Introduction to Spark SQL and Catalyst / Spark SQLおよびCalalystの紹介
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![SQL core in depth
o.a.s.sql.SchemaRDD
Extends RDD[Row].
Has logical plan tree.
Provides LINQ-like interfaces to construct
logical plan.
select, where, join, orderBy, …
Executes the plan.
28](https://image.slidesharecdn.com/sparksqlcatalyst-140912023144-phpapp02/75/introduction-to-spark-sql-and-catalyst-spark-sqlcalalyst-28-2048.jpg?cb=1668209580)
Introduction to Spark SQL and Catalyst / Spark SQLおよびCalalystの紹介
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Presentation material by Mr. Takuya Ueshin at ScalaMatsuri 2014 http://scalamatsuri.org/en/
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Introduction to Spark SQL and Catalyst / Spark SQLおよびCalalystの紹介
- 1. Introduction to SparkSQL & Catalyst Takuya UESHIN ! ScalaMatsuri2014 2014/09/06(Sat)
- 2. Who am I? Takuya UESHIN @ueshin github.com/ueshin Nautilus Technologies, Inc. A Spark contributor 2
- 3. Agenda What is SparkSQL? Catalyst in depth SparkSQL core in depth Interesting issues How to contribute 3
- 5. What is SparkSQL? SparkSQL is one of Spark components. Executes SQL on Spark Builds SchemaRDD like LINQ Optimizes execution plan. 5
- 6. What is SparkSQL? Catalyst provides a execution planning framework for relational operations. Including: SQL parser & analyzer Logical operators & general expressions Logical optimizer A framework to transform operator tree. 6
- 7. What is SparkSQL? To execute query needs some steps. Parse Analyze Logical Plan Optimize Physical Plan Execute 7
- 8. What is SparkSQL? To execute query needs some steps. Parse Analyze Logical Plan Optimize Physical Plan Execute 8 Catalyst
- 9. What is SparkSQL? To execute query needs some steps. Parse Analyze Logical Plan Optimize Physical Plan Execute 9 SQL core
- 11. Catalyst in depth Provides a execution planning framework for relational operations. Row & DataType’s Trees & Rules Logical Operators Expressions Optimizations 11
- 12. Row & DataType’s o.a.s.sql.catalyst.types.DataType Long, Int, Short, Byte, Float, Double, Decimal String, Binary, Boolean, Timestamp Array, Map, Struct o.a.s.sql.catalyst.expressions.Row Represents a single row. Can contain complex types. 12
- 13. Trees & Rules o.a.s.sql.catalyst.trees.TreeNode Provides transformations of tree. foreach, map, flatMap, collect transform, transformUp, transformDown Used for operator tree, expression tree. 13
- 14. Trees & Rules o.a.s.sql.catalyst.rules.Rule Represents a tree transform rule. o.a.s.sql.catalyst.rules.RuleExecutor A framework to transform trees based on rules. 14
- 15. Logical Operators Basic Operators Project, Filter, … Binary Operators Join, Except, Intersect, Union, … Aggregate Generate, Distinct Sort, Limit InsertInto, WriteToFile 15 Project Filter Join Table Table
- 16. Expressions Literal Arithmetics UnaryMinus, Sqrt, MaxOf Add, Subtract, Multiply, … Predicates EqualTo, LessThan, LessThanOrEqual, GreaterThan, GreaterThanOrEqual Not, And, Or, In, If, CaseWhen 16 + 1 2
- 17. Expressions Cast GetItem, GetField Coalesce, IsNull, IsNotNull StringOperations Like, Upper, Lower, Contains, StartsWith, EndsWith, Substring, … 17
- 18. Optimizations ConstantFolding NullPropagation ConstantFolding BooleanSimplification SimplifyFilters FilterPushdown CombineFilters PushPredicateThroughProject PushPredicateThroughJoin ColumnPruning 18
- 19. Optimizations NullPropagation, ConstantFolding Replace expressions that can be evaluated with some literal value to the value. ex) 1 + null => null 1 + 2 => 3 Count(null) => 0 19
- 20. Optimizations BooleanSimplification Simplifies boolean expressions that can be determined. ex) false AND $right => false true AND $right => $right true OR $right => true false OR $right => $right If(true, $then, $else) => $then 20
- 21. Optimizations SimplifyFilters Removes filters that can be evaluated trivially. ex) Filter(true, child) => child Filter(false, child) => empty 21
- 22. Optimizations CombineFilters Merges two filters. ex) Filter($fc, Filter($nc, child)) => Filter(AND($fc, $nc), child) 22
- 23. Optimizations PushPredicateThroughProject Pushes Filter operators through Project operator. ex) Filter(‘i === 1, Project(‘i, ‘j, child)) => Project(‘i, ‘j, Filter(‘i === 1, child)) 23
- 24. Optimizations PushPredicateThroughJoin Pushes Filter operators through Join operator. ex) Project(“left.i”.attr === 1, Join(left, right) => Join(Project(‘i === 1, left), right) 24
- 25. Optimizations ColumnPruning Eliminates the reading of unused columns. ex) Join(left, right, LeftSemi, “left.id”.attr === “right.id”.attr) => Join(Project(‘id, left), right, LeftSemi) 25
- 26. SQL core in depth
- 27. SQL core in depth Provides: Physical operators to build RDD Conversion from Existing RDD of Product to SchemaRDD support Parquet file read/write support JSON file read support Columnar in-memory table support 27
- 28. SQL core in depth o.a.s.sql.SchemaRDD Extends RDD[Row]. Has logical plan tree. Provides LINQ-like interfaces to construct logical plan. select, where, join, orderBy, … Executes the plan. 28
- 29. SQL core in depth o.a.s.sql.execution.SparkStrategies Converts logical plan to physical. Some rules are based on statistics of the operators. 29
- 30. SQL core in depth Parquet read/write support Parquet: Columnar storage format for Hadoop Reads existing Parquet files. Converts Parquet schema to row schema. Writes new Parquet files. Currently DecimalType and TimestampType are not supported. 30
- 31. SQL core in depth JSON read support Loads a JSON file (one object per line) Infers row schema from the entire dataset. Giving the schema is experimental. Inferring the schema by sampling is also experimental. 31
- 32. SQL core in depth Columnar in-memory table support Caches table like RDD.cache, but as columnar style. Can prune unnecessary columns when read data. 32
- 34. Interesting issues Support the GroupingSet/ROLLUP/CUBE https://issues.apache.org/jira/browse/SPARK-2663 Use statistics to skip partitions when reading from in-memory columnar data https://issues.apache.org/jira/browse/SPARK-2961 34
- 35. Interesting issues Pluggable interface for shuffles https://issues.apache.org/jira/browse/SPARK-2044 Sort-merge join https://issues.apache.org/jira/browse/SPARK-2213 Cost-based join reordering https://issues.apache.org/jira/browse/SPARK-2216 35
- 37. How to contribute See: Contributing to Spark Open an issue on JIRA Send pull-request at GitHub Communicate with committers and reviewers Congratulations! 37
- 38. Conclusion Introduced SparkSQL & Catalyst Now you know them very well! And you know how to contribute. ! Let’s contribute to Spark & SparkSQL!! 38