HDFSEventSink用于把数据从channel中拿出来（主动pull的形式）然后放到hdfs中，HDFSEventSink在启动时会启动两个线程池callTimeoutPool 和timedRollerPool ，callTimeoutPool 用于运行append/flush等操作hdfs的task（通过callWithTimeout方法调用，并实现timeout功能），用于运行翻转文件的计划任务timedRollerPool：
callTimeoutPool = Executors.newFixedThreadPool(threadsPoolSize, new ThreadFactoryBuilder().setNameFormat(timeoutName).build()); timedRollerPool = Executors.newScheduledThreadPool(rollTimerPoolSize, new ThreadFactoryBuilder().setNameFormat(rollerName).build());
channel到sink的操作最终调用了sink的process方法（由SinkProcessor实现类调用），比如HDFSEventSink的process方法,每个process方法中都是一个事务，用来提供原子性操作，process方法调用Channel的take方法从Channel中取出Event，每个transaction中最多的Event数量由hdfs.batchSize设定，默认是100，对每一个Event有如下操作：
1.获取文件的完整路径和名称lookupPath2.声明一个BucketWriter对象和HDFSWriter 对象，HDFSWriter由hdfs.fileType设定，负责实际数据的写入，BucketWriter可以理解成对hdfs文件和写入方法的封装，每个lookupPath对应一个BucketWriter对象，对应关系写入到sfWriters中（这里sfWriters是一个WriterLinkedHashMap对象，WriterLinkedHashMap是LinkedHashMap的子类（private static class WriterLinkedHashMap extends LinkedHashMap<String, BucketWriter>），用来存放文件到BucketWriter的对应关系，在start方法中初始化：this.sfWriters = new WriterLinkedHashMap( maxOpenFiles);长度为hdfs.maxOpenFiles的设置，默认为5000，这个代表最多能打开的文件数量)3.调用BucketWriter的append方法写入数据4.当操作的Event数量达到hdfs.batchSize设定后，循环调用每个BucketWriter对象的flush方法，并提交transaction5.如果出现异常则回滚事务6.最后关闭transactionprocess方法最后返回的是代表Sink状态的Status对象（BACKOFF或者READY），这个可以用于判断Sink的健康状态，比如failover的SinkProcessor就根据这个来判断Sink是否可以提供服务主要方法分析：1.构造函数声明一个HDFSWriterFactory对象在后面会使用HDFSWriterFactory的getWriter方法会根据file类型返回对应的HDFSWriter实现类2.configure 1)通过configure方法会根据Context设置各种参数项比如：inUseSuffix = context.getString( "hdfs.inUseSuffix", defaultInUseSuffix ); //正在写入的文件的后缀名，默认为".tmp"rollInterval = context.getLong( "hdfs.rollInterval", defaultRollInterval ); //文件翻转时间，默认30rollSize = context.getLong( "hdfs.rollSize", defaultRollSize ); //文件翻转大小，默认1024rollCount = context.getLong( "hdfs.rollCount", defaultRollCount ); //默认为10batchSize = context.getLong( "hdfs.batchSize", defaultBatchSize ); //默认为100idleTimeout = context.getInteger( "hdfs.idleTimeout", 0); //默认为String codecName = context.getString( "hdfs.codeC"); //压缩格式fileType = context.getString( "hdfs.fileType", defaultFileType ); //默认为HDFSWriterFactory.SequenceFileType，即sequencefilemaxOpenFiles = context.getInteger( "hdfs.maxOpenFiles", defaultMaxOpenFiles ); //默认为5000callTimeout = context.getLong( "hdfs.callTimeout", defaultCallTimeout ); //BucketWriter超时时间，默认为10000threadsPoolSize = context.getInteger( "hdfs.threadsPoolSize", defaultThreadPoolSize); //操作append/open/close/flush任务的线程池大小，默认为10rollTimerPoolSize = context.getInteger( "hdfs.rollTimerPoolSize", defaultRollTimerPoolSize); //文件翻转计时器线程池大小，默认为1tryCount = context.getInteger( "hdfs.closeTries", defaultTryCount ); //尝试close文件的此数（大于0）retryInterval = context.getLong( "hdfs.retryInterval", defaultRetryInterval); //间隔时间（大于0）
2)获取压缩格式
if (codecName == null) { //如果hdfs.codeC没有设置 codeC = null; //则没有压缩功能 compType = CompressionType. NONE; } else { codeC = getCodec(codecName); //调用getCodec方法获取压缩格式 // TODO : set proper compression type compType = CompressionType. BLOCK; //压缩类型为BLOCK类型 } 3）hdfs文件翻转相关设置，在实例化BucketWriter对象时会用到
needRounding = context.getBoolean( "hdfs.round", false ); if(needRounding) { String unit = context.getString( "hdfs.roundUnit", "second" ); if (unit.equalsIgnoreCase( "hour")) { this.roundUnit = Calendar.HOUR_OF_DAY; } else if (unit.equalsIgnoreCase("minute" )) { this.roundUnit = Calendar.MINUTE; } else if (unit.equalsIgnoreCase("second" )){ this.roundUnit = Calendar.SECOND; } else { LOG.warn("Rounding unit is not valid, please set one of" + "minute, hour, or second. Rounding will be disabled" ); needRounding = false ; } this. roundValue = context.getInteger("hdfs.roundValue" , 1); if(roundUnit == Calendar. SECOND || roundUnit == Calendar.MINUTE){ Preconditions. checkArgument(roundValue > 0 && roundValue <= 60, "Round value" + "must be > 0 and <= 60"); } else if (roundUnit == Calendar.HOUR_OF_DAY){ Preconditions. checkArgument(roundValue > 0 && roundValue <= 24, "Round value" + "must be > 0 and <= 24"); } } 4）最后初始化一个SinkCounter对象用来记录sink的性能数据
if (sinkCounter == null) { sinkCounter = new SinkCounter(getName()); } 3.start方法用来启动线程池等
public void start() { String timeoutName = "hdfs-" + getName() + "-call-runner-%d" ; callTimeoutPool = Executors. newFixedThreadPool(threadsPoolSize, new ThreadFactoryBuilder().setNameFormat(timeoutName).build()); String rollerName = "hdfs-" + getName() + "-roll-timer-%d" ; timedRollerPool = Executors. newScheduledThreadPool(rollTimerPoolSize, new ThreadFactoryBuilder().setNameFormat(rollerName).build()); this. sfWriters = new WriterLinkedHashMap(maxOpenFiles); //初始化WriterLinkedHashMap对象 sinkCounter.start(); super.start(); } 4.process方法，从channel中pull出数据并发送到hdfs中（每一个transaction中最多可以有batchSize条Event），获取对应的bucket，序列化数据并写入hdfs文件
public Status process() throws EventDeliveryException { Channel channel = getChannel(); //获取对应的channel Transaction transaction = channel.getTransaction(); //获取Transaction 对象，提供事务功能 List writers = Lists. newArrayList(); transaction.begin(); //事务开始 try { int txnEventCount = 0; for (txnEventCount = 0; txnEventCount < batchSize; txnEventCount++) {//这里一个transaction存放的数据最多由hdfs.batchSize指定 Event event = channel.take(); //循环调用Channel的take方法获取Event if (event == null) { break; } // reconstruct the path name by substituting place holders String realPath = BucketPath. escapeString(filePath, event.getHeaders(), timeZone, needRounding, roundUnit , roundValue ); //设置文件路径 String realName = BucketPath. escapeString(fileName, event.getHeaders(), timeZone, needRounding, roundUnit , roundValue ); //设置文件名称 String lookupPath = realPath + DIRECTORY_DELIMITER + realName; //完整的文件名称 BucketWriter bucketWriter = sfWriters.get(lookupPath); //根据文件获取对应的BucketWriter对象 // we haven't seen this file yet, so open it and cache the handle if (bucketWriter == null) { HDFSWriter hdfsWriter = writerFactory.getWriter(fileType ); //根据文件类型获取HDFSWriter 对象 WriterCallback idleCallback = null; if(idleTimeout != 0) { idleCallback = new WriterCallback() { @Override public void run(String bucketPath) { sfWriters.remove(bucketPath); //回调方法 } }; } bucketWriter = new BucketWriter(rollInterval , rollSize , rollCount , batchSize, context , realPath, realName, inUsePrefix, inUseSuffix, suffix, codeC, compType, hdfsWriter, timedRollerPool, proxyTicket, sinkCounter , idleTimeout , idleCallback, lookupPath); //实例化BucketWriter sfWriters.put(lookupPath, bucketWriter); //这里sfWriters是一个WriterLinkedHashMap对象，WriterLinkedHashMap是LinkedHashMap的子类，用来存放文件到BucketWriter的对应关系，在start方法中初始化：this .sfWriters = new WriterLinkedHashMap(maxOpenFiles);大小为hdfs.maxOpenFiles的设置，默认为5000 } // track the buckets getting written in this transaction if (!writers.contains(bucketWriter)) { //List writers = Lists.newArrayList(); writers.add(bucketWriter); } // Write the data to HDFS append(bucketWriter, event); //调用append方法写入Event数据 } if (txnEventCount == 0) { sinkCounter.incrementBatchEmptyCount(); } else if (txnEventCount == batchSize ) { sinkCounter.incrementBatchCompleteCount(); } else { sinkCounter.incrementBatchUnderflowCount(); } // flush all pending buckets before committing the transaction for (BucketWriter bucketWriter : writers) { flush(bucketWriter); //调用flush方法 } transaction.commit(); //事务提交 if (txnEventCount < 1) { return Status.BACKOFF ; } else { sinkCounter.addToEventDrainSuccessCount(txnEventCount); return Status.READY ; } } catch (IOException eIO) { //如果异常则回滚事务 transaction.rollback(); LOG.warn( "HDFS IO error", eIO); return Status. BACKOFF; } catch (Throwable th) { transaction.rollback(); LOG.error( "process failed", th); if (th instanceof Error) { throw (Error) th; } else { throw new EventDeliveryException(th); } } finally { transaction.close(); } } 5.同时定义了几个操作BucketWriter的方法append，flush，close
1） private void append(final BucketWriter bucketWriter, final Event event) throws IOException, InterruptedException { // Write the data to HDFS callWithTimeout(new Callable () { //注意这里使用callWithTimeout提供了调用的超时功能 public Void call() throws Exception { bucketWriter.append(event); //调用BucketWriter.append方法写入Event数据 return null; } }); }2）flush-->BucketWriter.flush()3) close-->BucketWriter.close()