[SPARK-2470] PEP8 fixes to PySpark

This pull request aims to resolve all outstanding PEP8 violations in PySpark.

Author: Nicholas Chammas <[email protected]>
Author: nchammas <[email protected]>

Closes apache#1505 from nchammas/master and squashes the following commits:

98171af [Nicholas Chammas] [SPARK-2470] revert PEP 8 fixes to cloudpickle
cba7768 [Nicholas Chammas] [SPARK-2470] wrap expression list in parentheses
e178dbe [Nicholas Chammas] [SPARK-2470] style - change position of line break
9127d2b [Nicholas Chammas] [SPARK-2470] wrap expression lists in parentheses
22132a4 [Nicholas Chammas] [SPARK-2470] wrap conditionals in parentheses
24639bc [Nicholas Chammas] [SPARK-2470] fix whitespace for doctest
7d557b7 [Nicholas Chammas] [SPARK-2470] PEP8 fixes to tests.py
8f8e4c0 [Nicholas Chammas] [SPARK-2470] PEP8 fixes to storagelevel.py
b3b96cf [Nicholas Chammas] [SPARK-2470] PEP8 fixes to statcounter.py
d644477 [Nicholas Chammas] [SPARK-2470] PEP8 fixes to worker.py
aa3a7b6 [Nicholas Chammas] [SPARK-2470] PEP8 fixes to sql.py
1916859 [Nicholas Chammas] [SPARK-2470] PEP8 fixes to shell.py
95d1d95 [Nicholas Chammas] [SPARK-2470] PEP8 fixes to serializers.py
a0fec2e [Nicholas Chammas] [SPARK-2470] PEP8 fixes to mllib
c85e1e5 [Nicholas Chammas] [SPARK-2470] PEP8 fixes to join.py
d14f2f1 [Nicholas Chammas] [SPARK-2470] PEP8 fixes to __init__.py
81fcb20 [Nicholas Chammas] [SPARK-2470] PEP8 fixes to resultiterable.py
1bde265 [Nicholas Chammas] [SPARK-2470] PEP8 fixes to java_gateway.py
7fc849c [Nicholas Chammas] [SPARK-2470] PEP8 fixes to daemon.py
ca2d28b [Nicholas Chammas] [SPARK-2470] PEP8 fixes to context.py
f4e0039 [Nicholas Chammas] [SPARK-2470] PEP8 fixes to conf.py
a6d5e4b [Nicholas Chammas] [SPARK-2470] PEP8 fixes to cloudpickle.py
f0a7ebf [Nicholas Chammas] [SPARK-2470] PEP8 fixes to rddsampler.py
4dd148f [nchammas] Merge pull request #5 from apache/master
f7e4581 [Nicholas Chammas] unrelated pep8 fix
a36eed0 [Nicholas Chammas] name ec2 instances and security groups consistently
de7292a [nchammas] Merge pull request #4 from apache/master
2e4fe00 [nchammas] Merge pull request #3 from apache/master
89fde08 [nchammas] Merge pull request #2 from apache/master
69f6e22 [Nicholas Chammas] PEP8 fixes
2627247 [Nicholas Chammas] broke up lines before they hit 100 chars
6544b7e [Nicholas Chammas] [SPARK-2065] give launched instances names
69da6cf [nchammas] Merge pull request #1 from apache/master

Author: nchammas

python/pyspark/__init__.py

@@ -59,4 +59,5 @@
 from pyspark.storagelevel import StorageLevel
 
 
-__all__ = ["SparkConf", "SparkContext", "SQLContext", "RDD", "SchemaRDD", "SparkFiles", "StorageLevel", "Row"]
+__all__ = ["SparkConf", "SparkContext", "SQLContext", "RDD", "SchemaRDD",
+           "SparkFiles", "StorageLevel", "Row"]

python/pyspark/conf.py

@@ -50,7 +50,8 @@
 spark.executorEnv.VAR4=value4
 spark.home=/path
 >>> sorted(conf.getAll(), key=lambda p: p[0])
-[(u'spark.executorEnv.VAR1', u'value1'), (u'spark.executorEnv.VAR3', u'value3'), (u'spark.executorEnv.VAR4', u'value4'), (u'spark.home', u'/path')]
+[(u'spark.executorEnv.VAR1', u'value1'), (u'spark.executorEnv.VAR3', u'value3'), \
+(u'spark.executorEnv.VAR4', u'value4'), (u'spark.home', u'/path')]
 """
 
 
@@ -118,9 +119,9 @@ def setExecutorEnv(self, key=None, value=None, pairs=None):
         """Set an environment variable to be passed to executors."""
         if (key is not None and pairs is not None) or (key is None and pairs is None):
             raise Exception("Either pass one key-value pair or a list of pairs")
-        elif key != None:
+        elif key is not None:
             self._jconf.setExecutorEnv(key, value)
-        elif pairs != None:
+        elif pairs is not None:
             for (k, v) in pairs:
                 self._jconf.setExecutorEnv(k, v)
         return self
@@ -137,7 +138,7 @@ def setAll(self, pairs):
 
     def get(self, key, defaultValue=None):
         """Get the configured value for some key, or return a default otherwise."""
-        if defaultValue == None:   # Py4J doesn't call the right get() if we pass None
+        if defaultValue is None:   # Py4J doesn't call the right get() if we pass None
             if not self._jconf.contains(key):
                 return None
             return self._jconf.get(key)

python/pyspark/context.py

@@ -29,7 +29,7 @@
 from pyspark.files import SparkFiles
 from pyspark.java_gateway import launch_gateway
 from pyspark.serializers import PickleSerializer, BatchedSerializer, UTF8Deserializer, \
-        PairDeserializer
+    PairDeserializer
 from pyspark.storagelevel import StorageLevel
 from pyspark import rdd
 from pyspark.rdd import RDD
@@ -50,12 +50,11 @@ class SparkContext(object):
     _next_accum_id = 0
     _active_spark_context = None
     _lock = Lock()
-    _python_includes = None # zip and egg files that need to be added to PYTHONPATH
-
+    _python_includes = None  # zip and egg files that need to be added to PYTHONPATH
 
     def __init__(self, master=None, appName=None, sparkHome=None, pyFiles=None,
-        environment=None, batchSize=1024, serializer=PickleSerializer(), conf=None,
-        gateway=None):
+                 environment=None, batchSize=1024, serializer=PickleSerializer(), conf=None,
+                 gateway=None):
         """
         Create a new SparkContext. At least the master and app name should be set,
         either through the named parameters here or through C{conf}.
@@ -138,8 +137,8 @@ def __init__(self, master=None, appName=None, sparkHome=None, pyFiles=None,
         self._accumulatorServer = accumulators._start_update_server()
         (host, port) = self._accumulatorServer.server_address
         self._javaAccumulator = self._jsc.accumulator(
-                self._jvm.java.util.ArrayList(),
-                self._jvm.PythonAccumulatorParam(host, port))
+            self._jvm.java.util.ArrayList(),
+            self._jvm.PythonAccumulatorParam(host, port))
 
         self.pythonExec = os.environ.get("PYSPARK_PYTHON", 'python')
 
@@ -165,7 +164,7 @@ def __init__(self, master=None, appName=None, sparkHome=None, pyFiles=None,
                 (dirname, filename) = os.path.split(path)
                 self._python_includes.append(filename)
                 sys.path.append(path)
-                if not dirname in sys.path:
+                if dirname not in sys.path:
                     sys.path.append(dirname)
 
         # Create a temporary directory inside spark.local.dir:
@@ -192,15 +191,19 @@ def _ensure_initialized(cls, instance=None, gateway=None):
                 SparkContext._writeToFile = SparkContext._jvm.PythonRDD.writeToFile
 
             if instance:
-                if SparkContext._active_spark_context and SparkContext._active_spark_context != instance:
+                if (SparkContext._active_spark_context and
+                   SparkContext._active_spark_context != instance):
                     currentMaster = SparkContext._active_spark_context.master
                     currentAppName = SparkContext._active_spark_context.appName
                     callsite = SparkContext._active_spark_context._callsite
 
                     # Raise error if there is already a running Spark context
-                    raise ValueError("Cannot run multiple SparkContexts at once; existing SparkContext(app=%s, master=%s)" \
-                        " created by %s at %s:%s " \
-                        % (currentAppName, currentMaster, callsite.function, callsite.file, callsite.linenum))
+                    raise ValueError(
+                        "Cannot run multiple SparkContexts at once; "
+                        "existing SparkContext(app=%s, master=%s)"
+                        " created by %s at %s:%s "
+                        % (currentAppName, currentMaster,
+                            callsite.function, callsite.file, callsite.linenum))
                 else:
                     SparkContext._active_spark_context = instance
 
@@ -290,7 +293,7 @@ def textFile(self, name, minPartitions=None):
         Read a text file from HDFS, a local file system (available on all
         nodes), or any Hadoop-supported file system URI, and return it as an
         RDD of Strings.
-        
+
         >>> path = os.path.join(tempdir, "sample-text.txt")
         >>> with open(path, "w") as testFile:
         ...    testFile.write("Hello world!")
@@ -584,11 +587,12 @@ def addPyFile(self, path):
         HTTP, HTTPS or FTP URI.
         """
         self.addFile(path)
-        (dirname, filename) = os.path.split(path) # dirname may be directory or HDFS/S3 prefix
+        (dirname, filename) = os.path.split(path)  # dirname may be directory or HDFS/S3 prefix
 
         if filename.endswith('.zip') or filename.endswith('.ZIP') or filename.endswith('.egg'):
             self._python_includes.append(filename)
-            sys.path.append(os.path.join(SparkFiles.getRootDirectory(), filename)) # for tests in local mode
+            # for tests in local mode
+            sys.path.append(os.path.join(SparkFiles.getRootDirectory(), filename))
 
     def setCheckpointDir(self, dirName):
         """
@@ -649,9 +653,9 @@ def setJobGroup(self, groupId, description, interruptOnCancel=False):
         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.
+        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)
 
@@ -688,7 +692,7 @@ def cancelAllJobs(self):
         """
         self._jsc.sc().cancelAllJobs()
 
-    def runJob(self, rdd, partitionFunc, partitions = None, allowLocal = False):
+    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.
@@ -703,7 +707,7 @@ def runJob(self, rdd, partitionFunc, partitions = None, allowLocal = False):
         >>> sc.runJob(myRDD, lambda part: [x * x for x in part], [0, 2], True)
         [0, 1, 16, 25]
         """
-        if partitions == None:
+        if partitions is None:
             partitions = range(rdd._jrdd.partitions().size())
         javaPartitions = ListConverter().convert(partitions, self._gateway._gateway_client)
 
@@ -714,6 +718,7 @@ def runJob(self, rdd, partitionFunc, partitions = None, allowLocal = False):
         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

python/pyspark/daemon.py

@@ -42,12 +42,12 @@ def should_exit():
 
 
 def compute_real_exit_code(exit_code):
-  # SystemExit's code can be integer or string, but os._exit only accepts integers
-  import numbers
-  if isinstance(exit_code, numbers.Integral):
-    return exit_code
-  else:
-    return 1
+    # SystemExit's code can be integer or string, but os._exit only accepts integers
+    import numbers
+    if isinstance(exit_code, numbers.Integral):
+        return exit_code
+    else:
+        return 1
 
 
 def worker(listen_sock):

python/pyspark/java_gateway.py

@@ -24,6 +24,7 @@
 from threading import Thread
 from py4j.java_gateway import java_import, JavaGateway, GatewayClient
 
+
 def launch_gateway():
     SPARK_HOME = os.environ["SPARK_HOME"]
 

python/pyspark/join.py

@@ -33,10 +33,11 @@
 
 from pyspark.resultiterable import ResultIterable
 
+
 def _do_python_join(rdd, other, numPartitions, dispatch):
     vs = rdd.map(lambda (k, v): (k, (1, v)))
     ws = other.map(lambda (k, v): (k, (2, v)))
-    return vs.union(ws).groupByKey(numPartitions).flatMapValues(lambda x : dispatch(x.__iter__()))
+    return vs.union(ws).groupByKey(numPartitions).flatMapValues(lambda x: dispatch(x.__iter__()))
 
 
 def python_join(rdd, other, numPartitions):
@@ -85,6 +86,7 @@ def make_mapper(i):
     vrdds = [rdd.map(make_mapper(i)) for i, rdd in enumerate(rdds)]
     union_vrdds = reduce(lambda acc, other: acc.union(other), vrdds)
     rdd_len = len(vrdds)
+
     def dispatch(seq):
         bufs = [[] for i in range(rdd_len)]
         for (n, v) in seq:

python/pyspark/mllib/_common.py

@@ -164,7 +164,7 @@ def _deserialize_double_vector(ba, offset=0):
     nb = len(ba) - offset
     if nb < 5:
         raise TypeError("_deserialize_double_vector called on a %d-byte array, "
-                "which is too short" % nb)
+                        "which is too short" % nb)
     if ba[offset] == DENSE_VECTOR_MAGIC:
         return _deserialize_dense_vector(ba, offset)
     elif ba[offset] == SPARSE_VECTOR_MAGIC:
@@ -272,6 +272,7 @@ def _serialize_labeled_point(p):
     header_float[0] = p.label
     return header + serialized_features
 
+
 def _deserialize_labeled_point(ba, offset=0):
     """Deserialize a LabeledPoint from a mutually understood format."""
     from pyspark.mllib.regression import LabeledPoint
@@ -283,6 +284,7 @@ def _deserialize_labeled_point(ba, offset=0):
     features = _deserialize_double_vector(ba, offset + 9)
     return LabeledPoint(label, features)
 
+
 def _copyto(array, buffer, offset, shape, dtype):
     """
     Copy the contents of a vector to a destination bytearray at the

python/pyspark/mllib/linalg.py

@@ -247,6 +247,7 @@ def stringify(vector):
         else:
             return "[" + ",".join([str(v) for v in vector]) + "]"
 
+
 def _test():
     import doctest
     (failure_count, test_count) = doctest.testmod(optionflags=doctest.ELLIPSIS)

python/pyspark/mllib/util.py

@@ -24,7 +24,6 @@
 from pyspark.serializers import NoOpSerializer
 
 
-
 class MLUtils:
     """
     Helper methods to load, save and pre-process data used in MLlib.
@@ -154,7 +153,6 @@ def saveAsLibSVMFile(data, dir):
         lines = data.map(lambda p: MLUtils._convert_labeled_point_to_libsvm(p))
         lines.saveAsTextFile(dir)
 
-
     @staticmethod
     def loadLabeledPoints(sc, path, minPartitions=None):
         """

python/pyspark/rddsampler.py

@@ -18,13 +18,16 @@
 import sys
 import random
 
+
 class RDDSampler(object):
     def __init__(self, withReplacement, fraction, seed=None):
         try:
             import numpy
             self._use_numpy = True
         except ImportError:
-            print >> sys.stderr, "NumPy does not appear to be installed. Falling back to default random generator for sampling."
+            print >> sys.stderr, (
+                "NumPy does not appear to be installed. "
+                "Falling back to default random generator for sampling.")
             self._use_numpy = False
 
         self._seed = seed if seed is not None else random.randint(0, sys.maxint)
@@ -61,7 +64,7 @@ def getUniformSample(self, split):
     def getPoissonSample(self, split, mean):
         if not self._rand_initialized or split != self._split:
             self.initRandomGenerator(split)
-        
+
         if self._use_numpy:
             return self._random.poisson(mean)
         else:
@@ -80,30 +83,27 @@ def getPoissonSample(self, split, mean):
                 num_arrivals += 1
 
             return (num_arrivals - 1)
-    
+
     def shuffle(self, vals):
         if self._random is None:
             self.initRandomGenerator(0)  # this should only ever called on the master so
             # the split does not matter
-        
+
         if self._use_numpy:
             self._random.shuffle(vals)
         else:
             self._random.shuffle(vals, self._random.random)
 
     def func(self, split, iterator):
-        if self._withReplacement:            
+        if self._withReplacement:
             for obj in iterator:
-                # For large datasets, the expected number of occurrences of each element in a sample with
-                # replacement is Poisson(frac). We use that to get a count for each element.                                   
-                count = self.getPoissonSample(split, mean = self._fraction)
+                # For large datasets, the expected number of occurrences of each element in
+                # a sample with replacement is Poisson(frac). We use that to get a count for
+                # each element.
+                count = self.getPoissonSample(split, mean=self._fraction)
                 for _ in range(0, count):
                     yield obj
         else:
             for obj in iterator:
                 if self.getUniformSample(split) <= self._fraction:
                     yield obj
-
-            
-            
-