Spark JDBC

ClickHouse Supported

JDBC 是 Spark 中最常用的数据源之一。 在本节中，我们将介绍如何使用 ClickHouse 官方 JDBC 连接器 与 Spark 集成。

• 读取数据
• 写入数据
• 并行度
• JDBC 限制

读取数据

Java

public static void main(String[] args) {
        // 初始化 Spark 会话
        SparkSession spark = SparkSession.builder().appName("example").master("local").getOrCreate();

        String jdbcURL = "jdbc:ch://localhost:8123/default";
        String query = "select * from example_table where id > 2";

        //---------------------------------------------------------------------------------------------------
        // 使用 JDBC 从 ClickHouse 读取表数据
        //---------------------------------------------------------------------------------------------------
        Properties jdbcProperties = new Properties();
        jdbcProperties.put("user", "default");
        jdbcProperties.put("password", "123456");

        Dataset<Row> df1 = spark.read().jdbc(jdbcURL, String.format("(%s)", query), jdbcProperties);

        df1.show();

        //---------------------------------------------------------------------------------------------------
        // 使用 load 方法从 ClickHouse 读取表数据
        //---------------------------------------------------------------------------------------------------
        Dataset<Row> df2 = spark.read()
                .format("jdbc")
                .option("url", jdbcURL)
                .option("user", "default")
                .option("password", "123456")
                .option("query", query)
                .load();

        df2.show();

        // 停止 Spark 会话
        spark.stop();
    }

Scala

object ReadData extends App {
  // 初始化 Spark 会话
  val spark: SparkSession = SparkSession.builder.appName("example").master("local").getOrCreate

  val jdbcURL = "jdbc:ch://localhost:8123/default"
  val query: String = "select * from example_table where id > 2"

  //---------------------------------------------------------------------------------------------------
  // 使用 JDBC 从 ClickHouse 读取表数据
  //---------------------------------------------------------------------------------------------------
  val connectionProperties = new Properties()
  connectionProperties.put("user", "default")
  connectionProperties.put("password", "123456")

  val df1: Dataset[Row] = spark.read.
    jdbc(jdbcURL, s"($query)", connectionProperties)

  df1.show()
  //---------------------------------------------------------------------------------------------------
  // 使用 load 方法从 ClickHouse 读取表数据
  //---------------------------------------------------------------------------------------------------
  val df2: Dataset[Row] = spark.read
    .format("jdbc")
    .option("url", jdbcURL)
    .option("user", "default")
    .option("password", "123456")
    .option("query", query)
    .load()

  df2.show()

  // 停止 Spark 会话// 停止 Spark 会话
  spark.stop()

}

Python

from pyspark.sql import SparkSession

jar_files = [
    "jars/clickhouse-jdbc-X.X.X-SNAPSHOT-all.jar"
]

使用 JAR 文件初始化 Spark 会话

spark = SparkSession.builder
.appName("example")
.master("local")
.config("spark.jars", ",".join(jar_files))
.getOrCreate()

url = "jdbc:ch://localhost:8123/default" user = "your_user" password = "your_password"
query = "select * from example_table where id > 2" driver = "com.clickhouse.jdbc.ClickHouseDriver"

df = (spark.read .format('jdbc') .option('driver', driver) .option('url', url) .option('user', user) .option('password', password).option( 'query', query).load())

df.show()

</TabItem>
<TabItem value="SparkSQL" label="Spark SQL">

```sql
   CREATE TEMPORARY VIEW jdbcTable
           USING org.apache.spark.sql.jdbc
           OPTIONS (
                   url "jdbc:ch://localhost:8123/default",
                   dbtable "schema.tablename",
                   user "username",
                   password "password",
                   driver "com.clickhouse.jdbc.ClickHouseDriver"
           );

   SELECT * FROM jdbcTable;

写入数据

Java

 public static void main(String[] args) {
        // 初始化 Spark 会话
        SparkSession spark = SparkSession.builder().appName("example").master("local").getOrCreate();

        // JDBC 连接详细信息
        String jdbcUrl = "jdbc:ch://localhost:8123/default";
        Properties jdbcProperties = new Properties();
        jdbcProperties.put("user", "default");
        jdbcProperties.put("password", "123456");

        // 创建示例 DataFrame
        StructType schema = new StructType(new StructField[]{
                DataTypes.createStructField("id", DataTypes.IntegerType, false),
                DataTypes.createStructField("name", DataTypes.StringType, false)
        });

        List<Row> rows = new ArrayList<Row>();
        rows.add(RowFactory.create(1, "John"));
        rows.add(RowFactory.create(2, "Doe"));

        Dataset<Row> df = spark.createDataFrame(rows, schema);

        //---------------------------------------------------------------------------------------------------
        // 使用 jdbc 方法将 DataFrame 写入 ClickHouse
        //---------------------------------------------------------------------------------------------------

        df.write()
                .mode(SaveMode.Append)
                .jdbc(jdbcUrl, "example_table", jdbcProperties);

        //---------------------------------------------------------------------------------------------------
        // 使用 save 方法将 DataFrame 写入 ClickHouse
        //---------------------------------------------------------------------------------------------------

        df.write()
                .format("jdbc")
                .mode("append")
                .option("url", jdbcUrl)
                .option("dbtable", "example_table")
                .option("user", "default")
                .option("password", "123456")
                .save();

        // 停止 Spark 会话
        spark.stop();
    }

Scala

object WriteData extends App {

  val spark: SparkSession = SparkSession.builder.appName("example").master("local").getOrCreate

  // JDBC 连接详细信息
  val jdbcUrl: String = "jdbc:ch://localhost:8123/default"
  val jdbcProperties: Properties = new Properties
  jdbcProperties.put("user", "default")
  jdbcProperties.put("password", "123456")

  // 创建示例 DataFrame

  val rows = Seq(Row(1, "John"), Row(2, "Doe"))

  val schema = List(
    StructField("id", DataTypes.IntegerType, nullable = false),
    StructField("name", StringType, nullable = true)
  )

  val df: DataFrame = spark.createDataFrame(
    spark.sparkContext.parallelize(rows),
    StructType(schema)
  )

  //---------------------------------------------------------------------------------------------------//---------------------------------------------------------------------------------------------------
  // 使用 jdbc 方法将 DataFrame 写入 ClickHouse
  //---------------------------------------------------------------------------------------------------//---------------------------------------------------------------------------------------------------

  df.write
    .mode(SaveMode.Append)
    .jdbc(jdbcUrl, "example_table", jdbcProperties)

  //---------------------------------------------------------------------------------------------------//---------------------------------------------------------------------------------------------------
  // 使用 save 方法将 DataFrame 写入 ClickHouse
  //---------------------------------------------------------------------------------------------------//---------------------------------------------------------------------------------------------------

  df.write
    .format("jdbc")
    .mode("append")
    .option("url", jdbcUrl)
    .option("dbtable", "example_table")
    .option("user", "default")
    .option("password", "123456")
    .save()

  // 停止 Spark 会话// 停止 Spark 会话
  spark.stop()

}

Python

from pyspark.sql import SparkSession
from pyspark.sql import Row

jar_files = [
    "jars/clickhouse-jdbc-X.X.X-SNAPSHOT-all.jar"
]

使用 JAR 包初始化 Spark 会话

spark = SparkSession.builder
.appName("example")
.master("local")
.config("spark.jars", ",".join(jar_files))
.getOrCreate()

创建 DataFrame

data = [Row(id=11, name="John"), Row(id=12, name="Doe")] df = spark.createDataFrame(data)

url = "jdbc:ch://localhost:8123/default" user = "your_user" password = "your_password"
driver = "com.clickhouse.jdbc.ClickHouseDriver"

将 DataFrame 写入 ClickHouse

df.write
.format("jdbc")
.option("driver", driver)
.option("url", url)
.option("user", user)
.option("password", password)
.option("dbtable", "example_table")
.mode("append")
.save()

</TabItem>
<TabItem value="SparkSQL" label="Spark SQL">

```sql
   CREATE TEMPORARY VIEW jdbcTable
           USING org.apache.spark.sql.jdbc
           OPTIONS (
                   url "jdbc:ch://localhost:8123/default",
                   dbtable "schema.tablename",
                   user "username",
                   password "password",
                   driver "com.clickhouse.jdbc.ClickHouseDriver"
           );
   -- resultTable 可以通过 df.createTempView 或 Spark SQL 创建
   INSERT INTO TABLE jdbcTable
                SELECT * FROM resultTable;

并行度

使用 Spark JDBC 时，Spark 默认只使用单个分区读取数据。要获得更高的并行度，你需要指定 partitionColumn、lowerBound、upperBound 和 numPartitions，用于定义在由多个 worker 并行读取时如何对表进行分区。 如需了解更多信息，请参阅 Apache Spark 官方文档中的 JDBC 配置。

JDBC 限制

• 截至目前，通过 JDBC 只能将数据插入到已存在的表中（目前无法像 Spark 使用其他连接器那样，在插入 DataFrame 时自动创建表）。