The Return of the Living Datalog
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The document discusses the reemergence of Datalog as a query language. Key points include: - Datalog allows querying over relationships between entities, handling sparse and multi-valued data. - Datalog has seen renewed interest with applications in declarative networking, security, and more. It offers recursion, implicit joins, and a simple yet powerful query syntax. - Extensions like linear negation, aggregation, and inductive logic programming have expanded Datalog's capabilities while retaining its desirable properties like termination and declarativity.
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The Return of the Living Datalog
- 1. The Reemergence of Datalog
- 2. Return of the Living Datalog
- 4. Data
- 5. Rectangles
- 6. RDL
- 8. Rectangulation • Relationship between entities • Sparse data • Multi-valued attributes • PLace-Oriented Programming
- 9. Java
- 10. Java
- 11. Where’s the Data? Java
- 12. Java
- 13. Java
- 15. RDL
- 16. ORMG!
- 17. Code as Code. Data as Data.
- 18. Code as Code. Data as Data.
- 20. Cookies User information Protocols Lisp Schemas Events Chess moves as Data. TX ...
- 21. Code as Code. Data as Data.
- 22. Unification
- 23. Your data
- 24. punching holes in Your data
- 25. fitting the holes in Your data
- 26. Variables
- 28. Substitution
- 31. MGU
- 32. Prolog
- 33. Prolog data
- 34. Prolog facts
- 35. Prolog rules
- 36. Prolog query
- 37. Data as Code!
- 38. Data as Code! Caveat Emptor
- 39. Caveats • Clause-order dependence • Non-termination • Infection of imperative
- 42. Non-termination
- 43. Non-termination
- 44. Fixed?
- 46. Cut
- 47. Cut
- 48. fail
- 49. Prolog is prelude • Powerful • Often beautiful • Not as declarative as we’d like
- 50. Datalog
- 51. Datalog is... • A query language • Not Turing complete • Explicit • Simple • to use • ... and implement • ... kinda
- 52. History • 1977: Gallaire and Minker's Symposium on Logic Data Bases • 1980s: Nail, LDL, Coral • 1995: Stonebraker and Hellerstein declare "no practical applications …" • The dark years...
- 53. History • 2002: Binder, a logic-based security language by DeTreville • 2000s: Declarative networking, bddbddb, Orchestra CDSS, Doop, SecureBlox, Dedalus, more • 2010: The Declarative Imperative by Hellerstein! • Today: Bloom, Cascalog, Datomic, LogicBlox, more
- 54. Datalog is also • Declarative logic programming with termination • Recursive queries • Implicit joins
- 55. EAV • “Entities” (objects?) - a grouping of tuples • Make that efficient
- 56. Query elements
- 57. Patterns
- 58. Simple query • Find all language entities with a website entry
- 59. Simple query • Find all language entities with a website entry entity attribute
- 60. Binding query • Find all language URLs with a website entry entity attribute value
- 61. Join • Repeating ?language indicates a join
- 62. Rules head body • All variables in the head, must appear in the body
- 63. Recursive rules
- 64. Recursive rules Simula LISP Smalltalk Dart
- 65. Datomic
- 66. Datalog plus • No need for a database • Time travel
- 67. Where’s the DB?
- 68. Where’s the DB?
- 69. Where’s the DB?
- 70. Where’s the DB?
- 71. now • How do you keep a notion of time in a relational database? • Ever write now()?
- 72. Always?
- 73. Time • Total ordering of transactions • Every datom retains a reference to its enclosing transaction • Transactions are first-class entities, can have their own attributes
- 74. Time • You can obtain the value of the db as-of, or since, a point in time, or both • without parameterizing your logic with a time argument • You can also get the entire history of an entity!
- 75. Datomic is also • Fully navigable lazy entity maps • Query across databases • Optimistic and pessimistic concurrency • “Upsertting” • http://datomic.com
- 76. Dedalus
- 77. Datalog plus • Time • State via rules
- 78. Time • Tick model • Time is an element of the tuple
- 79. Deductive time • “Right now”
- 80. Deductive time • “Right now” • All terms have the same time...
- 81. Inductive time • “Some other time”
- 82. Inductive time • “Some other time” • Next time tick
- 83. Async time • Unreliable network
- 84. State • At time tick 0
- 85. State • At time tick 0 • Facts
- 86. Update • At time tick 100 • Facts
- 87. Update • At time tick 300 • Facts
- 89. Mutable persistence rule • Update
- 90. Cascalog
- 91. Datalog plus • Map/reduce processing • Order independence a win
- 92. Cascalog
- 93. Three stages • Pre-aggregation • Aggregation • Post-aggregation Pre- Post- Aggregation aggregation aggregation
- 94. Pre-aggregation • Joins aggregator functions • Applies bindable functions and filters • Dataflow-esque Pre- Post- Aggregation aggregation aggregation
- 95. Aggregation • Partition result tuples along logic variables • Execute aggregators for each logic var Pre- Post- Aggregation aggregation aggregation
- 96. Post-aggregation • Execute the the remaining filters and functions on dependent aggregator output Pre- Post- Aggregation aggregation aggregation
- 97. Example Pre- Post- Aggregation aggregation aggregation
- 98. Pre-aggregation Pre- Post- Aggregation aggregation aggregation
- 99. Aggregation Pre- Post- Aggregation aggregation aggregation
- 100. Post-aggregation Pre- Post- Aggregation aggregation aggregation
- 101. Bacwn
- 103. Delicious!
- 104. Bacwn facts
- 105. Bacwn rules!
- 106. Bacwn query
- 107. Bacwn negation
- 108. Bacwn negation • Find all non-humans at a location
- 109. Open questions • Query plans • Optimizations
- 110. Query plans • The holy grail of DBs is that they do the right thing • Query plans • No runtime guarantees
- 111. Gaming the query • /*+ Hinting */ • Prolog - Ordering for termination • Datalog - Ordering for speed
- 112. Gaming the query • /*+ Hinting */ • Prolog - Ordering for termination • Datalog - Ordering for speed
- 113. Wut • Find all descendants of root number #100 with value = 4 • SLLLLLLOOOOOOWWWW
- 114. Wat • Find all descendants of root number #100 with value = 4 • FAST!!
- 115. Wat • Find all descendants of root number #100 with value = 4 • Most-bound
- 116. Order doesn’t matter Except when it does
- 117. Pluggable Optimizers • Order agnostic is a win • Some orders are better than others • Plug in your own optimizer • That knows your data • Will not affect other Datalog engine optimization techniques
- 118. • Rich Hickey, Stu THX! Halloway, Clojure/core • Clojure/dev • Manning Publications • The fam • You
- 119. double-secret slides
- 120. ? (foo) engine
- 121. ? (foo) engine
- 122. ? (foo) engine
- 123. ? (foo) engine
- 124. Data as Data.
- 125. Scalar
- 126. Scalar
- 127. Client ? Server
- 128. JSON Client Server JSON as Data
- 129. Client ? Server Clojure as Code 118
- 130. Client Server Clojure as Data 119
- 131. as Code. Data
- 132. Engine 121
- 133. Specification Engine 122
- 134. Specification Engine 123
- 135. I’m really doing something cool Specification Engine now 124
- 136. Code as Data.
- 137. Function call semantics: fn call arg (println "Hello World") structure: symbol string list 126
- 138. Function definition define a fn fn name docstring (defn greet "Returns a friendly greeting" [your-name] (str "Hello, " your-name)) arguments fn body 127
- 139. It's all data symbol symbol string (defn greet "Returns a friendly greeting" [your-name] (str "Hello, " your-name)) vector list 128
Editor's Notes
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- Cancer of the rectangle\n
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- Mistaking the menu for the meal.\n
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- * One Size Fits All: An Idea Whose Time has Come and Gone\n\n
- * With a severe line between\n
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- * We’re surrounded by data\n
- * We need better ways to leverage our data, in all of its facets\n* Unify the two?\n
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- * Ground terms surrounding variables\n
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- * The resulting substitution between two forms that provides a symmetry between the two forms\n* Amalgamation of the bindings of either gives the MGU on subst\n
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- * unromantic view of childbirth\n* genealogy is the killer app\n
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- * backtracking\n* depth-first tree traversals\n
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- * Bill’s descendents?\n
- * Flip order fixes!\n
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- * This is not completely bad, but writing Prolog is often many balancing acts.\n* Like this\n
- *generically cut is a way to prune branches from the search tree, but...\n
- * This kills the idea of data as code\n
- * Performing IO\n* This kills the idea of data as code\n
- * Make some compromises?\n
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- * Relation patterns unify across the existing data\n
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- * \n
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- * the gist of datalog\n* datalog is a family of languages\n
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- * Runs sentence generator and executes splitoperation\n* Binds ?word logic var\n\n
- * Executes the count aggregator\n* Binds ?count logic var\n\n
- * Executes the (> ?count 5) filter\n
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- * negation or a toy\n* bacwn facts\n
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- * Stratified negation\n** subtle\n* Soft-stratification\n** PhD dissertation\n\n
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- * Most DBs prioritze automatic planning\n** 1-bajillion man-years of effort\n* Hinting\n** unstable -- black art on a black box\n\n
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- * This is the conceptual model for data as code\n
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