Hi Maik,
> On Mar 8, 2017, at 7:47 PM, Musall, Maik wrote:
>
> Well, if I need them all in the same context to work with after this, I would
> then need to localObject() them and be back at locking, this time against the
> graph manager. Dang.
Yes. Unfortunately.
> It would be nice if Cayenn
Whoa. Parallel instantiation down to <2700 ms using multiple threads with a
local ObjectContext each.
Well, if I need them all in the same context to work with after this, I would
then need to localObject() them and be back at locking, this time against the
graph manager. Dang. It would be nice
If parallel is going to have any benefit you have to be using separate
object contexts to avoid locking the same DataRow cache.
On Wed, Mar 8, 2017 at 5:59 AM Musall, Maik wrote:
>
> > Am 08.03.2017 um 10:56 schrieb Aristedes Maniatis :
> >
> > On 8/3/17 6:54pm, Musall, Maik wrote:
> >
> >> regul
> Am 08.03.2017 um 10:56 schrieb Aristedes Maniatis :
>
> On 8/3/17 6:54pm, Musall, Maik wrote:
>
>> regular SelectQuery: 25888 ms for 1291644 objects
>> DataRowQuery alone: 14289 ms for 1291644 rows
>> DataRowQuery sequential instantiation: 6878 ms for 1291644 objects, sum =
>> 21167
>> DataRo
On 8/3/17 6:54pm, Musall, Maik wrote:
> regular SelectQuery: 25888 ms for 1291644 objects
> DataRowQuery alone: 14289 ms for 1291644 rows
> DataRowQuery sequential instantiation: 6878 ms for 1291644 objects, sum =
> 21167
> DataRowQuery parallel instantiation: 7351 ms for 1291644 objects, sum = 2
Hi Ari,
> Am 07.03.2017 um 23:14 schrieb Aristedes Maniatis :
>
> On 7/3/17 8:25am, Musall, Maik wrote:
>> Hi all,
>>
>> I have a number of statistics functions which need to fetch large amounts of
>> objects. I need the actual DataObjects because that's where the business
>> logic is that I n
On 7/3/17 8:25am, Musall, Maik wrote:
> Hi all,
>
> I have a number of statistics functions which need to fetch large amounts of
> objects. I need the actual DataObjects because that's where the business
> logic is that I need for the computations.
>
> Let's say I need to fetch 300.000 objects.
Hi Marcel,
I know how to do the actual computation in parallel. My question is how to
fetch and instantiate the DataObjects in parallel before I can start the
computations. An iterator would only slow down the fetch because of the added
roundtrips. Iterators are about reducing memory footprint,
Hi Maik,
maybe you can use the new iterator and split the iterator for parallel
computation?
public static Stream asStream(Iterator sourceIterator, boolean
parallel) {
Iterable iterable = () -> sourceIterator;
return StreamSupport.stream(iterable.spliterator(), parallel);
}
found at
http:
Hi all,
I have a number of statistics functions which need to fetch large amounts of
objects. I need the actual DataObjects because that's where the business logic
is that I need for the computations.
Let's say I need to fetch 300.000 objects. Let's also assume the database sits
on a fast SSD
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