Use SQLAlchemy ORMs to Access HDFS Data in Python

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HDFS Python Connector

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

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

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

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

Connecting to HDFS Data

Connecting to HDFS 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.

In order to authenticate, set the following connection properties:

  • Host: Set this value to the host of your HDFS installation.
  • Port: Set this value to the port of your HDFS installation. Default port: 50070

Follow the procedure below to install SQLAlchemy and start accessing HDFS 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 HDFS Data in Python

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

engine = create_engine("hdfs:///?")

Declare a Mapping Class for HDFS 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 Files 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 Files(base):
	__tablename__ = "Files"
	FileId = Column(String,primary_key=True)
	ChildrenNum = Column(String)

Query HDFS 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("hdfs:///?")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Files).filter_by(FileId="119116"):
	print("FileId: ", instance.FileId)
	print("ChildrenNum: ", instance.ChildrenNum)

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

Using the execute Method

Files_table = Files.metadata.tables["Files"]
for instance in session.execute( == "119116")):
	print("FileId: ", instance.FileId)
	print("ChildrenNum: ", instance.ChildrenNum)

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

Free Trial & More Information

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