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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 08, 2017 @jricardoferreir

Building Agile and Resilient Schema Transf. using Kafka and ESB's


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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?

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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 Bus Service 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.





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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


Initiator Service

Topic Topic

Topic


Services Design :: Third Iteration


Supplies Collection

Service

Scavenge Service


Initiator Service

Topic

Service Bus

Topic Topic

Service Bus

Topic Topic

Service Bus

Topic Topic

Service Bus

Topic



Services Design :: Fourth Iteration


Supplies Collection

Service

Scavenge 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

}





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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()

http://man7.org/linux/man-pages/man2/sendfile.2.html



• 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 - RainFocus€¦ · Building Agile and Resilient Schema Transf....

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