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我正在运行一个 spark 作业，我正在读取、操作并将大量 txt 文件合并到一个文件中，但我遇到了这个问题：

Py4JJavaError：调用 o8483.collectToPython 时出错。：org.apache.spark.SparkException：作业因阶段失败而中止：838个任务的序列化结果的总大小（1025.6 MB）大于spark.driver.maxResultSize（1024.0 MB）

是否可以增加尺寸spark.driver.maxResultSize？

注意：这个问题是关于 WS Spark“环境”而不是关于分析引擎。

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1 回答 1

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如果您使用“Analytics Engine”火花集群实例，您可以通过 Ambari 控制台增加默认值。您可以从 console.bluemix.net 中的 IAE 实例获取到 Ambari 控制台的链接和凭据。在 Ambari 控制台中，添加一个新属性

Spark2 -> “自定义 spark2-defaults” -> 添加属性 -> spark.driver.maxResultSize = 2GB

确保 spark.driver.maxResultSize 值小于设置的驱动程序内存

Spark2 -> “高级 spark2-env” -> 内容 -> SPARK_DRIVER_MEMORY

如果您只是尝试创建单个 CSV 文件并且不想更改 spark conf 值，因为您不知道最终文件有多大，那么另一个建议是使用如下函数，该函数使用 hdfs getmerge 函数像 pandas 一样创建一个 csv 文件。

def writeSparkDFAsCSV_HDFS(spark_df, file_location,file_name, csv_sep=',', csv_quote='"'):
    """
    It can be used to write large spark dataframe as a csv file without running 
    into memory issues while converting to pandas dataframe.
    It first writes the spark df to a temp hdfs location and uses getmerge to create 
    a single file. After adding a header, the merged file is moved to hdfs.

    Args:
        spark_df (spark dataframe) : Data object to be written to file.
        file_location (String) : Directory location of the file.
        file_name (String) : Name of file to write to.
        csv_sep (character) : Field separator to use in csv file
        csv_quote (character) : Quote character to use in csv file
    """
    # define temp and final paths
    file_path= os.path.join(file_location,file_name)
    temp_file_location = tempfile.NamedTemporaryFile().name 
    temp_file_path = os.path.join(temp_file_location,file_name)

    print("Create directories")
    #create directories if not exist in both local and hdfs
    !mkdir $temp_file_location
    !hdfs dfs -mkdir $file_location
    !hdfs dfs -mkdir $temp_file_location

    # write to temp hdfs location
    print("Write to temp hdfs location : {}".format("hdfs://" + temp_file_path))
    spark_df.write.csv("hdfs://" + temp_file_path, sep=csv_sep, quote=csv_quote)


    # merge file from hadoop to local
    print("Merge and put file at {}".format(temp_file_path))
    !hdfs dfs -getmerge $temp_file_path $temp_file_path

    # Add header to the merged file
    header = ",".join(spark_df.columns)
    !rm $temp_file_location/.*crc
    line_prepender(temp_file_path, header)

    #move the final file to hdfs
    !hdfs dfs -put -f $temp_file_path $file_path

    #cleanup temp locations
    print("Cleanup..")
    !rm -rf $temp_file_location
    !hdfs dfs -rm -r $temp_file_location
    print("Done!")
于 2018-11-30T00:54:39.493 回答