9909efc10a
This patch simply ports over the Scala implementation of RDD#take(), which reads the first partition at the driver, then decides how many more partitions it needs to read and will possibly start a real job if it's more than 1. (Note that SparkContext#runJob(allowLocal=true) only runs the job locally if there's 1 partition selected and no parent stages.) Author: Aaron Davidson <aaron@databricks.com> Closes #922 from aarondav/take and squashes the following commits: fa06df9 [Aaron Davidson] SPARK-1839: PySpark RDD#take() shouldn't always read from driver
582 lines
24 KiB
Python
582 lines
24 KiB
Python
#
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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import os
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import shutil
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import sys
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from threading import Lock
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from tempfile import NamedTemporaryFile
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from collections import namedtuple
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from pyspark import accumulators
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from pyspark.accumulators import Accumulator
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from pyspark.broadcast import Broadcast
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from pyspark.conf import SparkConf
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from pyspark.files import SparkFiles
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from pyspark.java_gateway import launch_gateway
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from pyspark.serializers import PickleSerializer, BatchedSerializer, UTF8Deserializer, \
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PairDeserializer
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from pyspark.storagelevel import StorageLevel
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from pyspark import rdd
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from pyspark.rdd import RDD
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from py4j.java_collections import ListConverter
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class SparkContext(object):
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"""
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Main entry point for Spark functionality. A SparkContext represents the
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connection to a Spark cluster, and can be used to create L{RDD}s and
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broadcast variables on that cluster.
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"""
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_gateway = None
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_jvm = None
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_writeToFile = None
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_next_accum_id = 0
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_active_spark_context = None
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_lock = Lock()
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_python_includes = None # zip and egg files that need to be added to PYTHONPATH
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def __init__(self, master=None, appName=None, sparkHome=None, pyFiles=None,
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environment=None, batchSize=1024, serializer=PickleSerializer(), conf=None,
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gateway=None):
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"""
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Create a new SparkContext. At least the master and app name should be set,
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either through the named parameters here or through C{conf}.
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@param master: Cluster URL to connect to
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(e.g. mesos://host:port, spark://host:port, local[4]).
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@param appName: A name for your job, to display on the cluster web UI.
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@param sparkHome: Location where Spark is installed on cluster nodes.
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@param pyFiles: Collection of .zip or .py files to send to the cluster
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and add to PYTHONPATH. These can be paths on the local file
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system or HDFS, HTTP, HTTPS, or FTP URLs.
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@param environment: A dictionary of environment variables to set on
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worker nodes.
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@param batchSize: The number of Python objects represented as a single
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Java object. Set 1 to disable batching or -1 to use an
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unlimited batch size.
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@param serializer: The serializer for RDDs.
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@param conf: A L{SparkConf} object setting Spark properties.
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@param gateway: Use an existing gateway and JVM, otherwise a new JVM
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will be instatiated.
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>>> from pyspark.context import SparkContext
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>>> sc = SparkContext('local', 'test')
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>>> sc2 = SparkContext('local', 'test2') # doctest: +IGNORE_EXCEPTION_DETAIL
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Traceback (most recent call last):
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...
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ValueError:...
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"""
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if rdd._extract_concise_traceback() is not None:
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self._callsite = rdd._extract_concise_traceback()
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else:
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tempNamedTuple = namedtuple("Callsite", "function file linenum")
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self._callsite = tempNamedTuple(function=None, file=None, linenum=None)
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SparkContext._ensure_initialized(self, gateway=gateway)
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self.environment = environment or {}
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self._conf = conf or SparkConf(_jvm=self._jvm)
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self._batchSize = batchSize # -1 represents an unlimited batch size
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self._unbatched_serializer = serializer
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if batchSize == 1:
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self.serializer = self._unbatched_serializer
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else:
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self.serializer = BatchedSerializer(self._unbatched_serializer,
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batchSize)
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# Set any parameters passed directly to us on the conf
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if master:
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self._conf.setMaster(master)
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if appName:
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self._conf.setAppName(appName)
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if sparkHome:
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self._conf.setSparkHome(sparkHome)
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if environment:
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for key, value in environment.iteritems():
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self._conf.setExecutorEnv(key, value)
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# Check that we have at least the required parameters
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if not self._conf.contains("spark.master"):
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raise Exception("A master URL must be set in your configuration")
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if not self._conf.contains("spark.app.name"):
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raise Exception("An application name must be set in your configuration")
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# Read back our properties from the conf in case we loaded some of them from
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# the classpath or an external config file
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self.master = self._conf.get("spark.master")
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self.appName = self._conf.get("spark.app.name")
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self.sparkHome = self._conf.get("spark.home", None)
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for (k, v) in self._conf.getAll():
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if k.startswith("spark.executorEnv."):
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varName = k[len("spark.executorEnv."):]
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self.environment[varName] = v
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# Create the Java SparkContext through Py4J
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self._jsc = self._initialize_context(self._conf._jconf)
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# Create a single Accumulator in Java that we'll send all our updates through;
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# they will be passed back to us through a TCP server
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self._accumulatorServer = accumulators._start_update_server()
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(host, port) = self._accumulatorServer.server_address
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self._javaAccumulator = self._jsc.accumulator(
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self._jvm.java.util.ArrayList(),
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self._jvm.PythonAccumulatorParam(host, port))
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self.pythonExec = os.environ.get("PYSPARK_PYTHON", 'python')
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# Broadcast's __reduce__ method stores Broadcast instances here.
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# This allows other code to determine which Broadcast instances have
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# been pickled, so it can determine which Java broadcast objects to
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# send.
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self._pickled_broadcast_vars = set()
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SparkFiles._sc = self
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root_dir = SparkFiles.getRootDirectory()
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sys.path.append(root_dir)
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# Deploy any code dependencies specified in the constructor
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self._python_includes = list()
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for path in (pyFiles or []):
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self.addPyFile(path)
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# Deploy code dependencies set by spark-submit; these will already have been added
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# with SparkContext.addFile, so we just need to add them to the PYTHONPATH
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for path in self._conf.get("spark.submit.pyFiles", "").split(","):
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if path != "":
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(dirname, filename) = os.path.split(path)
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self._python_includes.append(filename)
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sys.path.append(path)
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if not dirname in sys.path:
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sys.path.append(dirname)
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# Create a temporary directory inside spark.local.dir:
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local_dir = self._jvm.org.apache.spark.util.Utils.getLocalDir(self._jsc.sc().conf())
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self._temp_dir = \
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self._jvm.org.apache.spark.util.Utils.createTempDir(local_dir).getAbsolutePath()
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def _initialize_context(self, jconf):
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"""
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Initialize SparkContext in function to allow subclass specific initialization
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"""
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return self._jvm.JavaSparkContext(jconf)
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@classmethod
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def _ensure_initialized(cls, instance=None, gateway=None):
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"""
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Checks whether a SparkContext is initialized or not.
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Throws error if a SparkContext is already running.
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"""
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with SparkContext._lock:
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if not SparkContext._gateway:
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SparkContext._gateway = gateway or launch_gateway()
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SparkContext._jvm = SparkContext._gateway.jvm
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SparkContext._writeToFile = SparkContext._jvm.PythonRDD.writeToFile
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if instance:
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if SparkContext._active_spark_context and SparkContext._active_spark_context != instance:
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currentMaster = SparkContext._active_spark_context.master
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currentAppName = SparkContext._active_spark_context.appName
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callsite = SparkContext._active_spark_context._callsite
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# Raise error if there is already a running Spark context
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raise ValueError("Cannot run multiple SparkContexts at once; existing SparkContext(app=%s, master=%s)" \
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" created by %s at %s:%s " \
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% (currentAppName, currentMaster, callsite.function, callsite.file, callsite.linenum))
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else:
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SparkContext._active_spark_context = instance
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@classmethod
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def setSystemProperty(cls, key, value):
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"""
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Set a Java system property, such as spark.executor.memory. This must
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must be invoked before instantiating SparkContext.
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"""
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SparkContext._ensure_initialized()
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SparkContext._jvm.java.lang.System.setProperty(key, value)
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@property
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def defaultParallelism(self):
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"""
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Default level of parallelism to use when not given by user (e.g. for
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reduce tasks)
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"""
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return self._jsc.sc().defaultParallelism()
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@property
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def defaultMinPartitions(self):
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"""
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Default min number of partitions for Hadoop RDDs when not given by user
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"""
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return self._jsc.sc().defaultMinPartitions()
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def __del__(self):
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self.stop()
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def stop(self):
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"""
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Shut down the SparkContext.
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"""
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if self._jsc:
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self._jsc.stop()
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self._jsc = None
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if self._accumulatorServer:
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self._accumulatorServer.shutdown()
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self._accumulatorServer = None
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with SparkContext._lock:
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SparkContext._active_spark_context = None
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def parallelize(self, c, numSlices=None):
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"""
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Distribute a local Python collection to form an RDD.
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>>> sc.parallelize(range(5), 5).glom().collect()
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[[0], [1], [2], [3], [4]]
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"""
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numSlices = numSlices or self.defaultParallelism
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# Calling the Java parallelize() method with an ArrayList is too slow,
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# because it sends O(n) Py4J commands. As an alternative, serialized
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# objects are written to a file and loaded through textFile().
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tempFile = NamedTemporaryFile(delete=False, dir=self._temp_dir)
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# Make sure we distribute data evenly if it's smaller than self.batchSize
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if "__len__" not in dir(c):
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c = list(c) # Make it a list so we can compute its length
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batchSize = min(len(c) // numSlices, self._batchSize)
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if batchSize > 1:
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serializer = BatchedSerializer(self._unbatched_serializer,
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batchSize)
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else:
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serializer = self._unbatched_serializer
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serializer.dump_stream(c, tempFile)
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tempFile.close()
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readRDDFromFile = self._jvm.PythonRDD.readRDDFromFile
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jrdd = readRDDFromFile(self._jsc, tempFile.name, numSlices)
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return RDD(jrdd, self, serializer)
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def textFile(self, name, minPartitions=None):
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"""
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Read a text file from HDFS, a local file system (available on all
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nodes), or any Hadoop-supported file system URI, and return it as an
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RDD of Strings.
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>>> path = os.path.join(tempdir, "sample-text.txt")
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>>> with open(path, "w") as testFile:
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... testFile.write("Hello world!")
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>>> textFile = sc.textFile(path)
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>>> textFile.collect()
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[u'Hello world!']
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"""
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minPartitions = minPartitions or min(self.defaultParallelism, 2)
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return RDD(self._jsc.textFile(name, minPartitions), self,
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UTF8Deserializer())
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def wholeTextFiles(self, path, minPartitions=None):
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"""
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Read a directory of text files from HDFS, a local file system
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(available on all nodes), or any Hadoop-supported file system
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URI. Each file is read as a single record and returned in a
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key-value pair, where the key is the path of each file, the
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value is the content of each file.
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For example, if you have the following files::
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hdfs://a-hdfs-path/part-00000
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hdfs://a-hdfs-path/part-00001
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...
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hdfs://a-hdfs-path/part-nnnnn
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Do C{rdd = sparkContext.wholeTextFiles("hdfs://a-hdfs-path")},
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then C{rdd} contains::
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(a-hdfs-path/part-00000, its content)
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(a-hdfs-path/part-00001, its content)
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...
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(a-hdfs-path/part-nnnnn, its content)
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NOTE: Small files are preferred, as each file will be loaded
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fully in memory.
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>>> dirPath = os.path.join(tempdir, "files")
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>>> os.mkdir(dirPath)
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>>> with open(os.path.join(dirPath, "1.txt"), "w") as file1:
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... file1.write("1")
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>>> with open(os.path.join(dirPath, "2.txt"), "w") as file2:
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... file2.write("2")
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>>> textFiles = sc.wholeTextFiles(dirPath)
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>>> sorted(textFiles.collect())
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[(u'.../1.txt', u'1'), (u'.../2.txt', u'2')]
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"""
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minPartitions = minPartitions or self.defaultMinPartitions
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return RDD(self._jsc.wholeTextFiles(path, minPartitions), self,
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PairDeserializer(UTF8Deserializer(), UTF8Deserializer()))
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def _checkpointFile(self, name, input_deserializer):
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jrdd = self._jsc.checkpointFile(name)
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return RDD(jrdd, self, input_deserializer)
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def union(self, rdds):
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"""
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Build the union of a list of RDDs.
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This supports unions() of RDDs with different serialized formats,
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although this forces them to be reserialized using the default
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serializer:
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>>> path = os.path.join(tempdir, "union-text.txt")
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>>> with open(path, "w") as testFile:
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... testFile.write("Hello")
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>>> textFile = sc.textFile(path)
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>>> textFile.collect()
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[u'Hello']
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>>> parallelized = sc.parallelize(["World!"])
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>>> sorted(sc.union([textFile, parallelized]).collect())
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[u'Hello', 'World!']
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"""
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first_jrdd_deserializer = rdds[0]._jrdd_deserializer
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if any(x._jrdd_deserializer != first_jrdd_deserializer for x in rdds):
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rdds = [x._reserialize() for x in rdds]
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first = rdds[0]._jrdd
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rest = [x._jrdd for x in rdds[1:]]
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rest = ListConverter().convert(rest, self._gateway._gateway_client)
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return RDD(self._jsc.union(first, rest), self,
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rdds[0]._jrdd_deserializer)
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def broadcast(self, value):
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"""
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Broadcast a read-only variable to the cluster, returning a
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L{Broadcast<pyspark.broadcast.Broadcast>}
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object for reading it in distributed functions. The variable will be
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sent to each cluster only once.
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"""
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pickleSer = PickleSerializer()
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pickled = pickleSer.dumps(value)
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jbroadcast = self._jsc.broadcast(bytearray(pickled))
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return Broadcast(jbroadcast.id(), value, jbroadcast,
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self._pickled_broadcast_vars)
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def accumulator(self, value, accum_param=None):
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"""
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Create an L{Accumulator} with the given initial value, using a given
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L{AccumulatorParam} helper object to define how to add values of the
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data type if provided. Default AccumulatorParams are used for integers
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and floating-point numbers if you do not provide one. For other types,
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a custom AccumulatorParam can be used.
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"""
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if accum_param is None:
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if isinstance(value, int):
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accum_param = accumulators.INT_ACCUMULATOR_PARAM
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elif isinstance(value, float):
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accum_param = accumulators.FLOAT_ACCUMULATOR_PARAM
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elif isinstance(value, complex):
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accum_param = accumulators.COMPLEX_ACCUMULATOR_PARAM
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else:
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raise Exception("No default accumulator param for type %s" % type(value))
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SparkContext._next_accum_id += 1
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return Accumulator(SparkContext._next_accum_id - 1, value, accum_param)
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def addFile(self, path):
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"""
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Add a file to be downloaded with this Spark job on every node.
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The C{path} passed can be either a local file, a file in HDFS
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(or other Hadoop-supported filesystems), or an HTTP, HTTPS or
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FTP URI.
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To access the file in Spark jobs, use
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L{SparkFiles.get(path)<pyspark.files.SparkFiles.get>} to find its
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download location.
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>>> from pyspark import SparkFiles
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>>> path = os.path.join(tempdir, "test.txt")
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>>> with open(path, "w") as testFile:
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... testFile.write("100")
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>>> sc.addFile(path)
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>>> def func(iterator):
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... with open(SparkFiles.get("test.txt")) as testFile:
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... fileVal = int(testFile.readline())
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... return [x * 100 for x in iterator]
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>>> sc.parallelize([1, 2, 3, 4]).mapPartitions(func).collect()
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[100, 200, 300, 400]
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"""
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self._jsc.sc().addFile(path)
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def clearFiles(self):
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"""
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Clear the job's list of files added by L{addFile} or L{addPyFile} so
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that they do not get downloaded to any new nodes.
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"""
|
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# TODO: remove added .py or .zip files from the PYTHONPATH?
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self._jsc.sc().clearFiles()
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def addPyFile(self, path):
|
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"""
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|
Add a .py or .zip dependency for all tasks to be executed on this
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SparkContext in the future. The C{path} passed can be either a local
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file, a file in HDFS (or other Hadoop-supported filesystems), or an
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HTTP, HTTPS or FTP URI.
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"""
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self.addFile(path)
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(dirname, filename) = os.path.split(path) # dirname may be directory or HDFS/S3 prefix
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if filename.endswith('.zip') or filename.endswith('.ZIP') or filename.endswith('.egg'):
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self._python_includes.append(filename)
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sys.path.append(os.path.join(SparkFiles.getRootDirectory(), filename)) # for tests in local mode
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def setCheckpointDir(self, dirName):
|
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"""
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Set the directory under which RDDs are going to be checkpointed. The
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directory must be a HDFS path if running on a cluster.
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"""
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self._jsc.sc().setCheckpointDir(dirName)
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|
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def _getJavaStorageLevel(self, storageLevel):
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"""
|
|
Returns a Java StorageLevel based on a pyspark.StorageLevel.
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"""
|
|
if not isinstance(storageLevel, StorageLevel):
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raise Exception("storageLevel must be of type pyspark.StorageLevel")
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newStorageLevel = self._jvm.org.apache.spark.storage.StorageLevel
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return newStorageLevel(storageLevel.useDisk,
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storageLevel.useMemory,
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storageLevel.useOffHeap,
|
|
storageLevel.deserialized,
|
|
storageLevel.replication)
|
|
|
|
def setJobGroup(self, groupId, description, interruptOnCancel=False):
|
|
"""
|
|
Assigns a group ID to all the jobs started by this thread until the group ID is set to a
|
|
different value or cleared.
|
|
|
|
Often, a unit of execution in an application consists of multiple Spark actions or jobs.
|
|
Application programmers can use this method to group all those jobs together and give a
|
|
group description. Once set, the Spark web UI will associate such jobs with this group.
|
|
|
|
The application can use L{SparkContext.cancelJobGroup} to cancel all
|
|
running jobs in this group.
|
|
|
|
>>> import thread, threading
|
|
>>> from time import sleep
|
|
>>> result = "Not Set"
|
|
>>> lock = threading.Lock()
|
|
>>> def map_func(x):
|
|
... sleep(100)
|
|
... raise Exception("Task should have been cancelled")
|
|
>>> def start_job(x):
|
|
... global result
|
|
... try:
|
|
... sc.setJobGroup("job_to_cancel", "some description")
|
|
... result = sc.parallelize(range(x)).map(map_func).collect()
|
|
... except Exception as e:
|
|
... result = "Cancelled"
|
|
... lock.release()
|
|
>>> def stop_job():
|
|
... sleep(5)
|
|
... sc.cancelJobGroup("job_to_cancel")
|
|
>>> supress = lock.acquire()
|
|
>>> supress = thread.start_new_thread(start_job, (10,))
|
|
>>> supress = thread.start_new_thread(stop_job, tuple())
|
|
>>> supress = lock.acquire()
|
|
>>> print result
|
|
Cancelled
|
|
|
|
If interruptOnCancel is set to true for the job group, then job cancellation will result
|
|
in Thread.interrupt() being called on the job's executor threads. This is useful to help ensure
|
|
that the tasks are actually stopped in a timely manner, but is off by default due to HDFS-1208,
|
|
where HDFS may respond to Thread.interrupt() by marking nodes as dead.
|
|
"""
|
|
self._jsc.setJobGroup(groupId, description, interruptOnCancel)
|
|
|
|
def setLocalProperty(self, key, value):
|
|
"""
|
|
Set a local property that affects jobs submitted from this thread, such as the
|
|
Spark fair scheduler pool.
|
|
"""
|
|
self._jsc.setLocalProperty(key, value)
|
|
|
|
def getLocalProperty(self, key):
|
|
"""
|
|
Get a local property set in this thread, or null if it is missing. See
|
|
L{setLocalProperty}
|
|
"""
|
|
return self._jsc.getLocalProperty(key)
|
|
|
|
def sparkUser(self):
|
|
"""
|
|
Get SPARK_USER for user who is running SparkContext.
|
|
"""
|
|
return self._jsc.sc().sparkUser()
|
|
|
|
def cancelJobGroup(self, groupId):
|
|
"""
|
|
Cancel active jobs for the specified group. See L{SparkContext.setJobGroup}
|
|
for more information.
|
|
"""
|
|
self._jsc.sc().cancelJobGroup(groupId)
|
|
|
|
def cancelAllJobs(self):
|
|
"""
|
|
Cancel all jobs that have been scheduled or are running.
|
|
"""
|
|
self._jsc.sc().cancelAllJobs()
|
|
|
|
def runJob(self, rdd, partitionFunc, partitions = None, allowLocal = False):
|
|
"""
|
|
Executes the given partitionFunc on the specified set of partitions,
|
|
returning the result as an array of elements.
|
|
|
|
If 'partitions' is not specified, this will run over all partitions.
|
|
|
|
>>> myRDD = sc.parallelize(range(6), 3)
|
|
>>> sc.runJob(myRDD, lambda part: [x * x for x in part])
|
|
[0, 1, 4, 9, 16, 25]
|
|
|
|
>>> myRDD = sc.parallelize(range(6), 3)
|
|
>>> sc.runJob(myRDD, lambda part: [x * x for x in part], [0, 2], True)
|
|
[0, 1, 16, 25]
|
|
"""
|
|
if partitions == None:
|
|
partitions = range(rdd._jrdd.splits().size())
|
|
javaPartitions = ListConverter().convert(partitions, self._gateway._gateway_client)
|
|
|
|
# Implementation note: This is implemented as a mapPartitions followed
|
|
# by runJob() in order to avoid having to pass a Python lambda into
|
|
# SparkContext#runJob.
|
|
mappedRDD = rdd.mapPartitions(partitionFunc)
|
|
it = self._jvm.PythonRDD.runJob(self._jsc.sc(), mappedRDD._jrdd, javaPartitions, allowLocal)
|
|
return list(mappedRDD._collect_iterator_through_file(it))
|
|
|
|
def _test():
|
|
import atexit
|
|
import doctest
|
|
import tempfile
|
|
globs = globals().copy()
|
|
globs['sc'] = SparkContext('local[4]', 'PythonTest', batchSize=2)
|
|
globs['tempdir'] = tempfile.mkdtemp()
|
|
atexit.register(lambda: shutil.rmtree(globs['tempdir']))
|
|
(failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
|
|
globs['sc'].stop()
|
|
if failure_count:
|
|
exit(-1)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
_test()
|