Caching solutions with Redis

Feb 18, 2014Download as PPTX, PDF4 likes4,940 views

Redis is an open source, in-memory data structure store that can be used as a database, cache, or message broker. It supports basic data types like strings, hashes, lists, and sets. Redis features high performance, replication, publishing/subscribing, and Lua scripting. It is widely adopted by companies like GitHub, StackOverflow, and Blizzard for use cases like caching, sessions, queues, and as a real-time database.

Caching Solutions:
Redis
with Bogdan Hadadea

1. General Description: Redis
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NoSQL key-value date store
Like MongoDB, but better
Built-in persistence
More data types
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String
Hash
Set
List

• Built-in pub/sub feature
• Highly scalable

1. General Description: Redis
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Creator: Salvatore Sanfilippo
Released: 2009
Written in ANSI C
Single threaded
Open Source
Backed by VMWare
Early adopted by GitHub

2. Usage: Redis – Who?
• StackOverflow – 3 layers of caching
o Local cache
o Site cache
o Global cache

• Blizzard – 8 node Redis install
o 16GB/instance
o For storing auction house and serving avatars

• Amazon ElastiCache, Redis-to-go
o Cache in the cloud
o Cache hosting

2. Usage: Redis – What for?
• Redis for cache
o Good to place sessions – hashes
o A fine place for high score tracking – sorted sets

• Redis as database
o Persistence to disk
o High performance

• Redis as message bus
o Based on Pub/Sub functionality
o Queues with list structures
o Resque – Ruby library for creating background jobs

3. Architecture: Redis
• Request/Response Protocols:
o Redis is a TCP server using the client-server model
Request/Response protocol

• Pipelining:
o Process multiple requests even if response not read yet
o Not paying the RTT (Round Trip Time) for every command

• Limitations:
o Responses stored in a queue
o Recommended max: 10k commands

• Benchmark: ‘Ping’ – 10k times

3. Architecture: Redis
• Scripting
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Lua interpreter built in ver. > 2.6
Using EVAL and EVALSHA
Conversion between Lua and Redis data types
redis.call() & redis.pcall()

o Atomicity of scripts
o Emitting Redis logs from scripts
o EVALSHA in the context of pipelining

3. Architecture: Redis
• Replication:
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Master-slave: exact copies
Asynchronous replication
Master – multiple slaves
Non-blocking replication – master side
Non-blocking replication – slave side – old dataset
Slaves accept connections from other slaves
Replication for scalability
Replication for cost saving
Full/Partial synchronization
Configure: slaveof 192.168.1.1 6379
Read-only slaves
Slave authentication to master
Write only when N replicas

3. Architecture: Redis
• Redis Transactions:
o No rollback
o Optimistic locking

3. Architecture: Redis
• Publisher/Subscriber messaging paradigm
o Decoupling pub-sub
o Pattern matching subscriptions

• Redis clustering
o Not production ready

• Memory optimization
o Encoding of small data – up to 5x less space
o Using 32bit instances

• Using hashes to abstract a key-value on top of Redis

5. Conclusion
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Focused on speed
Reliable as database
Steeper learning curve than other solutions
Still improving

This document provides an overview of Redis, an open source, advanced key-value store. It supports a variety of data types including strings, lists, sets, sorted sets and hashes. Redis can be used across multiple programming languages and operating systems. It offers functionality like pub/sub messaging, Lua scripting, pipelining, transactions, persistence and replication. High availability options include Redis Sentinel, virtual IPs and twemproxy for load balancing. In summary, Redis is a versatile NoSQL database that supports advanced data structures and common database functions through a simple interface.

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Caching solutions with Redis

1. Caching Solutions: Redis with Bogdan Hadadea

2. 1. General Description: Redis • • • • NoSQL key-value date store Like MongoDB, but better Built-in persistence More data types o o o o String Hash Set List • Built-in pub/sub feature • Highly scalable

3. 1. General Description: Redis • • • • • • • Creator: Salvatore Sanfilippo Released: 2009 Written in ANSI C Single threaded Open Source Backed by VMWare Early adopted by GitHub

4. 2. Usage: Redis – Who? • StackOverflow – 3 layers of caching o Local cache o Site cache o Global cache • Blizzard – 8 node Redis install o 16GB/instance o For storing auction house and serving avatars • Amazon ElastiCache, Redis-to-go o Cache in the cloud o Cache hosting

5. 2. Usage: Redis – What for? • Redis for cache o Good to place sessions – hashes o A fine place for high score tracking – sorted sets • Redis as database o Persistence to disk o High performance • Redis as message bus o Based on Pub/Sub functionality o Queues with list structures o Resque – Ruby library for creating background jobs

6. 3. Architecture: Redis – Overview

7. 3. Architecture: Redis • Request/Response Protocols: o Redis is a TCP server using the client-server model Request/Response protocol • Pipelining: o Process multiple requests even if response not read yet o Not paying the RTT (Round Trip Time) for every command • Limitations: o Responses stored in a queue o Recommended max: 10k commands • Benchmark: ‘Ping’ – 10k times

8. 3. Architecture: Redis • Scripting o o o o Lua interpreter built in ver. > 2.6 Using EVAL and EVALSHA Conversion between Lua and Redis data types redis.call() & redis.pcall() o Atomicity of scripts o Emitting Redis logs from scripts o EVALSHA in the context of pipelining

9. 3. Architecture: Redis • Replication: o o o o o o o o o o o o o Master-slave: exact copies Asynchronous replication Master – multiple slaves Non-blocking replication – master side Non-blocking replication – slave side – old dataset Slaves accept connections from other slaves Replication for scalability Replication for cost saving Full/Partial synchronization Configure: slaveof 192.168.1.1 6379 Read-only slaves Slave authentication to master Write only when N replicas

10. 3. Architecture: Redis • Redis Transactions: o No rollback o Optimistic locking

11. 3. Architecture: Redis • Publisher/Subscriber messaging paradigm o Decoupling pub-sub o Pattern matching subscriptions • Redis clustering o Not production ready • Memory optimization o Encoding of small data – up to 5x less space o Using 32bit instances • Using hashes to abstract a key-value on top of Redis

12. 4. Comparison: Memcached

13. 4. Comparison: Memcached

14. 5. Conclusion • • • • Focused on speed Reliable as database Steeper learning curve than other solutions Still improving

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