Derek Hohls wrote:
OK, I'll bite here, as my curiosity is aroused (and, lets face it,
as XML gets wider use, its likely that file sizes will get larger)
You say "XSLT isn't that appropriate for that sort of thing";
I thought XSLT was *the* preferred way for processing XML?!
Because XSLT can, in various circumstances, build in memory versions of
our XML. If your XML is large, you will consume a lot of memory, which
could break things. STX (which I have never used), is intended to be a
streaming process, which means that you don't hold any of your XML in
memory, and you can thus stream as much XML as your recipient can handle.
Second; what are the advantages/disadvantagesof STX icw XSLT?
Advantage of STX? It is streamed. Disadvantage of STX? It is streamed.
As I say, I've never used it, but I suspect there are some things that
require 'knowledge' of different parts of the XML structure that could
be difficult to implement in STX. But then, I might be wrong.
Regards, Upayavira
[EMAIL PROTECTED] 2004/11/19 09:09:18 AM >>>
Tom Bloomfield wrote:
I'm planning to do xml -> text transformations (for tab-delimited
output) and xml -> FOP on large XML datasets. The XML I will be
processing will be 10-12 MB in size, and will grow from there. Based
on planning, the XSL will contain around 50 node traversals and will
iterate over my XML dataset around 46,000 times. Previous to this,
my
Cocoon transformations haven't been nearly this big.
The amount of JVM memory I have to deal with is limited (<256M).
This
transformation will need to run in real-time.
Does anyone have experience dealing with large datasets like this?
That sounds like quite a challenge. XSLT isn't that appropriate for
that
sort of thing. Firstly, in XSLT, avoid arbitrary wanders around your
XML
tree - stay as close to the context node as you can.
Alternatively, look at STX (there is an STX block). See if you can
manage your transformations with that. This is "streaming"
transformations for XML, i.e. it is designed for streaming, and thus
should be able to handle large datasets.
Regards, Upayavira
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