Scaling the Web: Databases & NoSQL

Scaling the Web: Databases & NoSQL Richard Schneeman Wed Nov 10 @schneems works for @Gowalla 2011

whoami • @Schneems • BSME with Honors from Georgia Tech • 5 + years experience Ruby & Rails • Work for @Gowalla • Rails 3.1 contributor : ) • 3 + years technical teaching

Compounding Traﬀic ex. Wikipedia

Gowalla • 50 best websites NYTimes 2010 • Founded 2009 @ SXSW • 1 million+ Users • Undisclosed Visitors • Loves/highlights/comments/stories/guides • Facebook/Foursquare/Twitter integration • iphone/android/web apps • public API

Gowalla Backend • Ruby on Rails • Uses the Ruby Language • Rails is the Framework

The Web is Data • Username => String • Birthday => Int/ Int/ Int • Blog Post => Text • Image => Binary-ﬁle/blob Data needs to be stored to be useful

Gowalla Database • PostgreSQL • Relational (RDBMS) • Open Source • Competitor to MySQL • ACID compliant • Running on a Dedicated Managed Server

Need for Speed • Throughput: • The number of operations per minute that can be performed • Pure Speed: • How long an individual operation takes.

Potential Problems • Hardware • Slow Network • Slow hard-drive • Insuﬀicient CPU • Insuﬀicient Ram • Software • too many Reads • too many Writes

Scaling Up versus Out • Scale Up: • More CPU, Bigger HD, More Ram etc. • Scale Out: • More machines • More machines • More machines • ...

Scale Up • Bigger faster machine • More Ram • More CPU • Bigger ethernet bus • ... • Moores Law • Diminishing returns

Scale Out • Forget Moores law... • Add more nodes • Master/ Slave Database • Sharding

Master/Slave Write Master DB Copy Slave DB Slave DB Slave DB Slave DB Read

Master & Slave +/- • Pro • Increased read speed • Takes read load oﬀ of master • Allows us to Join across all tables • Con • Doesn’t buy increased write throughput • Single Point of Failure in Master Node

Sharding Write Users in Users in Users in Users in USA Europe Asia Africa Read

Sharding +/- • Pro • Increased Write & Read throughput • No Single Point of failure • Individual features can fail • Con • Cannot Join queries between shards

What is a Database? • Relational Database Managment System (RDBMS) • Stores Data Using Schema • A.C.I.D. compliant • Atomic • Consistent • Isolated • Durable

RDBMS • Relational • Matches data on common characteristics in data • Enables “Join” & “Union” queries • Makes data modular

Relational +/- • Pros • Data is modular • Highly ﬂexible data layout • Cons • Getting desired data can be tricky • Over modularization leads to many join queries • Trade oﬀ performance for search-ability

Schema Storage • Blueprint for data storage • Break data into tables/columns/rows • Give data types to your data • Integer • String • Text • Boolean • ...

Schema +/- • Pros • Regularize our data • Helps keep data consistent • Converts to programming “types” easily • Cons • Must seperatly manage schema • Adding columns & indexes to existing large tables can be painful & slow

ACID • Properties that guarante a reliably transaction are processed database • Atomic • Consistent • Isolated • Durable

ACID • Atomic • Any database Transaction is all or nothing. • If one part of the transaction fails it all fails “An Incomplete Transaction Cannot Exist”

ACID • Consistent • Any transaction will take the another from one consistent state to database “Only Consistent data is allowed to be written”

ACID • Isolated • No transaction should be able to interfere with another transaction “the same ﬁeld cannot be updated by two sources at the exact same time” } a = 0 a += 1 a = ?? a += 2

ACID • Durable • Onceway that a transaction Is committed it will stay “Save it once, read it forever”

What is a Database? • RDBMS • Relational • Flexible • Has a schema • Most likely ACID compliant • Typically fast under low load or when optimized

What is SQL? • Structured Query Language • The language databases speak • Based on relational algebra • Insert • Query • Update • Delete “SELECT Company, Country FROM Customers WHERE Country = 'USA' ”

Why people <3 SQL • Relational algebra is powerful • SQL is proven • well understood • well documented

Why people </3 SQL • Relational algebra Is hard • Diﬀerent databases support diﬀerent SQL syntax • Yet another programming language to learn

SQL != Database • SQL is used to talk to a RDBMS (database) • SQL is not a RDBMS

What is NoSQL? Not A Relational Database

Types of NoSQL • Distributed Systems • Document Store • Graph Database • Key-Value Store • Eventually Consistent Systems Mix And Match ↑

Key Value Stores • Non Relational • Typically No Schema • Map one Key (a string) to a Value (some object) Example: Redis

Key Value Example redis = Redis.new redis.set(“foo”, “bar”) redis.get(“foo”) >> “bar”

Key Value Example redis = Redis.new Key Value redis.set(“foo”, “bar”) Key redis.get(“foo”) Value >> “bar”

Key Value • Like a databse that can only ever use primary Key (id) YES select * from users where id = ‘3’; NO select * from users where name = ‘schneems’;

NoSQL @ Gowalla • Redis (key-value store) • Store “Likes” & Analytics • Memcache (key-value store) • Cache Database results • Cassandra • (eventually consistent, with-schema, key value store) • Store “feeds” or “timelines” • Solr (search index)

Memcache • Key-Value Store • Open Source • Distributed • In memory (ram) only • fast, but volatile • Not ACID • Memory object caching system

Memcache Example memcache = Memcache.new memcache.set(“foo”, “bar”) memcache.get(“foo”) >> “bar”

Memcache • Can store whole objects memcache = Memcache.new user = User.where(:username => “schneems”) memcache.set(“user:3”, user) user_from_cache = memcache.get(“user:3”) user_from_cache == user >> true user_from_cache.username >> “Schneems”

Memcache @ Gowalla • Cache Common Queries • Decreases Load on DB (postgres) • Enables higher throughput from DB • Faster response than DB • Users see quicker page load time

What to Cache? • Objects that change infrequently • users • spots (places) • etc. • Expensive(ish) sql queries • Friend ids for users • User ids for people visiting spots • etc.

Memcache Distributed Easily add more nodes A D B C

Memcache <3’s DB • We use them Together • If memcache doesn’t have a value • Fetch from the database • Set the key from database • Hard • Cache Invalidation : (

Redis • Key Value Store • Open Source • Not Distributed (yet) • Extremely Quick • “Data structure server”

Redis Example, again redis = Redis.new redis.set(“foo”, “bar”) redis.get(“foo”) >> “bar”

Redis - Has Data Types • Strings • Hashes • Lists • Sets • Sorted Sets

Redis Example, sets redis = Redis.new redis.sadd(“foo”, “bar”) redis.members(“foo”) >> [“bar”] redis.sadd(“foo”, “fly”) redis.members(“foo”) >> [“bar”, “fly”]

Redis => Likeable • Very Fast response • ~ 50 queries per page view • ~ 1 ms per query • http://github.com/Gowalla/likeable

Cassandra • Open Source • Distributed • Key Value Store • Eventually Consistent • Sortof not ACID • Uses A Schema • ColumnFamilies

Cassandra Distributed Eventual Consistency A D Copied To Extra Nodes ... Eventually Data In B C

Cassandra { @ Gowalla Activity Feeds

Cassandra @ Gowalla • Chronologic • http://github.com/Gowalla/chronologic

Tradeoﬀs • Every Data store has them • Know your data store • Strengths • Weaknesses

NoSQL vs. RDBMS • No Magic Bullet • Use Both!!! • Model data in a datastore you understand • Switch to when/if you need to • Understand Your Options

Questions? Richard Schneeman @schneems works for @Gowalla

Scaling the Web: Databases & NoSQL

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