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author | Josh Rosen <joshrosen@databricks.com> | 2016-09-14 10:10:01 -0700 |
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committer | Davies Liu <davies.liu@gmail.com> | 2016-09-14 10:10:01 -0700 |
commit | 6d06ff6f7e2dd72ba8fe96cd875e83eda6ebb2a9 (patch) | |
tree | cee2c6043fc889682ec3827f10818ecb85502af0 | |
parent | 52738d4e099a19466ef909b77c24cab109548706 (diff) | |
download | spark-6d06ff6f7e2dd72ba8fe96cd875e83eda6ebb2a9.tar.gz spark-6d06ff6f7e2dd72ba8fe96cd875e83eda6ebb2a9.tar.bz2 spark-6d06ff6f7e2dd72ba8fe96cd875e83eda6ebb2a9.zip |
[SPARK-17514] df.take(1) and df.limit(1).collect() should perform the same in Python
## What changes were proposed in this pull request?
In PySpark, `df.take(1)` runs a single-stage job which computes only one partition of the DataFrame, while `df.limit(1).collect()` computes all partitions and runs a two-stage job. This difference in performance is confusing.
The reason why `limit(1).collect()` is so much slower is that `collect()` internally maps to `df.rdd.<some-pyspark-conversions>.toLocalIterator`, which causes Spark SQL to build a query where a global limit appears in the middle of the plan; this, in turn, ends up being executed inefficiently because limits in the middle of plans are now implemented by repartitioning to a single task rather than by running a `take()` job on the driver (this was done in #7334, a patch which was a prerequisite to allowing partition-local limits to be pushed beneath unions, etc.).
In order to fix this performance problem I think that we should generalize the fix from SPARK-10731 / #8876 so that `DataFrame.collect()` also delegates to the Scala implementation and shares the same performance properties. This patch modifies `DataFrame.collect()` to first collect all results to the driver and then pass them to Python, allowing this query to be planned using Spark's `CollectLimit` optimizations.
## How was this patch tested?
Added a regression test in `sql/tests.py` which asserts that the expected number of jobs, stages, and tasks are run for both queries.
Author: Josh Rosen <joshrosen@databricks.com>
Closes #15068 from JoshRosen/pyspark-collect-limit.
4 files changed, 26 insertions, 18 deletions
diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py index e5eac918a9..0f7d8fba3b 100644 --- a/python/pyspark/sql/dataframe.py +++ b/python/pyspark/sql/dataframe.py @@ -357,10 +357,7 @@ class DataFrame(object): >>> df.take(2) [Row(age=2, name=u'Alice'), Row(age=5, name=u'Bob')] """ - with SCCallSiteSync(self._sc) as css: - port = self._sc._jvm.org.apache.spark.sql.execution.python.EvaluatePython.takeAndServe( - self._jdf, num) - return list(_load_from_socket(port, BatchedSerializer(PickleSerializer()))) + return self.limit(num).collect() @since(1.3) def foreach(self, f): diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py index 769e454072..1be0b72304 100644 --- a/python/pyspark/sql/tests.py +++ b/python/pyspark/sql/tests.py @@ -1862,6 +1862,24 @@ class HiveContextSQLTests(ReusedPySparkTestCase): sorted(df.select(functions.collect_list(df.value).alias('r')).collect()[0].r), ["1", "2", "2", "2"]) + def test_limit_and_take(self): + df = self.spark.range(1, 1000, numPartitions=10) + + def assert_runs_only_one_job_stage_and_task(job_group_name, f): + tracker = self.sc.statusTracker() + self.sc.setJobGroup(job_group_name, description="") + f() + jobs = tracker.getJobIdsForGroup(job_group_name) + self.assertEqual(1, len(jobs)) + stages = tracker.getJobInfo(jobs[0]).stageIds + self.assertEqual(1, len(stages)) + self.assertEqual(1, tracker.getStageInfo(stages[0]).numTasks) + + # Regression test for SPARK-10731: take should delegate to Scala implementation + assert_runs_only_one_job_stage_and_task("take", lambda: df.take(1)) + # Regression test for SPARK-17514: limit(n).collect() should the perform same as take(n) + assert_runs_only_one_job_stage_and_task("collect_limit", lambda: df.limit(1).collect()) + if __name__ == "__main__": from pyspark.sql.tests import * diff --git a/sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala b/sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala index 3b3cb82078..9cfbdffd02 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala @@ -29,7 +29,7 @@ import org.apache.commons.lang3.StringUtils import org.apache.spark.annotation.{DeveloperApi, Experimental} import org.apache.spark.api.java.JavaRDD import org.apache.spark.api.java.function._ -import org.apache.spark.api.python.PythonRDD +import org.apache.spark.api.python.{PythonRDD, SerDeUtil} import org.apache.spark.broadcast.Broadcast import org.apache.spark.rdd.RDD import org.apache.spark.sql.catalyst._ @@ -2567,8 +2567,12 @@ class Dataset[T] private[sql]( } private[sql] def collectToPython(): Int = { + EvaluatePython.registerPicklers() withNewExecutionId { - PythonRDD.collectAndServe(javaToPython.rdd) + val toJava: (Any) => Any = EvaluatePython.toJava(_, schema) + val iter = new SerDeUtil.AutoBatchedPickler( + queryExecution.executedPlan.executeCollect().iterator.map(toJava)) + PythonRDD.serveIterator(iter, "serve-DataFrame") } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/python/EvaluatePython.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/python/EvaluatePython.scala index cf68ed4ec3..724025b464 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/python/EvaluatePython.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/python/EvaluatePython.scala @@ -24,9 +24,8 @@ import scala.collection.JavaConverters._ import net.razorvine.pickle.{IObjectPickler, Opcodes, Pickler} -import org.apache.spark.api.python.{PythonRDD, SerDeUtil} +import org.apache.spark.api.python.SerDeUtil import org.apache.spark.rdd.RDD -import org.apache.spark.sql.DataFrame import org.apache.spark.sql.catalyst.InternalRow import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.util.{ArrayBasedMapData, ArrayData, GenericArrayData, MapData} @@ -34,16 +33,6 @@ import org.apache.spark.sql.types._ import org.apache.spark.unsafe.types.UTF8String object EvaluatePython { - def takeAndServe(df: DataFrame, n: Int): Int = { - registerPicklers() - df.withNewExecutionId { - val iter = new SerDeUtil.AutoBatchedPickler( - df.queryExecution.executedPlan.executeTake(n).iterator.map { row => - EvaluatePython.toJava(row, df.schema) - }) - PythonRDD.serveIterator(iter, s"serve-DataFrame") - } - } def needConversionInPython(dt: DataType): Boolean = dt match { case DateType | TimestampType => true |