Batching of Dataset Spark scala - scala

Question

I am trying to create batches of rows of Dataset in Spark.
For maintaining the number of records sent to service I want to batch the items so that i can maintain the rate at which the data will be sent.
For,
case class Person(name:String, address: String)
case class PersonBatch(personBatch: List[Person])
For a given Dataset[Person] I want to create Dataset[PersonBatch]
For example if input Dataset[Person] has 100 records the output Dataset should be like Dataset[PersonBatch] where every PersonBatchshould be list of n records (Person).
I have tried this but it din't work.
object DataBatcher extends Logger {
var batchList: ListBuffer[PersonBatch] = ListBuffer[PersonBatch]()
var batchSize: Long = 500 //default batch size
def addToBatchList(batch: PersonBatch): Unit = {
batchList += batch
}
def clearBatchList(): Unit = {
batchList.clear()
}
def createBatches(ds: Dataset[Person]): Dataset[PersonBatch] = {
val dsCount = ds.count()
logger.info(s"Count of dataset passed for creating batches : ${dsCount}")
val batchElement = ListBuffer[Person]()
val batch = PersonBatch(batchElement)
ds.foreach(x => {
batch.personBatch += x
if(batch.personBatch.length == batchSize) {
addToBatchList(batch)
batch.requestBatch.clear()
}
})
if(batch.personBatch.length > 0) {
addToBatchList(batch)
batch.personBatch.clear()
}
sparkSession.createDataset(batchList)
}
}
I want to run this job on Hadoop cluster.
Can some help me with this ?

Answer 1

rdd.iterator has grouped function may be useful for you.
for example :
iter.grouped(batchSize)
Sample code snippet which does batch insert with iter.grouped(batchsize) here its 1000 and Im trying to insert in to database
df.repartition(numofpartitionsyouwant) // numPartitions ~ number of simultaneous DB connections you can planning to give...
def insertToTable(sqlDatabaseConnectionString: String,
sqlTableName: String): Unit = {
val tableHeader: String = dataFrame.columns.mkString(",")
dataFrame.foreachPartition { partition =>
//NOTE : EACH PARTITION ONE CONNECTION (more better way is to use connection pools)
val sqlExecutorConnection: Connection =
DriverManager.getConnection(sqlDatabaseConnectionString)
//Batch size of 1000 is used since some databases cant use batch size more than 1000 for ex : Azure sql
partition.grouped(1000).foreach { group =>
val insertString: scala.collection.mutable.StringBuilder =
new scala.collection.mutable.StringBuilder()
group.foreach { record =>
insertString.append("('" + record.mkString(",") + "'),")
}
sqlExecutorConnection
.createStatement()
.executeUpdate(f"INSERT INTO [$sqlTableName] ($tableHeader) VALUES "
+ insertString.stripSuffix(","))
}
sqlExecutorConnection.close() // close the connection so that connections wont exhaust.
}
}

Answer 2

val tableHeader: String = dataFrame.columns.mkString(",")
dataFrame.foreachPartition((it: Iterator[Row]) => {
println("partition index: " )
val url = "jdbc:...+ "user=;password=;"
val conn = DriverManager.getConnection(url)
conn.setAutoCommit(true)
val stmt = conn.createStatement()
val batchSize = 10
var i =0
while (it.hasNext) {
val row = it.next
import java.sql.SQLException
import java.sql.SQLIntegrityConstraintViolationException
try {
stmt.addBatch(" UPDATE TABLE SET STATUS = 0 , " +
" DATE ='" + new java.sql.Timestamp(System.currentTimeMillis()) +"'" +
" where id = " + row.getAs("IDNUM") )
i += 1
if ( i % batchSize == 0 ) {
stmt.executeBatch
conn.commit
}
} catch {
case e: SQLIntegrityConstraintViolationException =>
case e: SQLException =>
e.printStackTrace()
}
finally {
stmt.executeBatch
conn.commit
}
}
import java.util
val ret = stmt.executeBatch
System.out.println("Ret val: " + util.Arrays.toString(ret))
System.out.println("Update count: " + stmt.getUpdateCount)
conn.commit
stmt.close