Cassandra Day SV 2014: Fundamentals of Apache Cassandra Data Modeling
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Cassandra Day SV 2014: Fundamentals of Apache Cassandra Data Modeling

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You know you need Cassandra for it's uptime and scaling, but what about that data model? Let's bridge that gap and get you building your game changing app. We'll break down topics like storing objects ...

You know you need Cassandra for it's uptime and scaling, but what about that data model? Let's bridge that gap and get you building your game changing app. We'll break down topics like storing objects and indexing for fast retrieval. You will see by understanding a few things about Cassandra internals, you can put your data model in the spotlight. The goal of this talk is to get you comfortable working with data in Cassandra throughout the application lifecycle. What are you waiting for? The cameras are waiting!

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Cassandra Day SV 2014: Fundamentals of Apache Cassandra Data Modeling Presentation Transcript

  • 1. ©2013 DataStax Confidential. Do not distribute without consent. @PatrickMcFadin Patrick McFadin
 Chief Evangelist Data Modeling Time Series 1
  • 2. Internet Of Things • 15B devices by 2015 • 40B devices by 2020!
  • 3. Why Cassandra for Time Series Scales Resilient Good data model Efficient Storage Model What about that?
  • 4. Example 1: Weather Station • Weather station collects data • Cassandra stores in sequence • Application reads in sequence
  • 5. Use case • Store data per weather station • Store time series in order: first to last • Get all data for one weather station • Get data for a single date and time • Get data for a range of dates and times Needed Queries Data Model to support queries
  • 6. Data Model • Weather Station Id and Time are unique • Store as many as needed CREATE TABLE temperature ( weatherstation_id text, event_time timestamp, temperature text, PRIMARY KEY (weatherstation_id,event_time) ); INSERT INTO temperature(weatherstation_id,event_time,temperature) VALUES ('1234ABCD','2013-04-03 07:01:00','72F'); ! INSERT INTO temperature(weatherstation_id,event_time,temperature) VALUES ('1234ABCD','2013-04-03 07:02:00','73F'); ! INSERT INTO temperature(weatherstation_id,event_time,temperature) VALUES ('1234ABCD','2013-04-03 07:03:00','73F'); ! INSERT INTO temperature(weatherstation_id,event_time,temperature) VALUES ('1234ABCD','2013-04-03 07:04:00','74F');
  • 7. Storage Model - Logical View 2013-04-03 07:01:00 72F 2013-04-03 07:02:00 73F 2013-04-03 07:03:00 73F SELECT weatherstation_id,event_time,temperature FROM temperature WHERE weatherstation_id='1234ABCD'; 1234ABCD 1234ABCD 1234ABCD weatherstation_id event_time temperature 2013-04-03 07:04:00 74F 1234ABCD
  • 8. Storage Model - Disk Layout 2013-04-03 07:01:00 72F 2013-04-03 07:02:00 73F 2013-04-03 07:03:00 73F 1234ABCD 2013-04-03 07:04:00 74F SELECT weatherstation_id,event_time,temperature FROM temperature WHERE weatherstation_id='1234ABCD'; Merged, Sorted and Stored Sequentially 2013-04-03 07:05:00! ! 74F 2013-04-03 07:06:00! ! 75F
  • 9. Query patterns • Range queries • “Slice” operation on disk SELECT weatherstation_id,event_time,temperature FROM temperature WHERE weatherstation_id='1234ABCD' AND event_time >= '2013-04-03 07:01:00' AND event_time <= '2013-04-03 07:04:00'; 2013-04-03 07:01:00 72F 2013-04-03 07:02:00 73F 2013-04-03 07:03:00 73F 1234ABCD 2013-04-03 07:04:00 74F 2013-04-03 07:05:00! ! 74F 2013-04-03 07:06:00! ! 75F Single seek on disk
  • 10. Query patterns • Range queries • “Slice” operation on disk SELECT weatherstation_id,event_time,temperature FROM temperature WHERE weatherstation_id='1234ABCD' AND event_time >= '2013-04-03 07:01:00' AND event_time <= '2013-04-03 07:04:00'; 2013-04-03 07:01:00 72F 2013-04-03 07:02:00 73F 2013-04-03 07:03:00 73F 1234ABCD 2013-04-03 07:04:00 74F weatherstation_id event_time temperature 1234ABCD 1234ABCD 1234ABCD Programmers like this Sorted by event_time
  • 11. Additional help on the storage engine
  • 12. SSTable seeks • Each read minimum 1 seek • Cache and bloom filter help minimize Total seek time = Disk Latency * number of seeks
  • 13. The key to speed Use the first part of the primary key to get the node (data localization) Minimize seeks for SStables (Key Cache, Bloom Filter) Find the data fast in the SSTable (Indexes)
  • 14. Min/Max Value Hint • New since 2.0 • Range index on primary key values per SSTable • Minimizes seeks on range data CASSANDRA-5514 if you are interested in details SELECT temperature FROM event_time,temperature WHERE weatherstation_id='1234ABCD' AND event_time > '2013-04-03 07:01:00' AND event_time < '2013-04-03 07:04:00'; Row Key: 1234ABCD Min event_time: 2013-04-01 00:00:00 Max event_time: 2013-04-04 23:59:59 Row Key: 1234ABCD Min event_time: 2013-04-05 00:00:00 Max event_time: 2013-04-09 23:59:59 Row Key: 1234ABCD Min event_time: 2013-03-27 00:00:00 Max event_time: 2013-03-31 23:59:59 ? This one
  • 15. Ingestion models • Apache Kafka • Apache Flume • Storm • Custom Applications Apache Kafka Your totally! killer! application
  • 16. Kafka + Storm • Kafka provides reliable queuing • Storm processes (rollups, counts) • Cassandra stores at the same speed • Storm lookup on Cassandra Apache Kafka Apache Storm Queue Process Store
  • 17. Flume • Source accepts data • Channel buffers data • Sink processes and stores • Popular for log processing Sink Channel Source Application Load Balancer Syslog
  • 18. Dealing with data at speed • 1 million writes per second? • 1 insert every microsecond • Collisions? • Primary Key determines node placement • Random partitioning • Special data type - TimeUUID Your totally! killer! application weatherstation_id='1234ABCD' weatherstation_id='5678EFGH'
  • 19. How does data replicate?
  • 20. Primary key determines placement* Partitioning jim age: 36 car: camaro gender: M carol age: 37 car: subaru gender: F johnny age:12 gender: M suzy age:10 gender: F
  • 21. jim carol johnny suzy PK 5e02739678... a9a0198010... f4eb27cea7... 78b421309e... MD5 Hash MD5* hash operation yields a 128-bit number for keys of any size. Key Hashing
  • 22. Node A Node D Node C Node B The Token Ring
  • 23. 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
  • 24. 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
  • 25. 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
  • 26. 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
  • 27. 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
  • 28. Node A Node D Node C Node B carol a9a0198010... Replication
  • 29. Node A Node D Node C Node B carol a9a0198010... Replication
  • 30. Node A Node D Node C Node B carol a9a0198010... Replication Replication factor = 3 Consistency is a different topic for later
  • 31. TimeUUID • Also known as a Version 1 UUID • Sortable • Reversible Timestamp to Microsecond + UUID = TimeUUID 04d580b0-9412-11e3-baa8-0800200c9a66 Wednesday, February 12, 2014 6:18:06 PM GMT http://www.famkruithof.net/uuid/uuidgen =
  • 32. Example 2: Financial Transactions • Trading of stocks • When did they happen? • Massive speeds and volumes “Sirca, a non-profit university consortium based in Sydney, is the world’s biggest broker of financial data, ingesting into its database 2million pieces of information a second from every major trading exchange.”* * http://www.theage.com.au/it-pro/business-it/help-poverty-theres-an-app-for-that-20140120-hv948.html
  • 33. Use case • Store data per symbol and date • Store time series in reverse order: last to first • Make sure every transaction is unique • Get all trades for symbol and day • Get trade for a single date and time • Get last 10 trades for symbol and date Needed Queries Data Model to support queries
  • 34. Data Model • date is int of days since epoch • timeuuid keeps it unique • Reverse the times for later queries CREATE TABLE stock_ticks ( symbol text, date int, trade timeuuid, trade_details text, PRIMARY KEY ((symbol, date), trade) ) WITH CLUSTERING ORDER BY (trade DESC); INSERT INTO stock_ticks(symbol, date, trade, trade_details) VALUES (‘NFLX’,340,04d580b0-1431-1e33-baf8-0833200c98a6,'BUY:2000'); ! INSERT INTO stock_ticks(symbol, date, trade, trade_details) VALUES (‘NFLX’,340,05d580b0-6472-1ef3-a3a8-0430200c9a66,'BUY:300'); ! INSERT INTO stock_ticks(symbol, date, trade, trade_details) VALUES (‘NFLX’,340,02d580b0-9412-d223-55a8-0976200c9a25,'SELL:450'); ! INSERT INTO stock_ticks(symbol, date, trade, trade_details) VALUES (‘NFLX’,340,08d580b0-4482-11e3-5fd3-3421200c9a65,'SELL:3000');
  • 35. Storage Model - Logical View 08d580b0-4482-11e3-5fd3- 3421200c9a65 SELL:3000 02d580b0-9412- d223-55a8-0976200c9a25 SELL:450 05d580b0-6472-1ef3- a3a8-0430200c9a66 BUY:300 SELECT trade,trade_details FROM stock_ticks WHERE symbol =‘NFLX’ AND date=‘340’; NFLX:340 NFLX:340 NFLX:340 symbol:date trade trade_details 04d580b0-1431-1e33- baf8-0833200c98a6 BUY:2000 NFLX:340
  • 36. 04d580b0-1431-1e33- baf8-0833200c98a6 05d580b0-6472-1ef3- a3a8-0430200c9a66 02d580b0-9412-d223-55a8 BUY:2000BUY:300 08d580b0-4482-11e3-5fd3- 3421200c9a65 SELL:3000 SELL:450 Storage Model - Disk Layout NFLX:340 Order is from last trade to first SELECT trade,trade_details FROM stock_ticks WHERE symbol =‘NFLX’ AND date=‘340’;
  • 37. 04d580b0-1431-1e33- baf8-0833200c98a6 05d580b0-6472-1ef3- a3a8-0430200c9a66 02d580b0-9412- d223-55a8-0976200c9a25 Query patterns • Limit queries • Get last X trades From here SELECT trade,trade_details FROM stock_ticks WHERE symbol =‘NFLX’ AND date=‘340’ LIMIT 3; BUY:2000BUY:300 08d580b0-4482-11e3-5fd3- 3421200c9a65 SELL:3000 SELL:450 NFLX:340 to here
  • 38. Query patterns Reverse sorted by trade Last 3 trades 08d580b0-4482-11e3-5fd3- 3421200c9a65 SELL:3000 02d580b0-9412- d223-55a8-0976200c9a25 SELL:450 05d580b0-6472-1ef3- a3a8-0430200c9a66 BUY:300 NFLX:340 NFLX:340 NFLX:340 symbol:date trade trade_details • Limit queries • Get last X trades SELECT trade,trade_details FROM stock_ticks WHERE symbol =‘NFLX’ AND date=‘340’ LIMIT 3;
  • 39. Way more examples • 5 minute interviews • Use cases • Free training! ! www.planetcassandra.org
  • 40. Thank You! Follow me for more updates all the time: @PatrickMcFadin