这个问题与terasort的例子有关。是否有任何参数可以使用terasort更改输出记录的数量？使用teragen生成的输入为65'536'000但我们要求运行terasort并输出10'000'000条记录。此请求是Cloudera分发实践的一部分，不是真实案例，而是实施实践的基准。 Teragen：
时间hadoop jar opt / cloudera / parcels / CDH-5.13.1-1.cdh5.13.1.p0.2 / lib / hadoop-0.20-mapreduce / hadoop-examples.jar teragen -Dmapreduce.job.maps = 12 -Ddfs。 blocksize = 33554432 -Dmapreduce.map.memory.mb = 512 -Dyarn.app.mapreduce.am.containerlauncher.threadpool-initial-size = 512 65536000 / user / haley / tgen
结果：
17/12/20 10:31:00 INFO terasort.TeraSort: starting
17/12/20 10:31:02 INFO hdfs.DFSClient: Created token for haley: HDFS_DELEGATION_TOKEN [email protected], renewer=yarn, realUser=, issueDate=1513776662042, maxDate=1514381462042, sequenceNumber=6, masterKeyId=14 on 172.31.10.43:8020
17/12/20 10:31:02 INFO security.TokenCache: Got dt for hdfs://ip-172-31-10-43.us-west-2.compute.internal:8020; Kind: HDFS_DELEGATION_TOKEN, Service: 172.31.10.43:8020, Ident: (token for haley: HDFS_DELEGATION_TOKEN [email protected], renewer=yarn, realUser=, issueDate=1513776662042, maxDate=1514381462042, sequenceNumber=6, masterKeyId=14)
17/12/20 10:31:02 INFO input.FileInputFormat: Total input paths to process : 12
Spent 330ms computing base-splits.
Spent 4ms computing TeraScheduler splits.
Computing input splits took 335ms
Sampling 10 splits of 204
Making 12 from 100000 sampled records
Computing parititions took 522ms
Spent 858ms computing partitions.
17/12/20 10:31:02 INFO client.RMProxy: Connecting to ResourceManager at ip-172-31-15-85.us-west-2.compute.internal/172.31.15.85:8032
17/12/20 10:31:03 INFO mapreduce.JobSubmitter: number of splits:204
17/12/20 10:31:03 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1513773980733_0002
17/12/20 10:31:03 INFO mapreduce.JobSubmitter: Kind: HDFS_DELEGATION_TOKEN, Service: 172.31.10.43:8020, Ident: (token for haley: HDFS_DELEGATION_TOKEN [email protected], renewer=yarn, realUser=, issueDate=1513776662042, maxDate=1514381462042, sequenceNumber=6, masterKeyId=14)
17/12/20 10:31:03 INFO impl.YarnClientImpl: Submitted application application_1513773980733_0002
17/12/20 10:31:03 INFO mapreduce.Job: The url to track the job: http://ip-172-31-15-85.us-west-2.compute.internal:8088/proxy/application_1513773980733_0002/
17/12/20 10:31:03 INFO mapreduce.Job: Running job: job_1513773980733_0002
17/12/20 10:31:11 INFO mapreduce.Job: Job job_1513773980733_0002 running in uber mode : false
17/12/20 10:31:11 INFO mapreduce.Job: map 0% reduce 0%
17/12/20 10:31:19 INFO mapreduce.Job: map 1% reduce 0%
17/12/20 10:31:20 INFO mapreduce.Job: map 2% reduce 0%
17/12/20 10:31:23 INFO mapreduce.Job: map 4% reduce 0%
17/12/20 10:31:26 INFO mapreduce.Job: map 5% reduce 0%
17/12/20 10:31:27 INFO mapreduce.Job: map 6% reduce 0%
17/12/20 10:31:29 INFO mapreduce.Job: map 11% reduce 0%
17/12/20 10:31:30 INFO mapreduce.Job: map 12% reduce 0%
17/12/20 10:31:33 INFO mapreduce.Job: map 13% reduce 0%
17/12/20 10:31:34 INFO mapreduce.Job: map 14% reduce 0%
17/12/20 10:31:36 INFO mapreduce.Job: map 15% reduce 0%
17/12/20 10:31:37 INFO mapreduce.Job: map 16% reduce 0%
17/12/20 10:31:40 INFO mapreduce.Job: map 17% reduce 0%
17/12/20 10:31:41 INFO mapreduce.Job: map 22% reduce 0%
17/12/20 10:31:43 INFO mapreduce.Job: map 23% reduce 0%
17/12/20 10:31:44 INFO mapreduce.Job: map 24% reduce 0%
17/12/20 10:31:47 INFO mapreduce.Job: map 25% reduce 0%
17/12/20 10:31:50 INFO mapreduce.Job: map 26% reduce 0%
17/12/20 10:31:51 INFO mapreduce.Job: map 27% reduce 0%
17/12/20 10:31:54 INFO mapreduce.Job: map 31% reduce 0%
17/12/20 10:31:55 INFO mapreduce.Job: map 33% reduce 0%
17/12/20 10:31:58 INFO mapreduce.Job: map 34% reduce 0%
17/12/20 10:31:59 INFO mapreduce.Job: map 35% reduce 0%
17/12/20 10:32:02 INFO mapreduce.Job: map 37% reduce 0%
17/12/20 10:32:05 INFO mapreduce.Job: map 38% reduce 0%
17/12/20 10:32:06 INFO mapreduce.Job: map 43% reduce 0%
17/12/20 10:32:08 INFO mapreduce.Job: map 44% reduce 0%
17/12/20 10:32:09 INFO mapreduce.Job: map 45% reduce 0%
17/12/20 10:32:11 INFO mapreduce.Job: map 46% reduce 0%
17/12/20 10:32:12 INFO mapreduce.Job: map 47% reduce 0%
17/12/20 10:32:16 INFO mapreduce.Job: map 49% reduce 0%
17/12/20 10:32:17 INFO mapreduce.Job: map 50% reduce 0%
17/12/20 10:32:18 INFO mapreduce.Job: map 52% reduce 0%
17/12/20 10:32:19 INFO mapreduce.Job: map 54% reduce 0%
17/12/20 10:32:20 INFO mapreduce.Job: map 55% reduce 0%
17/12/20 10:32:23 INFO mapreduce.Job: map 56% reduce 0%
17/12/20 10:32:24 INFO mapreduce.Job: map 57% reduce 0%
17/12/20 10:32:26 INFO mapreduce.Job: map 58% reduce 0%
17/12/20 10:32:27 INFO mapreduce.Job: map 59% reduce 0%
17/12/20 10:32:29 INFO mapreduce.Job: map 60% reduce 0%
17/12/20 10:32:30 INFO mapreduce.Job: map 64% reduce 0%
17/12/20 10:32:31 INFO mapreduce.Job: map 65% reduce 0%
17/12/20 10:32:33 INFO mapreduce.Job: map 66% reduce 0%
17/12/20 10:32:34 INFO mapreduce.Job: map 67% reduce 0%
17/12/20 10:32:36 INFO mapreduce.Job: map 68% reduce 0%
17/12/20 10:32:37 INFO mapreduce.Job: map 69% reduce 0%
17/12/20 10:32:39 INFO mapreduce.Job: map 70% reduce 0%
17/12/20 10:32:42 INFO mapreduce.Job: map 73% reduce 0%
17/12/20 10:32:43 INFO mapreduce.Job: map 75% reduce 0%
17/12/20 10:32:45 INFO mapreduce.Job: map 76% reduce 0%
17/12/20 10:32:47 INFO mapreduce.Job: map 77% reduce 0%
17/12/20 10:32:48 INFO mapreduce.Job: map 78% reduce 0%
17/12/20 10:32:51 INFO mapreduce.Job: map 80% reduce 0%
17/12/20 10:32:52 INFO mapreduce.Job: map 81% reduce 0%
17/12/20 10:32:53 INFO mapreduce.Job: map 82% reduce 0%
17/12/20 10:32:54 INFO mapreduce.Job: map 84% reduce 0%
17/12/20 10:32:55 INFO mapreduce.Job: map 86% reduce 0%
17/12/20 10:32:58 INFO mapreduce.Job: map 88% reduce 0%
17/12/20 10:33:02 INFO mapreduce.Job: map 89% reduce 0%
17/12/20 10:33:05 INFO mapreduce.Job: map 90% reduce 0%
17/12/20 10:33:06 INFO mapreduce.Job: map 91% reduce 0%
17/12/20 10:33:07 INFO mapreduce.Job: map 92% reduce 0%
17/12/20 10:33:11 INFO mapreduce.Job: map 92% reduce 3%
17/12/20 10:33:12 INFO mapreduce.Job: map 93% reduce 10%
17/12/20 10:33:13 INFO mapreduce.Job: map 94% reduce 10%
17/12/20 10:33:14 INFO mapreduce.Job: map 95% reduce 13%
17/12/20 10:33:15 INFO mapreduce.Job: map 95% reduce 26%
17/12/20 10:33:17 INFO mapreduce.Job: map 96% reduce 26%
17/12/20 10:33:18 INFO mapreduce.Job: map 98% reduce 26%
17/12/20 10:33:20 INFO mapreduce.Job: map 98% reduce 27%
17/12/20 10:33:22 INFO mapreduce.Job: map 99% reduce 27%
17/12/20 10:33:23 INFO mapreduce.Job: map 100% reduce 27%
17/12/20 10:33:24 INFO mapreduce.Job: map 100% reduce 30%
17/12/20 10:33:26 INFO mapreduce.Job: map 100% reduce 33%
17/12/20 10:33:27 INFO mapreduce.Job: map 100% reduce 45%
17/12/20 10:33:28 INFO mapreduce.Job: map 100% reduce 51%
17/12/20 10:33:30 INFO mapreduce.Job: map 100% reduce 62%
17/12/20 10:33:32 INFO mapreduce.Job: map 100% reduce 64%
17/12/20 10:33:33 INFO mapreduce.Job: map 100% reduce 72%
17/12/20 10:33:34 INFO mapreduce.Job: map 100% reduce 80%
17/12/20 10:33:36 INFO mapreduce.Job: map 100% reduce 89%
17/12/20 10:33:37 INFO mapreduce.Job: map 100% reduce 91%
17/12/20 10:33:38 INFO mapreduce.Job: map 100% reduce 95%
17/12/20 10:33:39 INFO mapreduce.Job: map 100% reduce 96%
17/12/20 10:33:40 INFO mapreduce.Job: map 100% reduce 99%
17/12/20 10:33:43 INFO mapreduce.Job: map 100% reduce 100%
17/12/20 10:33:43 INFO mapreduce.Job: Job job_1513773980733_0002 completed successfully
17/12/20 10:33:43 INFO mapreduce.Job: Counters: 49
File System Counters
FILE: Number of bytes read=2907421533
FILE: Number of bytes written=5786194509
FILE: Number of read operations=0
FILE: Number of large read operations=0
FILE: Number of write operations=0
HDFS: Number of bytes read=6553630192
HDFS: Number of bytes written=6553600000
HDFS: Number of read operations=648
HDFS: Number of large read operations=0
HDFS: Number of write operations=24
Job Counters
Launched map tasks=204
Launched reduce tasks=12
Data-local map tasks=204
Total time spent by all maps in occupied slots (ms)=1572044
Total time spent by all reduces in occupied slots (ms)=441827
Total time spent by all map tasks (ms)=1572044
Total time spent by all reduce tasks (ms)=441827
Total vcore-milliseconds taken by all map tasks=1572044
Total vcore-milliseconds taken by all reduce tasks=441827
Total megabyte-milliseconds taken by all map tasks=1609773056
Total megabyte-milliseconds taken by all reduce tasks=452430848
Map-Reduce Framework
Map input records=65536000
Map output records=65536000
Map output bytes=6684672000
Map output materialized bytes=2846244178
Input split bytes=30192
Combine input records=0
Combine output records=0
Reduce input groups=65536000
Reduce shuffle bytes=2846244178
Reduce input records=65536000
Reduce output records=65536000
Spilled Records=131072000
Shuffled Maps =2448
Failed Shuffles=0
Merged Map outputs=2448
GC time elapsed (ms)=27275
CPU time spent (ms)=950620
Physical memory (bytes) snapshot=117459451904
Virtual memory (bytes) snapshot=345340637184
Total committed heap usage (bytes)=125787176960
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=6553600000
File Output Format Counters
Bytes Written=6553600000
17/12/20 10:33:43 INFO terasort.TeraSort: done
real 2m43.996s
user 0m7.229s
sys 0m0.361s
Terasort（到目前为止尝试了mapred.map.output.records没有运气）：
时间hadoop jar /opt/cloudera/parcels/CDH-5.13.1-1.cdh5.13.1.p0.2/lib/hadoop-0.20-mapreduce/hadoop-examples.jar terasort -D mapred.map.output.records = 10000000 / user / haley / tgen / user / haley / tsort1
结果：
17/12/20 10:56:12 INFO terasort.TeraSort: starting
17/12/20 10:56:13 INFO hdfs.DFSClient: Created token for haley: HDFS_DELEGATION_TOKEN [email protected], renewer=yarn, realUser=, issueDate=1513778173455, maxDate=1514382973455, sequenceNumber=7, masterKeyId=14 on 172.31.10.43:8020
17/12/20 10:56:13 INFO security.TokenCache: Got dt for hdfs://ip-172-31-10-43.us-west-2.compute.internal:8020; Kind: HDFS_DELEGATION_TOKEN, Service: 172.31.10.43:8020, Ident: (token for haley: HDFS_DELEGATION_TOKEN [email protected], renewer=yarn, realUser=, issueDate=1513778173455, maxDate=1514382973455, sequenceNumber=7, masterKeyId=14)
17/12/20 10:56:13 INFO input.FileInputFormat: Total input paths to process : 12
Spent 295ms computing base-splits.
Spent 4ms computing TeraScheduler splits.
Computing input splits took 299ms
Sampling 10 splits of 204
Making 12 from 100000 sampled records
Computing parititions took 558ms
Spent 860ms computing partitions.
17/12/20 10:56:14 INFO client.RMProxy: Connecting to ResourceManager at ip-172-31-15-85.us-west-2.compute.internal/172.31.15.85:8032
17/12/20 10:56:14 INFO mapreduce.JobSubmitter: number of splits:204
17/12/20 10:56:14 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1513773980733_0003
17/12/20 10:56:14 INFO mapreduce.JobSubmitter: Kind: HDFS_DELEGATION_TOKEN, Service: 172.31.10.43:8020, Ident: (token for haley: HDFS_DELEGATION_TOKEN [email protected], renewer=yarn, realUser=, issueDate=1513778173455, maxDate=1514382973455, sequenceNumber=7, masterKeyId=14)
17/12/20 10:56:15 INFO impl.YarnClientImpl: Submitted application application_1513773980733_0003
17/12/20 10:56:15 INFO mapreduce.Job: The url to track the job: http://ip-172-31-15-85.us-west-2.compute.internal:8088/proxy/application_1513773980733_0003/
17/12/20 10:56:15 INFO mapreduce.Job: Running job: job_1513773980733_0003
17/12/20 10:56:22 INFO mapreduce.Job: Job job_1513773980733_0003 running in uber mode : false
17/12/20 10:56:22 INFO mapreduce.Job: map 0% reduce 0%
17/12/20 10:56:30 INFO mapreduce.Job: map 1% reduce 0%
17/12/20 10:56:31 INFO mapreduce.Job: map 2% reduce 0%
17/12/20 10:56:34 INFO mapreduce.Job: map 4% reduce 0%
17/12/20 10:56:37 INFO mapreduce.Job: map 5% reduce 0%
17/12/20 10:56:38 INFO mapreduce.Job: map 6% reduce 0%
17/12/20 10:56:40 INFO mapreduce.Job: map 7% reduce 0%
17/12/20 10:56:41 INFO mapreduce.Job: map 12% reduce 0%
17/12/20 10:56:44 INFO mapreduce.Job: map 13% reduce 0%
17/12/20 10:56:45 INFO mapreduce.Job: map 14% reduce 0%
17/12/20 10:56:48 INFO mapreduce.Job: map 16% reduce 0%
17/12/20 10:56:51 INFO mapreduce.Job: map 17% reduce 0%
17/12/20 10:56:52 INFO mapreduce.Job: map 18% reduce 0%
17/12/20 10:56:53 INFO mapreduce.Job: map 22% reduce 0%
17/12/20 10:56:56 INFO mapreduce.Job: map 24% reduce 0%
17/12/20 10:56:58 INFO mapreduce.Job: map 25% reduce 0%
17/12/20 10:57:02 INFO mapreduce.Job: map 27% reduce 0%
17/12/20 10:57:05 INFO mapreduce.Job: map 28% reduce 0%
17/12/20 10:57:06 INFO mapreduce.Job: map 33% reduce 0%
17/12/20 10:57:09 INFO mapreduce.Job: map 34% reduce 0%
17/12/20 10:57:10 INFO mapreduce.Job: map 35% reduce 0%
17/12/20 10:57:12 INFO mapreduce.Job: map 36% reduce 0%
17/12/20 10:57:13 INFO mapreduce.Job: map 37% reduce 0%
17/12/20 10:57:16 INFO mapreduce.Job: map 38% reduce 0%
17/12/20 10:57:17 INFO mapreduce.Job: map 42% reduce 0%
17/12/20 10:57:18 INFO mapreduce.Job: map 43% reduce 0%
17/12/20 10:57:19 INFO mapreduce.Job: map 44% reduce 0%
17/12/20 10:57:20 INFO mapreduce.Job: map 45% reduce 0%
17/12/20 10:57:24 INFO mapreduce.Job: map 47% reduce 0%
17/12/20 10:57:26 INFO mapreduce.Job: map 48% reduce 0%
17/12/20 10:57:27 INFO mapreduce.Job: map 49% reduce 0%
17/12/20 10:57:28 INFO mapreduce.Job: map 50% reduce 0%
17/12/20 10:57:29 INFO mapreduce.Job: map 51% reduce 0%
17/12/20 10:57:30 INFO mapreduce.Job: map 54% reduce 0%
17/12/20 10:57:31 INFO mapreduce.Job: map 55% reduce 0%
17/12/20 10:57:33 INFO mapreduce.Job: map 56% reduce 0%
17/12/20 10:57:34 INFO mapreduce.Job: map 57% reduce 0%
17/12/20 10:57:37 INFO mapreduce.Job: map 58% reduce 0%
17/12/20 10:57:38 INFO mapreduce.Job: map 59% reduce 0%
17/12/20 10:57:40 INFO mapreduce.Job: map 61% reduce 0%
17/12/20 10:57:41 INFO mapreduce.Job: map 64% reduce 0%
17/12/20 10:57:42 INFO mapreduce.Job: map 65% reduce 0%
17/12/20 10:57:45 INFO mapreduce.Job: map 66% reduce 0%
17/12/20 10:57:46 INFO mapreduce.Job: map 67% reduce 0%
17/12/20 10:57:48 INFO mapreduce.Job: map 68% reduce 0%
17/12/20 10:57:49 INFO mapreduce.Job: map 69% reduce 0%
17/12/20 10:57:51 INFO mapreduce.Job: map 70% reduce 0%
17/12/20 10:57:52 INFO mapreduce.Job: map 72% reduce 0%
17/12/20 10:57:53 INFO mapreduce.Job: map 73% reduce 0%
17/12/20 10:57:54 INFO mapreduce.Job: map 74% reduce 0%
17/12/20 10:57:55 INFO mapreduce.Job: map 75% reduce 0%
17/12/20 10:57:56 INFO mapreduce.Job: map 76% reduce 0%
17/12/20 10:57:59 INFO mapreduce.Job: map 78% reduce 0%
17/12/20 10:58:01 INFO mapreduce.Job: map 79% reduce 0%
17/12/20 10:58:02 INFO mapreduce.Job: map 80% reduce 0%
17/12/20 10:58:03 INFO mapreduce.Job: map 82% reduce 0%
17/12/20 10:58:05 INFO mapreduce.Job: map 84% reduce 0%
17/12/20 10:58:06 INFO mapreduce.Job: map 86% reduce 0%
17/12/20 10:58:09 INFO mapreduce.Job: map 87% reduce 0%
17/12/20 10:58:12 INFO mapreduce.Job: map 88% reduce 0%
17/12/20 10:58:14 INFO mapreduce.Job: map 89% reduce 0%
17/12/20 10:58:15 INFO mapreduce.Job: map 90% reduce 0%
17/12/20 10:58:19 INFO mapreduce.Job: map 91% reduce 0%
17/12/20 10:58:20 INFO mapreduce.Job: map 91% reduce 5%
17/12/20 10:58:21 INFO mapreduce.Job: map 92% reduce 5%
17/12/20 10:58:22 INFO mapreduce.Job: map 92% reduce 10%
17/12/20 10:58:23 INFO mapreduce.Job: map 93% reduce 15%
17/12/20 10:58:24 INFO mapreduce.Job: map 94% reduce 15%
17/12/20 10:58:25 INFO mapreduce.Job: map 94% reduce 18%
17/12/20 10:58:26 INFO mapreduce.Job: map 95% reduce 26%
17/12/20 10:58:28 INFO mapreduce.Job: map 96% reduce 26%
17/12/20 10:58:29 INFO mapreduce.Job: map 97% reduce 26%
17/12/20 10:58:30 INFO mapreduce.Job: map 98% reduce 26%
17/12/20 10:58:32 INFO mapreduce.Job: map 98% reduce 27%
17/12/20 10:58:33 INFO mapreduce.Job: map 99% reduce 27%
17/12/20 10:58:34 INFO mapreduce.Job: map 100% reduce 27%
17/12/20 10:58:37 INFO mapreduce.Job: map 100% reduce 30%
17/12/20 10:58:38 INFO mapreduce.Job: map 100% reduce 44%
17/12/20 10:58:40 INFO mapreduce.Job: map 100% reduce 52%
17/12/20 10:58:41 INFO mapreduce.Job: map 100% reduce 58%
17/12/20 10:58:43 INFO mapreduce.Job: map 100% reduce 64%
17/12/20 10:58:44 INFO mapreduce.Job: map 100% reduce 73%
17/12/20 10:58:46 INFO mapreduce.Job: map 100% reduce 81%
17/12/20 10:58:47 INFO mapreduce.Job: map 100% reduce 85%
17/12/20 10:58:48 INFO mapreduce.Job: map 100% reduce 94%
17/12/20 10:58:49 INFO mapreduce.Job: map 100% reduce 98%
17/12/20 10:58:50 INFO mapreduce.Job: map 100% reduce 100%
17/12/20 10:58:51 INFO mapreduce.Job: Job job_1513773980733_0003 completed successfully
17/12/20 10:58:51 INFO mapreduce.Job: Counters: 49
File System Counters
FILE: Number of bytes read=2906318809
FILE: Number of bytes written=5785091778
FILE: Number of read operations=0
FILE: Number of large read operations=0
FILE: Number of write operations=0
HDFS: Number of bytes read=6553630192
HDFS: Number of bytes written=6553600000
HDFS: Number of read operations=648
HDFS: Number of large read operations=0
HDFS: Number of write operations=24
Job Counters
Launched map tasks=204
Launched reduce tasks=12
Data-local map tasks=204
Total time spent by all maps in occupied slots (ms)=1548516
Total time spent by all reduces in occupied slots (ms)=443076
Total time spent by all map tasks (ms)=1548516
Total time spent by all reduce tasks (ms)=443076
Total vcore-milliseconds taken by all map tasks=1548516
Total vcore-milliseconds taken by all reduce tasks=443076
Total megabyte-milliseconds taken by all map tasks=1585680384
Total megabyte-milliseconds taken by all reduce tasks=453709824
Map-Reduce Framework
Map input records=65536000
Map output records=65536000
Map output bytes=6684672000
Map output materialized bytes=2846244178
Input split bytes=30192
Combine input records=0
Combine output records=0
Reduce input groups=65536000
Reduce shuffle bytes=2846244178
Reduce input records=65536000
Reduce output records=65536000
Spilled Records=131072000
Shuffled Maps =2448
Failed Shuffles=0
Merged Map outputs=2448
GC time elapsed (ms)=26251
CPU time spent (ms)=946520
Physical memory (bytes) snapshot=117397381120
Virtual memory (bytes) snapshot=345217998848
Total committed heap usage (bytes)=123740356608
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=6553600000
File Output Format Counters
Bytes Written=6553600000
17/12/20 10:58:51 INFO terasort.TeraSort: done
real 2m40.756s
user 0m7.248s
sys 0m0.378s
提前致谢！！！
是否有任何参数可以使用terasort更改输出记录的数量？
据我所知，TeraSort.java的源代码，它似乎实现了自定义分区，分区和排序完整的输入。所以没有参数可以改变这种行为。