The saying goes that there are only two hard things in Computer Science: cache invalidation, and naming things. Well, turns out the first one is solved actually ;) Join us for this session to learn how to keep read views of your data in distributed caches close to your users, always kept in sync with your primary data stores change data capture. You will learn how to - Implement a low-latency data pipeline for cache updates based on Debezium, Apache Kafka, and Infinispan - Create denormalized views of your data using Kafka Streams and make them accessible via plain key look-ups from a cache cluster close by - Propagate updates between cache clusters using cross-site replication We'll also touch on some advanced concepts, such as detecting and rejecting writes to the system of record which are derived from outdated cached state, and show in a demo how all the pieces come together, of course connected via Apache Kafka.

1. Image © Nathalie https://flic.kr/p/21Ghf2g (CC BY 2.0)
Keep Your Cache Always Fresh With Debezium!
Gunnar Morling
Software Engineer, Red Hat
@gunnarmorling

2. #Debezium @gunnarmorling
The Challenge
Multi-site Application With Shared Database

3. #Debezium @gunnarmorling
The Challenge
Multi-site Application With Shared Database

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The Challenge
Multi-site Application With Shared Database

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The Challenge
● Deployments in different geographies, single DB
● 90% read requests
● Complex queries
Multi-site Application With Shared Database

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… Multi-site application with shared system-of-record database
… With local, denormalized read views (CQRS)
... Automatically kept in sync after writes
Today’s Mission
🤔
Explore How to Build a…

7. #Debezium @gunnarmorling
● Software engineer at Red Hat
○ Debezium
○ Quarkus
● kcctl 🧸, JfrUnit, ModiTect, MapStruct
● Spec Lead for Bean Validation 2.0
● Java Champion
Gunnar Morling

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The Idea
Caching to the Rescue!

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The Idea
Caching to the Rescue!

10. https://flic.kr/p/PFDvkY Public Domain, Angelo Brathot

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Infinispan
100% Open-source In-Memory Distributed Data Store
Interoperability
Resilient
Fault Tolerant Data
Clustered Processing Query
ACID Tx

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App 1
Data
App 2
Data
1, Maria
2, Jenny
2, Jenny
3, Juan
App 3
Data
1, Maria
3, Juan
Infinispan Deployment
Distributed Cache

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App 1
Data
App 2
Data
1, Maria
2, Jenny
2, Jenny
3, Juan
App 3
Data
1, Maria
3, Juan
Put 4 Will Put 4 Will
Infinispan Deployment
Distributed Cache

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App 1
Data
App 2
Data
1, Maria
2, Jenny
4, Will
2, Jenny
3, Juan
4, Will
App 3
Data
1, Maria
3, Juan
Infinispan Deployment
Distributed Cache

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Infinispan
Client/Server
Service 1
Service 2
Binary
(hot rod)
REST
...
Service 3
Data
Infinispan Cluster
Data
Data

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Infinispan Cross-Site Replication
AWS (LON)
GCP (NYC)
Load Balancer
APP
APP
Service
APP
APP
Service
Shared
State
Shared
State
Shared
State
Shared
State
Data
Data
Data NYC
Data LON
RELAY2

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The Question
How To Keep The Cache In Sync?

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Dual Writes
Easy to Get Wrong

20. https://flic.kr/p/PFDvkY Public Domain, Angelo Br

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Debezium in a Nutshell
Open-Source Change Data Capture
● A CDC Platform
○ Based on transaction logs
○ Snapshotting, filtering, etc.
○ Outbox support
○ Web-based UI
● Fully open-source, very active
community
● Large production deployments

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Debezium
Becoming the De-Facto CDC Standard
https://debezium.io/blog/2021/09/22/deep-dive-into-a-debezium-community-connector-scylla-cdc-source-connector/

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Debezium: Deployment Alternatives
Embedded Engine and Debezium Server

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Solution Overview
Capturing Changes From the Database

25. https://flic.kr/p/PFDvkY Public Domain, Angelo Brathot
Demo

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● Fast start-up, low memory consumption
● Developer joy
● Imperative and Reactive
● Best-of-breed libraries
● Run via HotSpot and GraalVM native binaries
Quarkus - Supersonic Subatomic Java
A Stack for Building Cloud-native Apps

28. https://flic.kr/p/PFDvkY Public Domain, Angelo Brathot
Demo

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Eventual Consistency
Dealing With Stale Data
● Cached views updated asynchronously
● How to ensure read your own writes semantics?
● How to prevent taking action based on stale data?
🤔

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Eventual Consistency
Read Your Own Writes

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Eventual Consistency
Read Your Own Writes

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Eventual Consistency
Read Your Own Writes

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Eventual Consistency
Read Your Own Writes

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Eventual Consistency
Detecting Writes Derived From Stale State

35. Wrap-Up

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… Multi-site application with shared system-of-record database ✅
… With local, denormalized read views (CQRS) ✅
... Automatically kept in sync after writes ✅
Today’s Mission
🤩
Explore How to Build a…

37. #Debezium @gunnarmorling
Discussion
What Have We Gained?
● Pro:
○ Lower latencies
○ Reduced load on primary database
○ Increased availability
● Con:
○ Increased complexity

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● Incremental snapshotting
● Postgres logical decoding messages
● Multi-DB support (SQL Server)
● Debezium Server sinks
● MongoDB change streams support
● Debezium UI
● Debezium 2.0
What’s New in Debezium?

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● Infinispan: @infinispan | https://infinispan.org/
● Debezium: @debezium | https://debezium.io/
● Demo: https://github.com/debezium/debezium-examples/ →
distributed-caching
● kcctl 🧸: https://github.com/kcctl/kcctl/
Learn More

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Q & A
gunnar@hibernate.org
@gunnarmorling
📧
Thank You!