我正在尝试收集特定站点的最高和最低温度，然后查找每个不同日期的温度总和，但是我在映射器中始终遇到错误，并且尝试了很多其他方法，例如使用stringtokenizer但还是一样，我得到一个错误。
样本输入。
站点日期（YYYYMMDD）元素温度标记1 flat2其他值
我只需要输入中的测站，日期（键），元素和温度
USW00003889,20180101,TMAX,122,7,1700
USW00003889,20180101,TMIN,-67,7,1700
UK000056225,20180101,TOBS,56,7,1700
UK000056225,20180101,PRCP,0,7,1700
UK000056225,20180101,SNOW,0,7
USC00264341,20180101,SNWD,0,7,1700
USC00256837,20180101,PRCP,0,7,800
UK000056225,20180101,SNOW,0,7
UK000056225,20180101,SNWD,0,7,800
USW00003889,20180102,TMAX,12,E
USW00003889,20180102,TMIN,3,E
UK000056225,20180101,PRCP,42,E
SWE00138880,20180101,PRCP,50,E
UK000056225,20180101,PRCP,0,a
USC00256480,20180101,PRCP,0,7,700
USC00256480,20180101,SNOW,0,7
USC00256480,20180101,SNWD,0,7,700
SWE00138880,20180103,TMAX,-228,7,800
SWE00138880,20180103,TMIN,-328,7,800
USC00247342,20180101,PRCP,0,7,800
UK000056225,20180101,SNOW,0,7
SWE00137764,20180101,PRCP,63,E
UK000056225,20180101,SNWD,0,E
USW00003889,20180104,TMAX,-43,W
USW00003889,20180104,TMIN,-177,W
public static class MaxMinMapper
extends Mapper<Object, Text, Text, IntWritable> {
private Text newDate = new Text();
public void map(Object key, Text value, Context context) throws
IOException,
InterruptedException {
String stationID = "USW00003889";
String[] tokens = value.toString().split(",");
String station = "";
String date = "";
String element = "";
int data = 0;
station = tokens[0];
date = tokens[1];
element = tokens[2];
data = Integer.parseInt(tokens[3]);
if (stationID.equals(station) && ( element.equals("TMAX") ||
element.equals("TMIN")) ) {
newDate.set(date);
context.write(newDate, new IntWritable(data));
}
}
}
public static class MaxMinReducer
extends Reducer<Text, Text, Text, IntWritable> {
private IntWritable result = new IntWritable();
public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
int sumResult = 0;
int val1 = 0;
int val2 = 0;
while (values.iterator().hasNext()) {
val1 = values.iterator().next().get();
val2 = values.iterator().next().get();
sumResult = val1 + val2;
}
result.set(sumResult);
context.write(key, result);
}
}
}
请帮助我，谢谢。
更新：使用条件验证每一行，并将数据变量更改为String（稍后再更改回Integer-> IntWritable）。
if (tokens.length <= 5) {
station = tokens[0];
date = tokens[1];
element = tokens[2];
data = tokens[3];
otherValue = tokens[4];
}else{
station = tokens[0];
date = tokens[1];
element = tokens[2];
data = tokens[3];
otherValue = tokens[4];
otherValue2 = tokens[5];
}
Update2：好的，我现在将输出写入文件，但是输出错误。我需要它来添加具有相同日期（键）的两个值。我在做什么错了？
OUTPUT:
20180101 -67
20180101 122
20180102 3
20180102 12
20180104 -177
20180104 -43
Desired Output
20180101 55
20180102 15
20180104 -220
这也是我收到的错误，即使我得到输出。
ERROR: (gcloud.dataproc.jobs.submit.hadoop) Job [8e31c44ccd394017a4a28b3b16471aca] failed with error:
Google Cloud Dataproc Agent reports job failure. If logs are available, they can be found at 'https://console.cloud.google.com/dataproc/jobs/8e31c44ccd394017a4a28b3b16471aca
?project=driven-airway-257512®ion=us-central1' and in 'gs://dataproc-261a376e-7874-4151-b6b7-566c18758206-us-central1/google-cloud-dataproc-metainfo/f912a2f0-107f-40b6-94
56-b6a72cc8bfc4/jobs/8e31c44ccd394017a4a28b3b16471aca/driveroutput'.
19/11/14 12:53:24 INFO client.RMProxy: Connecting to ResourceManager at cluster-1e8f-m/10.128.0.12:8032
19/11/14 12:53:25 INFO client.AHSProxy: Connecting to Application History server at cluster-1e8f-m/10.128.0.12:10200
19/11/14 12:53:26 WARN mapreduce.JobResourceUploader: Hadoop command-line option parsing not performed. Implement the Tool interface and execute your application with ToolRunner to remedy this.
19/11/14 12:53:26 INFO input.FileInputFormat: Total input files to process : 1
19/11/14 12:53:26 INFO mapreduce.JobSubmitter: number of splits:1
19/11/14 12:53:26 INFO Configuration.deprecation: yarn.resourcemanager.system-metrics-publisher.enabled is deprecated. Instead, use yarn.system-metrics-publisher.enabled
19/11/14 12:53:26 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1573654432484_0035
19/11/14 12:53:27 INFO impl.YarnClientImpl: Submitted application application_1573654432484_0035
19/11/14 12:53:27 INFO mapreduce.Job: The url to track the job: http://cluster-1e8f-m:8088/proxy/application_1573654432484_0035/
19/11/14 12:53:27 INFO mapreduce.Job: Running job: job_1573654432484_0035
19/11/14 12:53:35 INFO mapreduce.Job: Job job_1573654432484_0035 running in uber mode : false
19/11/14 12:53:35 INFO mapreduce.Job: map 0% reduce 0%
19/11/14 12:53:41 INFO mapreduce.Job: map 100% reduce 0%
19/11/14 12:53:52 INFO mapreduce.Job: map 100% reduce 20%
19/11/14 12:53:53 INFO mapreduce.Job: map 100% reduce 40%
19/11/14 12:53:54 INFO mapreduce.Job: map 100% reduce 60%
19/11/14 12:53:56 INFO mapreduce.Job: map 100% reduce 80%
19/11/14 12:53:57 INFO mapreduce.Job: map 100% reduce 100%
19/11/14 12:53:58 INFO mapreduce.Job: Job job_1573654432484_0035 completed successfully
19/11/14 12:53:58 INFO mapreduce.Job: Counters: 55
File System Counters
FILE: Number of bytes read=120
FILE: Number of bytes written=1247665
FILE: Number of read operations=0
FILE: Number of large read operations=0
FILE: Number of write operations=0
GS: Number of bytes read=846
GS: Number of bytes written=76
GS: Number of read operations=0
GS: Number of large read operations=0
GS: Number of write operations=0
HDFS: Number of bytes read=139
HDFS: Number of bytes written=0
HDFS: Number of read operations=1
HDFS: Number of large read operations=0
HDFS: Number of write operations=0
Job Counters
Killed reduce tasks=1
Launched map tasks=1
Launched reduce tasks=5
Rack-local map tasks=1
Total time spent by all maps in occupied slots (ms)=17348
Total time spent by all reduces in occupied slots (ms)=195920
Total time spent by all map tasks (ms)=4337
Total time spent by all reduce tasks (ms)=48980
Total vcore-milliseconds taken by all map tasks=4337
Total vcore-milliseconds taken by all reduce tasks=48980
Total megabyte-milliseconds taken by all map tasks=8882176
Total megabyte-milliseconds taken by all reduce tasks=100311040
Map-Reduce Framework
Map input records=25
Map output records=6
Map output bytes=78
Map output materialized bytes=120
Input split bytes=139
Combine input records=0
Combine output records=0
Reduce input groups=3
Reduce shuffle bytes=120
Reduce input records=6
Reduce output records=6
Spilled Records=12
Shuffled Maps =5
Failed Shuffles=0
Merged Map outputs=5
GC time elapsed (ms)=1409
CPU time spent (ms)=6350
Physical memory (bytes) snapshot=1900220416
Virtual memory (bytes) snapshot=21124952064
Total committed heap usage (bytes)=1492123648
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=846
File Output Format Counters
Bytes Written=76
Job output is complete
更新3：
我更新了Reducer（在LowKey说了之后），它给了我与上面相同的输出。它没有做我想要做的加法。它完全忽略了该操作。为什么？
public static class MaxMinReducer
extends Reducer<Text, Text, Text, IntWritable> {
public IntWritable result = new IntWritable();
public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
int value = 0;
int sumResult = 0;
Iterator<IntWritable> iterator = values.iterator();
while (values.iterator().hasNext()) {
value = iterator.next().get();
sumResult = sumResult + value;
}
result.set(sumResult);
context.write(key, result);
}
}
final int missing = -9999;
没有任何意义。
下面，您似乎有一些代码显然应该添加两个值，但似乎您不小心将列表中的项目扔掉了。查看您的位置：
if (values.iterator().next().get() != missing)
嗯...您从未保存过该值，所以这意味着您将其丢弃了。
另一个问题是您添加错误...由于某种原因，您试图为循环的每次迭代添加两个值。您应该添加一个，因此您的循环应如下所示：
IntWritable value = null;
Iterator iterator = values.iterator();
while (values.iterator().hasNext()) {
value = iterator.next().get();
if (value != missing){
sumResult = sumResult + value;
}
}
下一个明显的问题是将输出行放在while循环内：
while (values.iterator().hasNext()) { [...] context.write(key, result); }
这意味着，每当您将一个项目读入减速器时，您都会写出一个项目。我认为您要尝试做的是读入给定键的所有项，然后写一个减小的值（总和）。在这种情况下，您不应将输出放在循环内。应该在之后。
while ([...]) { [...] } result.set(sumResult); context.write(key, result);