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Building Stream
Processing Applications
with Apache® Kafka’sTM
Exactly-Once Processing
Guarantees
Matthias J. Sax | Software Engineer
matthias@confluent.io
@MatthiasJSax

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Apache Kafka
• A distributed Streaming Platform
Consumers
Producers
Connectors
Processing

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Confluent
• Founded by the original creators of Apache Kafka
• Headquarters based in Palo Alto, CA
KSQL: Streaming SQL for Apache Kafka
Developer Preview (https://github.com/confluentinc/ksql)

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How to Build Applications with Apache Kafka
• Streams API
• Client library (it’s actually much more, but you use it like one)
• DIY using Consumer/Producer API
<dependency>
<groupId>org.apache.kafka</groupId>
<artifactId>kafka-streams</artifactId>
<version>0.11.0.1</version>
</dependency>
<dependency>
<groupId>org.apache.kafka</groupId>
<artifactId>kafka-clients</artifactId>
<version>0.11.0.1</version>
</dependency>

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Apache Kafka is a Streaming Platform
Does NOT run inside
the Kafka brokers!

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Deploy as you wish

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Streams API
The easiest way to use exactly-once semantics!
• Easy to use and powerful DSL plus low level Processor API
• Filter, aggregations, windows, joins, tables, punctuations, …
• Rich time semantics (event time, ingestion time, processing time)
• Elastic, scalable, fault-tolerant (including state)
• S, M, L, XL, … use cases
• No need to change any code to use exactly-once!
• Config parameter processing.mode = “exactly_once”

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Apache Kafka’s Exactly-once Guarantees
• Avoids duplicates on writes
• Frees application code from de-duplication
• Simplifies application development
• Enables new use cases with strong consistency guarantees
• Stock market
• Financial industry
• Billing
• Etc…

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Core Concepts in Streams API

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Topics, Streams, and Tables

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Processing Streams
KStreamBuilder builder = new KStreamBuilder();
KStream<Long,String> inputStream =
builder.stream(“input-topic”);
KStream<Long,String> outputStream =
inputStream.mapValues(
value -> value.toLowerCase());
outputStream.to(“output-topic”);

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Using Tables
KTable<Long,String> inputTable = builder.table(“changelog-topic”);

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Using Tables
KStream<Long,String> enrichedStream =
inputStream.join(inputTable, …);

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Aggregating Streams
KTable<Long,Long> countPerKey =
enrichedStream.groupByKey()
.count();

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End-To-End Application and Exactly-Once
read – process – write
track input offsets – track state updates – write output

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Application Failure Scenarios: tracking offsets
Application
(k,v1) (k,v2)
Duplicate reads results in duplicate writes.
read(k,v1) -> process -> output -> commit offsets
read(k,v2) -> process -> output -> commit offsets
read(k,v2) -> process -> output -> CRASH

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Application Failure Scenarios: state update
Application
(k,v1) (k,v2)
read(k,v1) -> process/state -> output -> commit offsets
read(k,v2) -> process/state -> output -> commit offsets
read(k,v2) -> process/state -> CRASH
Duplicate reads results in corrupted state and thus
wrong results (e.g., over counting).

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Application Failure Scenarios: Error on Write
Producer
Application
Consumer
Application
(k,v1) (k,v2)
Duplicate writes lead to wrong result
and downstream duplicate reads.
write(k,v1)
write(k,v2)
read(k,v1)
read(k,v2)
read(k,v2)
(k,v2)

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Error Propagation
Application Application Application

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Exactly-Once in Kafka Streams API
Kafka’s Streams API provides
Exactly-Once Processing Guarantees,
by an atomic read-process-write pattern.
This allows for deep processing pipelines with
exactly-one guarantees.

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Exactly-Once in Apache Kafka Streams API

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Exactly-Once in Kafka Streams API (since v0.11.0)
• Builds on top of KafkaProducer and KafkaConsumer
• In v0.11.0, KafkaProducer adds:
• Idempotent writes
• Transactional API
• Includes offset commits in a producer transaction
• No offset commits via KafkaConsumer
• In v0.11.0, KafkaConsumer adds:
• read committed mode (vs. read uncommited)

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How to use exactly-once capabilities:
• Streams API (the easiest way to use exactly-once semantics)
• Config parameter processing.mode = “exactly_once”
• Idempotent Producer
• Config parameter enable.idempotence = true
• Transactional Producer
• Config parameter transactional.id = “my-unique-tid”
• And Transactional API (hard to use correctly – even if it look simple on the surface)
• Transactional Consumer
• Config parameter isolation.level = “read_committed”
(default: “read_uncommitted”)

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Transactional API
producer.initTransactions();
try {
producer.beginTransaction();
producer.send(message1);
producer.send(message2);
producer.sendOffsetsToTxn(…);
producer.commitTransaction();
} catch (ProducerFencedException e) {
producer.close();
} catch (KafkaException e) {
producer.abortTransaction();
}

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Summary
• Streams API is the easiest way to build applications with Apache Kafka
• It’s a library that enriches your application
• No compute cluster
• It provides end-to-end exactly-once processing guarantees
• Kafka’s exactly-once guarantees provide strong semantics that simplifies your
application code

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Material
• Download Confluent Open Source: https://www.confluent.io/product/confluent-
open-source/
• Check out the docs: https://docs.confluent.io/
• Check our blog:
• Exactly-Once: https://www.confluent.io/blog/exactly-once-semantics-are-possible-heres-how-
apache-kafka-does-it/
• Micro-Service Blog Series: https://www.confluent.io/blog/data-dichotomy-rethinking-the-way-
we-treat-data-and-services/
• Kafka Summit talks:
• Exactly-Once (NY): https://www.confluent.io/kafka-summit-nyc17/resource/#exactly-once-
semantics_slide
• Exactly-Once with Streams API (SF): https://www.confluent.io/kafka-summit-
sf17/resource/#Exactly-once-Stream-Processing-with-Kafka-Streams_slide
• Micro-Services (SF): https://www.confluent.io/kafka-summit-sf17/resource/#building-event-
driven-services-stateful-streams_slide

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Thank You
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