flink(三)

1.运行环境

　　有一些三种方式获取当前环境

　　　　getExecutionEnvironment()

　　　　createLocalEnvironment()

　　　　createRemoteEnvironment

2.批处理

　　ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();

　　DataSet<String> text = env.readTextFile("file:///o.txt");

　　text.print()

3.map操作

　　text.map(new MapFunction<String, Tuple2<String, Integer>>() {

　　　　@Override
　　　　public Tuple2<String, Integer> map(String s) throws Exception {
　　　　　　return new Tuple2<>(s, 1);
　　　　}
　　});

4.table操作

　　

import org.apache.flink.table.api.*

import static org.apache.flink.table.api.Expressions.*

// environment configuration
ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
BatchTableEnvironment tEnv = BatchTableEnvironment.create(env);

// register Orders table in table environment
// ...

// specify table program
Table orders = tEnv.from("Orders"); // schema (a, b, c, rowtime)

Table counts = orders
        .groupBy($("a"))
        .select($("a"), $("b").count().as("cnt"));

// conversion to DataSet
DataSet<Row> result = tEnv.toDataSet(counts, Row.class);
result.print();

Table orders = tEnv.from("Orders"); // schema (a, b, c, rowtime)

Table result = orders
        .filter(
            and(
                $("a").isNotNull(),
                $("b").isNotNull(),
                $("c").isNotNull()
            ))
        .select($("a").lowerCase().as("a"), $("b"), $("rowtime"))
        .window(Tumble.over(lit(1).hours()).on($("rowtime")).as("hourlyWindow"))
        .groupBy($("hourlyWindow"), $("a"))
        .select($("a"), $("hourlyWindow").end().as("hour"), $("b").avg().as("avgBillingAmount"));

 

5.batch和stream

Table orders = tableEnv.from("Orders");
// Distinct aggregation on group by
Table groupByDistinctResult = orders
    .groupBy($("a"))
    .select($("a"), $("b").sum().distinct().as("d"));
// Distinct aggregation on time window group by
Table groupByWindowDistinctResult = orders
    .window(Tumble
            .over(lit(5).minutes()))
            .on($("rowtime"))
            .as("w")
    )
    .groupBy($("a"), $("w"))
    .select($("a"), $("b").sum().distinct().as("d"));
// Distinct aggregation on over window
Table result = orders
    .window(Over
        .partitionBy($("a"))
        .orderBy($("rowtime"))
        .preceding(UNBOUNDED_RANGE)
        .as("w"))
    .select(
        $("a"), $("b").avg().distinct().over($("w")),
        $("b").max().over($("w")),
        $("b").min().over($("w"))
    );