kafka flinkclickhouse如何進行數據轉換

Kafka Flink ClickHouse 是一個基于 Apache Flink 和 ClickHouse 的實時數據處理和分析平臺

1. 添加依賴：首先，確保你的項目中已經添加了 Flink 和 ClickHouse 的相關依賴。在 Maven 項目的 pom.xml 文件中添加以下依賴：

<dependencies>
    <!-- Flink dependency -->
    <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>${flink.version}</version>
    </dependency>
    <dependency>
        <groupId>org.apache.flink</groupId>
        <artifactId>flink-streaming-scala_${scala.binary.version}</artifactId>
        <version>${flink.version}</version>
    </dependency>
    <dependency>
        <groupId>org.apache.flink</groupId>
        <artifactId>flink-connector-kafka_${scala.binary.version}</artifactId>
        <version>${flink.version}</version>
    </dependency>
    <dependency>
        <groupId>org.apache.flink</groupId>
        <artifactId>flink-connector-clickhouse_${scala.binary.version}</artifactId>
        <version>${flink.version}</version>
    </dependency>
</dependencies>

2. 創建 Flink 流處理程序：創建一個繼承 org.apache.flink.streaming.api.datastream.DataStream 的類，并實現數據轉換邏輯。例如，假設我們有一個 Kafka 主題 input_topic，包含以下字段：id（整數），name（字符串），timestamp（時間戳）。我們希望將其轉換為 ClickHouse 表結構，并將其寫入 ClickHouse 表 output_table。

import org.apache.flink.api.common.serialization.SimpleStringSchema;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer;
import org.apache.flink.streaming.connectors.clickhouse.ClickHouseSink;
import org.apache.flink.streaming.connectors.clickhouse.ClickHouseOptions;
import org.apache.flink.streaming.connectors.clickhouse.ClickHouseTableSchema;
import org.apache.flink.streaming.connectors.clickhouse.internal.ClickHouseConnectionOptions;
import org.apache.flink.streaming.connectors.clickhouse.internal.ClickHouseTableSchemaBuilder;

public class KafkaFlinkClickHouseExample {
    public static void main(String[] args) throws Exception {
        final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

        // 創建 Kafka 消費者
        FlinkKafkaConsumer<String> kafkaConsumer = new FlinkKafkaConsumer<>("input_topic", new SimpleStringSchema(), properties);

        // 讀取數據流
        DataStream<String> stream = env.addSource(kafkaConsumer);

        // 定義 ClickHouse 表結構
        ClickHouseTableSchema schema = ClickHouseTableSchemaBuilder
                .builder()
                .addPrimaryKey("id")
                .addColumn("name", "String")
                .addColumn("timestamp", "DateTime")
                .build();

        // 創建 ClickHouse 連接選項
        ClickHouseConnectionOptions connectionOptions = new ClickHouseConnectionOptions.Builder()
                .withUrl("jdbc:clickhouse://localhost:8123")
                .withUsername("default")
                .withPassword("")
                .build();

        // 創建 ClickHouse  sink
        ClickHouseSink<String> clickHouseSink = new ClickHouseSink<>(
                connectionOptions,
                "default",
                "output_table",
                schema,
                new ClickHouseOptions.ClickHouseWriteMode(),
                new ClickHouseOptions.ClickHouseFormatOption("JSONEachRow"),
                new ClickHouseOptions.ClickHouseCompression("LZ4"));

        // 將數據流寫入 ClickHouse
        stream.addSink(clickHouseSink);

        // 啟動 Flink 作業
        env.execute("Kafka Flink ClickHouse Example");
    }
}

在這個示例中，我們首先創建了一個 Kafka 消費者來讀取 input_topic 的數據。然后，我們定義了 ClickHouse 表結構，并創建了 ClickHouse 連接選項。接下來，我們創建了一個 ClickHouse Sink，將數據流寫入 output_table。最后，我們啟動了 Flink 作業。

注意：請根據實際情況修改 Kafka 和 ClickHouse 的配置參數，例如 URL、端口、用戶名、密碼等。