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## What changes were proposed in this pull request? In #24068, IvanVergiliev fixes the issue that OrcFilters.createBuilder has exponential complexity in the height of the filter tree due to the way the check-and-build pattern is implemented. Comparing to the approach in #24068, I propose a simple solution for the issue: 1. separate the logic of building a convertible filter tree and the actual SearchArgument builder, since the two procedures are different and their return types are different. Thus the new introduced class `ActionType`,`TrimUnconvertibleFilters` and `BuildSearchArgument` in #24068 can be dropped. The code is more readable. 2. For most of the leaf nodes, the convertible result is always Some(node), we can abstract it like this PR. 3. The code is actually small changes on the previous code. See https://github.com/apache/spark/pull/24783 ## How was this patch tested? Run the benchmark provided in #24068: ``` val schema = StructType.fromDDL("col INT") (20 to 30).foreach { width => val whereFilter = (1 to width).map(i => EqualTo("col", i)).reduceLeft(Or) val start = System.currentTimeMillis() OrcFilters.createFilter(schema, Seq(whereFilter)) println(s"With $width filters, conversion takes ${System.currentTimeMillis() - start} ms") } ``` Result: ``` With 20 filters, conversion takes 6 ms With 21 filters, conversion takes 0 ms With 22 filters, conversion takes 0 ms With 23 filters, conversion takes 0 ms With 24 filters, conversion takes 0 ms With 25 filters, conversion takes 0 ms With 26 filters, conversion takes 0 ms With 27 filters, conversion takes 0 ms With 28 filters, conversion takes 0 ms With 29 filters, conversion takes 0 ms With 30 filters, conversion takes 0 ms ``` Also verified with Unit tests. Closes #24910 from gengliangwang/refactorOrcFilters. Authored-by: Gengliang Wang <gengliang.wang@databricks.com> Signed-off-by: Wenchen Fan <wenchen@databricks.com> |
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| .. | ||
| catalyst | ||
| core | ||
| hive | ||
| hive-thriftserver | ||
| create-docs.sh | ||
| gen-sql-markdown.py | ||
| mkdocs.yml | ||
| README.md | ||
Spark SQL
This module provides support for executing relational queries expressed in either SQL or the DataFrame/Dataset API.
Spark SQL is broken up into four subprojects:
- Catalyst (sql/catalyst) - An implementation-agnostic framework for manipulating trees of relational operators and expressions.
- Execution (sql/core) - A query planner / execution engine for translating Catalyst's logical query plans into Spark RDDs. This component also includes a new public interface, SQLContext, that allows users to execute SQL or LINQ statements against existing RDDs and Parquet files.
- Hive Support (sql/hive) - Includes an extension of SQLContext called HiveContext that allows users to write queries using a subset of HiveQL and access data from a Hive Metastore using Hive SerDes. There are also wrappers that allow users to run queries that include Hive UDFs, UDAFs, and UDTFs.
- HiveServer and CLI support (sql/hive-thriftserver) - Includes support for the SQL CLI (bin/spark-sql) and a HiveServer2 (for JDBC/ODBC) compatible server.
Running sql/create-docs.sh generates SQL documentation for built-in functions under sql/site.