Use SQLAlchemy ORMs to Access Elasticsearch Data in Python

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Elasticsearch Python Connector

Python Connector Libraries for Elasticsearch Data Connectivity. Integrate Elasticsearch with popular Python tools like Pandas, SQLAlchemy, Dash & petl.



The CData Python Connector for Elasticsearch enables you to create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of Elasticsearch data.

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems effectively. With the CData Python Connector for Elasticsearch and the SQLAlchemy toolkit, you can build Elasticsearch-connected Python applications and scripts. This article shows how to use SQLAlchemy to connect to Elasticsearch data to query, update, delete, and insert Elasticsearch data.

With built-in optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Elasticsearch data in Python. When you issue complex SQL queries from Elasticsearch, the CData Connector pushes supported SQL operations, like filters and aggregations, directly to Elasticsearch and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to Elasticsearch Data

Connecting to Elasticsearch data looks just like connecting to any relational data source. Create a connection string using the required connection properties. For this article, you will pass the connection string as a parameter to the create_engine function.

Set the Server and Port connection properties to connect. To authenticate, set the User and Password properties, PKI (public key infrastructure) properties, or both. To use PKI, set the SSLClientCert, SSLClientCertType, SSLClientCertSubject, and SSLClientCertPassword properties.

The data provider uses X-Pack Security for TLS/SSL and authentication. To connect over TLS/SSL, prefix the Server value with 'https://'. Note: TLS/SSL and client authentication must be enabled on X-Pack to use PKI.

Once the data provider is connected, X-Pack will then perform user authentication and grant role permissions based on the realms you have configured.

Follow the procedure below to install SQLAlchemy and start accessing Elasticsearch through Python objects.

Install Required Modules

Use the pip utility to install the SQLAlchemy toolkit:

pip install sqlalchemy

Be sure to import the module with the following:

import sqlalchemy

Model Elasticsearch Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Elasticsearch data.

engine = create_engine("elasticsearch:///?Server=127.0.0.1&Port=9200&User=admin&Password=123456")

Declare a Mapping Class for Elasticsearch Data

After establishing the connection, declare a mapping class for the table you wish to model in the ORM (in this article, we will model the Orders table). Use the sqlalchemy.ext.declarative.declarative_base function and create a new class with some or all of the fields (columns) defined.

base = declarative_base()
class Orders(base):
	__tablename__ = "Orders"
	OrderName = Column(String,primary_key=True)
	Freight = Column(String)
	...

Query Elasticsearch Data

With the mapping class prepared, you can use a session object to query the data source. After binding the Engine to the session, provide the mapping class to the session query method.

Using the query Method

engine = create_engine("elasticsearch:///?Server=127.0.0.1&Port=9200&User=admin&Password=123456")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Orders).filter_by(ShipCity="New York"):
	print("OrderName: ", instance.OrderName)
	print("Freight: ", instance.Freight)
	print("---------")

Alternatively, you can use the execute method with the appropriate table object. The code below works with an active session.

Using the execute Method

Orders_table = Orders.metadata.tables["Orders"]
for instance in session.execute(Orders_table.select().where(Orders_table.c.ShipCity == "New York")):
	print("OrderName: ", instance.OrderName)
	print("Freight: ", instance.Freight)
	print("---------")

For examples of more complex querying, including JOINs, aggregations, limits, and more, refer to the Help documentation for the extension.

Insert Elasticsearch Data

To insert Elasticsearch data, define an instance of the mapped class and add it to the active session. Call the commit function on the session to push all added instances to Elasticsearch.

new_rec = Orders(OrderName="placeholder", ShipCity="New York")
session.add(new_rec)
session.commit()

Update Elasticsearch Data

To update Elasticsearch data, fetch the desired record(s) with a filter query. Then, modify the values of the fields and call the commit function on the session to push the modified record to Elasticsearch.

updated_rec = session.query(Orders).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()
updated_rec.ShipCity = "New York"
session.commit()

Delete Elasticsearch Data

To delete Elasticsearch data, fetch the desired record(s) with a filter query. Then delete the record with the active session and call the commit function on the session to perform the delete operation on the provided records (rows).

deleted_rec = session.query(Orders).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()
session.delete(deleted_rec)
session.commit()

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