Python client for Elasticsearch built on top of elasticsearch-dsl
Project description
fiqs
====
fiqs is an opinionated high-level library whose goal is to help you write concise queries
agains Elasticsearch and better consume the results. It is built on top of the awesome [Elasticsearch
DSL](<https://github.com/elastic/elasticsearch-dsl-py>) library.
fiqs exposes a ``flatten_result`` function which transforms an elasticsearch-dsl ``Result``, or a dictionary, into the list of its nodes.
fiqs also lets you create Model classes, a la Django, which automatically generates an Elasticsearch mapping.
Finally fiqs exposes a ``FQuery`` objects which, leveraging your models, lets you write less verbose queries against Elasticsearch.
Compatibility
-------------
fiqs is compatible with Elasticsearch 5.X and works with both Python 2.7 and Python 3.3
Code example
------------
You define a model, matching what is in your Elasticsearch cluster:
```python
from fiqs import models
class Sale(models.Model):
index = 'sale_data'
doc_type = 'sale'
id = fields.IntegerField()
shop_id = fields.IntegerField()
client_id = fields.KeywordField()
timestamp = fields.DateField()
price = fields.IntegerField()
payment_type = fields.KeywordField(choices=['wire_transfer', 'cash', 'store_credit'])
```
You can then write clean queries:
```python
from elasticsearch_dsl import Search
from fiqs.aggregations import Sum
from fiqs.query import FQuery
from .models import Sale
search = Search(...)
metric = FQuery(search).values(
total_sales=Sum(Sale.price),
).group_by(
Sale.shop_id,
Sale.client_id,
)
result = metric.eval()
```
And let fiqs organise the results:
```python
print result
# [
# {
# "shop_id": 1,
# "client_id": 1,
# "doc_count": 30,
# "total_sales": 12345.0,
# },
# {
# "shop_id": 2,
# "client_id": 1,
# "doc_count": 20,
# "total_sales": 23456.0,
# },
# {
# "shop_id": 3,
# "client_id": 1,
# "doc_count": 10,
# "total_sales": 34567.0,
# },
# [...]
# ]
```
Documentation
-------------
Documentation is available at https://fiqs.readthedocs.io/
Contributing
------------
The fiqs project is hosted on [GitLab](<https://gitlab.com/pmourlanne/fiqs>)
To run the tests on your machine use this command: ``python setup.py test`` Some tests are used to generate results output from Elasticsearch. To run them you will need to run a docker container on your machine: ``docker run -d -p 8200:9200 -p 8300:9300 elasticsearch:5.0.2`` and then run ``py.test -k docker``.
License
-------
See attached LICENSE file.
====
fiqs is an opinionated high-level library whose goal is to help you write concise queries
agains Elasticsearch and better consume the results. It is built on top of the awesome [Elasticsearch
DSL](<https://github.com/elastic/elasticsearch-dsl-py>) library.
fiqs exposes a ``flatten_result`` function which transforms an elasticsearch-dsl ``Result``, or a dictionary, into the list of its nodes.
fiqs also lets you create Model classes, a la Django, which automatically generates an Elasticsearch mapping.
Finally fiqs exposes a ``FQuery`` objects which, leveraging your models, lets you write less verbose queries against Elasticsearch.
Compatibility
-------------
fiqs is compatible with Elasticsearch 5.X and works with both Python 2.7 and Python 3.3
Code example
------------
You define a model, matching what is in your Elasticsearch cluster:
```python
from fiqs import models
class Sale(models.Model):
index = 'sale_data'
doc_type = 'sale'
id = fields.IntegerField()
shop_id = fields.IntegerField()
client_id = fields.KeywordField()
timestamp = fields.DateField()
price = fields.IntegerField()
payment_type = fields.KeywordField(choices=['wire_transfer', 'cash', 'store_credit'])
```
You can then write clean queries:
```python
from elasticsearch_dsl import Search
from fiqs.aggregations import Sum
from fiqs.query import FQuery
from .models import Sale
search = Search(...)
metric = FQuery(search).values(
total_sales=Sum(Sale.price),
).group_by(
Sale.shop_id,
Sale.client_id,
)
result = metric.eval()
```
And let fiqs organise the results:
```python
print result
# [
# {
# "shop_id": 1,
# "client_id": 1,
# "doc_count": 30,
# "total_sales": 12345.0,
# },
# {
# "shop_id": 2,
# "client_id": 1,
# "doc_count": 20,
# "total_sales": 23456.0,
# },
# {
# "shop_id": 3,
# "client_id": 1,
# "doc_count": 10,
# "total_sales": 34567.0,
# },
# [...]
# ]
```
Documentation
-------------
Documentation is available at https://fiqs.readthedocs.io/
Contributing
------------
The fiqs project is hosted on [GitLab](<https://gitlab.com/pmourlanne/fiqs>)
To run the tests on your machine use this command: ``python setup.py test`` Some tests are used to generate results output from Elasticsearch. To run them you will need to run a docker container on your machine: ``docker run -d -p 8200:9200 -p 8300:9300 elasticsearch:5.0.2`` and then run ``py.test -k docker``.
License
-------
See attached LICENSE file.
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
fiqs-0.3.32.tar.gz
(30.0 kB
view details)
File details
Details for the file fiqs-0.3.32.tar.gz.
File metadata
- Download URL: fiqs-0.3.32.tar.gz
- Upload date:
- Size: 30.0 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 2ef8317c1839e4627079589c918e222051bf4b232cd346d45b797f76b0461bff |
|
| MD5 | 1a717f22c1e12b98099612ff37be8b5d |
|
| BLAKE2b-256 | 79a8f19efeac9c8af206b4b6da876137979ed92888e70daf4f72dd1efc172081 |
