Event Driven Architectures with Apache Kafka

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Matt Masuda

An introduction to event driven architectures for microservice-based applications.

Software

Event Driven
Architectures with
Apache Kafka
MATT MASUDA
MMASUDA@GMAIL.COM
@MATTMASUDA

Agenda
OVERVIEW OF WEB-
BASED
ARCHITECTURES
EVENT DRIVEN
OVERVIEW
ADVANTAGES &
DISADVANTAGES
BRIEF KAFKA
OVERVIEW
EVENT DRIVEN WITH
KAFKA
DEMO

Web service
issues
• The service API is a form of coupling
• Service calls are synchronous
• All downstream calls must complete before your
operation can finish
• Load balancing and service location

Event Driven
Architectures
• Leverages asynchronous messaging platforms
• Maintains the micro in microservice
• Each service produces and/or consumes events
• Events are the contracts between services
• Asynchronous operation is tolerant of temporary service
disruptions

Hard things
about event
driven
• Transactions are right out
• Getting your result out means polling or notification
through other means (Websockets, SSE, SignalR)
• Events are your contracts => design is important
• Messaging APIs can be less mature than web frameworks

Kafka Basics
• It is a distributed messaging platform.
• It lets you publish and subscribe to streams of records. In
this respect it is similar to a message queue or enterprise
messaging system.
• It lets you store streams of records in a fault-tolerant way.
• It lets you process streams of records as they occur.

Kafka Concepts
• Kafka is run as a cluster on one or more servers.
• The Kafka cluster stores streams of records in categories
called topics.
• Each record consists of a key, a value, and a timestamp.
• Producers publish streams of records to one or more
topics.
• Topics can be divided into partitions for scalability.
• Consumers subscribe to one or more topics and process
the streams of records produced to them.
• Consumers have control over their position in a topic.
• Consumers can be grouped for scalability.

Event driven
with Kafka
Solves service location
Solves load balancing
Pub/sub model makes it easy to add new services to existing
event streams
Durable storage – you can keep a record of system behavior
and trends
Read positioning means you can reprocess old events
Requires Kafka

Resources
Confluent blog: Journey to Event Driven
Saga pattern -
http://microservices.io/patterns/data/saga.html
Cloud Native Patterns (Manning)
Microservices Patterns (Manning)
Designing Data-Intensive Applications (O’Reilly)
Apache Kafka web site - https://kafka.apache.org/intro

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