Scaling web applications with cassandra presentation

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Scaling web applications with cassandra presentation

  1. 1. introduction to cassandra eben hewitt september 29. 2010 web 2.0 expo new york city
  2. 2. @ebenhewitt• director, application architecture at a global corp• focus on SOA, SaaS, Events• i wrote this
  3. 3. agenda• context• features• data model• api
  4. 4. “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…
  5. 5. 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
  6. 6. 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
  7. 7. 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)
  8. 8. daniel abadi: pacelc
  9. 9. write consistencyLevel DescriptionZERO Good luck with thatANY 1 replica (hints count)ONE 1 replica. read repair in bkgndQUORUM (DCQ for RackAware) (N /2) + 1ALL N = replication factor read consistencyLevel DescriptionZERO Ummm…ANY Try ONE insteadONE 1 replicaQUORUM (DCQ for RackAware) Return most recent TS after (N /2) + 1 reportALL N = replication factor
  10. 10. agenda• context• features• data model• api
  11. 11. 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
  12. 12. write op
  13. 13. 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
  14. 14. instrumentation
  15. 15. data replication
  16. 16. partitioner smack-downRandom Preserving Order Preserving• system will use MD5(key) to • key distribution determined distribute data across nodes by token• even distribution of keys • lexicographical ordering from one CF across • required for range queries ranges/nodes – scan over rows like cursor in index • can specify the token for this node to use • ‘scrabble’ distribution
  17. 17. agenda• context• features• data model• api
  18. 18. structure
  19. 19. keyspace• ~= database• typically one per application• some settings are configurable only per keyspace
  20. 20. column family• group records of similar kind• not same kind, because CFs are sparse tables• ex: – User – Address – Tweet – PointOfInterest – HotelRoom
  21. 21. think of cassandra as row-oriented• each row is uniquely identifiable by key• rows group columns and super columns
  22. 22. column familykey nickname= user=eben The123 Situationkey icon= n= user=alison456 42
  23. 23. json-like notationUser { 123 : { email: alison@foo.com, icon: }, 456 : { email: eben@bar.com, location: The Danger Zone}}
  24. 24. 0.6 example$cassandra –f$bin/cassandra-clicassandra> connect localhost/9160cassandra> 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)
  25. 25. a column has 3 parts1. name – byte[] – determines sort order – used in queries – indexed1. value – byte[] – you don’t query on column values1. timestamp – long (clock) – last write wins conflict resolution
  26. 26. column comparators• byte• utf8• long• timeuuid• lexicaluuid• <pluggable> – ex: lat/long
  27. 27. super columnsuper columns group columns under a common name
  28. 28. super column family <<SCF>>PointOfInterest <<SC>>Central <<SC>> Park Empire State Bldg10017 desc=Fun to desc=Great phone=212. walk in. view from 555.11212 102nd floor! <<SC>>85255 Phoenix Zoo
  29. 29. super column family super column familyPointOfInterest { key: 85255 { column 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 super column key: 10019 { flexible schema Central Park { desc: Walk around. Its pretty.} , s Empire State Building { phone: 212-777-7777, desc: Great view from 102nd floor. } } //end nyc}
  30. 30. 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
  31. 31. agenda• context• features• data model• api
  32. 32. 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)
  33. 33. • get() : Column – get the Col or SC at given ColPath read api 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);
  34. 34. client.insert(userKeyBytes, parent, write api 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)
  35. 35. //create param batch_mutateMap<byte[], Map<String, List<Mutation>>> mutationMap = new HashMap<byte[], Map<String, List<Mutation>>>();//create Cols for MutsColumn 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, etcMap<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 namemutationMap.put(rowKey.getBytes(), muts);//send to serverclient.batch_mutate(mutationMap, CL);
  36. 36. raw thrift: for masochists only• pycassa (python)• fauna (ruby)• hector (java)• pelops (java)• kundera (JPA)• hectorSharp (C#)
  37. 37. ? what about… SELECT WHERE ORDER BY JOIN ON GROUP
  38. 38. rdbms: domain-based model what answers do I have?cassandra: query-based model what questions do I have?
  39. 39. SELECT WHERE cassandra is an index factory<<cf>>USERKey: UserIDCols: username, email, birth date, city, stateHow to support this query?SELECT * FROM User WHERE city = ‘Scottsdale’Create a new CF called UserCity:<<cf>>USERCITYKey: cityCols: IDs of the users in that city.Also uses the Valueless Column pattern
  40. 40. SELECT WHERE pt 2• 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
  41. 41. ORDER BYColumns Rowsare sorted according to are placed according to their Partitioner:CompareWith orCompareSubcolumnsWith •Random: MD5 of key •Order-Preserving: actual key are sorted by key, regardless of partitioner
  42. 42. is cassandra a good fit?• you need really fast writes • your programmers can deal• you need durability – documentation• you have lots of data – complexity – consistency model > GBs – change >= three servers – visibility tools• your app is evolving • your operations can deal – startup mode, fluid data – hardware considerations structure – can move data• loose domain data – JMX monitoring – “points of interest”
  43. 43. thank you!@ebenhewitt

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