时隔两年再次发一篇Blog,记录一下这个Flink测试过程中遇到各种问题,一个简单的Job竟然耗费了6个小时才完成。Job要求如下:

自定义 TableSource,从标准输入中按行读取数据,每行按逗号分隔字段。表的 schema 为 (id int, name string, age int)
自定义 TableSink,将 Table 数据写到标准输出
通过 SQL 实现 select name, max(age) from student group by name

实现代码如下:

/**
 * Flink版本 1.9.1
 */
public class StreamTableSQLDemo {

    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment streamEnv = StreamExecutionEnvironment.getExecutionEnvironment();
        StreamTableEnvironment streamTableEnv = StreamTableEnvironment.create(streamEnv);

        //使用自定义的dataSource
        String[] fieldNames = {"id", "name", "age"};
        DataType[] fieldTypes = {DataTypes.INT(), DataTypes.STRING(), DataTypes.INT()};
        TableSource studentSource = new SystemInDataSource(fieldNames, fieldTypes);
        //注册为table
        streamTableEnv.registerTableSource("student", studentSource);

        //使用自定义的sink,打印数据到标准输出
        TableSink studentSink = new PrintTableSink(fieldNames, fieldTypes);
        streamTableEnv.registerTableSink("result_student", studentSink );
        streamTableEnv.sqlUpdate("insert into result_student select id, name, age from student");

        //执行SQL
        Table tableAfterGroup = streamTableEnv.sqlQuery("select name, max(age) from student group by name");
        DataStream<Tuple2<Boolean, Row>> result = streamTableEnv.toRetractStream(tableAfterGroup, Row.class);
        //打印输出
        result.print();

        streamEnv.execute("Stream Table SQL Demo");
    }

    /**
     * 自定义标准输入Source
     */
    public static class SystemInDataSource implements StreamTableSource<Row>, DefinedFieldMapping {

        private TableSchema tableSchema;

        private SystemInDataSource(String[] fieldNames, DataType[] fieldTypes){
            this.tableSchema = TableSchema.builder().fields(fieldNames,fieldTypes).build();
        }

        @Override
        public DataType getProducedDataType() {
            return DataTypes.ROW(DataTypes.FIELD("f0", DataTypes.INT()),
                    DataTypes.FIELD("f1", DataTypes.STRING()),
                    DataTypes.FIELD("f2", DataTypes.INT()));
        }

        @Override
        public DataStream<Row> getDataStream(StreamExecutionEnvironment streamExecutionEnvironment) {
            return streamExecutionEnvironment.addSource(new SystemInSourceFunction());
        }

        @Override
        public TableSchema getTableSchema() {
            return tableSchema;
        }

        /**
         * 定义映射关系
         * @return
         */
        @Nullable
        @Override
        public Map<String, String> getFieldMapping() {
            Map<String, String> mapping = new HashMap<>();
            mapping.put("id", "f0");
            mapping.put("name", "f1");
            mapping.put("age", "f2");
            return mapping;
        }
    }

    /**
     * 自定义的标准输入SourceFunction
     */
    public static class SystemInSourceFunction implements SourceFunction<Row>, ResultTypeQueryable<Row> {

        @Override
        public TypeInformation<Row> getProducedType() {
            return Types.ROW(Types.INT, Types.STRING, Types.INT);
        }

        @Override
        public void run(SourceContext<Row> sourceContext) throws Exception {
            while(true){
                System.out.print("请输入: ");
                Scanner scanner = new Scanner(System.in);
                String line = scanner.next();
                String [] str = line.split(",");

                if(str.length == 3){
                    Row element = Row.of(Integer.valueOf(str[0]), str[1], Integer.valueOf(str[2]));
                    sourceContext.collect(element);
                } else {
                    System.err.println("输入格式错误.");
                }
            }
        }

        @Override
        public void cancel() {

        }
    }

    /**
     * 自定义标准输出Sink
     */
    public static class PrintTableSink implements AppendStreamTableSink<Row> {

        private TableSchema tableSchema;

        private PrintTableSink(String[] fieldNames, DataType[] fieldTypes){
            this.tableSchema = TableSchema.builder().fields(fieldNames,fieldTypes).build();
        }

        @Override
        public TableSchema getTableSchema() {
            return tableSchema;
        }

        @Override
        public DataType getConsumedDataType() {
            return tableSchema.toRowDataType();
        }

        @Override
        public DataStreamSink<?> consumeDataStream(DataStream<Row> dataStream) {
            return dataStream.addSink(new SinkFunction());
        }

        @Override
        public TableSink configure(String[] strings, TypeInformation<?>[] typeInformations) {
            return this;
        }

        @Override
        public void emitDataStream(DataStream dataStream) {
        }

        private static class SinkFunction extends RichSinkFunction<Row> {
            public SinkFunction() {
            }

            @Override
            public void invoke(Row value, Context context) throws Exception {
                System.out.println("收到了新的输入: " + value);
            }
        }
    }

}

运行测试,依次输入以下内容:

zhangsan
1,zhangsan,20
2,lisi,30
3,zhangsan,25
4,lisi,36

理论上首先会输出格式错误,然后依次输出结构,并且采用Retract打印当前max(age)结果。
截图如下:

Flink自定义TableSource/TableSink Job
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