Discover how a bimodal integration strategy can address the major data management challenges facing your organization today.
Get the Report →
How to use SQLAlchemy ORM to access HBase Data in Python
Create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of HBase data.
The rich ecosystem of Python modules lets you get to work quickly and integrate your systems effectively. With the CData Python Connector for HBase and the SQLAlchemy toolkit, you can build HBase-connected Python applications and scripts. This article shows how to use SQLAlchemy to connect to HBase data to query, update, delete, and insert HBase data.
With built-in optimized data processing, the CData Python Connector offers unmatched performance for interacting with live HBase data in Python. When you issue complex SQL queries from HBase, the CData Connector pushes supported SQL operations, like filters and aggregations, directly to HBase and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).
Connecting to HBase Data
Connecting to HBase 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 Port and Server to connect to Apache HBase.
Follow the procedure below to install SQLAlchemy and start accessing HBase through Python objects.
Install Required Modules
Use the pip utility to install the SQLAlchemy toolkit and SQLAlchemy ORM package:
pip install sqlalchemy
pip install sqlalchemy.orm
Be sure to import the appropriate modules:
from sqlalchemy import create_engine, String, Column
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker
Model HBase Data in Python
You can now connect with a connection string. Use the create_engine function to create an Engine for working with HBase data.
NOTE: Users should URL encode the any connection string properties that include special characters. For more information, refer to the SQL Alchemy documentation.
engine = create_engine("apachehbase:///?Server=127.0.0.1&Port=8080")
Declare a Mapping Class for HBase 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 Customers 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 Customers(base):
__tablename__ = "Customers"
CustomerName = Column(String,primary_key=True)
Price = Column(String)
...
Query HBase 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("apachehbase:///?Server=127.0.0.1&Port=8080")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Customers).filter_by(ShipCity="New York"):
print("CustomerName: ", instance.CustomerName)
print("Price: ", instance.Price)
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
Customers_table = Customers.metadata.tables["Customers"]
for instance in session.execute(Customers_table.select().where(Customers_table.c.ShipCity == "New York")):
print("CustomerName: ", instance.CustomerName)
print("Price: ", instance.Price)
print("---------")
For examples of more complex querying, including JOINs, aggregations, limits, and more, refer to the Help documentation for the extension.
Insert HBase Data
To insert HBase 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 HBase.
new_rec = Customers(CustomerName="placeholder", ShipCity="New York")
session.add(new_rec)
session.commit()
Update HBase Data
To update HBase 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 HBase.
updated_rec = session.query(Customers).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()
updated_rec.ShipCity = "New York"
session.commit()
Delete HBase Data
To delete HBase 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(Customers).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()
session.delete(deleted_rec)
session.commit()
Free Trial & More Information
Download a free, 30-day trial of the CData Python Connector for HBase to start building Python apps and scripts with connectivity to HBase data. Reach out to our Support Team if you have any questions.
Ready to get started?
Download a free trial of the HBase Connector to get started:
Download NowLearn more:
HBase Python Connector
Python Connector Libraries for Apache Hbase Data Connectivity. Integrate Apache Hbase with popular Python tools like Pandas, SQLAlchemy, Dash & petl.
Data Connectors
ETL/ ELT Solutions
Cloud & API Connectivity
OEM & Custom Drivers