You are viewing a plain text version of this content. The canonical link for it is here.
Posted to dev@lucene.apache.org by "Varun Thacker (JIRA)" <ji...@apache.org> on 2016/05/21 08:32:15 UTC
[jira] [Updated] (SOLR-9142) Improve JSON nested facets effeciency
[ https://issues.apache.org/jira/browse/SOLR-9142?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Varun Thacker updated SOLR-9142:
--------------------------------
Description:
I indexed a dataset of 2M docs
{{top_facet_s}} has a cardinality of 1000 which is the top level facet.
For nested facets it has two fields {{sub_facet_unique_s}} and {{sub_facet_unique_td}} which are string and double and have cardinality 2M
The nested query for the double field returns in the 1s mark always. The nested query for the string field takes roughly 10s to execute.
{code:title=nested string facet|borderStyle=solid}
q=*:*&rows=0&json.facet=
{
"top_facet_s": {
"type": "terms",
"limit": -1,
"field": "top_facet_s",
"mincount": 1,
"excludeTags": "ANY",
"facet": {
"sub_facet_unique_s": {
"type": "terms",
"limit": 1,
"field": "sub_facet_unique_s",
"mincount": 1
}
}
}
}
{code}
{code:title=nested double facet|borderStyle=solid}
q=*:*&rows=0&json.facet=
{
"top_facet_s": {
"type": "terms",
"limit": -1,
"field": "top_facet_s",
"mincount": 1,
"excludeTags": "ANY",
"facet": {
"sub_facet_unique_s": {
"type": "terms",
"limit": 1,
"field": "sub_facet_unique_td",
"mincount": 1
}
}
}
}
{code}
I tried to dig deeper to understand why are string nested faceting that slow compared to numeric field
Since the top facet has a cardinality of 1000 we have to calculate sub facets on each of them. Now the key difference was in the implementation of the two .
For the string field, In {{FacetField#getFieldCacheCounts}} we call {{createCollectAcc}} with nDocs=0 and numSlots=2M . This then initializes an array of 2M. So we create a 2M array 1000 times for this one query which from what I understand makes this query slow.
For numeric fields {{FacetFieldProcessorNumeric#calcFacets}} uses a CountSlotAcc which doesn't assign a huge array. In this query it calls {{createCollectAcc}} with numDocs=2k and numSlots=1024 .
In string faceting, we create the 2M array because the cardinality is 2M and we use the array position as the ordinal and value as the count. If we could improve on this it would speed things up significantly? For sub-facets we know the maximum cardinality can be at max the top level bucket count.
was:
I indexed a dataset of 2M docs
{{top_facet_s}} has a cardinality of 1000 which is the top level facet.
For nested facets it has two fields {{sub_facet_unique_s}} and {{sub_facet_unique_td}} which are string and double and have cardinality 2M
The nested query for the double field returns in the 1s mark always. The nested query for the string field takes roughly 10s to execute.
{code:title=nested string facet|borderStyle=solid}
q=*:*&rows=0&json.facet=
{
"top_facet_s": {
"type": "terms",
"limit": -1,
"field": "top_facet_s",
"mincount": 1,
"excludeTags": "ANY",
"facet": {
"sub_facet_unique_s": {
"type": "terms",
"limit": 1,
"field": "sub_facet_unique_s",
"mincount": 1
}
}
}
}
{code}
{code:title=nested double facet|borderStyle=solid}
q=*:*&rows=0&json.facet=
{
"top_facet_s": {
"type": "terms",
"limit": -1,
"field": "top_facet_s",
"mincount": 1,
"excludeTags": "ANY",
"facet": {
"sub_facet_unique_s": {
"type": "terms",
"limit": 1,
"field": "sub_facet_unique_td",
"mincount": 1
}
}
}
}
{code}
I tried to dig deeper to understand why are string nested faceting that slow compared to numeric field
Since the top facet has a cardinality of 1000 we have to calculate sub facets on each of them. Now the key difference was in the implementation of the two .
For the string field, In {{FacetField#getFieldCacheCounts}} we call {{createCollectAcc}} with nDocs=0 and numSlots=2M . This then initializes an array of 2M. So we create a 2M array 1000 times for this one query which from what I understand makes this query slow.
For numeric fields {{FacetFieldProcessorNumeric#calcFacets}} uses a CountSlotAcc which doesn't assign a huge array. In this query it calls {{createCollectAcc}} with numDocs=2k and numSlots=1024 .
In string faceting, we create the 2M array because the cardinality if 2M and we use the array position as the ordinal and value as the count. If we could improve on this it would speed things up significantly?
> Improve JSON nested facets effeciency
> -------------------------------------
>
> Key: SOLR-9142
> URL: https://issues.apache.org/jira/browse/SOLR-9142
> Project: Solr
> Issue Type: Bug
> Reporter: Varun Thacker
>
> I indexed a dataset of 2M docs
> {{top_facet_s}} has a cardinality of 1000 which is the top level facet.
> For nested facets it has two fields {{sub_facet_unique_s}} and {{sub_facet_unique_td}} which are string and double and have cardinality 2M
> The nested query for the double field returns in the 1s mark always. The nested query for the string field takes roughly 10s to execute.
> {code:title=nested string facet|borderStyle=solid}
> q=*:*&rows=0&json.facet=
> {
> "top_facet_s": {
> "type": "terms",
> "limit": -1,
> "field": "top_facet_s",
> "mincount": 1,
> "excludeTags": "ANY",
> "facet": {
> "sub_facet_unique_s": {
> "type": "terms",
> "limit": 1,
> "field": "sub_facet_unique_s",
> "mincount": 1
> }
> }
> }
> }
> {code}
> {code:title=nested double facet|borderStyle=solid}
> q=*:*&rows=0&json.facet=
> {
> "top_facet_s": {
> "type": "terms",
> "limit": -1,
> "field": "top_facet_s",
> "mincount": 1,
> "excludeTags": "ANY",
> "facet": {
> "sub_facet_unique_s": {
> "type": "terms",
> "limit": 1,
> "field": "sub_facet_unique_td",
> "mincount": 1
> }
> }
> }
> }
> {code}
> I tried to dig deeper to understand why are string nested faceting that slow compared to numeric field
> Since the top facet has a cardinality of 1000 we have to calculate sub facets on each of them. Now the key difference was in the implementation of the two .
> For the string field, In {{FacetField#getFieldCacheCounts}} we call {{createCollectAcc}} with nDocs=0 and numSlots=2M . This then initializes an array of 2M. So we create a 2M array 1000 times for this one query which from what I understand makes this query slow.
> For numeric fields {{FacetFieldProcessorNumeric#calcFacets}} uses a CountSlotAcc which doesn't assign a huge array. In this query it calls {{createCollectAcc}} with numDocs=2k and numSlots=1024 .
> In string faceting, we create the 2M array because the cardinality is 2M and we use the array position as the ordinal and value as the count. If we could improve on this it would speed things up significantly? For sub-facets we know the maximum cardinality can be at max the top level bucket count.
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: dev-unsubscribe@lucene.apache.org
For additional commands, e-mail: dev-help@lucene.apache.org