My talk at Strata Data Conference, London, May 2017. https://conferences.oreilly.com/strata/strata-eu/public/schedule/detail/57619 Abstract: Modern businesses have data at their core, but this data is changing continuously. How can you harness this torrent of information in real time? The answer: stream processing. The core platform for streaming data is Apache Kafka, and thousands of companies are using Kafka to transform and reshape their industries, including Netflix, Uber, PayPal, Airbnb, Goldman Sachs, Cisco, and Oracle. Unfortunately, today’s common architectures for real-time data processing at scale suffer from complexity: to succeed, many technologies need to be stitched and operated together, and each individual technology is often complex by itself. This has led to a strong discrepancy between how we engineers would like to work and how we actually end up working in practice. Michael Noll explains how Apache Kafka helps you radically simplify your data processing architectures by building normal applications to serve your real-time processing needs rather than building clusters or similar special-purpose infrastructure—while still benefiting from properties typically associated exclusively with cluster technologies, like high scalability, distributed computing, and fault tolerance. Michael also covers Kafka’s Streams API, its abstractions for streams and tables, and its recently introduced interactive queries functionality. Along the way, Michael shares common use cases that demonstrate that stream processing in practice often requires database-like functionality and how Kafka allows you to bridge the worlds of streams and databases when implementing your own core business applications (for example, in the form of event-driven, containerized microservices). As you’ll see, Kafka makes such architectures equally viable for small-, medium-, and large-scale use cases.

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Rethinking Stream Processing
with Apache Kafka:
Applications vs. Clusters,
Streams vs. Databases
Michael G. Noll
Confluent
Strata Data Conference, London, May 2017

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0.11* Exactly-once
semantics
0.10 Data processing (Streams API)
0.9 Data integration (Connect API)
Intra-cluster
replication
0.8
2012 2014 2015 2016 2017
Cluster mirroring0.7
2013
Apache Kafka: birthed as a messaging system, now a streaming platform

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,

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,

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(Does NOT run inside
the Kafka brokers!)

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(Does NOT run inside
the Kafka brokers!)

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http://docs.confluent.io/current/cp-docker-images/docs/tutorials/kafka-streams-examples.html

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Before

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Before
With Kafka’s
Streams API

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KStream<Integer, Integer> input =
builder.stream("numbers-topic");
// Stateless computation
KStream<Integer, Integer> doubled =
input.mapValues(v -> v * 2);
// Stateful computation
KTable<Integer, Integer> sumOfOdds = input
.filter((k,v) -> v % 2 != 0)
.selectKey((k, v) -> 1)
.groupByKey()
.reduce((v1, v2) -> v1 + v2, "sum-of-odds");
class PrintToConsoleProcessor
implements Processor<K, V> {
@Override
public void init(ProcessorContext context) {}
@Override
void process(K key, V value) {
System.out.println("Got value " + value);
}
@Override
void punctuate(long timestamp) {}
@Override
void close() {}
}

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Linux Windows

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http://www.confluent.io/blog/introducing-kafka-streams-stream-processing-made-simple
https://kafka.apache.org/documentation/streams#streams_duality

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…and more…

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…and more…

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2016 2017
First release of Kafka’s
Streams API (0.10.0.0)
today
Kafka Streams API in the wild
Kafka 0.10.2.1
In production at LINE Corp., Japan
220+ million active users, processing millions of msg/s
“Applying Kafka Streams for internal message delivery pipeline”
https://engineering.linecorp.com/en/blog/detail/80

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…and more…

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53. 53*Available in Apache Kafka 0.11 (June 2017)

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$ curl -sXGET http://localhost:7070/kafka-music/charts/top-five
[
{
"artist": "Subhumans",
"album": "Live In A Dive",
"name": "All Gone Dead",
"plays": 126
},
{
"artist": "Wheres The Pope?",
"album": "PSI",
"name": "Fear Of God",
"plays": 115
},
...
]

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…and more…

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https://kafka.apache.org/documentation/streams
https://www.confluent.io/downloads/
http://docs.confluent.io/current/streams/

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Kafka Summit San Francisco
August 28, 2017
www.kafka-summit.org
Discount code: kafcom17
Use the Apache Kafka community discount code to get $50 off
Presented by Questions? We’re at booth #317 in the Exhibition Hall.