粉丝：什么情况下，hive 只会产生一个reduce任务，而没有maptask-技术圈

今天下午，在微信群里看到粉丝聊天，提到了一个某公司的面试题：

什么情况下，hive 只会产生一个reduce任务，而没有maptask

这个问题是不是很神奇？

我们常规使用的mapreducer任务执行过程大致如下图：

appmaster通过某种策略计算数据源可以做多少分片（getSplits方法），对应的生成固定数量的maptask，假如存在shuffle的话，就根据默认或者指定的reducer数，将数据分散给特定数量的reducer。不存在shuffle的话，reducer可以为零的。

正常逻辑：

mapTask的职责就是负责读数据，做ETL，也可以利用combiner局部聚合；ReduceTask输入严重依赖于mapper输出，所以‘一直’的逻辑仅有reducer无法执行的。

没有maptask，仅有一个reducerTask的hive任务。有点违背我们的使用常识了哦。

其实，正常使用的情况下，hive的sql模式执行引擎还是主要依赖于hadoop的mapreduce计算框架。

但是仅仅依赖于sql，然后依赖于hadoop的mapreduce，显然不能满足hive的野心的。所以hive想提供一个单独的可以使用java编写的hive自己的map/reduce的框架。

你在hive搜索reducer可以直接找到hive自己的reducer。

hql常规解析后依赖的mapreduce主要还是ExecReducer/ExecMapper--通过实现hadoop的mapper和reducer来实现具体执行逻辑。

hive自己的mapreducer有一个实现案例，就是GenericMR，实现源码如下：

public final class GenericMR {  public void map(final InputStream in, final OutputStream out,      final Mapper mapper) throws Exception {    map(new InputStreamReader(in), new OutputStreamWriter(out), mapper);  }
  public void map(final Reader in, final Writer out, final Mapper mapper) throws Exception {    handle(in, out, new RecordProcessor() {      @Override      public void processNext(RecordReader reader, Output output) throws Exception {        mapper.map(reader.next(), output);      }    });  }
  public void reduce(final InputStream in, final OutputStream out,      final Reducer reducer) throws Exception {    reduce(new InputStreamReader(in), new OutputStreamWriter(out), reducer);  }
  public void reduce(final Reader in, final Writer out, final Reducer reducer) throws Exception {    handle(in, out, new RecordProcessor() {      @Override      public void processNext(RecordReader reader, Output output) throws Exception {        reducer.reduce(reader.peek()[0], new KeyRecordIterator(            reader.peek()[0], reader), output);      }    });  }
  private void handle(final Reader in, final Writer out,      final RecordProcessor processor) throws Exception {    final RecordReader reader = new RecordReader(in);    final OutputStreamOutput output = new OutputStreamOutput(out);
    try {      while (reader.hasNext()) {        processor.processNext(reader, output);      }    } finally {      try {        output.close();      } finally {        reader.close();      }    }  }
  private static interface RecordProcessor {    void processNext(final RecordReader reader, final Output output) throws Exception;  }
  private static final class KeyRecordIterator implements Iterator<String[]> {    private final String key;    private final RecordReader reader;
    private KeyRecordIterator(final String key, final RecordReader reader) {      this.key = key;      this.reader = reader;    }
    @Override    public boolean hasNext() {      return (reader.hasNext() && key.equals(reader.peek()[0]));    }
    @Override    public String[] next() {      if (!hasNext()) {        throw new NoSuchElementException();      }
      return reader.next();    }
    @Override    public void remove() {      throw new UnsupportedOperationException();    }  }
  private static final class RecordReader {    private final BufferedReader reader;    private String[] next;
    private RecordReader(final InputStream in) {      this(new InputStreamReader(in));    }
    private RecordReader(final Reader in) {      reader = new BufferedReader(in);      next = readNext();    }
    private String[] next() {      final String[] ret = next;
      next = readNext();
      return ret;    }
    private String[] readNext() {      try {        final String line = reader.readLine();        return (line == null ? null : line.split("\t"));      } catch (final Exception e) {        throw new RuntimeException(e);      }    }
    private boolean hasNext() {      return next != null;    }
    private String[] peek() {      return next;    }
    private void close() throws Exception {      reader.close();          }  }
  private static final class OutputStreamOutput implements Output {    private final PrintWriter out;
    private OutputStreamOutput(final OutputStream out) {      this(new OutputStreamWriter(out));    }
    private OutputStreamOutput(final Writer out) {      this.out = new PrintWriter(out);    }
    public void close() throws Exception {      out.close();    }
    @Override    public void collect(String[] record) throws Exception {      out.println(_join(record, "\t"));    }
    private static String _join(final String[] record, final String separator) {      if (record == null || record.length == 0) {        return "";      }      final StringBuilder sb = new StringBuilder();      for (int i = 0; i < record.length; i++) {        if (i > 0) {          sb.append(separator);        }        sb.append(record[i]);      }      return sb.toString();    }  }}

重点：

常规的mapreducer的reducer输入依赖于mapper的输出的，所以无法单独执行。但是GenericMR的实现reducer里可以直接支持InputStream/Reader，所以就可以直接生成java的指定输入流或者reader即可。

new GenericMR().reduce(new StringReader("a\tb\tc"), new StringWriter(),          new Reducer() {        public void reduce(String key, Iterator<String[]> records,            Output output) throws Exception {          while (true) {            records.next();          }        }});

这个问题确实很另类，浪尖估计是被面试者简历写了精通hive源码，才被大佬由此一问。要不正常使用者，不会注意到这个框架。

不过这个也给大家提个醒，关注框架使用的同时，也要关注框架的历史及发展。

总结一下就是：hive野心很大，不想仅仅限于hql，想提供一个单独的可以用java编写的hive自己的map/reduce计算框架。

欢迎关注浪尖，留言更多比较有趣的问题哦！