[jira] [Created] (SPARK-40847) SPARK: Load Data from Dataframe or RDD to DynamoDB

["Vivek Garg (Jira)" <ji...@apache.org>]

Vivek Garg created SPARK-40847:
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             Summary: SPARK: Load Data from Dataframe or RDD to DynamoDB 
                 Key: SPARK-40847
                 URL: https://issues.apache.org/jira/browse/SPARK-40847
             Project: Spark
          Issue Type: Question
          Components: Deploy
    Affects Versions: 2.1.1
            Reporter: Vivek Garg


I am using spark 2.1 on EMR and i have a dataframe like this:

ClientNum | Value_1 | Value_2 | Value_3 | Value_4
14 | A | B | C | null
19 | X | Y | null | null
21 | R | null | null | null
I want to load data into DynamoDB table with ClientNum as key fetching:

Analyze Your Data on Amazon DynamoDB with apche Spark11

Using Spark SQL for ETL3

here is my code that I tried to solve:

var jobConf = new JobConf(sc.hadoopConfiguration)
jobConf.set("dynamodb.servicename", "dynamodb")
jobConf.set("dynamodb.input.tableName", "table_name")
jobConf.set("dynamodb.output.tableName", "table_name")
jobConf.set("dynamodb.endpoint", "dynamodb.eu-west-1.amazonaws.com")
jobConf.set("dynamodb.regionid", "eu-west-1")
jobConf.set("dynamodb.throughput.read", "1")
jobConf.set("dynamodb.throughput.read.percent", "1")
jobConf.set("dynamodb.throughput.write", "1")
jobConf.set("dynamodb.throughput.write.percent", "1")

jobConf.set("mapred.output.format.class", "org.apache.hadoop.dynamodb.write.DynamoDBOutputFormat")
jobConf.set("mapred.input.format.class", "org.apache.hadoop.dynamodb.read.DynamoDBInputFormat")

#Import Data
val df = sqlContext.read.format("com.databricks.spark.csv").option("header", "true").option("inferSchema", "true").load(path)
I performed 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.

Create a new RDD with those types in it, in the following map call:

#Convert the dataframe to rdd
val df_rdd = df.rdd
> df_rdd: org.apache.spark.rdd.RDD[org.apache.spark.sql.Row] = MapPartitionsRDD[10] at rdd at <console>:41

#Print first rdd
df_rdd.take(1)
> res12: Array[org.apache.spark.sql.Row] = Array([14,A,B,C,null])

var ddbInsertFormattedRDD = df_rdd.map(a =>

{ var ddbMap = new HashMap[String, AttributeValue]() var ClientNum = new AttributeValue() ClientNum.setN(a.get(0).toString) ddbMap.put("ClientNum", ClientNum) var Value_1 = new AttributeValue() Value_1.setS(a.get(1).toString) ddbMap.put("Value_1", Value_1) var Value_2 = new AttributeValue() Value_2.setS(a.get(2).toString) ddbMap.put("Value_2", Value_2) var Value_3 = new AttributeValue() Value_3.setS(a.get(3).toString) ddbMap.put("Value_3", Value_3) var Value_4 = new AttributeValue() Value_4.setS(a.get(4).toString) ddbMap.put("Value_4", Value_4) var item = new DynamoDBItemWritable() item.setItem(ddbMap) (new Text(""), item) }
)
This last call uses the job configuration that defines the EMR-DDB connector to write out the new RDD you created in the expected format:

ddbInsertFormattedRDD.saveAsHadoopDataset(jobConf)
fails with the follwoing error:

Caused by: java.lang.NullPointerException
null values caused the error, if I try with ClientNum and Value_1 it works data is correctly inserted on DynamoDB table.



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