Hi Michael,
In general the more buckets being returned by the parent aggregator the
top_hits is nested in, the more work the top_hits agg needs to do, but I
didn't come across performance issues with `size` on terms agg being set to
50 and the time it takes to execute increasing 30 times when top_hits is
used. To exclude this on your side, can you play around with the `size`
option on terms agg?
Also perhaps the _source of your documents are relatively large. How does
the top_hits agg perform without the `_source` option on the top_hits agg?
Martijn
On 6 January 2015 at 22:29, Michael Irani <irani.mich...@gmail.com> wrote:
> Sure. I simplified the query to keep things focused.
>
> This query takes about 3 seconds to run:
>
> {
>
> "size": 0,
>
> "aggs": {
> "top-fingerprints": {
> "terms": {
> "field": "fingerprint",
> "size": 50
> },
> "aggs": {
> "top_tag_hits": {
> "top_hits": {
> "size": 1,
> "_source": {
> "include": [
> "title"
> ]
> }
> }
> }
> }
> }
> }
>
> }
>
>
> This one takes about 80 milliseconds:
>
> {
>
> "size": 0,
>
> "aggs": {
> "fingerprints": {
> "terms": {
> "field": "fingerprint",
> "size": 100
> }
> }
> }
>
> }
>
>
> The result's a bit too big to paste here. Anything specific about it you want
> me to expose?
>
>
> Michael.
>
>
> On Tuesday, January 6, 2015 12:14:55 PM UTC-8, Itamar Syn-Hershko wrote:
>>
>> Can you share the query and example results please?
>>
>> --
>>
>> Itamar Syn-Hershko
>> http://code972.com | @synhershko <https://twitter.com/synhershko>
>> Freelance Developer & Consultant
>> Author of RavenDB in Action <http://manning.com/synhershko/>
>>
>> On Tue, Jan 6, 2015 at 10:11 PM, Michael Irani <irani....@gmail.com>
>> wrote:
>>
>>> Hello,
>>> I'm working on a corpus of size approximately 10 million documents. The
>>> issue I'm running into right now is that the top scoring documents that
>>> come back from my query are essentially all the same result. I'm trying to
>>> find a way to get back unique results.
>>>
>>> I've looked into modeling the data differently with nested objects or
>>> parent-child relationships, but neither layout seems to fit the bill. The
>>> nested model won't work because some of the documents have too many closely
>>> related objects. On the flip side there are also too many unique documents
>>> for the parent-child relationship to fit.
>>>
>>> I then tried the "top hits aggregation" and it's exactly what I'm
>>> looking for, except the running time of the query is approximately 30x
>>> slower than the query without the aggregation. Are there known performance
>>> issues with "top hits"? Any ideas on what I should use to make these
>>> queries? Here's the aggregation piece:
>>> "aggs": {
>>>
>>> "top-fingerprints": {
>>> "terms": {
>>> "field": "fingerprint",
>>> "size": 50
>>> },
>>> "aggs": {
>>> "top_tag_hits": {
>>> "top_hits": {
>>> "size": 1,
>>> "_source": {
>>> "include": [
>>> "title"
>>> ]
>>> }
>>> }
>>> }
>>> }
>>> }
>>> }
>>>
>>>
>>> Thanks,
>>> Michael
>>>
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--
Met vriendelijke groet,
Martijn van Groningen
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