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 <e...@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.klo...@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 <jornfra...@gmail.com> wrote:
>>
>>> What database are you using?
>>>
>>> Le 28 févr. 2015 18:15, "Michal Klos" <michal.klo...@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
>
