Re: Function query matching
: The bottom line for Peter is still the same: using scale() wrapped arround
: a function/query does involve a computing hte results for every document,
: and that is going to scale linearly as the size of hte index grows -- but
: it it is *only* because of the scale function.
Another problem with this approach is that the scale() function will likely
generate incorrect values because it occurs before any filters. If the
filters drop high scoring docs, the scaled values will never include the
'maxTarget' value (and may not include the 'minTarget' value, either).
Peter
On Sat, Dec 7, 2013 at 2:30 PM, Chris Hostetter hossman_luc...@fucit.orgwrote:
(This is why i shouldn't send emails just before going to bed.)
I woke up this morning realizing that of course I was completley wrong
when i said this...
: I want to be clear for 99% of the people reading this, if you find
: yourself writting a query structure like this...
:
: q={!func}..functions involving wrapping $qq ...
...
: ...Try to restructure the match you want to do into the form of a
: multiplier
...
: Because the later case is much more efficient and Solr will only compute
: the function values for hte docs it needs to (that match the wrapped $qq
: query)
The reason i was wrong...
Even though function queries do by default match all documents, and even
if the main query is a function query (ie: q={!func}...), if there is
an fq that filters down the set of documents, then the (main) function
query will only be calculated for the documents that match the filter.
It was trivial to ammend the test i mentioned last night to show this (and
i feel silly for not doing that last night and stoping myself from saying
something foolish)...
https://svn.apache.org/viewvc?view=revisionrevision=r1548955
The bottom line for Peter is still the same: using scale() wrapped arround
a function/query does involve a computing hte results for every document,
and that is going to scale linearly as the size of hte index grows -- but
it it is *only* because of the scale function.
-Hoss
http://www.lucidworks.com/
Re: Function query matching
But for your specific goal Peter: Yes, if the whole point of a function
you have is to wrap generated a scaled score of your base $qq, ...
Thanks for the confirmation, Chris. So, to do this efficiently, I think I
need to implement a custom Collector that performs the scaling (and other
math) after collecting the matching dismax query docs. I started a separate
thread asking about the state of configurable collectors.
Thanks,
Peter
On Sat, Dec 7, 2013 at 1:45 AM, Chris Hostetter hossman_luc...@fucit.orgwrote:
I had to do a double take when i read this sentence...
: Even with any improvements to 'scale', all function queries will add a
: linear increase to the Qtime as index size increases, since they match
all
: docs.
...because that smelled like either a bug in your methodology, or a bug in
Solr. To convince myself there wasn't a bug in Solr, i wrote a test case
(i'll commit tomorow, bunch of churn in svn right now making ant
precommit unhappy) to prove that when wrapping boost functions arround
queries, Solr will only evaluate the functions for docs matching the
wrapped query -- so there is no linear increase as the index size
increases, just the (neccessary) libera increase as the number of
*matching* docs grows. (for most functions anyway -- as mentioned scale
is special).
BUT! ... then i remembered how this thread started, and your goal of
scaling the scores from a wrapped query.
I want to be clear for 99% of the people reading this, if you find
yourself writting a query structure like this...
q={!func}..functions involving wrapping $qq ...
qq={!edismax ...lots of stuff but still only matching subset of the
index...}
fq={!query v=$qq}
...Try to restructure the match you want to do into the form of a
multiplier
q={!boost b=$b v=$qq}
b=...functions producing a score multiplier...
qq={!edismax ...lots of stuff but still only matching subset of the
index...}
Because the later case is much more efficient and Solr will only compute
the function values for hte docs it needs to (that match the wrapped $qq
query)
But for your specific goal Peter: Yes, if the whole point of a function
you have is to wrap generated a scaled score of your base $qq, then the
function (wrapping the scale(), wrapping the query()) is going to have to
be evaluated for every doc -- that will definitely be linear based on the
size of the index.
-Hoss
http://www.lucidworks.com/
Re: Function query matching
(This is why i shouldn't send emails just before going to bed.)
I woke up this morning realizing that of course I was completley wrong
when i said this...
: I want to be clear for 99% of the people reading this, if you find
: yourself writting a query structure like this...
:
: q={!func}..functions involving wrapping $qq ...
...
: ...Try to restructure the match you want to do into the form of a
: multiplier
...
: Because the later case is much more efficient and Solr will only compute
: the function values for hte docs it needs to (that match the wrapped $qq
: query)
The reason i was wrong...
Even though function queries do by default match all documents, and even
if the main query is a function query (ie: q={!func}...), if there is
an fq that filters down the set of documents, then the (main) function
query will only be calculated for the documents that match the filter.
It was trivial to ammend the test i mentioned last night to show this (and
i feel silly for not doing that last night and stoping myself from saying
something foolish)...
https://svn.apache.org/viewvc?view=revisionrevision=r1548955
The bottom line for Peter is still the same: using scale() wrapped arround
a function/query does involve a computing hte results for every document,
and that is going to scale linearly as the size of hte index grows -- but
it it is *only* because of the scale function.
-Hoss
http://www.lucidworks.com/
Re: Function query matching
I added some timing logging to IndexSearcher and ScaleFloatFunction and
compared a simple DisMax query with a DisMax query wrapped in the scale
function. The index size was 500K docs, 61K docs match the DisMax query.
The simple DisMax query took 33 ms, the function query took 89 ms. What I
found was:
1. The scale query only normalized the scores once (in
ScaleInfo.createScaleInfo) and added 33 ms to the Qtime. Subsequent calls
to ScaleFloatFuntion.getValues bypassed 'createScaleInfo and added ~0 time.
2. The FunctionQuery 'nextDoc' iterations added 16 ms over the DisMax
'nextDoc' iterations.
Here's the breakdown:
Simple DisMax query:
weight.scorer: 3 ms (get term enum)
scorer.score: 23 ms (nextDoc iterations)
other: 3 ms
Total: 33 ms
DisMax wrapped in ScaleFloatFunction:
weight.scorer: 39 ms (get scaled values)
scorer.score: 39 ms (nextDoc iterations)
other: 11 ms
Total: 89 ms
Even with any improvements to 'scale', all function queries will add a
linear increase to the Qtime as index size increases, since they match all
docs.
Trey: I'd be happy to test any patch that you find improves the speed.
On Mon, Dec 2, 2013 at 11:21 PM, Trey Grainger solrt...@gmail.com wrote:
We're working on the same problem with the combination of the
scale(query(...)) combination, so I'd like to share a bit more information
that may be useful.
*On the scale function:*
Even thought the scale query has to calculate the scores for all documents,
it is actually doing this work twice for each ValueSource (once to
calculate the min and max values, and then again when actually scoring the
documents), which is inefficient.
To solve the problem, we're in the process of putting a cache inside the
scale function to remember the values for each document when they are
initially computed (to find the min and max) so that the second pass can
just use the previously computed values for each document. Our theory is
that most of the extra time due to the scale function is really just the
result of doing duplicate work.
No promises this won't be overly costly in terms of memory utilization, but
we'll see what we get in terms of speed improvements and will share the
code if it works out well. Alternate implementation suggestions (or
criticism of a cache like this) are also welcomed.
*On the NoOp product function: scale(prod(1, query(...))):*
We do the same thing, which ultimately is just an unnecessary waste of a
loop through all documents to do an extra multiplication step. I just
debugged the code and uncovered the problem. There is a Map (called
context) that is passed through to each value source to store intermediate
state, and both the query and scale functions are passing the ValueSource
for the query function in as the KEY to this Map (as opposed to using some
composite key that makes sense in the current context). Essentially, these
lines are overwriting each other:
Inside ScaleFloatFunction: context.put(this.source, scaleInfo);
//this.source refers to the QueryValueSource, and the scaleInfo refers to
a ScaleInfo object
Inside QueryValueSource: context.put(this, w); //this refers to the same
QueryValueSource from above, and the w refers to a Weight object
As such, when the ScaleFloatFunction later goes to read the ScaleInfo from
the context Map, it unexpectedly pulls the Weight object out instead and
thus the invalid case exception occurs. The NoOp multiplication works
because it puts an different ValueSource between the query and the
ScaleFloatFunction such that this.source (in ScaleFloatFunction) != this
(in QueryValueSource).
This should be an easy fix. I'll create a JIRA ticket to use better key
names in these functions and push up a patch. This will eliminate the need
for the extra NoOp function.
-Trey
On Mon, Dec 2, 2013 at 12:41 PM, Peter Keegan peterlkee...@gmail.com
wrote:
I'm persuing this possible PostFilter solution, I can see how to collect
all the hits and recompute the scores in a PostFilter, after all the hits
have been collected (for scaling). Now, I can't see how to get the custom
doc/score values back into the main query's HitQueue. Any advice?
Thanks,
Peter
On Fri, Nov 29, 2013 at 9:18 AM, Peter Keegan peterlkee...@gmail.com
wrote:
Instead of using a function query, could I use the edismax query (plus
some low cost filters not shown in the example) and implement the
scale/sum/product computation in a PostFilter? Is the query's maxScore
available there?
Thanks,
Peter
On Wed, Nov 27, 2013 at 1:58 PM, Peter Keegan peterlkee...@gmail.com
wrote:
Although the 'scale' is a big part of it, here's a closer breakdown.
Here
are 4 queries with increasing functions, and theei response times
(caching
turned off in solrconfig):
100 msec:
select?q={!edismax v='news' qf='title^2 body'}
135 msec:
select?qq={!edismax v='news' qf='title^2
Re: Function query matching
In my previous posting, I said: Subsequent calls to ScaleFloatFuntion.getValues bypassed 'createScaleInfo and added ~0 time. These subsequent calls are for the remaining segments in the index reader (21 segments). Peter On Fri, Dec 6, 2013 at 2:10 PM, Peter Keegan peterlkee...@gmail.com wrote: I added some timing logging to IndexSearcher and ScaleFloatFunction and compared a simple DisMax query with a DisMax query wrapped in the scale function. The index size was 500K docs, 61K docs match the DisMax query. The simple DisMax query took 33 ms, the function query took 89 ms. What I found was: 1. The scale query only normalized the scores once (in ScaleInfo.createScaleInfo) and added 33 ms to the Qtime. Subsequent calls to ScaleFloatFuntion.getValues bypassed 'createScaleInfo and added ~0 time. 2. The FunctionQuery 'nextDoc' iterations added 16 ms over the DisMax 'nextDoc' iterations. Here's the breakdown: Simple DisMax query: weight.scorer: 3 ms (get term enum) scorer.score: 23 ms (nextDoc iterations) other: 3 ms Total: 33 ms DisMax wrapped in ScaleFloatFunction: weight.scorer: 39 ms (get scaled values) scorer.score: 39 ms (nextDoc iterations) other: 11 ms Total: 89 ms Even with any improvements to 'scale', all function queries will add a linear increase to the Qtime as index size increases, since they match all docs. Trey: I'd be happy to test any patch that you find improves the speed. On Mon, Dec 2, 2013 at 11:21 PM, Trey Grainger solrt...@gmail.com wrote: We're working on the same problem with the combination of the scale(query(...)) combination, so I'd like to share a bit more information that may be useful. *On the scale function:* Even thought the scale query has to calculate the scores for all documents, it is actually doing this work twice for each ValueSource (once to calculate the min and max values, and then again when actually scoring the documents), which is inefficient. To solve the problem, we're in the process of putting a cache inside the scale function to remember the values for each document when they are initially computed (to find the min and max) so that the second pass can just use the previously computed values for each document. Our theory is that most of the extra time due to the scale function is really just the result of doing duplicate work. No promises this won't be overly costly in terms of memory utilization, but we'll see what we get in terms of speed improvements and will share the code if it works out well. Alternate implementation suggestions (or criticism of a cache like this) are also welcomed. *On the NoOp product function: scale(prod(1, query(...))):* We do the same thing, which ultimately is just an unnecessary waste of a loop through all documents to do an extra multiplication step. I just debugged the code and uncovered the problem. There is a Map (called context) that is passed through to each value source to store intermediate state, and both the query and scale functions are passing the ValueSource for the query function in as the KEY to this Map (as opposed to using some composite key that makes sense in the current context). Essentially, these lines are overwriting each other: Inside ScaleFloatFunction: context.put(this.source, scaleInfo); //this.source refers to the QueryValueSource, and the scaleInfo refers to a ScaleInfo object Inside QueryValueSource: context.put(this, w); //this refers to the same QueryValueSource from above, and the w refers to a Weight object As such, when the ScaleFloatFunction later goes to read the ScaleInfo from the context Map, it unexpectedly pulls the Weight object out instead and thus the invalid case exception occurs. The NoOp multiplication works because it puts an different ValueSource between the query and the ScaleFloatFunction such that this.source (in ScaleFloatFunction) != this (in QueryValueSource). This should be an easy fix. I'll create a JIRA ticket to use better key names in these functions and push up a patch. This will eliminate the need for the extra NoOp function. -Trey On Mon, Dec 2, 2013 at 12:41 PM, Peter Keegan peterlkee...@gmail.com wrote: I'm persuing this possible PostFilter solution, I can see how to collect all the hits and recompute the scores in a PostFilter, after all the hits have been collected (for scaling). Now, I can't see how to get the custom doc/score values back into the main query's HitQueue. Any advice? Thanks, Peter On Fri, Nov 29, 2013 at 9:18 AM, Peter Keegan peterlkee...@gmail.com wrote: Instead of using a function query, could I use the edismax query (plus some low cost filters not shown in the example) and implement the scale/sum/product computation in a PostFilter? Is the query's maxScore available there? Thanks, Peter On Wed, Nov 27, 2013 at 1:58 PM, Peter Keegan peterlkee...@gmail.com wrote: Although
Re: Function query matching
I had to do a double take when i read this sentence...
: Even with any improvements to 'scale', all function queries will add a
: linear increase to the Qtime as index size increases, since they match all
: docs.
...because that smelled like either a bug in your methodology, or a bug in
Solr. To convince myself there wasn't a bug in Solr, i wrote a test case
(i'll commit tomorow, bunch of churn in svn right now making ant
precommit unhappy) to prove that when wrapping boost functions arround
queries, Solr will only evaluate the functions for docs matching the
wrapped query -- so there is no linear increase as the index size
increases, just the (neccessary) libera increase as the number of
*matching* docs grows. (for most functions anyway -- as mentioned scale
is special).
BUT! ... then i remembered how this thread started, and your goal of
scaling the scores from a wrapped query.
I want to be clear for 99% of the people reading this, if you find
yourself writting a query structure like this...
q={!func}..functions involving wrapping $qq ...
qq={!edismax ...lots of stuff but still only matching subset of the index...}
fq={!query v=$qq}
...Try to restructure the match you want to do into the form of a
multiplier
q={!boost b=$b v=$qq}
b=...functions producing a score multiplier...
qq={!edismax ...lots of stuff but still only matching subset of the index...}
Because the later case is much more efficient and Solr will only compute
the function values for hte docs it needs to (that match the wrapped $qq
query)
But for your specific goal Peter: Yes, if the whole point of a function
you have is to wrap generated a scaled score of your base $qq, then the
function (wrapping the scale(), wrapping the query()) is going to have to
be evaluated for every doc -- that will definitely be linear based on the
size of the index.
-Hoss
http://www.lucidworks.com/
Re: Function query matching
I'm persuing this possible PostFilter solution, I can see how to collect
all the hits and recompute the scores in a PostFilter, after all the hits
have been collected (for scaling). Now, I can't see how to get the custom
doc/score values back into the main query's HitQueue. Any advice?
Thanks,
Peter
On Fri, Nov 29, 2013 at 9:18 AM, Peter Keegan peterlkee...@gmail.comwrote:
Instead of using a function query, could I use the edismax query (plus
some low cost filters not shown in the example) and implement the
scale/sum/product computation in a PostFilter? Is the query's maxScore
available there?
Thanks,
Peter
On Wed, Nov 27, 2013 at 1:58 PM, Peter Keegan peterlkee...@gmail.comwrote:
Although the 'scale' is a big part of it, here's a closer breakdown. Here
are 4 queries with increasing functions, and theei response times (caching
turned off in solrconfig):
100 msec:
select?q={!edismax v='news' qf='title^2 body'}
135 msec:
select?qq={!edismax v='news' qf='title^2
body'}q={!func}product(field(myfield),query($qq)fq={!query v=$qq}
200 msec:
select?qq={!edismax v='news' qf='title^2
body'}q={!func}sum(product(0.75,query($qq)),product(0.25,field(myfieldfq={!query
v=$qq}
320 msec:
select?qq={!edismax v='news' qf='title^2
body'}scaledQ=scale(product(query($qq),1),0,1)q={!func}sum(product(0.75,$scaledQ),product(0.25,field(myfield)))fq={!query
v=$qq}
Btw, that no-op product is necessary, else you get this exception:
org.apache.lucene.search.BooleanQuery$BooleanWeight cannot be cast to
org.apache.lucene.queries.function.valuesource.ScaleFloatFunction$ScaleInfo
thanks,
peter
On Wed, Nov 27, 2013 at 1:30 PM, Chris Hostetter
hossman_luc...@fucit.org wrote:
: So, this query does just what I want, but it's typically 3 times slower
: than the edismax query without the functions:
that's because the scale() function is inhernetly slow (it has to
compute the min max value for every document in order to know how to
scale them)
what you are seeing is the price you have to pay to get that query with a
normalized 0-1 value.
(you might be able to save a little bit of time by eliminating that
no-Op multiply by 1: product(query($qq),1) ... but i doubt you'll even
notice much of a chnage given that scale function.
: Is there any way to speed this up? Would writing a custom function
query
: that compiled all the function queries together be any faster?
If you can find a faster implementation for scale() then by all means let
us konw, and we can fold it back into Solr.
-Hoss
Re: Function query matching
We're working on the same problem with the combination of the
scale(query(...)) combination, so I'd like to share a bit more information
that may be useful.
*On the scale function:*
Even thought the scale query has to calculate the scores for all documents,
it is actually doing this work twice for each ValueSource (once to
calculate the min and max values, and then again when actually scoring the
documents), which is inefficient.
To solve the problem, we're in the process of putting a cache inside the
scale function to remember the values for each document when they are
initially computed (to find the min and max) so that the second pass can
just use the previously computed values for each document. Our theory is
that most of the extra time due to the scale function is really just the
result of doing duplicate work.
No promises this won't be overly costly in terms of memory utilization, but
we'll see what we get in terms of speed improvements and will share the
code if it works out well. Alternate implementation suggestions (or
criticism of a cache like this) are also welcomed.
*On the NoOp product function: scale(prod(1, query(...))):*
We do the same thing, which ultimately is just an unnecessary waste of a
loop through all documents to do an extra multiplication step. I just
debugged the code and uncovered the problem. There is a Map (called
context) that is passed through to each value source to store intermediate
state, and both the query and scale functions are passing the ValueSource
for the query function in as the KEY to this Map (as opposed to using some
composite key that makes sense in the current context). Essentially, these
lines are overwriting each other:
Inside ScaleFloatFunction: context.put(this.source, scaleInfo);
//this.source refers to the QueryValueSource, and the scaleInfo refers to
a ScaleInfo object
Inside QueryValueSource: context.put(this, w); //this refers to the same
QueryValueSource from above, and the w refers to a Weight object
As such, when the ScaleFloatFunction later goes to read the ScaleInfo from
the context Map, it unexpectedly pulls the Weight object out instead and
thus the invalid case exception occurs. The NoOp multiplication works
because it puts an different ValueSource between the query and the
ScaleFloatFunction such that this.source (in ScaleFloatFunction) != this
(in QueryValueSource).
This should be an easy fix. I'll create a JIRA ticket to use better key
names in these functions and push up a patch. This will eliminate the need
for the extra NoOp function.
-Trey
On Mon, Dec 2, 2013 at 12:41 PM, Peter Keegan peterlkee...@gmail.comwrote:
I'm persuing this possible PostFilter solution, I can see how to collect
all the hits and recompute the scores in a PostFilter, after all the hits
have been collected (for scaling). Now, I can't see how to get the custom
doc/score values back into the main query's HitQueue. Any advice?
Thanks,
Peter
On Fri, Nov 29, 2013 at 9:18 AM, Peter Keegan peterlkee...@gmail.com
wrote:
Instead of using a function query, could I use the edismax query (plus
some low cost filters not shown in the example) and implement the
scale/sum/product computation in a PostFilter? Is the query's maxScore
available there?
Thanks,
Peter
On Wed, Nov 27, 2013 at 1:58 PM, Peter Keegan peterlkee...@gmail.com
wrote:
Although the 'scale' is a big part of it, here's a closer breakdown.
Here
are 4 queries with increasing functions, and theei response times
(caching
turned off in solrconfig):
100 msec:
select?q={!edismax v='news' qf='title^2 body'}
135 msec:
select?qq={!edismax v='news' qf='title^2
body'}q={!func}product(field(myfield),query($qq)fq={!query v=$qq}
200 msec:
select?qq={!edismax v='news' qf='title^2
body'}q={!func}sum(product(0.75,query($qq)),product(0.25,field(myfieldfq={!query
v=$qq}
320 msec:
select?qq={!edismax v='news' qf='title^2
body'}scaledQ=scale(product(query($qq),1),0,1)q={!func}sum(product(0.75,$scaledQ),product(0.25,field(myfield)))fq={!query
v=$qq}
Btw, that no-op product is necessary, else you get this exception:
org.apache.lucene.search.BooleanQuery$BooleanWeight cannot be cast to
org.apache.lucene.queries.function.valuesource.ScaleFloatFunction$ScaleInfo
thanks,
peter
On Wed, Nov 27, 2013 at 1:30 PM, Chris Hostetter
hossman_luc...@fucit.org wrote:
: So, this query does just what I want, but it's typically 3 times
slower
: than the edismax query without the functions:
that's because the scale() function is inhernetly slow (it has to
compute the min max value for every document in order to know how to
scale them)
what you are seeing is the price you have to pay to get that query
with a
normalized 0-1 value.
(you might be able to save a little bit of time by eliminating that
no-Op multiply by 1: product(query($qq),1) ... but i doubt you'll
even
notice much of a chnage
Re: Function query matching
Instead of using a function query, could I use the edismax query (plus some
low cost filters not shown in the example) and implement the
scale/sum/product computation in a PostFilter? Is the query's maxScore
available there?
Thanks,
Peter
On Wed, Nov 27, 2013 at 1:58 PM, Peter Keegan peterlkee...@gmail.comwrote:
Although the 'scale' is a big part of it, here's a closer breakdown. Here
are 4 queries with increasing functions, and theei response times (caching
turned off in solrconfig):
100 msec:
select?q={!edismax v='news' qf='title^2 body'}
135 msec:
select?qq={!edismax v='news' qf='title^2
body'}q={!func}product(field(myfield),query($qq)fq={!query v=$qq}
200 msec:
select?qq={!edismax v='news' qf='title^2
body'}q={!func}sum(product(0.75,query($qq)),product(0.25,field(myfieldfq={!query
v=$qq}
320 msec:
select?qq={!edismax v='news' qf='title^2
body'}scaledQ=scale(product(query($qq),1),0,1)q={!func}sum(product(0.75,$scaledQ),product(0.25,field(myfield)))fq={!query
v=$qq}
Btw, that no-op product is necessary, else you get this exception:
org.apache.lucene.search.BooleanQuery$BooleanWeight cannot be cast to
org.apache.lucene.queries.function.valuesource.ScaleFloatFunction$ScaleInfo
thanks,
peter
On Wed, Nov 27, 2013 at 1:30 PM, Chris Hostetter hossman_luc...@fucit.org
wrote:
: So, this query does just what I want, but it's typically 3 times slower
: than the edismax query without the functions:
that's because the scale() function is inhernetly slow (it has to
compute the min max value for every document in order to know how to
scale them)
what you are seeing is the price you have to pay to get that query with a
normalized 0-1 value.
(you might be able to save a little bit of time by eliminating that
no-Op multiply by 1: product(query($qq),1) ... but i doubt you'll even
notice much of a chnage given that scale function.
: Is there any way to speed this up? Would writing a custom function query
: that compiled all the function queries together be any faster?
If you can find a faster implementation for scale() then by all means let
us konw, and we can fold it back into Solr.
-Hoss
Re: Function query matching
Hi,
So, this query does just what I want, but it's typically 3 times slower
than the edismax query without the functions:
select?qq={!edismax v='news' qf='title^2 body'}scaledQ=scale(product(
query($qq),1),0,1)q={!func}sum(product(0.75,$scaledQ),
product(0.25,field(myfield)))fq={!query v=$qq}
Is there any way to speed this up? Would writing a custom function query
that compiled all the function queries together be any faster?
Thanks,
Peter
On Mon, Nov 11, 2013 at 1:31 PM, Peter Keegan peterlkee...@gmail.comwrote:
Thanks
On Mon, Nov 11, 2013 at 11:46 AM, Yonik Seeley yo...@heliosearch.comwrote:
On Mon, Nov 11, 2013 at 11:39 AM, Peter Keegan peterlkee...@gmail.com
wrote:
fq=$qq
What is the proper syntax?
fq={!query v=$qq}
-Yonik
http://heliosearch.com -- making solr shine
Re: Function query matching
: So, this query does just what I want, but it's typically 3 times slower : than the edismax query without the functions: that's because the scale() function is inhernetly slow (it has to compute the min max value for every document in order to know how to scale them) what you are seeing is the price you have to pay to get that query with a normalized 0-1 value. (you might be able to save a little bit of time by eliminating that no-Op multiply by 1: product(query($qq),1) ... but i doubt you'll even notice much of a chnage given that scale function. : Is there any way to speed this up? Would writing a custom function query : that compiled all the function queries together be any faster? If you can find a faster implementation for scale() then by all means let us konw, and we can fold it back into Solr. -Hoss
Re: Function query matching
Although the 'scale' is a big part of it, here's a closer breakdown. Here
are 4 queries with increasing functions, and theei response times (caching
turned off in solrconfig):
100 msec:
select?q={!edismax v='news' qf='title^2 body'}
135 msec:
select?qq={!edismax v='news' qf='title^2
body'}q={!func}product(field(myfield),query($qq)fq={!query v=$qq}
200 msec:
select?qq={!edismax v='news' qf='title^2
body'}q={!func}sum(product(0.75,query($qq)),product(0.25,field(myfieldfq={!query
v=$qq}
320 msec:
select?qq={!edismax v='news' qf='title^2
body'}scaledQ=scale(product(query($qq),1),0,1)q={!func}sum(product(0.75,$scaledQ),product(0.25,field(myfield)))fq={!query
v=$qq}
Btw, that no-op product is necessary, else you get this exception:
org.apache.lucene.search.BooleanQuery$BooleanWeight cannot be cast to
org.apache.lucene.queries.function.valuesource.ScaleFloatFunction$ScaleInfo
thanks,
peter
On Wed, Nov 27, 2013 at 1:30 PM, Chris Hostetter
hossman_luc...@fucit.orgwrote:
: So, this query does just what I want, but it's typically 3 times slower
: than the edismax query without the functions:
that's because the scale() function is inhernetly slow (it has to
compute the min max value for every document in order to know how to
scale them)
what you are seeing is the price you have to pay to get that query with a
normalized 0-1 value.
(you might be able to save a little bit of time by eliminating that
no-Op multiply by 1: product(query($qq),1) ... but i doubt you'll even
notice much of a chnage given that scale function.
: Is there any way to speed this up? Would writing a custom function query
: that compiled all the function queries together be any faster?
If you can find a faster implementation for scale() then by all means let
us konw, and we can fold it back into Solr.
-Hoss
Re: Function query matching
I replaced the frange filter with the following filter and got the correct
no. of results and it was 3X faster:
select?qq={!edismax v='news' qf='title^2
body'}scaledQ=scale(product(query($qq),1),0,1)q={!func}sum(product(0.75,$scaledQ),product(0.25,field(myfield)))fq={!edismax
v='news' qf='title^2 body'}
Then, I tried to simplify the query with parameter substitution, but 'fq'
didn't parse correctly:
select?qq={!edismax v='news' qf='title^2
body'}scaledQ=scale(product(query($qq),1),0,1)q={!func}sum(product(0.75,$scaledQ),product(0.25,field(myfield)))fq=$qq
What is the proper syntax?
Thanks,
Peter
On Thu, Nov 7, 2013 at 2:16 PM, Peter Keegan peterlkee...@gmail.com wrote:
I'm trying to used a normalized score in a query as I described in a
recent thread titled Re: How to get similarity score between 0 and 1 not
relative score
I'm using this query:
select?qq={!edismax v='news' qf='title^2
body'}scaledQ=scale(product(query($qq),1),0,1)q={!func}sum(product(0.75,$scaledQ),product(0.25,field(myfield)))fq={!frange
l=0.001}$q
Is there another way to accomplish this using dismax boosting?
On Thu, Nov 7, 2013 at 12:55 PM, Jason Hellman
jhell...@innoventsolutions.com wrote:
You can, of course, us a function range query:
select?q=text:newsfq={!frange l=0 u=100}sum(x,y)
http://lucene.apache.org/solr/4_5_1/solr-core/org/apache/solr/search/FunctionRangeQParserPlugin.html
This will give you a bit more flexibility to meet your goal.
On Nov 7, 2013, at 7:26 AM, Erik Hatcher erik.hatc...@gmail.com wrote:
Function queries score (all) documents, but don't filter them. All
documents effectively match a function query.
Erik
On Nov 7, 2013, at 1:48 PM, Peter Keegan peterlkee...@gmail.com
wrote:
Why does this function query return docs that don't match the embedded
query?
select?qq=text:newsq={!func}sum(query($qq),0)
Re: Function query matching
On Mon, Nov 11, 2013 at 11:39 AM, Peter Keegan peterlkee...@gmail.com wrote:
fq=$qq
What is the proper syntax?
fq={!query v=$qq}
-Yonik
http://heliosearch.com -- making solr shine
Re: Function query matching
Thanks
On Mon, Nov 11, 2013 at 11:46 AM, Yonik Seeley yo...@heliosearch.comwrote:
On Mon, Nov 11, 2013 at 11:39 AM, Peter Keegan peterlkee...@gmail.com
wrote:
fq=$qq
What is the proper syntax?
fq={!query v=$qq}
-Yonik
http://heliosearch.com -- making solr shine
Re: Function query matching
Function queries score (all) documents, but don't filter them. All documents
effectively match a function query.
Erik
On Nov 7, 2013, at 1:48 PM, Peter Keegan peterlkee...@gmail.com wrote:
Why does this function query return docs that don't match the embedded
query?
select?qq=text:newsq={!func}sum(query($qq),0)
Re: Function query matching
You can, of course, us a function range query:
select?q=text:newsfq={!frange l=0 u=100}sum(x,y)
http://lucene.apache.org/solr/4_5_1/solr-core/org/apache/solr/search/FunctionRangeQParserPlugin.html
This will give you a bit more flexibility to meet your goal.
On Nov 7, 2013, at 7:26 AM, Erik Hatcher erik.hatc...@gmail.com wrote:
Function queries score (all) documents, but don't filter them. All documents
effectively match a function query.
Erik
On Nov 7, 2013, at 1:48 PM, Peter Keegan peterlkee...@gmail.com wrote:
Why does this function query return docs that don't match the embedded
query?
select?qq=text:newsq={!func}sum(query($qq),0)
Re: Function query matching
I'm trying to used a normalized score in a query as I described in a recent
thread titled Re: How to get similarity score between 0 and 1 not relative
score
I'm using this query:
select?qq={!edismax v='news' qf='title^2
body'}scaledQ=scale(product(query($qq),1),0,1)q={!func}sum(product(0.75,$scaledQ),product(0.25,field(myfield)))fq={!frange
l=0.001}$q
Is there another way to accomplish this using dismax boosting?
On Thu, Nov 7, 2013 at 12:55 PM, Jason Hellman
jhell...@innoventsolutions.com wrote:
You can, of course, us a function range query:
select?q=text:newsfq={!frange l=0 u=100}sum(x,y)
http://lucene.apache.org/solr/4_5_1/solr-core/org/apache/solr/search/FunctionRangeQParserPlugin.html
This will give you a bit more flexibility to meet your goal.
On Nov 7, 2013, at 7:26 AM, Erik Hatcher erik.hatc...@gmail.com wrote:
Function queries score (all) documents, but don't filter them. All
documents effectively match a function query.
Erik
On Nov 7, 2013, at 1:48 PM, Peter Keegan peterlkee...@gmail.com wrote:
Why does this function query return docs that don't match the embedded
query?
select?qq=text:newsq={!func}sum(query($qq),0)
