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

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Yet another Redis presentation: ...

Yet another Redis presentation:

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

- Redis 101: basics, scripting, some examples…

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

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

    • 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 ! 127.0.0.1:6379> SET the-answer 42 OK ! 127.0.0.1:6379> QUIT ! rulo:~$
    • SELECT * FROM… $ redis-cli -n 0 ! 127.0.0.1:6379> GET the-answer "42" ! 127.0.0.1:6379> 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/