[SPARK-4172] [PySpark] Progress API in Python

core/src/main/scala/org/apache/spark/scheduler/TaskResultGetter.scala

@@ -18,6 +18,7 @@
 package org.apache.spark.scheduler
 
 import java.nio.ByteBuffer
+import java.util.concurrent.RejectedExecutionException
 
 import scala.language.existentials
 import scala.util.control.NonFatal
@@ -95,25 +96,30 @@ private[spark] class TaskResultGetter(sparkEnv: SparkEnv, scheduler: TaskSchedul
   def enqueueFailedTask(taskSetManager: TaskSetManager, tid: Long, taskState: TaskState,
     serializedData: ByteBuffer) {
     var reason : TaskEndReason = UnknownReason
-    getTaskResultExecutor.execute(new Runnable {
-      override def run(): Unit = Utils.logUncaughtExceptions {
-        try {
-          if (serializedData != null && serializedData.limit() > 0) {
-            reason = serializer.get().deserialize[TaskEndReason](
-              serializedData, Utils.getSparkClassLoader)
+    try {
+      getTaskResultExecutor.execute(new Runnable {
+        override def run(): Unit = Utils.logUncaughtExceptions {
+          try {
+            if (serializedData != null && serializedData.limit() > 0) {
+              reason = serializer.get().deserialize[TaskEndReason](
+                serializedData, Utils.getSparkClassLoader)
+            }
+          } catch {
+            case cnd: ClassNotFoundException =>
+              // Log an error but keep going here -- the task failed, so not catastrophic
+              // if we can't deserialize the reason.
+              val loader = Utils.getContextOrSparkClassLoader
+              logError(
+                "Could not deserialize TaskEndReason: ClassNotFound with classloader " + loader)
+            case ex: Exception => {}
           }
-        } catch {
-          case cnd: ClassNotFoundException =>
-            // Log an error but keep going here -- the task failed, so not catastrophic if we can't
-            // deserialize the reason.
-            val loader = Utils.getContextOrSparkClassLoader
-            logError(
-              "Could not deserialize TaskEndReason: ClassNotFound with classloader " + loader)
-          case ex: Exception => {}
+          scheduler.handleFailedTask(taskSetManager, tid, taskState, reason)
         }
-        scheduler.handleFailedTask(taskSetManager, tid, taskState, reason)
-      }
-    })
+      })
+    } catch {
+      case e: RejectedExecutionException if sparkEnv.isStopped =>
+        // ignore it
+    }
   }
 
   def stop() {

examples/src/main/python/status_api_demo.py

@@ -0,0 +1,67 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import time
+import threading
+import Queue
+
+from pyspark import SparkConf, SparkContext
+
+
+def delayed(seconds):
+    def f(x):
+        time.sleep(seconds)
+        return x
+    return f
+
+
+def call_in_background(f, *args):
+    result = Queue.Queue(1)
+    t = threading.Thread(target=lambda: result.put(f(*args)))
+    t.daemon = True
+    t.start()
+    return result
+
+
+def main():
+    conf = SparkConf().set("spark.ui.showConsoleProgress", "false")
+    sc = SparkContext(appName="PythonStatusAPIDemo", conf=conf)
+
+    def run():
+        rdd = sc.parallelize(range(10), 10).map(delayed(2))
+        reduced = rdd.map(lambda x: (x, 1)).reduceByKey(lambda x, y: x + y)
+        return reduced.map(delayed(2)).collect()
+
+    result = call_in_background(run)
+    status = sc.statusTracker()
+    while result.empty():
+        ids = status.getJobIdsForGroup()
+        for id in ids:
+            job = status.getJobInfo(id)
+            print "Job", id, "status: ", job.status
+            for sid in job.stageIds:
+                info = status.getStageInfo(sid)
+                if info:
+                    print "Stage %d: %d tasks total (%d active, %d complete)" % \
+                          (sid, info.numTasks, info.numActiveTasks, info.numCompletedTasks)
+        time.sleep(1)
+
+    print "Job results are:", result.get()
+    sc.stop()
+
+if __name__ == "__main__":
+    main()

python/pyspark/__init__.py

@@ -22,17 +22,17 @@
 
   - :class:`SparkContext`:
       Main entry point for Spark functionality.
-  - L{RDD}
+  - :class:`RDD`:
       A Resilient Distributed Dataset (RDD), the basic abstraction in Spark.
-  - L{Broadcast}
+  - :class:`Broadcast`:
       A broadcast variable that gets reused across tasks.
-  - L{Accumulator}
+  - :class:`Accumulator`:
       An "add-only" shared variable that tasks can only add values to.
-  - L{SparkConf}
+  - :class:`SparkConf`:
       For configuring Spark.
-  - L{SparkFiles}
+  - :class:`SparkFiles`:
       Access files shipped with jobs.
-  - L{StorageLevel}
+  - :class:`StorageLevel`:
       Finer-grained cache persistence levels.
 
 """
@@ -45,6 +45,7 @@
 from pyspark.accumulators import Accumulator, AccumulatorParam
 from pyspark.broadcast import Broadcast
 from pyspark.serializers import MarshalSerializer, PickleSerializer
+from pyspark.status import *
 from pyspark.profiler import Profiler, BasicProfiler
 
 # for back compatibility
@@ -53,5 +54,5 @@
 __all__ = [
     "SparkConf", "SparkContext", "SparkFiles", "RDD", "StorageLevel", "Broadcast",
     "Accumulator", "AccumulatorParam", "MarshalSerializer", "PickleSerializer",
-    "Profiler", "BasicProfiler",
+    "StatusTracker", "SparkJobInfo", "SparkStageInfo", "Profiler", "BasicProfiler",
 ]

python/pyspark/context.py

@@ -32,6 +32,7 @@
 from pyspark.storagelevel import StorageLevel
 from pyspark.rdd import RDD
 from pyspark.traceback_utils import CallSite, first_spark_call
+from pyspark.status import StatusTracker
 from pyspark.profiler import ProfilerCollector, BasicProfiler
 
 from py4j.java_collections import ListConverter
@@ -808,6 +809,12 @@ def cancelAllJobs(self):
         """
         self._jsc.sc().cancelAllJobs()
 
+    def statusTracker(self):
+        """
+        Return :class:`StatusTracker` object
+        """
+        return StatusTracker(self._jsc.statusTracker())
+
     def runJob(self, rdd, partitionFunc, partitions=None, allowLocal=False):
         """
         Executes the given partitionFunc on the specified set of partitions,

python/pyspark/status.py

@@ -0,0 +1,96 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from collections import namedtuple
+
+__all__ = ["SparkJobInfo", "SparkStageInfo", "StatusTracker"]
+
+
+class SparkJobInfo(namedtuple("SparkJobInfo", "jobId stageIds status")):
+    """
+    Exposes information about Spark Jobs.
+    """
+
+
+class SparkStageInfo(namedtuple("SparkStageInfo",
+                                "stageId currentAttemptId name numTasks numActiveTasks "
+                                "numCompletedTasks numFailedTasks")):
+    """
+    Exposes information about Spark Stages.
+    """
+
+
+class StatusTracker(object):
+    """
+    Low-level status reporting APIs for monitoring job and stage progress.
+
+    These APIs intentionally provide very weak consistency semantics;
+    consumers of these APIs should be prepared to handle empty / missing
+    information. For example, a job's stage ids may be known but the status
+    API may not have any information about the details of those stages, so
+    `getStageInfo` could potentially return `None` for a valid stage id.
+
+    To limit memory usage, these APIs only provide information on recent
+    jobs / stages.  These APIs will provide information for the last
+    `spark.ui.retainedStages` stages and `spark.ui.retainedJobs` jobs.
+    """
+    def __init__(self, jtracker):
+        self._jtracker = jtracker
+
+    def getJobIdsForGroup(self, jobGroup=None):
+        """
+        Return a list of all known jobs in a particular job group.  If
+        `jobGroup` is None, then returns all known jobs that are not
+        associated with a job group.
+
+        The returned list may contain running, failed, and completed jobs,
+        and may vary across invocations of this method. This method does
+        not guarantee the order of the elements in its result.
+        """
+        return list(self._jtracker.getJobIdsForGroup(jobGroup))
+
+    def getActiveStageIds(self):
+        """
+        Returns an array containing the ids of all active stages.
+        """
+        return sorted(list(self._jtracker.getActiveStageIds()))
+
+    def getActiveJobsIds(self):
+        """
+        Returns an array containing the ids of all active jobs.
+        """
+        return sorted((list(self._jtracker.getActiveJobIds())))
+
+    def getJobInfo(self, jobId):
+        """
+        Returns a :class:`SparkJobInfo` object, or None if the job info
+        could not be found or was garbage collected.
+        """
+        job = self._jtracker.getJobInfo(jobId)
+        if job is not None:
+            return SparkJobInfo(jobId, job.stageIds(), str(job.status()))
+
+    def getStageInfo(self, stageId):
+        """
+        Returns a :class:`SparkStageInfo` object, or None if the stage
+        info could not be found or was garbage collected.
+        """
+        stage = self._jtracker.getStageInfo(stageId)
+        if stage is not None:
+            # TODO: fetch them in batch for better performance
+            attrs = [getattr(stage, f)() for f in SparkStageInfo._fields[1:]]
+            return SparkStageInfo(stageId, *attrs)

python/pyspark/tests.py

@@ -1550,6 +1550,37 @@ def test_with_stop(self):
             sc.stop()
         self.assertEqual(SparkContext._active_spark_context, None)
 
+    def test_progress_api(self):
+        with SparkContext() as sc:
+            sc.setJobGroup('test_progress_api', '', True)
+
+            rdd = sc.parallelize(range(10)).map(lambda x: time.sleep(100))
+            t = threading.Thread(target=rdd.collect)
+            t.daemon = True
+            t.start()
+            # wait for scheduler to start
+            time.sleep(1)
+
+            tracker = sc.statusTracker()
+            jobIds = tracker.getJobIdsForGroup('test_progress_api')
+            self.assertEqual(1, len(jobIds))
+            job = tracker.getJobInfo(jobIds[0])
+            self.assertEqual(1, len(job.stageIds))
+            stage = tracker.getStageInfo(job.stageIds[0])
+            self.assertEqual(rdd.getNumPartitions(), stage.numTasks)
+
+            sc.cancelAllJobs()
+            t.join()
+            # wait for event listener to update the status
+            time.sleep(1)
+
+            job = tracker.getJobInfo(jobIds[0])
+            self.assertEqual('FAILED', job.status)
+            self.assertEqual([], tracker.getActiveJobsIds())
+            self.assertEqual([], tracker.getActiveStageIds())
+
+            sc.stop()
+
 
 @unittest.skipIf(not _have_scipy, "SciPy not installed")
 class SciPyTests(PySparkTestCase):