Spark：每个Spark RDD分区的数据库连接，并执行mapPartition

Question

I want to do a mapPartitions on my spark rdd,

    val newRd = myRdd.mapPartitions(
      partition => {

        val connection = new DbConnection /*creates a db connection per partition*/

        val newPartition = partition.map(
           record => {
             readMatchingFromDB(record, connection)
         })
        connection.close()
        newPartition
      })

But, this gives me a connection already closed exception, as expected because before the control reaches the .map() my connection is closed. I want to create a connection per RDD partition, and close it properly. How can I achieve this?

Thanks!

Answer 1

As mentioned in the discussion here - the issue stems from the laziness of map operation on the iterator partition . This laziness means that for each partition, a connection is created and closed, and only later (when RDD is acted upon), readMatchingFromDB is called.

To resolve this, you should force an eager traversal of the iterator before closing the connection, eg by converting it into a list (and then back):

val newRd = myRdd.mapPartitions(partition => {
  val connection = new DbConnection /*creates a db connection per partition*/

  val newPartition = partition.map(record => {
    readMatchingFromDB(record, connection)
  }).toList // consumes the iterator, thus calls readMatchingFromDB 

  connection.close()
  newPartition.iterator // create a new iterator
})

Answer 2

rdd.foreachPartitionAsync(iterator->{

// this object will be cached inside each executor JVM. For the first time, the //connection will be created and hence forward, it will be reused. 
// Very useful for streaming apps
DBConn conn=DBConn.getConnection()
while(iterator.hasNext()) {
  conn.read();
}

});

public class DBConn{
private static dbObj=null;

//Create a singleton method that returns only one instance of this object
}

}