Building Agile and Resilient Schema Transformations using Apache Kafka and ESB's

Copyright © 2017, Oracle and/or its affiliates. All rights reserved. |
Building Agile and Resilient Schema
Transformations using Apache Kafka
and ESB's
Transformation-free Data Pipelines by combining the Power
of Apache Kafka and the Flexibility of the ESB's
Ricardo Ferreira
Principal Solutions Architect
Cloud Solution Architects Team (A-Team)
March 21, 2017
@jricardoferreir
Building Agile and Resilient Schema Transf. using Kafka and ESB's

Safe Harbor Statement
The following is intended to outline our general product direction. It is intended for
information purposes only, and may not be incorporated into any contract. It is not a
commitment to deliver any material, code, or functionality, and should not be relied upon
in making purchasing decisions. The development, release, and timing of any features or
functionality described for Oracle’s products remains at the sole discretion of Oracle.
Building Agile and Resilient Schema Transf. using Kafka and ESB's 3

• Ricardo Ferreira
– Principal Solutions Architect, Oracle
• Freaking nerd, proud husband and father
• I have been writing code since 1997
• Currently working in the Oracle A-Team
• Author of couple Kafka-based projects:
 Service Bus Transport for Kafka
 Stream Explorer Adapter for Kafka
@jricardoferreir
Who am I ?

API changes, Transformations and Decoupling
Hands-on Demonstration using the Oracle Cloud
Why Apache Kafka instead of other Options?
1
2
3
Agenda

Wait… did you say ESB's? Aren't they evil?
• Modern Cloud-Native applications are built on top of design principles that
doesn't include ESB's. One of them is the Smart endpoints and Dumb Pipes
Service A
Service BusService B
Service C
Service D
Service E
Service F

Smart Endpoints and Dump Pipes
• Let the endpoints handle the transformation logic required to expose
and/or invoke other endpoints. Pipes must only carry the messages in/out.
Service A Service B
REST or Messaging

Copyright © 2017, Oracle and/or its affiliates. All rights reserved. | Building Agile and Resilient Schema Transf. using Kafka and ESB's 9
Wait… but what if
the API changes?

We might have some options. And along with… trade-offs
Widely accepted solution among the
developer community, but it is not
bullet-proof and creates lots of code
and operational overhead. Jamie
Zawinski's used to say:
- "I'll use versioning. Now you have
2.1.0 problems."
Effective but relies on the assumption
that developers must coordinate to
handle the API changes. This might
work for certain cases, but is reactive
and creates organization overhead.
It creates a more complex architecture
upfront but handle changes almost on-
the-fly and allows services to react with
higher uptime while teams coordinate
to evolve their systems.
Service Versioning Deployment Pipelines Design for Change Principle

Design for Change Principle
• Whenever possible, exchange messages using messaging technologies. This
allows you to decouple the communication while providing a way to filter
and correct message schemas. Use REST for synchronous use cases only.
Phase 1 :: Leveraging messaging in-between microservices
Service A Service B
Messaging using Topics
Benefits of this design:
 Messages will never be lost.
 Reliability while maintenance.
 Possibility of schema handling
 UI-ready using reactive coding
 Scalability using less hardware

• API providers hook up directly while API consumers hook up indirectly. By
using different message channels you can plugin schema transformations
on-the-fly. By default, just perform messages pass-through.
Phase 2 :: Allow schema transformation by foreseen message channels
Service A Service B
Two topics per service

• When the time comes and your service API need to be changed, simply do
the change and allow a third-party service to handle the schema evolution.
This service responsibility is exclusively handle schemas. Ring any bells? 
Phase 3 :: Plugin schema transformation engines on-the-fly
Service A Service B
Two topics per service
Transformation Service
("A.K.A Service C")
This can be implemented in a variety
of ways but using ESB's seems to be a
natural fit. If this is a one time thing,
then maybe Serverless could work.

1
2
3
Agenda

Understanding the "Use Case"

More specifically... this use case is about the Saviors/Negan

Collecting Supplies from the Communities
Supplies
Collection
Scavenge
Lesson
Teaching
• Is it time to collect?
• What do we need?
• Half of their assets?
• Check Inventory
• Check their Demands
• Do scavenging…
• Is 50% of their assets?
• There was disrespect?
• Is Lucille angry?

Services Design :: First Iteration
Supplies
Collection
Service
Scavenge
Service
Lesson
Teaching
Service
Initiator
Service

Services Design :: Second Iteration
Supplies
Collection
Service
Scavenge
Service
Lesson
Teaching
Service
Initiator
Service
Topic Topic
Topic

Services Design :: Third Iteration
Supplies
Collection
Service
Scavenge
Service
Lesson
Teaching
Service
Initiator
Service
Topic
Service Bus
Topic Topic
Service Bus
TopicTopic
Service Bus
Topic Topic
Service Bus
Topic

Services Design :: Fourth Iteration
Supplies
Collection
Service
Scavenge
Service
Lesson
Teaching
Service
Initiator
Service
Topic
Service Bus
Oracle Java Cloud Service Oracle SOA Cloud Service
Topic
Oracle IaaS Cloud Service
Kafka Transport

Enough slides,
show me code

Do you want to win a Negan Souvenir?
http://141.144.29.149:8080/oracle-code-2017/collectSupplies?name=XYZ
{
"timestamp" : "MM/dd/yyyy hh:mm:ss",
"requestorName" : "XYZ",
"outcome" : true
}

1
2
3
Agenda

What is Apache Kafka?
• Simply Put, Kafka is a Distributed Streaming Platform
– Allows ingestion and consumption of streams of records
– Allows streams of records to be persisted with fault tolerance
– Allows streams of records to be processed as they happen
• It is Comprised of Six Main Modules:
– Kafka Cluster
– Producer API
– Consumer API
– Connector API
– Streams API
– REST Proxy *

Kafka Design "Crash Course"
• For implementation simplicity; Kafka does not support the concept of many
destinations, topics are the only abstraction. However, both Queuing and
Pub/Sub scenarios are supported.
• In Kafka, Queuing is just a matter of how consumers are grouped together.
• Each consumer has a property called group
id. When two (or more) consumers has the
same group id value, that means that they
belong to the same group.
• Consumers belonging to the same
group will load balance themselves
to fetch records from the partition.

• The basic abstraction in Kafka is called topics.
• Queue and Pub/Sub scenarios are supported.
• Topics are broken down in multiple partitions.
• Partitions are spread over the Kafka cluster.
• Each partition is an ordered, immutable
sequence of records that is continuously
appended to a structured commit log.
• Committed records has an offset that
uniquely identifies it within the partition.

• Each partition has an ever-growing commit log.
• A log can be simply view as a list of linked files.
• Partition logs always carries the topic name and
the identifier of the partition (i.e.: "topic-0")
• Kafka's commit log has been designed for
maximum efficiency, therefore:
File journaling (O1 structures) may be faster than RAM
There is no JVM caching. Page Caching is used instead
Zero-copy transfer: CPU offloading using sendfile()

• As mentioned before, Kafka supports fault tolerance. That is achieved with
the concept of replication. Replication happens in a per partition basis.
• Partition backups are spread over
the cluster based on the number
defined in the replication factor.
• When replication is in place, one
broker is elected to be the leader
of a given partition. Only leaders
can read/write records. The rest
of the brokers (hosting replicas)
are called followers or ISR's.

• Any Kafka deployment must include a Zookeeper setup. That is mandatory.
Therefore, it is important to understand what role Zookeeper plays in Kafka.
• Zookeeper is used to store lots of metadata:
Controllers election. Keeps track of leaders and followers.
Cluster details. Which brokers are alive? Dead? Got stuck?
Topic configuration. Which ones, partitions, replicas, etc.
Quotas. How much each client is allowed to read/write?
ACLs. Who is allowed to read and/or write to which topic?
Old 0.8 Consumers. Offset storage and rewind operations.
Producer
Producer
Producer
Topic
Consumer
Consumer
Consumer
Kafka Cluster
Leaders Membership Metadata Quotas
Zookeeper Cluster

Building Agile and Resilient Schema Transformations using Apache Kafka and ESB's

Building Agile and Resilient Schema Transformations using Apache Kafka and ESB's

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Similar to Building Agile and Resilient Schema Transformations using Apache Kafka and ESB's

Similar to Building Agile and Resilient Schema Transformations using Apache Kafka and ESB's (20)

Recently uploaded

Recently uploaded (18)

Building Agile and Resilient Schema Transformations using Apache Kafka and ESB's