Elastisticsearch_dsl python to create pivot tables

Mike_5 · March 30, 2015, 8:55pm

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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honzakral · March 30, 2015, 9:06pm

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 almugabo@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.kral@elastic.co

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For more options, visit https://groups.google.com/d/optout.

Mike_5 · March 30, 2015, 9:21pm

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

--
You received this message because you are subscribed to the Google Groups
"elasticsearch" group.
To unsubscribe from this group and stop receiving emails from it, send an
email to elasticsearc...@googlegroups.com <javascript:>.
To view this discussion on the web visit
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https://groups.google.com/d/msgid/elasticsearch/fa471fcf-9ed7-49f9-9e34-4cbefb90abb8%40googlegroups.com?utm_medium=email&utm_source=footer
.
For more options, visit https://groups.google.com/d/optout.

--
Honza Král
Python Engineer
honza...@elastic.co <javascript:>

--
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To unsubscribe from this group and stop receiving emails from it, send an email to elasticsearch+unsubscribe@googlegroups.com.
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For more options, visit https://groups.google.com/d/optout.

honzakral · March 30, 2015, 9:26pm

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 almugabo@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

--
You received this message because you are subscribed to the Google
Groups "elasticsearch" group.
To unsubscribe from this group and stop receiving emails from it, send
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Mike_5 · March 30, 2015, 10:04pm

Thanks Honza.

You made my day (my night rather, it is midnight here in Brussels).

I quickly tested the code and it gives the same results as the manually
chained expression.

I will test with various metrics tomorrow, I will then mark the question as
“completed”.

Thanks a lot and have a nice day.

P.S:This opens the possibilty to perform any “pivot” table in ES . The
challenge will be to parse the resulting json resuts (see
http://stackoverflow.com/questions/29280480/), but I hope to find a way.

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Mike_5 · March 31, 2015, 7:18pm

tested it. works as expected.
Thanks again for your help

Am Dienstag, 31. März 2015 00:04:39 UTC+2 schrieb Mike:

Thanks Honza.

You made my day (my night rather, it is midnight here in Brussels).

I quickly tested the code and it gives the same results as the manually
chained expression.

I will test with various metrics tomorrow, I will then mark the question
as “completed”.

Thanks a lot and have a nice day.

P.S:This opens the possibilty to perform any “pivot” table in ES . The
challenge will be to parse the resulting json resuts (see
python - pandas dataframe from a nested dictionary (elasticsearch result) - Stack Overflow), but I hope to find a way.

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