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gatorsmile 422a45cf04 [SPARK-18766][SQL] Push Down Filter Through BatchEvalPython (Python UDF) 
### What changes were proposed in this pull request?
Currently, when users use Python UDF in Filter, BatchEvalPython is always generated below FilterExec. However, not all the predicates need to be evaluated after Python UDF execution. Thus, this PR is to push down the determinisitc predicates through `BatchEvalPython`.
```Python
>>> df = spark.createDataFrame([(1, "1"), (2, "2"), (1, "2"), (1, "2")], ["key", "value"])
>>> from pyspark.sql.functions import udf, col
>>> from pyspark.sql.types import BooleanType
>>> my_filter = udf(lambda a: a < 2, BooleanType())
>>> sel = df.select(col("key"), col("value")).filter((my_filter(col("key"))) & (df.value < "2"))
>>> sel.explain(True)
```
Before the fix, the plan looks like
```
== Optimized Logical Plan ==
Filter ((isnotnull(value#1) && <lambda>(key#0L)) && (value#1 < 2))
+- LogicalRDD [key#0L, value#1]

== Physical Plan ==
*Project [key#0L, value#1]
+- *Filter ((isnotnull(value#1) && pythonUDF0#9) && (value#1 < 2))
   +- BatchEvalPython [<lambda>(key#0L)], [key#0L, value#1, pythonUDF0#9]
      +- Scan ExistingRDD[key#0L,value#1]
```

After the fix, the plan looks like
```
== Optimized Logical Plan ==
Filter ((isnotnull(value#1) && <lambda>(key#0L)) && (value#1 < 2))
+- LogicalRDD [key#0L, value#1]

== Physical Plan ==
*Project [key#0L, value#1]
+- *Filter pythonUDF0#9: boolean
   +- BatchEvalPython [<lambda>(key#0L)], [key#0L, value#1, pythonUDF0#9]
      +- *Filter (isnotnull(value#1) && (value#1 < 2))
         +- Scan ExistingRDD[key#0L,value#1]
```

### How was this patch tested?
Added both unit test cases for `BatchEvalPythonExec` and also add an end-to-end test case in Python test suite.

Author: gatorsmile <gatorsmile@gmail.com>

Closes #16193 from gatorsmile/pythonUDFPredicatePushDown.
2016-12-10 08:47:45 -08:00
..
catalyst [SPARK-17460][SQL] Make sure sizeInBytes in Statistics will not overflow 2016-12-10 22:41:40 +08:00
core [SPARK-18766][SQL] Push Down Filter Through BatchEvalPython (Python UDF) 2016-12-10 08:47:45 -08:00
hive [SPARK-18637][SQL] Stateful UDF should be considered as nondeterministic 2016-12-09 16:35:06 +08:00
hive-thriftserver [SPARK-18695] Bump master branch version to 2.2.0-SNAPSHOT 2016-12-02 21:09:37 -08:00
README.md [SPARK-16557][SQL] Remove stale doc in sql/README.md 2016-07-14 19:24:42 -07:00

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 allows 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.