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From "Derek Rendall" <>
Subject Re: OutOfMemoryError: reading a large number of objects one by one
Date Wed, 16 May 2007 11:50:02 GMT
My patch seemed to scale well - it got to about (I think) 40 Meg or so then
stabilized with only nominal growth from there. I did not test much higher
than 100 K records as I did not need to. I guess the size will be related to
how big each record is when represented as a data row * number of rows.

I think that this is a pretty common "problem" to ORM tools. When I looked
at it a couple of years ago, it appeared to be the case that neither
Hibernate nor Kodo JDO addressed this issue with any better approach. I did
not check TopLink. ORM tools tend to focus on simplifying user tasks rather
than batch type tasks.

Note: some people will advocate sitting such logic on top of a standard JDBC
result set (I'm not commenting one way or another ;-). Thats really the only
way to avoid loading at least something for each record up front.

Also, you should probably start tracking new objects before the while loop
as well (for the first 100 :-)


On 5/16/07, Andrus Adamchik <> wrote:
> On May 15, 2007, at 12:47 AM, Tomi N/A wrote:
> > Reduced the max number of objects to 1000. The result? A NPE at:
> >               for (MyClassC mcc :
> > (List<MyClassC>)mca.getToMyClassC().getToParentClass
> > ().getMyClassCArray())
> > {
> Ok, so the cache size will have to be big enough to hold all resolved
> objects within the lifetime of a context. So let's try another
> strategy. Return the max objects back to 10000 and uncheck "use
> shared cache" for the DataDomain.
> If this doesn't work, I suggest to run the app in profiler to see
> exactly how objects are allocated and collected.
> > The database referential integrity ensures there can be no nulls if
> > (mcc != null), which it is.
> > As far as -Xmx is concerned, it's at it's default value (64M), which
> > should be several times more than necessary for the job.
> Agreed - the default 64m should be enough if there's no leaks.
> Andrus

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