{"id":49311,"date":"2025-07-30T17:23:13","date_gmt":"2025-07-30T09:23:13","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/49311.html"},"modified":"2025-07-30T17:23:13","modified_gmt":"2025-07-30T09:23:13","slug":"spark%e7%9a%84%e7%b4%af%e5%8a%a0%e5%99%a8%ef%bc%88accumulator%ef%bc%89","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/49311.html","title":{"rendered":"Spark\u7684\u7d2f\u52a0\u5668\uff08Accumulator\uff09"},"content":{"rendered":"

\u5728 PySpark \u4e2d，\u7d2f\u52a0\u5668（Accumulator）\u662f\u4e00\u79cd\u7279\u6b8a\u7684\u5171\u4eab\u53d8\u91cf，\u7528\u4e8e\u5728\u5206\u5e03\u5f0f\u8ba1\u7b97\u4e2d\u5b89\u5168\u5730\u805a\u5408\u6765\u81ea\u591a\u4e2a Executor \u8282\u70b9\u7684\u6570\u636e\u5230 Driver \u8282\u70b9\u3002\u5b83\u4e3b\u8981\u89e3\u51b3\u4e86\u5206\u5e03\u5f0f\u73af\u5883\u4e0b\u8de8\u8282\u70b9\u72b6\u6001\u5171\u4eab\u7684\u95ee\u9898，\u7279\u522b\u9002\u5408\u8ba1\u6570\u3001\u6c42\u548c\u7b49\u805a\u5408\u64cd\u4f5c\u3002<\/p>\n

\u7d2f\u52a0\u5668\u7684\u6838\u5fc3\u7279\u6027<\/h4>\n
  • \u5355\u5411\u901a\u4fe1：Executor \u53ea\u80fd\u5bf9\u7d2f\u52a0\u5668\u6267\u884c\u66f4\u65b0\u64cd\u4f5c，\u4e0d\u80fd\u8bfb\u53d6\u5176\u503c；\u53ea\u6709 Driver \u53ef\u4ee5\u8bfb\u53d6\u7d2f\u52a0\u5668\u7684\u6700\u7ec8\u7ed3\u679c<\/li>\n
  • \u5206\u5e03\u5f0f\u5b89\u5168：Spark \u4f1a\u81ea\u52a8\u5904\u7406\u4efb\u52a1\u91cd\u8bd5\u65f6\u7684\u7d2f\u52a0\u5668\u66f4\u65b0，\u786e\u4fdd\u7ed3\u679c\u6b63\u786e\u6027<\/li>\n
  • \u60f0\u6027\u6267\u884c：\u7d2f\u52a0\u5668\u7684\u66f4\u65b0\u53ea\u6709\u5728 Action \u64cd\u4f5c\u89e6\u53d1\u65f6\u624d\u4f1a\u771f\u6b63\u6267\u884c<\/li>\n
  • \u5bb9\u9519\u6027：\u5f53\u4efb\u52a1\u5931\u8d25\u91cd\u65b0\u8ba1\u7b97\u65f6，Spark \u4f1a\u6b63\u786e\u5904\u7406\u7d2f\u52a0\u5668\u7684\u72b6\u6001<\/li>\n

    \u7d2f\u52a0\u5668\u7684\u7c7b\u578b<\/h4>\n
  • \n

    \u5185\u7f6e\u7d2f\u52a0\u5668：<\/p>\n

      \n
    • \u6570\u503c\u578b\u7d2f\u52a0\u5668（LongAccumulator\u3001DoubleAccumulator）<\/li>\n
    • \u96c6\u5408\u7d2f\u52a0\u5668（CollectionAccumulator）<\/li>\n<\/ul>\n<\/li>\n
    • \n

      \u81ea\u5b9a\u4e49\u7d2f\u52a0\u5668：
      \n\u5f53\u5185\u7f6e\u7d2f\u52a0\u5668\u65e0\u6cd5\u6ee1\u8db3\u9700\u6c42\u65f6，\u53ef\u4ee5\u901a\u8fc7\u7ee7\u627fAccumulatorV2\u7c7b\u5b9e\u73b0\u81ea\u5b9a\u4e49\u903b\u8f91<\/p>\n<\/li>\n

      \u5185\u7f6e\u7d2f\u52a0\u5668\u4f7f\u7528\u793a\u4f8b<\/h4>\n

      1. \u6570\u503c\u578b\u7d2f\u52a0\u5668<\/h5>\n

      from pyspark.sql import SparkSession<\/p>\n

      # \u521d\u59cb\u5316SparkSession
      \nspark = SparkSession.builder \\\\
      \n .appName("PySpark Accumulator Example") \\\\
      \n .master("local[*]") \\\\
      \n .getOrCreate()<\/p>\n

      sc = spark.sparkContext<\/p>\n

      # \u521b\u5efa\u7d2f\u52a0\u5668
      \ncount_acc = sc.longAccumulator("count_accumulator") # \u6574\u6570\u7d2f\u52a0\u5668
      \nsum_acc = sc.doubleAccumulator("sum_accumulator") # \u6d6e\u70b9\u6570\u7d2f\u52a0\u5668<\/p>\n

      # \u51c6\u5907\u6570\u636e
      \ndata = sc.parallelize(range(1, 11)) # 1\u523010\u7684\u6570\u5b57<\/p>\n

      # \u4f7f\u7528\u7d2f\u52a0\u5668
      \ndef process_number(num):
      \n count_acc.add(1) # \u8ba1\u6570
      \n sum_acc.add(num) # \u6c42\u548c<\/p>\n

      data.foreach(process_number)<\/p>\n

      # \u5728Driver\u7aef\u83b7\u53d6\u7d2f\u52a0\u5668\u7ed3\u679c
      \nprint(f"\u603b\u8bb0\u5f55\u6570: {count_acc.value}") # \u8f93\u51fa: \u603b\u8bb0\u5f55\u6570: 10
      \nprint(f"\u603b\u548c: {sum_acc.value}") # \u8f93\u51fa: \u603b\u548c: 55.0
      \nprint(f"\u5e73\u5747\u503c: {sum_acc.value \/ count_acc.value}") # \u8f93\u51fa: \u5e73\u5747\u503c: 5.5<\/p>\n

      spark.stop()<\/p>\n

      2. \u96c6\u5408\u7d2f\u52a0\u5668<\/h5>\n

      \u96c6\u5408\u7d2f\u52a0\u5668\u7528\u4e8e\u6536\u96c6\u5206\u5e03\u5f0f\u8ba1\u7b97\u4e2d\u7684\u5143\u7d20：<\/p>\n

      from pyspark.sql import SparkSession<\/p>\n

      spark = SparkSession.builder \\\\
      \n .appName("Collection Accumulator Example") \\\\
      \n .master("local[*]") \\\\
      \n .getOrCreate()<\/p>\n

      sc = spark.sparkContext<\/p>\n

      # \u521b\u5efa\u96c6\u5408\u7d2f\u52a0\u5668
      \ncollection_acc = sc.collectionAccumulator("collection_accumulator")<\/p>\n

      # \u51c6\u5907\u6570\u636e
      \ndata = sc.parallelize(["apple", "banana", "apple", "orange", "banana"])<\/p>\n

      # \u4f7f\u7528\u7d2f\u52a0\u5668\u6536\u96c6\u5143\u7d20
      \ndata.foreach(lambda x: collection_acc.add(x))<\/p>\n

      # \u83b7\u53d6\u7ed3\u679c
      \nprint(f"\u6536\u96c6\u5230\u7684\u5143\u7d20: {collection_acc.value}")
      \n# \u53ef\u80fd\u8f93\u51fa: \u6536\u96c6\u5230\u7684\u5143\u7d20: ['apple', 'banana', 'apple', 'orange', 'banana']<\/p>\n

      # \u53bb\u91cd\u5904\u7406
      \nunique_elements = list(set(collection_acc.value))
      \nprint(f"\u552f\u4e00\u5143\u7d20: {unique_elements}")
      \n# \u53ef\u80fd\u8f93\u51fa: \u552f\u4e00\u5143\u7d20: ['apple', 'banana', 'orange']<\/p>\n

      spark.stop()<\/p>\n

      \u81ea\u5b9a\u4e49\u7d2f\u52a0\u5668\u793a\u4f8b<\/h4>\n

      \u5f53\u5185\u7f6e\u7d2f\u52a0\u5668\u4e0d\u80fd\u6ee1\u8db3\u9700\u6c42\u65f6，\u53ef\u4ee5\u5b9e\u73b0\u81ea\u5b9a\u4e49\u7d2f\u52a0\u5668\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7edf\u8ba1\u5b57\u7b26\u4e32\u957f\u5ea6\u7684\u7d2f\u52a0\u5668：<\/p>\n

      from pyspark.sql import SparkSession
      \nfrom pyspark.util import AccumulatorV2
      \nfrom typing import Tuple, List<\/p>\n

      class StringLengthAccumulator(AccumulatorV2[str, Tuple[int, int]]):
      \n """
      \n \u81ea\u5b9a\u4e49\u7d2f\u52a0\u5668，\u7edf\u8ba1\u5b57\u7b26\u4e32\u603b\u957f\u5ea6\u548c\u5b57\u7b26\u4e32\u6570\u91cf
      \n \u7ed3\u679c\u4e3a\u4e00\u4e2a\u5143\u7ec4: (\u603b\u957f\u5ea6, \u603b\u6570\u91cf)
      \n """
      \n def __init__(self):
      \n self.total_length = 0 # \u603b\u957f\u5ea6
      \n self.count = 0 # \u603b\u6570\u91cf
      \n self.is_zero = True<\/p>\n

      def reset(self):
      \n """\u91cd\u7f6e\u7d2f\u52a0\u5668\u4e3a\u521d\u59cb\u72b6\u6001"""
      \n self.total_length = 0
      \n self.count = 0
      \n self.is_zero = True<\/p>\n

      def add(self, value: str):
      \n """\u6dfb\u52a0\u4e00\u4e2a\u5b57\u7b26\u4e32\u5e76\u66f4\u65b0\u72b6\u6001"""
      \n self.total_length += len(value)
      \n self.count += 1
      \n self.is_zero = False<\/p>\n

      def merge(self, other: "StringLengthAccumulator"):
      \n """\u5408\u5e76\u53e6\u4e00\u4e2a\u7d2f\u52a0\u5668\u7684\u7ed3\u679c"""
      \n self.total_length += other.total_length
      \n self.count += other.count
      \n self.is_zero = (self.total_length == 0 and self.count == 0)<\/p>\n

      def value(self) -> Tuple[int, int]:
      \n """\u8fd4\u56de\u5f53\u524d\u7d2f\u52a0\u5668\u7684\u503c"""
      \n return (self.total_length, self.count)<\/p>\n

      def isZero(self) -> bool:
      \n """\u68c0\u67e5\u7d2f\u52a0\u5668\u662f\u5426\u5904\u4e8e\u521d\u59cb\u72b6\u6001"""
      \n return self.is_zero<\/p>\n

      def copy(self) -> "StringLengthAccumulator":
      \n """\u590d\u5236\u7d2f\u52a0\u5668"""
      \n new_acc = StringLengthAccumulator()
      \n new_acc.total_length = self.total_length
      \n new_acc.count = self.count
      \n new_acc.is_zero = self.is_zero
      \n return new_acc<\/p>\n

      # \u4f7f\u7528\u81ea\u5b9a\u4e49\u7d2f\u52a0\u5668
      \nif __name__ == "__main__":
      \n spark = SparkSession.builder \\\\
      \n .appName("Custom String Length Accumulator") \\\\
      \n .master("local[*]") \\\\
      \n .getOrCreate()<\/p>\n

      sc = spark.sparkContext<\/p>\n

      # \u6ce8\u518c\u81ea\u5b9a\u4e49\u7d2f\u52a0\u5668
      \n str_acc = StringLengthAccumulator()
      \n sc.register(str_acc, "string_length_accumulator")<\/p>\n

      # \u6d4b\u8bd5\u6570\u636e
      \n data = sc.parallelize(["apple", "banana", "cherry", "date", "elderberry"])<\/p>\n

      # \u4f7f\u7528\u7d2f\u52a0\u5668
      \n data.foreach(lambda s: str_acc.add(s))<\/p>\n

      # \u83b7\u53d6\u7ed3\u679c
      \n total_length, count = str_acc.value
      \n print(f"\u603b\u5b57\u7b26\u4e32\u6570\u91cf: {count}") # \u8f93\u51fa: \u603b\u5b57\u7b26\u4e32\u6570\u91cf: 5
      \n print(f"\u5b57\u7b26\u4e32\u603b\u957f\u5ea6: {total_length}") # \u8f93\u51fa: \u5b57\u7b26\u4e32\u603b\u957f\u5ea6: 25
      \n print(f"\u5e73\u5747\u5b57\u7b26\u4e32\u957f\u5ea6: {total_length \/ count}") # \u8f93\u51fa: \u5e73\u5747\u5b57\u7b26\u4e32\u957f\u5ea6: 5.0<\/p>\n

      spark.stop()<\/p>\n

      \u7d2f\u52a0\u5668\u4f7f\u7528\u6ce8\u610f\u4e8b\u9879<\/h4>\n
    • \n

      \u4e0d\u8981\u5728 Transformation \u4e2d\u8bfb\u53d6\u7d2f\u52a0\u5668\u503c：
      \nTransformation \u662f\u60f0\u6027\u6267\u884c\u7684，\u4e14\u53ef\u80fd\u88ab\u591a\u6b21\u8ba1\u7b97，\u5728\u5176\u4e2d\u8bfb\u53d6\u7d2f\u52a0\u5668\u503c\u4f1a\u5f97\u5230\u4e0d\u53ef\u9760\u7684\u7ed3\u679c\u3002<\/p>\n<\/li>\n

    • \n

      \u7d2f\u52a0\u5668\u66f4\u65b0\u53ef\u80fd\u88ab\u91cd\u590d\u6267\u884c：
      \n\u5f53\u4efb\u52a1\u5931\u8d25\u91cd\u8bd5\u65f6，Spark \u4f1a\u91cd\u65b0\u6267\u884c\u4efb\u52a1，\u5bfc\u81f4\u7d2f\u52a0\u5668\u88ab\u591a\u6b21\u66f4\u65b0\u3002\u4e0d\u8fc7 Spark \u5185\u90e8\u4f1a\u5904\u7406\u8fd9\u79cd\u60c5\u51b5，\u786e\u4fdd\u6bcf\u4e2a\u4efb\u52a1\u7684\u66f4\u65b0\u53ea\u88ab\u8ba1\u7b97\u4e00\u6b21\u3002<\/p>\n<\/li>\n

    • \n

      \u7d2f\u52a0\u5668\u4e0d\u5e94\u7528\u4e8e\u63a7\u5236\u6d41：
      \n\u4e0d\u8981\u6839\u636e\u7d2f\u52a0\u5668\u7684\u503c\u6765\u51b3\u5b9a\u7a0b\u5e8f\u7684\u6267\u884c\u8def\u5f84，\u56e0\u4e3a\u5728 Transformation \u4e2d\u65e0\u6cd5\u83b7\u53d6\u5230\u6b63\u786e\u7684\u7d2f\u52a0\u5668\u503c\u3002<\/p>\n<\/li>\n

    • \n

      \u7d2f\u52a0\u5668\u6027\u80fd\u8003\u91cf：
      \n\u9891\u7e41\u66f4\u65b0\u7d2f\u52a0\u5668\u4f1a\u4ea7\u751f\u7f51\u7edc\u5f00\u9500，\u56e0\u4e3a\u6bcf\u4e2a\u66f4\u65b0\u90fd\u9700\u8981\u4e0e Driver \u901a\u4fe1\u3002\u5bf9\u4e8e\u6027\u80fd\u654f\u611f\u7684\u573a\u666f，\u5e94\u6279\u91cf\u66f4\u65b0\u6216\u8003\u8651\u5176\u4ed6\u65b9\u6848\u3002<\/p>\n<\/li>\n

    • \n

      \u7d2f\u52a0\u5668\u4e0e\u5e7f\u64ad\u53d8\u91cf\u7684\u533a\u522b：<\/p>\n

        \n
      • \u7d2f\u52a0\u5668：\u4ece Executor \u5230 Driver \u7684\u5355\u5411\u6570\u636e\u805a\u5408<\/li>\n
      • \u5e7f\u64ad\u53d8\u91cf：\u4ece Driver \u5230 Executor \u7684\u53ea\u8bfb\u6570\u636e\u5206\u53d1<\/li>\n<\/ul>\n<\/li>\n

        \u5408\u7406\u4f7f\u7528\u7d2f\u52a0\u5668\u53ef\u4ee5\u7b80\u5316\u5206\u5e03\u5f0f\u8ba1\u7b97\u4e2d\u7684\u72b6\u6001\u805a\u5408\u64cd\u4f5c，\u5c24\u5176\u662f\u5728\u9700\u8981\u7edf\u8ba1\u3001\u8ba1\u6570\u6216\u6536\u96c6\u7279\u5b9a\u6570\u636e\u65f6，\u80fd\u663e\u8457\u63d0\u9ad8\u4ee3\u7801\u7684\u7b80\u6d01\u6027\u548c\u6548\u7387\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"

        \u6587\u7ae0\u6d4f\u89c8\u9605\u8bfb55\u6b21\u3002\u6458\u8981\uff1aPySpark\u7d2f\u52a0\u5668\u662f\u7528\u4e8e\u5206\u5e03\u5f0f\u8ba1\u7b97\u4e2d\u5b89\u5168\u805a\u5408\u6570\u636e\u7684\u5171\u4eab\u53d8\u91cf\uff0c\u652f\u6301\u6570\u503c\u578b\u548c\u96c6\u5408\u578b\u5185\u7f6e\u7d2f\u52a0\u5668\uff0c\u4e5f\u53ef\u901a\u8fc7\u7ee7\u627fAccumulatorV2\u7c7b\u5b9e\u73b0\u81ea\u5b9a\u4e49\u903b\u8f91\u3002\u7d2f\u52a0\u5668\u5177\u6709\u5355\u5411\u901a\u4fe1\u3001\u5206\u5e03\u5f0f\u5b89\u5168\u3001\u60f0\u6027\u6267\u884c\u548c\u5bb9\u9519\u6027\u7b49\u7279\u70b9\uff0c\u9002\u7528\u4e8e\u8ba1\u6570\u3001\u6c42\u548c\u7b49\u805a\u5408\u64cd\u4f5c\u3002\u4f7f\u7528\u65f6\u9700\u6ce8\u610f\u4e0d\u8981\u5728\u8f6c\u6362\u64cd\u4f5c\u4e2d\u8bfb\u53d6\u503c\uff0c\u66f4\u65b0\u53ef\u80fd\u91cd\u590d\u6267\u884c\u7b49\u95ee\u9898\u3002\u5408\u7406\u4f7f\u7528\u7d2f\u52a0\u5668\u80fd\u6709\u6548\u7b80\u5316\u5206\u5e03\u5f0f\u72b6\u6001\u805a\u5408\uff0c\u63d0\u5347\u4ee3\u7801\u6548\u7387\u3002\u5185\u7f6e\u7d2f\u52a0\u5668\u5305\u62ec\u6570\u503c\u578b\u548c\u96c6\u5408\u578b\uff0c\u81ea\u5b9a\u4e49\u7d2f\u52a0\u5668\u5219\u80fd\u6269\u5c55\u529f\u80fd\u6ee1\u8db3\u7279\u5b9a\u9700\u6c42\u3002<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[365,323,100],"topic":[],"class_list":["post-49311","post","type-post","status-publish","format-standard","hentry","category-server","tag-spark","tag-323","tag-100"],"yoast_head":"\nSpark\u7684\u7d2f\u52a0\u5668\uff08Accumulator\uff09 - 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