Zero-Downtime Deployment with Kubernetes, SpringBoot & Flyway

•

3 likes•1,431 views

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
ZERO-DOWNTIME
DEPLOYMENT
With Kubernetes, Flyway and Spring Boot

@nicolas_frankel
Me, myself and I
● Developer Advocate
● Former dev/architect
● DevOps and Cloud curious

@nicolas_frankel
European alternative to the “big”
cloud-computing players
● Privacy-minded
● Great support

@nicolas_frankel
Why zero-downtime?
1. Company’s reputation
2. Users’ expectations
3. Missing revenue

@nicolas_frankel
Blue-Green deployment variant

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

@nicolas_frankel
Rolling updates principle
1.0 1.01.02.0 2.0 2.0

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

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

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

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

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

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

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

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

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

@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. Remove extra columns in the PERSON table

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

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

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

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

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

@nicolas_frankel
Thanks!
● https://blog.frankel.ch/
● @nicolas_frankel
● https://git.io/fhZrq

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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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While a microservices architecture is more scalable than a monolith, it has a direct hit on performance. To cope with that, one performance improvement is to set up a cache. It can be configured for database access, for REST calls or just to store session state across a cluster of server nodes. In this demo-based talk, I'll show how Hazelcast In-Memory Data Grid can help you in each one of those areas and how to configure it.

ConFoo - 3 performance improvements

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While microservices offer better scalability, they actually decrease performance and resiliency. I’ll show 3 areas in which it’s possible to cope with that. While a microservices architecture is more scalable than a monolith, it has a direct hit on performance. To cope with that, one performance improvement is to set up a cache. It can be configured for database access, for REST calls or just to store session state across a cluster of server nodes. In this demo-based talk, I’ll show how Hazelcast In-Memory Data Grid can help you in each one of those areas and how to configure it. Hint: it’s much easier than one would expect.

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