performance - Spark 示例程序运行很慢

Question

我尝试使用 Spark 来解决简单的图形问题。我在 Spark 源文件夹中找到了一个示例程序:transitive_closure.py，它在一个不超过 200 个边和顶点的图中计算传递闭包。但是在我自己的笔记本电脑中，它运行了 10 多分钟并且没有终止。我使用的命令行是:spark-submit transitionive_closure.py。

我想知道为什么即使计算这么小的传递闭包结果，spark 也这么慢？这是一个常见的情况吗？有没有我想念的配置？

该程序如下所示，可以在他们网站的 spark install 文件夹中找到。

from __future__ import print_function

import sys
from random import Random

from pyspark import SparkContext

numEdges = 200
numVertices = 100
rand = Random(42)


def generateGraph():
    edges = set()
    while len(edges) < numEdges:
        src = rand.randrange(0, numEdges)
        dst = rand.randrange(0, numEdges)
        if src != dst:
            edges.add((src, dst))
    return edges


if __name__ == "__main__":
    """
    Usage: transitive_closure [partitions]
    """
    sc = SparkContext(appName="PythonTransitiveClosure")
    partitions = int(sys.argv[1]) if len(sys.argv) > 1 else 2
    tc = sc.parallelize(generateGraph(), partitions).cache()

    # Linear transitive closure: each round grows paths by one edge,
    # by joining the graph's edges with the already-discovered paths.
    # e.g. join the path (y, z) from the TC with the edge (x, y) from
    # the graph to obtain the path (x, z).

    # Because join() joins on keys, the edges are stored in reversed order.
    edges = tc.map(lambda x_y: (x_y[1], x_y[0]))

    oldCount = 0
    nextCount = tc.count()
    while True:
        oldCount = nextCount
        # Perform the join, obtaining an RDD of (y, (z, x)) pairs,
        # then project the result to obtain the new (x, z) paths.
        new_edges = tc.join(edges).map(lambda __a_b: (__a_b[1][1], __a_b[1][0]))
        tc = tc.union(new_edges).distinct().cache()
        nextCount = tc.count()
        if nextCount == oldCount:
            break

    print("TC has %i edges" % tc.count())

    sc.stop()

Answer 1

这段代码在您的机器上表现不佳的原因有很多，但很可能这只是 Spark iteration time increasing exponentially when using join 中描述的问题的另一种变体。 .检查是否确实如此的最简单方法是提供 spark.default.parallelism提交参数:

bin/spark-submit --conf spark.default.parallelism=2 \
  examples/src/main/python/transitive_closure.py

如无其他限制， SparkContext.union , RDD.join和 RDD.union将子分区的数量设置为父分区的总数。通常这是一种理想的行为，但如果反复应用，可能会变得非常低效。