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[EP-tech] Re: Memory usage in 3.2, Sword 1.3 and epdata packages

Correct. In 3.2 the HTTP post is all worked on in memory. In 3.3 XML
data are streamed and will be written to disk as it arrives.


On Fri, 2013-07-12 at 08:26 +0100, Ian Stuart wrote:
> With no real knowledge, and certainly no investigation.... I would 
> suspect the problem is actually with how the base64 files are handled, 
> rather then being an EPrints memory leak per sae.
>  From the SWORD importers I've written, the process seems to be to
> 1) read in the deposit
> 2) unpack the deposit (zip into disk space, XML into memory)
> 3) create the eprint object
> 4) attach the files
> 5) write everything out
> So I would suspect that what's happening is that all your base64 files 
> are created (in memory) from the XML (which is also in memory)
> On 12/07/13 03:57, Mark Gregson wrote:
> > We’re using SWORD with epdata packages to deposit documents and
> > multimedia into our repository (3.2). This works fine for small file
> > sizes but at CPU and memory increases quickly until with a ~200MB file
> > the httpd process consumes all available memory and dies.  This is on a
> > RHEL5 64bit box with 8GB memory with a separate DB server.
> >
> > Clearly, the epdata format is not the most appropriate for this size
> > file due to the increased file size as a result of the base64 encoding
> > and because the document is embedded within the XML.  Changing package
> > format may alleviate/resolve the problem but as this is definitely going
> > to be a challenge in our environment I’m hoping it will be easier to
> > deal with the issue within EPrints.
> >
> > Note, I’ve already ascertained that is not related to libxm2’s
> > XML_PARSE_HUGE option being disabled, the failure occurs trying to run df.
> >
> > I’m about to start hunting for memory leaks and then doing additional
> > memory profiling.  If anyone has any suggestions about likely locations
> > for memory leaks in the code, information about expected memory usage
> > for SWORD with epdata packages, data from previous profiling, etc, it
> > would be very valuable.

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