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Posted to issues@spark.apache.org by "Vivek Garg (Jira)" <ji...@apache.org> on 2022/10/19 16:18:00 UTC
[jira] [Updated] (SPARK-40847) SPARK: Load Data from Dataframe or RDD to DynamoDB
[ https://issues.apache.org/jira/browse/SPARK-40847?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Vivek Garg updated SPARK-40847:
-------------------------------
Description:
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.
Thank you.
[Power BI Certification|https://www.igmguru.com/data-science-bi/power-bi-certification-training/]
was:
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.
> 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
> Priority: Major
> Labels: spark
>
> 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.
> Thank you.
> [Power BI Certification|https://www.igmguru.com/data-science-bi/power-bi-certification-training/]
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