spark streaming 实现根据文件内容自定义文件名，并实现文件内容追加

spark streaming 从kafka拉取数据，根绝消息内容，需要将不容的消息放到不同的文件夹下，大致内容为 从消息中拆分出域名，不同域名分不到不同目录，域名下按消息中的时间分年月日目录，底层目录下自定义文件个数，实现追加
由于sparkstreaming 10秒运行一次job，需要重写 OutputFormat，来实现按内容分目录，文件追加

val lines = KafkaUtils.createStream(ssc, zkQuorum, group, topicMap).map(_._2)

val line = lines.map(x => (x, 1)).repartition(20)  

line.saveAsHadoopFiles("","",classOf[Text],classOf[NullWritable],classOf[MyMultipleTextOutputFormat[Text,NullWritable]])

MyMultipleTextOutputFormat 即为我们重写的类

package com.linkingcloud.bigdata.common;

import com.linkingcloud.bigdata.common.interpret.LineInterpret;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.mapred.*;
import org.apache.hadoop.mapred.lib.MultipleTextOutputFormat;
import org.apache.hadoop.util.Progressable;

import org.apache.hadoop.mapred.RecordWriter;

import java.io.IOException;
import java.util.Iterator;
import java.util.Random;
import java.util.TreeMap;

/**
 * Created by apple on 2017/2/15.
 */
public class MyMultipleTextOutputFormat<K, V> extends MultipleTextOutputFormat<K, V> {

private TextOutputFormat<K, V> theTextOutputFormat = null;

    public RecordWriter getRecordWriter(final FileSystem fs, final JobConf job, final String name, final Progressable arg3) throws IOException {

return new RecordWriter() {

            TreeMap<String, RecordWriter<K, V>> recordWriters = new TreeMap();

            public void write(Object key, Object value) throws IOException {

//key中为消息内容，value无意义
                String line = key.toString();

//根据消息内容，定义输出路径和输出内容（同时清洗数据）
                String[] ss = LineInterpret.interpretLine(line, "/test/spark/kafka");

if (ss != null && ss.length == 2) {

//name的最后两位为jobid，同一个文件只能同时允许一个job写入，多个job写一个文件会报错，将jobid作为文件名的一部分
//能解决此问题
                    String finalPath = ss[1] + "-" + name.substring(name.length() - 2);

                    RecordWriter rw = (RecordWriter) this.recordWriters.get(finalPath);

try {

if (rw == null) {

                            rw = getBaseRecordWriter(fs, job, finalPath, arg3);

this.recordWriters.put(finalPath, rw);
                        }

                        rw.write(ss[0], null);

                    } catch (Exception e) {
//一个周期内，job不能完成，下一个job启动，会造成同时写一个文件的情况，变更文件名，添加后缀
this.rewrite(finalPath + "-", ss[0]);
                    }
                }
            }

            public void rewrite(String path, String line) {

                String finalPath = path + new Random().nextInt(10);

                RecordWriter rw = (RecordWriter) this.recordWriters.get(finalPath);

try {

if (rw == null) {

                        rw = getBaseRecordWriter(fs, job, finalPath, arg3);

this.recordWriters.put(finalPath, rw);
                    }

                    rw.write(line, null);

                } catch (Exception e) {
//重试
this.rewrite(finalPath, line);
                }
            }

            public void close(Reporter reporter) throws IOException {
                Iterator keys = this.recordWriters.keySet().iterator();

while (keys.hasNext()) {
                    RecordWriter rw = (RecordWriter) this.recordWriters.get(keys.next());
                    rw.close(reporter);
                }

this.recordWriters.clear();
            }
        };
    }


protected RecordWriter<K, V> getBaseRecordWriter(FileSystem fs, JobConf job, String path, Progressable arg3) throws IOException {
if (this.theTextOutputFormat == null) {
this.theTextOutputFormat = new MyTextOutputFormat();
        }

return this.theTextOutputFormat.getRecordWriter(fs, job, path, arg3);
    }
}

MyTextOutputFormat中实现对存在的文件进行append，不覆盖

package com.linkingcloud.bigdata.common;

import org.apache.hadoop.fs.FSDataOutputStream;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.compress.CompressionCodec;
import org.apache.hadoop.io.compress.GzipCodec;
import org.apache.hadoop.mapred.FileOutputFormat;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapred.RecordWriter;
import org.apache.hadoop.mapred.TextOutputFormat;
import org.apache.hadoop.util.Progressable;
import org.apache.hadoop.util.ReflectionUtils;

import java.io.DataOutputStream;
import java.io.IOException;

/**
 * Created by apple on 2017/2/15.
 */

public class MyTextOutputFormat<K, V> extends TextOutputFormat<K, V> {

    public MyTextOutputFormat() {
    }

    @Override
    public RecordWriter<K, V> getRecordWriter(FileSystem ignored, JobConf job, String path, Progressable progress) throws IOException {

        String keyValueSeparator = job.get("mapreduce.output.textoutputformat.separator", "\t");

        CompressionCodec codec = ReflectionUtils.newInstance(GzipCodec.class, job);

        Path file = FileOutputFormat.getTaskOutputPath(job, path + codec.getDefaultExtension());

        FileSystem fs = file.getFileSystem(job);

        String file_path = path + codec.getDefaultExtension();

        Path newFile = new Path(FileOutputFormat.getOutputPath(job), file_path);

        FSDataOutputStream fileOut;

        if (fs.exists(newFile)) {
            fileOut = fs.append(newFile,4096,progress);
        } else {
            fileOut = fs.create(newFile, progress);
        }
        return new TextOutputFormat.LineRecordWriter(new DataOutputStream(codec.createOutputStream(fileOut)), keyValueSeparator);

    }
}

结果如下：

spark streaming 采用gzip压缩，会导致derect memory泄露，暂时没有找到好的解决方法，只能不使用压缩，谁解决了此问题，可以留言，感谢！