Hadoop学习之第一个MapReduce程序
期望
通过这个mapreduce程序了解mapreduce程序执行的流程，着重从程序解执行的打印信息中提炼出有用信息。
执行前
程序代码
程序代码基本上是《hadoop权威指南》上原封不动搬下来的，目的为求出某一年份中最高气温，相关代码如下：
public class NcdcWeather {
private String USAF_station_id;
private String WBAN_station_id;
private String date;
private String time;
private String latitude;
private String longitude;
/** 海拔*/
private String elevation;
/** 风向*/
private String wind_direction;
private String wind_direction_quality_code;
private String sky_ceiling_height;
private String sky_ceiling_height_quality_code;
private String visibility_distance;
private String visibility_distance_quality_code;
private String air_temperature;
private String air_temperature_quality_code;
private String dew_point_temperature;
private String dew_point_temperature_quality_code;
private String atmospheric_pressure;
private String atmospheric_pressure_quality_code;
public NcdcWeather(String rowData) {
if (StringUtils.isEmpty(rowData) || rowData.length() < 105) {
return;
}
USAF_station_id = rowData.substring(4, 10);
WBAN_station_id = rowData.substring(10, 15);
date = rowData.substring(15, 23);
time = rowData.substring(23, 27);
latitude = rowData.substring(28, 34);
longitude = rowData.substring(34, 41);
elevation = rowData.substring(46, 51);
wind_direction = rowData.substring(60, 63);
wind_direction_quality_code = rowData.substring(63, 64);
sky_ceiling_height = rowData.substring(70, 75);
sky_ceiling_height_quality_code = rowData.substring(75, 76);
visibility_distance = rowData.substring(78, 84);
visibility_distance_quality_code = rowData.substring(84, 85);
air_temperature = rowData.substring(87, 92);
air_temperature_quality_code = rowData.substring(92, 93);
dew_point_temperature = rowData.substring(93, 98);
dew_point_temperature_quality_code = rowData.substring(98, 99);
atmospheric_pressure = rowData.substring(99, 104);
atmospheric_pressure_quality_code = rowData.substring(104, 105);
}
public String getUSAF_station_id() {
return USAF_station_id;
}
public void setUSAF_station_id(String USAF_station_id) {
this.USAF_station_id = USAF_station_id;
}
public String getWBAN_station_id() {
return WBAN_station_id;
}
public void setWBAN_station_id(String WBAN_station_id) {
this.WBAN_station_id = WBAN_station_id;
}
public String getDate() {
return date;
}
public void setDate(String date) {
this.date = date;
}
public String getTime() {
return time;
}
public void setTime(String time) {
this.time = time;
}
public String getLatitude() {
return latitude;
}
public void setLatitude(String latitude) {
this.latitude = latitude;
}
public String getLongitude() {
return longitude;
}
public void setLongitude(String longitude) {
this.longitude = longitude;
}
public String getElevation() {
return elevation;
}
public void setElevation(String elevation) {
this.elevation = elevation;
}
public String getWind_direction() {
return wind_direction;
}
public void setWind_direction(String wind_direction) {
this.wind_direction = wind_direction;
}
public String getWind_direction_quality_code() {
return wind_direction_quality_code;
}
public void setWind_direction_quality_code(String wind_direction_quality_code) {
this.wind_direction_quality_code = wind_direction_quality_code;
}
public String getSky_ceiling_height() {
return sky_ceiling_height;
}
public void setSky_ceiling_height(String sky_ceiling_height) {
this.sky_ceiling_height = sky_ceiling_height;
}
public String getSky_ceiling_height_quality_code() {
return sky_ceiling_height_quality_code;
}
public void setSky_ceiling_height_quality_code(String sky_ceiling_height_quality_code) {
this.sky_ceiling_height_quality_code = sky_ceiling_height_quality_code;
}
public String getVisibility_distance() {
return visibility_distance;
}
public void setVisibility_distance(String visibility_distance) {
this.visibility_distance = visibility_distance;
}
public String getVisibility_distance_quality_code() {
return visibility_distance_quality_code;
}
public void setVisibility_distance_quality_code(String visibility_distance_quality_code) {
this.visibility_distance_quality_code = visibility_distance_quality_code;
}
public String getAir_temperature() {
return air_temperature;
}
public void setAir_temperature(String air_temperature) {
this.air_temperature = air_temperature;
}
public String getAir_temperature_quality_code() {
return air_temperature_quality_code;
}
public void setAir_temperature_quality_code(String air_temperature_quality_code) {
this.air_temperature_quality_code = air_temperature_quality_code;
}
public String getDew_point_temperature() {
return dew_point_temperature;
}
public void setDew_point_temperature(String dew_point_temperature) {
this.dew_point_temperature = dew_point_temperature;
}
public String getDew_point_temperature_quality_code() {
return dew_point_temperature_quality_code;
}
public void setDew_point_temperature_quality_code(String dew_point_temperature_quality_code) {
this.dew_point_temperature_quality_code = dew_point_temperature_quality_code;
}
public String getAtmospheric_pressure() {
return atmospheric_pressure;
}
public void setAtmospheric_pressure(String atmospheric_pressure) {
this.atmospheric_pressure = atmospheric_pressure;
}
public String getAtmospheric_pressure_quality_code() {
return atmospheric_pressure_quality_code;
}
public void setAtmospheric_pressure_quality_code(String atmospheric_pressure_quality_code) {
this.atmospheric_pressure_quality_code = atmospheric_pressure_quality_code;
}
@Override
public String toString() {
return "NcdcWeather{" +
"USAF_station_id='" + USAF_station_id + '\'' +
", WBAN_station_id='" + WBAN_station_id + '\'' +
", date='" + date + '\'' +
", time='" + time + '\'' +
", latitude='" + latitude + '\'' +
", longitude='" + longitude + '\'' +
", elevation='" + elevation + '\'' +
", wind_direction='" + wind_direction + '\'' +
", wind_direction_quality_code='" + wind_direction_quality_code + '\'' +
", sky_ceiling_height='" + sky_ceiling_height + '\'' +
", sky_ceiling_height_quality_code='" + sky_ceiling_height_quality_code + '\'' +
", visibility_distance='" + visibility_distance + '\'' +
", visibility_distance_quality_code='" + visibility_distance_quality_code + '\'' +
", air_temperature='" + air_temperature + '\'' +
", air_temperature_quality_code='" + air_temperature_quality_code + '\'' +
", dew_point_temperature='" + dew_point_temperature + '\'' +
", dew_point_temperature_quality_code='" + dew_point_temperature_quality_code + '\'' +
", atmospheric_pressure='" + atmospheric_pressure + '\'' +
", atmospheric_pressure_quality_code='" + atmospheric_pressure_quality_code + '\'' +
'}';
}
}
Weather Bean
public class MaxTemperatureMapper extends Mapper<LongWritable, Text, Text, IntWritable> {
private static int MISS_CODE = 9999;
@Override
protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
String line = value.toString();
NcdcWeather ncdcWeather = new NcdcWeather(line);
String year = ncdcWeather.getDate().substring(0, 4);
int temperature = 0;
if (ncdcWeather.getAir_temperature().startsWith("+")) {
temperature = Integer.parseInt(ncdcWeather.getAir_temperature().substring(1));
} else {
temperature = Integer.parseInt(ncdcWeather.getAir_temperature());
}
if (temperature != MISS_CODE && ncdcWeather.getAir_temperature_quality_code().matches("[01459]")) {
context.write(new Text(year), new IntWritable(temperature));
}
}
}
Mapper
public class MaxTemperatureReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
@Override
protected void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
int max = Integer.MIN_VALUE;
for (IntWritable temp : values) {
max = Math.max(max, temp.get());
}
context.write(key, new IntWritable(max));
}
}
Reducer
public class MaxTemperature {
public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
if (args.length != 2) {
System.err.println("Usage: MaxTemperature <input path> <output path>");
System.exit(-1);
}
Job job = Job.getInstance();
job.setJarByClass(MaxTemperature.class);
job.setJobName("Max Temperature");
FileInputFormat.addInputPath(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
job.setMapperClass(MaxTemperatureMapper.class);
job.setReducerClass(MaxTemperatureReducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}
数据准备
提前往hdfs中放了1901、1902两个年份的天气数据如下图所示：
验证执行
将以上代码打成一个jar，push到我们的虚拟机Hadoop环境中，为防止程序bug也为了节省执行时间，我们先在一个小集群、小数据量中验证代码。为此，我已经提前准备好了一个伪分布式（所谓为分布式即只有一个节点的全分布式）集群环境，下面开始在该环境中执行以上程序：
yarn jar ~/max-temperature-1.0-SNAPSHOT-jar-with-dependencies.jar /ncdc/ /max_temperature_out/
成功执行时的日志打印：
-- ::, INFO client.RMProxy: Connecting to ResourceManager at localhost/127.0.0.1:
-- ::, WARN mapreduce.JobResourceUploader: Hadoop command-line option parsing not performed. Implement the Tool interface and execute your application with ToolRunner to remedy this.
-- ::, INFO mapreduce.JobResourceUploader: Disabling Erasure Coding for path: /tmp/hadoop-yarn/staging/hadoopuser/.staging/job_1568096329203_0001
-- ::, INFO input.FileInputFormat: Total input files to process :
-- ::, INFO mapreduce.JobSubmitter: number of splits:
-- ::, INFO Configuration.deprecation: yarn.resourcemanager.system-metrics-publisher.enabled is deprecated. Instead, use yarn.system-metrics-publisher.enabled
-- ::, INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1568096329203_0001
-- ::, INFO mapreduce.JobSubmitter: Executing with tokens: []
-- ::, INFO conf.Configuration: resource-types.xml not found
-- ::, INFO resource.ResourceUtils: Unable to find 'resource-types.xml'.
-- ::, INFO impl.YarnClientImpl: Submitted application application_1568096329203_0001
-- ::, INFO mapreduce.Job: The url to track the job: http://slave1:8088/proxy/application_1568096329203_0001/
-- ::, INFO mapreduce.Job: Running job: job_1568096329203_0001
-- ::, INFO mapreduce.Job: Job job_1568096329203_0001 running in uber mode : false
-- ::, INFO mapreduce.Job: map % reduce %
-- ::, INFO mapreduce.Job: map % reduce %
-- ::, INFO mapreduce.Job: map % reduce %
-- ::, INFO mapreduce.Job: Job job_1568096329203_0001 completed successfully
-- ::, INFO mapreduce.Job: Counters:
File System Counters
FILE: Number of bytes read=
FILE: Number of bytes written=
FILE: Number of read operations=
FILE: Number of large read operations=
FILE: Number of write operations=
HDFS: Number of bytes read=
HDFS: Number of bytes written=
HDFS: Number of read operations=
HDFS: Number of large read operations=
HDFS: Number of write operations=
HDFS: Number of bytes read erasure-coded=
Job Counters
Launched map tasks=
Launched reduce tasks=
Data-local map tasks=
Total time spent by all maps in occupied slots (ms)=
Total time spent by all reduces in occupied slots (ms)=
Total time spent by all map tasks (ms)=
Total time spent by all reduce tasks (ms)=
Total vcore-milliseconds taken by all map tasks=
Total vcore-milliseconds taken by all reduce tasks=
Total megabyte-milliseconds taken by all map tasks=
Total megabyte-milliseconds taken by all reduce tasks=
Map-Reduce Framework
Map input records=
Map output records=
Map output bytes=
Map output materialized bytes=
Input split bytes=
Combine input records=
Combine output records=
Reduce input groups=
Reduce shuffle bytes=
Reduce input records=
Reduce output records=
Spilled Records=
Shuffled Maps =
Failed Shuffles=
Merged Map outputs=
GC time elapsed (ms)=
CPU time spent (ms)=
Physical memory (bytes) snapshot=
Virtual memory (bytes) snapshot=
Total committed heap usage (bytes)=
Peak Map Physical memory (bytes)=
Peak Map Virtual memory (bytes)=
Peak Reduce Physical memory (bytes)=
Peak Reduce Virtual memory (bytes)=
Shuffle Errors
BAD_ID=
CONNECTION=
IO_ERROR=
WRONG_LENGTH=
WRONG_MAP=
WRONG_REDUCE=
File Input Format Counters
Bytes Read=
File Output Format Counters
Bytes Written=
接下来逐行理解日志含义：
-- ::, INFO client.RMProxy: Connecting to ResourceManager at localhost/127.0.0.1:
#emm 我要先找到资源管理器 -- ::, WARN mapreduce.JobResourceUploader: Hadoop command-line option parsing not performed. Implement the Tool interface and execute your application with ToolRunner to remedy this.
#ohoh，代码里用的运行方式过时了，推荐使用继承Tool接口的方式来实现它 -- ::, INFO mapreduce.JobResourceUploader: Disabling Erasure Coding for path: /tmp/hadoop-yarn/staging/hadoopuser/.staging/job_1568096329203_0001
-- ::, INFO input.FileInputFormat: Total input files to process :
#输入文件数 2 -- ::, INFO mapreduce.JobSubmitter: number of splits:
#分片数2（与输入块数出奇的一致呢~） -- ::, INFO Configuration.deprecation: yarn.resourcemanager.system-metrics-publisher.enabled is deprecated. Instead, use yarn.system-metrics-publisher.enabled
-- ::, INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1568096329203_0001
#提交作业token，终于看到作业命名规则了，大概是job_<时间戳>_<4位数的递增序号> -- ::, INFO mapreduce.JobSubmitter: Executing with tokens: []
-- ::, INFO conf.Configuration: resource-types.xml not found
-- ::, INFO resource.ResourceUtils: Unable to find 'resource-types.xml'.
-- ::, INFO impl.YarnClientImpl: Submitted application application_1568096329203_0001
#似乎是提交作业到Yarn了 -- ::, INFO mapreduce.Job: The url to track the job: http://slave1:8088/proxy/application_1568096329203_0001/
#em，提供了一个追踪作业执行进度的url -- ::, INFO mapreduce.Job: Running job: job_1568096329203_0001
-- ::, INFO mapreduce.Job: Job job_1568096329203_0001 running in uber mode : false
#作业不满足uber作业的条件，将以非uber模式执行 -- ::, INFO mapreduce.Job: map % reduce %
-- ::, INFO mapreduce.Job: map % reduce %
-- ::, INFO mapreduce.Job: map % reduce %
-- ::, INFO mapreduce.Job: Job job_1568096329203_0001 completed successfully
#em 作业执行成功了 -- ::, INFO mapreduce.Job: Counters:
#下面是作业的一些计数器数据
File System Counters
FILE: Number of bytes read=
FILE: Number of bytes written=
FILE: Number of read operations=
FILE: Number of large read operations=
FILE: Number of write operations=
HDFS: Number of bytes read=
HDFS: Number of bytes written=
HDFS: Number of read operations=
HDFS: Number of large read operations=
HDFS: Number of write operations=
HDFS: Number of bytes read erasure-coded=
Job Counters
Launched map tasks=
Launched reduce tasks=
Data-local map tasks=
Total time spent by all maps in occupied slots (ms)=
Total time spent by all reduces in occupied slots (ms)=
Total time spent by all map tasks (ms)=
Total time spent by all reduce tasks (ms)=
Total vcore-milliseconds taken by all map tasks=
Total vcore-milliseconds taken by all reduce tasks=
Total megabyte-milliseconds taken by all map tasks=
Total megabyte-milliseconds taken by all reduce tasks=
Map-Reduce Framework
Map input records=
Map output records=
Map output bytes=
Map output materialized bytes=
Input split bytes=
Combine input records=
Combine output records=
Reduce input groups=
Reduce shuffle bytes=
Reduce input records=
Reduce output records=
Spilled Records=
Shuffled Maps =
Failed Shuffles=
Merged Map outputs=
GC time elapsed (ms)=
CPU time spent (ms)=
Physical memory (bytes) snapshot=
Virtual memory (bytes) snapshot=
Total committed heap usage (bytes)=
Peak Map Physical memory (bytes)=
Peak Map Virtual memory (bytes)=
Peak Reduce Physical memory (bytes)=
Peak Reduce Virtual memory (bytes)=
Shuffle Errors
BAD_ID=
CONNECTION=
IO_ERROR=
WRONG_LENGTH=
WRONG_MAP=
WRONG_REDUCE=
File Input Format Counters
Bytes Read=
File Output Format Counters
Bytes Written=
再看看我们指定的输出目录下到底输出了什么：
可以看到，输出目录包含两个文件，其中名为_SUCCESS的空文件标识作业执行成功，名为part-r-00000的文件记录Reduce任务的输出。查看part-r-00000文件内容：
可以看到，顺利得出了1901年和1902年的最高气温分别是317和244。
这里我们可以通过非mapreduce的方式去计算一下最高气温，以验证这个mapred程序的气温计算是否正确，但由于这不是我们的重点，而且程序逻辑比较简单，姑且认为至结果可信。
猜想
基于输入输入和程序执行过程中的日志打印，我们做如下猜想：
猜想1、分片数等于输入的块数
猜想2、计数器中File Input Format Counters等于读入的文件总字节数
猜想3、计数器中File Output Format Counters等于写入输出目录的总字节数
验证猜想
在搭建的分布式环境中运行以上代码，以验证猜想。分布式环境中准备好的输入文件如下：
即这个集群中准备了44个年份的数据文件，总大小为3137231401字节，分成了61个数据块。
下面开始提交执行作业：
yarn jar ~/max-temperature-1.0-SNAPSHOT-jar-with-dependencies.jar /ncdc/raw/ /max_temperature_out/
作业提交和执行并非一帆风顺，期间遇到过很多问题，比较典型的是java.net.NoRouteToHostException，对这个错误的解决过程记录在《Hadoop学习问题记录之基础篇》的问题一。
解决遇到的问题后，成功运行的日志打印如下：
[hadoop_user@master hadoop-3.2.]$ yarn jar ~/max-temperature-1.0-SNAPSHOT-jar-with-dependencies.jar /ncdc/raw/ /max_out
-- ::, INFO client.RMProxy: Connecting to ResourceManager at master/192.168.212.132:
-- ::, WARN mapreduce.JobResourceUploader: Hadoop command-line option parsing not performed. Implement the Tool interface and execute your application with ToolRunner to remedy this.
-- ::, INFO mapreduce.JobResourceUploader: Disabling Erasure Coding for path: /tmp/hadoop-yarn/staging/hadoop_user/.staging/job_1568605271327_0002
-- ::, INFO input.FileInputFormat: Total input files to process :
-- ::, INFO mapreduce.JobSubmitter: number of splits:
-- ::, INFO Configuration.deprecation: yarn.resourcemanager.system-metrics-publisher.enabled is deprecated. Instead, use yarn.system-metrics-publisher.enabled
-- ::, INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1568605271327_0002
-- ::, INFO mapreduce.JobSubmitter: Executing with tokens: []
-- ::, INFO conf.Configuration: resource-types.xml not found
-- ::, INFO resource.ResourceUtils: Unable to find 'resource-types.xml'.
-- ::, INFO impl.YarnClientImpl: Submitted application application_1568605271327_0002
-- ::, INFO mapreduce.Job: The url to track the job: http://master:8088/proxy/application_1568605271327_0002/
-- ::, INFO mapreduce.Job: Running job: job_1568605271327_0002
-- ::, INFO mapreduce.Job: Job job_1568605271327_0002 running in uber mode : false
-- ::, INFO mapreduce.Job: map % reduce %
-- ::, INFO mapreduce.Job: map % reduce %
-- ::, INFO mapreduce.Job: map % reduce %
-- ::, INFO mapreduce.Job: map % reduce %
-- ::, INFO mapreduce.Job: map % reduce %
-- ::, INFO mapreduce.Job: map % reduce %
-- ::, INFO mapreduce.Job: map % reduce %
-- ::, INFO mapreduce.Job: map % reduce %
-- ::, INFO mapreduce.Job: map % reduce %
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-- ::, INFO mapreduce.Job: map % reduce %
-- ::, INFO mapreduce.Job: map % reduce %
-- ::, INFO mapreduce.Job: Job job_1568605271327_0002 completed successfully
-- ::, INFO mapreduce.Job: Counters:
File System Counters
FILE: Number of bytes read=
FILE: Number of bytes written=
FILE: Number of read operations=
FILE: Number of large read operations=
FILE: Number of write operations=
HDFS: Number of bytes read=
HDFS: Number of bytes written=
HDFS: Number of read operations=
HDFS: Number of large read operations=
HDFS: Number of write operations=
HDFS: Number of bytes read erasure-coded=
Job Counters
Killed map tasks=
Launched map tasks=
Launched reduce tasks=
Data-local map tasks=
Rack-local map tasks=
Total time spent by all maps in occupied slots (ms)=
Total time spent by all reduces in occupied slots (ms)=
Total time spent by all map tasks (ms)=
Total time spent by all reduce tasks (ms)=
Total vcore-milliseconds taken by all map tasks=
Total vcore-milliseconds taken by all reduce tasks=
Total megabyte-milliseconds taken by all map tasks=
Total megabyte-milliseconds taken by all reduce tasks=
Map-Reduce Framework
Map input records=
Map output records=
Map output bytes=
Map output materialized bytes=
Input split bytes=
Combine input records=
Combine output records=
Reduce input groups=
Reduce shuffle bytes=
Reduce input records=
Reduce output records=
Spilled Records=
Shuffled Maps =
Failed Shuffles=
Merged Map outputs=
GC time elapsed (ms)=
CPU time spent (ms)=
Physical memory (bytes) snapshot=
Virtual memory (bytes) snapshot=
Total committed heap usage (bytes)=
Peak Map Physical memory (bytes)=
Peak Map Virtual memory (bytes)=
Peak Reduce Physical memory (bytes)=
Peak Reduce Virtual memory (bytes)=
Shuffle Errors
BAD_ID=
CONNECTION=
IO_ERROR=
WRONG_LENGTH=
WRONG_MAP=
WRONG_REDUCE=
File Input Format Counters
Bytes Read=
File Output Format Counters
Bytes Written=
通过日志可以得出
猜想1错误，输入分片数是58，并不等于输入文件的块数61。
猜想2错误，File Input Format Counters并不总是等于实际输入文件字节数，这里mapred任务统计的读入字节数为3137288745，略大于fsck工具检测出的字节数3137231401。
猜想3正确，File Output Format Counters等于实际输出的part-r-*的文件总字节数。
结论
- 分片数不会一定等于输入块数。
- File Input Format Conters并不总等于fsck工具检测出的输入文件总字节数。
- File Output Format Conters会等于实际输出的所有part-r-*文件总字节数
ps1：以上结论只是在处理文本输入的情况下得出的，对于其它类型的InputFormat是否会违背上述结论，这里先打个问号。
ps2：关于分片数的问题，将另起篇幅叙述，这里暂且不话。见这里。
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