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How to use SQLAlchemy ORM to access Spark Data in Python
Create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of Spark data.
The rich ecosystem of Python modules lets you get to work quickly and integrate your systems effectively. With the CData Python Connector for Apache Spark and the SQLAlchemy toolkit, you can build Spark-connected Python applications and scripts. This article shows how to use SQLAlchemy to connect to Spark data to query, update, delete, and insert Spark data.
With built-in optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Spark data in Python. When you issue complex SQL queries from Spark, the CData Connector pushes supported SQL operations, like filters and aggregations, directly to Spark and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).
Connecting to Spark Data
Connecting to Spark 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, Database, User, and Password connection properties to connect to SparkSQL.
Follow the procedure below to install SQLAlchemy and start accessing Spark 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 Spark Data in Python
You can now connect with a connection string. Use the create_engine function to create an Engine for working with Spark 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("sparksql:///?Server=127.0.0.1")
Declare a Mapping Class for Spark 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"
City = Column(String,primary_key=True)
Balance = Column(String)
...
Query Spark 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("sparksql:///?Server=127.0.0.1")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Customers).filter_by(Country="US"):
print("City: ", instance.City)
print("Balance: ", instance.Balance)
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.Country == "US")):
print("City: ", instance.City)
print("Balance: ", instance.Balance)
print("---------")
For examples of more complex querying, including JOINs, aggregations, limits, and more, refer to the Help documentation for the extension.
Insert Spark Data
To insert Spark 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 Spark.
new_rec = Customers(City="placeholder", Country="US")
session.add(new_rec)
session.commit()
Update Spark Data
To update Spark 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 Spark.
updated_rec = session.query(Customers).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()
updated_rec.Country = "US"
session.commit()
Delete Spark Data
To delete Spark 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 Apache Spark to start building Python apps and scripts with connectivity to Spark data. Reach out to our Support Team if you have any questions.
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