Beyond (No)SQL
Download as KEY, PDF7 likes6,695 views
Slides from my RailsConf 2010 talk, Beyond No(SQL). Conceptual tools for evaluating both SQL and NoSQL databases.
![[OpenStack Days Korea 2016] Track1 - All flash CEPH 구성 및 최적화](https://cdn.slidesharecdn.com/ss_thumbnails/12skt-160226171513-thumbnail.jpg?width=560&fit=bounds)
![[Oracle DBA & Developer Day 2016] しばちょう先生の特別講義！！ストレージ管理のベストプラクティス ～ASMからExada...](https://cdn.slidesharecdn.com/ss_thumbnails/mktgdd2-3stragemanagementfordl2-200702092359-thumbnail.jpg?width=560&fit=bounds)
Ad
More Related Content
What's hot (20)
Recently uploaded (20)
Ad
Beyond (No)SQL
- 1. Beyond (No)SQL Sarah Mei Pivotal Labs @sarahmei [email protected]
- 2. ?
- 3. Fsck CS. -DHH
- 4. Fsck CS. -DHH (paraphrased)
- 5. Photo by jasonwg (http://www.flickr.com/photos/jasonwg/1382036808)
- 6. Photo by jasonwg (http://www.flickr.com/photos/jasonwg/1382036808)
- 7. Agenda
- 8. Agenda • The data storage landscape
- 9. Agenda • The data storage landscape • Relational model & SQL
- 10. Agenda • The data storage landscape • Relational model & SQL RELATIONAL ALGEBRA
- 11. Agenda • The data storage landscape • Relational model & SQL RELATIONAL ALGEBRA • Evaluating data stores
- 12. Photo by TANAKA Juuyoh http://www.flickr.com/photos/tanaka_juuyoh/3121538767/
- 13. Photo by TANAKA Juuyoh http://www.flickr.com/photos/tanaka_juuyoh/3121538767/
- 14. Photo by TANAKA Juuyoh http://www.flickr.com/photos/tanaka_juuyoh/3121538767/
- 15. Photo by TANAKA Juuyoh http://www.flickr.com/photos/tanaka_juuyoh/3121538767/
- 16. Photo by TANAKA Juuyoh http://www.flickr.com/photos/tanaka_juuyoh/3121538767/
- 17. “NoSQL”
- 18. “NoSQL” MongoDB
- 27. “NoSQL” MongoDB Memcache Bigtable CouchDB Cassandra Project Voldemort Hbase Riak Redis Tokyo Cabinet
- 28. “NoSQL” MongoDB Memcache Key-value Bigtable CouchDB Cassandra Project Voldemort Hbase Riak Redis Tokyo Cabinet
- 29. “NoSQL” MongoDB Memcache Key-value Bigtable CouchDB Document Cassandra Project Voldemort Hbase Riak Redis Tokyo Cabinet
- 30. “NoSQL” MongoDB Memcache Key-value Bigtable CouchDB Document Cassandra Project Voldemort Hbase Riak Redis Tokyo Cabinet
- 31. “NoSQL” MongoDB Memcache Key-value Bigtable CouchDB Document Cassandra Other Project Voldemort Hbase Riak Redis Tokyo Cabinet
- 32. Photo from mga (http://www.flickr.com/photos/mgiraldo/420642350)
- 33. NO.
- 34. The Relational Model Diagram by Wikipedia user AutumnSnow (http://en.wikipedia.org/wiki/File:Relational_model_concepts.png)
- 35. Sets
- 37. Sets • No duplicates • Unordered
- 38. More sets
- 39. More sets • TABLE: a set of columns and a set of rows
- 40. More sets • TABLE: a set of columns and a set of rows • COLUMN: a unique name and a type
- 41. More sets • TABLE: a set of columns and a set of rows • COLUMN: a unique name and a type • ROW: a set of name-value pairs
- 42. Relational Algebra Operations you can do on tables (“relations”)
- 43. Operations • projection: subset of available columns • selection: subset of available rows • cartesian product • set union • set intersection • rename
- 44. Operations • projection: subset of available columns • selection: subset of available rows • cartesian product • set union • set intersection • rename
- 45. A Join select * from foo, bar where foo.ipsum = bar.lorem First you take the cartesian product.... foo: bat ipsum bar: cat lorem me 5 X us 5 you 4 them 4 bat ipsum cat lorem me 5 us 5 = me 5 them 4 you 4 us 5 you 4 them 4
- 46. A Join select * from foo, bar where foo.ipsum = bar.lorem Then you select the rows that satisfy the join condition: bat ipsum cat lorem bat ipsum cat lorem me 5 us 5 me 5 us 5 me 5 them 4 => you 4 them 4 you 4 us 5 you 4 them 4
- 47. A Join select * from foo, bar where foo.ipsum = bar.lorem First you take the cartesian product.... foo: bat ipsum bar: cat lorem me 5 X us 5 you 4 them 4 bat ipsum cat lorem me 5 us 5 = me 5 them 4 you 4 us 5 you 4 them 4
- 48. A Join select * from foo, bar where foo.ipsum = bar.lorem First you take the cartesian product.... foo: bat ipsum bar: cat lorem me 5 X us 5 8,000,000 you 4 them 4 bat ipsum cat lorem me 5 us 5 = me 5 them 4 you 4 us 5 you 4 them 4
- 49. A Join select * from foo, bar where foo.ipsum = bar.lorem First you take the cartesian product.... foo: bat ipsum bar: cat lorem me 5 X us 5 8,000,000 100,000 you 4 them 4 bat ipsum cat lorem me 5 us 5 = me 5 them 4 you 4 us 5 you 4 them 4
- 50. A Join select * from foo, bar where foo.ipsum = bar.lorem First you take the cartesian product.... foo: bat ipsum bar: cat lorem me 5 X us 5 8,000,000 100,000 you 4 them 4 bat ipsum cat lorem me 5 us 5 = me 5 them 4 800,000,000,000 you 4 us 5 you 4 them 4
- 51. Your choices
- 52. Your choices • Scale the database
- 53. Your choices • Scale the database • Try a different approach
- 54. What SQL Gets You
- 55. What SQL Gets You • Speed
- 56. What SQL Gets You • Speed (when data is highly structured and small enough)
- 57. What SQL Gets You • Speed (when data is highly structured and small enough) • Aggregation
- 58. What SQL Gets You • Speed (when data is highly structured and small enough) • Aggregation • Relational searching
- 59. What SQL Gets You • Speed (when data is highly structured and small enough) • Aggregation • Relational searching • ACID - guaranteed full consistency
- 60. Image by captcreate (http://www.flickr.com/photos/27845211@N02/2662264721)
- 61. What SQL Gets You
- 62. What SQL Gets You • Speed
- 63. What SQL Gets You • Speed (when data is highly structured and small enough)
- 64. What SQL Gets You • Speed (when data is highly structured and small enough) • Aggregation
- 65. What SQL Gets You • Speed (when data is highly structured and small enough) • Aggregation • Relational searching
- 66. What SQL Gets You • Speed (when data is highly structured and small enough) • Aggregation • Relational searching • ACID - guaranteed full consistency
- 67. What if you gave up data aggregation?
- 68. What if you gave up data aggregation? Document databases: MongoDB CouchDB Riak
- 69. What if you gave up where clauses?
- 70. What if you gave up where clauses? Key-value stores: Memcache Project Voldemort Redis Tokyo Cabinet
- 71. What if you gave up consistency?
- 72. What if you gave up consistency? Then things get interesting.
- 73. ACID & BASE
- 74. ACID & BASE • Atomicity
- 75. ACID & BASE • Atomicity • Consistency
- 76. ACID & BASE • Atomicity • Consistency • Isolation
- 77. ACID & BASE • Atomicity • Consistency • Isolation • Durability
- 78. ACID & BASE • Atomicity • Consistency • Isolation • Durability
- 79. ACID & BASE • Atomicity • Basically Available • Consistency • Isolation • Durability
- 80. ACID & BASE • Atomicity • Basically Available • Consistency • Soft state • Isolation • Durability
- 81. ACID & BASE • Atomicity • Basically Available • Consistency • Soft state • Isolation • Eventually consistent • Durability
- 82. Photo by Marcus Vegas (http://www.flickr.com/photos/vegas/413159909)
- 83. Fully ACID Fully BASE Photo by Marcus Vegas (http://www.flickr.com/photos/vegas/413159909)
- 84. Fully ACID Fully BASE Redis Photo by Marcus Vegas (http://www.flickr.com/photos/vegas/413159909)
- 85. Fully ACID Fully BASE CouchDB Redis Photo by Marcus Vegas (http://www.flickr.com/photos/vegas/413159909)
- 86. Fully ACID Fully BASE CouchDB Redis Bigtable Photo by Marcus Vegas (http://www.flickr.com/photos/vegas/413159909)
- 87. Questions to ask about data • Where can I compromise aggregation? • Where can I compromise where clauses? • Where can I compromise consistency? Where can I localize consistency?
- 88. CAP Theorem Pick any two:
- 89. CAP Theorem Pick any two: • Consistency
- 90. CAP Theorem Pick any two: • Consistency • Availability
- 91. CAP Theorem Pick any two: • Consistency • Availability • Partition tolerance
- 92. Summary
- 93. Summary Every system at scale will have to compromise consistency at some level.
- 94. Summary Every system at scale will have to compromise consistency at some level.
- 95. Summary Every system at scale will have to compromise consistency at some level. Do it mindfully.
- 96. Questions? Twitter: @sarahmei Email: [email protected] Slides: http://bit.ly/9xS2PK Please rate this talk! http://bit.ly/9MCtX9
Editor's Notes
- #32: A table is called a “relation” in this model, and it’s defined by a list of column definitions. Each column definition specifies a unique column name and the type of the values that are permitted for that column. The body of the table is a set of rows, which are called tuples in the model. Each row is a set of key-value pairs, or tuple, consisting of the name of the column and the value of the data. Each tuple has to be unique in the table.
- #38: They transform tables into other tables. Another word for this is “query.”