# (new at ES): can this be done with ES?
**URL:** https://discuss.elastic.co/t/new-at-es-can-this-be-done-with-es/11180
**Category:** Elasticsearch
**Created:** [March 16, 2013, 11:38pm UTC](https://discuss.elastic.co/t/new-at-es-can-this-be-done-with-es/11180 "2013-03-16T23:38:40Z")
**Posts on this page:** 14
**Page:** 1
### Author:  [@Matei](https://discuss.elastic.co/u/Matei)
#### Post date: [March 16, 2013, 11:38pm UTC](https://discuss.elastic.co/t/new-at-es-can-this-be-done-with-es/11180/1 "2013-03-16T23:38:40Z")
Hi guys,
I'm looking for a storage/search solution for the following scenario:
I have \*50M \*documents of this form:
{title: "ABC", tags: [{id: 1, weight: 10, type: 5}, {id: 4, weight: 50,
type: 6},.....]},
Each document can have \*thousands \*of tags.
I want to:
1. search for documents that match all of a given array of tags [1,4,...]
2. return top 30 tags based on the _sum_ of their weights from all the
matched documents, grouped by type.
So, if tag \*4 _appears in 50 documents, in each having a weight of 10,
it's total weight would be 5010 = 100_.
I'm currently doing this with Mongo DB's aggregation framework, but have
serious concerns of it running out of memory since everything is done
in-memory.
Can this be achieved efficiently with ES?
Thank you for your input!
Matei
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### Author:  [@polyfractal](https://discuss.elastic.co/u/polyfractal)
#### Post date: [March 18, 2013, 11:23am UTC](https://discuss.elastic.co/t/new-at-es-can-this-be-done-with-es/11180/2 "2013-03-18T11:23:40Z")
I think this should be doable. You will need to use Nested[http://www.elasticsearch.org/guide/reference/mapping/nested-type.html](http://www.elasticsearch.org/guide/reference/mapping/nested-type.html)or
Parent/Child[http://www.elasticsearch.org/guide/reference/mapping/parent-field.html](http://www.elasticsearch.org/guide/reference/mapping/parent-field.html)mapping, since inner-objects will collapse your 'tagged' fields into a
single, large array. IDs can be filtered using a simple Term filter[http://www.elasticsearch.org/guide/reference/query-dsl/term-filter.html](http://www.elasticsearch.org/guide/reference/query-dsl/term-filter.html).
That will give you the list of matching documents.
To get the sum of weights for each type, you'd probably want to use a
filtered, Statistical[http://www.elasticsearch.org/guide/reference/api/search/facets/statistical-facet.html](http://www.elasticsearch.org/guide/reference/api/search/facets/statistical-facet.html)facet.
-Zach
On Saturday, March 16, 2013 7:38:40 PM UTC-4, Matei wrote:
> Hi guys,
>
> I'm looking for a storage/search solution for the following scenario:
>
> I have \*50M \*documents of this form:
>
> {title: "ABC", tags: [{id: 1, weight: 10, type: 5}, {id: 4, weight: 50,
> type: 6},.....]},
>
> Each document can have \*thousands \*of tags.
>
> I want to:
>
> 1. search for documents that match all of a given array of tags [1,4,...]
> 2. return top 30 tags based on the _sum_ of their weights from all the
> matched documents, grouped by type.
>
> So, if tag \*4 _appears in 50 documents, in each having a weight of 10,
> it's total weight would be 5010 = 100_.
>
> I'm currently doing this with Mongo DB's aggregation framework, but have
> serious concerns of it running out of memory since everything is done
> in-memory.
>
> Can this be achieved efficiently with ES?
>
> Thank you for your input!
>
> Matei
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### Author:  [@Matei](https://discuss.elastic.co/u/Matei)
#### Post date: [March 18, 2013, 11:29am UTC](https://discuss.elastic.co/t/new-at-es-can-this-be-done-with-es/11180/3 "2013-03-18T11:29:09Z")
Hi Zach, thank you for taking the time yo reply. As I read in the docs,
Statistical facets are also performed in-memory, just like in MongoDB so
the out-of-memory concerns would still be there, right?
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### Author:  [@polyfractal](https://discuss.elastic.co/u/polyfractal)
#### Post date: [March 18, 2013, 11:50am UTC](https://discuss.elastic.co/t/new-at-es-can-this-be-done-with-es/11180/4 "2013-03-18T11:50:53Z")
Yep, that is correct - faceting (and sorting) are in-memory operations.
However, ES will only load into memory what is required to complete the
facet operation. If you filter the facet by ID value, you should be
operating on a subset of your 50M doc dataset. The memory requirements
will be lighter than if you were to facet over the entire dataset without a
filter. There is also the consideration that, as you add nodes to your
cluster, the memory burden of each node decreases since each node is
responsible for faceting a smaller portion of the data.
On Monday, March 18, 2013 7:29:09 AM UTC-4, Matei wrote:
> Hi Zach, thank you for taking the time yo reply. As I read in the docs,
> Statistical facets are also performed in-memory, just like in MongoDB so
> the out-of-memory concerns would still be there, right?
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### Author:  [@Clinton\_Gormley](https://discuss.elastic.co/u/Clinton_Gormley)
#### Post date: [March 18, 2013, 12:57pm UTC](https://discuss.elastic.co/t/new-at-es-can-this-be-done-with-es/11180/5 "2013-03-18T12:57:18Z")
> However, ES will only load into memory what is required to complete
> the facet operation. If you filter the facet by ID value, you should
> be operating on a subset of your 50M doc dataset. The memory
> requirements will be lighter than if you were to facet over the entire
> dataset without a filter. There is also the consideration that, as
> you add nodes to your cluster, the memory burden of each node
> decreases since each node is responsible for faceting a smaller
> portion of the data.
Unfortunately, this is not correct.
For efficiency's sake, Elasticsearch assumes that, if you need field
values for docs 1..10 in this query, the chances are good that you'll
need docs 11.1000 in a later query, so it loads the field values for ALL
docs in your index into memory.
On top of that, in versions of ES before 0.90, multi-value fields use a
lot of memory: num\_of\_docs x max\_num\_of\_vals\_per\_field
If you have docs with thousands of tags, that is going to consume an
enormous amount of memory. Chances are good you will get OOM failures.
The situation will be better when the next version of ES is released,
but you may want to reconsider this structure.
clint
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### Author:  [@polyfractal](https://discuss.elastic.co/u/polyfractal)
#### Post date: [March 18, 2013, 1:11pm UTC](https://discuss.elastic.co/t/new-at-es-can-this-be-done-with-es/11180/6 "2013-03-18T13:11:37Z")
Whoops, ignore what I said then. I assumed that a filtered facet would
operate on smaller datasets (similar to how queries operate on filtered
results).
Sorry about that! =/
On Monday, March 18, 2013 8:57:18 AM UTC-4, Clinton Gormley wrote:
> > However, ES will only load into memory what is required to complete
> > the facet operation. If you filter the facet by ID value, you should
> > be operating on a subset of your 50M doc dataset. The memory
> > requirements will be lighter than if you were to facet over the entire
> > dataset without a filter. There is also the consideration that, as
> > you add nodes to your cluster, the memory burden of each node
> > decreases since each node is responsible for faceting a smaller
> > portion of the data.
>
> Unfortunately, this is not correct.
>
> For efficiency's sake, Elasticsearch assumes that, if you need field
> values for docs 1..10 in this query, the chances are good that you'll
> need docs 11.1000 in a later query, so it loads the field values for ALL
> docs in your index into memory.
>
> On top of that, in versions of ES before 0.90, multi-value fields use a
> lot of memory: num\_of\_docs x max\_num\_of\_vals\_per\_field
>
> If you have docs with thousands of tags, that is going to consume an
> enormous amount of memory. Chances are good you will get OOM failures.
>
> The situation will be better when the next version of ES is released,
> but you may want to reconsider this structure.
>
> clint
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### Author:  [@Matei](https://discuss.elastic.co/u/Matei)
#### Post date: [March 19, 2013, 11:35am UTC](https://discuss.elastic.co/t/new-at-es-can-this-be-done-with-es/11180/7 "2013-03-19T11:35:43Z")
Thanks Clint, Zach for your replies. It seems this is an increasingly
difficult problem to solve. Any suggestions on how I should restructure my
data for similar results, without risking OOM?
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### Author:  [@Clinton\_Gormley](https://discuss.elastic.co/u/Clinton_Gormley)
#### Post date: [March 19, 2013, 12:49pm UTC](https://discuss.elastic.co/t/new-at-es-can-this-be-done-with-es/11180/8 "2013-03-19T12:49:15Z")
Hi Matei
On Tue, 2013-03-19 at 04:35 -0700, Matei wrote:
> Thanks Clint, Zach for your replies. It seems this is an increasingly
> difficult problem to solve. Any suggestions on how I should
> restructure my data for similar results, without risking OOM?
Difficult to say. It kinda depends what you really want to achieve. I
know you explained your algorithm earlier, but it sounds like a
poor-man's ranking engine. I'm wondering if the native functionality
available in ES might do a better job of it.
Perhaps step back and restate the problem: what user experience do you
want to provide.
From that, the answer may be clearer
clint
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### Author:  [@Matei](https://discuss.elastic.co/u/Matei)
#### Post date: [March 21, 2013, 3:14pm UTC](https://discuss.elastic.co/t/new-at-es-can-this-be-done-with-es/11180/9 "2013-03-21T15:14:13Z")
Hi Clint,
I have about 50M events, each being described by tags. There are 4 tag
types (places, speakers, topics, industries).
1. Tags are hierarchical, for example, "Eric Schmidt" is filed under
Google who is filed under Tech companies. So, whenever Eric is at an event,
all three tags are associated with the event.
2. Different tags can have different popularity, meaning "Eric Schmidt"
would have a popularity of 100, but "Eileen Naughton" would have a
popularity of "10".
3. The popularity does not apply hierarchically. That means that, if "Eric
Schmidt" would leave Google for Foursquare, his popularity would still be
100 and Foursquare would still have popularity 50.
Now, imagine a left-hand menu with 4 sections:
## Places
Paris
London
New York
[more]
## Speakers
Google
Facebook
Marc Zuckerberg
[more]
and so on.
Whenever the user clicks on a tag, I want the menu to reflect the results
(faceted search). The twist is that when deciding to show "Google" vs "Eric
Schmidt" vs"Foursquare" in the first three tags in each section, I want to
make sure the most popular tag is shown higher, based on the [number of
matching events] \* [tag popularity]. That means that if there are 3
matching events for "Foursquare" and only one for "Eric Schmidt" it should
show Foursquare first, with a score of 3\*50 = 150 vs Schmidt's 1 \* 100.
Also, ideally, if I select "Google" then, for the "speakers" section, the
system should not return people outside Google, even if the matching events
also have "Zuckerberg" listed, with a huge popularity of 200. So, the
returned tags should reside "beneath" the current selection in each
section, and their sorting should be based on the above scoring logic.
I hope I managed to explain what I'm trying to achieve.
Any help would be greatly appreciated!
Thanks
Matei
On Tue, Mar 19, 2013 at 2:49 PM, Clinton Gormley [clint@traveljury.com](mailto:clint@traveljury.com)wrote:
> Hi Matei
>
> On Tue, 2013-03-19 at 04:35 -0700, Matei wrote:
>
> > Thanks Clint, Zach for your replies. It seems this is an increasingly
> > difficult problem to solve. Any suggestions on how I should
> > restructure my data for similar results, without risking OOM?
>
> Difficult to say. It kinda depends what you really want to achieve. I
> know you explained your algorithm earlier, but it sounds like a
> poor-man's ranking engine. I'm wondering if the native functionality
> available in ES might do a better job of it.
>
> Perhaps step back and restate the problem: what user experience do you
> want to provide.
>
> From that, the answer may be clearer
>
> clint
>
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### Author:  [@Clinton\_Gormley](https://discuss.elastic.co/u/Clinton_Gormley)
#### Post date: [March 21, 2013, 5:43pm UTC](https://discuss.elastic.co/t/new-at-es-can-this-be-done-with-es/11180/10 "2013-03-21T17:43:24Z")
Hi Matei
> I have about 50M events, each being described by tags. There are 4 tag
> types (places, speakers, topics, industries).
>
> 1. Tags are hierarchical, for example, "Eric Schmidt" is filed under
> Google who is filed under Tech companies. So, whenever Eric is at an
> event, all three tags are associated with the event.
> 2. Different tags can have different popularity, meaning "Eric
> Schmidt" would have a popularity of 100, but "Eileen Naughton" would
> have a popularity of "10".
> 3. The popularity does not apply hierarchically. That means that, if
> "Eric Schmidt" would leave Google for Foursquare, his popularity would
> still be 100 and Foursquare would still have popularity 50.
> Now, imagine a left-hand menu with 4 sections:
>
> ## Places
>
> Paris
> London
> New York
> [more]
>
> ## Speakers
>
> Google
> Facebook
> Marc Zuckerberg
> [more]
>
> and so on.
>
> Whenever the user clicks on a tag, I want the menu to reflect the
> results (faceted search). The twist is that when deciding to show
> "Google" vs "Eric Schmidt" vs"Foursquare" in the first three tags in
> each section, I want to make sure the most popular tag is shown
> higher, based on the [number of matching events] \* [tag popularity].
> That means that if there are 3 matching events for "Foursquare" and
> only one for "Eric Schmidt" it should show Foursquare first, with a
> score of 3\*50 = 150 vs Schmidt's 1 \* 100.
>
> Also, ideally, if I select "Google" then, for the "speakers" section,
> the system should not return people outside Google, even if the
> matching events also have "Zuckerberg" listed, with a huge popularity
> of 200. So, the returned tags should reside "beneath" the current
> selection in each section, and their sorting should be based on the
> above scoring logic.
>
> I hope I managed to explain what I'm trying to achieve.
OK - that makes a lot of sense now. Much easier to understand with
"real" data.
I think this will be quite easy to do using version 0.90.0.RC1 (which
will be released as stable in the near future), which has support for
sorting on values in nested documents (min,max,sum,avg).
So, index your tags as nested documents:
```
curl -XPUT 'http://127.0.0.1:9200/events/?pretty=1' -d '
{
"mappings" : {
"event" : {
"properties" : {
"name" : {
"type" : "string"
},
"tags" : {
"type" : "nested",
"properties" : {
"value" : {
"index" : "not_analyzed",
"type" : "string"
},
"weight" : {
"type" : "integer"
},
"type" : {
"index" : "not_analyzed",
"type" : "string"
}
}
}
}
}
}
}
'
```
Then, some data, eg:
```
curl -XPOST 'http://127.0.0.1:9200/events/event?pretty=1' -d '
{
"title" : "Paris in the springtime",
"tags" : [
{
"value" : "Paris",
"weight" : 10,
"type" : "place"
},
{
"value" : "Eric Schmidt",
"weight" : 100,
"type" : "speaker"
},
{
"value" : "Google",
"weight" : 50,
"type" : "company"
}
]
}
'
curl -XPOST 'http://127.0.0.1:9200/events/event?pretty=1' -d '
{
"title" : "Barcelona is the bollocks",
"tags" : [
{
"value" : "Barcelona",
"weight" : 30,
"type" : "place"
},
{
"value" : "Mark Zuckerberg",
"weight" : 30,
"type" : "speaker"
},
{
"value" : "Facebook",
"weight" : 40,
"type" : "company"
}
]
}
'
```
Now we can do our search, and sort on the sum of the weights in the
nested docs:
Look for the 'sort' value:
>
You can filter on (eg) just speakers from Google:
>
Or you can include all events, but score just on companies named
'Google':
>
Because we're sorting on a field value (ie 'tags.weight'), its values
need to be loaded into memory. But it has only a single value per field,
and you shouldn't run into the memory problems that you might have had
with other designs
clint
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### Author:  [@Matei](https://discuss.elastic.co/u/Matei)
#### Post date: [March 21, 2013, 5:55pm UTC](https://discuss.elastic.co/t/new-at-es-can-this-be-done-with-es/11180/11 "2013-03-21T17:55:51Z")
Hi Clint,
If I understand correctly, the examples you sent are for retrieving and
sorting \*events \*based on weight sum of selected tags which is great, but
not exactly the problem ๐ What I am looking for is a way to retrieve _the
facets_ for the menu, for each tag type, so that I can efficiently allow
the users to drill down by showing only the most relevant tags, based on
the weight-sum logic.
Sorry for not being clear enough, but I will surely use the examples once I
get to the sorting of events (for now, the event searching is done, but I
haven't thought about sorting them yet)
On Thursday, 21 March 2013 19:43:24 UTC+2, Clinton Gormley wrote:
> Hi Matei
>
> > I have about 50M events, each being described by tags. There are 4 tag
> > types (places, speakers, topics, industries).
> >
> > 1. Tags are hierarchical, for example, "Eric Schmidt" is filed under
> > Google who is filed under Tech companies. So, whenever Eric is at an
> > event, all three tags are associated with the event.
> > 2. Different tags can have different popularity, meaning "Eric
> > Schmidt" would have a popularity of 100, but "Eileen Naughton" would
> > have a popularity of "10".
> > 3. The popularity does not apply hierarchically. That means that, if
> > "Eric Schmidt" would leave Google for Foursquare, his popularity would
> > still be 100 and Foursquare would still have popularity 50.
>
> > Now, imagine a left-hand menu with 4 sections:
> >
> > ## Places
> >
> > Paris
> > London
> > New York
> > [more]
> >
> > ## Speakers
> >
> > Google
> > Facebook
> > Marc Zuckerberg
> > [more]
> >
> > and so on.
> >
> > Whenever the user clicks on a tag, I want the menu to reflect the
> > results (faceted search). The twist is that when deciding to show
> > "Google" vs "Eric Schmidt" vs"Foursquare" in the first three tags in
> > each section, I want to make sure the most popular tag is shown
> > higher, based on the [number of matching events] \* [tag popularity].
> > That means that if there are 3 matching events for "Foursquare" and
> > only one for "Eric Schmidt" it should show Foursquare first, with a
> > score of 3\*50 = 150 vs Schmidt's 1 \* 100.
> >
> > Also, ideally, if I select "Google" then, for the "speakers" section,
> > the system should not return people outside Google, even if the
> > matching events also have "Zuckerberg" listed, with a huge popularity
> > of 200. So, the returned tags should reside "beneath" the current
> > selection in each section, and their sorting should be based on the
> > above scoring logic.
> >
> > I hope I managed to explain what I'm trying to achieve.
>
> OK - that makes a lot of sense now. Much easier to understand with
> "real" data.
>
> I think this will be quite easy to do using version 0.90.0.RC1 (which
> will be released as stable in the near future), which has support for
> sorting on values in nested documents (min,max,sum,avg).
>
> So, index your tags as nested documents:
>
> ```
> curl -XPUT 'http://127.0.0.1:9200/events/?pretty=1' -d '
> {
> "mappings" : {
> "event" : {
> "properties" : {
> "name" : {
> "type" : "string"
> },
> "tags" : {
> "type" : "nested",
> "properties" : {
> "value" : {
> "index" : "not_analyzed",
> "type" : "string"
> },
> "weight" : {
> "type" : "integer"
> },
> "type" : {
> "index" : "not_analyzed",
> "type" : "string"
> }
> }
> }
> }
> }
> }
> }
> '
>
> ```
>
> Then, some data, eg:
>
> ```
> curl -XPOST 'http://127.0.0.1:9200/events/event?pretty=1' -d '
> {
> "title" : "Paris in the springtime",
> "tags" : [
> {
> "value" : "Paris",
> "weight" : 10,
> "type" : "place"
> },
> {
> "value" : "Eric Schmidt",
> "weight" : 100,
> "type" : "speaker"
> },
> {
> "value" : "Google",
> "weight" : 50,
> "type" : "company"
> }
> ]
> }
> '
>
> curl -XPOST 'http://127.0.0.1:9200/events/event?pretty=1' -d '
> {
> "title" : "Barcelona is the bollocks",
> "tags" : [
> {
> "value" : "Barcelona",
> "weight" : 30,
> "type" : "place"
> },
> {
> "value" : "Mark Zuckerberg",
> "weight" : 30,
> "type" : "speaker"
> },
> {
> "value" : "Facebook",
> "weight" : 40,
> "type" : "company"
> }
> ]
> }
> '
>
> ```
>
> Now we can do our search, and sort on the sum of the weights in the
> nested docs:
>
> Look for the 'sort' value:
>
> [curl.json ยท GitHub](https://gist.github.com/clintongormley/5215023)
>
> You can filter on (eg) just speakers from Google:
>
> [curl.json ยท GitHub](https://gist.github.com/clintongormley/5215015)
>
> Or you can include all events, but score just on companies named
> 'Google':
>
> [curl.json ยท GitHub](https://gist.github.com/clintongormley/5214991)
>
> Because we're sorting on a field value (ie 'tags.weight'), its values
> need to be loaded into memory. But it has only a single value per field,
> and you shouldn't run into the memory problems that you might have had
> with other designs
>
> clint
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### Author:  [@Clinton\_Gormley](https://discuss.elastic.co/u/Clinton_Gormley)
#### Post date: [March 21, 2013, 6:20pm UTC](https://discuss.elastic.co/t/new-at-es-can-this-be-done-with-es/11180/12 "2013-03-21T18:20:06Z")
On Thu, 2013-03-21 at 10:55 -0700, Matei wrote:
> Hi Clint,
>
> If I understand correctly, the examples you sent are for retrieving
> and sorting events based on weight sum of selected tags which is
> great, but not exactly the problem ๐ What I am looking for is a way
> to retrieve the facets for the menu, for each tag type, so that I can
> efficiently allow the users to drill down by showing only the most
> relevant tags, based on the weight-sum logic.
>
> Sorry for not being clear enough, but I will surely use the examples
> once I get to the sorting of events (for now, the event searching is
> done, but I haven't thought about sorting them yet)
Fortunately, this seems easy enough to do with a terms\_stats facet.
Look at the 'total' value.
here are the facets for all docs:
>
and facets just for docs with company:Google
>
clint
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### Author:  [@Matei](https://discuss.elastic.co/u/Matei)
#### Post date: [March 21, 2013, 8:35pm UTC](https://discuss.elastic.co/t/new-at-es-can-this-be-done-with-es/11180/13 "2013-03-21T20:35:01Z")
That looks great, I'm going to give everything you pointed out a test.
Hopefully, even though they are statistical facets I won't run out of
memory ๐
Thanks a lot! I owe you one. Let me know if you ever need front-end or PHP
assistance ๐
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### Author:  [@system](https://discuss.elastic.co/u/system)
#### Post date: [July 6, 2017, 2:45am UTC](https://discuss.elastic.co/t/new-at-es-can-this-be-done-with-es/11180/14 "2017-07-06T02:45:13Z")