FLINK基础（141）：DS流与表转换(7) Handling of Changelog Streams(2) fromChangelogStream

The following code shows how to use fromChangelogStream for different scenarios.

import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.table.api.Schema;
import org.apache.flink.table.api.Table;
import org.apache.flink.table.connector.ChangelogMode;
import org.apache.flink.types.Row;
import org.apache.flink.types.RowKind;

// === EXAMPLE 1 ===

// interpret the stream as a retract stream

// create a changelog DataStream
DataStream<Row> dataStream =
    env.fromElements(
        Row.ofKind(RowKind.INSERT, "Alice", 12),
        Row.ofKind(RowKind.INSERT, "Bob", 5),
        Row.ofKind(RowKind.UPDATE_BEFORE, "Alice", 12),
        Row.ofKind(RowKind.UPDATE_AFTER, "Alice", 100));

// interpret the DataStream as a Table
Table table = tableEnv.fromChangelogStream(dataStream);

// register the table under a name and perform an aggregation
tableEnv.createTemporaryView("InputTable", table);
tableEnv
    .executeSql("SELECT f0 AS name, SUM(f1) AS score FROM InputTable GROUP BY f0")
    .print();

// prints:
// +----+--------------------------------+-------------+
// | op |                           name |       score |
// +----+--------------------------------+-------------+
// | +I |                            Bob |           5 |
// | +I |                          Alice |          12 |
// | -D |                          Alice |          12 |
// | +I |                          Alice |         100 |
// +----+--------------------------------+-------------+


// === EXAMPLE 2 ===

// interpret the stream as an upsert stream (without a need for UPDATE_BEFORE)

// create a changelog DataStream
DataStream<Row> dataStream =
    env.fromElements(
        Row.ofKind(RowKind.INSERT, "Alice", 12),
        Row.ofKind(RowKind.INSERT, "Bob", 5),
        Row.ofKind(RowKind.UPDATE_AFTER, "Alice", 100));

// interpret the DataStream as a Table
Table table =
    tableEnv.fromChangelogStream(
        dataStream,
        Schema.newBuilder().primaryKey("f0").build(),
        ChangelogMode.upsert());

// register the table under a name and perform an aggregation
tableEnv.createTemporaryView("InputTable", table);
tableEnv
    .executeSql("SELECT f0 AS name, SUM(f1) AS score FROM InputTable GROUP BY f0")
    .print();

// prints:
// +----+--------------------------------+-------------+
// | op |                           name |       score |
// +----+--------------------------------+-------------+
// | +I |                            Bob |           5 |
// | +I |                          Alice |          12 |
// | -D |                          Alice |          12 |
// | +I |                          Alice |         100 |
// +----+--------------------------------+-------------+

The default ChangelogMode shown in example 1 should be sufficient for most use cases as it accepts all kinds of changes.

However, example 2 shows how to limit the kinds of incoming changes for efficiency by reducing the number of update messages by 50%.