support chained Python UDFs

python/pyspark/sql/tests.py

@@ -305,6 +305,15 @@ def test_udf2(self):
         [res] = self.sqlCtx.sql("SELECT strlen(a) FROM test WHERE strlen(a) > 1").collect()
         self.assertEqual(4, res[0])
 
+    def test_chained_python_udf(self):
+        self.sqlCtx.registerFunction("double", lambda x: x + x, IntegerType())
+        [row] = self.sqlCtx.sql("SELECT double(1)").collect()
+        self.assertEqual(row[0], 2)
+        [row] = self.sqlCtx.sql("SELECT double(double(1))").collect()
+        self.assertEqual(row[0], 4)
+        [row] = self.sqlCtx.sql("SELECT double(double(1) + 1)").collect()
+        self.assertEqual(row[0], 6)
+
     def test_udf_with_array_type(self):
         d = [Row(l=list(range(3)), d={"key": list(range(5))})]
         rdd = self.sc.parallelize(d)

python/pyspark/worker.py

@@ -57,6 +57,11 @@ def read_command(serializer, file):
     return command
 
 
+def chain(f, g):
+    """chain two function together """
+    return lambda x: g(f(x))
+
+
 def main(infile, outfile):
     try:
         boot_time = time.time()
@@ -112,8 +117,7 @@ def main(infile, outfile):
                 if row_func is None:
                     row_func = f
                 else:
-                    # chain multiple UDF together
-                    row_func = lambda x: f(row_func(x))
+                    row_func = chain(row_func, f)
             serializer = deserializer
             func = lambda _, it: map(lambda x: returnType.toInternal(row_func(*x)), it)
         else:

sql/core/src/main/scala/org/apache/spark/sql/execution/python/BatchPythonEvaluation.scala

@@ -22,10 +22,10 @@ import scala.collection.JavaConverters._
 import net.razorvine.pickle.{Pickler, Unpickler}
 
 import org.apache.spark.TaskContext
-import org.apache.spark.api.python.PythonRunner
+import org.apache.spark.api.python.{PythonFunction, PythonRunner}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.expressions.{Attribute, GenericMutableRow, JoinedRow, UnsafeProjection}
+import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.execution.SparkPlan
 import org.apache.spark.sql.types.{StructField, StructType}
 
@@ -45,6 +45,18 @@ case class BatchPythonEvaluation(udf: PythonUDF, output: Seq[Attribute], child:
 
   def children: Seq[SparkPlan] = child :: Nil
 
+  private def collectFunctions(udf: PythonUDF): (Seq[PythonFunction], Seq[Expression]) = {
+    udf.children match {
+      case Seq(u: PythonUDF) =>
+        val (fs, children) = collectFunctions(u)
+        (fs ++ Seq(udf.func), children)
+      case children =>
+        // There should not be any other UDFs, or the children can't be evaluated directly.
+        assert(children.forall(_.find(_.isInstanceOf[PythonUDF]).isEmpty))
+        (Seq(udf.func), udf.children)
+    }
+  }
+
   protected override def doExecute(): RDD[InternalRow] = {
     val inputRDD = child.execute().map(_.copy())
     val bufferSize = inputRDD.conf.getInt("spark.buffer.size", 65536)
@@ -57,9 +69,11 @@ case class BatchPythonEvaluation(udf: PythonUDF, output: Seq[Attribute], child:
       // combine input with output from Python.
       val queue = new java.util.concurrent.ConcurrentLinkedQueue[InternalRow]()
 
+      val (pyFuncs, children) = collectFunctions(udf)
+
       val pickle = new Pickler
-      val currentRow = newMutableProjection(udf.children, child.output)()
-      val fields = udf.children.map(_.dataType)
+      val currentRow = newMutableProjection(children, child.output)()
+      val fields = children.map(_.dataType)
       val schema = new StructType(fields.map(t => new StructField("", t, true)).toArray)
 
       // Input iterator to Python: input rows are grouped so we send them in batches to Python.
@@ -75,7 +89,7 @@ case class BatchPythonEvaluation(udf: PythonUDF, output: Seq[Attribute], child:
       val context = TaskContext.get()
 
       // Output iterator for results from Python.
-      val outputIterator = new PythonRunner(Seq(udf.func), bufferSize, reuseWorker, true)
+      val outputIterator = new PythonRunner(pyFuncs, bufferSize, reuseWorker, true)
         .compute(inputIterator, context.partitionId(), context)
 
       val unpickle = new Unpickler

sql/core/src/main/scala/org/apache/spark/sql/execution/python/ExtractPythonUDFs.scala

@@ -17,6 +17,7 @@
 
 package org.apache.spark.sql.execution.python
 
+import org.apache.spark.sql.catalyst.expressions.Expression
 import org.apache.spark.sql.catalyst.plans.logical
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
 import org.apache.spark.sql.catalyst.rules.Rule
@@ -29,13 +30,31 @@ import org.apache.spark.sql.catalyst.rules.Rule
  * multiple child operators.
  */
 private[spark] object ExtractPythonUDFs extends Rule[LogicalPlan] {
+
+  private def hasUDF(e: Expression): Boolean = {
+    e.find(_.isInstanceOf[PythonUDF]).isDefined
+  }
+
+  private def canEvaluate(e: PythonUDF): Boolean = {
+    e.children match {
+      case Seq(u: PythonUDF) => canEvaluate(u)
+      case children => !children.exists(hasUDF)
+    }
+  }
+
+  private def collectEvaluatableUDF(expr: Expression): Seq[PythonUDF] = {
+    expr.collect {
+      case udf: PythonUDF if canEvaluate(udf) => udf
+    }
+  }
+
   def apply(plan: LogicalPlan): LogicalPlan = plan resolveOperators {
     // Skip EvaluatePython nodes.
     case plan: EvaluatePython => plan
 
     case plan: LogicalPlan if plan.resolved =>
       // Extract any PythonUDFs from the current operator.
-      val udfs = plan.expressions.flatMap(_.collect { case udf: PythonUDF => udf })
+      val udfs = plan.expressions.flatMap(collectEvaluatableUDF)
       if (udfs.isEmpty) {
         // If there aren't any, we are done.
         plan