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PRESENTED BY

May 2, 2023

Data Loss and Data Duplication

in Kafka

Jayesh Thakrar

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Kafka is a distributed, partitioned, replicated, durable commit log service. It provides the functionality of a messaging system, but with a unique design.

Exactly once - each message is delivered once and only once

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Kafka Overview Data Loss Data Duplication Data Loss and Duplicate Prevention Monitoring

AGENDA

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Kafka Overview

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Kafka As A Log Abstraction

Client: Producer

Client: Consumer BClient: Consumer A

Kafka Server = Kafka Broker

Topic: app_events

Source: https://engineering.linkedin.com/distributed-systems/log-what-every-software-engineer-should-know-about-real-time-datas-unifying

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Topic Partitioning . . .

Kafka Broker

Client: Producer or Consumer

Source: https://engineering.linkedin.com/distributed-systems/log-what-every-software-engineer-should-know-about-real-time-datas-unifying

Topic: app_events

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Topic Partitioning – Scalability

Clients: Producer, Consumer

Leader

Replica

Replica

Leader

Replica

Replica

Leader

Replica

Replica

Kafka Broker 0

Kafka Broker 1

Kafka Broker 2

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Topic Partitioning – redundancy

Client: Producer, Consumer

Kafka Broker 2

Leader

Replica

Replica

Leader

Replica

Replica

Leader

Replica

Replica

Kafka Broker 0

Kafka Broker 1

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Topic Partitioning – Redundancy/durabilityKafka Broker 2

Leader

Replica

Replica

Leader

Replica

Replica

Leader

Replica

Replica

Kafka Broker 0

Kafka Broker 1

Pull-based inter-broker replication

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Topic Partitioning – summary Log sharded into partitions

Messages assigned to partitions by API or custom partitioner

Partitions assigned to brokers (manual or automatic)

Partitions replicated (as needed)

Messages ordered within each partition

Message offset = absolute position in partition

Partitions stored on filesystem as ordered sequence of log segments (files)

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Other Key Concepts Cluster = collection of brokers

Broker-id = a unique id (integer) assigned to each broker Controller = functionality within each broker responsible for leader

assignment and management, with one being the active controller

Replica = partition copy, represented (identified) by the broker-id Assigned replicas = set of all replicas (broker-ids) for a partition

ISR = In-Sync Replicas = subset of assigned replicas (brokers) that are “in-sync/caught-up”* with the leader (ISR always includes the leader)

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Data Loss

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Data Loss : Inevitable

Upto 0.01% data lossFor 700 billion messages / day,

that's up to 7 million / day

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Data loss at the producer

Kafka Producer API

API Call-tree

kafkaProducer.send() …. accumulator.append() // buffer …. sender.send() // network I/O

•Messages accumulate in buffer in batches•Batched by partition, retry at batch level•Expired batches dropped after retries•Error count and other metrics via JMX

Data Loss at Producer

•Failure to close / flush producer on termination

•Dropped batches due to communication or other errors when acks = 0 or retry exhaustion

•Data produced faster than delivery, causing BufferExhaustedException(deprecated in 0.10+)

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dATA LOSS AT The CLUSTER (BY BROKERS)

Was it a

leader?

Detected by Controller via

zookeeper

Was it in ISR?

Other replicas in ISR?

Elect another leader

Allow unclean election?

ISR >= min.insync.replicas?

Relax, everything will be fine

Partition unavailable !!

Other replicas

available?

Y Y

N

N

Y

Y

Y

Y

N

Broker Crashes

N

N

N

1

2

4

5 6

3

7

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Non-leader broker crash

Was it a

leader?

Detected by Controller via

zookeeper

Was it in ISR?

Other replicas in ISR?

Elect another leader

Allow unclean election?

ISR >= min.insync.replicas?

Relax, everything will be fine

Partition unavailable !!

Other replicas

available?

Y Y

N

N

Y

Y

Y

Y

N

Broker Crashes

N

N

N

1

2

4

5 6

3

7

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Leader broker crash: Scenario 1

Was it a

leader?

Detected by Controller via

zookeeper

Was it in ISR?

Other replicas in ISR?

Elect another leader

Allow unclean election?

ISR >= min.insync.replicas?

Relax, everything will be fine

Partition unavailable !!

Other replicas

available?

Y Y

N

N

Y

Y

Y

Y

N

Broker Crashes

N

N

N

1

2

4

5 6

3

7

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Leader broker crash: Scenario 2

Was it a

leader?

Detected by Controller via

zookeeper

Was it in ISR?

Other replicas in ISR?

Elect another leader

Allow unclean election?

ISR >= min.insync.replicas?

Relax, everything will be fine

Partition unavailable !!

Other replicas

available?

Y Y

N

N

Y

Y

Y

Y

N

Broker Crashes

N

N

N

1

2

4

5 6

3

7

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dATA LOSS AT The CLUSTER (BY BROKERS)

Was it a

leader?

Detected by Controller via

zookeeper

Was it in ISR?

Other replicas in ISR?

Elect another leader

Allow unclean election?

ISR >= min.insync.replicas?

Relax, everything will be fine

Partition unavailable !!

Other replicas

available?

Y Y

N

N

Y

Y

Y

Y

N

Potential data-loss depending upon acks

config at producer. See KAFKA-3919 KAFKA-4215

Broker Crashes

N

N

N

1

2

4

5 6

3

7

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FROM KAFKA-3919

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FROM KAFKA-4215

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Config for Data Durability and Consistency Producer config

- acks = -1 (or all)- max.block.ms (blocking on buffer full, default = 60000) and retries- request.timeout.ms (default = 30000) – it triggers retries

Topic config- min.insync.replicas = 2 (or higher)

Broker config- unclean.leader.election.enable = false

- timeout.ms (default = 30000) – inter-broker timeout for acks

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Config for Availability and Throughput

Producer config- acks = 0 (or 1)- buffer.memory, batch.size, linger.ms (default = 100)- request.timeout.ms, max.block.ms (default = 60000), retries- max.in.flight.requests.per.connection

Topic config- min.insync.replicas = 1 (default)

Broker config- unclean.leader.election.enable = true

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Data Duplication

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Data Duplication: How it occursClient: Producer

Client: Consumer BClient: Consumer A

Kafka Broker

Topic: app_events

Producer (API) retries = messages resent after timeout

when retries > 1

Consumer consumes messages more than once after restart from unclean

shutdown / crash

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Data Loss & Duplication Detection

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How to Detect Data loss & Duplication - 1

Memcache /HBase /Cassandra / Other

Producer Kafka Consumer

Topic, Partition, Offset | Msg Key or Hash

KEY | VALUE

1) Msg from producer to Kafka

2) Ack from Kafka with details

3) Producer inserts into store

4) Consumer reads msg

5) Consumer validates msg If exists not duplicate consume msg delete msg If missing duplicate msg Audit: Remaining msgs in store are "lost" or "unconsumed" msgs

1

2

3

4

5Store

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How to Detect Data loss & Duplication - 2

Memcache /HBase /Cassandra / Other

Producer Kafka Consumer

Source, time-window | Msg count or some other checksum (e.g. totals, etc)

KEY | VALUE

1) Msg from producer to Kafka

2) Ack from Kafka with details

3) Producer maintains window stats

4) Consumer reads msg

5) Consumer validates window stats at end of interval

1

2

3

4

5Store

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Data Duplication: How to minimize at consumerClient: Producer

Client: Consumer BClient: Consumer A

Kafka Broker

Topic: app_events

If possible, lookup last

processed offset in destination at

startup

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Monitoring

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Monitoring and Operations: JMX Metrics

Producer JMX Consumer JMX

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Questions?

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Jayesh Thakrarjthakrar@conversantmedia.com