VoxxedDays Cluj - Zero-downtime deployment with Kubernetes, Spring Boot and Flyway

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Kubernetes allows a lot. After discovering its features, it’s easy to think it can magically transform your application deployment process into a painless no-event. For Hello World applications, that is the case. Unfortunately, not many of us do deploy such applications day-to-day. You need to think about application backward compatibility, possible rollback, database schema migration, etc. I believe the later is one of the biggest pain point. In this talk, I’ll demo how to update a Spring Boot app deployed on a Kubernetes cluster with a non-trivial database schema migration with the help of Flyway, while keeping the service up during the entire update process.

Software

@nicolas_frankel
With Kubernetes, Flyway and Spring Boot
Zero-downtime Deployment

@nicolas_frankel
• Developer, team lead, architect,
whatever it takes
• Recently Developer Advocate
• Interested in DevOps
• Curious about Kubernetes
Me, myself and I

@nicolas_frankel
Hazelcast
HAZELCAST IMDG is an operational,
in-memory, distributed computing
platform that manages data using
in-memory storage, and performs
parallel execution for breakthrough
application speed and scale.
HAZELCAST JET is the ultra fast,
application embeddable, 3rd
generation stream processing
engine for low latency batch
and stream processing.

@nicolas_frankel
1. Engineers want it
• It’s ”cool”
2. Users want it
• It’s expected
3. Business want it
• It’s revenue
Why zero-downtime?

@nicolas_frankel
Blue-Green deployment variant

@nicolas_frankel
• Routing latency
• “Lagging” users
• Database management
• Migration
• Or compatibility
Issues with Blue-Green deployment

@nicolas_frankel
1.0 1.01.02.0 2.0 2.0
Rolling updates principle

@nicolas_frankel
Kubernetes to the rescue!
apiVersion: apps/v1
kind: Deployment
spec:
replicas: 3
strategy:
rollingUpdate:
maxSurge: 0
maxUnavailable: 1
type: RollingUpdate

@nicolas_frankel
• maxSurge:
• Number of extra pods
• maxUnavalaible:
• Number of down pods allowed
Rolling update options

@nicolas_frankel
• Kubernetes need to know the
application’s state
• To mark the pod ready
• Spring Boot offers the actuator
• With the /health endpoint
Apps don’t start instantly

@nicolas_frankel
spec:
containers:
- name: spring-boot-app
image: zerodowntime:1.0
readinessProbe:
httpGet:
path: /actuator/health
port: 8080
Kubernetes-Spring Boot integration
magic

@nicolas_frankel
• Let’s pat ourselves on the shoulder
• Hello world apps FTW!
Most talks stop there...

@nicolas_frankel
It’s the database, stupid!
1.0 1.01.02.0
1.02.0

@nicolas_frankel
Consecutive app versions must be
schema-compatible
Version compatibility

@nicolas_frankel
It’s still the database...
1.0 1.01.02.0
1.0
2.0 2.0
2.0

@nicolas_frankel
Break down a change into a serie of
side-by-side compatible changes
Making changes for-/backward
compatible

@nicolas_frankel
Separating a single table into 2 tables
with a Primary Key - Foreign Key
relation
A sample use-case

@nicolas_frankel
Database schema migration

@nicolas_frankel
Steps decomposition
1. Create ADDRESS table
• App use the PERSON table data as previously
• Trigger copy data to ADDRESS table
2. The ADDRESS becomes the “source of truth”
• App now uses the ADDRESS table data
• Trigger copy data to the PERSON table
3. Stop copying the data
4. (Remove extra columns in the PERSON table)

@nicolas_frankel
• Disable automatic DDL
creation/update
• Delegate to Flyway
• (Or Liquibase)
Spring Boot specifics

@nicolas_frankel
“Version control for your database.
Robust schema evolution across all
your environments.”
Flyway

@nicolas_frankel
• Based on migrations
• A migration is a SQL script
• Each migration is versioned
• Flyway records migrations in a
dedicated table
Flyway 101

@nicolas_frankel
• Migrations are stored in the
db/migrations folder
• Automatically applied at
application startup
Spring Boot Flyway integration

@nicolas_frankel
• Spring and JPA listeners don’t go
along well
• Implementation dependent
(Hibernate)
JPA specifics

@nicolas_frankel
1. Zero-downtime is possible
2. It’s not free
3. It has an impact on how to
code/design/plan
Takeaways

@nicolas_frankel
• https://blog.frankel.ch/
• @nicolas_frankel
• https://git.io/JeGJd
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As “the Cloud” becomes more and more widespread, now is a good time to assess how you can containerize your Java application. I assume you’re able to write a a Dockerfile around the generated JAR. However, each time the application’s code will change, the whole image will need to be rebuilt. If you’re deploying to a local Kubernetes cluster environment, this increases that much the length of the feedback loop. In this demo-based talk, I’ll present different ways to get your Java app in a container: Dockerfile, Jib, and Cloud Native Buildpacks. We will also have a look at what kind of Docker image they generate, how they layer the images, whether those images are compatible with skaffold, etc.

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JUG Tirana - Introduction to data streaming

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This talk will be about the reasons behind the new stream processing model, how it compare to the old batch model, what are their pros and cons, and a list of existing technologies implementing stream processing with their most prominent characteristics. It will contain details of one possible use-case of data streaming that is not possible with batches: display in (near) real-time all trains in Switzerland and their position on a map, beginning with an overview of all the requirements and the design. Finally, using an OpenData endpoint and the Hazelcast platform,showing a working demo implementation of it.

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The document discusses stream processing and provides an overview of Hazelcast Jet. It begins with explaining why streaming is useful and describes different streaming approaches like event-driven programming. It then provides details on Hazelcast Jet, including its concepts of pipelines and jobs. The document also discusses open data standards like GTFS and demonstrates a sample streaming pipeline that enriches public transportation data from open APIs.

London Java Community - An Experiment in Continuous Deployment of JVM applica...

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A couple of years ago, continuous integration in the JVM ecosystem meant Jenkins. Since that time, a lot of other tools have been made available. But new tools don’t mean new features, just new ways. Besides that, what about continuous deployment? There’s no tool that allows deploying new versions of a JVM-based application without downtime. The only way to achieve zero downtime is to have multiple nodes deployed on a platform, and let that platform achieve that e.g. Kubernetes. And yet, achieving true continuous deployment of bytecode on one single JVM instance is possible if one changes one’s way of looking at things. What if the compilation could be seen as changes? What if those changes could be stored in a data store, and a listener on this data store could stream those changes to the running production JVM via the Attach API? In this talk, we'll demo exactly that using Hazelcast and Hazelcast Jet - but it’s possible to re-use the principles that will be shown using other streaming technologies.

OSCONF - Your own Kubernetes controller: not only in Go

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This document discusses creating Kubernetes operators and controllers using different programming languages besides Go. It suggests that a Java-based controller is possible using the GraalVM, which allows creating native executables from Java bytecode. Key points covered include what controllers and operators are, that no specific technology stack is required, and that the JVM could be a good option for controller development with GraalVM's support for polyglot programming and creating native applications.

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Nicolas Fränkel proposes an experimental use case of continuous deployment of bytecode by dynamically loading Java agents into a running JVM using the Attach API. The agent would use the Instrumentation API to modify bytecode of running applications by redefining classes in order to deploy changes made by a continuous integration pipeline without restarting the JVM. Current limitations include a lack of tagging and an inability to add/remove fields or methods via redefinition. Improvements could include streaming changes directly from the CI pipeline and adding metadata to classes from the Git tag.

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JUG SF - Introduction to data streaming

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While “software is eating the world”, those who are able to best manage the huge mass of data will emerge out on the top. The batch processing model has been faithfully serving us for decades. However, it might have reached the end of its usefulness for all but some very specific use-cases. As the pace of businesses increases, most of the time, decision makers prefer slightly wrong data sooner, than 100% accurate data later. Stream processing - or data streaming - exactly matches this usage: instead of managing the entire bulk of data, manage pieces of them as soon as they become available. In this talk, I’ll define the context in which the old batch processing model was born, the reasons that are behind the new stream processing one, how they compare, what are their pros and cons, and a list of existing technologies implementing the latter with their most prominent characteristics. I’ll conclude by describing in detail one possible use-case of data streaming that is not possible with batches: display in (near) real-time all trains in Switzerland and their position on a map. I’ll go through the all the requirements and the design. Finally, using an OpenData endpoint and the Hazelcast platform, I’ll try to impress attendees with a working demo implementation of it.

SCALE - Stream processing and Open Data, a match made in Heaven

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While “software is eating the world”, those who are able to best manage the huge mass of data will emerge out on the top. Some countries in Europe understand the potential there's in existing data that sits behind closed fences, and passed laws to make this data available to everyone. On the other hand, the batch processing model gets more and more obsolete: users want the information as soon as possible. While there’s a trade-off between correctness of data, and its speed of delivery, most business decisions do not rely on 100% correct data. In this talk, I’ll explain how one can leverage the data related to public transportation in Switzerland, to display them in (near) real-time on a map.

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VoxxedDays Cluj - Zero-downtime deployment with Kubernetes, Spring Boot and Flyway

1. @nicolas_frankel With Kubernetes, Flyway and Spring Boot Zero-downtime Deployment

2. @nicolas_frankel • Developer, team lead, architect, whatever it takes • Recently Developer Advocate • Interested in DevOps • Curious about Kubernetes Me, myself and I

3. @nicolas_frankel Hazelcast HAZELCAST IMDG is an operational, in-memory, distributed computing platform that manages data using in-memory storage, and performs parallel execution for breakthrough application speed and scale. HAZELCAST JET is the ultra fast, application embeddable, 3rd generation stream processing engine for low latency batch and stream processing.

4. @nicolas_frankel 1. Engineers want it • It’s ”cool” 2. Users want it • It’s expected 3. Business want it • It’s revenue Why zero-downtime?

5. @nicolas_frankel Blue-Green deployment

6. @nicolas_frankel Blue-Green deployment

7. @nicolas_frankel Blue-Green deployment variant

8. @nicolas_frankel • Routing latency • “Lagging” users • Database management • Migration • Or compatibility Issues with Blue-Green deployment

9. @nicolas_frankel 1.0 1.01.02.0 2.0 2.0 Rolling updates principle

10. @nicolas_frankel Kubernetes to the rescue! apiVersion: apps/v1 kind: Deployment spec: replicas: 3 strategy: rollingUpdate: maxSurge: 0 maxUnavailable: 1 type: RollingUpdate

11. @nicolas_frankel • maxSurge: • Number of extra pods • maxUnavalaible: • Number of down pods allowed Rolling update options

12. @nicolas_frankel

13. @nicolas_frankel

14. @nicolas_frankel

15. @nicolas_frankel • Kubernetes need to know the application’s state • To mark the pod ready • Spring Boot offers the actuator • With the /health endpoint Apps don’t start instantly

16. @nicolas_frankel spec: containers: - name: spring-boot-app image: zerodowntime:1.0 readinessProbe: httpGet: path: /actuator/health port: 8080 Kubernetes-Spring Boot integration magic

17. @nicolas_frankel • Let’s pat ourselves on the shoulder • Hello world apps FTW! Most talks stop there...

18. @nicolas_frankel It’s the database, stupid! 1.0 1.01.02.0 1.02.0

19. @nicolas_frankel Consecutive app versions must be schema-compatible Version compatibility

20. @nicolas_frankel It’s still the database... 1.0 1.01.02.0 1.0 2.0 2.0 2.0

21. @nicolas_frankel Break down a change into a serie of side-by-side compatible changes Making changes for-/backward compatible

22. @nicolas_frankel Separating a single table into 2 tables with a Primary Key - Foreign Key relation A sample use-case

23. @nicolas_frankel Database schema migration

24. @nicolas_frankel Steps decomposition 1. Create ADDRESS table • App use the PERSON table data as previously • Trigger copy data to ADDRESS table 2. The ADDRESS becomes the “source of truth” • App now uses the ADDRESS table data • Trigger copy data to the PERSON table 3. Stop copying the data 4. (Remove extra columns in the PERSON table)

25. @nicolas_frankel • Disable automatic DDL creation/update • Delegate to Flyway • (Or Liquibase) Spring Boot specifics

26. @nicolas_frankel “Version control for your database. Robust schema evolution across all your environments.” Flyway

27. @nicolas_frankel • Based on migrations • A migration is a SQL script • Each migration is versioned • Flyway records migrations in a dedicated table Flyway 101

28. @nicolas_frankel • Migrations are stored in the db/migrations folder • Automatically applied at application startup Spring Boot Flyway integration

29. @nicolas_frankel • Spring and JPA listeners don’t go along well • Implementation dependent (Hibernate) JPA specifics

30. @nicolas_frankel

31. @nicolas_frankel 1. Zero-downtime is possible 2. It’s not free 3. It has an impact on how to code/design/plan Takeaways

32. @nicolas_frankel • https://blog.frankel.ch/ • @nicolas_frankel • https://git.io/JeGJd Thanks!

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