Have you already tried using the Vertica hadoop input format with spark?  I don't know how it's implemented, but I'd hope that it has some notion of vertica-specific shard locality (which JdbcRDD does not).

If you're really constrained to consuming the result set in a single thread, whatever processing you're doing of the results must be time-consuming enough to make the overhead of distributing it in a spark job still worthwhile?   I guess you might take a look at doing a custom DStream receiver that iterates over the result set and makes micro-batches out of it.

On Sun, Mar 1, 2015 at 9:59 AM, michal.klos81@gmail.com <michal.klos81@gmail.com> wrote:
Yes exactly.

The temp table is an approach but then we need to manage the deletion of it etc.

I'm sure we won't be the only people with this crazy use case. 

If there isn't a feasible way to do this "within the framework" then that's okay. But if there is a way we are happy to write the code and PR it back :)

M



On Mar 1, 2015, at 10:02 AM, eric <eric@ericjbell.com> wrote:

What you're saying is that, due to the intensity of the query, you need to run a single query and partition the results, versus running one query for each partition.

I assume it's not viable to throw the query results into another table in your database and then query that using the normal approach?

--eric

On 3/1/15 4:28 AM, michal.klos81@gmail.com wrote:
Jorn: Vertica 

Cody: I posited the limit just as an example of how jdbcrdd could be used least invasively. Let's say we used a partition on a time field -- we would still need to have N executions of those queries. The queries we have are very intense and concurrency is an issue even if the the N partitioned queries are smaller. Some queries require evaluating the whole data set first. If our use case a simple select * from table.. Then the partitions would be an easier sell if it wasn't for the concurrency problem :) Long story short -- we need only one execution of the query and would like to just divy out the result set.

M



On Mar 1, 2015, at 5:18 AM, Jörn Franke <jornfranke@gmail.com> wrote:

What database are you using?

Le 28 févr. 2015 18:15, "Michal Klos" <michal.klos81@gmail.com> a écrit :
Hi Spark community,

We have a use case where we need to pull huge amounts of data from a SQL query against a database into Spark. We need to execute the query against our huge database and not a substitute (SparkSQL, Hive, etc) because of a couple of factors including custom functions used in the queries that only our database has.

We started by looking at JDBC RDD, which utilizes a prepared statement with two parameters that are meant to be used to partition the result set to the workers... e.g.:

select * from table limit ?,?

turns into

select * from table limit 1,100 on worker 1
select * from table limit 101,200 on worker 2

This will not work for us because our database cannot support multiple execution of these queries without being crippled. But, additionally, our database doesn't support the above LIMIT syntax and we don't have a generic way of partitioning the various queries.

As a result -- we stated by forking JDBCRDD and made a version that executes the SQL query once in getPartitions into a Vector and then hands each worker node an index and iterator. Here's a snippet of getPartitions and compute:

override def getPartitions: Array[Partition] = {
//Compute the DB query once here
val results = computeQuery
 
(0 until numPartitions).map(i => {
// TODO: would be better to do this partitioning when scrolling through result set if still loading into memory
val partitionItems = results.drop(i).sliding(1, numPartitions).flatten.toVector
new DBPartition(i, partitionItems)
}).toArray
}
override def compute(thePart: Partition, context: TaskContext) = new NextIterator[T] {
val part = thePart.asInstanceOf[DBPartition[T]]
 
//Shift the result vector to our index number and then do a sliding iterator over it
val iterator = part.items.iterator
 
override def getNext : T = {
if (iterator.hasNext) {
iterator.next()
} else {
finished = true
null.asInstanceOf[T]
}
}
 
override def close: Unit = ()
}
This is a little better since we can just execute the query once. However, the result-set needs to fit in memory. 
We've been trying to brainstorm a way to 
A) have that result set distribute out to the worker RDD partitions as it's streaming in from the cursor?
B) have the result set spill to disk if it exceeds memory and do something clever around the iterators?
C) something else?
We're not familiar enough yet with all of the workings of Spark to know how to proceed on this.
We also thought of the worker-around of having the DB query dump to HDFS/S3 and then pick it up for there, but it adds more moving parts and latency to our processing. 
Does anyone have a clever suggestion? Are we missing something? 
thanks,
Michal