developing real-time data pipelines with apache kafka

SPRINGONE2GXWASHINGTON, DC

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Developing Real-Time Data

Pipelines with Apache Kafka

Joe Stein@allthingshadoop

Unless otherwise indicated, these slides are © 2013-2015 Pivotal Software, Inc. and licensed under aCreative Commons Attribution-NonCommercial license: http://creativecommons.org/licenses/by-nc/3.0/

CEO of Elodina http://www.elodina.net/ a big data as a service platform built on top open source software. The Elodina platform enables customers to analyze data streams and programmatically react to the results in real-time. We solve today’s data analytics needs by providing the tools and support necessary to utilize open source technologies. As users, contributors and committers, Elodina also provides support for frameworks that run on Mesos including Apache Kafka, Exhibitor (Zookeeper), Apache Storm, Apache Cassandra and a whole lot more!

Apache Kafka Committer & PMC Member

LinkedIn: http://linkedin.com/in/charmalloc Twitter : @allthingshadoop

whoami

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Contents• Introduction To Kafka

•Overview•Topics, Partitions &

Segments•Data Durability•Replication•Producers•Consumers•Performance•Integration•Quick Start•Operations

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• Designs• Distributed RPC

o Requesto Processo Response

• Storage & Analyticso Streamo Transformo Analyzeo Storeo Search

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Apache Kafka

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Apache KafkaApache Kafka was first open sourced by LinkedIn in 2011Papers

● Building a Replicated Logging System with Apache Kafka http://www.vldb.org/pvldb/vol8/p1654-wang.pdf

● Kafka: A Distributed Messaging System for Log Processing http://research.microsoft.com/en-us/um/people/srikanth/netdb11/netdb11papers/netdb11-final12.pdf

● Building LinkedIn’s Real-time Activity Data Pipeline http://sites.computer.org/debull/A12june/pipeline.pdf

● The Log: What Every Software Engineer Should Know About Real-time Data's Unifying Abstraction http://engineering.linkedin.com/distributed-systems/log-what-every-software-engineer-should-know-about-real-time-datas-unifying

http://kafka.apache.org/

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How Big Data Usually Starts

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More Big Data!

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Ah!

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eesh

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Kafka de-couples data pipelines

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Distributed Replicated LogRead and writeIn real timeAs much as you wantAs fast as your network can go

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Topics and Partitions

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Log Segments

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Distributed Replicated Log

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Data Durability

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Replication

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Producers

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Consumers

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Consumer Failover

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Producer Performance

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https://engineering.linkedin.com/kafka/benchmarking-apache-kafka-2-million-writes-second-three-cheap-machines

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Consumer Performance

http://kafka.apache.org/documentation.html#maximizingefficiency

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Client LibrariesCommunity Clients https://cwiki.apache.org/confluence/display/KAFKA/Clients

● Go (aka golang) Pure Go implementation with full protocol support. Consumer and Producer implementations included, GZIP and Snappy compression supported.

● Python - Pure Python implementation with full protocol support. Consumer and Producer implementations included, GZIP and Snappy compression supported.

● C - High performance C library with full protocol support● Ruby - Pure Ruby, Consumer and Producer implementations included, GZIP and Snappy

compression supported. Ruby 1.9.3 and up (CI runs MRI 2.● Clojure - Clojure DSL for the Kafka API● JavaScript (NodeJS) - NodeJS client in a pure JavaScript implementation

Wire Protocol Developer's Guide https://cwiki.apache.org/confluence/display/KAFKA/A+Guide+To+The+Kafka+Protocol

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Spring IntegrationGood blog about it

https://spring.io/blog/2015/04/15/using-apache-kafka-for-integration-and-data-processing-pipelines-with-spring

Kafka Integration Source https://github.com/spring-projects/spring-integration-kafka

Spring XD sampleshttps://github.com/spring-projects/spring-xd-samples/tree/master/kafka-source

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Quick Starthttps://kafka.apache.org/documentation.html#quickstartDownload the 0.8.2.2 release and un-tar it.> tar -xzf kafka_2.10-0.8.2.2.tgz> cd kafka_2.10-0.8.2.2(use at least four terminal windows)> bin/zookeeper-server-start.sh config/zookeeper.properties> bin/kafka-server-start.sh config/server.properties> bin/kafka-topics.sh --create --zookeeper localhost:2181 --replication-factor 1 --partitions 1 --topic test> bin/kafka-console-producer.sh --broker-list localhost:9092 --topic testThis is a messageThis is another message> bin/kafka-console-consumer.sh --zookeeper localhost:2181 --topic test --from-beginningThis is a messageThis is another message

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Operationalizing Kafkahttps://kafka.apache.org/documentation.html#basic_ops

Basic Kafka Operations

● Adding and removing topics

● Modifying topics

● Graceful shutdown

● Balancing leadership

● Checking consumer position

● Mirroring data between clusters

● Expanding your cluster

● Decommissioning brokers

● Increasing replication factor

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Running on Mesos

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Static Partitioning

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Scaling is manual (even if orchestrated)

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Static failures require manual intervention

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Application Elasticity

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An operating system for your data center

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Everything goes on Mesos

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Kafka on Mesoshttps://github.com/mesos/kafka

● smart broker.id assignment.● preservation of broker placement (through constraints and/or new features).● ability to-do configuration changes.● rolling restarts (for things like configuration changes).● scaling the cluster up and down with automatic, programmatic and manual

options.● smart partition assignment via constraints visa vi roles, resources and

attributes.

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Kafka on MesosScheduler

● Provides the operational automation for a Kafka Cluster.

● Manages the changes to the broker's configuration.

● Exposes a REST API for the CLI to use or any other client.

● Runs on Marathon for high availability.

Executor● The executor interacts with the kafka broker as an intermediary to the scheduler

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REST API & CLI● scheduler - starts the scheduler.● add - adds one more more brokers to the cluster.

● update - changes resources, constraints or broker properties one or more brokers.● remove - take a broker out of the cluster.● start - starts a broker up.● stop - this can either a graceful shutdown or will force kill it (./kafka-mesos.sh help stop)● rebalance - allows you to rebalance a cluster either by selecting the brokers or topics to rebalance. Manual

assignment is still possible using the Apache Kafka project tools. Rebalance can also change the replication factor

on a topic.

● help - ./kafka-mesos.sh help || ./kafka-mesos.sh help {command}

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Launch 20 brokers in seconds

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Kafka 0.9KIP (Kafka Improvement Process)

• https://cwiki.apache.org/confluence/display/KAFKA/Kafka+Improvement+Proposals

New Consumer• https://cwiki.apache.org/confluence/display/KAFKA/Kafka+0.9+Consumer+Rewrite+De

sign

Security • https://cwiki.apache.org/confluence/display/KAFKA/Security• https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=51809888

JIRA• https://issues.apache.org/jira/browse/KAFKA/fixforversion/12328745/?selectedTab=com

.atlassian.jira.jira-projects-plugin:version-issues-panel

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Distributed RPC

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Reference Architecture

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Questions?

http://www.elodina.net

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