Caching Patterns

Media Partner
Organizers
Top Media Partner
General Partner
Supporters
Daniel Rezny, Tomi Vanek
Accenture
Digital in Space & Time Caching Patterns and Strategies
October 17, 2014

Copyright © 2014 Accenture All rights reserved.
2
Daniel Rezny
senior technology architect
daniel.rezny@accenture.com
@danielrezny
Tomi Vanek
senior software architect
tomas.vanek@accenture.com
http://tomi.vanek.sk

3
Data
Everything what is DIGITAL is DATA.

4
Data = Identity & Content
Time
Volume
Identity
Individual
Collective
Content

5
Data = Identity & Content
Time
Volume
Identity
Individual
Collective

6

7
Good scalability
Transparency
Easy to maintain
Reduced load on source
Fast recovery after failover

8
Cache Patterns Read Through Cache

9
Read Through
Source
Cache
Client 1
Cache
Client 2

10
Client 1
Client 2
Source
Read Through
Cache
Cache

11
Client 1
Client 2
Source
Read Through
Cache
Cache

12
Client 1
Client 2
Source
Read Through
Cache
Cache

13
Client 1
Client 2
Source
Read Through
Cache
Cache

14
Client 1
Client 2
Source
Read Through
Cache
Cache

15
•Simple, transparent
•Scalable
•Faster response time by repeating requests
•Decreases load to data source
•Read only
•Only for repeating requests
•For data with rare changes
•Requests for unique data hit data source

16
Read Through usage examples
Content Delivery Network (CDN)
HTTP reverse proxy
Browser proxy
API management tools
Reusable data closer to consumption

17
Cache Patterns Write Through / Write behind

18
Cache
Cache
Write Through / Write behind
Client 1
Client 2
Destination

19
Client 1
Client 2
Destination
Cache
Cache

20
Client 1
Client 2
Destination
Cache
Cache

21
Client 1
Client 2
Destination
Cache
Cache

22
•Transparently hide destination systems
•The only application’s source of data
•Limit amount of requests to the source
•Avoid cache misses
•Database unknown for application
•Split of responsibilities
•Does not improve writes speed
•Possible data inconsistences
•Cache transaction finish before database’ starts

23
Write Through usage examples
Additional application layer
API management tools
Content delivery network
Lot of writes and reads

24
Cache Patterns Pre-Fetch / Pre-Caching

25
Trigger
Cache
Pre-Fetch / Pre-Caching
Client
Source

26
Trigger
Cache
Client
Source

27
Trigger
Cache
Client
Source

28
Trigger
Cache
Client
Source

29
Trigger
Cache
Client
Source

30
•Fast read of non-repetitive data
•Prepare data for usage in advance
•Transparent work "behind the scene"
•Increased load on resources (data source, network, cache size)
•Redundant data in cache
•Complex trigger logic

31
Pre-Fetch usage examples
Master-detail pattern (search)
Asynchronous page refresh
Database pre-fetch
Guided navigation
Preload of web assets
Automated and manual pre-fetching

33
Cache
Side Cache
Source
Client
Consumer

34
Cache
Side Cache
Source
Client
Consumer

35
Cache
Side Cache
Source
Client
Consumer

36
Cache
Side Cache
Source
Client
Consumer

37
•Lower load to the data source
•Read Through emulation possible
•Effectively loads data on demand
•Data lifecycle management possible
•Supporting pre-caching might be used
•Not fully transparent
•Additional hops to the cache
•Not good for static data

38
Side cache usage examples
When dynamic data used
Resource demand is not predictable
Local storage in browser
Large amount of data

39
Cache Patterns Publisher / Refresh Ahead

40
Index
Cache
Publisher / Refresh Ahead
Source
Client
Publisher

41
Index
Cache
Source
Client
Publisher

42
Index
Cache
Source
Client
Publisher

43
Index
Cache
Source
Client
Publisher

44
•Very high scalability
•Controlled “freshness” of data
•Distributed
•Data source does not limit the scaling of the application
•Read Only
•Does not reflect frequent changes
•Snapshot of data in history

45
Publisher usage examples
Good for static data
Optimized images for responsive design
Reports, graphs
Documents in PDF / EPub / Mobi
Stock amount in Eshop

47
CDN
•Static files and images
•Websites
Front-end
•HTTP requests and session data
•User’s data
Application logic
•Short-term code results
•External calls and sessions
Integration
•Request to external systems
•Responses from requests
Database layer
•SQL queries and parameters
•Connection strings and configurations
CDN
Frontend
Application Logic
Integration
Database Layer

48
Performing
Application
DATA
Right caching pattern
Application improvements

Caching Patterns

More Related Content

What's hot (20)

Viewers also liked (17)

Similar to Caching Patterns (20)

Recently uploaded (20)

Caching Patterns