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About Eland is a Python Elasticsearch client for exploring and analyzing data in Elasticsearch with a familiar Pandas-compatible API. Where possible the package uses existing Python APIs and data structures…
About
Eland is a Python Elasticsearch client for exploring and analyzing data in Elasticsearch with a familiar
Pandas-compatible API.
Where possible the package uses existing Python APIs and data structures to make it easy to switch between numpy,
pandas, or 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.
Eland also provides tools to upload trained machine learning models from common libraries like
scikit-learn, XGBoost, and
LightGBM into Elasticsearch.
Getting Started
Eland can be installed from PyPI with Pip:
bash
$ python -m pip install eland
If using Eland to upload NLP models to Elasticsearch install the PyTorch extras:
bash
$ python -m pip install eland[pytorch]
Eland can also be installed from Conda Forge with Conda:
bash
$ conda install -c conda-forge eland
Compatibility
- Supports Python 3.8, 3.9, 3.10 and Pandas 1.5
- Supports Elasticsearch clusters that are 7.11+, recommended 8.3 or later for all features to work.
- You need to use PyTorch
1.13.1or earlier to import an NLP model.
pip install torch==1.13.1 to install the aproppriate version of PyTorch.
Prerequisites
Users installing Eland on Debian-based distributions may need to install prerequisite packages for the transitive
dependencies of Eland:
bash
$ sudo apt-get install -y \
build-essential pkg-config cmake \
python3-dev libzip-dev libjpeg-dev
Note that other distributions such as CentOS, RedHat, Arch, etc. may require using a different package manager and
specifying different package names.
Docker
Users wishing to use Eland without installing it, in order to just run the available scripts, can build the Docker
container:
bash
$ docker build -t elastic/eland .
The container can now be used interactively:
bash
$ docker run -it --rm --network host elastic/eland
Running installed scripts is also possible without an interactive shell, e.g.:
bash
$ docker run -it --rm --network host \
elastic/eland \
eland_import_hub_model \
--url http://host.docker.internal:9200/ \
--hub-model-id elastic/distilbert-base-cased-finetuned-conll03-english \
--task-type ner
Connecting to Elasticsearch
Eland uses the Elasticsearch low level client to connect to Elasticsearch.
This client supports a range of connection options and authentication options.
You can pass either an instance of elasticsearch.Elasticsearch to Eland APIs
or a string containing the host to connect to:
python
import eland as ed
# Connecting to an Elasticsearch instance running on 'localhost:9200'
df = ed.DataFrame("localhost:9200", es_index_pattern="flights")
# Connecting to an Elastic Cloud instance
from elasticsearch import Elasticsearch
es = Elasticsearch(
cloud_id="cluster-name:...",
http_auth=("elastic", "")
)
df = ed.DataFrame(es, es_index_pattern="flights")
DataFrames in Eland
eland.DataFrame wraps an Elasticsearch index in a Pandas-like API
and defers all processing and filtering of data to Elasticsearch
instead of your local machine. This means you can process large
amounts of data within Elasticsearch from a Jupyter Notebook
without overloading your machine.
➤ Eland DataFrame API documentation
➤ Advanced examples in a Jupyter Notebook
python
>>> import eland as ed
>>> # Connect to 'flights' index via localhost Elasticsearch node
>>> df = ed.DataFrame('localhost:9200', 'flights')
# eland.DataFrame instance has the same API as pandas.DataFrame
# except all data is in Elasticsearch. See .info() memory usage.
>>> 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.info()
Index: 13059 entries, 0 to 13058
Data columns (total 27 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 AvgTicketPrice 13059 non-null float64
1 Cancelled 13059 non-null bool
2 Carrier 13059 non-null object
...
24 OriginWeather 13059 non-null object
25 dayOfWeek 13059 non-null int64
26 timestamp 13059 non-null datetime64[ns]
dtypes: bool(2), datetime64[ns](1), float64(5), int64(2), object(17)
memory usage: 80.0 bytes
Elasticsearch storage usage: 5.043 MB
# Filtering of rows using comparisons
>>> 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]
# Running aggregations across an index
>>> 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
Machine Learning in Eland
Regression and classification
Eland allows transforming trained regression and classification models from scikit-learn, XGBoost, and LightGBM
libraries to be serialized and used as an inference model in Elasticsearch.
➤ Eland Machine Learning API documentation
➤ Read more about Machine Learning in Elasticsearch
python
>>> from xgboost import XGBClassifier
>>> from eland.ml import MLModel
# Train and exercise an XGBoost ML model locally
>>> xgb_model = XGBClassifier(booster="gbtree")
>>> xgb_model.fit(training_data[0], training_data[1])
>>> xgb_model.predict(training_data[0])
[0 1 1 0 1 0 0 0 1 0]
# Import the model into Elasticsearch
>>> es_model = MLModel.import_model(
es_client="localhost:9200",
model_id="xgb-classifier",
model=xgb_model,
feature_names=["f0", "f1", "f2", "f3", "f4"],
)
# Exercise the ML model in Elasticsearch with the training data
>>> es_model.predict(training_data[0])
[0 1 1 0 1 0 0 0 1 0]
NLP with PyTorch
For NLP tasks, Eland allows importing PyTorch trained BERT models into Elasticsearch. Models can be either plain PyTorch
models, or supported transformers models from the
Hugging Face model hub.
bash
$ eland_import_hub_model \
--url http://localhost:9200/ \
--hub-model-id elastic/distilbert-base-cased-finetuned-conll03-english \
--task-type ner \
--start
The example above will automatically start a model deployment. This is a
good shortcut for initial experimentation, but for anything that needs
good throughput you should omit the --start argument from the Eland
command line and instead start the model using the ML UI in Kibana.
The --start argument will deploy the model with one allocation and one
thread per allocation, which will not offer good performance. When starting
the model deployment using the ML UI in Kibana or the Elasticsearch
API
you will be able to set the threading options to make the best use of your
hardware.
python
>>> import elasticsearch
>>> from pathlib import Path
>>> from eland.common import es_version
>>> from eland.ml.pytorch import PyTorchModel
>>> from eland.ml.pytorch.transformers import TransformerModel
>>> es = elasticsearch.Elasticsearch("http://elastic:mlqa_admin@localhost:9200")
>>> es_cluster_version = es_version(es)
# Load a Hugging Face transformers model directly from the model hub
>>> tm = TransformerModel(model_id="elastic/distilbert-base-cased-finetuned-conll03-english", task_type="ner", es_version=es_cluster_version)
Downloading: 100%|██████████| 257/257 [00:00<00:00, 108kB/s]
Downloading: 100%|██████████| 954/954 [00:00<00:00, 372kB/s]
Downloading: 100%|██████████| 208k/208k [00:00<00:00, 668kB/s]
Downloading: 100%|██████████| 112/112 [00:00<00:00, 43.9kB/s]
Downloading: 100%|██████████| 249M/249M [00:23<00:00, 11.2MB/s]
# Export the model in a TorchScrpt representation which Elasticsearch uses
>>> tmp_path = "models"
>>> Path(tmp_path).mkdir(parents=True, exist_ok=True)
>>> model_path, config, vocab_path = tm.save(tmp_path)
# Import model into Elasticsearch
>>> ptm = PyTorchModel(es, tm.elasticsearch_model_id())
>>> ptm.import_model(model_path=model_path, config_path=None, vocab_path=vocab_path, config=config)
100%|██████████| 63/63 [00:12<00:00, 5.02it/s]-
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Dépot ZDS
Python ★ 0 8y agoExplain → -
karpathy.github.io ⑂
my blog
CSS ★ 0 10y agoExplain → -
cnn ⑂
C++ neural network library
C++ ★ 0 10y agoExplain → -
pquentin.github.io
No description.
HTML ★ 0 10y agoExplain → -
pylama ⑂
Code audit tool for python.
Python ★ 0 10y agoExplain → -
support ⑂
Customers can report issues in this repo.
★ 0 10y agoExplain → -
wesnoth ⑂
Free, turn-based strategy game with a high fantasy theme, featuring both single-player, and online/hotseat multiplayer combat.
C++ ★ 0 6y agoExplain → -
django-debug-toolbar-template-profiler ⑂
Displays template rendering time on the timeline
Python ★ 0 11y agoExplain → -
sample-slack-notifications ⑂
No description.
Ruby ★ 0 11y agoExplain → -
jQuery-File-Upload ⑂
File Upload widget with multiple file selection, drag&drop support, progress bar, validation and preview images, audio and video for jQuery. Supports cross-domain, chunked and resumable file uploads. Works with any server-side platform (Google App Engine, PHP, Python, Ruby on Rails, Java, etc.) that supports standard HTML form file uploads.
JavaScript ★ 0 11y agoExplain → -
langid.py ⑂
Stand-alone language identification system
Python ★ 0 11y agoExplain → -
python-cld2 ⑂
Python bindings for CLD2.
Python ★ 0 11y agoExplain → -
select2 ⑂
Select2 is a jQuery based replacement for select boxes. It supports searching, remote data sets, and infinite scrolling of results.
JavaScript ★ 0 11y agoExplain → -
dateutil ⑂
Useful extensions to the standard Python datetime features
Python ★ 0 2y agoExplain → -
vault ⑂
A tool for managing secrets.
Go ★ 0 11y agoExplain → -
Kalman-and-Bayesian-Filters-in-Python ⑂
Kalman Filter textbook using Ipython Notebook. This book takes a minimally mathematical approach, focusing on building intuition and experience, not formal proofs. Includes Kalman filters, Extended Kalman filters, unscented filters, and more. Includes exercises with solutions.
Python ★ 0 11y agoExplain → -
elasticsearch-dsl-py ⑂
High level Python client for Elasticsearch
Python ★ 0 1y agoExplain → -
django-mptt ⑂
Utilities for implementing a modified pre-order traversal tree in django.
Python ★ 0 11y agoExplain → -
django-rest-swagger ⑂
Swagger Documentation Generator for Django REST Framework
JavaScript ★ 0 11y agoExplain → -
sitebuild
No description.
PHP ★ 0 11y agoExplain → -
thesis
No description.
TeX ★ 0 11y agoExplain → -
12factor ⑂
No description.
CSS ★ 0 11y agoExplain → -
www ⑂
files for http://mozfr.org/
PHP ★ 0 11y agoExplain → -
paste-py ⑂
Paste service that uses Pygments
CSS ★ 0 11y agoExplain → -
bibliography
Bibliography for my PhD thesis
TeX ★ 0 11y agoExplain → -
firefox.html ⑂
Firefox.html is an experiment: trying to re-implement the Firefox UI in HTML.
JavaScript ★ 0 6y agoExplain → -
django ⑂
The Web framework for perfectionists with deadlines.
Python ★ 0 6y agoExplain → -
pomodorock ⑂
A single webpage pomodoro timer / tracker that keeps ZERO KNOWLEDGE of your data on the server-side.
JavaScript ★ 0 11y agoExplain → -
dxr ⑂
An intelligent source code browser
★ 0 11y agoExplain → -
verbnet ⑂
VerbNet history from v1.0 + some of my fixes in another branch
★ 0 11y agoExplain → -
airmozilla ⑂
AirMozilla is a the video broadcasting site for the Mozilla project
Python ★ 0 11y agoExplain → -
TurboParser ⑂
A multilingual dependency parser based on linear programming relaxations.
★ 0 11y agoExplain → -
newsome ⑂
It's gonna be awesom!
★ 0 11y agoExplain → -
bedrock ⑂
Making mozilla.org awesome, one pebble at a time
★ 0 11y agoExplain → -
BedrockInstallScript ⑂
Linux Install Script for Bedrock on Ubuntu
★ 0 11y agoExplain → -
bbdocs ⑂
Historical documentation for buildbot
JavaScript ★ 0 11y agoExplain → -
qtile ⑂
A small, flexible, scriptable tiling window manager written in Python
Python ★ 0 11y agoExplain → -
buildbot ⑂
Python-based continuous integration testing framework; send pull requests for your patches!
Python ★ 0 11y agoExplain → -
botherders ⑂
Buildbot Administration
★ 0 11y agoExplain → -
python-packaging-user-guide ⑂
Python Packaging User Guide
★ 0 11y agoExplain → -
erenyagdiran.github.io ⑂
my blg
CSS ★ 0 11y agoExplain → -
nltk_data ⑂
NLTK Data
★ 0 11y agoExplain → -
codefirefox ⑂
Video and exercise based tutorial site for coding Firefox and other Mozilla related technology
JavaScript ★ 0 11y agoExplain → -
ocaml.org ⑂
Website for the OCaml community.
OCaml ★ 0 12y agoExplain → -
NLP-RNNs-Representations-Post ⑂
A blog post using word embeddings and RNNs to explain representations.
★ 0 12y agoExplain → -
lima ⑂
A multilingual Natural Language Processing (NLP) suite
C++ ★ 0 12y agoExplain →
No repos match these filters.