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Cassandra By Example:
Data Modelling with CQL3


Eric Evans
eevans@opennms.com
@jericevans
CQL is...
● Query language for Apache Cassandra
● Almost SQL (almost)
● Alternative query interface First class citizen
● More performant!
● Available since Cassandra 0.8.0 (almost 2
  years!)
Bad Old Days: Thrift RPC
Bad Old Days: Thrift RPC
// Your Column
Column col = new Column(ByteBuffer.wrap("name".getBytes()));
col.setValue(ByteBuffer.wrap("value".getBytes()));
col.setTimestamp(System.currentTimeMillis());


// Don't ask
ColumnOrSuperColumn cosc = new ColumnOrSuperColumn();
cosc.setColumn(col);


// Prepare to be amazed
Mutation mutation = new Mutation();
mutation.setColumnOrSuperColumn(cosc);


List<Mutation> mutations = new ArrayList<Mutation>();
mutations.add(mutation);


Map mutations_map = new HashMap<ByteBuffer, Map<String, List<Mutation>>>();
Map cf_map = new HashMap<String, List<Mutation>>();
cf_map.set("Standard1", mutations);
mutations_map.put(ByteBuffer.wrap("key".getBytes()), cf_map);


cassandra.batch_mutate(mutations_map, consistency_level);
Better, no?




INSERT INTO (id, name) VALUES ('key', 'value');
But before we begin...
Partitioning

               Z   A




        Q              E




               M   I
Partitioning

               Z         A




        Q          Cat       E




               M         I
Partitioning

               Z         A




        Q          Cat       E




               M         I
Partitioning

         A



                                   Pets

                   Animal   Type     Size    Youtub-able

               E   Cat      mammal   small   true

                                     ...




         I
Twissandra
● Twitter-inspired sample application
● Originally by Eric Florenzano, June 2009
● Python (Django)
● DBAPI-2 driver for CQL
● Favors simplicity over correctness!
● https://github.com/eevans/twissandra
   ○ See: cass.py
Twissandra
Twissandra
Twissandra
Twissandra
Twissandra
Twissandra Explained
users
users

-- User storage
CREATE TABLE users (
   username text PRIMARY KEY,
   password text
);
users

-- Adding users (signup)
INSERT INTO users (username, password)
    VALUES ('meg', 's3kr3t')
users
users

-- Lookup password (login)
SELECT password FROM users
    WHERE username = 'meg'
following / followers
following

-- Users a user is following
CREATE TABLE following (
   username text,
   followed text,
   PRIMARY KEY(username, followed)
);
following

-- Meg follows Stewie
INSERT INTO following (username, followed)
    VALUES ('meg', 'stewie')

-- Get a list of who Meg follows
SELECT followed FROM following
    WHERE username = 'meg'
users @meg is following
  followed
----------
    brian
    chris
     lois
    peter
   stewie
 quagmire
      ...
followers

-- The users who follow username
CREATE TABLE followers (
   username text,
   following text,
   PRIMARY KEY(username, following)
);
followers

-- Meg follows Stewie
INSERT INTO followers (username, followed)
    VALUES ('stewie', 'meg')

-- Get a list of who follows Stewie
SELECT followers FROM following
    WHERE username = 'stewie'
redux: following / followers

-- @meg follows @stewie
BEGIN BATCH
  INSERT INTO following (username, followed)
      VALUES ('meg', 'stewie')
  INSERT INTO followers (username, followed)
      VALUES ('stewie', 'meg')
APPLY BATCH
tweets
Denormalization Ahead!
tweets

-- Tweet storage (think: permalink)
CREATE TABLE tweets (
   tweetid uuid PRIMARY KEY,
   username text,
   body text
);
tweets
-- Store a tweet
INSERT INTO tweets (
   tweetid,
   username,
   body
) VALUES (
   60780342-90fe-11e2-8823-0026c650d722,
   'stewie',
   'victory is mine!'
)
Query tweets by ... ?
● author, time descending
● followed authors, time descending
● date starting / date ending
userline
tweets, by user
userline
-- Materialized view of the tweets
-- created by user.
CREATE TABLE userline (
   username text,
   tweetid timeuuid,
   body text,
   PRIMARY KEY(username, tweetid)
);
Wait, WTF is a timeuuid?
● Aka "Type 1 UUID" (http://goo.gl/SWuCb)
● 100 nano second units since Oct. 15, 1582
● Timestamp is first 60 bits (sorts temporally!)
● Used like timestamp, but:
   ○ more granular
   ○ globally unique
userline
-- Range of tweets for a user
SELECT
  dateOf(tweetid), body
FROM
  userline
WHERE
  username = 'stewie' AND
  tweetid > minTimeuuid('2013-03-01 12:10:09')
ORDER BY
  tweetid DESC
LIMIT 40
@stewie's most recent tweets
 dateOf(posted_at)        | body
--------------------------+-------------------------------
 2013-03-19 14:43:15-0500 |               victory is mine!
 2013-03-19 13:23:24-0500 |      generate killer bandwidth
 2013-03-19 13:23:24-0500 |            grow B2B e-business
 2013-03-19 13:23:24-0500 |   innovate vertical e-services
 2013-03-19 13:23:24-0500 | deploy e-business experiences
 2013-03-19 13:23:24-0500 | grow intuitive infrastructures
 ...
timeline
tweets from those a user follows
timeline
-- Materialized view of tweets from
-- the users username follows.
CREATE TABLE timeline (
   username text,
   tweetid timeuuid,
   posted_by text,
   body text,
   PRIMARY KEY(username, tweetid)
);
timeline
-- Range of tweets for a user
SELECT
  dateOf(tweetid), posted_by, body
FROM
  timeline
WHERE
  username = 'stewie' AND
  tweetid > '2013-03-01 12:10:09'
ORDER BY
  tweetid DESC
LIMIT 40
most recent tweets for @meg
 dateOf(posted_at)        | posted_by | body
--------------------------+-----------+-------------------
 2013-03-19 14:43:15-0500 |    stewie |   victory is mine!
 2013-03-19 13:23:25-0500 |       meg |   evolve intuit...
 2013-03-19 13:23:25-0500 |       meg | whiteboard bric...
 2013-03-19 13:23:25-0500 |    stewie |      brand clic...
 2013-03-19 13:23:25-0500 |     brian | synergize gran...
 2013-03-19 13:23:24-0500 |     brian | expedite real-t...
 2013-03-19 13:23:24-0500 |    stewie |    generate kil...
 2013-03-19 13:23:24-0500 |    stewie |       grow B2B ...
 2013-03-19 13:23:24-0500 |       meg | generate intera...
 ...
redux: tweets
-- @stewie tweets
BEGIN BATCH
  INSERT INTO tweets ...
  INSERT INTO userline ...
  INSERT INTO timeline ...
  INSERT INTO timeline ...
  INSERT INTO timeline ...
  ...
APPLY BATCH
In Conclusion:
● Think in terms of your queries, store that
● Don't fear duplication; Space is cheap to scale
● Go wide; Rows can have 2 billion columns!
● The only thing better than NoSQL, is MoSQL
● Python hater? Java ❤'r?
   ○ https://github.com/eevans/twissandra-j
● http://goo.gl/zPOD
The   End

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Cassandra By Example: Data Modelling with CQL3

  • 1. Cassandra By Example: Data Modelling with CQL3 Eric Evans eevans@opennms.com @jericevans
  • 2. CQL is... ● Query language for Apache Cassandra ● Almost SQL (almost) ● Alternative query interface First class citizen ● More performant! ● Available since Cassandra 0.8.0 (almost 2 years!)
  • 3. Bad Old Days: Thrift RPC
  • 4. Bad Old Days: Thrift RPC // Your Column Column col = new Column(ByteBuffer.wrap("name".getBytes())); col.setValue(ByteBuffer.wrap("value".getBytes())); col.setTimestamp(System.currentTimeMillis()); // Don't ask ColumnOrSuperColumn cosc = new ColumnOrSuperColumn(); cosc.setColumn(col); // Prepare to be amazed Mutation mutation = new Mutation(); mutation.setColumnOrSuperColumn(cosc); List<Mutation> mutations = new ArrayList<Mutation>(); mutations.add(mutation); Map mutations_map = new HashMap<ByteBuffer, Map<String, List<Mutation>>>(); Map cf_map = new HashMap<String, List<Mutation>>(); cf_map.set("Standard1", mutations); mutations_map.put(ByteBuffer.wrap("key".getBytes()), cf_map); cassandra.batch_mutate(mutations_map, consistency_level);
  • 5. Better, no? INSERT INTO (id, name) VALUES ('key', 'value');
  • 6. But before we begin...
  • 7. Partitioning Z A Q E M I
  • 8. Partitioning Z A Q Cat E M I
  • 9. Partitioning Z A Q Cat E M I
  • 10. Partitioning A Pets Animal Type Size Youtub-able E Cat mammal small true ... I
  • 11.
  • 12. Twissandra ● Twitter-inspired sample application ● Originally by Eric Florenzano, June 2009 ● Python (Django) ● DBAPI-2 driver for CQL ● Favors simplicity over correctness! ● https://github.com/eevans/twissandra ○ See: cass.py
  • 19. users
  • 20. users -- User storage CREATE TABLE users ( username text PRIMARY KEY, password text );
  • 21. users -- Adding users (signup) INSERT INTO users (username, password) VALUES ('meg', 's3kr3t')
  • 22. users
  • 23. users -- Lookup password (login) SELECT password FROM users WHERE username = 'meg'
  • 25. following -- Users a user is following CREATE TABLE following ( username text, followed text, PRIMARY KEY(username, followed) );
  • 26. following -- Meg follows Stewie INSERT INTO following (username, followed) VALUES ('meg', 'stewie') -- Get a list of who Meg follows SELECT followed FROM following WHERE username = 'meg'
  • 27. users @meg is following followed ---------- brian chris lois peter stewie quagmire ...
  • 28.
  • 29. followers -- The users who follow username CREATE TABLE followers ( username text, following text, PRIMARY KEY(username, following) );
  • 30. followers -- Meg follows Stewie INSERT INTO followers (username, followed) VALUES ('stewie', 'meg') -- Get a list of who follows Stewie SELECT followers FROM following WHERE username = 'stewie'
  • 31. redux: following / followers -- @meg follows @stewie BEGIN BATCH INSERT INTO following (username, followed) VALUES ('meg', 'stewie') INSERT INTO followers (username, followed) VALUES ('stewie', 'meg') APPLY BATCH
  • 34. tweets -- Tweet storage (think: permalink) CREATE TABLE tweets ( tweetid uuid PRIMARY KEY, username text, body text );
  • 35. tweets -- Store a tweet INSERT INTO tweets ( tweetid, username, body ) VALUES ( 60780342-90fe-11e2-8823-0026c650d722, 'stewie', 'victory is mine!' )
  • 36. Query tweets by ... ? ● author, time descending ● followed authors, time descending ● date starting / date ending
  • 38. userline -- Materialized view of the tweets -- created by user. CREATE TABLE userline ( username text, tweetid timeuuid, body text, PRIMARY KEY(username, tweetid) );
  • 39. Wait, WTF is a timeuuid? ● Aka "Type 1 UUID" (http://goo.gl/SWuCb) ● 100 nano second units since Oct. 15, 1582 ● Timestamp is first 60 bits (sorts temporally!) ● Used like timestamp, but: ○ more granular ○ globally unique
  • 40. userline -- Range of tweets for a user SELECT dateOf(tweetid), body FROM userline WHERE username = 'stewie' AND tweetid > minTimeuuid('2013-03-01 12:10:09') ORDER BY tweetid DESC LIMIT 40
  • 41. @stewie's most recent tweets dateOf(posted_at) | body --------------------------+------------------------------- 2013-03-19 14:43:15-0500 | victory is mine! 2013-03-19 13:23:24-0500 | generate killer bandwidth 2013-03-19 13:23:24-0500 | grow B2B e-business 2013-03-19 13:23:24-0500 | innovate vertical e-services 2013-03-19 13:23:24-0500 | deploy e-business experiences 2013-03-19 13:23:24-0500 | grow intuitive infrastructures ...
  • 42. timeline tweets from those a user follows
  • 43. timeline -- Materialized view of tweets from -- the users username follows. CREATE TABLE timeline ( username text, tweetid timeuuid, posted_by text, body text, PRIMARY KEY(username, tweetid) );
  • 44. timeline -- Range of tweets for a user SELECT dateOf(tweetid), posted_by, body FROM timeline WHERE username = 'stewie' AND tweetid > '2013-03-01 12:10:09' ORDER BY tweetid DESC LIMIT 40
  • 45. most recent tweets for @meg dateOf(posted_at) | posted_by | body --------------------------+-----------+------------------- 2013-03-19 14:43:15-0500 | stewie | victory is mine! 2013-03-19 13:23:25-0500 | meg | evolve intuit... 2013-03-19 13:23:25-0500 | meg | whiteboard bric... 2013-03-19 13:23:25-0500 | stewie | brand clic... 2013-03-19 13:23:25-0500 | brian | synergize gran... 2013-03-19 13:23:24-0500 | brian | expedite real-t... 2013-03-19 13:23:24-0500 | stewie | generate kil... 2013-03-19 13:23:24-0500 | stewie | grow B2B ... 2013-03-19 13:23:24-0500 | meg | generate intera... ...
  • 46. redux: tweets -- @stewie tweets BEGIN BATCH INSERT INTO tweets ... INSERT INTO userline ... INSERT INTO timeline ... INSERT INTO timeline ... INSERT INTO timeline ... ... APPLY BATCH
  • 47. In Conclusion: ● Think in terms of your queries, store that ● Don't fear duplication; Space is cheap to scale ● Go wide; Rows can have 2 billion columns! ● The only thing better than NoSQL, is MoSQL ● Python hater? Java ❤'r? ○ https://github.com/eevans/twissandra-j ● http://goo.gl/zPOD
  • 48. The End