Everything you always wanted to know about Redis but were afraid to ask

50 %
50 %
Information about Everything you always wanted to know about Redis but were afraid to ask

Published on March 8, 2014

Author: carlosabalde

Source: slideshare.net


Yet another Redis presentation:

- Introduction: context, popular Redis users, latest releases…

- Redis 101: basics, scripting, some examples…

- Mastering Redis: persistence, replication, performance, sharding…

Redis Everything you always wanted to know about Redis but were afraid to ask Carlos Abalde carlos.abalde@gmail.com March 2014

Introduction Remote dictionary server

INSERT INTO… $ redis-cli -n 0 !> SET the-answer 42 OK !> QUIT ! rulo:~$

SELECT * FROM… $ redis-cli -n 0 !> GET the-answer "42" !> QUIT ! rulo:~$

The end

The end

Agenda I. Introduction ‣ Context, popular Redis users, latest releases… II. Redis 101 ‣ Basics, scripting, some examples… III.Mastering Redis ‣ Persistence, replication, performance, sharding…

I. Introduction http://www.flickr.com/photos/verino77/5616332196/

NoSQL / NoREL mess ๏ Document DBs ‣ ๏ Graph DBs ‣ ๏ Neo4j, FlockDB… Column oriented DBs ‣ ๏ MongoDB, CouchDB, Riak… HBase, Cassandra, BigTable… Key-Value DBs ‣ Memcache, MemcacheDB, Redis, Voldemort, Dynamo…

Who’s behind Redis? ๏ Created by Salvatore Sanfilippo ‣ ‣ ๏ http://antirez.com @antirez at Twitter Currently sponsored by Pivotal ‣ Previously to May 2013 sponsored by VMware

Who’s using Redis? I

Who’s using Redis? II ๏ The architecture Twitter uses to deal with 150M active users, 300K QPS, a 22 MB/S Firehose, and send tweets in under 5 seconds. High Scalability (2013)▸ ๏ Storing hundreds of millions of simple key-value pairs in Redis. Instagram Engineering Blog (2012)▸ ๏ The Instagram architecture Facebook bought for a cool billion dollars. High Scalability (2012)▸ ๏ Facebook’s Instagram: making the switch to Cassandra from Redis, a 75% ‘insta’ savings. Planet Cassandra (2013)▸

Who’s using Redis? III ๏ Highly available real time push notifications and you. Flickr Engineering Blog (2012)▸ ๏ Using Redis as a secondary index for MySQL. Flickr Engineering Blog (2013)▸ ๏ How we made GitHub fast. The GitHub Blog (2009)▸ ๏ Real world Redis. Agora Games (2012)▸ ๏ Disqus discusses migration from Redis to Cassandra for horizontal Scalability. Planet Cassandra (2013)▸

Memory is the new disk ๏ BSD licensed in-memory data structure server ‣ Strings, hashes, lists, sets… ๏ Optional durability ๏ Bindings to almost all relevant languages “Memory is the new disk, disk is the new tape” — Jim Gray

A fight against complexity ๏ Simple & robust foundations ‣ ‣ ๏ Single threaded No map-reduce, no indexes, no vector clocks, no Paxos, no Merkle trees, no gossip protocols… Blazingly fast ‣ Implemented in C (20K LoC for the 2.2 release) ‣ No dependencies

A fight against complexity ! … 5. We’re against complexity. We believe designing systems is a fight against complexity. […] Most of the time the best way to fight complexity is by not creating it at all. … The Redis Manifesto▸

Most popular K-V DB ๏ Currently most popular key-value DB▸ ๏ Redis 1.0 (April’09) ↝ Redis 2.8.6 (March’14) Google Trends▸

Latest releases I ๏ Redis 2.6 (October’12) ‣ LUA scripting ‣ New commands ‣ Milliseconds precision expires ‣ Unlimited number of clients ‣ Improved AOF generation

Latest releases II ๏ Redis 2.8 (November’13) ‣ Redis 2.7 removing clustering stuff ‣ Partial resynchronization with slaves ‣ IPv6 support ‣ Config rewriting ‣ Key-space changes notifications via Pub/Sub

Latest releases III ๏ Redis 3.0 ‣ Next beta release planned to March’14 ‣ Redis Cluster ‣ Speed improvements under certain workloads

Commands ๏ redis-server ๏ redis-cli ‣ ๏ redis-benchmark ‣ ๏ Command line interface Benchmarking utility redis-check-dump & redis-check-aof ‣ Corrupted RDB/AOF files utilities

Performance Sample benchmark ๏ Redis 2.6.14 ๏ Intel Xeon CPU E5520 @ 2.27GHz ๏ 50 simultaneous clients performing 2M requests ๏ Loopback interface ๏ Key space of 1M keys

Performance No pipelining $ redis-benchmark -r 1000000 -n 2000000 -t get,set,lpush,lpop -q ! SET: 122556.53 requests per second GET: 123601.76 requests per second LPUSH: 136752.14 requests per second LPOP: 132424.03 requests per second

Performance 16 command per pipeline $ redis-benchmark -r 1000000 -n 2000000 -t get,set,lpush,lpop -P 16 -q ! SET: 552028.75 requests per second GET: 707463.75 requests per second LPUSH: 767459.75 requests per second LPOP: 770119.38 requests per second

Summary ✓ Simple ✓ Fast ✓ Predictable ✓ Widely supported ✓ Reliable ✓ Lightweight

II. Redis 101 http://www.flickr.com/photos/caseycanada/2058552752/

Overview ๏ Family of fundamental data structures ‣ ‣ Accessed / indexed by key ‣ ๏ Strings and string containers Directly exposed — No abstraction layers Rich set of atomic operations over the structures ‣ ๏ Detailed reference using big-O notation for complexities Basic publish / subscribe infrastructure

Keys ๏ Arbitrary ASCII strings ‣ ‣ ๏ Define some format convention and adhere to it Key length matters! Multiple name spaces are available ‣ Separate DBs indexed by an integer value - ๏ SELECT command Multiples DBs vs. Single DB + key prefixes Keys can expire automatically

Data structures I ๏ Strings ‣ ๏ Hashes ‣ ๏ Caching, counters, realtime metrics… “Object” storage… Lists ‣ Logs, queues, message passing…

Data structures II ๏ Sets ‣ ๏ Membership, tracking… Ordered sets ‣ Leaderboards, activity feeds… RTFM, please :) ▸

Publish / Subscribe Overview ๏ Classic pattern decoupling publishers & subscribers ‣ ‣ ๏ You can subscribe to channels; when someone publish in a channel matching your interests Redis will send it to you SUBSCRIBE, UNSUBSCRIBE & PUBLISH commands Fire and forget notifications ‣ ๏ Not suitable for reliable off-line notification of events Pattern-matching subscriptions ‣ PSUBSCRIBE & PUNSUBSCRIBE commands

Publish / Subscribe Key-space notifications ๏ Available since Redis 2.8 ‣ ‣ ๏ Disabled in the default configuration Key-space vs. keys-event notifications Delay of key expiration events ‣ Expired events are generated when Redis deletes the key; not when the TTL is consumed - Lazy (i.e. on access time) key eviction - Background key eviction process

Pipelining ๏ Redis pipelines are just a RTT optimization ‣ Deliver multiple commands together without waiting for replies ‣ Fetch all replies in a single step - Server needs to buffer all replies! ๏ Pipelines are NOT transactional or atomic ๏ Redis scripting FTW! ‣ Much more flexible alternative

Transactions ๏ Or, more precisely, “transactions” ‣ Commands are executed as an atomic & single isolated operation - ‣ ๏ Rollback is not supported! MULTI, EXEC & DISCARD commands ‣ ๏ Partial execution is possible due to pre/post EXEC failures! Conditional EXEC with WATCH Redis scripting FTW! ‣ Redis transactions are complex and cumbersome

Scripting Overview I ๏ Added in Redis 2.6 ๏ Uses the LUA 5.1 programming language▸ ‣ Base, Table, String, Math & Debug libraries ‣ Built-in support for JSON and MessagePack ‣ No global variables ‣ redis.{call(), pcall()} ‣ redis.{error_reply(), status_reply(), log()}

Scripting Overview II ๏ Scripts are atomic, like any other command ๏ Scripts add minimal overhead ‣ ๏ Shared LUA context Scripts are replicated on slaves by sending the script (i.e. not the resulting commands) ‣ ‣ Single thread Scripts are required to be pure functions Maximum execution time vs. Atomic execution

Scripting What is fixed with scripting? ๏ Server side manipulation of data ๏ Minimizes latency ‣ ๏ No round trip delay Maximizes CPU usage ‣ ‣ ๏ Less parsing Less OS system calls Simpler & faster alternative to WATCH

Scripting Scripts vs. Stored procedures ๏ Stored procedures are evil ๏ Backend logic should be 100% application side ‣ ‣ ๏ No hidden behaviors No crazy version management Redis keys are explicitly declared as parameters of the script ‣ Cluster friendly ‣ Hashed scripts

Scripting Hello world! > EVAL " return redis.call('SET', KEYS[1], ARGV[1])" 1 foo 42 OK ! > GET foo "42"

Scripting DECREMENT-IF-GREATER-THAN EVAL " local res = redis.call('GET', KEYS[1]); ! if res ~= nil then res = tonumber(res); if res ~= nil and res > tonumber(ARGV[1]) then res = redis.call('DECR', KEYS[1]); end end ! return res" 1 foo 100

Scripting Some more commands ๏ EVALSHA sha1 nkeys key [key…] arg [arg…] ‣ Client libraries optimistically use EVALSHA - ‣ ๏ On NOSCRIPT error, EVAL is used Automatic version management SCRIPT LOAD script ‣ Cached scripts are no flushed until server restart ‣ Ensures EVALSHA will not fail (e.g. MULTI/EXEC)

Dangerous commands ๏ KEYS pattern ๏ SAVE ๏ FLUSHALL & FLUSHDB ๏ CONFIG

Some examples I ๏ 11 common web use cases solved in Redis▸ ๏ How to take advantage of Redis just adding it to your stack▸ ๏ A case study: design and implementation of a simple Twitter clone using only PHP and Redis▸ ๏ Scaling Crashlytics: building analytics on Redis 2.6▸

Some examples II ๏ Fast, easy, realtime metrics using Redis bitmaps▸ ๏ Redis - NoSQL data store▸ ๏ Auto complete with Redis▸ ๏ Multi user high performance web chat▸

III. Mastering Redis http://www.fotolia.com/id/19245921

Persistence Overview ๏ The whole dataset needs to feet in memory ‣ ‣ Very high read & write rates ‣ ๏ Durability is optional Optimal & simple memory and disk representations What if Redis runs out of memory? ‣ Swapping Performance degradation ‣ Hit maxmemory limit Failed writes or eviction policy

Persistence Snapshotting — RDB ๏ Periodic asynchronous point-in-time dump to disk ‣ Every S seconds and C changes ‣ Fast service restarts ๏ Possible data lost during a crash ๏ Compact files ๏ Minimal overhead during operation ๏ Huge data sets may experience short delays during fork() ๏ Copy-on-write fork() semantics 2x memory problem

Persistence Append only file — AOF ๏ Journal file logging every write operation ‣ ‣ ๏ Configurable fsync frequency: speed vs. safety Commands replayed when server restarts No as compact as RDB ‣ Safe background AOF file rewrite fork() ๏ Overhead during operation depends on fsync behavior ๏ Recommended to use both RDB + AOF ‣ RDB is the way to of for backups & disaster recovery

Security ๏ Designed for trusted clients in trusted environments ‣ ๏ Basic unencrypted AUTH command ‣ ๏ No users, no access control, no connection filtering… requirepass s3cr3t Command renaming ‣ rename-command FLUSHALL f1u5hc0mm4nd ‣ rename-command FLUSHALL ""

Replication Overview I ๏ One master — Multiple slaves ‣ Scalability & redundancy - ‣ Client side failover, eviction, query routing… Lightweight master ๏ Slaves are able to accept other slave connections ๏ Non-blocking in the master, but blocking on the slaves ๏ Asynchronous but periodically acknowledged

Replication Overview II ๏ Automatic slave reconnection ๏ Partial resynchronization: PSYNC vs. SYNC ‣ ๏ RDB snapshots are used during initial SYNC Read-write slaves ‣ ‣ ๏ slave-read-only no Ephemeral data storage Minimum replication factor

Replication Some commands & configuration ๏ Trivial setup ‣ ‣ ๏ slaveof <host> <port> SLAVEOF [<host> <port >| NO ONE] Some more configuration tips ‣ slave-serve-stale-data [yes|no] ‣ repl-ping-slave-period <seconds> ‣ masterauth <password>

Replication Final tips ๏ Inconsistencies are possible when using some eviction policy in a replicated setup ‣ Set slave’s maxmemory to 0

Performance General tips ๏ Fast CPUs with large caches and not many cores ๏ Do not invest on expensive fast memory modules ๏ Avoid virtual machines ๏ Use UNIX domain sockets when possible ๏ Aggregate commands when possible ๏ Keep low the number of client connections

Performance Advanced optimization ๏ Special encoding of small aggregate data types ๏ 32 vs. 64 bit instances ๏ Consider using bit & byte level operations ๏ Use hashes when possible ๏ Alway check big-O notation complexities

Performance Understanding metrics I ๏ redis-cli --latency ‣ Typical latency for 1 GBits/s network is 200 μs ‣ SHOWLOG GET ‣ Monitor number of client connections and consider using multiplexing proxy ‣ Improve memory management

Performance Understanding metrics II ๏ redis-cli INFO | grep … ๏ used_memory ‣ Usually inferior to used_memory_rss - Used memory as seen by the OS ‣ Swapping risk when approaching 45% / 95% ‣ Reduce Redis footprint when possible

Performance Understanding metrics III ๏ total_commands_processed ‣ ๏ Use multi-argument commands, scripts and pipelines when possible mem_fragmentation_ratio ‣ used_memory_rss ÷ used_memory ‣ Execute SHUTDOWN SAVE and restart the instance ‣ Consider alternative memory allocators

Performance Understanding metrics IV ๏ evicted_keys ‣ Keys removed when hitting maxmemory limit ‣ Increase maxmemory when possible ‣ Reduce Redis footprint when possible ‣ Consider sharding

Redis pools ๏ Redis is extremely small footprint and lightweight ๏ Multiple Redis instances per node ‣ ‣ Mitigated RDB 2x memory problem ‣ ๏ Full CPU usage Fine tuned instances How to use multiple instances? ‣ Sharding ‣ Specialized instances

Redis Sentinel Overview I ๏ Official Redis HA / failover solution ‣ ‣ On master failure, choose slave & promote to master ‣ ๏ Periodically check liveness of Redis instances Notify clients & slaves about the new master Multiple Sentinels ‣ Complex distributed system ‣ Gossip, quorum & leader election algorithms

Redis Sentinel Overview II ๏ Work in progress not ready for production ๏ Master pub/sub capabilities ‣ Auto discovery of other sentinels & slaves ‣ Notification of master failover ๏ Explicit client support required ๏ Redis Sentinel is a monitoring system with support for automatic failover. It does not turn Redis into a distributed data store. CAP discussions do not apply▸

Redis Sentinel Apache Zookeeper ๏ Set of primitives to ease building distributed systems ‣ ‣ Handling of network partitions, leader election, quorum management… ‣ ๏ http://zookeeper.apache.org Replicated, highly available, well-known… Ad-hoc Redis HA alternative to Sentinel ‣ Explicit client implementation required

Redis Cluster ๏ Long term project to be released in Redis 3.0 ๏ High performance & linearly scalable complex distributed DB ‣ ‣ ๏ Sharding across multiple nodes Graceful handling of network partitions Implemented subset ‣ ‣ ๏ Commands dealing with multiple keys, etc. not supported Multiple databases are not supported Keys hash tags

Sharding Overview I ๏ Distribute data into multiple Redis instances ‣ ‣ ๏ Allows much larger databases Allows to scale the computational power Data distribution strategies ‣ Directory based ‣ Ranges ‣ Hash + Module ‣ Consistent hashing

Sharding Overview II ๏ Data distribution responsibility ‣ ‣ Proxy assisted ‣ ๏ Client side Query routing Do I really need sharding? ‣ Very unlikely CPU becomes bottleneck with Redis ‣ 500K requests per second!

Sharding Disadvantages ๏ Multi-key commands are not supported ๏ Multi-key transactions are not supported ๏ Sharding unit is the key ๏ Harder client logics ๏ Complex to scale up/down when used as a store

Sharding Presharding ๏ Hard to scale up/down sharded databases ‣ ๏ Take advantage of small Redis footprint ‣ ๏ But data storage needs may vary over the time Think big! Redis replication allows moving instances with minimal downtime

Sharding Twimproxy overview I ๏ Redis Cluster is currently not production ready ‣ Mix between query routing & client side partitioning ๏ Not all Redis clients support sharding ๏ Automatic sharding Redis & Memcache (ASCII) proxy ‣ Developed by Twitter & Apache 2.0 licensed ‣ https://github.com/twitter/twemproxy/ ‣ Single threaded & extremely fast

Sharding Twimproxy overview II ๏ Also known as nutcracker ๏ Connection multiplexer pipelining requests and responses ‣ Original motivation ๏ No bottleneck or single point of failure ๏ Optional node ejection ‣ Only useful when using Redis as a cache

Sharding Why Twimproxy ? ๏ Multiplexed persistent server connections ๏ Automatic sharding and protocol pipelining ๏ Multiple distribution algorithms supporting nicknames ๏ Simple dumb clients ๏ Automatic fault tolerance capabilities ๏ Zero copy

Sharding Why not Twimproxy ? ๏ Extra network hop ‣ ๏ Pipelining is your friend Not all commands supported ‣ ‣ ๏ Transactions Pub / Sub HA not supported ‣ Redis Sentinel Twemproxy agent

Thanks! http://www.flickr.com/photos/62337512@N00/3958637561/

Add a comment

Related presentations

Presentación que realice en el Evento Nacional de Gobierno Abierto, realizado los ...

In this presentation we will describe our experience developing with a highly dyna...

Presentation to the LITA Forum 7th November 2014 Albuquerque, NM

Un recorrido por los cambios que nos generará el wearabletech en el futuro

Um paralelo entre as novidades & mercado em Wearable Computing e Tecnologias Assis...

Microsoft finally joins the smartwatch and fitness tracker game by introducing the...

Related pages

Everything You Always Wanted to Know About Sex* (*But Were ...

Everything You Always Wanted to Know ... You haven't seen anything until you've seen everything; If you want to know how ... *But Were Afraid to Ask" you ...
Read more

Everything You Wanted to Know about Indians But Were ...

Buy Everything You Wanted to Know about Indians But Were ... What have you always wanted to know ... Know about Indians But Were Afraid to Ask cuts ...
Read more

Everything You Always Wanted to Know About Sex (But Were ...

Everything You Always Wanted to Know ... Wanted to Know About Sex* (*But Were Afraid to Ask), ... Everything You Always Wanted Know ...
Read more

Everything You Always Wanted to Know About Synchronization ...

Everything You Always Wanted to Know About Synchronization but Were Afraid to Ask Tudor David, Rachid Guerraoui, Vasileios Trigonakis School of Computer ...
Read more