登录
当前位置： 首页 > 知识库问答 >
问题：

pyspark，在大型RDD中寻找最大值？

高建本
2023-03-14

我想出了四种方法来解决这个问题，但没有一种方法真正适用于大型RDD，我希望有人能帮助我。

我有一个格式为((x，y)，(sim，sim ')的火花RDD，其中x和y是两个索引，sim和sim '是x和y的两个不同的相似性度量。我有兴趣寻找具有最大sim '值的元组。我想出了一些方法来做到这一点，但每种方法都有其问题，最终这些方法都不能应用于大规模的RDD，如10亿元组的RDD。

假设res_dict是((x，y)，(sim，sim '))元组的RDD，在pyspark中调用< code>res_dict.collect()时，返回< code >[(0，4)，(0.84482865216358305，-0.15517134783641684)，(0，5)，(0.814141945629517345，-0实际上，初始res_dict要大得多，它经历(n-1)次迭代，并且在每次迭代中，res_dict中的元组数量减少多达(n-ite)。n是初始res_dict中的元组总数，ite是当前迭代的索引，ite=1，....，n-1。

方法1：

res_dict最初由 .repartition（k） 划分为 n 个分区，（k

    def f(iterator): yield max(iterator, key = lambda d:d[1][1])
    max_list = res_dict.mapPartitions(f)
    i_j_sim = max_list.max(key = lambda d:d[1][1])

随着res_dict的大小在每次迭代中减小，显然需要动态决定其分区数，否则将出现空分区并导致错误。所以在传递上面的代码之前，我查找了当前迭代中 res_dict 的非空分区数，并用这个数字重新分区res_dict：

    def numNonEmptyPar(anRDD):
        par_ind_size = anRDD.mapPartitionsWithIndex(length)
        numNonEmp = par_ind_size.filter(lambda d: d[1] != 0).map(lambda x:1).reduce(add) # reduce is quite slow
        return numNonEmp

    numNonEmpar = numNonEmptyPar(res_dict)
    if numNonEmpar < resPar:
        resPar = numNonEmpar
        res_dict = res_dict.repartition(resPar)

在我看来，.repartition（） 不能保证每个分区都是非空的（.coalesce（） 也不是）。那么如何使方法 1 起作用呢？

方法2：

i_j_sim = res_dict.glom().
\map(lambda ls : None if len(ls)==0 else max(ls, key=lambda d:d[1][1])).
\filter(lambda d: d!= None).max(lambda d:d[1][1]) # pyspark built-in func: rdd.max()

方法三:

i_j_sim = res_dict.max(key=lambda d: d[1][1])

对于方法 2 和方法 3，看起来 max（lambda d：d[1][1]） 是问题所在。我观察到他们为10000个元组的res_dict工作，但对10亿个元组不起作用。所以rdd.max（）应该只用一个小的rdd喂食吗？

PS:方法3的完整追溯是

ssh://hduser@159.84.139.244:22/usr/bin/python -u /home/hduser/Documents/test_Spark/ahc_sim_v1.py
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel).
16/10/14 14:47:30 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
[Stage 5:==>(23 + 4) / 32][Stage 6:>  (0 + 12) / 32][Stage 8:>   (0 + 0) / 32]16/10/14 14:48:30 WARN TaskSetManager: Lost task 4.0 in stage 6.0 (TID 68, 159.84.139.245): java.io.StreamCorruptedException: invalid stream header: 12018301
at java.io.ObjectInputStream.readStreamHeader(ObjectInputStream.java:804)
at java.io.ObjectInputStream.<init>(ObjectInputStream.java:299)
at org.apache.spark.serializer.JavaDeserializationStream$$anon$1.<init>(JavaSerializer.scala:63)
at org.apache.spark.serializer.JavaDeserializationStream.<init>(JavaSerializer.scala:63)
at org.apache.spark.serializer.JavaSerializerInstance.deserializeStream(JavaSerializer.scala:122)
at org.apache.spark.serializer.SerializerManager.dataDeserializeStream(SerializerManager.scala:146)
at org.apache.spark.storage.BlockManager$$anonfun$getRemoteValues$1.apply(BlockManager.scala:524)
at org.apache.spark.storage.BlockManager$$anonfun$getRemoteValues$1.apply(BlockManager.scala:522)
at scala.Option.map(Option.scala:146)
at org.apache.spark.storage.BlockManager.getRemoteValues(BlockManager.scala:522)
at org.apache.spark.storage.BlockManager.get(BlockManager.scala:609)
at org.apache.spark.storage.BlockManager.getOrElseUpdate(BlockManager.scala:661)
at org.apache.spark.rdd.RDD.getOrCompute(RDD.scala:330)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:281)
at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:63)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
at org.apache.spark.api.python.PairwiseRDD.compute(PythonRDD.scala:390)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:79)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:47)
at org.apache.spark.scheduler.Task.run(Task.scala:85)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
[Stage 5:==>(24 + 4) / 32][Stage 6:>  (0 + 12) / 32][Stage 8:>   (0 + 0) / 32]16/10/14 14:48:31 ERROR TaskSetManager: Task 10 in stage 6.0 failed 4 times; aborting job
Traceback (most recent call last):
  File "/home/hduser/Documents/test_Spark/ahc_sim_v1.py", line 320, in <module>
    i_j_sim = res_dict.max(key=lambda d: d[1][1]) # get one pair with max s'(x,y)
  File "/home/hduser/spark-2.0.0-bin-hadoop2.7/python/pyspark/rdd.py", line 974, in max
    return self.reduce(lambda a, b: max(a, b, key=key))
  File "/home/hduser/spark-2.0.0-bin-hadoop2.7/python/pyspark/rdd.py", line 802, in reduce
    vals = self.mapPartitions(func).collect()
  File "/home/hduser/spark-2.0.0-bin-hadoop2.7/python/pyspark/rdd.py", line 776, in collect
    port = self.ctx._jvm.PythonRDD.collectAndServe(self._jrdd.rdd())
  File "/usr/local/lib/python2.7/dist-packages/py4j/java_gateway.py", line 1133, in __call__
    answer, self.gateway_client, self.target_id, self.name)
  File "/usr/local/lib/python2.7/dist-packages/py4j/protocol.py", line 319, in get_return_value
    format(target_id, ".", name), value)
py4j.protocol.Py4JJavaError: An error occurred while calling z:org.apache.spark.api.python.PythonRDD.collectAndServe.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 10 in stage 6.0 failed 4 times, most recent failure: Lost task 10.3 in stage 6.0 (TID 101, 159.84.139.247): java.io.StreamCorruptedException: invalid stream header: 12018301
at java.io.ObjectInputStream.readStreamHeader(ObjectInputStream.java:804)
at java.io.ObjectInputStream.<init>(ObjectInputStream.java:299)
at org.apache.spark.serializer.JavaDeserializationStream$$anon$1.<init>(JavaSerializer.scala:63)
at org.apache.spark.serializer.JavaDeserializationStream.<init>(JavaSerializer.scala:63)
at org.apache.spark.serializer.JavaSerializerInstance.deserializeStream(JavaSerializer.scala:122)
at org.apache.spark.serializer.SerializerManager.dataDeserializeStream(SerializerManager.scala:146)
at org.apache.spark.storage.BlockManager$$anonfun$getRemoteValues$1.apply(BlockManager.scala:524)
at org.apache.spark.storage.BlockManager$$anonfun$getRemoteValues$1.apply(BlockManager.scala:522)
at scala.Option.map(Option.scala:146)
at org.apache.spark.storage.BlockManager.getRemoteValues(BlockManager.scala:522)
at org.apache.spark.storage.BlockManager.get(BlockManager.scala:609)
at org.apache.spark.storage.BlockManager.getOrElseUpdate(BlockManager.scala:661)
at org.apache.spark.rdd.RDD.getOrCompute(RDD.scala:330)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:281)
at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:63)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
at org.apache.spark.api.python.PairwiseRDD.compute(PythonRDD.scala:390)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:79)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:47)
at org.apache.spark.scheduler.Task.run(Task.scala:85)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1450)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1438)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1437)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1437)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:811)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:811)
at scala.Option.foreach(Option.scala:257)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:811)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1659)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1618)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1607)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:632)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1871)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1884)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1897)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1911)
at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:893)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:358)
at org.apache.spark.rdd.RDD.collect(RDD.scala:892)
at org.apache.spark.api.python.PythonRDD$.collectAndServe(PythonRDD.scala:453)
at org.apache.spark.api.python.PythonRDD.collectAndServe(PythonRDD.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:606)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:237)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
at py4j.Gateway.invoke(Gateway.java:280)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:128)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:211)
at java.lang.Thread.run(Thread.java:745)
Caused by: java.io.StreamCorruptedException: invalid stream header: 12018301
at java.io.ObjectInputStream.readStreamHeader(ObjectInputStream.java:804)
at java.io.ObjectInputStream.<init>(ObjectInputStream.java:299)
at org.apache.spark.serializer.JavaDeserializationStream$$anon$1.<init>(JavaSerializer.scala:63)
at org.apache.spark.serializer.JavaDeserializationStream.<init>(JavaSerializer.scala:63)
at org.apache.spark.serializer.JavaSerializerInstance.deserializeStream(JavaSerializer.scala:122)
at org.apache.spark.serializer.SerializerManager.dataDeserializeStream(SerializerManager.scala:146)
at org.apache.spark.storage.BlockManager$$anonfun$getRemoteValues$1.apply(BlockManager.scala:524)
at org.apache.spark.storage.BlockManager$$anonfun$getRemoteValues$1.apply(BlockManager.scala:522)
at scala.Option.map(Option.scala:146)
at org.apache.spark.storage.BlockManager.getRemoteValues(BlockManager.scala:522)
at org.apache.spark.storage.BlockManager.get(BlockManager.scala:609)
at org.apache.spark.storage.BlockManager.getOrElseUpdate(BlockManager.scala:661)
at org.apache.spark.rdd.RDD.getOrCompute(RDD.scala:330)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:281)
at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:63)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
at org.apache.spark.api.python.PairwiseRDD.compute(PythonRDD.scala:390)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:79)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:47)
at org.apache.spark.scheduler.Task.run(Task.scala:85)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
... 1 more
Process finished with exit code 1

方法四：

    max_sim_pr = res_dict.values().values().treeReduce(lambda a,b: max(a,b))
    i_j_sim = res_dict.filter(lambda d:d[1][1] == max_sim_pr).first()

似乎对于方法2、3和4，真正的问题发生在.减少（）上，但我不知道为什么，也不知道如何解决它。

共有1个答案

徐昆
2023-03-14

我发现了问题。错误是因为我没有相同的火花默认值。conf文件。在我将这个conf文件的内容与集群中的每个节点保持一致之后，结果证明方法2、3和4工作正常。但方法1不起作用，在我看来，rdd。repartition（）无法确保分区是非空的，即使此rdd中的元素数大于分区数。我还注意到.repartition（）需要大量的洗牌，所以我改为.colease（），它在我的测试中运行得更快。

 类似资料：
  • 在有向图中寻找最大乘积

  • 在RDD[LabeledPoint]Spark/Scala中查找最小、最大标签

    我有一个RDD[标签点]，我想找到标签的最小值和最大值，并应用一些转换，例如从所有这些标签中减去数字5。问题是我已经尝试了各种方法来获取标签，但没有任何工作正常。 如何仅访问 RDD 的标签和功能？有没有办法将它们作为列表[双精度]和列表[向量]例如？ 我无法转到数据帧。

  • 为密钥寻找最大价值

    我想找出面积最大的国家。 我的数据集如下 有人能帮我写mapreduce程序吗？ 我的映射器和缩减器代码如下 制图员 减速机

  • 寻找局部最大值和最小值

    我有一个熊猫数据框，有两列，一列是温度，另一列是时间。 我想做第三和第四列，叫做最小和最大。这些列中的每一个都将填充nan's，除非有一个局部min或max，那么它将具有该极值的值。 这里是一个数据的样本，本质上我试图识别图中所有的峰值和低点。 有没有内置的熊猫工具可以做到这一点？

  • 寻找一数组中前K个最大的数

    本文向大家介绍寻找一数组中前K个最大的数相关面试题，主要包含被问及寻找一数组中前K个最大的数时的应答技巧和注意事项，需要的朋友参考一下 考察点：数组    

  • 在MongoDB中查找最大文档大小

    是否可以在MongoDB中找到最大的文档大小？ 显示的是平均大小，这并不具有代表性，因为在我的例子中，大小可能会有很大差异。

相关问答

在MongoDB中查找最大文档大小使用最小最大值和Alpha-Beta修剪寻找最佳移动在laravel中查找列的最大值为什么pyspark在查找列的最大值时要慢得多？如何在PySpark中找到DataFrame的大小或形状？

相关文章

Go中int类型的最大值如何找到最大堆栈大小？从for循环中找到最大值在Python中的元组列表中找到最大值如何设置搜寻栏的最小值和最大值

相关阅读

Java求数组元素的最大和最小值理想+ 大模型面经人工智能的最小最大算法JavaScript算法-最大子序和NLP大模型实习面经，25届找到实习啦

相关工具

大众ERP放置大地大实惠 APP黑白棋大师加拿大点击

相关文档

Flask 大型教程资源大全中文版PySpark 中文教程Git 命令大全中文版C++大学教程
快捷导航： 新手教程 算法原理 架构设计 Java进阶 数据库进阶 大厂专栏 面试经验 编程笔记 编程问答 所有专题 文档资料 工具软件 电子书籍 小牛导航
在线工具： 房贷计算器 个税计算器 Linux命令查询 Json格式化 正则表达式 颜色转换 AES加解密 SHA1加密 MD5加密 毒鸡汤 字数统计 随机密码生成 进制转换 Base64编解码 励志句子
Copyright © 2019-2024 小牛知识库@xnip.cn. All Rights Reserved.