Scala 脚本示例 - 流式处理 ETL

以下示例脚本连接到 Amazon Kinesis Data Streams，使用来自数据目录的架构解析数据流，将流联接到 Amazon S3 上的静态数据集，并以 parquet 格式将联接结果输出到 Amazon S3。

// This script connects to an Amazon Kinesis stream, uses a schema from the data catalog to parse the stream,
// joins the stream to a static dataset on Amazon S3, and outputs the joined results to Amazon S3 in parquet format.
import com.amazonaws.services.glue.GlueContext
import com.amazonaws.services.glue.util.GlueArgParser
import com.amazonaws.services.glue.util.Job
import java.util.Calendar
import org.apache.spark.SparkContext
import org.apache.spark.sql.Dataset
import org.apache.spark.sql.Row
import org.apache.spark.sql.SaveMode
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.functions.from_json
import org.apache.spark.sql.streaming.Trigger
import scala.collection.JavaConverters._

object streamJoiner {
  def main(sysArgs: Array[String]) {
    val spark: SparkContext = new SparkContext()
    val glueContext: GlueContext = new GlueContext(spark)
    val sparkSession: SparkSession = glueContext.getSparkSession
    import sparkSession.implicits._
    // @params: [JOB_NAME]
    val args = GlueArgParser.getResolvedOptions(sysArgs, Seq("JOB_NAME").toArray)
    Job.init(args("JOB_NAME"), glueContext, args.asJava)

    val staticData = sparkSession.read          // read() returns type DataFrameReader
      .format("csv")
      .option("header", "true")
      .load("s3://awsexamplebucket-streaming-demo2/inputs/productsStatic.csv")  // load() returns a DataFrame

    val datasource0 = sparkSession.readStream   // readstream() returns type DataStreamReader
      .format("kinesis")
      .option("streamName", "stream-join-demo")
      .option("endpointUrl", "https://kinesis.us-east-1.amazonaws.com")
      .option("startingPosition", "TRIM_HORIZON")
      .load                                     // load() returns a DataFrame

    val selectfields1 = datasource0.select(from_json($"data".cast("string"), glueContext.getCatalogSchemaAsSparkSchema("stream-demos", "stream-join-demo2")) as "data").select("data.*")

    val datasink2 = selectfields1.writeStream.foreachBatch { (dataFrame: Dataset[Row], batchId: Long) => {   //foreachBatch() returns type DataStreamWriter
      val joined = dataFrame.join(staticData, "product_id")
      val year: Int = Calendar.getInstance().get(Calendar.YEAR)
      val month :Int = Calendar.getInstance().get(Calendar.MONTH) + 1
      val day: Int = Calendar.getInstance().get(Calendar.DATE)
      val hour: Int = Calendar.getInstance().get(Calendar.HOUR_OF_DAY)

      if (dataFrame.count() > 0) {
        joined.write                           // joined.write returns type DataFrameWriter
          .mode(SaveMode.Append)
          .format("parquet")
          .option("quote", " ")
          .save("s3://awsexamplebucket-streaming-demo2/output/" + "/year=" + "%04d".format(year) + "/month=" + "%02d".format(month) + "/day=" + "%02d".format(day) + "/hour=" + "%02d".format(hour) + "/")
      }
    }
    }  // end foreachBatch()
      .trigger(Trigger.ProcessingTime("100 seconds"))
      .option("checkpointLocation", "s3://awsexamplebucket-streaming-demo2/checkpoint/")
      .start().awaitTermination()              // start() returns type StreamingQuery
    Job.commit()
  }
}