datax 原理解析和性能优化
datax简介
datax是阿里开源的用于异构数据源之间的同步工具，由于其精巧的设计和抽象，数据同步效率极高，在很多公司数据部门都有广泛的使用。本司基于datax在阿里云普通版的rds服务器上实现了通过公网，从阿里云杭州到美国西部俄勒冈aws emr集群峰值30M以上带宽的传输效率。全量传输上亿条记录、大小30G的数据，最快不到30分钟。要知道如果拉跨洋专线的话，1M带宽每个月至少需要1千大洋呢。走公网照样能达到类似的稳定性，本文通过原理设计来阐述我们是如何基于datax做到的。
datax工作原理
在讲解datax原理之前，需要明确一些概念：
Job: Job是DataX用以描述从一个源头到一个目的端的同步作业，是DataX数据同步的最小业务单元。比如：从一张mysql的表同步到hive的一个表的特定分区。
Task: Task是为最大化而把Job拆分得到的最小执行单元。比如：读一张有1024个分表的mysql分库分表的Job，拆分成1024个读Task，若干个任务并发执行。或者将一个大表按照id拆分成1024个分片，若干个分片任务并发执行。
TaskGroup: 描述的是一组Task集合。在同一个TaskGroupContainer执行下的Task集合称之为TaskGroup。
JobContainer: Job执行器，负责Job全局拆分、调度、前置语句和后置语句等工作的工作单元。
TaskGroupContainer: TaskGroup执行器，负责执行一组Task的工作单元。
job和task是datax两种维度的抽象，后面源码分析中还会涉及到。
datax的处理过程可描述为：
DataX完成单个数据同步的作业，我们称之为Job，DataX接受到一个Job之后，将启动一个进程来完成整个作业同步过程。DataX Job模块是单个作业的中枢管理节点，承担了数据清理、子任务切分(将单一作业计算转化为多个子Task)、TaskGroup管理等功能。
DataXJob启动后，会根据不同的源端切分策略，将Job切分成多个小的Task(子任务)，以便于并发执行。Task便是DataX作业的最小单元，每一个Task都会负责一部分数据的同步工作。
切分多个Task之后，DataX Job会调用Scheduler模块，根据配置的并发数据量，将拆分成的Task重新组合，组装成TaskGroup(任务组)。每一个TaskGroup负责以一定的并发运行完毕分配好的所有Task，默认单个任务组的并发数量为5。
每一个Task都由TaskGroup负责启动，Task启动后，会固定启动Reader—>Channel—>Writer的线程来完成任务同步工作。
DataX作业运行起来之后， Job监控并等待多个TaskGroup模块任务完成，等待所有TaskGroup任务完成后Job成功退出。否则，异常退出，进程退出值非0。
上述流程可图像化描述为：
其中Channel是连接Reader和Writer的数据交换通道，所有的数据都会经由Channel进行传输，一个channel代表一个并发传输通道，通过该通道实现传输速率控制。接下来我们通过源码的角度，在抽取其核心逻辑，以mysql到hdfs的传输为例分析其工作流程。通过分析源码将会有以下几点收获：
datax 工作流程
datax 插件机制
datax 同步实现
datax源码分析
datax 工作流程
public class Engine {
private static final Logger LOG = LoggerFactory.getLogger(Engine.class);
private static String RUNTIME_MODE;
public void start(Configuration allConf) {
boolean isJob = !("taskGroup".equalsIgnoreCase(allConf.getString(CoreConstant.DATAX_CORE_CONTAINER_MODEL)));
//JobContainer会在schedule后再行进行设置和调整值
int channelNumber =0;
AbstractContainer container;
long instanceId;
int taskGroupId = -1;
if (isJob) {
allConf.set(CoreConstant.DATAX_CORE_CONTAINER_JOB_MODE, RUNTIME_MODE);
container = new JobContainer(allConf);
instanceId = allConf.getLong(
CoreConstant.DATAX_CORE_CONTAINER_JOB_ID, 0);
} else {
container = new TaskGroupContainer(allConf);
instanceId = allConf.getLong(
CoreConstant.DATAX_CORE_CONTAINER_JOB_ID);
taskGroupId = allConf.getInt(
CoreConstant.DATAX_CORE_CONTAINER_TASKGROUP_ID);
channelNumber = allConf.getInt(
CoreConstant.DATAX_CORE_CONTAINER_TASKGROUP_CHANNEL);
}
container.start();
}
job实例运行在jobContainer容器中，它是所有任务的master，负责初始化、拆分、调度、运行、回收、监控和汇报，但它并不做实际的数据同步操作
public class JobContainer extends AbstractContainer {
private static final Logger LOG = LoggerFactory
.getLogger(JobContainer.class);
public JobContainer(Configuration configuration) {
super(configuration);
}
/**
* jobContainer主要负责的工作全部在start()里面，包括init、prepare、split、scheduler以及destroy和statistics
*/
@Override
public void start() {
LOG.info("DataX jobContainer starts job.");
try{
userConf = configuration.clone();
this.init();
this.prepare();
this.totalStage = this.split();
this.schedule();
} catch (Throwable e) {
Communication communication = super.getContainerCommunicator().collect();
// 汇报前的状态，不需要手动进行设置
// communication.setState(State.FAILED);
communication.setThrowable(e);
communication.setTimestamp(this.endTimeStamp);
Communication tempComm = new Communication();
tempComm.setTimestamp(this.startTransferTimeStamp);
Communication reportCommunication = CommunicationTool.getReportCommunication(communication, tempComm, this.totalStage);
super.getContainerCommunicator().report(reportCommunication);
throw DataXException.asDataXException(
FrameworkErrorCode.RUNTIME_ERROR, e);
}
}
/**
* reader和writer的初始化
*/
private void init() {
Thread.currentThread().setName("job-" + this.jobId);
JobPluginCollector jobPluginCollector = new DefaultJobPluginCollector(
this.getContainerCommunicator());
//必须先Reader ，后Writer
this.jobReader = this.initJobReader(jobPluginCollector);
this.jobWriter = this.initJobWriter(jobPluginCollector);
}
/**
*schedule首先完成的工作是把上一步reader和writer split的结果整合到具体taskGroupContainer中,
* 同时不同的执行模式调用不同的调度策略，将所有任务调度起来
*/
private void schedule() {
/**
* 这里的全局speed和每个channel的速度设置为B/s
*/
int channelsPerTaskGroup = this.configuration.getInt(
CoreConstant.DATAX_CORE_CONTAINER_TASKGROUP_CHANNEL, 5);
int taskNumber = this.configuration.getList(
CoreConstant.DATAX_JOB_CONTENT).size();
this.needChannelNumber = Math.min(this.needChannelNumber, taskNumber);
/**
* 通过获取配置信息得到每个taskGroup需要运行哪些tasks任务。
会考虑 task 中对资源负载作的 load 标识进行更均衡的作业分配操作。
*/
List<Configuration> taskGroupConfigs = JobAssignUtil.assignFairly(this.configuration,
this.needChannelNumber, channelsPerTaskGroup);
LOG.info("Scheduler starts [{}] taskGroups.", taskGroupConfigs.size());
AbstractScheduler scheduler;
try {
scheduler = initStandaloneScheduler(this.configuration);
this.startTransferTimeStamp = System.currentTimeMillis();
scheduler.schedule(taskGroupConfigs);
this.endTransferTimeStamp = System.currentTimeMillis();
} catch (Exception e) {
LOG.error("运行scheduler出错.");
this.endTransferTimeStamp = System.currentTimeMillis();
throw DataXException.asDataXException(
FrameworkErrorCode.RUNTIME_ERROR, e);
}
}
private AbstractScheduler initStandaloneScheduler(Configuration configuration) {
AbstractContainerCommunicator containerCommunicator = new StandAloneJobContainerCommunicator(configuration);
super.setContainerCommunicator(containerCommunicator);
return new StandAloneScheduler(containerCommunicator);
}
}
public abstract class AbstractScheduler {
private static final Logger LOG = LoggerFactory
.getLogger(AbstractScheduler.class);
public void schedule(List<Configuration> configurations) {
/**
* 给 taskGroupContainer 的 Communication 注册
*/
this.containerCommunicator.registerCommunication(configurations);
int totalTasks = calculateTaskCount(configurations);
startAllTaskGroup(configurations);
try {
while (true) {
Communication nowJobContainerCommunication = this.containerCommunicator.collect();
//汇报周期
long now = System.currentTimeMillis();
if (now - lastReportTimeStamp > jobReportIntervalInMillSec) {
Communication reportCommunication = CommunicationTool
.getReportCommunication(nowJobContainerCommunication, lastJobContainerCommunication, totalTasks);
this.containerCommunicator.report(reportCommunication);
if (nowJobContainerCommunication.getState() == State.SUCCEEDED) {
LOG.info("Scheduler accomplished all tasks.");
break;
}
if (nowJobContainerCommunication.getState() == State.FAILED) {
dealFailedStat(this.containerCommunicator, nowJobContainerCommunication.getThrowable());
}
Thread.sleep(jobSleepIntervalInMillSec);
}
} catch (InterruptedException e) {
// 以 failed 状态退出
LOG.error("捕获到InterruptedException异常!", e);
throw DataXException.asDataXException(
FrameworkErrorCode.RUNTIME_ERROR, e);
}
}
@Override
public void startAllTaskGroup(List<Configuration> configurations) {
this.taskGroupContainerExecutorService = Executors
.newFixedThreadPool(configurations.size());
for (Configuration taskGroupConfiguration : configurations) {
TaskGroupContainerRunner taskGroupContainerRunner = newTaskGroupContainerRunner(taskGroupConfiguration);
this.taskGroupContainerExecutorService.execute(taskGroupContainerRunner);
}
this.taskGroupContainerExecutorService.shutdown();
}
@Override
public void dealFailedStat(AbstractContainerCommunicator frameworkCollector, Throwable throwable) {
this.taskGroupContainerExecutorService.shutdownNow();
}
}
public class TaskGroupContainer extends AbstractContainer {
private static final Logger LOG = LoggerFactory
.getLogger(TaskGroupContainer.class);
@Override
public void start() {
try {
while (true) {
//1.判断task状态
boolean failedOrKilled = false;
Map<Integer, Communication> communicationMap = containerCommunicator.getCommunicationMap();
for(Map.Entry<Integer, Communication> entry : communicationMap.entrySet()){
Integer taskId = entry.getKey();
Communication taskCommunication = entry.getValue();
if(!taskCommunication.isFinished()){
continue;
}
TaskExecutor taskExecutor = removeTask(runTasks, taskId);
if(taskCommunication.getState() == State.FAILED){
failedOrKilled = true;
break;
}
else if(taskCommunication.getState() == State.SUCCEEDED){
Long taskStartTime = taskStartTimeMap.get(taskId);
if(taskStartTime != null){
Long usedTime = System.currentTimeMillis() - taskStartTime;
LOG.info("taskGroup[{}] taskId[{}] is successed, used[{}]ms",
this.taskGroupId, taskId, usedTime);
//usedTime*1000*1000
taskStartTimeMap.remove(taskId);
taskConfigMap.remove(taskId);
}
}
}
// 2.发现该taskGroup下taskExecutor的总状态失败则汇报错误
if (failedOrKilled) {
lastTaskGroupContainerCommunication = reportTaskGroupCommunication(
lastTaskGroupContainerCommunication, taskCountInThisTaskGroup);
throw DataXException.asDataXException(
FrameworkErrorCode.PLUGIN_RUNTIME_ERROR, lastTaskGroupContainerCommunication.getThrowable());
}
//3.有任务未执行，且正在运行的任务数小于最大通道限制
Iterator<Configuration> iterator = taskQueue.iterator();
while(iterator.hasNext() && runTasks.size() < channelNumber){
Configuration taskConfig = iterator.next();
Integer taskId = taskConfig.getInt(CoreConstant.TASK_ID);
Configuration taskConfigForRun =taskConfig.clone()
TaskExecutor taskExecutor = new TaskExecutor(taskConfigForRun);
taskStartTimeMap.put(taskId, System.currentTimeMillis());
taskExecutor.doStart();
terator.remove();
runTasks.add(taskExecutor);
LOG.info("taskGroup[{}] taskId[{}] is started",
this.taskGroupId, taskId);
}
//4.任务列表为空，executor已结束, 搜集状态为success--->成功
if (taskQueue.isEmpty() && isAllTaskDone(runTasks) && containerCommunicator.collectState() == State.SUCCEEDED) {
// 成功的情况下，也需要汇报一次。否则在任务结束非常快的情况下，采集的信息将会不准确
lastTaskGroupContainerCommunication = reportTaskGroupCommunication(
lastTaskGroupContainerCommunication, taskCountInThisTaskGroup);
LOG.info("taskGroup[{}] completed it's tasks.", this.taskGroupId);
break;
}
} catch (Throwable e) {
Communication nowTaskGroupContainerCommunication = this.containerCommunicator.collect();
if (nowTaskGroupContainerCommunication.getThrowable() == null) {
nowTaskGroupContainerCommunication.setThrowable(e);
}
nowTaskGroupContainerCommunication.setState(State.FAILED);
this.containerCommunicator.report(nowTaskGroupContainerCommunication);
throw DataXException.asDataXException(
FrameworkErrorCode.RUNTIME_ERROR, e);
}
}
}
/**
* TaskExecutor是一个完整task的执行器
* 其中包括1：1的reader和writer
*/
class TaskExecutor {
private Thread readerThread;
private Thread writerThread;
private ReaderRunner readerRunner;
private WriterRunner writerRunner;
public TaskExecutor(Configuration taskConf, int attemptCount) {
writerRunner = (WriterRunner) generateRunner(PluginType.WRITER);
//生成writerThread
this.writerThread = new Thread(writerRunner,
String.format("%d-%d-%d-writer",
jobId, taskGroupId, this.taskId));
//生成readerThread
readerRunner = (ReaderRunner) generateRunner(PluginType.READER,transformerInfoExecs);
this.readerThread = new Thread(readerRunner,
String.format("%d-%d-%d-reader",
jobId, taskGroupId, this.taskId));
}
public void doStart() {
this.writerThread.start();
// reader没有起来，writer不可能结束
if (!this.writerThread.isAlive() || this.taskCommunication.getState() == State.FAILED) {
throw DataXException.asDataXException(
FrameworkErrorCode.RUNTIME_ERROR,
this.taskCommunication.getThrowable());
}
this.readerThread.start();
// 这里reader可能很快结束
if (!this.readerThread.isAlive() && this.taskCommunication.getState() == State.FAILED) {
// 这里有可能出现Reader线上启动即挂情况 对于这类情况 需要立刻抛出异常
throw DataXException.asDataXException(
FrameworkErrorCode.RUNTIME_ERROR,
this.taskCommunication.getThrowable());
}
}
}
从上面总体流程中可以看到JobContainer通过线程池调度起所有的TaskGroupContainer，然后轮训TaskGroupContainer的运行状态。每个TaskGroupContainer则是根据分配的chanel并发数量依次执行分配的Task实例。
插件机制
在工作流程中的init步骤，我们看到的jobReader和jobWriter的实现就是通过插件动态生成的。jobReader和jobWriter就对应datax中的Job概念模型。而在TaskExecutor中初始化的readerRunner和writerRunner对应的是Task模型。通过插件datax插件机制支持了数十种不同的数据源之间的读写同步，同时也很方便的支持新的数据源接入。
Job初始化过程
public class JobContainer extends AbstractContainer {
//reader job的初始化，返回Reader.Job
private Reader.Job initJobReader(
JobPluginCollector jobPluginCollector) {
this.readerPluginName = this.configuration.getString(
CoreConstant.DATAX_JOB_CONTENT_READER_NAME);
Reader.Job jobReader = (Reader.Job) LoadUtil.loadJobPlugin(
PluginType.READER, this.readerPluginName);
// 设置reader的jobConfig
jobReader.setPluginJobConf(this.configuration.getConfiguration(
CoreConstant.DATAX_JOB_CONTENT_READER_PARAMETER));
// 设置reader的readerConfig
jobReader.setPeerPluginJobConf(this.configuration.getConfiguration(
CoreConstant.DATAX_JOB_CONTENT_WRITER_PARAMETER));
jobReader.setJobPluginCollector(jobPluginCollector);
jobReader.init();
classLoaderSwapper.restoreCurrentThreadClassLoader();
return jobReader;
}
}
插件加载器，大体上分reader、transformer（还未实现）和writer三中插件类型，
reader和writer在执行时又可能出现Job和Task两种运行时（加载的类不同）
public class LoadUtil {
//加载JobPlugin，reader、writer都可能要加载
public static AbstractJobPlugin loadJobPlugin(PluginType pluginType,
String pluginName) {
Class<? extends AbstractPlugin> clazz = LoadUtil.loadPluginClass(
pluginType, pluginName, ContainerType.Job);
try {
AbstractJobPlugin jobPlugin = (AbstractJobPlugin) clazz
.newInstance();
jobPlugin.setPluginConf(getPluginConf(pluginType, pluginName));
return jobPlugin;
} catch (Exception e) {
throw DataXException.asDataXException(
FrameworkErrorCode.RUNTIME_ERROR,
String.format("DataX找到plugin[%s]的Job配置.",
pluginName), e);
}
}
//反射出具体plugin实例
private static synchronized Class<? extends AbstractPlugin> loadPluginClass(
PluginType pluginType, String pluginName,
ContainerType pluginRunType) {
Configuration pluginConf = getPluginConf(pluginType, pluginName);
JarLoader jarLoader = LoadUtil.getJarLoader(pluginType, pluginName);
try {
return (Class<? extends AbstractPlugin>) jarLoader
.loadClass(pluginConf.getString("class") + "$"
+ pluginRunType.value());
} catch (Exception e) {
throw DataXException.asDataXException(FrameworkErrorCode.RUNTIME_ERROR, e);
}
}
public static synchronized JarLoader getJarLoader(PluginType pluginType,
String pluginName) {
Configuration pluginConf = getPluginConf(pluginType, pluginName);
JarLoader jarLoader = jarLoaderCenter.get(generatePluginKey(pluginType,
pluginName));
if (null == jarLoader) {
String pluginPath = pluginConf.getString("path");
if (StringUtils.isBlank(pluginPath)) {
throw DataXException.asDataXException(
FrameworkErrorCode.RUNTIME_ERROR,
String.format(
"%s插件[%s]路径非法!",
pluginType, pluginName));
}
jarLoader = new JarLoader(new String[]{pluginPath});
jarLoaderCenter.put(generatePluginKey(pluginType, pluginName),
jarLoader);
}
return jarLoader;
}
}
//提供Jar隔离的加载机制，会把传入的路径、及其子路径、以及路径中的jar文件加入到class path。
public class JarLoader extends URLClassLoader {
public JarLoader(String[] paths) {
this(paths, JarLoader.class.getClassLoader());
}
public JarLoader(String[] paths, ClassLoader parent) {
super(getURLs(paths), parent);
}
private static URL[] getURLs(String[] paths) {
Validate.isTrue(null != paths && 0 != paths.length,
"jar包路径不能为空.");
List<String> dirs = new ArrayList<String>();
for (String path : paths) {
dirs.add(path);
JarLoader.collectDirs(path, dirs);
}
List<URL> urls = new ArrayList<URL>();
for (String path : dirs) {
urls.addAll(doGetURLs(path));
}
return urls.toArray(new URL[0]);
}
private static void collectDirs(String path, List<String> collector) {
if (null == path || StringUtils.isBlank(path)) {
return;
}
File current = new File(path);
if (!current.exists() || !current.isDirectory()) {
return;
}
for (File child : current.listFiles()) {
if (!child.isDirectory()) {
continue;
}
collector.add(child.getAbsolutePath());
collectDirs(child.getAbsolutePath(), collector);
}
}
}
Task 初始化过程
class TaskExecutor {
private AbstractRunner generateRunner(PluginType pluginType) {
return generateRunner(pluginType, null);
}
private AbstractRunner generateRunner(PluginType pluginType, List<TransformerExecution> transformerInfoExecs) {
AbstractRunner newRunner = null;
TaskPluginCollector pluginCollector;
switch (pluginType) {
case READER:
newRunner = LoadUtil.loadPluginRunner(pluginType,
this.taskConfig.getString(CoreConstant.JOB_READER_NAME));
newRunner.setJobConf(this.taskConfig.getConfiguration(
CoreConstant.JOB_READER_PARAMETER));
pluginCollector = ClassUtil.instantiate(
taskCollectorClass, AbstractTaskPluginCollector.class,
configuration, this.taskCommunication,
PluginType.READER);
RecordSender recordSender;
if (transformerInfoExecs != null && transformerInfoExecs.size() > 0) {
recordSender = new BufferedRecordTransformerExchanger(taskGroupId, this.taskId, this.channel,this.taskCommunication ,pluginCollector, transformerInfoExecs);
} else {
recordSender = new BufferedRecordExchanger(this.channel, pluginCollector);
}
((ReaderRunner) newRunner).setRecordSender(recordSender);
/**
* 设置taskPlugin的collector，用来处理脏数据和job/task通信
*/
newRunner.setTaskPluginCollector(pluginCollector);
break;
case WRITER:
newRunner = LoadUtil.loadPluginRunner(pluginType,
this.taskConfig.getString(CoreConstant.JOB_WRITER_NAME));
newRunner.setJobConf(this.taskConfig
.getConfiguration(CoreConstant.JOB_WRITER_PARAMETER));
pluginCollector = ClassUtil.instantiate(
taskCollectorClass, AbstractTaskPluginCollector.class,
configuration, this.taskCommunication,
PluginType.WRITER);
((WriterRunner) newRunner).setRecordReceiver(new BufferedRecordExchanger(
this.channel, pluginCollector));
/**
* 设置taskPlugin的collector，用来处理脏数据和job/task通信
*/
newRunner.setTaskPluginCollector(pluginCollector);
break;
default:
throw DataXException.asDataXException(FrameworkErrorCode.ARGUMENT_ERROR, "Cant generateRunner for:" + pluginType);
}
newRunner.setTaskGroupId(taskGroupId);
newRunner.setTaskId(this.taskId);
newRunner.setRunnerCommunication(this.taskCommunication);
return newRunner;
}
}
public class LoadUtil {
/**
* 根据插件类型、名字和执行时taskGroupId加载对应运行器
*
* @param pluginType
* @param pluginName
* @return
*/
public static AbstractRunner loadPluginRunner(PluginType pluginType, String pluginName) {
AbstractTaskPlugin taskPlugin = LoadUtil.loadTaskPlugin(pluginType,
pluginName);
switch (pluginType) {
case READER:
return new ReaderRunner(taskPlugin);
case WRITER:
return new WriterRunner(taskPlugin);
default:
throw DataXException.asDataXException(
FrameworkErrorCode.RUNTIME_ERROR,
String.format("插件[%s]的类型必须是[reader]或[writer]!",
pluginName));
}
}
}
同步实现
这部分就是经过split后的具体的Task的执行逻辑。Task的划分逻辑如下：
public class JobContainer extends AbstractContainer {
private static final Logger LOG = LoggerFactory
.getLogger(JobContainer.class);
/**
* 执行reader和writer最细粒度的切分，需要注意的是，writer的切分结果要参照reader的切分结果，
* 达到切分后数目相等，才能满足1：1的通道模型，所以这里可以将reader和writer的配置整合到一起，
* 然后，为避免顺序给读写端带来长尾影响，将整合的结果shuffler掉
*/
private int split() {
this.adjustChannelNumber();
if (this.needChannelNumber <= 0) {
this.needChannelNumber = 1;
}
List<Configuration> readerTaskConfigs = this
.doReaderSplit(this.needChannelNumber);
int taskNumber = readerTaskConfigs.size();
List<Configuration> writerTaskConfigs = this
.doWriterSplit(taskNumber);
List<Configuration> transformerList = this.configuration.getListConfiguration(CoreConstant.DATAX_JOB_CONTENT_TRANSFORMER);
LOG.debug("transformer configuration: "+ JSON.toJSONString(transformerList));
/**
* 输入是reader和writer的parameter list，输出是content下面元素的list
*/
List<Configuration> contentConfig = mergeReaderAndWriterTaskConfigs(
readerTaskConfigs, writerTaskConfigs, transformerList);
LOG.debug("contentConfig configuration: "+ JSON.toJSONString(contentConfig));
this.configuration.set(CoreConstant.DATAX_JOB_CONTENT, contentConfig);
return contentConfig.size();
}
}
每个Task都执行相同的逻辑和流程，下面以读mysql和写hdfs为例，展示其读写过程。
//单个slice的reader执行调用
public class ReaderRunner extends AbstractRunner implements Runnable {
@Override
public void run() {
Reader.Task taskReader = (Reader.Task) this.getPlugin();
taskReader.init();
taskReader.prepare();
taskReader.startRead(recordSender);
recordSender.terminate();
}
}
public class MysqlReader extends Reader {
@Override
public void startRead(RecordSender recordSender) {
int fetchSize = this.readerSliceConfig.getInt(Constant.FETCH_SIZE);
this.commonRdbmsReaderTask.startRead(this.readerSliceConfig, recordSender,
super.getTaskPluginCollector(), fetchSize);
}
}
public class CommonRdbmsReader {
public static class Task {
private static final Logger LOG = LoggerFactory
.getLogger(Task.class);
public void startRead(Configuration readerSliceConfig,
RecordSender recordSender,
TaskPluginCollector taskPluginCollector, int fetchSize) {
String querySql = readerSliceConfig.getString(Key.QUERY_SQL);
String table = readerSliceConfig.getString(Key.TABLE);
PerfTrace.getInstance().addTaskDetails(taskId, table + "," + basicMsg);
LOG.info("Begin to read record by Sql: [{}\n] {}.",
querySql, basicMsg);
Connection conn = DBUtil.getConnection(this.dataBaseType, jdbcUrl,
username, password);
int columnNumber = 0;
ResultSet rs = null;
try {
rs = DBUtil.query(conn, querySql, fetchSize);
while (rs.next()) {
//将数据记录放入channel通道，writer从中获取写数据
this.transportOneRecord(recordSender, rs,
metaData, columnNumber, mandatoryEncoding, taskPluginCollector);
}
}catch (Exception e) {
throw RdbmsException.asQueryException(this.dataBaseType, e, querySql, table, username);
} finally {
DBUtil.closeDBResources(null, conn);
}
}
}
}
//单个slice的writer执行调用
public class WriterRunner extends AbstractRunner implements Runnable {
@Override
public void run() {
Writer.Task taskWriter = (Writer.Task) this.getPlugin();
taskWriter.init();
taskWriter.prepare();
taskWriter.startWrite(recordReceiver);
}
}
public class HdfsWriter extends Writer {
public static class Task extends Writer.Task {
private static final Logger LOG = LoggerFactory.getLogger(Task.class);
@Override
public void startWrite(RecordReceiver lineReceiver) {
LOG.info("begin do write...");
LOG.info(String.format("write to file : [%s]", this.fileName));
if(fileType.equalsIgnoreCase("TEXT")){
//写TEXT FILE
hdfsHelper.textFileStartWrite(lineReceiver,this.writerSliceConfig, this.fileName,
this.getTaskPluginCollector());
}else if(fileType.equalsIgnoreCase("ORC")){
//写ORC FILE
hdfsHelper.orcFileStartWrite(lineReceiver,this.writerSliceConfig, this.fileName,
this.getTaskPluginCollector());
}
LOG.info("end do write");
}
}
}
public class HdfsHelper {
public void textFileStartWrite(RecordReceiver lineReceiver, Configuration config, String fileName,TaskPluginCollector taskPluginCollector){
try {
RecordWriter writer = outFormat.getRecordWriter(fileSystem, conf, outputPath.toString(), Reporter.NULL);
Record record = null;
while ((record = lineReceiver.getFromReader()) != null) {
MutablePair<Text, Boolean> transportResult = transportOneRecord(record, fieldDelimiter, columns, taskPluginCollector);
if (!transportResult.getRight()) {
writer.write(NullWritable.get(),transportResult.getLeft());
}
}
writer.close(Reporter.NULL);
} catch (Exception e) {
String message = String.format("写文件文件[%s]时发生IO异常,请检查您的网络是否正常！", fileName);
LOG.error(message);
Path path = new Path(fileName);
deleteDir(path.getParent());
throw DataXException.asDataXException(HdfsWriterErrorCode.Write_FILE_IO_ERROR, e);
}
}
}
reader和writer通过BufferedRecordExchanger建立联系，在其内部实现了基于ArrayBlockingQueue的MemoryChannel。
public class BufferedRecordExchanger implements RecordSender, RecordReceiver {
@Override
public void sendToWriter(Record record) {
if(shutdown){
throw DataXException.asDataXException(CommonErrorCode.SHUT_DOWN_TASK, "");
}
Validate.notNull(record, "record不能为空.");
if (record.getMemorySize() > this.byteCapacity) {
this.pluginCollector.collectDirtyRecord(record, new Exception(String.format("单条记录超过大小限制，当前限制为:%s", this.byteCapacity)));
return;
}
boolean isFull = (this.bufferIndex >= this.bufferSize || this.memoryBytes.get() + record.getMemorySize() > this.byteCapacity);
if (isFull) {
flush();
}
this.buffer.add(record);
this.bufferIndex++;
memoryBytes.addAndGet(record.getMemorySize());
}
@Override
public void flush() {
if(shutdown){
throw DataXException.asDataXException(CommonErrorCode.SHUT_DOWN_TASK, "");
}
this.channel.pushAll(this.buffer);
this.buffer.clear();
this.bufferIndex = 0;
this.memoryBytes.set(0);
}
@Override
public Record getFromReader() {
if(shutdown){
throw DataXException.asDataXException(CommonErrorCode.SHUT_DOWN_TASK, "");
}
boolean isEmpty = (this.bufferIndex >= this.buffer.size());
if (isEmpty) {
receive();
}
Record record = this.buffer.get(this.bufferIndex++);
if (record instanceof TerminateRecord) {
record = null;
}
return record;
}
datax性能优化
通过datax原理和实现的理解，自然可以知道如何提升datax的同步效率。以mysql同步hdfs为例，自然最直接的方式就是提高mysql和hdfs的硬件性能如cpu、内存、IOPS、网络带宽等。当硬件资源受限的情况下，可以有如下几种办法：
将不同的集群划分到同一个网络或者区域内，减少跨网络的不稳定性，如将阿里云集群迁移到amazon集群，或者同一个amazon集群中不同区域划分到同一个子网络内。
对数据库按照主键划分。datax对单个表默认一个通道，如果指定拆分主键，将会大大提升同步并发数和吞吐量。
在cpu、内存以及mysql负载满足的情况下，提升通道并发数。通道并发数意味着更多的内存开销，jvm调优是重中之重。
当无法提升通道数量时，而且每个拆分依然很大的时候，可以考虑对每个拆分再次拆分。
设定合适的参数，如mysql超时等。
总结
本文通过原理介绍和源码分析，逐步理清datax的工作流程和实现原理，并结合实际经验给出几点优化建议。然而在实际中涉及到更多的分库分表和特大量级的表，数据库的承载压力也是一大考虑因素，否则遭到dba的吊打肯定会在所难免。尤其是我们涉及到跨大洋数据同步，网络的稳定性也是一大挑战，此时基于增量同步方案或许是更好的选择。