This document summarizes Kafka's performance in handling data pipelines and ETL workloads. It discusses Kafka's high-level architecture, scalability, fault tolerance, and monitoring capabilities. The document also includes results from benchmark tests showing Kafka can process over 47 million transactions in under 6 minutes with latency under 2 milliseconds. It proposes using Kafka to integrate data pipelines between various systems and services at a company.

1. Kafka Evaluation for
Data Pipeline and ETL
Shafaq Abdullah @ GREE
Date: 10/21/2013

2. High-Level Architecture
Performance
Scalability
Fault-Tolerance/Error Recovery
Operations and Monitoring
Summary
Outline

3. Kafka -
A distributed messaging pub/sub system
In Production: Linkedin, FourSquare, Twitter, GREE (almost)
Usage:
- Log aggregation (user activity stream)
- Real-time events
- Monitoring
- Queuing
Intro

4. Point-to-Point Data Pipelines
Game
Server 1
Logs
Hadoop
...
User
Tracking
SecuritySearch
Social
Graph
Rules/
Recomm
endation/
Engine
VerticaOps
DataWare
House

5. Message- Central Data Channel
Game
Server 1
Logs
Hadoop
...
User
Tracking
SecuritySearch
Social
Graph
Rules/
Recomm
endation/
Engine
VerticaOps
Data
Warehouse
Message Queue

6. Topic-
A String representing Message Stream Id
Partition-
Logical Division per topic-level within Broker, for
writing logs generated by producer.
e.g: kafkaTopic - kafkaTopic1, kafkaTopic2
Replica-Sets-
Replica within Broker for a certain partition, with
Leader(writes) and follower (read) balanced using hash-
key modulu.
Kafka Jargon

7. Log- Message Queue

8. Log- Message Queue

9. AWS m1.Large instance
Dual Core Intel Xeon 64-
bit@2.27GHz
7.5 GB RAM
2 x 420 GB hardisk
Hardware Spec of Broker Cluster

10. Performance Results
Producer thread Transactions Processing time (s)
Throughput
(transaction/sec)
1 488396 64.129 7616
2 1195748 110.868 10785
4 1874713 140.375 13355
10 47269410 338.094 13981
17 7987317 568.028 14061

11. Latency vs Durability
Ack Status TIme to publish (ms) Tradeoff
No Ack 0.7 Greater data loss
Wait for ack 1.5 Lesser data loss

12. 1. Create a Kafka-Replica Sink
2. Feed data to Copier via Kafka-Sink
3. Benchmark Kakfa-Copier-Vertica Pipeline
4. Improve/Refactor for Performance
Integration Plan

13. C- Consistency (Producer Sync Mode)
A- Availability (Replication)
P- Partition Tolerance (Cluster in same
network- No Network delay)
Strongly Consistent and
Highly Available
CA-P theorem

14. Conventional Quoram Replication:
2f+1 replicas → f failures (e.g. ZooKeeper)
Kafka Replication:
f+1 replicas → f failures
Failure Recovery

16. Leader :
➢ Message is propagated to follower
➢ Commit offset is checkpointed to disk
Follower failure and Recovery:
➢ Kicked out of ISR
➢ After restart, truncates log to last commit
➢ Catches up with leader → ISR
Error Handling: Follower Failure

17. ➢ Embedded Controller via ZK detects
leader failure
➢ Leader election from ISR
➢ Committed message not lost
Error Handling: Leader Failure

18. - Horizontally scalable
Add partitions in Broker Cluster as higher
throughput needed (~10Mb/s /server)
- Balancing for producer and consumer
Scalability

19. • Number of messages the consumer lags behind the producer by
• Max lag in messages btw follower and leader replica
• Unclean leader election rate
• Is controller active on broke
Monitoring via JMX

20. 3 Node cluster ~ 25 MB/s, < 20 ms latency
(end2end)
having replication factor of 3 with 6
consumer group
Monthly operations
$500 + $250 (zookeeper 3 node) + 1 mm
Operation Costs

21. - No callback in producer’s send()
- Fully automatic balancing till script is
manually run- Balancing layer of topics
via ZK
Nothing is perfect

22. • Infinite scaling with 10Mb/s /server with
< 10 ms latency
• Costs <$1000 /month + 1 man-month
• No impact on current pipeline
• 0.8 final release due in days to be used
in production
Summary