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http://jira.dspace.org/jira/browse/DS-470?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=11115#action_11115
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Tim Donohue commented on DS-470:
--------------------------------
Hi Simon,
Just wanted to double check a few things. If my math is right, hypothetically
speaking, if we were loading 4,000 items into your repository of 120,000 you
are saying you would see the following:
* Before patch (4.7 secs/item): about 313 minutes of process time (5 hrs,
13mins)
* After patch (4.9 items/sec or about 0.2secs/ item): about 13 minutes of
process time
So, you've found the patch would decrease your processing time by 5 hours in
this hypothetical situation?
Also, did you see a much larger load on the server after the patch (in terms of
memory / cpu usage, etc) than before the patch? Trying to get a sense of
whether decreasing processing time this drastically causes a large increase in
server load.
We're trying to look at this from all angles to see what we can come up with in
terms of recommendations, etc. We appreciate any other help you can provide,
and I'm sure others will add more comments here, as this has been a topic of
much discussion amongst the developers today (esp in #dspace IRC channel).
> Batch import times increase drastically as repository size increases; patch
> to mitigate the problem
> ---------------------------------------------------------------------------------------------------
>
> Key: DS-470
> URL: http://jira.dspace.org/jira/browse/DS-470
> Project: DSpace 1.x
> Issue Type: Improvement
> Components: DSpace API
> Affects Versions: 1.6.0
> Reporter: Simon Brown
> Priority: Minor
> Fix For: 1.6.1
>
> Attachments: batch_importer_speedup.patch
>
>
> As mentioned by my colleague Tom De Mulder on dspace-tech and at
> http://tdm27.wordpress.com/2010/01/19/dspace-1-6-scalability-testing/
> As the repository grows, the time taken for batch imports to run also
> increases. Having profiled the importer in our 1.6.0-RC1 install we
> determined that most (80%-90%) of the time was spent in calls to
> IndexBrowse.pruneIndexes().
> The reason for this is that IndexBrowse.indexItem() calls pruneIndexes(), so
> every time an item is indexed, the indexes are pruned. For any batch of size
> n, where n > 1, this is (n - 1) times more than is necessary.
> Increasing the visibility of pruneIndexes(), removing the call from
> IndexBrowse.indexItem(), and making a single call at the end of the
> BrowseConsumer.end() method reduces this to once per event queue run.
> However, the batch importer calls Context.commit() after each item is
> imported. Context.commit() runs the event queue, thus causing one event queue
> run per imported item.
> This patch addresses both of these issues in a way which has a minimal effect
> on the rest of the code base; I don't necessarily consider it to be the
> "best" way, but I wanted to keep the patch small so it could be put out. What
> it does is:
> 1. create an IndexBrowse.indexItemNoPrune() method, which is called from the
> BrowseConsumer class instead of indexItem(). Other calls to indexItem() are
> not affected.
> 2. Call pruneIndexes() from BrowseConsumer.end()
> 3. Change the call in the batch importer from Context.commit() to
> Context.getDBConnection.commit(). The only effective difference between the
> two is that the event queue is not run; I think that a better solution might
> be to move the code to run the event queue from the Context.commit() method
> to the Context.complete() method, but I don't know what effect that will have
> on the rest of the code.
> As noted in Tom's blog post linked above, these changes, on a repository with
> in excess of 120,000 items, brought import time from 4.7 seconds/item down to
> 4.9 items/second.
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