ElasticSearch group by multiple fields
The only close thing that I've found was: Multiple group-by in Elasticsearch
Basically I'm trying to get the ES equivalent of the following MySql query:
select gender, age_range, count(distinct profile_id) as count
FROM TABLE group by age_range, gender
The age and gender by themselves were easy to get:
{
"query": {
"match_all": {}
},
"facets": {
"ages": {
"terms": {
"field": "age_range",
"size": 20
}
},
"gender_by_age": {
"terms": {
"fields": [
"age_range",
"gender"
]
}
}
},
"size": 0
}
which gives:
{
"ages": {
"_type": "terms",
"missing": 0,
"total": 193961,
"other": 0,
"terms": [
{
"term": 0,
"count": 162643
},
{
"term": 3,
"count": 10683
},
{
"term": 4,
"count": 8931
},
{
"term": 5,
"count": 4690
},
{
"term": 6,
"count": 3647
},
{
"term": 2,
"count": 3247
},
{
"term": 1,
"count": 120
}
]
},
"total_gender": {
"_type": "terms",
"missing": 0,
"total": 193961,
"other": 0,
"terms": [
{
"term": 1,
"count": 94799
},
{
"term": 2,
"count": 62645
},
{
"term": 0,
"count": 36517
}
]
}
}
But now I need something that looks like this:
[breakdown_gender] => Array
(
[1] => Array
(
[0] => 264
[1] => 1
[2] => 6
[3] => 67
[4] => 72
[5] => 40
[6] => 23
)
[2] => Array
(
[0] => 153
[2] => 2
[3] => 21
[4] => 35
[5] => 22
[6] => 11
)
)
Please note that 0,1,2,3,4,5,6 are "mappings" for the age ranges so they actually mean something :) and not just numbers. e.g. Gender[1] (which is "male") breaks down into age range [0] (which is "under 18") with a count of 246.
Answer
Starting from version 1.0 of ElasticSearch, the new aggregations API allows grouping by multiple fields, using sub-aggregations. Suppose you want to group by fields field1, field2 and field3:
{
"aggs": {
"agg1": {
"terms": {
"field": "field1"
},
"aggs": {
"agg2": {
"terms": {
"field": "field2"
},
"aggs": {
"agg3": {
"terms": {
"field": "field3"
}
}
}
}
}
}
}
}
Of course this can go on for as many fields as you'd like.
Update:
For completeness, here is how the output of the above query looks. Also below is python code for generating the aggregation query and flattening the result into a list of dictionaries.
{
"aggregations": {
"agg1": {
"buckets": [{
"doc_count": <count>,
"key": <value of field1>,
"agg2": {
"buckets": [{
"doc_count": <count>,
"key": <value of field2>,
"agg3": {
"buckets": [{
"doc_count": <count>,
"key": <value of field3>
},
{
"doc_count": <count>,
"key": <value of field3>
}, ...
]
},
{
"doc_count": <count>,
"key": <value of field2>,
"agg3": {
"buckets": [{
"doc_count": <count>,
"key": <value of field3>
},
{
"doc_count": <count>,
"key": <value of field3>
}, ...
]
}, ...
]
},
{
"doc_count": <count>,
"key": <value of field1>,
"agg2": {
"buckets": [{
"doc_count": <count>,
"key": <value of field2>,
"agg3": {
"buckets": [{
"doc_count": <count>,
"key": <value of field3>
},
{
"doc_count": <count>,
"key": <value of field3>
}, ...
]
},
{
"doc_count": <count>,
"key": <value of field2>,
"agg3": {
"buckets": [{
"doc_count": <count>,
"key": <value of field3>
},
{
"doc_count": <count>,
"key": <value of field3>
}, ...
]
}, ...
]
}, ...
]
}
}
}
The following python code performs the group-by given the list of fields. I you specify include_missing=True, it also includes combinations of values where some of the fields are missing (you don't need it if you have version 2.0 of Elasticsearch thanks to this)
def group_by(es, fields, include_missing):
current_level_terms = {'terms': {'field': fields[0]}}
agg_spec = {fields[0]: current_level_terms}
if include_missing:
current_level_missing = {'missing': {'field': fields[0]}}
agg_spec[fields[0] + '_missing'] = current_level_missing
for field in fields[1:]:
next_level_terms = {'terms': {'field': field}}
current_level_terms['aggs'] = {
field: next_level_terms,
}
if include_missing:
next_level_missing = {'missing': {'field': field}}
current_level_terms['aggs'][field + '_missing'] = next_level_missing
current_level_missing['aggs'] = {
field: next_level_terms,
field + '_missing': next_level_missing,
}
current_level_missing = next_level_missing
current_level_terms = next_level_terms
agg_result = es.search(body={'aggs': agg_spec})['aggregations']
return get_docs_from_agg_result(agg_result, fields, include_missing)
def get_docs_from_agg_result(agg_result, fields, include_missing):
current_field = fields[0]
buckets = agg_result[current_field]['buckets']
if include_missing:
buckets.append(agg_result[(current_field + '_missing')])
if len(fields) == 1:
return [
{
current_field: bucket.get('key'),
'doc_count': bucket['doc_count'],
}
for bucket in buckets if bucket['doc_count'] > 0
]
result = []
for bucket in buckets:
records = get_docs_from_agg_result(bucket, fields[1:], include_missing)
value = bucket.get('key')
for record in records:
record[current_field] = value
result.extend(records)
return result