Note, this project is still very much a work in progress and in an alpha state; input and contributions welcome!
| PyPI |
|
| Conda Forge |
|
| Package Status |
|
| License |
|
| Build Status |
|
eland is a Elasticsearch client Python package to analyse, explore and manipulate data that resides in Elasticsearch. Where possible the package uses existing Python APIs and data structures to make it easy to switch between numpy, pandas, scikit-learn to their Elasticsearch powered equivalents. In general, the data resides in Elasticsearch and not in memory, which allows eland to access large datasets stored in Elasticsearch.
For example, to explore data in a large Elasticsearch index, simply create an eland DataFrame from an Elasticsearch index pattern, and explore using an API that mirrors a subset of the pandas.DataFrame API:
>>> import eland as ed
>>> # Connect to 'flights' index via localhost Elasticsearch node
>>> df = ed.DataFrame('localhost:9200', 'flights')
>>> df.head()
AvgTicketPrice Cancelled ... dayOfWeek timestamp
0 841.265642 False ... 0 2018-01-01 00:00:00
1 882.982662 False ... 0 2018-01-01 18:27:00
2 190.636904 False ... 0 2018-01-01 17:11:14
3 181.694216 True ... 0 2018-01-01 10:33:28
4 730.041778 False ... 0 2018-01-01 05:13:00
[5 rows x 27 columns]
>>> df.describe()
AvgTicketPrice DistanceKilometers ... FlightTimeMin dayOfWeek
count 13059.000000 13059.000000 ... 13059.000000 13059.000000
mean 628.253689 7092.142457 ... 511.127842 2.835975
std 266.386661 4578.263193 ... 334.741135 1.939365
min 100.020531 0.000000 ... 0.000000 0.000000
25% 410.008918 2470.545974 ... 251.739008 1.000000
50% 640.387285 7612.072403 ... 503.148975 3.000000
75% 842.262193 9735.660463 ... 720.505705 4.239865
max 1199.729004 19881.482422 ... 1902.901978 6.000000
[8 rows x 7 columns]
>>> df[['Carrier', 'AvgTicketPrice', 'Cancelled']]
Carrier AvgTicketPrice Cancelled
0 Kibana Airlines 841.265642 False
1 Logstash Airways 882.982662 False
2 Logstash Airways 190.636904 False
3 Kibana Airlines 181.694216 True
4 Kibana Airlines 730.041778 False
... ... ... ...
13054 Logstash Airways 1080.446279 False
13055 Logstash Airways 646.612941 False
13056 Logstash Airways 997.751876 False
13057 JetBeats 1102.814465 False
13058 JetBeats 858.144337 False
[13059 rows x 3 columns]
>>> df[(df.Carrier=="Kibana Airlines") & (df.AvgTicketPrice > 900.0) & (df.Cancelled == True)].head()
AvgTicketPrice Cancelled ... dayOfWeek timestamp
8 960.869736 True ... 0 2018-01-01 12:09:35
26 975.812632 True ... 0 2018-01-01 15:38:32
311 946.358410 True ... 0 2018-01-01 11:51:12
651 975.383864 True ... 2 2018-01-03 21:13:17
950 907.836523 True ... 2 2018-01-03 05:14:51
[5 rows x 27 columns]
>>> df[['DistanceKilometers', 'AvgTicketPrice']].aggregate(['sum', 'min', 'std'])
DistanceKilometers AvgTicketPrice
sum 9.261629e+07 8.204365e+06
min 0.000000e+00 1.000205e+02
std 4.578263e+03 2.663867e+02
>>> df[['Carrier', 'Origin', 'Dest']].nunique()
Carrier 4
Origin 156
Dest 156
dtype: int64
>>> s = df.AvgTicketPrice * 2 + df.DistanceKilometers - df.FlightDelayMin
>>> s
0 18174.857422
1 10589.365723
2 381.273804
3 739.126221
4 14818.327637
...
13054 10219.474121
13055 8381.823975
13056 12661.157104
13057 20819.488281
13058 18315.431274
Length: 13059, dtype: float64
>>> print(s.info_es())
index_pattern: flights
Index:
index_field: _id
is_source_field: False
Mappings:
capabilities:
es_field_name is_source es_dtype es_date_format pd_dtype is_searchable is_aggregatable is_scripted aggregatable_es_field_name
NaN script_field_None False double None float64 True True True script_field_None
Operations:
tasks: []
size: None
sort_params: None
_source: ['script_field_None']
body: {'script_fields': {'script_field_None': {'script': {'source': "(((doc['AvgTicketPrice'].value * 2) + doc['DistanceKilometers'].value) - doc['FlightDelayMin'].value)"}}}}
post_processing: []
>>> pd_df = ed.eland_to_pandas(df)
>>> pd_df.head()
AvgTicketPrice Cancelled ... dayOfWeek timestamp
0 841.265642 False ... 0 2018-01-01 00:00:00
1 882.982662 False ... 0 2018-01-01 18:27:00
2 190.636904 False ... 0 2018-01-01 17:11:14
3 181.694216 True ... 0 2018-01-01 10:33:28
4 730.041778 False ... 0 2018-01-01 05:13:00
[5 rows x 27 columns]See docs and demo_notebook.ipynb for more examples.
Eland can be installed from PyPI via pip:
$ python -m pip install elandEland can also be installed from Conda Forge with Conda:
$ conda install -c conda-forge elandThe source code is currently available on GitHub.
Officially Python 3.6 and above.
eland depends on pandas version 1.0.0+.
eland is versioned like the Elastic stack (eland 7.5.1 is compatible with Elasticsearch 7.x up to 7.5.1)
A major version of the client is compatible with the same major version of Elasticsearch.
No compatibility assurances are given between different major versions of the client and Elasticsearch. Major differences likely exist between major versions of Elasticsearch, particularly around request and response object formats, but also around API urls and behaviour.
eland uses the Elasticsearch low level client to connect to Elasticsearch. This client supports a range of [connection options and authentication mechanisms] (https://elasticsearch-py.readthedocs.io/en/master/api.html#elasticsearch).
>>> import eland as ed
>>> # Connect to flights index via localhost Elasticsearch node
>>> ed.DataFrame('localhost', 'flights')
>>> # Connect to flights index via localhost Elasticsearch node on port 9200
>>> ed.DataFrame('localhost:9200', 'flights')
>>> # Connect to flights index via localhost Elasticsearch node on port 9200 with <user>:<password> credentials
>>> ed.DataFrame('http://<user>:<password>@localhost:9200', 'flights')
>>> # Connect to flights index via ssl
>>> es = Elasticsearch(
'https://<user>:<password>@localhost:443',
use_ssl=True,
verify_certs=True,
ca_certs='/path/to/ca.crt'
)
>>> ed.DataFrame(es, 'flights')
>>> # Connect to flights index via ssl using Urllib3HttpConnection options
>>> es = Elasticsearch(
['localhost:443', 'other_host:443'],
use_ssl=True,
verify_certs=True,
ca_certs='/path/to/CA_certs',
client_cert='/path/to/clientcert.pem',
client_key='/path/to/clientkey.pem'
)
>>> ed.DataFrame(es, 'flights')>>> import eland as ed
>>> from elasticsearch import Elasticsearch
>>> es = Elasticsearch(cloud_id="<cloud_id>", http_auth=('<user>','<password>'))
>>> es.info()
{'name': 'instance-0000000000', 'cluster_name': 'bf900cfce5684a81bca0be0cce5913bc', 'cluster_uuid': 'xLPvrV3jQNeadA7oM4l1jA', 'version': {'number': '7.4.2', 'build_flavor': 'default', 'build_type': 'tar', 'build_hash': '2f90bbf7b93631e52bafb59b3b049cb44ec25e96', 'build_date': '2019-10-28T20:40:44.881551Z', 'build_snapshot': False, 'lucene_version': '8.2.0', 'minimum_wire_compatibility_version': '6.8.0', 'minimum_index_compatibility_version': '6.0.0-beta1'}, 'tagline': 'You Know, for Search'}
>>> df = ed.read_es(es, 'reviews')Naming is difficult, but as we had to call it something:
Pronunciation: /ˈeːlɑnt/