From 0180b849dbaf191826231eda7dfaaf146a19602b Mon Sep 17 00:00:00 2001 From: Jian Feng Date: Mon, 21 Sep 2015 23:36:41 -0700 Subject: [SPARK-10577] [PYSPARK] DataFrame hint for broadcast join https://issues.apache.org/jira/browse/SPARK-10577 Author: Jian Feng Closes #8801 from Jianfeng-chs/master. --- python/pyspark/sql/functions.py | 9 +++++++++ python/pyspark/sql/tests.py | 18 ++++++++++++++++++ 2 files changed, 27 insertions(+) (limited to 'python') diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py index 26b8662718..fa04f4cd83 100644 --- a/python/pyspark/sql/functions.py +++ b/python/pyspark/sql/functions.py @@ -29,6 +29,7 @@ from pyspark.rdd import _prepare_for_python_RDD, ignore_unicode_prefix from pyspark.serializers import PickleSerializer, AutoBatchedSerializer from pyspark.sql.types import StringType from pyspark.sql.column import Column, _to_java_column, _to_seq +from pyspark.sql.dataframe import DataFrame def _create_function(name, doc=""): @@ -189,6 +190,14 @@ def approxCountDistinct(col, rsd=None): return Column(jc) +@since(1.6) +def broadcast(df): + """Marks a DataFrame as small enough for use in broadcast joins.""" + + sc = SparkContext._active_spark_context + return DataFrame(sc._jvm.functions.broadcast(df._jdf), df.sql_ctx) + + @since(1.4) def coalesce(*cols): """Returns the first column that is not null. diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py index 3e680f1030..645133b2b2 100644 --- a/python/pyspark/sql/tests.py +++ b/python/pyspark/sql/tests.py @@ -1075,6 +1075,24 @@ class SQLTests(ReusedPySparkTestCase): self.assertRaises(TypeError, foo) + # add test for SPARK-10577 (test broadcast join hint) + def test_functions_broadcast(self): + from pyspark.sql.functions import broadcast + + df1 = self.sqlCtx.createDataFrame([(1, "1"), (2, "2")], ("key", "value")) + df2 = self.sqlCtx.createDataFrame([(1, "1"), (2, "2")], ("key", "value")) + + # equijoin - should be converted into broadcast join + plan1 = df1.join(broadcast(df2), "key")._jdf.queryExecution().executedPlan() + self.assertEqual(1, plan1.toString().count("BroadcastHashJoin")) + + # no join key -- should not be a broadcast join + plan2 = df1.join(broadcast(df2))._jdf.queryExecution().executedPlan() + self.assertEqual(0, plan2.toString().count("BroadcastHashJoin")) + + # planner should not crash without a join + broadcast(df1)._jdf.queryExecution().executedPlan() + class HiveContextSQLTests(ReusedPySparkTestCase): -- cgit v1.2.3