目标是：cassandra中的数百万行需要被提取并尽可能快速高效地压缩到一个文件中（每天）。
当前的安装程序使用googledataproc集群运行spark作业，将数据直接提取到google云存储桶中。我试过两种方法：
使用fileutil.copymerge（）将大约9000个spark分区文件合并成一个未压缩的文件，然后提交一个hadoopmapreduce作业来压缩这个文件。
保留大约9000个spark分区文件作为原始输出，并提交hadoopmapreduce作业，将这些文件合并并压缩为单个文件。
一些作业细节：约8亿行。由spark作业输出的大约9000个spark分区文件。spark作业在一个1主4辅（4vcpu，每个15gb）dataproc集群上运行大约需要一个小时。默认的dataproc hadoop块大小，我认为是128mb。
一些spark配置细节：

spark.task.maxFailures=10
spark.executor.cores=4

spark.cassandra.input.consistency.level=LOCAL_ONE
spark.cassandra.input.reads_per_sec=100
spark.cassandra.input.fetch.size_in_rows=1000
spark.cassandra.input.split.size_in_mb=64

hadoop作业：

hadoop jar file://usr/lib/hadoop-mapreduce/hadoop-streaming-2.8.4.jar
-Dmapred.reduce.tasks=1
-Dmapred.output.compress=true
-Dmapred.compress.map.output=true
-Dstream.map.output.field.separator=,
-Dmapred.textoutputformat.separator=,
-Dmapred.output.compression.codec=org.apache.hadoop.io.compress.GzipCodec
-input gs://bucket/with/either/single/uncompressed/csv/or/many/spark/partition/file/csvs
-output gs://output/bucket
-mapper /bin/cat
-reducer /bin/cat
-inputformat org.apache.hadoop.mapred.TextInputFormat
-outputformat org.apache.hadoop.mapred.TextOutputFormat

spark的工作花了大约1个小时将cassandra数据提取到gcs bucket中。使用fileutil.copymerge（）增加了大约45分钟，这是由dataproc集群执行的，但资源利用不足，因为它似乎使用1个节点。压缩单个文件的hadoop作业又花了50分钟。这并不是一个最佳的方法，因为集群必须保持更长的时间，即使它没有使用它的全部资源。
该作业的信息输出：

INFO mapreduce.Job: Counters: 55
File System Counters
    FILE: Number of bytes read=5072098452
    FILE: Number of bytes written=7896333915
    FILE: Number of read operations=0
    FILE: Number of large read operations=0
    FILE: Number of write operations=0
    GS: Number of bytes read=47132294405
    GS: Number of bytes written=2641672054
    GS: Number of read operations=0
    GS: Number of large read operations=0
    GS: Number of write operations=0
    HDFS: Number of bytes read=57024
    HDFS: Number of bytes written=0
    HDFS: Number of read operations=352
    HDFS: Number of large read operations=0
    HDFS: Number of write operations=0
Job Counters 
    Killed map tasks=1
    Launched map tasks=353
    Launched reduce tasks=1
    Rack-local map tasks=353
    Total time spent by all maps in occupied slots (ms)=18495825
    Total time spent by all reduces in occupied slots (ms)=7412208
    Total time spent by all map tasks (ms)=6165275
    Total time spent by all reduce tasks (ms)=2470736
    Total vcore-milliseconds taken by all map tasks=6165275
    Total vcore-milliseconds taken by all reduce tasks=2470736
    Total megabyte-milliseconds taken by all map tasks=18939724800
    Total megabyte-milliseconds taken by all reduce tasks=7590100992
Map-Reduce Framework
    Map input records=775533855
    Map output records=775533855
    Map output bytes=47130856709
    Map output materialized bytes=2765069653
    Input split bytes=57024
    Combine input records=0
    Combine output records=0
    Reduce input groups=2539721
    Reduce shuffle bytes=2765069653
    Reduce input records=775533855
    Reduce output records=775533855
    Spilled Records=2204752220
    Shuffled Maps =352
    Failed Shuffles=0
    Merged Map outputs=352
    GC time elapsed (ms)=87201
    CPU time spent (ms)=7599340
    Physical memory (bytes) snapshot=204676702208
    Virtual memory (bytes) snapshot=1552881852416
    Total committed heap usage (bytes)=193017675776
Shuffle Errors
    BAD_ID=0
    CONNECTION=0
    IO_ERROR=0
    WRONG_LENGTH=0
    WRONG_MAP=0
    WRONG_REDUCE=0
File Input Format Counters 
    Bytes Read=47132294405
File Output Format Counters 
    Bytes Written=2641672054

我期望这种方法的性能与另一种方法一样好或更好，但它的性能要差得多。spark的工作没有改变。跳过fileutil.copymerge（）并直接跳转到hadoop mapreduce作业。。。一个半小时后，这份工作的Map部分只占50%左右。这项工作在那个时候被取消了，因为很明显这是行不通的。
我完全可以控制spark和hadoop的工作。我知道我们可以创建一个更大的集群，但我宁愿在确保作业本身得到优化之后再这样做。感谢您的帮助。谢谢。