apache kafka - scalable message-processing and more !

BASEL BERN BRUGG DÜSSELDORF FRANKFURT A.M. FREIBURG I.BR. GENF HAMBURG KOPENHAGEN LAUSANNE MÜNCHEN STUTTGART WIEN ZÜRICH

Apache KafkaScalable Message Processing and more!

Guido Schmutz

@gschmutz guidoschmutz.wordpress.com

Guido Schmutz

Working at Trivadis for more than 20 yearsOracle ACE Director for Fusion Middleware and SOAConsultant, Trainer Software Architect for Java, Oracle, SOA andBig Data / Fast DataMember of Trivadis Architecture BoardTechnology Manager @ Trivadis

More than 30 years of software development experience

Contact: [email protected]: http://guidoschmutz.wordpress.comSlideshare: http://www.slideshare.net/gschmutzTwitter: gschmutz

2 8.12.2016 Big Data & Fast Data

Agenda

1. Introduction & Motivation2. Kafka Core

3. Kafka Connect

4. Kafka Streams

5. Kafka and ”Big Data” / ”Fast Data” Ecosystem

6. Confluent Data Platform7. Summary

Apache Kafka - Scalable Message Processing and more!3

Introduction & Motivation


Hadoop ClusterdHadoop Cluster

Big Data Cluster

Traditional Big Data Architecture

BITools

Enterprise Data Warehouse

Billing &Ordering

CRM / Profile

MarketingCampaigns

File Import / SQL Import

SQL

Search

Online&MobileApps

Search

NoSQL

ParallelProcessing

DistributedFilesystem

• MachineLearning• GraphAlgorithms• NaturalLanguageProcessing

Event HubEvent

Hub

Hadoop ClusterdHadoop Cluster

Big Data Cluster

Event Hub – handle event stream data

BITools


Location

Social

Clickstream

Sensor Data

Billing &Ordering

CRM / Profile

MarketingCampaigns

Event Hub

CallCenter

WeatherData

MobileApps

SQL

Search

Online&MobileApps

Search

Data Flow

NoSQL

ParallelProcessing


• MachineLearning• GraphAlgorithms• NaturalLanguageProcessing

Hadoop ClusterdHadoop ClusterBig Data Cluster

Event Hub – taking Velocity into account

Location

Social

Clickstream

Sensor Data

Billing &Ordering

CRM / Profile

MarketingCampaigns

CallCenter

MobileApps

Batch Analytics

Streaming Analytics

Event HubEvent

HubEvent Hub

NoSQL

ParallelProcessing


Stream AnalyticsNoSQL

Reference /Models

SQL

Search

Dashboard

BITools


Search

Online&MobileApps

File Import / SQL Import

WeatherData


Kafka Stream Data Platform

Source:ConfluentApache Kafka - Scalable Message Processing and more!8

Kafka Core


Apache Kafka - Overview

Distributed publish-subscribe messaging system

Designed for processing of real time activity stream data (logs, metrics collections, social media streams, …)

Initially developed at LinkedIn, now part of Apache

Does not use JMS API and standards

Kafka maintains feeds of messages in topics


Apache Kafka - Motivation

LinkedIn’s motivation for Kafka was:

• “A unified platform for handling all the real-time data feeds a large company might have.”

Must haves

• High throughput to support high volume event feeds.

• Support real-time processing of these feeds to create new, derived feeds.

• Support large data backlogs to handle periodic ingestion from offline systems.

• Support low-latency delivery to handle more traditional messaging use cases.

• Guarantee fault-tolerance in the presence of machine failures.


Kafka High Level Architecture

The who is who• Producers write data to brokers.• Consumers read data from

brokers.• All this is distributed.

The data• Data is stored in topics.• Topics are split into partitions,

which are replicated.

Kafka Cluster

Consumer Consumer Consumer

Producer Producer Producer

Broker 1 Broker 2 Broker 3

ZookeeperEnsemble


Apache Kafka - Architecture

Kafka Broker

Movement Processor

MovementTopic

Engine-MetricsTopic

1 2 3 4 5 6

EngineProcessor1 2 3 4 5 6

Truck



Kafka Broker

Movement Processor

MovementTopic

Engine-MetricsTopic

1 2 3 4 5 6

EngineProcessor

Partition0

1 2 3 4 5 6Partition0

1 2 3 4 5 6Partition1 Movement

ProcessorTruck


ApacheKafka

Kafka Broker 1

Movement Processor

Truck

MovementTopicP0

Movement Processor

1 2 3 4 5

P2 1 2 3 4 5

Kafka Broker 2MovementTopic

P2 1 2 3 4 5

P1 1 2 3 4 5

Kafka Broker 3MovementTopic

P0 1 2 3 4 5

P1 1 2 3 4 5

Movement Processor



• Write Ahead Log / Commit Log

• Producers always append to tail

• think append to file

Kafka Broker

MovementTopic

1 2 3 4 5

Truck

6 6


Durability Guarantees

Producer can configure acknowledgements

Value Impact Durability0 • Producerdoesn’twaitforleader weak

1(default) • Producerwaitsforleader• Leadersends ack whenmessagewrittentolog• Nowaitforfollowers

medium

all • Producerwaitsforleader• Leadersendsack when allIn-SyncReplicahave

acknowledged

strong


Apache Kafka - Partition offsets

Offset: messages in the partitions are each assigned a unique (per partition) and sequential id called the offset

• Consumers track their pointers via (offset, partition, topic) tuples

ConsumerGroupA ConsumerGroupB


Source:ApacheKafka

Data Retention – 3 options

1. Never

2. Time based (TTL) log.retention.{ms | minutes | hours}

3. Size based log.retention.bytes

4. Log compaction based (entries with same key are removed)kafka-topics.sh --zookeeper localhost:2181 \

--create --topic customers \--replication-factor 1 --partitions 1 \--config cleanup.policy=compact


Apache Kafka – Some numbers

Kafka at LinkedIn => over 1800+ broker machines / 79K+ Topics

Kafka Performance at our own infrastructure => 6 brokers (VM) / 1 cluster

• 445’622 messages/second• 31 MB / second • 3.0405 ms average latency between producer / consumer

1.3Trillionmessagesperday

330Terabytesin/day

1.2Petabytesout/day

Peakloadforasinglecluster2millionmessages/sec4.7Gigabits/secinbound15Gigabits/secoutbound

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

https://engineering.linkedin.com/kafka/running-kafka-scale


Kafka Topics

Creating a topic• Command line interface

• Using AdminUtils.createTopic method

• Auto-create via auto.create.topics.enable = true

Modifying a topichttps://kafka.apache.org/documentation.html#basic_ops_modify_topic

Deleting a topic

• Command Line interface

$ kafka-topics.sh –zookeeper zk1:2181 --create \--topic my.topic –-partitions 3 \–-replication-factor 2 --config x=y


Inspecting the current state of a topic

Use the --describe option

• Leader: brokerID of the currently elected leader broker

• Replica ID’s = broker ID’s

• ISR = “in-sync replica”, replicas that are in sync with the leader. In this example:

• Broker 0 is leader for partition 1.

• Broker 1 is leader for partitions 0 and 2.

• All replicas are in-sync with their respective leader partitions.

$ kafka-topics.sh –zookeeper zk1:2181 –-describe --topic my.topicTopic:zerg2.hydra PartitionCount:3 ReplicationFactor:2 Configs:Topic: my.topic Partition: 0 Leader: 1 Replicas: 1,0 Isr: 1,0Topic: my.topic Partition: 1 Leader: 0 Replicas: 0,1 Isr: 0,1Topic: my.topic Partition: 2 Leader: 1 Replicas: 1,0 Isr: 1,0


Kafka Connect


Kafka Connect Architecture


Source:Confluent

Kafka Connector Hub – Certified Connectors

Source:http://www.confluent.io/product/connectors


Kafka Connector Hub – Additional Connectors

Source:http://www.confluent.io/product/connectors


Kafka Streams


Kafka Streams

• Designed as a simple and lightweight library in Apache Kafka

• no external dependencies on systems other than Apache Kafka

• Leverages Kafka as its internal messaging layer

• agnostic to resource management and configuration tools

• Supports fault-tolerant local state

• Event-at-a-time processing (not microbatch) with millisecond latency

• Windowing with out-of-order data using a Google DataFlow-like model


Kafka Streams - Architecture


topology defines the stream processing computational logic for your application

topology is a graph of stream processors (nodes) that are connected by streams (edges)

source processor is a stream processor that does not have any upstream processors

sink processor is a special type of stream processor that does not have down-stream processors.

Source:Confluent

Kafka Streams - Processor Topology


topology defines the stream processing computational logic for your application

topology is a graph of stream processors(nodes) that are connected by streams (edges)

source processor is a stream processor that does not have any upstream processors. Consumes one or Kafka topics.

sink processor is a special type of stream processor that does not have down-stream processors. Produces to a single Kafka topic.

Source:Confluent

Kafka and ”Big Data” / ”Fast Data” Ecosystem


Kafka and the Big Data / Fast Data ecosystem

Kafka integrates with many popular products / frameworks

• Apache Spark Streaming

• Apache Flink

• Apache Storm

• Apache NiFi

• Streamsets

• Apache Flume

• Oracle Stream Analytics

• Oracle Service Bus

• Oracle GoldenGate

• Spring Integration Kafka Support

• …Stormbuilt-inKafkaSpouttoconsumeeventsfromKafka


Confluent Platform


Confluent Data Platform 3.1


Source:Confluent

Summary


WeatherData

SQL ImportHadoop ClusterdHadoop Cluster

Hadoop Cluster

Location

Social

Clickstream

Sensor Data

Billing &Ordering

CRM / Profile

MarketingCampaigns

CallCenter

MobileApps

Batch Analytics

Streaming Analytics

Event HubEvent

HubEvent Hub

NoSQL

ParallelProcessing


Stream AnalyticsNoSQL

Reference /Models

SQL

Search

Dashboard

BITools


Search

Online&MobileApps

Customer Event Hub – mapping of technologies


Summary

• Kafka can scale to millions of messages per second, and more

• Easy to start with for a PoC

• A bit more to invest to setup production environment

• Monitoring is key

• Vibrant community and ecosystem

• Fast pace technology

• Confluent provides Kafka Distribution


Guido SchmutzTechnology Manager

[email protected]


@gschmutz guidoschmutz.wordpress.com