Here are slides from my talk on introducing exactly once semantics to Apache Kafka. The talk was given at the Kafka Summit NYC, 8 May 2017. The slides dive into the design of transactions in Apache Kafka.

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Introducing Exactly Once
Semantics in Apache Kafka
Jason Gustafson, Guozhang Wang, Sriram
Subramaniam, and Apurva Mehta

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On deck..
• Kafka’s existing delivery semantics.
• Why did we improve them?
• What’s new?
• How do you use it?
• Summary.

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Apache Kafka’s existing semantics

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Existing Semantics

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Existing Semantics

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Existing Semantics

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Existing Semantics

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Existing Semantics

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Existing Semantics

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Existing Semantics

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Existing Semantics

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Existing Semantics

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

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Why improve?

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Why improve?
• Stream processing is becoming an ever bigger part of the
data landscape.
• Apache Kafka is the heart of the streams platform.
• Strengthening Kafka’s semantics expands the universe of
streaming applications.

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A motivating example..
A peer to peer lending platform which processes micro-loans
between users.

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A Peer to Peer Lender

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The Basic Flow

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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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What’s new?

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What’s new
• Exactly once in order delivery per partition
• Atomic writes across multiple partitions
• Performance considerations

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What’s new, Part 1
Exactly once, in order, delivery per partition

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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
• Sequence numbers and producer ids:
• enable de-dup
• are in the log.
• Hence de-dup works transparently across leader
changes.
• Will not de-dup application-level resends.
• Works transparently – no API changes.

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What’s new, part 2
Multi partition writes.

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Introducing ‘transactions’
producer.initTransactions();
try {
producer.beginTransaction();
producer.send(record0);
producer.send(record1);
producer.sendOffsetsToTxn(…);
producer.commitTransaction();
} catch (ProducerFencedException e) {
producer.close();
} catch (KafkaException e) {
producer.abortTransaction();
}

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Introducing ‘transactions’

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Initializing ‘transactions’

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Transactional sends – part 1

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Transactional sends – part 2

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Commit – phase 1

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Commit – phase 2

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Commit – phase 2

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Success!

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Let’s review the APIs
producer.initTransactions();
try {
producer.beginTransaction();
producer.send(record0);
producer.send(record1);
producer.sendOffsetsToTxn(…);
producer.commitTransaction();
} catch (ProducerFencedException e) {
producer.close();
} catch (KafkaException e) {
producer.abortTransaction();
}

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Let’s review the APIs
producer.initTransactions();
try {
producer.beginTransaction();
producer.send(record0);
producer.send(record1);
producer.sendOffsetsToTxn(…);
producer.commitTransaction();
} catch (ProducerFencedException e) {
producer.close();
} catch (KafkaException e) {
producer.abortTransaction();
}

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Let’s review the APIs
producer.initTransactions();
try {
producer.beginTransaction();
producer.send(record0);
producer.send(record1);
producer.sendOffsetsToTxn(…);
producer.commitTransaction();
} catch (ProducerFencedException e) {
producer.close();
} catch (KafkaException e) {
producer.abortTransaction();
}

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Let’s review the APIs
producer.initTransactions();
try {
producer.beginTransaction();
producer.send(record0);
producer.send(record1);
producer.sendOffsetsToTxn(…);
producer.commitTransaction();
} catch (ProducerFencedException e) {
producer.close();
} catch (KafkaException e) {
producer.abortTransaction();
}

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Let’s review the APIs
producer.initTransactions();
try {
producer.beginTransaction();
producer.send(record0);
producer.send(record1);
producer.sendOffsetsToTxn(…);
producer.commitTransaction();
} catch (ProducerFencedException e) {
producer.close();
} catch (KafkaException e) {
producer.abortTransaction();
}

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Consumer returns only committed messages

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Some notes on consuming transactions
• Two ‘isolation levels’ : read_committed, and
read_uncommitted.
• Messages read in offset order.
• read_committed consumers read to the point where there
are no open transactions.

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TL;DR
• Transaction coordinator and transaction log maintain
transaction state.
• Use the new producer APIs for transactions.
• Consumers can read only committed messages.

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Part 3
Performance!

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What’s new, part 3: Performance boost!
• Up to +20% producer throughput
• Up to +50% consumer throughput
• Up to -20% disk utilization
• Savings start when you batch
• Details: https://bit.ly/kafka-eos-perf

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Too good to be true?
Let’s understand how!

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The old message format

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The new format

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The new format -> new fields

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The new format -> new fields

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The new format -> delta encoding

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A visual comparison with 7 records, 10 bytes each

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TL;DR
• With a batch size of 2, the new format starts saving
space.
• Savings are maximal for large batches of small
messages.
• Hence higher throughput when IO bound.
• Works as soon as you upgrade to the new format.

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Cool!
But how do I use this?

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Producer Configs
• enable.idempotence = true
• max.inflight.requests.per.connection=1
• acks = “all”
• retries > 1 (preferably MAX_INT)
• transactional.id = ‘some unique id’
• enable.idempotence = true

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

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Streams config
• processing.mode = “exactly_once”

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Putting it together
• We understood Kafka’s existing delivery semantics
• Understood why we want to improve them
• Learned how these have been strengthened
• Learned how the new semantics work

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

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Thank You!