本文主要是介绍Hadoop入门之自定义groupingcomparator和outputformat的使用，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
自定义outputformat输出demo类:
/*** maptask或者reducetask在最终输出时，先调用OutputFormat的getRecordWriter方法拿到一个RecordWriter* 然后再调用RecordWriter的write(k,v)方法将数据写出* * @author* */
public class LogEnhanceOutputFormat extends FileOutputFormat<Text, NullWritable> {@Overridepublic RecordWriter<Text, NullWritable> getRecordWriter(TaskAttemptContext context) throws IOException, InterruptedException {FileSystem fs = FileSystem.get(context.getConfiguration());Path enhancePath = new Path("D:/temp/en/log.dat");Path tocrawlPath = new Path("D:/temp/crw/url.dat");FSDataOutputStream enhancedOs = fs.create(enhancePath);FSDataOutputStream tocrawlOs = fs.create(tocrawlPath);return new EnhanceRecordWriter(enhancedOs, tocrawlOs);}/*** 构造一个自己的recordwriter* * @author* */static class EnhanceRecordWriter extends RecordWriter<Text, NullWritable> {FSDataOutputStream enhancedOs = null;FSDataOutputStream tocrawlOs = null;public EnhanceRecordWriter(FSDataOutputStream enhancedOs, FSDataOutputStream tocrawlOs) {super();this.enhancedOs = enhancedOs;this.tocrawlOs = tocrawlOs;}@Overridepublic void write(Text key, NullWritable value) throws IOException, InterruptedException {String result = key.toString();// 如果要写出的数据是待爬的url，则写入待爬清单文件 /logenhance/tocrawl/url.datif (result.contains("tocrawl")) {tocrawlOs.write(result.getBytes());} else {// 如果要写出的数据是增强日志，则写入增强日志文件 /logenhance/enhancedlog/log.datenhancedOs.write(result.getBytes());}}@Overridepublic void close(TaskAttemptContext context) throws IOException, InterruptedException {if (tocrawlOs != null) {tocrawlOs.close();}if (enhancedOs != null) {enhancedOs.close();}}}} job.setOutputFormatClass(LogEnhanceOutputFormat.class);自定义groupingcomparator的使用Demo:
有如下订单数据
| 订单id | 商品id | 成交金额 |
| Order_0000001 | Pdt_01 | 222.8 |
| Order_0000001 | Pdt_05 | 25.8 |
| Order_0000002 | Pdt_03 | 522.8 |
| Order_0000002 | Pdt_04 | 122.4 |
| Order_0000002 | Pdt_05 | 722.4 |
| Order_0000003 | Pdt_01 | 222.8 |
现在需要求出每一个订单中成交金额最大的一笔交易
1、利用“订单id和成交金额”作为key，可以将map阶段读取到的所有订单数据按照id分区，按照金额排序，发送到reduce
2、在reduce端利用groupingcomparator将订单id相同的kv聚合成组，然后取第一个即是最大值
自定义groupingcomparator
/*** 用于控制shuffle过程中reduce端对kv对的聚合逻辑* @author duanhaitao@itcast.cn**/
public class ItemidGroupingComparator extends WritableComparator {protected ItemidGroupingComparator() {super(OrderBean.class, true);}@Overridepublic int compare(WritableComparable a, WritableComparable b) {OrderBean abean = (OrderBean) a;OrderBean bbean = (OrderBean) b;//将item_id相同的bean都视为相同，从而聚合为一组return abean.getItemid().compareTo(bbean.getItemid());}
}/*** 订单信息bean，实现hadoop的序列化机制* @author duanhaitao@itcast.cn**/
public class OrderBean implements WritableComparable<OrderBean>{private Text itemid;private DoubleWritable amount;public OrderBean() {}public OrderBean(Text itemid, DoubleWritable amount) {set(itemid, amount);}public void set(Text itemid, DoubleWritable amount) {this.itemid = itemid;this.amount = amount;}public Text getItemid() {return itemid;}public DoubleWritable getAmount() {return amount;}
@Overridepublic int compareTo(OrderBean o) {int cmp = this.itemid.compareTo(o.getItemid());if (cmp == 0) {cmp = -this.amount.compareTo(o.getAmount());}return cmp;}@Overridepublic void write(DataOutput out) throws IOException {out.writeUTF(itemid.toString());out.writeDouble(amount.get());}@Overridepublic void readFields(DataInput in) throws IOException {String readUTF = in.readUTF();double readDouble = in.readDouble();this.itemid = new Text(readUTF);this.amount= new DoubleWritable(readDouble);}@Overridepublic String toString() {return itemid.toString() + "\t" + amount.get();}
}
/*** 利用secondarysort机制输出每种item订单金额最大的记录* @author duanhaitao@itcast.cn**/
public class SecondarySort {static class SecondarySortMapper extends Mapper<LongWritable, Text, OrderBean, NullWritable>{
OrderBean bean = new OrderBean();@Overrideprotected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {String line = value.toString();String[] fields = StringUtils.split(line, "\t");bean.set(new Text(fields[0]), new DoubleWritable(Double.parseDouble(fields[1])));context.write(bean, NullWritable.get());}}static class SecondarySortReducer extends Reducer<OrderBean, NullWritable, OrderBean, NullWritable>{//在设置了groupingcomparator以后，这里收到的kv数据 就是： <1001 87.6>,null <1001 76.5>,null .... //此时，reduce方法中的参数key就是上述kv组中的第一个kv的key：<1001 87.6>//要输出同一个item的所有订单中最大金额的那一个，就只要输出这个key@Overrideprotected void reduce(OrderBean key, Iterable<NullWritable> values, Context context) throws IOException, InterruptedException {context.write(key, NullWritable.get());}}public static void main(String[] args) throws Exception {Configuration conf = new Configuration();Job job = Job.getInstance(conf);job.setJarByClass(SecondarySort.class);job.setMapperClass(SecondarySortMapper.class);job.setReducerClass(SecondarySortReducer.class);job.setOutputKeyClass(OrderBean.class);job.setOutputValueClass(NullWritable.class);FileInputFormat.setInputPaths(job, new Path(args[0]));FileOutputFormat.setOutputPath(job, new Path(args[1]));//指定shuffle所使用的GroupingComparator类job.setGroupingComparatorClass(ItemidGroupingComparator.class);//指定shuffle所使用的partitioner类job.setPartitionerClass(ItemIdPartitioner.class);job.setNumReduceTasks(3);job.waitForCompletion(true);}}
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