Source code for pyspark.profiler

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import cProfile
import pstats
import os
import atexit

from pyspark.accumulators import AccumulatorParam

class ProfilerCollector(object):
    This class keeps track of different profilers on a per
    stage basis. Also this is used to create new profilers for
    the different stages.

    def __init__(self, profiler_cls, dump_path=None):
        self.profiler_cls = profiler_cls
        self.profile_dump_path = dump_path
        self.profilers = []

    def new_profiler(self, ctx):
        """ Create a new profiler using class `profiler_cls` """
        return self.profiler_cls(ctx)

    def add_profiler(self, id, profiler):
        """ Add a profiler for RDD `id` """
        if not self.profilers:
            if self.profile_dump_path:
                atexit.register(self.dump_profiles, self.profile_dump_path)

        self.profilers.append([id, profiler, False])

    def dump_profiles(self, path):
        """ Dump the profile stats into directory `path` """
        for id, profiler, _ in self.profilers:
            profiler.dump(id, path)
        self.profilers = []

    def show_profiles(self):
        """ Print the profile stats to stdout """
        for i, (id, profiler, showed) in enumerate(self.profilers):
            if not showed and profiler:
                # mark it as showed
                self.profilers[i][2] = True

[docs]class Profiler(object): """ .. note:: DeveloperApi PySpark supports custom profilers, this is to allow for different profilers to be used as well as outputting to different formats than what is provided in the BasicProfiler. A custom profiler has to define or inherit the following methods: profile - will produce a system profile of some sort. stats - return the collected stats. dump - dumps the profiles to a path add - adds a profile to the existing accumulated profile The profiler class is chosen when creating a SparkContext >>> from pyspark import SparkConf, SparkContext >>> from pyspark import BasicProfiler >>> class MyCustomProfiler(BasicProfiler): ... def show(self, id): ... print("My custom profiles for RDD:%s" % id) ... >>> conf = SparkConf().set("spark.python.profile", "true") >>> sc = SparkContext('local', 'test', conf=conf, profiler_cls=MyCustomProfiler) >>> sc.parallelize(range(1000)).map(lambda x: 2 * x).take(10) [0, 2, 4, 6, 8, 10, 12, 14, 16, 18] >>> sc.parallelize(range(1000)).count() 1000 >>> sc.show_profiles() My custom profiles for RDD:1 My custom profiles for RDD:3 >>> sc.stop() """ def __init__(self, ctx): pass
[docs] def profile(self, func): """ Do profiling on the function `func`""" raise NotImplemented
[docs] def stats(self): """ Return the collected profiling stats (pstats.Stats)""" raise NotImplemented
[docs] def show(self, id): """ Print the profile stats to stdout, id is the RDD id """ stats = self.stats() if stats: print("=" * 60) print("Profile of RDD<id=%d>" % id) print("=" * 60) stats.sort_stats("time", "cumulative").print_stats()
[docs] def dump(self, id, path): """ Dump the profile into path, id is the RDD id """ if not os.path.exists(path): os.makedirs(path) stats = self.stats() if stats: p = os.path.join(path, "rdd_%d.pstats" % id) stats.dump_stats(p)
class PStatsParam(AccumulatorParam): """PStatsParam is used to merge pstats.Stats""" @staticmethod def zero(value): return None @staticmethod def addInPlace(value1, value2): if value1 is None: return value2 value1.add(value2) return value1
[docs]class BasicProfiler(Profiler): """ BasicProfiler is the default profiler, which is implemented based on cProfile and Accumulator """ def __init__(self, ctx): Profiler.__init__(self, ctx) # Creates a new accumulator for combining the profiles of different # partitions of a stage self._accumulator = ctx.accumulator(None, PStatsParam)
[docs] def profile(self, func): """ Runs and profiles the method to_profile passed in. A profile object is returned. """ pr = cProfile.Profile() pr.runcall(func) st = pstats.Stats(pr) = None # make it picklable st.strip_dirs() # Adds a new profile to the existing accumulated value self._accumulator.add(st)
[docs] def stats(self): return self._accumulator.value
if __name__ == "__main__": import doctest (failure_count, test_count) = doctest.testmod() if failure_count: exit(-1)