Intro to Cassandra and the Python driver

Presented at the Austin Web Python Users Group on February 27th, 2014.

There's a video recording of the talk here: http://capitalfactory.lifesize.com/videos/video/322/?access_token=shr00000003227541148304880759831091078996279

Transcript

1. ©2014 DataStax Cassandra committer, drivers eng at DataStax tyler@datastax.com @tylhobbs Tyler Hobbs Cassandra + Python 1
2. Five years of Cassandra Jul-09 May-10 Feb-11 Dec-11 Oct-12 Jul-13 0.1 0.3 0.6 0.7 1.0 1.2 ... 2.0 DSE Jul-08
4. Application/Use Case • Social Signals: like/want/own features for eBay product and item pages • Hunch taste graph for eBay users and items • Many time series use cases Why Cassandra? • Multi-datacenter • Scalable • Write performance • Distributed counters • Hadoop support ACE
5. Time series data
6. Multi-datacenter support
8. Distributed counters
9. Hadoop support
10. Application/Use Case • Adobe AudienceManager: web analytics, content management, and online advertising Why Cassandra? • Low-latency • Scalable • Multi-datacenter • Tuneable consistency ACE
11. Low latency
12. Bootstrapping
13. Bootstrapping
14. Bootstrapping s d s d s d s d
15. Bootstrapping s d s d s d s d
16. Bootstrapping
17. Tuneable consistency •(We’ll come back to this)
18. Application/Use Case • Logging • Notifications Why Cassandra? • Efficient writes • Durable • Scalable • High availability ACE
19. Durable + efficient writes Memory Hard drive Memtable write( , ) k1 c1:v1 Commit log
20. Memory Hard drive Memtable write( , k1 c1:v Commit log k1 c1:v k1 c1:v
21. Memory Hard drive write( , k1 c2:v k1 c1:v k1 c1:v k1 c2:v c2:v
22. Memory Hard drive k1 c1:v k1 c1:v k1 c2:v c2:v write( , ) k2 c1:v c2:v k2 c1:v c2:v k2 c1:v c2:v
23. Memory Hard drive k1 c1:v k1 c1:v k1 c2:v c2:v write( , ) k1 c1:v c3:v k2 c1:v c2:v k2 c1:v c2:v k1 c1:v c3:v c3:v
24. Memory Hard drive SSTable flush k1 c1:v c2:v k2 c1:v c2:v c3:v index / BF cleanup
25. High availability •99.9999% availability on Cassandra •(We’ll come back to this, too)
26. Core values •Massive scalability •High performance •Ease of use •Reliability/ Cassandra HBase Redis MySQL
27. 0 20000 40000 60000 80000 0 2 4 6 8 10 12 Cassandra HBase Redis MySQL NUMBER OF NODES THROUGHPUT OPS/SEC) CASSANDRA VLDB benchmark (RWS)
28. 0 8750 17500 26250 35000 1 2 4 8 16 32 Cassandra HBase MongoDB CA SSA N D RA Endpoint benchmark (RW) THROUGHPUT OPS/SEC) NUMBER OF NODES
29. Ease of use CREATE TABLE users ( id uuid PRIMARY KEY, name text, state text, birth_date int ); CREATE INDEX ON users(state); SELECT * FROM users WHERE state=‘Texas’ AND birth_date > 1950;
31. Classic partitioning (SPOF) partition 1 partition 2 partition 3 partition 4 router client
32. (Not a theoretical problem) https://speakerdeck.com/mitsuhiko/a-year-of-mongodb http://aphyr.com/posts/288-the-network-is-reliable
33. Fully distributed, no SPOF p1 p1 p1 p3 p6 Client
34. Primary key determines placement* Partitioning jim carol johnny suzy age: 36 car: camaro gender: M age: 37 car: subaru gender: F age:12 gender: M age:10 gender: F
35. jim carol johnny suzy PK 5e02739678... a9a0198010... f4eb27cea7... 78b421309e... Murmur Hash Murmur* hash operation yields a 64-bit number for keys of any size.
36. Node D Node C Node B Node A The “token ring”
37. jim 5e02739678... carol a9a0198010... johnny f4eb27cea7... suzy 78b421309e... Start End A 0xc000000000..1 0x0000000000..0 B 0x0000000000..1 0x4000000000..0 C 0x4000000000..1 0x8000000000..0 D 0x8000000000..1 0xc000000000..0
38. jim 5e02739678... carol a9a0198010... johnny f4eb27cea7... suzy 78b421309e... Start End A 0xc000000000..1 0x0000000000..0 B 0x0000000000..1 0x4000000000..0 C 0x4000000000..1 0x8000000000..0 D 0x8000000000..1 0xc000000000..0
39. jim 5e02739678... carol a9a0198010... johnny f4eb27cea7... suzy 78b421309e... Start End A 0xc000000000..1 0x0000000000..0 B 0x0000000000..1 0x4000000000..0 C 0x4000000000..1 0x8000000000..0 D 0x8000000000..1 0xc000000000..0
40. jim 5e02739678... carol a9a0198010... johnny f4eb27cea7... suzy 78b421309e... Start End A 0xc000000000..1 0x0000000000..0 B 0x0000000000..1 0x4000000000..0 C 0x4000000000..1 0x8000000000..0 D 0x8000000000..1 0xc000000000..0
41. jim 5e02739678... carol a9a0198010... johnny f4eb27cea7... suzy 78b421309e... Start End A 0xc000000000..1 0x0000000000..0 B 0x0000000000..1 0x4000000000..0 C 0x4000000000..1 0x8000000000..0 D 0x8000000000..1 0xc000000000..0
42. Node D Node C Node B Node A carol a9a0198010... Replication
43. Node D Node C Node B Node A carol a9a0198010...
44. Node D Node C Node B Node A carol a9a0198010...
45. C’’ A’’ D’ C’ A’ D A B’ C B Virtual nodes Node D Node C Node B Node A Without vnodes With vnodes
46. A closer look at reads Client Coordinator 40% busy 90% busy 30% busy
47. A closer look at reads Client Coordinator 40% busy 90% busy 30% busy
48. A closer look at reads Client Coordinator 40% busy 90% busy 30% busy
49. A closer look at reads Client Coordinator 40% busy 90% busy 30% busy
50. A closer look at reads Client Coordinator 40% busy 90% busy 30% busy
51. Consistency levels Client Coordinator 40% busy 90% busy 30% busy
52. Consistency levels Client Coordinator 40% busy 90% busy 30% busy
53. Consistency levels Client Coordinator 40% busy 90% busy 30% busy
54. Consistency levels Client Coordinator 40% busy 90% busy 30% busy
55. Consistency levels Client Coordinator 40% busy 90% busy 30% busy
56. Consistency levels •ONE •QUORUM •LOCAL_QUORUM •EACH_QUORUM •ALL
57. #CASSANDRAEU Race condition SELECT name FROM users WHERE username = 'pmcfadin';
58. #CASSANDRAEU Race condition SELECT name FROM users WHERE username = 'pmcfadin'; (0 rows) SELECT name FROM users WHERE username = 'pmcfadin';
59. #CASSANDRAEU Race condition SELECT name FROM users WHERE username = 'pmcfadin'; (0 rows) SELECT name FROM users WHERE username = 'pmcfadin'; INSERT INTO users (username, name, password, created_date) VALUES ('pmcfadin', 'Patrick McFadin', 'ba27e03fd9...', '2011-06-20 13:50:00'); (0 rows)
60. #CASSANDRAEU Race condition SELECT name FROM users WHERE username = 'pmcfadin'; (0 rows) SELECT name FROM users WHERE username = 'pmcfadin'; INSERT INTO users (username, name, password, created_date) VALUES ('pmcfadin', 'Patrick McFadin', 'ba27e03fd9...', '2011-06-20 13:50:00'); (0 rows) INSERT INTO users (username, name, password, created_date) VALUES ('pmcfadin', 'Patrick McFadin', 'ea24e13ad9...', '2011-06-20 13:50:01');
61. #CASSANDRAEU Race condition SELECT name FROM users WHERE username = 'pmcfadin'; This one wins (0 rows) SELECT name FROM users WHERE username = 'pmcfadin'; INSERT INTO users (username, name, password, created_date) VALUES ('pmcfadin', 'Patrick McFadin', 'ba27e03fd9...', '2011-06-20 13:50:00'); (0 rows) INSERT INTO users (username, name, password, created_date) VALUES ('pmcfadin', 'Patrick McFadin', 'ea24e13ad9...', '2011-06-20 13:50:01');
62. #CASSANDRAEU Lightweight transactions INSERT INTO users (username, name, password, created_date) VALUES ('pmcfadin', 'Patrick McFadin', 'ba27e03fd9...', '2011-06-20 13:50:00') IF NOT EXISTS;
63. #CASSANDRAEU Lightweight transactions INSERT INTO users (username, name, password, created_date) VALUES ('pmcfadin', 'Patrick McFadin', 'ba27e03fd9...', '2011-06-20 13:50:00') IF NOT EXISTS; [applied] ----------- INSERT INTO users (username, name, password, created_date) VALUES ('pmcfadin', 'Patrick McFadin', 'ea24e13ad9...', '2011-06-20 13:50:01') IF NOT EXISTS;
64. #CASSANDRAEU Lightweight transactions [applied] | username | created_date | name -----------+----------+----------------+---------------- False | pmcfadin | 2011-06-20 ... | Patrick McFadin INSERT INTO users (username, name, password, created_date) VALUES ('pmcfadin', 'Patrick McFadin', 'ba27e03fd9...', '2011-06-20 13:50:00') IF NOT EXISTS; [applied] ----------- INSERT INTO users (username, name, password, created_date) VALUES ('pmcfadin', 'Patrick McFadin', 'ea24e13ad9...', '2011-06-20 13:50:01') IF NOT EXISTS;
65. Paxos •All operations are quorum-based •Each replica sends information about unfinished operations to the leader during prepare •Paxos made Simple
66. Details •4 round trips vs 1 for normal updates •Paxos state is durable •Immediate consistency with no leader election or failover •ConsistencyLevel.SERIAL •http://www.datastax.com/dev/blog/lightweight- transactions-in-cassandra-2-0
67. Use with caution •Great for 1% of your application •Eventual consistency is your friend •http://www.slideshare.net/planetcassandra/c- summit-2013-eventual-consistency-hopeful-consistency- by-christos-kalantzis
68. Cassandra Questions?
69. Python Driver •github.com/datastax/python-driver •pip install cassandra-driver •Apache License
70. Connecting from cassandra.cluster import Cluster cluster = Cluster([‘192.168.1.1’, ‘192.168.1.2’]) session = cluster.connect() rows = session.execute( “SELECT * FROM users.accounts”)
71. Connecting cluster = Cluster([‘192.168.1.1’, ‘192.168.1.2’]) Provide contact points, and the rest of the cluster is automatically discovered.
72. Executing Queries rows = session.execute( “SELECT username, id FROM users.accounts”) for row in rows: print row.username, row.id
73. Executing Queries rows = session.execute( “SELECT username, id FROM users.accounts”) for username, id in rows: print username, id
74. Executing Queries rows = session.execute(“”” INSERT INTO accounts (id, username) VALUES (%s, %s) “””, (123, “j’doe”))
75. Prepared Statements fetch_user = session.prepare( “SELECT * FROM accounts WHERE id=?”) for id in users_to_fetch: user = session.execute(fetch_user, [id])
76. Async Queries future = session.execute_async( “SELECT username, id FROM users.accounts”) # do some other work... rows = future.result()
77. Async Queries fetch = session.prepare( “SELECT * FROM accounts WHERE id=?”) futures = [] for id in users_to_fetch: future = session.execute_async(fetch, [id]) futures.append(future) users = [f.result()[0] for f in futures]
78. Async Queries def process_user(query_results): # do some work ... def log_error(query_exception): logger.error(“Bad stuff:”, query_exception) future = session.execute_async( “SELECT username, id FROM users.accounts”) future.add_callbacks(process_user, log_error)
79. Tracing •Breakdown of how long each step of the query took •Trace covers coordinator and replica nodes
80. Tracing query = SimpleStatement( “SELECT * FROM accounts WHERE id=%s”) session.execute(query, [user_id], trace=True) print query.trace
81. Python Driver Questions?
82. Twissandra •Twitter clone using Cassandra •github.com/twissandra/twissandra •‘cql3’ branch
83. Followers CREATE TABLE followers ( username text, follower text, since timestamp, PRIMARY KEY (username, follower) )
84. Followers CREATE TABLE followers ( username text, follower text, since timestamp, PRIMARY KEY (username, follower) ) •Partition by username •Cluster by follower •SELECT follower FROM followers WHERE username=?
85. Tweets CREATE TABLE tweets_by_user ( username text, tweet_id timeuuid, body text, PRIMARY KEY (username, tweet_id) ) WITH CLUSTERING ORDER BY (tweet_id DESC)
86. Tweets CREATE TABLE tweets_by_user ( username text, tweet_id timeuuid, body text, PRIMARY KEY (username, tweet_id) ) WITH CLUSTERING ORDER BY (tweet_id DESC) •Partition by user •Cluster by timeuuid (timestamp) •SELECT * FROM tweets_by_user WHERE username=? LIMIT 100
87. Tweets by Followed CREATE TABLE tweets_by_followed ( username text, tweet_id timeuuid, author_username text, body text, PRIMARY KEY (username, tweet_id) ) WITH CLUSTERING ORDER BY (tweet_id DESC)
88. Tweets by Followed CREATE TABLE tweets_by_followed ( username text, tweet_id timeuuid, author_username text, body text, PRIMARY KEY (username, tweet_id) ) WITH CLUSTERING ORDER BY (tweet_id DESC) •Denormalize, append to each follower’s timeline •Extra writes, cheaper reads