When your application starts slowing down, the reason is probably a bottleneck somewhere in the execution chain. Sometimes, this bottleneck is due to a bug. Sometimes, somebody didn’t set up the optimal configuration. And sometimes, the process of fetching the data is the bottleneck. One option would be to change your whole architecture. Before moving to such a drastic, and probably expensive measure, one can consider a trade-off: instead of getting remote data every time, you can store the data locally after the first read. This is the trade-off that caching offers: stale data vs. speed. Deciding to use caching is just the first step in a long journey. The next step is to think about how your application and the cache will interact. This talk focuses on options available regarding those interactions.

1. @nicolas_frankel
A Guided Tour of Caching
Patterns
Nicolas Fränkel

2. @nicolas_frankel
Me, myself and I
 Developer
 Developer Advocate
 Former Lecturer

3. @nicolas_frankel
Hazelcast
HAZELCAST IMDG is an operational,
in-memory, distributed computing
platform that manages data using
in-memory storage and performs
execution for breakthrough
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
Caching is a trade-off
 Stale data vs. fast data access
 Stale data vs. available data

5. @nicolas_frankel
Don’t roll your own crypto cache!

6. @nicolas_frankel
Basic features of a professional cache solution
 Size limit
 Cache invalidation
• Least Recently Used
• Least Frequently Used
 Eviction
• Time-To-Live

7. @nicolas_frankel
Optional features of a professional cache solution
 Distributed
• Auto-discovery
• Split-brain scenarios
 Serialization strategies
 Polyglot
 etc.

8. @nicolas_frankel
Cache-Aside

9. @nicolas_frankel
Cache-Aside (Write)

10. @nicolas_frankel
Read-Through

11. @nicolas_frankel
Write-Through

12. @nicolas_frankel
Write-Behind

13. @nicolas_frankel
Refresh-Ahead
 When the entry is “close” to invalidation
• Fetches the value from the store
 Requires Read-Through

14. @nicolas_frankel
Cache-Ahead

15. @nicolas_frankel
Summary
Pattern Consider Cons
Cache-Aside
When you’re limited by the capabilities of your
cache provider
The application is responsible for the
cache orchestration flow
Read-Through Solid default
Write-Through Solid default
Write-Behind
When performance considerations outweigh
short-term consistency
Asynchronous systems are harder to
reason with
Refresh-Ahead
When fetching data from the datastore
impairs throughput
Additional component to develop,
deploy and maintain
Cache-Ahead
When you need the cache to be always in-
sync
Additional component to develop,
deploy and maintain

16. @nicolas_frankel
Thanks for your attention!
 https://blog.frankel.ch/
 @nicolas_frankel
 https://hazelcast.com/blog/a-hitchhikers-
guide-to-caching-patterns/
 http://bit.ly/caching-patterns
 https://slack.hazelcast.com/
 https://training.hazelcast.com/