apache flink 入门

配置环境
包括
JAVA_HOME
jobmanager.rpc.address  
jobmanager.heap.mb 和 taskmanager.heap.mb
taskmanager.numberOfTaskSlots
taskmanager.tmp.dirs
slaves文件

启动关闭
bin/start-cluster.sh
bin/stop-cluster.sh

 

初步使用

    public static void main(String[] args) throws Exception {

        if (args.length != 2){
            System.err.println("USAGE:\nSocketTextStreamWordCount <hostname> <port>");
            return;
        }

        String hostName = args[0];
        Integer port = Integer.parseInt(args[1]);

        // set up the execution environment
        final StreamExecutionEnvironment env = StreamExecutionEnvironment
                .getExecutionEnvironment();

        // get input data
        DataStream<String> text = env.socketTextStream(hostName, port);

        DataStream<Tuple2<String, Integer>> counts =
        // split up the lines in pairs (2-tuples) containing: (word,1)
        text.flatMap(new LineSplitter())
        // group by the tuple field "0" and sum up tuple field "1"
                .keyBy(0)
                .sum(1);

        counts.print();

        // execute program
        env.execute("WordCount from SocketTextStream Example");
    }

    public static final class LineSplitter implements FlatMapFunction<String, Tuple2<String, Integer>> {

        @Override
        public void flatMap(String value, Collector<Tuple2<String, Integer>> out) {
            // normalize and split the line
            String[] tokens = value.toLowerCase().split("\\W+");

            // emit the pairs
            for (String token : tokens) {
                if (token.length() > 0) {
                    out.collect(new Tuple2<String, Integer>(token, 1));
                }
            }
        }
    }    

 

编程步骤，和spark很类似
Obtain an execution environment,
Load/create the initial data,
Specify transformations on this data,
Specify where to put the results of your computations,
Trigger the program execution

 

连接flink的接口 StreamExecutionEnvironment
getExecutionEnvironment()
createLocalEnvironment()
createRemoteEnvironment(String host, int port, String... jarFiles)

Accumulators & Counters 用于求和和计数
步骤包括定义，添加到上下文，操作，最后获取
private IntCounter numLines = new IntCounter();
getRuntimeContext().addAccumulator("num-lines", this.numLines);
this.numLines.add(1);
myJobExecutionResult=env.execute("xxx");
myJobExecutionResult.getAccumulatorResult("num-lines")

 

 

并发数设置
System Level:
parallelism.default=10
Client Level:
./bin/flink run -p 10 example.jar
client.run(program, 10, true);

Execution Environment Level:
env.setParallelism(3);

Operator Level:
DataStream<Tuple2<String, Integer>> wordCounts = text
    .flatMap(new LineSplitter())
    .keyBy(0)
    .timeWindow(Time.seconds(5))
    .sum(1).setParallelism(5);

 

 

最后上架构图和执行流程图，看起来和spark很类似