See my reply to John. The context is using weak references for committed objects, so it is self-cleaning. Though of course having a second (long- or short- lived context) is probably safer in a general case.
Andrus > On May 19, 2017, at 5:06 PM, Michael Gentry <[email protected]> wrote: > > Hi Andrus, > > In your example, wouldn't "context" continue growing and possibly > exhausting memory as more Artists are brought into it? (And this would > also slow down the commitChanges() call as it has more evaluation to do as > the context grows.) > > mrg > > > On Fri, May 19, 2017 at 9:49 AM, Andrus Adamchik <[email protected]> > wrote: > >> I concur with Mike on the suggestion. Though I would recommend using >> vastly improved 4.0 API: >> >> http://cayenne.apache.org/docs/4.0/cayenne-guide/performance-tuning.html# >> iterated-queries >> >>> As you iterate over your entire record set, you can convert the DataRows >>> into Cayenne objects >> >> In 4.0 you can iterate over objects. >> >>> Gather up 50 or 100 or 1000 >> >> In 4.0 you can use batch iterator to receive the stream already split in >> batches. Docs example actually has a typo. Batch iterator looks like this: >> >> try(ResultBatchIterator<Artist> it = >> ObjectSelect.query(Artist.class).batchIterator(context, batchSize)) { >> >> for(List<Artist> list : it) { >> ... >> context.commitChanges(); >> } >> } >> >> Andrus >> >> >>> On May 19, 2017, at 4:39 PM, Michael Gentry <[email protected]> wrote: >>> >>> Hi Pascal, >>> >>> I suspect you need to utilize an iterated query: >>> >>> http://cayenne.apache.org/docs/3.1/cayenne-guide/ >> performance-tuning.html#iterated-queries >>> >>> As you iterate over your entire record set, you can convert the DataRows >>> into Cayenne objects (see the section in the documentation above the >>> iterated queries documentation) in a *different* DataContext. Gather up >> 50 >>> or 100 or 1000 (whatever number feels good to you) in that second >>> DataContext and then commit them, throw away that DataContext and create >> a >>> new one. Repeat. This should keep your memory usage fairly constant and >>> allow you to process arbitrarily large record sizes. >>> >>> mrg >>> >>> >>> On Fri, May 19, 2017 at 9:27 AM, Pascal Robert <[email protected]> >> wrote: >>> >>>> Hi, >>>> >>>> I’m still in my FileMaker -> MySQL migration project. This time, I want >> to >>>> migrate a FileMaker table who have 445 244 records in it. If I fetch >>>> everything into an object entity for each row, I’m getting a Java heap >>>> space problem, which is somewhat expected by the size of the result set. >>>> >>>> If I call setFetchLimit() with a 10 000 limit, works fine. FileMaker >>>> doesn’t support fetch limits, so I can’t do something on that side. >>>> >>>> Any tips? >> >>
