Introduction to Apache Kafka

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Jim Plush

from Orange County Big Data meetup A tour of Kafka and what makes it unique in the queue space

Ingénierie

INTROTO KAFKA
Jim Plush, Director of Cloud Engineering, CrowdStrike.com
Twitter: @jimplush

ABOUT ME
Jim Plush, Director of Cloud Engineering @ CrowdStrike.com
Architect of distributed cloud services for catching bad guys
Previously Director of Engineering at gravity.com
personalization service, ingesting clickstream fromYahoo!, New
York Times,WSJ, etc…
wrote most of the ETL workﬂow

ABOUT CROWDSTRIKE
“Big Data” Security Company
Near term focus on targeted, state sponsored attacks and
attribution
Single customer can generate 2.2TB of machine data per day we
process in our cloud
Horizontally scalable, distributed infrastructure
Uses goodies like Kafka, Cassandra, Elastic Search, Hadoop, Scala, Go

–Said everyone, always
“Some people, when confronted with a problem, think
“I know, I'll use a message queue.”
Now they have two problems.”

APACHE KAFKA
It’s not a so much a queue, but an activity stream system
Trades stability and speed for consumer complexity
It’s scalable by nature
Supports data replication
You can rewind time
It’s fast!
Persistent messaging with O(1) disk structures that provide constant time
performance even with many TB of stored messages.

APACHE KAFKA - CONS
Consumer Complexity
Not “Rack Aware” replication
Lack of tooling/monitoring
Still pre 1.0 release
Operationally, it’s more manual than desired
Requires ZooKeeper

BASIC CONCEPTS
Topics - logical namespace for data (clickstream, app logs)
Partition - physical separation of data to allow for horizontal
scalability
Consumer Groups/Offsets - Where your consumer group last
check pointed in the stream
Replica - allows for partitions to be replicated across nodes for
availability, only one is the active leader

USE CASES
First point for data ingestion, provide back pressure to
downstream
Provide a data ﬁrehose for clients (with seeks)
Friendly to Blue/Green deployment architectures
Mirroring test data easily
Data Center log aggregation

Producer
API Server
Customer A Customer B
Data Stream
Serving a Firehose

Data Afﬁnity w/ Key Partitioning
Producer
Consumer B
Data Stream P0
Data Stream P1
UserIds 0-100
Consumer A
UserIds 0-100 UserIds 101-200

Producer
Blue
Consumer
InactiveTopic
ActiveTopic
Blue/Green Deployment
ZooKeeper
Controller

Producer
Blue
Consumer
InactiveTopic
ActiveTopic
Blue/Green Deployment
ZooKeeper
Controller
Green
Consumer

Producer
Blue
Consumer
InactiveTopic
ActiveTopic
Blue/Green Deployment
ZooKeeper
Green
Consumer
Controller
User: 555

Producer
Blue
Consumer
InactiveTopic
ActiveTopic
Blue/Green Deployment
Green
Consumer
Controller
User: 555ZooKeeper

SCALING OUT
1 partition = 1 consumer
1 partition needs to ﬁt on a single machine
Partitions = the scalability of your system from the producer and
consumer side
For high scale apps you will probably start out with 100
partitions

Producer
Consumer A
P1
P0
P2
Consumer B
Consumer C

MONITORING
http://quantiﬁnd.com/KafkaOffsetMonitor/

ZOOKEEPER
http://techblog.netﬂix.com/2012/04/introducing-exhibitor-supervisor-
system.html

WE’RE HIRING!
jim@crowdstrike.com
@jimplush
crowdstrike.com/about-us/careers

Producer A
Producer B
ZooKeeper
Partition 1
Partition 2
ClickStream
Partition Offsets
Commit Offset
Consumer A

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1. INTROTO KAFKA Jim Plush, Director of Cloud Engineering, CrowdStrike.com Twitter: @jimplush

2. ABOUT ME Jim Plush, Director of Cloud Engineering @ CrowdStrike.com Architect of distributed cloud services for catching bad guys Previously Director of Engineering at gravity.com personalization service, ingesting clickstream fromYahoo!, New York Times,WSJ, etc… wrote most of the ETL workﬂow

3. ABOUT CROWDSTRIKE “Big Data” Security Company Near term focus on targeted, state sponsored attacks and attribution Single customer can generate 2.2TB of machine data per day we process in our cloud Horizontally scalable, distributed infrastructure Uses goodies like Kafka, Cassandra, Elastic Search, Hadoop, Scala, Go

4. –Said everyone, always “Some people, when confronted with a problem, think “I know, I'll use a message queue.” Now they have two problems.”

5. APACHE KAFKA It’s not a so much a queue, but an activity stream system Trades stability and speed for consumer complexity It’s scalable by nature Supports data replication You can rewind time It’s fast! Persistent messaging with O(1) disk structures that provide constant time performance even with many TB of stored messages.

6. APACHE KAFKA - CONS Consumer Complexity Not “Rack Aware” replication Lack of tooling/monitoring Still pre 1.0 release Operationally, it’s more manual than desired Requires ZooKeeper

7. BASIC CONCEPTS Topics - logical namespace for data (clickstream, app logs) Partition - physical separation of data to allow for horizontal scalability Consumer Groups/Offsets - Where your consumer group last check pointed in the stream Replica - allows for partitions to be replicated across nodes for availability, only one is the active leader

10.

11. USE CASES First point for data ingestion, provide back pressure to downstream Provide a data ﬁrehose for clients (with seeks) Friendly to Blue/Green deployment architectures Mirroring test data easily Data Center log aggregation

12. Seamless Integration with Storm

13. Data Center Aggregation

14. Producer API Server Customer A Customer B Data Stream Serving a Firehose

15. Data Afﬁnity w/ Key Partitioning Producer Consumer B Data Stream P0 Data Stream P1 UserIds 0-100 Consumer A UserIds 0-100 UserIds 101-200

16. Producer Blue Consumer InactiveTopic ActiveTopic Blue/Green Deployment ZooKeeper Controller

17. Producer Blue Consumer InactiveTopic ActiveTopic Blue/Green Deployment ZooKeeper Controller Green Consumer

18. Producer Blue Consumer InactiveTopic ActiveTopic Blue/Green Deployment ZooKeeper Green Consumer Controller User: 555

19. Producer Blue Consumer InactiveTopic ActiveTopic Blue/Green Deployment Green Consumer Controller User: 555ZooKeeper

20. SCALING OUT 1 partition = 1 consumer 1 partition needs to ﬁt on a single machine Partitions = the scalability of your system from the producer and consumer side For high scale apps you will probably start out with 100 partitions

21. Producer Consumer AP1 P0 P2

22. Producer Consumer A P1 P0 P2 Consumer B Consumer C

23. MONITORING http://quantiﬁnd.com/KafkaOffsetMonitor/

24.

25.

26. ZOOKEEPER http://techblog.netﬂix.com/2012/04/introducing-exhibitor-supervisor- system.html

27. WE’RE HIRING! jim@crowdstrike.com @jimplush crowdstrike.com/about-us/careers

28. Producer A Producer B ZooKeeper Partition 1 Partition 2 ClickStream Partition Offsets Commit Offset Consumer A