传统的大数据处理方式一般是批处理式的，也就是说，今天所收集的数据，我们明天再把今天收集到的数据算出来，以供大家使用，但是在很多情况下，数据的时效性对于业务的成败是非常关键的。
Spark 和 Flink 都是通用的开源大规模处理引擎，目标是在一个系统中支持所有的数据处理以带来效能的提升。两者都有相对比较成熟的生态系统。是下一代大数据引擎有力的竞争者。
Spark 的生态总体更完善一些，在机器学习的集成和易用性上暂时领先。
Flink 在流计算上有明显优势，核心架构和模型也更透彻和灵活一些。
本文主要通过实例来分析flink的流式处理过程，并通过源码的方式来介绍流式处理的内部机制。
DataStream整体概述
主要分5部分，下面我们来分别介绍：
1.运行环境StreamExecutionEnvironment
StreamExecutionEnvironment是个抽象类，是流式处理的容器，实现类有两个，分别是
LocalStreamEnvironment：
RemoteStreamEnvironment：
/**
* The StreamExecutionEnvironment is the context in which a streaming program is executed. A
* {@link LocalStreamEnvironment} will cause execution in the current JVM, a
* {@link RemoteStreamEnvironment} will cause execution on a remote setup.
*
* <p>The environment provides methods to control the job execution (such as setting the parallelism
* or the fault tolerance/checkpointing parameters) and to interact with the outside world (data access).
*
* @see org.apache.flink.streaming.api.environment.LocalStreamEnvironment
* @see org.apache.flink.streaming.api.environment.RemoteStreamEnvironment
*/
2.数据源DataSource数据输入
包含了输入格式InputFormat
/**
* Creates a new data source.
*
* @param context The environment in which the data source gets executed.
* @param inputFormat The input format that the data source executes.
* @param type The type of the elements produced by this input format.
*/
public DataSource(ExecutionEnvironment context, InputFormat<OUT, ?> inputFormat, TypeInformation<OUT> type, String dataSourceLocationName) {
super(context, type);
this.dataSourceLocationName = dataSourceLocationName;
if (inputFormat == null) {
throw new IllegalArgumentException("The input format may not be null.");
}
this.inputFormat = inputFormat;
if (inputFormat instanceof NonParallelInput) {
this.parallelism = 1;
}
}
flink将数据源主要分为内置数据源和第三方数据源，内置数据源有 文件，网络socket端口及集合类型数据；第三方数据源实用Connector的方式来连接如kafka Connector，es connector等，自己定义的话，可以实现SourceFunction，封装成Connector来做。
3.DataStream转换
DataStream：同一个类型的流元素，DataStream可以通过transformation转换成另外的DataStream,示例如下
@link DataStream#map
@link DataStream#filter
StreamOperator:流式算子的基本接口，三个实现类
AbstractStreamOperator：
OneInputStreamOperator：
TwoInputStreamOperator：
/**
* Basic interface for stream operators. Implementers would implement one of
* {@link org.apache.flink.streaming.api.operators.OneInputStreamOperator} or
* {@link org.apache.flink.streaming.api.operators.TwoInputStreamOperator} to create operators
* that process elements.
*
* <p>The class {@link org.apache.flink.streaming.api.operators.AbstractStreamOperator}
* offers default implementation for the lifecycle and properties methods.
*
* <p>Methods of {@code StreamOperator} are guaranteed not to be called concurrently. Also, if using
* the timer service, timer callbacks are also guaranteed not to be called concurrently with
* methods on {@code StreamOperator}.
*
* @param <OUT> The output type of the operator
*/
4.DataStreamSink输出
/**
* Adds the given sink to this DataStream. Only streams with sinks added
* will be executed once the {@link StreamExecutionEnvironment#execute()}
* method is called.
*
* @param sinkFunction
* The object containing the sink's invoke function.
* @return The closed DataStream.
*/
public DataStreamSink<T> addSink(SinkFunction<T> sinkFunction) {
// read the output type of the input Transform to coax out errors about MissingTypeInfo
transformation.getOutputType();
// configure the type if needed
if (sinkFunction instanceof InputTypeConfigurable) {
((InputTypeConfigurable) sinkFunction).setInputType(getType(), getExecutionConfig());
}
StreamSink<T> sinkOperator = new StreamSink<>(clean(sinkFunction));
DataStreamSink<T> sink = new DataStreamSink<>(this, sinkOperator);
getExecutionEnvironment().addOperator(sink.getTransformation());
return sink;
}
5.执行
/**
* Executes the JobGraph of the on a mini cluster of ClusterUtil with a user
* specified name.
*
* @param jobName
* name of the job
* @return The result of the job execution, containing elapsed time and accumulators.
*/
@Override
public JobExecutionResult execute(String jobName) throws Exception {
// transform the streaming program into a JobGraph
StreamGraph streamGraph = getStreamGraph();
streamGraph.setJobName(jobName);
JobGraph jobGraph = streamGraph.getJobGraph();
jobGraph.setAllowQueuedScheduling(true);
Configuration configuration = new Configuration();
configuration.addAll(jobGraph.getJobConfiguration());
configuration.setString(TaskManagerOptions.MANAGED_MEMORY_SIZE, "0");
// add (and override) the settings with what the user defined
configuration.addAll(this.configuration);
if (!configuration.contains(RestOptions.BIND_PORT)) {
configuration.setString(RestOptions.BIND_PORT, "0");
}
int numSlotsPerTaskManager = configuration.getInteger(TaskManagerOptions.NUM_TASK_SLOTS, jobGraph.getMaximumParallelism());
MiniClusterConfiguration cfg = new MiniClusterConfiguration.Builder()
.setConfiguration(configuration)
.setNumSlotsPerTaskManager(numSlotsPerTaskManager)
.build();
if (LOG.isInfoEnabled()) {
LOG.info("Running job on local embedded Flink mini cluster");
}
MiniCluster miniCluster = new MiniCluster(cfg);
try {
miniCluster.start();
configuration.setInteger(RestOptions.PORT, miniCluster.getRestAddress().get().getPort());
return miniCluster.executeJobBlocking(jobGraph);
}
finally {
transformations.clear();
miniCluster.close();
}
}
6.总结
Flink的执行方式类似于管道，它借鉴了数据库的一些执行原理，实现了自己独特的执行方式。
7.展望
Stream涉及的内容还包括Watermark，window等概念，因篇幅限制，这篇仅介绍flink DataStream API使用及原理。
下篇将介绍Watermark，下下篇是windows窗口计算。
