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Introducing Exactly Once
Semantics in Apache Kafka™
Apurva Mehta, Software Engineer,
Gehrig Kunz, Technical Product Marketing Manager

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Agenda
• Why exactly-once?
• An overview of messaging semantics
• Why are duplicates introduced?
• What is exactly-once semantics?
• Exactly-once semantics in Kafka: Is it Practical?
• Next Steps

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Exactly Once Semantics is a hard problem

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An overview of messaging semantics
• At-most once
• At-least once
• Exactly-once

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Why exactly-once?
• Stream processing is becoming the norm; it’s more natural.
• Apache Kafka is the most popular streaming platform.
• Mission critical applications require stronger guarantees.

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Why exactly-once?
• Stream processing is becoming the norm; it’s more natural.
• Apache Kafka is the most popular streaming platform.
• Mission critical applications require stronger guarantees.
In other words: make stream processing easy,
simple, and reliable enough for everyone.

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Apache Kafka’s existing semantics
At Least Once

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Kafka’s Existing Semantics

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Kafka’s Existing Semantics

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Kafka’s Existing Semantics

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Kafka’s Existing Semantics

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Kafka’s Existing Semantics

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Kafka’s Existing Semantics

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What do we do now???
Kafka’s Existing Semantics

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Kafka’s Existing Semantics: At Least Once

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Kafka’s Existing Semantics: At Least Once

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Kafka’s Existing Semantics: At Least Once

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Why are duplicates introduced?
Various failures must be handled correctly:
• Broker can fail
• Producer-to-Broker RPC can fail
• Producer or Consumer client can fail

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TL;DR – What we have today
• At least once in order delivery per partition.
• Producer retries can introduce duplicates and headaches.

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The age old engineering question
Before we make this work, are we sure we should?

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KafkaCash: A Peer to Peer Lending App
A peer-to-peer lending platform.

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Help Bob reach $1000, send him $10

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KafkaCash, powered by Kafka

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Offset commits

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Reprocessed transfer, eek!

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Lost money! Eek eek!

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How did Kafka add exactly once semantics?

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Exactly-once semantics in Kafka, explained
Apache Kafka’s guarantees are stronger in 3 ways:
• Idempotent producer: Exactly-once, in-order, delivery
per partition.
• Transactions: Atomic writes across partitions.
• Exactly-once stream processing across read-process-
write tasks.

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Part 1/3 : Idempotent Producer
Exactly-once, in-order, delivery per partition

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Idempotent Producer Semantics
A single --successful!-- producer.send will result in
exactly one copy of the message in the log in all
circumstances.

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Producer Configs
• enable.idempotence = true
• max.inflight.requests.per.connection=1
• acks = “all”
• retries > 0 (preferably MAX_INT)

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The idempotent producer

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The idempotent producer

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The idempotent producer

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The idempotent producer

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The idempotent producer

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The idempotent producer

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The idempotent producer

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The idempotent producer

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TL;DR: idempotent producer
• Works transparently -- only one config change.
• Sequence numbers and producer ids are in the log.
• Resilient to broker failures, producer retries, etc.

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Part 2/3 : Transactions
Atomic writes across multiple partitions.

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Transactions semantics
• Atomic writes across multiple partitions.
• All messages in a transaction are made visible together,
or none are.
• Consumers must be configured to skip uncommitted
messages.

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Producer config for transactions
• transactional.id = ‘some string’
• Typically based on the partition identifier in a partitioned,
stateful, app.
• Enables transaction recovery across producer sessions.

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The transaction API
producer.initTransactions();
try {
producer.beginTransaction();
producer.send(record0);
producer.send(record1);
producer.commitTransaction();
} catch (KafkaException e) {
producer.abortTransaction();
}

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Transactions

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1. Initialize the producer
producer.initTransactions();
try {
producer.beginTransaction();
producer.send(record0);
producer.send(record1);
producer.commitTransaction();
} catch (KafkaException e) {
producer.abortTransaction();
}

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Initializing ‘transactions’

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2. Begin transactions and send data
producer.initTransactions();
try {
producer.beginTransaction();
producer.send(record0);
producer.send(record1);
producer.commitTransaction();
} catch (KafkaException e) {
producer.abortTransaction();
}

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Transactional sends

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Transactional sends

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3. Commit transaction
producer.initTransactions();
try {
producer.beginTransaction();
producer.send(record0);
producer.send(record1);
producer.commitTransaction();
} catch (KafkaException e) {
producer.abortTransaction();
}

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Commit

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Commit

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Commit

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Success!

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Consumer configs
• isolation.level:
• “read_committed”, or
• “read_uncommitted”

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What do you get with isolation levels?
• read_committed: consumers read to the point where there
are no open transactions.
• read_uncommitted: will read everything.
• Messages read in offset order.

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TL;DR: Transactions
• Atomic, multi-partition, writes.
• Use the new producer APIs for transactions.
• Consumers can filter out uncommitted or aborted
transactional messages.

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Part 3/3 : Stream Processing
Stream Processing with
Exactly Once Semantics

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Streams config
• processing.mode = “exactly_once”

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End-to-end exactly-once semantics
• The read-process-write operation is atomic.
• Thus streams tasks produce valid answers even when
failures happen.

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Back to KafkaCash

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Exactly Once Semantics in Kafka
Is it practical?

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Performance boost for Apache Kafka 0.11!
• Up to +20% producer throughput
• Up to +50% consumer throughput
• Up to -20% disk utilization
• Details: https://bit.ly/kafka-eos-perf

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Gains due to more efficient message format

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What about the idempotent producer and transactions?
• Transactions: 3-5% overhead for 100ms transactions, 1KB
messages.
• Longer transactions and better batching result in better
performance.
• 20% overhead relative to at-most once delivery without
ordering guarantees.
• Idempotent producer alone has negligible overhead.

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Putting it together
• We talked through an idempotent producer
• How we added transactions with atomic writes
• The impact it has on stream processing

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When is it available?
Available to use in Kafka 0.11, June 2017.

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Where we’ve come
2007
High throughput
messaging broker
2008
Highly available
replicated log 2012
Top Level
Apache Project
2016
Streams API
Connect API
2017
Exactly Once
Semantics

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San Francisco
August 28, 2017
Organized by Confluent

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What’s next for you
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Thank You!