> $word is provided as a value? $word looks like a variable to the
optimizer so the FILTER is after the whole of the BGP.
Sorry I should have written" :
FILTER(REGEX(?title, "some word", 'i'))
my '$word' meant 'any value to complete the query string'.
> With GRAPH, or using just the {} part, the scope of the filter is
reduced.
Do you mean that using a named graph reduces the number of triples to
check, so that the query is faster?
Here's what I understand of named graphs : the triples are stored in
different 'partitions' or 'tables' instead of being in the same place ;
so there are fewer entries in the triple store index to check to find a
matching pattern, therefore the query is more efficient.
Le 17/12/2018 à 15:24, Andy Seaborne a écrit :
On 17/12/2018 07:53, Vincent Ventresque wrote:
Are you sure that named graphs have better performance?
I'm not a specialist, and I'd like to know other users' opinion about
that question. I thinks it depends both on the structure of your data
and the queries you run.
My use case consisted in using a dataset with +/- 168 M triples,
including +/- 10 M titles (triples like ?s dcterms:title "some
words"), and running queries with FILTER(REGEX()) :
-- a query like this took a long time (1 min or more) :
SELECT * WHERE {
?edition dcterms:title ?title .
FILTER(REGEX(?title, $word, 'i')) .
?edition rdarelationships:expressionManifested ?expr .
?expression bnf-roles:r70 ?author .
?author foaf:familyName $name
$word is provided as a value?
$word looks like a variable to the optimizer so the FILTER is after
the whole of the BGP. With GRAPH, or using just the {} part, the scope
of the filter is reduced.
Add {}
SELECT * WHERE {
{
?edition dcterms:title ?title .
FILTER(REGEX(?title, $word, 'i')) .
}
?edition rdarelationships:expressionManifested ?expr .
?expression bnf-roles:r70 ?author .
?author foaf:familyName $name
}
}
-------------
-- whereas a query like this one takes about 1 sec :
SELECT * WHERE {
GRAPH <:titles> {
?edition dcterms:title ?title .
FILTER(REGEX(?title, $word, 'i'))
}
?edition rdarelationships:expressionManifested ?expr .
?expression bnf-roles:r70 ?author .
?author foaf:familyName $name
}
----------------
So, depending on your data, it might be more efficient to use named
graphs, e. g. :
SELECT ?rel (count (?rel) as ?co)
where {
GRAPH <:names> { ?object MKG:English_name 'Pyrilamine' } # <--
HERE : named graph for names
?RelAttr owl:annotatedTarget ?object ;
owl:annotatedSource ?subject ;
owl:annotatedProperty ?rel ;
MKG:pyear '1967' .
}
group by ?rel
limit 10
-------------------------------
Then how to build named graphs?
There are several ways, here are 3 methods :
#1) when uploading files in Fuseki web interface, specify the graph
URI for the file
#2) use tdbloader : java -Xms4096m -Xmx4096m -cp ./fuseki-server.jar
tdb.tdbloader --graph=$namedGraph --tdb=$configFile $f
#3) use SPARQL INSERT + DELETE
#1 & #2 are fast, but all the triples of the file go into the same
graph, so maybe you have to have to modify your files first.
#3 is slower, but you don't have to modify your files. see my
question on StackOverflow :
https://stackoverflow.com/questions/48500404/sparql-offset-whithout-order-by-to-get-all-results-of-a-query
Le 07/12/2018 à 18:16, HYP a écrit :
OK. I explain my project in the following
The KG schema is composed of a set of semantic types like disease or
drugs, and a set of relations like treated_by(disease, drug).
Then each instance relation, like treated_by(disease_1, drug_1) has
an annotation property 'year' which means this triple occur in the
'year'.
My query has two steps. Firstly, query the related triples about
some drug, like Pyrilamine, and group them according to the relation
types and give a count. Secondly, query the related nodes in one
relation type.
The first step query, like:
SELECT ?rel (count (?rel) as ?co)
where {
?object MKG:English_name 'Pyrilamine' .
?RelAttr owl:annotatedTarget ?object ;
owl:annotatedSource ?subject ;
owl:annotatedProperty ?rel ;
MKG:pyear '1967' .
}
group by ?rel
limit 10
On 12/8/2018 01:00，ajs6f<[email protected]> wrote：
Let's slow down here a bit.
We can't give you any reasonable advice until you tell us _much_
more about your work. What is the data like? What kinds of queries
are you doing? How are you running them? What do you expect to happen?
Please give us a great deal more context.
ajs6f
On Dec 7, 2018, at 11:45 AM, HYP <[email protected]> wrote:
I store the 1.4B triples in two steps. Firstly, I made 886 rdf
files, each of which contains 1615837 triples. Then, I upload them
into TDB using Fuseki.
This is a huge job. Are you sure that named graphs have better
performance?
Then how to build named graphs?
On 12/7/2018 23:48，Vincent
Ventresque<[email protected]> wrote：
Do you mean -Xms = 64G ?
N.B. : with 1.4 B triples, you should have better performance using
named graphs.
Le 07/12/2018 à 16:37, 胡云苹 a écrit :
My memory is 64G and my setting is no upper limit.
On 12/7/2018 23:34，Vincent Ventresque<[email protected]>
<mailto:[email protected]> wrote：
Hello
How do you run fuseki? you can increase java memory limit with
java options :
java -jar -Xms4096m -Xmx4096m fuseki-server.jar
(where 4096m = 4 Go, but could be 8192m or more)
N.B. : I'm not a specialist, don't know if -Xms and -Xmx must be
the same
If I remember correctly the memory limit is 1.2 Go when you run
'./fuseki start' or './fuseki-server'
Vincent
Le 07/12/2018 à 16:23, 胡云苹 a écrit :
Dear jena,
I have built a graph with 1.4 billion triples and store it as a
data set in TDB through Fuseki upload system. Now, I try to make
some sparql search, the speed is very slow.
For example, when I make the sqarql in Fuseki in the following,
it takes 50 seconds. How can I improve the speed?
-
