flink自定义数据落地到hive代码示例

以下是一个简单的 Flink 代码示例，它将自定义数据流落地到 Hive 表中：

import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.common.serialization.SimpleStringEncoder;
import org.apache.flink.api.common.serialization.SimpleStringSchema;
import org.apache.flink.core.fs.Path;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.sink.filesystem.StreamingFileSink;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer;
import org.apache.flink.table.api.EnvironmentSettings;
import org.apache.flink.table.api.Table;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
import org.apache.flink.table.catalog.hive.HiveCatalog;
import org.apache.hadoop.hive.conf.HiveConf;

import java.util.Properties;

public class FlinkHiveSink {

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

        // 设置执行环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);

        // 设置 Table 环境
        EnvironmentSettings settings = EnvironmentSettings.newInstance().useBlinkPlanner().inStreamingMode().build();
        StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env, settings);

        // 设置 Hive catalog
        String catalogName = "my_hive_catalog";
        String defaultDatabase = "default";
        String hiveConfDir = "/etc/hadoop/conf";
        HiveConf hiveConf = new HiveConf();
        hiveConf.addResource(new Path(hiveConfDir + "/hive-site.xml"));
        HiveCatalog hiveCatalog = new HiveCatalog(catalogName, defaultDatabase, hiveConf);
        tableEnv.registerCatalog(catalogName, hiveCatalog);

        // 设置 Kafka 数据源
        Properties kafkaProps = new Properties();
        kafkaProps.setProperty("bootstrap.servers", "<your-bootstrap-servers>");
        kafkaProps.setProperty("group.id", "<your-group-id>");
        FlinkKafkaConsumer<String> kafkaConsumer = new FlinkKafkaConsumer<>("my-topic", new SimpleStringSchema(), kafkaProps);
        DataStream<String> dataStream = env.addSource(kafkaConsumer);

        // 将数据流转换为 Table
        Table table = tableEnv.fromDataStream(dataStream, "value");

        // 定义输出的 Table
        String tableName = "my_hive_table";
        String createTableStmt = String.format("CREATE TABLE IF NOT EXISTS %s (value STRING) STORED AS TEXTFILE", tableName);
        tableEnv.executeSql(createTableStmt);
        tableEnv.useCatalog(catalogName);
        tableEnv.useDatabase(defaultDatabase);
        tableEnv.createTemporaryView("temp_table", table);
        String insertStmt = String.format("INSERT INTO %s SELECT * FROM temp_table", tableName);
        tableEnv.executeSql(insertStmt);

        // 将数据流落地到 HDFS
        StreamingFileSink<String> sink = StreamingFileSink
                .forRowFormat(new Path("hdfs://<your-hdfs-path>"), new SimpleStringEncoder<String>("UTF-8"))
                .build();
        dataStream.map(new MapFunction<String, String>() {
            @Override
            public String map(String value) throws Exception {
                return value;
            }
        }).addSink(sink);

        // 执行任务
        env.execute("Flink Hive Sink Example");
    }
}

在这个示例中，我们首先设置了执行环境和 Table 环境。然后，我们设置了 Hive catalog 和 Kafka 数据源。接着，我们将数据流转换为 Table 并定义要输出的 Table。最后，我们使用 Flink 的 StreamingFileSink 将数据写入 HDFS 中。