Welcome to OGeek Q&A Community for programmer and developer-Open, Learning and Share
Welcome To Ask or Share your Answers For Others

Categories

0 votes
692 views
in Technique[技术] by (71.8m points)

scala - Spark : DB connection per Spark RDD partition and do mapPartition

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!

See Question&Answers more detail:os

与恶龙缠斗过久,自身亦成为恶龙;凝视深渊过久,深渊将回以凝视…
Welcome To Ask or Share your Answers For Others

1 Reply

0 votes
by (71.8m points)

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, e.g. 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
})

与恶龙缠斗过久,自身亦成为恶龙;凝视深渊过久,深渊将回以凝视…
OGeek|极客中国-欢迎来到极客的世界,一个免费开放的程序员编程交流平台!开放,进步,分享!让技术改变生活,让极客改变未来! Welcome to OGeek Q&A Community for programmer and developer-Open, Learning and Share
Click Here to Ask a Question

...