C*ollege Credit: An Introduction to Apache Cassandra
Download as KEY, PDF5 likes2,354 views
This document serves as an introduction to Apache Cassandra, detailing its origin, key features, and data model components such as clusters, replication strategies, and data types. It highlights the importance of consistency levels, data modeling practices, and provides examples of using the Cassandra Query Language (CQL). The presentation aims to determine suitability for applications and offering insight into operational advantages of using Cassandra for scaling and managing data.
![RPC via Python pycassa...
# pycassa - Python
>>> col_fam.get('row_key')
{'col_name': 'col_val', 'col_name2': 'col_val2'}
>>> col_fam.multi_get(['row_key'], [‘col_name’])
{‘row_key’ : {'col_name': 'col_val'}}](https://image.slidesharecdn.com/anintroductiontoapachecassandra1-121012122921-phpapp01/85/C-ollege-Credit-An-Introduction-to-Apache-Cassandra-54-320.jpg)
![RPC via Python pycassa...
# pycassa - Python
>>> col_fam.remove('row_key')
>>> col_fam.remove('row_key', [‘col_name’])](https://image.slidesharecdn.com/anintroductiontoapachecassandra1-121012122921-phpapp01/85/C-ollege-Credit-An-Introduction-to-Apache-Cassandra-55-320.jpg)
C*ollege Credit: An Introduction to Apache Cassandra
- 1. DATASTAX C*OLLEGE CREDIT: AN INTRODUCTION TO APACHE CASSANDRA Aaron Morton Apache Cassandra Committer, Data Stax MVP for Apache Cassandra @aaronmorton www.thelastpickle.com Licensed under a Creative Commons Attribution-NonCommercial 3.0 New Zealand License
- 2. Overview The Cluster The Data Model The API
- 3. Cassandra - Started at Facebook - Open sourced in 2008 - Top Level Apache project since 2010.
- 4. Used by... Netflix, Twitter, Reddit, Rackspace...
- 5. Inspiration - Google Big Table (2006) - Amazon Dynamo (2007)
- 6. Why Cassandra? - Scale - Operations - Data Model
- 7. Why Cassandra? Is My App a Good Fit for Apache Cassandra? Eric Lubow (CTO, SimpleReach) Wednesday October 24 @ 8:30AM PST http://www.datastax.com/resources/webinars/collegecredit
- 8. Overview The Cluster The Data Model The API
- 9. Store ‘foo’ key with Replication Factor 3. Node 1 - 'foo' Node 4 Node 2 - 'foo' Node 3 - 'foo'
- 10. Consistent Hashing. - Evenly map keys to nodes - Minimise key movements when nodes join or leave
- 11. Partitioner. RandomPartitioner transforms Keys to Tokens using MD5. (Default, there are others.)
- 12. Keys and Tokens? key 'fop' 'foo' token 0 10 90 99
- 13. Token Ring. 99 0 'foo' 'fop' token: 90 token: 10
- 14. Token Ranges. Node 1 token: 0 76-0 1-25 Node 4 Node 2 token: 75 token: 25 Node 3 token: 50
- 15. Locate Token Range. Node 1 token: 0 'foo' token: 90 Node 4 Node 2 token: 75 token: 25 Node 3 token: 50
- 16. Replication Strategy selects Replication Factor number of nodes for a row.
- 17. SimpleStrategy with RF 3. Node 1 token: 0 'foo' token: 90 Node 4 Node 2 token: 75 token: 25 Node 3 token: 50
- 18. NetworkTopologyStrategy uses a Replication Factor per Data Centre. (Default.)
- 19. Multi DC Replication with RF 3 and RF 2. Node 1 Node 10 token: 0 token: 1 'foo' token: 90 Node 4 West DC Node 2 Node 40 East DC Node 20 token: 75 token: 25 token: 76 token: 26 Node 3 Node 30 token: 50 token: 51
- 20. The Snitch knows which Data Centre and Rack the Node is in.
- 21. SimpleSnitch. Places all nodes in the same DC and Rack. (Default, there are others.)
- 22. PropertyFileSnitch. DC and Rack is specified per node via configuration.
- 23. EC2Snitch. DC is set to AWS Region and a Rack to Availability Zone.
- 24. DynamicSnitch. Re-orders nodes according to their observed performance. (Wraps other snitch.)
- 25. The Client and the Coordinator. Node 1 token: 0 'foo' token: 90 Node 4 Node 2 token: 75 token: 25 Node 3 Client token: 50
- 26. Gossip. Nodes share information with a small number of neighbours. Who share information with a small number of neigh..
- 27. Multi DC Client and the Coordinator. Node 1 Node 10 token: 0 token: 1 'foo' token: 90 Node 4 Node 2 Node 40 Node 20 token: 75 token: 25 token: 76 token: 26 Node 3 Node 30 Client token: 50 token: 51
- 28. Consistency Level (CL). - Specified for each request - Number of nodes to wait for.
- 29. Consistency Level (CL) - Any* - One, Two Three - QUORUM - LOCAL_QUORUM, EACH_QUOURM*
- 30. QUOURM at Replication Factor... Replication 2 or 3 4 or 5 6 or 7 Factor QUOURM 2 3 4
- 31. QUOURM at with RF3. Node 1 token: 0 'foo' token: 90 Node 4 Node 2 token: 75 token: 25 Node 3 Client token: 50
- 32. Write ‘foo’ at QUOURM with Hinted Handoff. Node 1 'foo' 'foo' token: 90 Node 4 Node 2 'foo' for #3 'foo' Node 3 Client
- 33. Read ‘foo’ at QUOURM. Node 1 'foo' 'foo' token: 90 Node 4 Node 2 'foo' Node 3 Client
- 34. Consistency Level nodes must agree.
- 35. Column Timestamps used to resolve differences.
- 36. Resolving differences. Column Node 1 Node 2 Node 3 cromulent cromulent purple <missing> (timestamp 10) (timestamp 10) embiggens embiggens debigulator monkey (timestamp 10) (timestamp 10) (timestamp 5) tomato tomato tomacco dishwasher (timestamp 10) (timestamp 10) (timestamp 15)
- 37. Consistent read for ‘foo’ at QUOURM. Node 1 Node 1 cromulent cromulent Node 4 Node 2 Node 4 Node 2 embiggins cromulent cromulent Client Client Node 3 Node 3
- 38. Strong Consistency W+R>N (#Write Nodes + #Read Nodes> Replication Factor)
- 39. Achieving Strong Consistency. - QUOURM Read + QUORUM Write - ALL Read + ONE Write - ONE Read + ALL Write
- 40. Eventual Consistency. W + R <= N
- 41. Achieving Consistency. - Hinted Handoff - Read Repair - Scheduled nodetool repair
- 42. Overview The Cluster The Data Model The API
- 43. Data Model so far. Row Key: Column Column Column (Incomplete.)
- 44. Data Model. Keyspace Column Family Column Family Column Family Column Column Column Row Key: Column Column Column Column Column Column (Column Family and Table mean the same.)
- 45. Rows are the unit of replication.
- 46. The Column Family is the unit of storage.
- 47. Inside the Column Family. Keyspace Column Family Column: name, value, timestamp Row Key: Column: name, value, timestamp Column: name, value, timestamp (Also TTL Columns)
- 48. Basic Data Types - ASCII, UTF8 - Integer, Long, Float, Double, Boolean - Date - UUID - Bytes - Counter*
- 49. Composite Data Types - Two or more Basic types - Ordered by each component - e.g. (IntegerType, UTF8) to hold (timestamp, user_name)
- 50. Data Modelling. Data Modelling for Apache Cassandra Aaron Morton (Cassandra Committer) Wednesday November 7 @ 11AM PST http://www.datastax.com/resources/webinars/collegecredit
- 51. Overview The Cluster The Data Model The API
- 52. The API. - Original Thrift based RPC - Declarative Cassandra Query Language (CQL)
- 53. RPC via Python pycassa. # pycassa - Python >>> col_fam = pycassa.ColumnFamily(connection_pool, 'ColumnFamily1') >>> col_fam.insert('row_key', {'col_name': 'col_val'})
- 54. RPC via Python pycassa... # pycassa - Python >>> col_fam.get('row_key') {'col_name': 'col_val', 'col_name2': 'col_val2'} >>> col_fam.multi_get(['row_key'], [‘col_name’]) {‘row_key’ : {'col_name': 'col_val'}}
- 55. RPC via Python pycassa... # pycassa - Python >>> col_fam.remove('row_key') >>> col_fam.remove('row_key', [‘col_name’])
- 56. CQL. # Cassandra Query Language (CQL) INSERT INTO ColumnFamily1 (KEY, col_name) VALUES ('row_key', 'col_value');
- 57. CQL... # Cassandra Query Language (CQL) SELECT * FROM ColumnFamily1 IN (‘row_key_1’); SELECT col_name FROM ColumnFamily1 WHERE KEY IN (‘row_key_1’, ‘row_key_2’);
- 58. CQL... # Cassandra Query Language (CQL) DELETE FROM ColumnFamily1 WHERE key IN ('row_key',); DELETE col_name FROM ColumnFamily1 WHERE key = 'row_key';
- 59. Thanks.
- 60. Aaron Morton @aaronmorton www.thelastpickle.com Licensed under a Creative Commons Attribution-NonCommercial 3.0 New Zealand License
Editor's Notes
- #2: \n
- #3: \n
- #4: \n
- #5: \n
- #6: \n
- #7: \n
- #8: \n
- #9: \n
- #10: \n
- #11: \n
- #12: \n
- #13: \n
- #14: \n
- #15: \n
- #16: \n
- #17: \n
- #18: \n
- #19: \n
- #20: \n
- #21: \n
- #22: \n
- #23: \n
- #24: \n
- #25: \n
- #26: \n
- #27: \n
- #28: \n
- #29: \n
- #30: \n
- #31: \n
- #32: \n
- #33: \n
- #34: \n
- #35: \n
- #36: \n
- #37: \n
- #38: \n
- #39: \n
- #40: \n
- #41: \n
- #42: \n
- #43: \n
- #44: \n
- #45: \n
- #46: \n
- #47: \n
- #48: \n
- #49: \n
- #50: \n
- #51: \n
- #52: \n
- #53: \n
- #54: \n
- #55: \n
- #56: \n
- #57: \n
- #58: \n
- #59: \n
- #60: \n
- #61: \n