ABSTRACT: Always running the same queries over and over... and waiting seconds after seconds, minutes after minutes. It's not about being lazy, a dev first of all is a person with immense patience. Patience doesn't last forever, at some point, it expires, you don't have it anymore. Exactly like data in a cache. But the cache can be configured to retain data as long as you want. But, what about your patience? Still wanna be slow? Stressed about slowness? Start caching your queries. Start caching your web API call. Start caching anything you need, just for the sake of getting it back at the speed of light. That's the purpose of a cache. Retrieve your data instantly. But retrieving data from the cache is just one side of the coin, what happens when you flip it? Well, you need a mechanism to load, to feed your cache, and that's what you will discover in this presentation. Best practices, patterns, and anti-patterns to load your cache, using Redis Stack as distributed cache and Spring Data as your Swiss army knife. You will also learn how to distribute your cached data and get them updated automatically. PITCH Nowadays, retrieving data in real-time it's a must for any business application, and a caching platform is the only solution available. Everyone knows what a cache is, but only a few know how to use it properly. Even fewer know what a distributed cache can do. Leveraging Spring Data and Redis Stack, I'll show how to properly implement the most common caching patterns and avoid anti-patterns.

1. Caching patterns
for lazy devs
for lazy loading
Luigi Fugaro
Solution Architect @Redis

2. More than 20 years of experience in IT.
I started my career in 1998 as a webmaster doing HTML, JavaScript,
Applets, and some graphics with Paint Shop Pro.
I then switched to Delphi, Clipper, Visual Basic, and then he finally started
working on real Java projects. I’ve been developing all kinds of web
applications, dealing with both backend and frontend frameworks.
Jumping from project to project, from company to company, in 2012 I
joined Red Hat as a consultant. An amazing experience lasted more than
7 years.
Next, I had the privilege to learn all about observability and monitoring
with Datadog working with all kind of companies.
In 2021 I received a call from Redis, a technology made in Italy, where
apps and data merge together and they enjoy it.
Luigi Fugaro
Solutions Architect
luigi.fugaro@redis.com
#VOXXEDTICINO 2

3. ❖ Caching is everywhere
❖ Software Architecture
❖ Caching Patterns
❖ Demo
❖ Q&A
Agenda
#VOXXEDTICINO 3

4. Caching is everywhere
4

5. #VOXXEDTICINO 5
Caching is everywhere
❖ The first time I heard about cache,
it was about browser cache with
Netscape Navigator.

6. #VOXXEDTICINO 6
❖ The first time I heard about cache,
it was about browser cache with
Netscape Navigator.
❖ Second time for CPUs, L1&L2
Cache and Cache Mode
(Write-Back).
Caching is everywhere

7. #VOXXEDTICINO 7
❖ The first time I heard about cache,
it was about browser cache with
Netscape Navigator.
❖ Second time for CPUs, L1&L2 Cache
and Cache Mode (Write-Back).
❖ Third time for HTTP Server Cache
and CDN for “static-content”
(images/js/css/…).
Caching is everywhere

8. #VOXXEDTICINO 8
❖ The first time I heard about cache,
it was about browser cache with
Netscape Navigator.
❖ Second time for CPUs, L1&L2 Cache
and Cache Mode (Write-Back).
❖ Third time for HTTP Server Cache
and CDN for “static-content”
(images/js/css/…).
❖ Session management,
One-Time-Password, and so on…
Caching is everywhere

9. #VOXXEDTICINO 9
❖ The first time I heard about cache,
it was about browser cache with
Netscape Navigator.
❖ Second time for CPUs, L1&L2 Cache
and Cache Mode (Write-Back).
❖ Third time for HTTP Server Cache
and CDN for “static-content”
(images/js/css/…).
❖ Session management,
One-Time-Password, and so on…
❖ On backend, L1 Cache (session)
and L2 Cache with data access
framework such as Hibernate.
Caching is everywhere

10. 10
Software Architecture
https://www.microservices.com/reference-architecture/

11. #VOXXEDTICINO 11
Software Architecture
https://martinfowler.com/articles/microservices/images/sketch.png

12. #VOXXEDTICINO 12
Software Architecture
https://martinfowler.com/articles/microservices/images/sketch.png

13. Colocated cache
#VOXXEDTICINO 13
Synching with source
Synching with source
Synching with source
Software Architecture

14. Colocated cache
Distributed cache
#VOXXEDTICINO 14
Synching with source
Software Architecture

15. 15
Caching Patterns

16. #VOXXEDTICINO 16
Caching Patterns
❖ Cache-aside (Lazy-loading)
❖ Read-Through
❖ Write-Through
❖ Write-Behind (Write-Back)
❖ Refresh-Ahead
❖ Read-Replica

17. #VOXXEDTICINO 17
Caching Patterns
❖ Cache-aside (Lazy-loading)
❖ Read-Through
❖ Write-Through
❖ Write-Behind (Write-Back)
❖ Refresh-Ahead
❖ Read-Replica

18. #VOXXEDTICINO 18
Caching Patterns
❖ Cache-aside (Lazy-loading)
❖ Read-Through
❖ Write-Through
❖ Write-Behind (Write-Back)
❖ Refresh-Ahead
❖ Read-Replica
@Service
@CacheConfig(cacheNames={"person"})
public class PersonService {
private final PersonRepository repository;
public PersonService(PersonRepository repository) {
this.repository = repository;
}
@Cacheable(unless="#result == null")
public Optional<Person> findById(Long id) {
return repository.findById(id);
}
@Cacheable(unless="#result == null")
public List<Person> findAll() {
return repository.findAll();
}
@CachePut(key = "#result.id", unless = "#result == null")
public Person save(Person Person) {
return repository.save(Person);
}
@CacheEvict
public void deleteById(Long id) {
repository.deleteById(id);
}
@CacheEvict
public void deleteAll() {
repository.deleteAll();
}
}

19. #VOXXEDTICINO 19
Caching Patterns
❖ Cache-aside (Lazy-loading)
❖ Read-Through
❖ Write-Through
❖ Write-Behind (Write-Back)
❖ Refresh-Ahead
❖ Read-Replica

20. #VOXXEDTICINO 20
Caching Patterns
❖ Cache-aside (Lazy-loading)
❖ Read-Through
❖ Write-Through
❖ Write-Behind (Write-Back)
❖ Refresh-Ahead
❖ Read-Replica

21. #VOXXEDTICINO 21
Caching Patterns
❖ Cache-aside (Lazy-loading)
❖ Read-Through
❖ Write-Through
❖ Write-Behind (Write-Back)
❖ Refresh-Ahead
❖ Read-Replica

22. #VOXXEDTICINO 22
Caching Patterns
❖ Cache-aside (Lazy-loading)
❖ Read-Through
❖ Write-Through
❖ Write-Behind (Write-Back)
❖ Refresh-Ahead
❖ Read-Replica

23. #VOXXEDTICINO 23
Caching Patterns
❖ Cache-aside (Lazy-loading)
❖ Read-Through
❖ Write-Through
❖ Write-Behind (Write-Back)
❖ Refresh-Ahead
❖ Read-Replica

24. 24
Demo

25. #VOXXEDTICINO 25
Demo
★ Cache Provider
redisGears
Implementation details…
❏ Serverless Engine

26. Q&A

27. #VOXXEDTICINO 27
Redis Enterprise Real-Time Data Platform
Hashes
Bitmaps
Strings
Bit field
Streams
Hyperloglog
Sorted Sets
Sets
Geospatial
Redis Core Redis Modules
Lists
Linear Scalability HA Geo-Distribution
Durability Backup & Restore Tiered-Memory Access Security
Multi-Tenant
Data Structures
BloomFilter
Search
Graph
TimeSeries
AI
JSON
Gears

28. Grazie!
l.fugaro@gmail.com
@foogaro
@foogaro
https:/
/www.linkedin.com/in/luigifugaro/