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Nikhil Sharma commented on SPARK-22588: --------------------------------------- Thank you for sharing the information. [React Native Online Course|[https://www.igmguru.com/digital-marketing-programming/react-native-training/]] is an integrated professional course aimed at providing learners with the skills and knowledge of React Native, a mobile application framework used for the development of mobile applications for Android, iOS, UWP (Universal Windows Platform), and web. > SPARK: Load Data from Dataframe or RDD to DynamoDB / dealing with null values > ----------------------------------------------------------------------------- > > Key: SPARK-22588 > URL: https://issues.apache.org/jira/browse/SPARK-22588 > Project: Spark > Issue Type: Question > Components: Deploy > Affects Versions: 2.1.1 > Reporter: Saanvi Sharma > Priority: Minor > Labels: dynamodb, spark > Original Estimate: 24h > Remaining Estimate: 24h > > 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. > Thanks for your help !! -- This message was sent by Atlassian Jira (v8.20.10#820010) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org