that's what happens when I try to be quick and don't verify my code - there
is an error in my example:
b = s.aggs
for bucket in xVarBuckets:
# this needs to be b = b.bucket instead of what I had previously
b = b.bucket(bucket['label'], 'terms', field=bucket['field'])
for metric in xVarMetrics:
b.metric(metric['label'], metric['agg_function'], field=metric['field'])
On Mon, Mar 30, 2015 at 11:21 PM, Mike <almug...@googlemail.com> wrote:
> 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> 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
>>
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--
Honza Král
Python Engineer
honza.k...@elastic.co
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