FLINK基础（142）：DS流与表转换(8) Handling of Changelog Streams(3) toChangelogStream

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

import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.functions.ProcessFunction;
import org.apache.flink.table.api.DataTypes;
import org.apache.flink.table.api.Schema;
import org.apache.flink.table.api.Table;
import org.apache.flink.table.data.StringData;
import org.apache.flink.types.Row;
import org.apache.flink.util.Collector;
import static org.apache.flink.table.api.Expressions.*;

// create Table with event-time
tableEnv.executeSql(
    "CREATE TABLE GeneratedTable "
    + "("
    + "  name STRING,"
    + "  score INT,"
    + "  event_time TIMESTAMP_LTZ(3),"
    + "  WATERMARK FOR event_time AS event_time - INTERVAL '10' SECOND"
    + ")"
    + "WITH ('connector'='datagen')");

Table table = tableEnv.from("GeneratedTable");


// === EXAMPLE 1 ===

// convert to DataStream in the simplest and most general way possible (no event-time)

Table simpleTable = tableEnv
    .fromValues(row("Alice", 12), row("Alice", 2), row("Bob", 12))
    .as("name", "score")
    .groupBy($("name"))
    .select($("name"), $("score").sum());

tableEnv
    .toChangelogStream(simpleTable)
    .executeAndCollect()
    .forEachRemaining(System.out::println);

// prints:
// +I[Bob, 12]
// +I[Alice, 12]
// -U[Alice, 12]
// +U[Alice, 14]


// === EXAMPLE 2 ===

// convert to DataStream in the simplest and most general way possible (with event-time)

DataStream<Row> dataStream = tableEnv.toChangelogStream(table);

// since `event_time` is a single time attribute in the schema, it is set as the
// stream record's timestamp by default; however, at the same time, it remains part of the Row

dataStream.process(
    new ProcessFunction<Row, Void>() {
        @Override
        public void processElement(Row row, Context ctx, Collector<Void> out) {

             // prints: [name, score, event_time]
             System.out.println(row.getFieldNames(true));

             // timestamp exists twice
             assert ctx.timestamp() == row.<Instant>getFieldAs("event_time").toEpochMilli();
        }
    });
env.execute();


// === EXAMPLE 3 ===

// convert to DataStream but write out the time attribute as a metadata column which means
// it is not part of the physical schema anymore

DataStream<Row> dataStream = tableEnv.toChangelogStream(
    table,
    Schema.newBuilder()
        .column("name", "STRING")
        .column("score", "INT")
        .columnByMetadata("rowtime", "TIMESTAMP_LTZ(3)")
        .build());

// the stream record's timestamp is defined by the metadata; it is not part of the Row

dataStream.process(
    new ProcessFunction<Row, Void>() {
        @Override
        public void processElement(Row row, Context ctx, Collector<Void> out) {

            // prints: [name, score]
            System.out.println(row.getFieldNames(true));

            // timestamp exists once
            System.out.println(ctx.timestamp());
        }
    });
env.execute();


// === EXAMPLE 4 ===

// for advanced users, it is also possible to use more internal data structures for efficiency

// note that this is only mentioned here for completeness because using internal data structures
// adds complexity and additional type handling

// however, converting a TIMESTAMP_LTZ column to `Long` or STRING to `byte[]` might be convenient,
// also structured types can be represented as `Row` if needed

DataStream<Row> dataStream = tableEnv.toChangelogStream(
    table,
    Schema.newBuilder()
        .column(
            "name",
            DataTypes.STRING().bridgedTo(StringData.class))
        .column(
            "score",
            DataTypes.INT())
        .column(
            "event_time",
            DataTypes.TIMESTAMP_LTZ(3).bridgedTo(Long.class))
        .build());

// leads to a stream of Row(name: StringData, score: Integer, event_time: Long)

For more information about which conversions are supported for data types in Example 4, see the Table API’s Data Types page.

The behavior of toChangelogStream(Table).executeAndCollect() is equal to calling Table.execute().collect(). However, toChangelogStream(Table) might be more useful for tests because it allows to access the produced watermarks in a subsequent ProcessFunction in DataStream API.