1.
ZAPR MEDIA LABS
Presented by: Chhavi Parasher and Ankit Timbadia

2.
Agenda
● What is Apache Kafka?
● Why Apache Kafka?
● Fundamentals
○ Terminologies
○ Architecture
○ Protocol
● What does kafka offer?
● Where Kafka is used?

3.
What is Apache Kafka?

4.
What is kafka
“A high throughput distributed pub-sub messaging system.

5.
What is kafka
“A pub-sub messaging system rethought as a distributed commit log
storage.”

6.
Why Apache Kafka?

7.
Why Kafka
Client Source
Data Pipelines Start like this.

8.
Why Kafka
Client Source
Client
Client
Client
Then we reuse them

9.
Why Kafka
Client Backend1
Client
Client
Client
Then we add additional endpoints to the
existing sources
Backend2

10.
Why Kafka
Client Backend1
Client
Client
Client
Then it starts to look like this
Backend2
Backend3
Backend4

11.
Birth of Kafka
Kafka decouples Data Pipelines
Source System Source System Source System Source System
Hadoop Security Systems
Real-time
monitoring
Data Warehouse
Kafka
Producers
Brokers
Consumers

12.
Kafka Fundamentals

13.
Terminologies

14.
Terminologies
• Topic is a message stream (queue)
• Partitions - Topic is divided into partitions
• Ordered, immutable, a structured commit log
• Segments - Partition has log segments on disk
• Partitions broken down into physical files.
• Offset - Segment has messages
• Each message is assigned a sequential id

15.
Anatomy of a topic
0 1 2 3 4 5 6 7 8 9
1
0
1
1
1
2
1
3
0 1 2 3 4 5 6 7 8 9
1
0
1
1
0 1 2 3 4 5 6 7 8 9
1
0
1
1
1
2
1
3
Partition
1
Partition
2
Partition
3
Writes
Old New
Segment1 Segment2

16.
Kafka Message (Write once read many)

17.
Partitions and Brokers
• Each broker can be
a leader or a replica
for a partition
• All writes & reads to
a topic go through
the leader

18.
Producer writing to partition
• Producers write to a
single leader
• Each write can be
serviced by a
separate broker

19.
Producer writing to second partition.

20.
Keyed Messages and Partitioning
Producer

21.
Partitioning Distributes clients across consumers
Consumer
Consumer
Consumer
Consumer
Consumer
Consumer
Consumer
Producer
Producer
Producer
Producer
Producer

22.
Architecture

23.
What does kafka offer?

24.
What does kafka offer?
• Fast
• Scalable
• Durable
• Distributed by Design

25.
What does kafka offer?
• Fast
• A single Kafka broker can handle 100Mbps of reads & writes from 1000 of clients.
• Scalable
• Durable
• Distributed by Design

26.
What does kafka offer?
• Fast
• Scalable
• Data streams are partitioned and spread over a cluster of machines to allow data
streams larger than the capability of any single machine
• Durable
• Distributed by Design

27.
What does kafka offer?
• Fast
• Scalable
• Durable
• Messages are persisted on disk and replicated within the cluster to prevent data
loss. Each broker can handle terabytes of messages without performance impact.
• Distributed by Design

28.
What does kafka offer
• Fast
• Scalable
• Durable
• Distributed by Design
• Kafka has a modern cluster-centric design that offers strong durability and
fault-tolerance guarantees.

29.
Efficiency - high throughput & low latency
• Disks are fast when used sequentially
• Append to end of log.
• Fetch messages from a partition beginning from a particular message id.
• Batching makes best use of network/IO
• Batched send and receive
• Batched compression (GZIP, Snappy and LZ4)

30.
Efficiency - high throughput & low latency
• No message caching in JVM
• Zero-copy from file to socket (Java NIO)

31.
●
●
●
●
●
●
Comparison to other messaging systems

32.
Where is Kafka used?

33.
Who uses Apache Kafka?

34.
What do people use it for?
• Activity tracking
• page views or click tracking, profile updates
• Real-time event processing
• Provides low latency
• Good integration with spark, samza. storm etc.
• Collecting Operational Metrics
• Monitoring & alerting
• Commit log
• database changes can be published to Kafka

35.
Why Replication?
• Broker can go down
• controlled: rolling restart for code/config push
• uncontrolled: isolated broker failure
• If broker is down
• some partitions are unavailable
• could be permanent data loss
• Replication ensures higher availability & durability

36.
Caveats
• Not designed for large payloads.
• Decoding is done for a whole message (no streaming decoding).
• Rebalancing can screw things up.
• if you're doing any aggregation in the consumer by the partition key
• Number of partitions cannot be easily changed (chose wisely).
• Lots of topics can hurt I/O performance.

37.
Guarantees
• At least once delivery
• In order delivery, per partition
• For a topic with replication factor N, Kafka can tolerate up to N-1 server
failures without “losing” any messages committed to the log

38.
Summary
• A high throughput distributed messaging system rethought as commit log
• Originally developed by LinkedIn, Open Sourced in 2011
• Written in Scala, Clients for every popular language
• Used by LinkedIn, Twitter, Netflix and many more companies.
• When should we use Kafka ?
• When should we not use kafka?

39.
Kafka in Production @ Zapr

40.
Agenda
• Kafka Clusters at @Zapr
• Kafka in AWS
• Challenges Managing Kafka Brokers.
• Monitoring of Kafka Clusters.

41.
Kafka Version: 0.8.2
Total Kafka Clusters: 5
Number of Topics: 185
Brokers: 17
Partitions: 7827
Regions: 2 (us-east-1,ap-southeast-1)
Kafka at Zapr

42.
Kafka Clusters on Amazon Web Services.
Quorum of 3 Zookeeper Nodes for High Availability
Kafka Brokers

43.
Resource Utilization
Family Type vCPU Memory CPU Pricing
Compute
Optimized
C4.2xlarge 8 16 ~75% 338$
General
Purpose
M4.2xlarge 8 32 ~25% 366$
After moving from C4 Class to M4 Class Machines 40% Improvement in CPU Usage

44.
Production Kafka Broker Configurations

45.
#Default Partitions and Replication Factor for Kafka Topics
num.partitions=10
default.replication.factor=2
#Controlled shutdown for the proper shutdown of Kafka Broker for maintenance.
num.recovery.threads.per.data.dir=4
controlled.shutdown.enable=true
controlled.shutdown.max.retries=5
controlled.shutdown.retry.backoff.ms=60000
#Creation and Deletion of Kafka Topics
delete.topic.enable=true
auto.create.topics.enable=true
Production Kafka Broker
Configurations

46.
Challenges

47.
Dynamic Load
• Kafka Scales Horizontally
• For a topic, Kafka has N (Replicated Partitions)
• Adding a Broker causes Partition to rebalanced

48.
Current State 2 Brokers, 1 Topic and 6
Partitions & replication 2
Desired State after adding 3rd
Broker

49.
Kafka approach

50.
Introducing Kafka Manager
https://github.com/yahoo/kafka-manager
It supports the following :
● Manage multiple clusters.
● Easy inspection of cluster state (topics, consumers, offsets, brokers)
● Generate partition assignments with option to select brokers to use
● Run reassignment of partition (based on generated assignments)
● Create a topic with optional topic configs (0.8.1.1 has different configs than 0.8.2+)
● Delete topic (only supported on 0.8.2+ and remember set delete.topic.enable=true in
broker config)
● Add partitions to existing topic
● Update config for existing topic
● Optionally enable JMX polling for broker level and topic level metrics.

51.
Clusters

52.
Topic List

53.
Topic View

54.
Consumed Topic View

55.
Broker List

56.
Broker View

57.
Reassign Partition

58.
Monitoring

59.
Kafka Graphite Metric Plugin
https://github.com/damienclaveau/kafka-graphite
#Graphite Reporter
kafka.metrics.reporters=com.criteo.kafka.KafkaGraphiteM
etricsReporter
kafka.graphite.metrics.reporter.enabled=true
kafka.metrics.polling.interval.secs = 60
kafka.graphite.metrics.host= graphite.zapr.com
kafka.graphite.metrics.port=2003
kafka.graphite.metrics.group=PROD.kafka-cluster-1.10-0-1-
90

60.
Metrics @Graphite

61.
Kafka Offset Monitoring
https://github.com/quantifind/KafkaOffsetMonitor
● This is an app to monitor your kafka consumers and their position (offset) in
the queue.
● You can see the current consumer groups, for each group the topics that they
are consuming
● Offset are useful to understand how quick you are consuming from a queue
and how fast the queue is growing

62.
Kafka Offset Checker

63.
Kafka offset monitoring @Graphite

64.
Future plans
• Upgrading EC2 instances to next generations from M4 to M5
• Upgrading Kafka Version from 0.8.2 to latest version (1.2)
• Using ST1 (throughput optimized HDD) EBS

65.
Thank you!