集成 Amazon Glue、ClickHouse 与 Spark

集成 ClickHouse 与 Amazon Glue

Amazon Glue 是 Amazon Web Services (AWS) 提供的一项全托管、无服务器数据集成服务。它简化了为分析、机器学习和应用开发而发现、准备和转换数据的过程。

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安装

要将你的 Glue 代码与 ClickHouse 集成，可以通过以下任一方式在 Glue 中使用我们的官方 Spark Connector：

• 从 AWS Marketplace 安装 ClickHouse Glue 连接器 (推荐) 。
• 手动将 Spark Connector 的 JAR 添加到你的 Glue 作业中。

AWS Marketplace

Glue 连接器中使用的 JAR 是为 Spark 3.3、Scala 2 和 Python 3 构建的。配置 Glue 作业时，请确保选择这些版本。

手动安装

如需手动添加所需的 JAR，请按以下步骤操作：

1. 将以下 JAR 上传到一个 S3 bucket：clickhouse-jdbc-0.6.X-all.jar 和 clickhouse-spark-runtime-3.X_2.X-0.8.X.jar。
2. 确保 Glue 作业可以访问该 bucket。
3. 在 Job details 选项卡下，向下滚动并展开 Advanced properties 下拉菜单，然后在 Dependent JARs path 中填写 JAR 路径：

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示例

Scala

import com.amazonaws.services.glue.GlueContext
import com.amazonaws.services.glue.util.GlueArgParser
import com.amazonaws.services.glue.util.Job
import com.clickhouseScala.Native.NativeSparkRead.spark
import org.apache.spark.sql.SparkSession

import scala.collection.JavaConverters._
import org.apache.spark.sql.types._
import org.apache.spark.sql.functions._

object ClickHouseGlueExample {
  def main(sysArgs: Array[String]) {
    val args = GlueArgParser.getResolvedOptions(sysArgs, Seq("JOB_NAME").toArray)

    val sparkSession: SparkSession = SparkSession.builder
      .config("spark.sql.catalog.clickhouse", "com.clickhouse.spark.ClickHouseCatalog")
      .config("spark.sql.catalog.clickhouse.host", "<your-clickhouse-host>")
      .config("spark.sql.catalog.clickhouse.protocol", "https")
      .config("spark.sql.catalog.clickhouse.http_port", "<your-clickhouse-port>")
      .config("spark.sql.catalog.clickhouse.user", "default")
      .config("spark.sql.catalog.clickhouse.password", "<your-password>")
      .config("spark.sql.catalog.clickhouse.database", "default")
      // 用于 ClickHouse Cloud
      .config("spark.sql.catalog.clickhouse.option.ssl", "true")
      .config("spark.sql.catalog.clickhouse.option.ssl_mode", "NONE")
      .getOrCreate

    val glueContext = new GlueContext(sparkSession.sparkContext)
    Job.init(args("JOB_NAME"), glueContext, args.asJava)
    import sparkSession.implicits._

    val url = "s3://{path_to_cell_tower_data}/cell_towers.csv.gz"

    val schema = StructType(Seq(
      StructField("radio", StringType, nullable = false),
      StructField("mcc", IntegerType, nullable = false),
      StructField("net", IntegerType, nullable = false),
      StructField("area", IntegerType, nullable = false),
      StructField("cell", LongType, nullable = false),
      StructField("unit", IntegerType, nullable = false),
      StructField("lon", DoubleType, nullable = false),
      StructField("lat", DoubleType, nullable = false),
      StructField("range", IntegerType, nullable = false),
      StructField("samples", IntegerType, nullable = false),
      StructField("changeable", IntegerType, nullable = false),
      StructField("created", TimestampType, nullable = false),
      StructField("updated", TimestampType, nullable = false),
      StructField("averageSignal", IntegerType, nullable = false)
    ))

    val df = sparkSession.read
      .option("header", "true")
      .schema(schema)
      .csv(url)

    // 写入 ClickHouse
    df.writeTo("clickhouse.default.cell_towers").append()

    // 从 ClickHouse 读取
    val dfRead = spark.sql("select * from clickhouse.default.cell_towers")
    Job.commit()
  }
}

Python

import sys
from awsglue.transforms import *
from awsglue.utils import getResolvedOptions
from pyspark.context import SparkContext
from awsglue.context import GlueContext
from awsglue.job import Job
from pyspark.sql import Row

## @params: [JOB_NAME]
args = getResolvedOptions(sys.argv, ['JOB_NAME'])

sc = SparkContext()
glueContext = GlueContext(sc)
logger = glueContext.get_logger()
spark = glueContext.spark_session
job = Job(glueContext)
job.init(args['JOB_NAME'], args)

spark.conf.set("spark.sql.catalog.clickhouse", "com.clickhouse.spark.ClickHouseCatalog")
spark.conf.set("spark.sql.catalog.clickhouse.host", "<your-clickhouse-host>")
spark.conf.set("spark.sql.catalog.clickhouse.protocol", "https")
spark.conf.set("spark.sql.catalog.clickhouse.http_port", "<your-clickhouse-port>")
spark.conf.set("spark.sql.catalog.clickhouse.user", "default")
spark.conf.set("spark.sql.catalog.clickhouse.password", "<your-password>")
spark.conf.set("spark.sql.catalog.clickhouse.database", "default")
spark.conf.set("spark.clickhouse.write.format", "json")
spark.conf.set("spark.clickhouse.read.format", "arrow")
# 适用于 ClickHouse Cloud
spark.conf.set("spark.sql.catalog.clickhouse.option.ssl", "true")
spark.conf.set("spark.sql.catalog.clickhouse.option.ssl_mode", "NONE")

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

# 将 DataFrame 写入 ClickHouse
df.writeTo("clickhouse.default.example_table").append()

# 从 ClickHouse 读取 DataFrame
df_read = spark.sql("select * from clickhouse.default.example_table")
logger.info(str(df.take(10)))

job.commit()

更多详情，请参阅我们的 Spark 文档。