.incubator.apache.org

38. Hello Samza (try Samza in 5 mins)

39. Thank you!
Samza:
• Getting started: samza.incubator.apache.org
• Underlying thinking: bit.ly/jay_on_logs
• Start contributing: bit.ly/samza_newbie_issues
Me:
• Twitter: @martinkl
• Blog: martinkl.com