Accumulo design
- 1. APACHE ACCUMULO From a design perspective
- 2. SCALABLE KEY-VALUE STORE BASED ON GOOGLE'S BIGTABLE
- 3. BIGTABLE FEATURES • Distributes data across many commodity servers • Sorts data by key for fast lookup of values by key • Scan across multiple key value pairs • Highly consistent writes to single row • Support for MapReduce jobs
- 5. Row ID Col Fam Col Qual Timestamp Value Bob Email id0023 20120301 Hey joe, can you send ... Bob Email id0024 20120302 Re: next Thursday ... Bob UserPrefs Background 20130101 Grey Fred Email id0001 20080302 Welcome to gmail ... Sarah Email id0004 20130201 Hi again ... Sara Videos ytid009 20100303 nsu736:)jdudjd k$:)378;'$$)
- 6. Tablet servers HDFS DataNodes Commit Layer Replication Layer
- 7. SINCE 2006 • Several BigTable implementations • Apache Hbase • Apache Cassandra • Apache Accumulo • others …
- 8. BIGTABLE IS BIGTABLE RIGHT?
- 9. HBASE
- 10. HBASE • Open source Apache project started by developers at Powerset, bought by Microsoft • Now used at Facebook, StumbleUpon, other big web sites • Fast reads • Row-oriented API • Each column family has it's own set of files
- 11. CASSANDRA
- 12. CASSANDRA • Apache project started at Facebook • Combines elements of BigTable and Amazon's Dynamo into one system • Used at Netflix, other web sites • Fast writes • Tunable consistency
- 13. Tablet servers Commit and Replication Layer
- 14. CONSISTENCY • Highly consistent means: writes in one place • Eventually consistent: writes in > one place • Writes in > one place: network partition tolerance • Partition tolerance: geographically distributed servers • *Google uses Spanner to synchronize multiple dbs
- 15. Tablet servers Data Center A Data Center B
- 16. Data Center A Data Center B Tablet servers
- 17. OVERVIEW • Both highly scalable • Used to build web applications that can serve millions of users at once • Serves as a low-latency persistence layer for real time service of requests • Available in single data center or cross data center options
- 18. USE CASE • Most data comes from users • Schema defined by the application • Data builds up over time
- 20. ACCUMULO
- 21. ACCUMULO • Can support the web application use-case • But what are those other extra features for?
- 22. ACCUMULO ‘EXTRAS’ • Dynamic Column Families • ColumnVisibility • Key-value oriented API • Iterators • Batch Scanners
- 23. BIG ORGANIZATIONS • Missions other than internet services • Various disparate operational systems that generate data • Desire to look across and analyze that data • Desire to deliver results to their own population
- 24. USE CASE IS DISCOVERING AND ANALYZING ALL DATA
- 25. ISSUES • Scale • Unknown / multiple schema • Support for analysis without data movement • Varying levels of sensitivity in the same system • Support a high number of low-latency user requests
- 27. SCALE?
- 29. NO CONTROL OVER OR MANY DIFFERENT SCHEMA?
- 30. MAP EXISTING FIELDSTO COLUMNS DYNAMICALLY
- 32. VARYING LEVELS OF DATA SENSITIVITY?
- 33. COLUMNVISIBILITY
- 34. DATA MODEL Key Value Row ID Column Time stamp Family Qualifier Visibility
- 35. Row ID Col Fam Col Qual Col Vis Timestamp Value Bob Email id0023 personal comms 20120301 Hey joe, can you send ... Bob Email id0024 personal comms 20120302 Re: next Thursday ... Bob UserPrefs Background prefs 20130101 Grey Fred Email id0001 personal comms 20080302 Welcome to gmail ... Sarah Email id0004 personal comms 20130201 Hi again ... Sara Videos ytid009 public post 20100303 nsu736:)jdu djdk $:)378;'$$)
- 36. DATA OFVARYING SENSITIVITY LEVELS CAN BE PHYSICALLY CO-LOCATED
- 37. FRAMEWORKS LIKE HADOOP MAP REDUCE LOVE IT WHEN DATA IS ALLTOGETHER
- 40. SECONDARY INDICES • Application-created data: known • Pre-existing data? unknown
- 41. DATA DISCOVERY!
- 42. SECONDARY INDICES RowID Col Qual Value RID00001 age 54 RID00001 name bob RID00002 name fred RID00003 age 43 RID00003 height 5’9” RID00003 name harry RID00004 name carl RID00005 name evan RowID Col Fam Col Qual 43 age RID00003 54 age RID00001 5’9” height RID00003 bob name RID00001 carl name RID00004 evan name RID00005 fred name RID00002 harry name RID00003
- 44. BATCH SCANNERS
- 45. RowID Col Qual Value RID00001 age 54 RID00001 name bob RID00002 name fred RID00003 age 43 RID00003 height 5’9” RID00003 name harry RID00004 name carl RID00005 name evan Batch Scanner
- 47. ANALYSIS?
- 48. MAPREDUCE: CHECK
- 49. SHUFFLE-SORTED? • Between Map and Reduce phases is shuffle-sort • Sorting by key is necessary so all the values for a given key end up next to each other … • BigTable also sorts keys …
- 50. ITERATORS
- 52. PRE-COMPUTATION
- 54. ACCUMULO