Hi Roy,
I suggest that you try the pf/tijah module for MonetDB/XQuery.
http://dbappl.cs.utwente.nl/pftijah/
This will create specific indices for your queries to facilitate text search.
Hope this helps for now. We will also investigate were the time is
spent in your case (without pf/tijah) and come back to you. How many p
elements your documents have? The problem might be that because monet
does not build inverted indices on text by itself, it has to visit
each p element and search with the help of the pcre library. Pf/tijah
was build for that purpose and should help alot.
Please feel free to contact us for further clarification and new
findings from your tests:)
cheers,
lefteris
On Mon, Jul 20, 2009 at 6:28 PM, Roy Walter
Running MonetDB/XQuery on a 2.6GHz 32-bit Windows XP box with 1GB of RAM.
What is the best way to organise XML in MonetDB for rapid text searching? A run down of my recent experience might help.
I created a collection of around 450 documents (153MB approx.). I ran the following query from the command line:
collection("papers")//p[contains(., 'wind farm')]
The query time is at best 19 seconds. That's bad. (It's worse than querying a Postgres database with documents stored in the XML field type.)
So to get a reference point I loaded up the 114MB XMark document and ran this query:
doc("standard")//text[contains(., "yoke")]
The query time varies from 2 to 4 seconds. Better, but still not great.
Now, adding more RAM (and moving to 64-bit) would speed things up I hope! But hardware aside:
1. Is it better to have big documents rather than big collections?
2. Is having small collections (<10 docs) of big documents also inefficient?
Ideally I need to query collections comprising several thousand documents using 'text search' predicates. Are there other, better ways to run this type of query against a MonetDB XML database? Or should I really be using some other platform for this task?
Thanks in advance for any pointers.
-- Roy
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