问题
I want my Spark application to read a table from DynamoDB, do stuff, then write the result in DynamoDB.
Read the table into a DataFrame
Right now, I can read the table from DynamoDB into Spark as a hadoopRDD and convert it to a DataFrame. However, I had to use a regular expression to extract the value from AttributeValue. Is there a better/more elegant way? Couldn't find anything in the AWS API.
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")
}
}
Write the DataFrame to DynamoDB
Many answers in stackoverflow and elsewhere only point to the blog post and the emr-dynamodb-hadoop github. None of those resources actually demonstrate how to write to DynamoDB.
I tried converting my DataFrame to RDD[Row] unsuccessfully.
df_add.rdd.saveAsHadoopDataset(jobConf_add)
What are the steps to write this DataFrame to DynamoDB? (Bonus Points if you tell me how to control overwrite vs putItem ;)
Note: df_add has the same schema as MyTable in DynamoDB.
EDIT: I am following the recommendation from this answer which points to this post on Using Spark SQL for ETL:
// 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)
However, now I am getting java.lang.ClassCastException: java.lang.String cannot be cast to org.apache.hadoop.io.Text despite following the documentation ... Do you have any suggestion ?
EDIT 2: Reading more carefully this post on Using Spark SQL for ETL:
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
DynamoDBItemWritabletypes.
Taking this into account, the code below is exactly what theAWS blog post suggest, except I cast output_df as an rdd otherwise saveAsHadoopDataset doesn't work. And now, I am getting Exception in thread "main" scala.reflect.internal.Symbols$CyclicReference: illegal cyclic reference involving object InterfaceAudience. I am at the end of my rope!
// 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)
回答1:
I was following that "Using Spark SQL for ETL" link, and found the same "illegal cyclic reference" exception. The solution for that exception is quite simple (but it cost me 2 days to figure out) as below. The key point is to use map function on the RDD of the dataframe, not the dataframe itself.
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)
回答2:
This is somewhat simpler working example.
For Writing to DynamoDB from Kinesis Stream for Example using Hadoop RDD:-
https://github.com/kali786516/Spark2StructuredStreaming/blob/master/src/main/scala/com/dataframe/part11/kinesis/consumer/KinesisSaveAsHadoopDataSet/TransactionConsumerDstreamToDynamoDBHadoopDataSet.scala
For reading from DynamoDB using Hadoop RDD and using spark SQL without regex.
val ddbConf = new JobConf(spark.sparkContext.hadoopConfiguration)
//ddbConf.set("dynamodb.output.tableName", "student")
ddbConf.set("dynamodb.input.tableName", "student")
ddbConf.set("dynamodb.throughput.write.percent", "1.5")
ddbConf.set("dynamodb.endpoint", "dynamodb.us-east-1.amazonaws.com")
ddbConf.set("dynamodb.regionid", "us-east-1")
ddbConf.set("dynamodb.servicename", "dynamodb")
ddbConf.set("dynamodb.throughput.read", "1")
ddbConf.set("dynamodb.throughput.read.percent", "1")
ddbConf.set("mapred.input.format.class", "org.apache.hadoop.dynamodb.read.DynamoDBInputFormat")
ddbConf.set("mapred.output.format.class", "org.apache.hadoop.dynamodb.write.DynamoDBOutputFormat")
//ddbConf.set("dynamodb.awsAccessKeyId", credentials.getAWSAccessKeyId)
//ddbConf.set("dynamodb.awsSecretAccessKey", credentials.getAWSSecretKey)
val data = spark.sparkContext.hadoopRDD(ddbConf, classOf[DynamoDBInputFormat], classOf[Text], classOf[DynamoDBItemWritable])
val simple2: RDD[(String)] = data.map { case (text, dbwritable) => (dbwritable.toString)}
spark.read.json(simple2).registerTempTable("gooddata")
spark.sql("select replace(replace(split(cast(address as string),',')[0],']',''),'[','') as housenumber from gooddata").show(false)
来源：https://stackoverflow.com/questions/47722648/spark-2-2-0-how-to-write-read-dataframe-to-dynamodb