This document discusses caching patterns and strategies for digital data. It provides an overview of common caching patterns including read-through caching, write-through caching, pre-fetch caching, side caching, and publisher/refresh ahead caching. For each pattern there is a brief description of how it works and examples of suitable uses cases. The document also discusses how to distribute data across different caching layers from content delivery networks to the database layer.

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Daniel Rezny, Tomi Vanek
Accenture
Digital in Space & Time Caching Patterns and Strategies
October 17, 2014

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Daniel Rezny
senior technology architect
daniel.rezny@accenture.com
@danielrezny
Tomi Vanek
senior software architect
tomas.vanek@accenture.com
http://tomi.vanek.sk

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Data
Everything what is DIGITAL is DATA.

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Data = Identity & Content
Time
Volume
Identity
Individual
Collective
Content

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Data = Identity & Content
Time
Volume
Identity
Individual
Collective

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

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Cache Patterns Read Through Cache

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Read Through
Source
Cache
Client 1
Cache
Client 2

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Client 1
Client 2
Source
Read Through
Cache
Cache

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Client 1
Client 2
Source
Read Through
Cache
Cache

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Client 1
Client 2
Source
Read Through
Cache
Cache

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Client 1
Client 2
Source
Read Through
Cache
Cache

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Client 1
Client 2
Source
Read Through
Cache
Cache

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•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

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

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Cache Patterns Write Through / Write behind

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

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

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

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

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•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

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

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Cache Patterns Pre-Fetch / Pre-Caching

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Trigger
Cache
Pre-Fetch / Pre-Caching
Client
Source

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Trigger
Cache
Pre-Fetch / Pre-Caching
Client
Source

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Trigger
Cache
Pre-Fetch / Pre-Caching
Client
Source

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Trigger
Cache
Pre-Fetch / Pre-Caching
Client
Source

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Trigger
Cache
Pre-Fetch / Pre-Caching
Client
Source

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•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

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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

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Cache Patterns Side Cache

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Cache
Side Cache
Source
Client
Consumer

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Cache
Side Cache
Source
Client
Consumer

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Cache
Side Cache
Source
Client
Consumer

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Cache
Side Cache
Source
Client
Consumer

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•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

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

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Cache Patterns Publisher / Refresh Ahead

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Index
Cache
Publisher / Refresh Ahead
Source
Client
Publisher

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Index
Cache
Publisher / Refresh Ahead
Source
Client
Publisher

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Index
Cache
Publisher / Refresh Ahead
Source
Client
Publisher

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Index
Cache
Publisher / Refresh Ahead
Source
Client
Publisher

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•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

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

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Data Distribution

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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

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Performing
Application
DATA
Right caching pattern
Application improvements

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Daniel Rezny Tomi Vanek