Scaling Web Applications with Cassandra Presentation (1).ppt

introduction to cassandra eben hewitt september 29. 2010 web 2.0 expo new york city

• director, application architecture at a global corp • focus on SOA, SaaS, Events • i wrote this @ebenhewitt

agenda • context • features • data model • api

“nosql”  “big data” • mongodb • couchdb • tokyo cabinet • redis • riak • what about? – Poet, Lotus, Xindice – they’ve been around forever… – rdbms was once the new kid…

innovation at scale • google bigtable (2006) – consistency model: strong – data model: sparse map – clones: hbase, hypertable • amazon dynamo (2007) – O(1) dht – consistency model: client tune-able – clones: riak, voldemort cassandra ~= bigtable + dynamo

proven • The Facebook stores 150TB of data on 150 nodes web 2.0 • used at Twitter, Rackspace, Mahalo, Reddit, Cloudkick, Cisco, Digg, SimpleGeo, Ooyala, OpenX, others

cap theorem •consistency – all clients have same view of data •availability – writeable in the face of node failure •partition tolerance – processing can continue in the face of network failure (crashed router, broken network)

write consistency Level Description ZERO Good luck with that ANY 1 replica (hints count) ONE 1 replica. read repair in bkgnd QUORUM (DCQ for RackAware) (N /2) + 1 ALL N = replication factor Level Description ZERO Ummm… ANY Try ONE instead ONE 1 replica QUORUM (DCQ for RackAware) Return most recent TS after (N /2) + 1 report ALL N = replication factor read consistency

cassandra properties • tuneably consistent • very fast writes • highly available • fault tolerant • linear, elastic scalability • decentralized/symmetric • ~12 client languages – Thrift RPC API • ~automatic provisioning of new nodes • 0(1) dht • big data

Staged Event-Driven Architecture • A general-purpose framework for high concurrency & load conditioning • Decomposes applications into stages separated by queues • Adopt a structured approach to event-driven concurrency

partitioner smack-down Random Preserving • system will use MD5(key) to distribute data across nodes • even distribution of keys from one CF across ranges/nodes Order Preserving • key distribution determined by token • lexicographical ordering • required for range queries – scan over rows like cursor in index • can specify the token for this node to use • ‘scrabble’ distribution

keyspace • ~= database • typically one per application • some settings are configurable only per keyspace

column family • group records of similar kind • not same kind, because CFs are sparse tables • ex: – User – Address – Tweet – PointOfInterest – HotelRoom

think of cassandra as row-oriented • each row is uniquely identifiable by key • rows group columns and super columns

column family n= 42 user=eben key 123 key 456 user=alison icon= nickname= The Situation

json-like notation User { 123 : { email: alison@foo.com, icon: }, 456 : { email: eben@bar.com, location: The Danger Zone} }

0.6 example $cassandra –f $bin/cassandra-cli cassandra> connect localhost/9160 cassandra> set Keyspace1.Standard1[‘eben’] [‘age’]=‘29’ cassandra> set Keyspace1.Standard1[‘eben’] [‘email’]=‘e@e.com’ cassandra> get Keyspace1.Standard1[‘eben'][‘age'] => (column=6e616d65, value=39, timestamp=1282170655390000)

a column has 3 parts 1. name – byte[] – determines sort order – used in queries – indexed 2. value – byte[] – you don’t query on column values 3. timestamp – long (clock) – last write wins conflict resolution

column comparators • byte • utf8 • long • timeuuid • lexicaluuid • <pluggable> – ex: lat/long

super column super columns group columns under a common name

<<SCF>>PointOfInterest super column family <<SC>>Central Park 10017 <<SC>> Empire State Bldg <<SC>> Phoenix Zoo 85255 desc=Fun to walk in. phone=212. 555.11212 desc=Great view from 102nd floor!

PointOfInterest { key: 85255 { Phoenix Zoo { phone: 480-555-5555, desc: They have animals here. }, Spring Training { phone: 623-333-3333, desc: Fun for baseball fans. }, }, //end phx key: 10019 { Central Park { desc: Walk around. It's pretty.} , Empire State Building { phone: 212-777-7777, desc: Great view from 102nd floor. } } //end nyc } s super column super column family flexible schema key column super column family

about super column families • sub-column names in a SCF are not indexed – top level columns (SCF Name) are always indexed • often used for denormalizing data from standard CFs

slice predicate • data structure describing columns to return – SliceRange • start column name • finish column name (can be empty to stop on count) • reverse • count (like LIMIT)

read api • get() : Column – get the Col or SC at given ColPath COSC cosc = client.get(key, path, CL); • get_slice() : List<ColumnOrSuperColumn> – get Cols in one row, specified by SlicePredicate: List<ColumnOrSuperColumn> results = client.get_slice(key, parent, predicate, CL); • multiget_slice() : Map<key, List<CoSC>> – get slices for list of keys, based on SlicePredicate Map<byte[],List<ColumnOrSuperColumn>> results = client.multiget_slice(rowKeys, parent, predicate, CL); • get_range_slices() : List<KeySlice> – returns multiple Cols according to a range – range is startkey, endkey, starttoken, endtoken: List<KeySlice> slices = client.get_range_slices( parent, predicate, keyRange, CL);

write api client.insert(userKeyBytes, parent, new Column(“band".getBytes(UTF8), “Funkadelic".getBytes(), clock), CL); batch_mutate – void batch_mutate( map<byte[], map<String, List<Mutation>>> , CL) remove – void remove(byte[], ColumnPath column_path, Clock, CL)

batch_mutate //create param Map<byte[], Map<String, List<Mutation>>> mutationMap = new HashMap<byte[], Map<String, List<Mutation>>>(); //create Cols for Muts Column nameCol = new Column("name".getBytes(UTF8), “Funkadelic”.getBytes("UTF-8"), new Clock(System.nanoTime());); Mutation nameMut = new Mutation(); nameMut.column_or_supercolumn = nameCosc; //also phone, etc Map<String, List<Mutation>> muts = new HashMap<String, List<Mutation>>(); List<Mutation> cols = new ArrayList<Mutation>(); cols.add(nameMut); cols.add(phoneMut); muts.put(CF, cols); //outer map key is a row key; inner map key is the CF name mutationMap.put(rowKey.getBytes(), muts); //send to server client.batch_mutate(mutationMap, CL);

raw thrift: for masochists only • pycassa (python) • fauna (ruby) • hector (java) • pelops (java) • kundera (JPA) • hectorSharp (C#)

what about… SELECT WHERE ORDER BY JOIN ON GROUP ?

rdbms: domain-based model what answers do I have? cassandra: query-based model what questions do I have?

SELECT WHERE cassandra is an index factory <<cf>>USER Key: UserID Cols: username, email, birth date, city, state How to support this query? SELECT * FROM User WHERE city = ‘Scottsdale’ Create a new CF called UserCity: <<cf>>USERCITY Key: city Cols: IDs of the users in that city. Also uses the Valueless Column pattern

• Use an aggregate key state:city: { user1, user2} • Get rows between AZ: & AZ; for all Arizona users • Get rows between AZ:Scottsdale & AZ:Scottsdale1 for all Scottsdale users SELECT WHERE pt 2

ORDER BY Rows are placed according to their Partitioner: •Random: MD5 of key •Order-Preserving: actual key are sorted by key, regardless of partitioner Columns are sorted according to CompareWith or CompareSubcolumnsWith

is cassandra a good fit? • you need really fast writes • you need durability • you have lots of data > GBs >= three servers • your app is evolving – startup mode, fluid data structure • loose domain data – “points of interest” • your programmers can deal – documentation – complexity – consistency model – change – visibility tools • your operations can deal – hardware considerations – can move data – JMX monitoring

Scaling Web Applications with Cassandra Presentation (1).ppt

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Scaling Web Applications with Cassandra Presentation (1).ppt