NoSQL Containers get Rich
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Are NOSQL Datastores just containers? Is typical classification based on data model everything? Highlighting some pros and cons of using different NoSQL solutions. It also introduces two key concepts: NoSQL aren't replacements, but adjuvants to RDBMS, and schemaless is a lie.
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NoSQL Containers get Rich
- 1. Containers get Rich NoSQL Day, Udine – Italy 15-11-2013
- 4. THE BIG DATA WE USE EVERY DAY
- 6. OMG! How to choose?
- 7. DATA MODEL
- 8. key value
- 9. nosqlday http://nosqlday.it // Store value > SET nosqlday http://nosqlday.it // Retrieve value > GET nosqlday http://nosqlday.it
- 10. the_godfather
- 11. KEY - VALUE
- 12. the_godfather
- 13. DOCUMENT
- 14. COLUMN
- 15. Year Cast Rating 1994 Tom Hanks, Robin Wright, Gary Sinise 8,7 Braveheart 1995 Mel Gibson, Sophie Marceau, Patrick McGoohan Fast & Furious 7 2014 Forrest Gump
- 16. GRAPH
- 21. Classification by data model Key - value Column 21 Document Graph
- 22. Is all about data model?
- 23. Let’s suppose exists a RDBMS «category»… 23
- 24. Let’s suppose exists a RDBMS «category»… 24
- 25. NOSQL ARE SIMPLY DATA CONTAINERS?
- 26. Many more to say about NoSQL datastores
- 32. KEY-VALUE + SETS
- 33. VALUE EXPIRATION
- 36. ALL useful things! but don’t limit to features-first comparison
- 38. Durability vs Performance _______ _______ _______ _______ _______ 38 safe mode? off = data loss risk
- 39. Durability vs Performance safe mode? off = data loss risk _______ _______ _______ _______ _______ file system 39
- 40. Durability vs Performance safe mode? off = data loss risk _______ _______ _______ _______ _______ Consider use of Journaling file system 40
- 41. Durability vs Performance safe mode? off = data loss risk _______ _______ _______ _______ _______ Disk / RAID cache? file system 41
- 44. Goldfish, not thoroughbreds Scale out Scale up
- 45. Goldfish, not thoroughbreds Allow for fast, cost-effective, on-demand growth (or shrink) Scale out Scale up
- 46. "I know two companies that collapsed due to inability to reduce operating costs when the utilization of their sites diminished" Theo Schlossnagle From Theo’s book "Scalable Internet Architectures"
- 47. Ease of scalability KeyValue stores ColumnFamily stores Document databases Graph databases > 90% of use cases Complexity Adapted from http://www.slideshare.net/emileifrem/an-overview-of-nosql-jfokus-2011
- 49. Query capability function(doc) { if (doc.city == ‘London’) { emit(doc._id, null) } } 49
- 50. Query capability We couldn’t use user input here function(doc) { if (doc.city == ???) { emit(doc._id, null) } } 50
- 51. Query capability db.events.find( { city: ‘Rome’ } ) Here we could use user input! function(doc) { if (doc.city == ???) { emit(doc._id, null) } } 51
- 53. FILTERED MULTI-MASTER S Distribution model: MREPLICATION MASTER all data SLAVE
- 54. FILTERED MULTI-MASTER M Distribution model: MREPLICATION MASTER MASTER MASTER
- 55. FILTERED MULTI-MASTER Filtered multi-master REPLICATION Product list MASTER MASTER (eg. head quarter) (eg. customer plant) Purchases
- 56. FILTERED MULTI-MASTER REPLICATION Scaling reads MASTER MASTER MASTER
- 57. FILTERED MULTI-MASTER REPLICATION Scaling reads client client MASTER MASTER MASTER client
- 58. FILTERED MULTI-MASTER REPLICATION Scaling writes? MASTER MASTER MASTER
- 59. FILTERED MULTI-MASTER REPLICATION Scaling writes? client MASTER MASTER MASTER
- 60. FILTERED ShardingMULTI-MASTER REPLICATION Shard 1 [A to F] Shard 2 [G to N] Shard 3 [O to T] Shard 4 [U to Z]
- 61. FILTERED MULTI-MASTER REPLICATION Scaling writes client Shard 1 [A to F] Shard 2 [G to N] Shard 3 [O to T] Shard 4 [U to Z]
- 62. FILTERED MULTI-MASTER REPLICATION Scaling writes client Shard 1 [A to F] Shard 2 [G to N] Shard 3 [O to T] Shard 4 [U to Z]
- 63. R / W data from 2 nodes 63
- 64. R / W data from 2 nodes T1 Node 1 C X=0 Node 2 C X=0 64
- 65. R / W data from 2 nodes T1 T2 Node 1 C X=0 C X=1 Node 2 C X=0 C X=0 65
- 66. R / W data from 2 nodes T1 T2 Node 1 C X=0 C X=1 Node 2 C X=0 C X=0 66
- 69. (Some of) available solutions CP: BigTable Hbase MongoDB Redis MemcacheDB etc. PA: Dynamo CouchDB Cassandra SimpleDB Tokyo Cabinet Voldemort etc. 69 CA: RDBMS etc. Consistency Partition Tolerance Availability
- 70. from CONSISTENCY
- 74. Aggregates Source: AggregateOrientedDatabase - http://martinfowler.com/bliki/AggregateOrientedDatabase.html 74
- 75. Aggregates Atomicity and Isolation are guaranteed inside an aggregate Source: AggregateOrientedDatabase - http://martinfowler.com/bliki/AggregateOrientedDatabase.html 75
- 76. ARE YOU SURE WE NEED ACID?
- 77. ARE YOU SURE WE NEED ACID?
- 81. Data Model STARTING FROM DATA MODEL…
- 82. Scalability model Data durability Query model Position on CAP Some needful feature Performance Data Model MANY OTHER THINGS TO CONSIDER
- 83. RELATIONAL DBMS
- 84. RELATIONAL DBMS
- 85. "the relational model is pretty magical" Laurie Voss http://seldo.com/weblog/2010/07/12/in_defence_of_sql
- 86. NOSQL DATASTORES
- 87. "Big data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it..." Dan Ariely https://www.facebook.com/dan.ariely/posts/904383595868
- 88. Schemaless
- 89. Schemaless $doc = $myDb->getDoc('the_godfather'); $year = $doc['year']; $castCount = count($doc['cast']); if ($castCount > 0) { $firstCastName = $doc['cast'][0]['name']; }
- 90. Schemaless …are you sure? $doc = $myDb->getDoc('the_godfather'); $year = $doc['year']; $castCount = count($doc['cast']); if ($castCount > 0) { $firstCastName = $doc['cast'][0]['name']; } The application is aware of document schema!
- 91. Polyglot persistence Farm Node 1 Provisioning LAPP stack Node n 91 Devices status Data aggregation
- 92. Polyglot persistence made safe Other component Provisioning layer 92
- 93. Polyglot persistence made safe Other component Data Store as a Service Provisioning layer 93
- 94. Polyglot persistence made safe Other component Provisioning Anti Corruption Layer 94 layer
- 96. GOOD APPLICATION DESIGN THINK ABOUT DATA LIFECYCLE
- 97. GOOD APPLICATION DESIGN THINK ABOUT DATA LIFECYCLE NOT ONLY DATA MODEL
- 98. That’s all, folks! Stefano Valle @stefanovalle s.valle@mvassociati.it