Flink导入StarRocks

1、pom依赖


  <properties>
    <maven.compiler.source>8</maven.compiler.source>
    <maven.compiler.target>8</maven.compiler.target>
    <flink.version>1.13.6</flink.version>
    <scala.binary.version>2.12</scala.binary.version>
  </properties>

  <dependencies>
    <!-- Apache Flink 的依赖, 这些依赖项,生产环境可以不打包到JAR文件中. -->
    <dependency>
      <groupId>org.apache.flink</groupId>
      <artifactId>flink-java</artifactId>
      <version>${flink.version}</version>
    </dependency>
    <dependency>
      <groupId>org.apache.flink</groupId>
      <artifactId>flink-streaming-java_${scala.binary.version}</artifactId>
      <version>${flink.version}</version>
    </dependency>
    <dependency>
      <groupId>org.apache.flink</groupId>
      <artifactId>flink-runtime-web_${scala.binary.version}</artifactId>
      <version>${flink.version}</version>
    </dependency>
    <dependency>
      <groupId>org.apache.flink</groupId>
      <artifactId>flink-table-planner_${scala.binary.version}</artifactId>
      <version>${flink.version}</version>
    </dependency>
    <!-- flink-connector-starrocks -->
    <dependency>
      <groupId>com.starrocks</groupId>
      <artifactId>flink-connector-starrocks</artifactId>
      <version>1.2.5_flink-1.13_2.12</version>
    </dependency>
  </dependencies>

2、代码编写

public class LoadJsonRecords {
    public static void main(String[] args) throws Exception {
        // To run the example, you should prepare in the following steps
        // 1. create a primary key table in your StarRocks cluster. The DDL is
        //  CREATE DATABASE `test`;
        //    CREATE TABLE `test`.`score_board`
        //    (
        //        `id` int(11) NOT NULL COMMENT "",
        //        `name` varchar(65533) NULL DEFAULT "" COMMENT "",
        //        `score` int(11) NOT NULL DEFAULT "0" COMMENT ""
        //    )
        //    ENGINE=OLAP
        //    PRIMARY KEY(`id`)
        //    COMMENT "OLAP"
        //    DISTRIBUTED BY HASH(`id`)
        //    PROPERTIES(
        //        "replication_num" = "1"
        //    );
        //
        // 2. replace the connector options "jdbc-url" and "load-url" with your cluster configurations
        MultipleParameterTool params = MultipleParameterTool.fromArgs(args);
        String jdbcUrl = params.get("jdbcUrl", "jdbc:mysql://fe-ip:9030");
        String loadUrl = params.get("loadUrl", "be-ip:8040;be-ip:8040;be-ip:8040");

        //String jdbcUrl = params.get("jdbcUrl", "jdbc:mysql://fe-ip:9030");
        //String loadUrl = params.get("loadUrl", "be-ip:8040;be-ip:8040;be-ip:8040");

        //String jdbcUrl = params.get("jdbcUrl", "jdbc:mysql://fe-ip:9030");
        //String loadUrl = params.get("loadUrl", "be-ip:8040,be-ip:8040,be-ip:8040");

        final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

        // Generate json-format records. Each record has three fields correspond to
        // the columns `id`, `name`, and `score` in StarRocks table.
        String[] records = new String[]{
                "{\"id\":1111, \"name\":\"starrocks-json\", \"score\":100}",
                "{\"id\":2222, \"name\":\"flink-json\", \"score\":100}",
        };
        DataStream<String> source = env.fromElements(records);

        // Configure the connector with the required properties, and you also need to add properties
        // "sink.properties.format" and "sink.properties.strip_outer_array" to tell the connector the
        // input records are json-format.
        StarRocksSinkOptions options = StarRocksSinkOptions.builder()
                .withProperty("jdbc-url", jdbcUrl)
                .withProperty("load-url", loadUrl)
                .withProperty("database-name", "tmp")
                .withProperty("table-name", "score_board")
                .withProperty("username", "")
                .withProperty("password", "")
                .withProperty("sink.properties.format", "json")
                .withProperty("sink.properties.strip_outer_array", "true")
                .withProperty("sink.parallelism","1")
                //.withProperty("sink.version","V1")
                .build();
        // Create the sink with the options
        SinkFunction<String> starRockSink = StarRocksSink.sink(options);
        source.addSink(starRockSink);

        env.execute("LoadJsonRecords");
    }
}

### 集成 FlinkStarRocks 的方法 Flink 是一种分布式流处理框架,而 StarRocks 是一款高性能的 OLAP 数据库。两者可以很好地协同工作,特别是在实时数据处理和分析场景下。以下是关于如何将 FlinkStarRocks 集成的一些关键点: #### 1. **通过 JDBC 连接器实现写入** Flink 提供了一个通用的 JDBC 输出连接器,可以通过该连接器将数据写入到支持 JDBC 协议的关系型数据库中,包括 StarRocks- 使用 `JDBCOutputFormat` 或者 `JdbcSink` 将数据从 Flink 流式作业写入到 StarRocks 表中。 - 下面是一个简单的代码示例展示如何配置并使用 JdbcSink 来向 StarRocks 写入数据[^5]: ```java import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment; import org.apache.flink.connector.jdbc.JdbcConnectionOptions; import org.apache.flink.connector.jdbc.JdbcSink; public class FlinkToStarRocks { public static void main(String[] args) throws Exception { StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment(); String url = "jdbc:mysql://<starrocks-host>:<port>/<database>"; String username = "<username>"; String password = "<password>"; env.fromElements(new Tuple2<>(1L, "Hello"), new Tuple2<>(2L, "World")) .addSink(JdbcSink.sink( "INSERT INTO my_table (id, message) VALUES (?, ?)", (ps, t) -> { ps.setLong(1, t.f0); ps.setString(2, t.f1); }, new JdbcConnectionOptions.JdbcConnectionOptionsBuilder() .withUrl(url) .withDriverName("com.mysql.cj.jdbc.Driver") .withUsername(username) .withPassword(password) .build())); env.execute("Write to StarRocks"); } } ``` #### 2. **利用 StarRocks 的 Streaming Load 功能** 除了传统的批量加载方式外,StarRocks 支持 Streaming Load 方式来实现实时的数据导入。这种方式允许用户通过 HTTP 接口直接上传 JSON/CSV 文件或者标准输入流中的数据至目标表。 - 可以在 Flink 中自定义 SinkFunction 实现对 StarRocks 的 Streaming Load 调用[^6]: ```java import java.io.BufferedReader; import java.io.InputStreamReader; import java.net.HttpURLConnection; import java.net.URL; public class StarRocksStreamingLoad extends RichSinkFunction<String> { private final String loadUrl; public StarRocksStreamingLoad(String host, int port, String db, String table){ this.loadUrl = String.format("http://%s:%d/api/%s/%s/_stream_load", host, port, db, table); } @Override public void invoke(String value, Context context) throws Exception { URL obj = new URL(loadUrl); HttpURLConnection con = (HttpURLConnection)obj.openConnection(); con.setRequestMethod("PUT"); // Set headers as required by StarRocks. con.setRequestProperty("label", System.currentTimeMillis()+""); con.setRequestProperty("format", "json"); con.setDoOutput(true); try(BufferedWriter wr = new BufferedWriter(new OutputStreamWriter(con.getOutputStream(), StandardCharsets.UTF_8))){ wr.write(value); } BufferedReader in = new BufferedReader(new InputStreamReader(con.getInputStream())); StringBuilder response = new StringBuilder(); String inputLine; while ((inputLine = in.readLine()) != null) { response.append(inputLine); } in.close(); } } ``` #### 3. **性能优化建议** 为了提高整个系统的吞吐量以及降低延迟,在实际部署过程中需要注意以下几个方面: - 合理设置并发度:根据硬件资源情况调整 TaskManager 数目及其 slot 大小,从而达到最佳平衡状态[^7]。 - 批量提交机制:对于频繁的小规模更新操作来说,启用批处理模式能够显著减少网络开销并提升效率[^8]。 ---
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