Trivadis TechEvent 2016 Apache Kafka - Scalable Massage Processing and more! by Guido Schmutz

BASEL BERN BRUGG DÜSSELDORF FRANKFURT A.M. FREIBURG I.BR. GENF
HAMBURG KOPENHAGEN LAUSANNE MÜNCHEN STUTTGART WIEN ZÜRICH
Apache Kafka
Scalable Message Processing and more!
Guido Schmutz
@gschmutz guidoschmutz.wordpress.com

Agenda
1. Introduction & Motivation
2. Kafka Core
3. Kafka Connect
4. Kafka Streams
5. Kafka and ”Big Data” / ”Fast Data” Ecosystem
6. Confluent Data Platform
7. Kafka in Architecture
8. Summary
Apache Kafka - Scalable Message Processing and more!3

Introduction & Motivation

A little story of a “real-life” customer situation
Traditional system interact with its clients
and does its work
Implemented using legacy technologies (i.e.
PL/SQL)
New requirement:
• Offer notification service to notify
customer when goods are shipped
• Subscription and inform over different
channels
• Existing technology doesn’t fit
delivery
Logistic
System
Oracle
Mobile Apps
Sensor ship
sort
5
Rich (Web)
Client Apps
D
B
schedule
Logic
(PL/SQL)
delivery

Events are “owned” by traditional
application (as well as the channels
they are transported over)
Implement notification as a new Java-based
application/system
But we need the events ! => so let’s
integrate
delivery
Logistic
System
Oracle
Mobile Apps
Sensor ship
sort
6
Rich (Web)
Client Apps
D
B
schedule
Notification
Logic
(PL/SQL)
Logic
(Java)
delivery
SMS
Email
…

integrate in order to get the information!
Oracle Service Bus was already there
Rule Engine implemented in Java and invoked from
OSB message flow
Notification system informed via queue
Higher Latency introduced (good enough in this
case)
delivery
Logistic
System
Oracle
Oracle
Service Bus
Mobile Apps
Sensor AQship
sort
7
Rich (Web)
Client Apps
D
B
schedule
Filter
Notification
Logic
(PL/SQL)
JMS
Rule Engine
(Java)
Logic
(Java)
delivery
shipdelivery
delivery true SMS
Email
…

Treat events as first-class citizens
Events belong to the “enterprise” and
not an individual system => Catalog of
Events similar to Catalog of
Services/APIs !!
Event (stream) processing can be
introduced and by that latency reduced!
delivery
Logistic
System
Oracle
Oracle
Service Bus
Mobile Apps
Sensor AQship
sort
8
Rich (Web)
Client Apps
D
B
schedule
Filter
Notification
Logic
(PL/SQL)
JMS
Rule Engine
(Java)
Logic
(Java)
delivery
shipdelivery
delivery true SMS
Email
…

Treat Events as Events and share them!
delivery
Logistic
System
Oracle
Oracle
Service Bus
Mobile Apps
Sensor
ship
sort
9
Rich (Web)
Client Apps
D
B
schedule
Filter
Notification
Logic
(PL/SQL)
JMS
Rule Engine
(Java)
Logic
(Java)
delivery
ship
delivery true SMS
Email
…
Event
Bus/Hub
Stream/Event
Processing

Treat Events as Events,share and make use of them!
delivery
Logistic
System
Oracle
Mobile Apps
Sensor
ship
sort
10
Rich (Web)
Client Apps
D
B
schedule
Filter
Notification
Logic
(PL/SQL)
Rule Engine
(Java)
Logic
(Java)
delivery
SMS
Email
…
Event
Bus/Hub
Stream/Event
Processing
notifiableDelivery
notifiableDelivery
delivery

Kafka Stream Data Platform
Source: Confluent

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
Zookeeper
Ensemble

Apache Kafka - Architecture
Kafka Broker
Movement
Processor
Movement Topic
Engine-Metrics Topic
1 2 3 4 5 6
Engine
Processor1 2 3 4 5 6
Truck

Kafka Broker
Movement
Processor
Movement Topic
Engine-Metrics Topic
1 2 3 4 5 6
Engine
Processor
Partition 0
1 2 3 4 5 6
Partition 0
1 2 3 4 5 6
Partition 1 Movement
Processor
Truck

Apache
Kafka
Kafka Broker 1
Movement
Processor
Truck
Movement Topic
P 0
Movement
Processor
1 2 3 4 5
P 2 1 2 3 4 5
Kafka Broker 2
Movement Topic
P 2 1 2 3 4 5
P 1 1 2 3 4 5
Kafka Broker 3
Movement Topic
P 0 1 2 3 4 5
P 1 1 2 3 4 5
Movement
Processor

• Write Ahead Log / Commit Log
• Producers always append to tail
• think append to file
Kafka Broker
Movement Topic
1 2 3 4 5
Truck
6 6

Durability Guarantees
Producer can configure acknowledgements
Value Impact Durability
0 • Producer doesn’t wait for leader weak
1 (default) • Producer waits for leader
• Leader sends ack when message written to log
• No wait for followers
medium
all • Producer waits for leader
• Leader sends ack when all In-Sync Replica have 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
Consumer Group A Consumer Group B
Source: Apache Kafka

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
1.3 Trillion messages per
day
330 Terabytes in/day
1.2 Petabytes out/day
Peak load for a single cluster
2 million messages/sec
4.7 Gigabits/sec inbound
15 Gigabits/sec outbound
http://engineering.linkedin.com/kafka/benchmarking-apache-kafka-2-million-writes-second-three-cheap-machines
https://engineering.linkedin.com/kafka/running-kafka-scale

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
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
• …Storm built-in Kafka Spout to consume events from Kafka

Confluent Platform

Confluent Data Platform 3.0
Source: Confluent

Kafka in Architecture

Hadoop Clusterd
Hadoop Cluster
Hadoop Cluster
Customer Event Hub – taking Velocity into account
Location
Social
Click
stream
Sensor
Data
Billing &
Ordering
CRM /
Profile
Marketing
Campaigns
Call
Center
Mobile
Apps
Batch Analytics
Streaming Analytics
Event
Hub
Event
Hub
Event
Hub
NoSQL
Parallel
Processing
Distributed
Filesystem
Stream Analytics
NoSQL
Reference /
Models
SQL
Search
Dashboard
BI Tools
Enterprise Data
Warehouse
Search
Online & Mobile
Apps
File Import / SQL Import
Weather
Data

Weather
Data
SQL Import
Hadoop Clusterd
Hadoop Cluster
Hadoop Cluster
Location
Social
Click
stream
Sensor
Data
Billing &
Ordering
CRM /
Profile
Marketing
Campaigns
Call
Center
Mobile
Apps
Batch Analytics
Streaming Analytics
Event
Hub
Event
Hub
Event
Hub
NoSQL
Parallel
Processing
Distributed
Filesystem
Stream Analytics
NoSQL
Reference /
Models
SQL
Search
Dashboard
BI Tools
Enterprise Data
Warehouse
Search
Online & Mobile
Apps
Customer Event Hub – mapping of technologies

Summary

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 Schmutz
Technology Manager
guido.schmutz@trivadis.com
@gschmutz guidoschmutz.wordpress.com

Trivadis TechEvent 2016 Apache Kafka - Scalable Massage Processing and more! by Guido Schmutz

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