本文為您介紹MapReduce的Pipeline樣本。
測試準備
- 準備好測試程式的JAR包,假設名字為mapreduce-examples.jar,本地存放路徑為data\resources。
- 準備好Pipeline的測試表和資源。
- 建立測試表。
create table wc_in (key string, value string); create table wc_out(key string, cnt bigint);
- 添加測試資源。
add jar data\resources\mapreduce-examples.jar -f;
- 建立測試表。
- 使用Tunnel匯入資料。
tunnel upload data wc_in;
匯入wc_in表的資料檔案data的內容。hello,odps
測試步驟
在MaxCompute用戶端中執行WordCountPipeline。
jar -resources mapreduce-examples.jar -classpath data\resources\mapreduce-examples.jar
com.aliyun.odps.mapred.open.example.WordCountPipeline wc_in wc_out;
預期結果
作業成功結束後,輸出表wc_out中的內容如下。
+------------+------------+
| key | cnt |
+------------+------------+
| hello | 1 |
| odps | 1 |
+------------+------------+
程式碼範例
package com.aliyun.odps.mapred.open.example;
import java.io.IOException;
import java.util.Iterator;
import com.aliyun.odps.Column;
import com.aliyun.odps.OdpsException;
import com.aliyun.odps.OdpsType;
import com.aliyun.odps.data.Record;
import com.aliyun.odps.data.TableInfo;
import com.aliyun.odps.mapred.Job;
import com.aliyun.odps.mapred.MapperBase;
import com.aliyun.odps.mapred.ReducerBase;
import com.aliyun.odps.pipeline.Pipeline;
public class WordCountPipelineTest {
public static class TokenizerMapper extends MapperBase {
Record word;
Record one;
@Override
public void setup(TaskContext context) throws IOException {
word = context.createMapOutputKeyRecord();
one = context.createMapOutputValueRecord();
one.setBigint(0, 1L);
}
@Override
public void map(long recordNum, Record record, TaskContext context)
throws IOException {
for (int i = 0; i < record.getColumnCount(); i++) {
String[] words = record.get(i).toString().split("\\s+");
for (String w : words) {
word.setString(0, w);
context.write(word, one);
}
}
}
}
public static class SumReducer extends ReducerBase {
private Record value;
@Override
public void setup(TaskContext context) throws IOException {
value = context.createOutputValueRecord();
}
@Override
public void reduce(Record key, Iterator<Record> values, TaskContext context)
throws IOException {
long count = 0;
while (values.hasNext()) {
Record val = values.next();
count += (Long) val.get(0);
}
value.set(0, count);
context.write(key, value);
}
}
public static class IdentityReducer extends ReducerBase {
private Record result;
@Override
public void setup(TaskContext context) throws IOException {
result = context.createOutputRecord();
}
@Override
public void reduce(Record key, Iterator<Record> values, TaskContext context)
throws IOException {
while (values.hasNext()) {
result.set(0, key.get(0));
result.set(1, values.next().get(0));
context.write(result);
}
}
}
public static void main(String[] args) throws OdpsException {
if (args.length != 2) {
System.err.println("Usage: WordCountPipeline <in_table> <out_table>");
System.exit(2);
}
Job job = new Job();
/**構造Pipeline的過程中,如果不指定Mapper的OutputKeySortColumns、PartitionColumns、OutputGroupingColumns,架構會預設使用其OutputKey作為此三者的預設配置。
*/
Pipeline pipeline = Pipeline.builder()
.addMapper(TokenizerMapper.class)
.setOutputKeySchema(
new Column[] { new Column("word", OdpsType.STRING) })
.setOutputValueSchema(
new Column[] { new Column("count", OdpsType.BIGINT) })
.setOutputKeySortColumns(new String[] { "word" })
.setPartitionColumns(new String[] { "word" })
.setOutputGroupingColumns(new String[] { "word" })
.addReducer(SumReducer.class)
.setOutputKeySchema(
new Column[] { new Column("word", OdpsType.STRING) })
.setOutputValueSchema(
new Column[] { new Column("count", OdpsType.BIGINT)})
.addReducer(IdentityReducer.class).createPipeline();
/**將pipeline的設定到jobconf中,如果需要設定combiner,是通過jobconf來設定。*/
job.setPipeline(pipeline);
/**設定輸入輸出表。*/
job.addInput(TableInfo.builder().tableName(args[0]).build());
job.addOutput(TableInfo.builder().tableName(args[1]).build());
/**作業提交並等待結束。*/
job.submit();
job.waitForCompletion();
System.exit(job.isSuccessful() == true ? 0 : 1);
}
}