scala - Spark 2.2.0 - 如何将 DataFrame 写入/读取 DynamoDB

Question

我希望我的 Spark 应用程序从 DynamoDB 读取表，执行操作，然后将结果写入 DynamoDB。

将表读入 DataFrame

现在，我可以将表从 DynamoDB 读取到 Spark 中，格式为 hadoopRDD并将其转换为 DataFrame。但是，我必须使用正则表达式从 AttributeValue 中提取值。 。有更好/更优雅的方式吗？在 AWS API 中找不到任何内容。

package main.scala.util

import org.apache.spark.sql.SparkSession
import org.apache.spark.SparkContext
import org.apache.spark.sql.SQLContext
import org.apache.spark.sql.functions._
import org.apache.spark.sql.types._
import org.apache.spark.rdd.RDD
import scala.util.matching.Regex
import java.util.HashMap

import com.amazonaws.services.dynamodbv2.model.AttributeValue
import org.apache.hadoop.io.Text;
import org.apache.hadoop.dynamodb.DynamoDBItemWritable
/* Importing DynamoDBInputFormat and DynamoDBOutputFormat */
import org.apache.hadoop.dynamodb.read.DynamoDBInputFormat
import org.apache.hadoop.dynamodb.write.DynamoDBOutputFormat
import org.apache.hadoop.mapred.JobConf
import org.apache.hadoop.io.LongWritable

object Tester {

  // {S: 298905396168806365,} 
  def extractValue : (String => String) = (aws:String) => {
    val pat_value = "\\s(.*),".r

    val matcher = pat_value.findFirstMatchIn(aws)
                matcher match {
                case Some(number) => number.group(1).toString
                case None => ""
        }
  }


   def main(args: Array[String]) {
    val spark = SparkSession.builder().getOrCreate()
    val sparkContext = spark.sparkContext

      import spark.implicits._

      // UDF to extract Value from AttributeValue 
      val col_extractValue = udf(extractValue)

  // Configure connection to DynamoDB
  var jobConf_add = new JobConf(sparkContext.hadoopConfiguration)
      jobConf_add.set("dynamodb.input.tableName", "MyTable")
      jobConf_add.set("dynamodb.output.tableName", "MyTable")
      jobConf_add.set("mapred.output.format.class", "org.apache.hadoop.dynamodb.write.DynamoDBOutputFormat")
      jobConf_add.set("mapred.input.format.class", "org.apache.hadoop.dynamodb.read.DynamoDBInputFormat")


      // org.apache.spark.rdd.RDD[(org.apache.hadoop.io.Text, org.apache.hadoop.dynamodb.DynamoDBItemWritable)]
      var hadooprdd_add = sparkContext.hadoopRDD(jobConf_add, classOf[DynamoDBInputFormat], classOf[Text], classOf[DynamoDBItemWritable])

      // Convert HadoopRDD to RDD
      val rdd_add: RDD[(String, String)] = hadooprdd_add.map {
      case (text, dbwritable) => (dbwritable.getItem().get("PIN").toString(), dbwritable.getItem().get("Address").toString())
      }

      // Convert RDD to DataFrame and extract Values from AttributeValue
      val df_add = rdd_add.toDF()
                  .withColumn("PIN", col_extractValue($"_1"))
                  .withColumn("Address", col_extractValue($"_2"))
                  .select("PIN","Address")
   }
}

将 DataFrame 写入 DynamoDB

stackoverflow 和其他地方的许多答案都只指向 blog post和 emr-dynamodb-hadoop github 。这些资源都没有实际演示如何写入 DynamoDB。

I tried converting我的DataFrame至RDD[Row]没有成功。

df_add.rdd.saveAsHadoopDataset(jobConf_add)

将此 DataFrame 写入 DynamoDB 的步骤是什么？ (如果你告诉我如何控制 overwrite 与 putItem 即可获得奖励积分；)

注:df_add与 MyTable 具有相同的架构在 DynamoDB 中。

编辑:我遵循 this answer 的建议它指向 Using Spark SQL for ETL 上的这篇文章:

// Format table to DynamoDB format
  val output_rdd =  df_add.as[(String,String)].rdd.map(a => {
    var ddbMap = new HashMap[String, AttributeValue]()

    // Field PIN
    var PINValue = new AttributeValue() // New AttributeValue
    PINValue.setS(a._1)                 // Set value of Attribute as String. First element of tuple
    ddbMap.put("PIN", PINValue)         // Add to HashMap

    // Field Address
    var AddValue = new AttributeValue() // New AttributeValue
    AddValue.setS(a._2)                 // Set value of Attribute as String
    ddbMap.put("Address", AddValue)     // Add to HashMap

    var item = new DynamoDBItemWritable()
    item.setItem(ddbMap)

    (new Text(""), item)
  })             

  output_rdd.saveAsHadoopDataset(jobConf_add) 

但是，现在我得到 java.lang.ClassCastException: java.lang.String cannot be cast to org.apache.hadoop.io.Text尽管遵循文档...您有什么建议吗？

编辑 2:更仔细地阅读 Using Spark SQL for ETL 上的这篇文章:

After you have the DataFrame, perform a transformation to have an RDD that matches the types that the DynamoDB custom output format knows how to write. The custom output format expects a tuple containing the Text and DynamoDBItemWritable types.

考虑到这一点，下面的代码正是 AWS 博客文章所建议的，除了我转换 output_df否则作为 rdd saveAsHadoopDataset不起作用。现在，我得到 Exception in thread "main" scala.reflect.internal.Symbols$CyclicReference: illegal cyclic reference involving object InterfaceAudience 。我已经束手无策了!

      // Format table to DynamoDB format
  val output_df =  df_add.map(a => {
    var ddbMap = new HashMap[String, AttributeValue]()

    // Field PIN
    var PINValue = new AttributeValue() // New AttributeValue
    PINValue.setS(a.get(0).toString())                 // Set value of Attribute as String
    ddbMap.put("PIN", PINValue)         // Add to HashMap

    // Field Address
    var AddValue = new AttributeValue() // New AttributeValue
    AddValue.setS(a.get(1).toString())                 // Set value of Attribute as String
    ddbMap.put("Address", AddValue)     // Add to HashMap

    var item = new DynamoDBItemWritable()
    item.setItem(ddbMap)

    (new Text(""), item)
  })             

  output_df.rdd.saveAsHadoopDataset(jobConf_add)   

Answer 1

我跟踪了“使用 Spark SQL 进行 ETL”链接，并发现了相同的“非法循环引用”异常。该异常的解决方案非常简单(但我花了 2 天时间才弄清楚)，如下所示。关键点是在数据框的 RDD 上使用映射函数，而不是数据框本身。

val ddbConf = new JobConf(spark.sparkContext.hadoopConfiguration)
ddbConf.set("dynamodb.output.tableName", "<myTableName>")
ddbConf.set("dynamodb.throughput.write.percent", "1.5")
ddbConf.set("mapred.input.format.class", "org.apache.hadoop.dynamodb.read.DynamoDBInputFormat")
ddbConf.set("mapred.output.format.class", "org.apache.hadoop.dynamodb.write.DynamoDBOutputFormat")


val df_ddb =  spark.read.option("header","true").parquet("<myInputFile>")
val schema_ddb = df_ddb.dtypes

var ddbInsertFormattedRDD = df_ddb.rdd.map(a => {
    val ddbMap = new HashMap[String, AttributeValue]()

    for (i <- 0 to schema_ddb.length - 1) {
        val value = a.get(i)
        if (value != null) {
            val att = new AttributeValue()
            att.setS(value.toString)
            ddbMap.put(schema_ddb(i)._1, att)
        }
    }

    val item = new DynamoDBItemWritable()
    item.setItem(ddbMap)

    (new Text(""), item)
}
)

ddbInsertFormattedRDD.saveAsHadoopDataset(ddbConf)