easierai-elasticsearchlib

Elastic search wrapper for Python

This library allows access to a Elasticsearch from a Python program.

https://pypi.org/project/elasticsearchlib/

Installation

Command line

This library can be installed with the following command:

pip3 install elasticsearchlib

Dockerfile

You can add these lines in your Dockerfile to include this library in your image:

RUN pip3 install elasticsearchlib

Uploading to pip

These steps are needed to distribute the library on Pip repository manager:

Prerequisites

First, these packages must be installed on your Python environment:

• Setuptools
• Wheel
• Twine
• Tqdm

sudo python -m pip install --upgrade pip setuptools wheel
sudo python -m pip install tqdm
sudo python -m pip install --user --upgrade twine

Customization

On the setup.py file, these fields can be customized:

• Version: The current version of the build.

Execution

python3 setup.py bdist_wheel

This command will generate a .whl file inside the dist folder of the root of the project. Then, execute the following command to upload this file to PyPi repository:

python3 -m twine upload dist/*

Usage

This section will explain the usage of this library.

Constructor

Elasticsearchlib()

start_connection

This function creates the connection to the elasticsearch database, checking if the server is up. Returns True if the database answered correctly.

def start_connection(self, host, port, request_retries=3, total_retries=9):

• host: Base IP address for the elasticsearch database.
• port: Port where the elasticsearch database is published.
• request_retries: number of times a request will be retried before being dropped (defaults to 3).
• total_retries: number of consecutive retries before dropping the connection and throwing an Exception (defaults to 9).

create_index

This function checks if an index is already created and, if not, creates it, according to the provided mapping.

def create_index(self, index, mapping=None): 

• index: Name of the index to create.
• mapping: Mapping provided as the template for this index.

add_to index

This function adds a document to the provided index. If the index does not exist, it will be created first.

def add_to_index(self, index, body, id=''):

• index: Index where the document will be added.
• body: Body for the document.
• id: Optional argument for the document id on the database. If not provided, a random one will be created.

search_last_n_measures

This functions allows for the retrieval of the last n measures of one dataset item.

def search_last_n_measures(self, index, id_dataset, n):

• index: Index to search in.
• id_dataset: Dataset id to retrieve the measures.
• n: Number of measures desired.

scrolled_query

This function returns all measures stored in an index, following a query.

def scrolled_query(self, query, index, filter_path=None):

• query: Query to use on the request.
• index: Index on which to use the query.
• filter_path: Filter that can be applied to the request.

get_entities_ids

This function returns all the entity ids stored under an index.

def get_entities_ids(self, index):

• index: Index on which to request the ids.

get_last_document

This function returns the last document stored under an index, for a specific device_id.

def get_last_document(self, index, device_id):

• index: Index on which to request the document.
• device_id: Id of the device to query.

get_data_history

This function returns the data history for a specific device in a period of time.

def get_data_history(self, index, device_id, gte, lte='now'):

• index: Index on which to request the data.
• device_id: Id of the device to query.
• gte: Lower bound for the time period.
• lte: Upper bound for the time period (defaults to now).