我有两个python脚本mapper和reducer（此时基本上是reducer只打印其他内容）而本地我得到4个结果 - 字符串 在hadoop我得到3.这是如何工作的？
我使用Hadoop使用Amazon Elastic Map Reduce
mapper.py
#!/usr/bin/env python
import sys
import re
import os
# Constants declaration
WINDOW = 10
OVERLAP = 4
START_POSITION = 0
END_POSITION = 0
# regular expressions
pattern = re.compile("[a-z]*", re.IGNORECASE)
a_to_f_pattern = re.compile("[a-f]", re.IGNORECASE)
g_to_l_pattern = re.compile("[g-l]", re.IGNORECASE)
m_to_r_pattern = re.compile("[m-r]", re.IGNORECASE)
s_to_z_pattern = re.compile("[s-z]", re.IGNORECASE)
# variables initialization
converted_word = ""
next_word = ""
new_character = ""
filename = ""
prev_filename = ""
i = 0
# Read pairs as lines of input from STDIN
for line in sys.stdin:
line.strip()
filename = os.environ['mapreduce_map_input_file']
filename = filename.replace("s3://source123/input/","")
# check if its a new file, and reset start position
if filename != prev_filename:
START_POSITION = 0
next_word = ""
converted_word = ""
prev_filename = filename
# loop through every word that matches the pattern
for word in pattern.findall(line):
new_character = convert(word)
converted_word = converted_word + new_character
if len(converted_word) > (WINDOW - OVERLAP):
next_word = next_word + new_character
# print "word= ", word
# print "converted_word= ", converted_word
else:
END_POSITION = START_POSITION + (len(converted_word) - 1)
print converted_word + "," + str(filename) + "," + str(START_POSITION) + "," + str(END_POSITION)
START_POSITION = START_POSITION + (WINDOW - OVERLAP)
new_character = convert(word)
converted_word = next_word + new_character
日志
2016-04-27 19:58:41,293 INFO com.amazon.ws.emr.hadoop.fs.EmrFileSystem (main): Consistency disabled, using com.amazon.ws.emr.hadoop.fs.s3n.S3NativeFileSystem as filesystem implementation
2016-04-27 19:58:41,512 INFO amazon.emr.metrics.MetricsSaver (main): MetricsConfigRecord disabledInCluster: false instanceEngineCycleSec: 60 clusterEngineCycleSec: 60 disableClusterEngine: true maxMemoryMb: 3072 maxInstanceCount: 500 lastModified: 1461784308237
2016-04-27 19:58:41,512 INFO amazon.emr.metrics.MetricsSaver (main): Created MetricsSaver j-KCDMFZJGYO89:i-995f5a41:RunJar:16480 period:60 /mnt/var/em/raw/i-995f5a41_20160427_RunJar_16480_raw.bin
2016-04-27 19:58:43,477 INFO org.apache.hadoop.yarn.client.RMProxy (main): Connecting to ResourceManager at ip-172-31-38-52.us-west-2.compute.internal/172.31.38.52:8032
2016-04-27 19:58:43,673 INFO org.apache.hadoop.yarn.client.RMProxy (main): Connecting to ResourceManager at ip-172-31-38-52.us-west-2.compute.internal/172.31.38.52:8032
2016-04-27 19:58:44,156 INFO com.amazon.ws.emr.hadoop.fs.s3n.S3NativeFileSystem (main): Opening 's3://source123/mapper.py' for reading
2016-04-27 19:58:44,267 INFO amazon.emr.metrics.MetricsSaver (main): Thread 1 created MetricsLockFreeSaver 1
2016-04-27 19:58:44,439 INFO com.amazon.ws.emr.hadoop.fs.s3n.S3NativeFileSystem (main): Opening 's3://source123/source_reducer.py' for reading
2016-04-27 19:58:44,628 INFO com.hadoop.compression.lzo.GPLNativeCodeLoader (main): Loaded native gpl library
2016-04-27 19:58:44,630 INFO com.hadoop.compression.lzo.LzoCodec (main): Successfully loaded & initialized native-lzo library [hadoop-lzo rev 426d94a07125cf9447bb0c2b336cf10b4c254375]
2016-04-27 19:58:45,046 INFO com.amazon.ws.emr.hadoop.fs.s3n.S3NativeFileSystem (main): listStatus s3://source123/input with recursive false
2016-04-27 19:58:45,265 INFO org.apache.hadoop.mapred.FileInputFormat (main): Total input paths to process : 1
2016-04-27 19:58:45,336 INFO org.apache.hadoop.mapreduce.JobSubmitter (main): number of splits:9
2016-04-27 19:58:45,565 INFO org.apache.hadoop.mapreduce.JobSubmitter (main): Submitting tokens for job: job_1461784297295_0004
2016-04-27 19:58:45,710 INFO org.apache.hadoop.yarn.client.api.impl.YarnClientImpl (main): Submitted application application_1461784297295_0004
2016-04-27 19:58:45,743 INFO org.apache.hadoop.mapreduce.Job (main): The url to track the job: http://ip-172-31-38-52.us-west-2.compute.internal:20888/proxy/application_1461784297295_0004/
2016-04-27 19:58:45,744 INFO org.apache.hadoop.mapreduce.Job (main): Running job: job_1461784297295_0004
2016-04-27 19:58:53,876 INFO org.apache.hadoop.mapreduce.Job (main): Job job_1461784297295_0004 running in uber mode : false
2016-04-27 19:58:53,877 INFO org.apache.hadoop.mapreduce.Job (main): map 0% reduce 0%
2016-04-27 19:59:11,063 INFO org.apache.hadoop.mapreduce.Job (main): map 11% reduce 0%
2016-04-27 19:59:14,081 INFO org.apache.hadoop.mapreduce.Job (main): map 22% reduce 0%
2016-04-27 19:59:16,094 INFO org.apache.hadoop.mapreduce.Job (main): map 33% reduce 0%
2016-04-27 19:59:18,106 INFO org.apache.hadoop.mapreduce.Job (main): map 56% reduce 0%
2016-04-27 19:59:19,114 INFO org.apache.hadoop.mapreduce.Job (main): map 67% reduce 0%
2016-04-27 19:59:26,159 INFO org.apache.hadoop.mapreduce.Job (main): map 78% reduce 0%
2016-04-27 19:59:29,178 INFO org.apache.hadoop.mapreduce.Job (main): map 89% reduce 0%
2016-04-27 19:59:30,184 INFO org.apache.hadoop.mapreduce.Job (main): map 100% reduce 0%
2016-04-27 19:59:32,196 INFO org.apache.hadoop.mapreduce.Job (main): map 100% reduce 33%
2016-04-27 19:59:34,207 INFO org.apache.hadoop.mapreduce.Job (main): map 100% reduce 67%
2016-04-27 19:59:38,228 INFO org.apache.hadoop.mapreduce.Job (main): map 100% reduce 100%
2016-04-27 19:59:40,246 INFO org.apache.hadoop.mapreduce.Job (main): Job job_1461784297295_0004 completed successfully
2016-04-27 19:59:40,409 INFO org.apache.hadoop.mapreduce.Job (main): Counters: 55
File System Counters
FILE: Number of bytes read=190
FILE: Number of bytes written=1541379
FILE: Number of read operations=0
FILE: Number of large read operations=0
FILE: Number of write operations=0
HDFS: Number of bytes read=873
HDFS: Number of bytes written=0
HDFS: Number of read operations=9
HDFS: Number of large read operations=0
HDFS: Number of write operations=0
S3: Number of bytes read=864
S3: Number of bytes written=130
S3: Number of read operations=0
S3: Number of large read operations=0
S3: Number of write operations=0
Job Counters
Killed map tasks=1
Launched map tasks=9
Launched reduce tasks=3
Data-local map tasks=9
Total time spent by all maps in occupied slots (ms)=6351210
Total time spent by all reduces in occupied slots (ms)=2449170
Total time spent by all map tasks (ms)=141138
Total time spent by all reduce tasks (ms)=27213
Total vcore-milliseconds taken by all map tasks=141138
Total vcore-milliseconds taken by all reduce tasks=27213
Total megabyte-milliseconds taken by all map tasks=203238720
Total megabyte-milliseconds taken by all reduce tasks=78373440
Map-Reduce Framework
Map input records=5
Map output records=3
Map output bytes=124
Map output materialized bytes=562
Input split bytes=873
Combine input records=0
Combine output records=0
Reduce input groups=3
Reduce shuffle bytes=562
Reduce input records=3
Reduce output records=6
Spilled Records=6
Shuffled Maps =27
Failed Shuffles=0
Merged Map outputs=27
GC time elapsed (ms)=2785
CPU time spent (ms)=11670
Physical memory (bytes) snapshot=5282500608
Virtual memory (bytes) snapshot=28472725504
Total committed heap usage (bytes)=5977407488
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=864
File Output Format Counters
Bytes Written=130
2016-04-27 19:59:40,409 INFO org.apache.hadoop.streaming.StreamJob (main): Output directory: s3://source123/output/
答案 0 :(得分：2)
映射器任务将其输入转换为行并将行提供给进程的标准输入。
在这种情况下，您有多个输入文件，并且您假设来自不同文件的所有行都按顺序（即逐个文件）提供，但是可能在 parallel 中进行处理，因此映射器（获取几个输入文件）可能会重置其计数器，而不是顺序分发。