Thanks Honza. (also for the great work you are doing for the python
community).
I may have mistated my problem.
what I am really looking for is to have a bucket, inside a bucket , inside
a bucket and then metrics
the following expression does this
s1.aggs.bucket('xColor', a).bucket('xMake', b).bucket('xCity',
c).metric('xMyPriceSum', 'sum', field = 'price').metric('xMyPriceAvg',
'avg', field = 'price')
the problem is that it has to be written manually (at least I haven't fond
a way to do this automatically).
I tried the second approach you suggest and it gives me a different result
:
- bucket 1, bucket 2 , bucket 3 and their metrics but not one inside
another one.
I hope my question makes sense otherwise I am happy to provide a more
complete example
Best regards,
Mike
On Monday, March 30, 2015 at 11:06:28 PM UTC+2, Honza Král wrote:
>
> Hello,
>
> you can access buckets already created using ['name'] syntax, in your case
> you can do (instead of the chaining):
>
> s.aggs['xColor']['xMake']['xCity'].metric(...)
> s.aggs['xColor']['xMake']['xCity'].metric(...)
>
> This way you can add aggregations to already created buckets.
>
> Also you can just use an approach where you keep the pointer to the
> inner-most bucket 9start with s.aggs) and go from there in your case (bunch
> of nested buckets and then metrics inside):
>
> b = s.aggs
> for bucket in xVarBuckets:
> b = s.aggs.bucket(bucket['label'], 'terms', field=bucket['field'])
>
> for metric in xVarMetrics:
> b.metric(metric['label'], metric['agg_function'],
> field=metric['field'])
>
>
> Hope this helps,
>
> On Mon, Mar 30, 2015 at 10:55 PM, Mike <almu...@googlemail.com
> <javascript:>> wrote:
>
>> the python elasticsearch , elasticsearch dsl packages are life-saver and
>> got me converted to ES.
>>
>> Now I am trying to use elasticsearch dsl package to create pivot tables
>> in ES but am having hard time figuring out how to chain the buckets
>> programmatically.
>> while chaining the buckets / metrics manually works, to chain them
>> programmatically seems impossible
>>
>> here is an example
>>
>>
>> from elasticsearch import Elasticsearch
>> from elasticsearch_dsl import Search as dsl_search, A, Q, F
>> # create client
>> es = Elasticsearch('localhost:9200')
>> # data : from the definitive guide, slighlty modified
>> #data from the definitive guide
>> xData = [
>> {'doc_id' : 1, 'price' : 10000, 'color' : 'red', 'make' : 'honda',
>> 'sold' : '2014-10-28', 'city': 'ROME', 'insurance': 'y'},
>> {'doc_id' : 2, 'price' : 20000, 'color' : 'red', 'make' : 'honda',
>> 'sold' : '2014-11-05', 'city': 'ROME', 'insurance': 'n'},
>> {'doc_id' : 3, 'price' : 30000, 'color' : 'green', 'make' : 'ford',
>> 'sold' : '2014-05-18', 'city': 'Berlin', 'insurance': 'y'},
>> {'doc_id' : 4, 'price' : 15000, 'color' : 'blue', 'make' : 'toyota',
>> 'sold' : '2014-07-02', 'city': 'Berlin', 'insurance': 'n'},
>> {'doc_id' : 5, 'price' : 12000, 'color' : 'green', 'make' : 'toyota',
>> 'sold' : '2014-08-19', 'city': 'Berlin', 'insurance': 'n'},
>> {'doc_id' : 6, 'price' : 20000, 'color' : 'red', 'make' : 'honda',
>> 'sold' : '2014-11-05', 'city': 'Paris', 'insurance': 'n'},
>> {'doc_id' : 7, 'price' : 80000, 'color' : 'red', 'make' : 'bmw',
>> 'sold' : '2014-01-01', 'city': 'Paris', 'insurance': 'y'},
>> {'doc_id' : 8, 'price' : 25000, 'color' : 'blue', 'make' : 'ford',
>> 'sold' : '2014-02-12', 'city': 'Paris', 'insurance': 'y'}]
>>
>> #create a mapping
>> my_mapping = {
>> 'my_example': {
>> 'properties': {
>> 'doc_id': {'type': 'integer'},
>> 'price': {'type': 'integer'},
>> 'color': {'type': 'string', 'index': 'not_analyzed'},
>> 'make': {'type': 'string', 'index': 'not_analyzed'},
>> 'city': {'type': 'string', 'index': 'not_analyzed'},
>> 'insurance': {'type': 'string', 'index': 'not_analyzed'},
>> 'sold': {'type': 'date'}
>> }}}
>>
>>
>> #create an index and add the mapping
>> if es.indices.exists('my_index_test'):
>> es.indices.delete(index="my_index_test")
>> es.indices.create('my_index_test')
>>
>> # mapping for the document type
>> if es.indices.exists_type(index = 'my_index_test', doc_type =
>> 'my_example'):
>> es.indices.delete_mapping(index='my_index_test',doc_type='my_example')
>>
>> es.indices.put_mapping(index='my_index_test',doc_type='my_example',body=my_mapping)
>>
>> # indexing
>> for xRow in xData:
>> es.index(index = 'my_index',
>> doc_type= 'my_example',
>> id = xRow['doc_id'],
>> body = xRow
>> )
>>
>>
>> ### MANUALLY CHAINING WORKS
>>
>> a = A('terms', field = 'color')
>> b = A('terms', field = 'make')
>> c = A('terms', field = 'city')
>>
>> s1 = dsl_search(es, index = 'my_index', doc_type= 'my_example')
>> s1.aggs.bucket('xColor', a).bucket('xMake', b).bucket('xCity', c)\
>> .metric('xMyPriceSum', 'sum', field = 'price')\
>> .metric('xMyPriceAvg', 'avg', field = 'price')
>> resp = s1.execute()
>> #get results
>> q1 = resp.aggregations
>> q1
>>
>>
>>
>> #### but not PROGRAMMATICALLY
>> # Programmatically chaining
>>
>> xVarBuckets = [{'field': 'color', 'label': 'xColor'},
>> {'field': 'make', 'label': 'xMake'},
>> {'field': 'city', 'label': 'xCity'}]
>>
>> xVar_Metrics = [{'field': 'price', 'agg_function': 'sum', 'label':
>> 'xMyPriceSum'},
>> {'field': 'price', 'agg_function': 'avg', 'label':
>> 'xMyPriceAvg'}]
>>
>>
>> s2 = None
>> s2 = dsl_search(es, index = 'my_index', doc_type = 'my_example')
>>
>> #add buckets
>> for xBucketVar in xVarBuckets:
>> xAgg = A('terms', field= xBucketVar['field'])
>> s2.aggs.bucket(xBucketVar['label'], xAgg)
>> resp2 = s2.execute()
>> #get results
>> q2 = resp2.aggregations
>>
>>
>> I guess it has to do with the fact that the newly create bucket is
>> overwritten by the new bucket, but how can append the new bucket to the
>> previous one
>>
>>
>> Any help appreciated
>>
>>
>>
>>
>>
>>
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>
>
>
> --
> Honza Král
> Python Engineer
> honza...@elastic.co <javascript:>
>
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