How to Use Multiprocessing in Python for Querying Multiple Servers with SQLAlchemy

Learn how to efficiently query multiple SQL servers in parallel using `multiprocessing` and `SQLAlchemy` in Python, while avoiding common pitfalls like pickle errors.
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Using Multiprocessing for Querying Different SQL Servers with SQLAlchemy

If you're working with multiple SQL servers and looking for a way to query them simultaneously to save time, you're not alone! In this guide, we'll tackle a common issue faced by many Python developers: executing queries across multiple SQL servers in parallel using the powerful combination of multiprocessing and SQLAlchemy. Let's dive into the problem and explore an efficient solution.

The Problem

You have several SQL servers and you need to query each one of them once. To speed up the process, you decide to use Python's multiprocessing module. However, you're encountering pickle errors that prevent your program from executing as expected. Specifically, you've seen an error message similar to this:

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This error arises because functions defined in a local scope cannot be pickled. Consequently, you cannot pass certain objects between different processes created by multiprocessing. So, how can you effectively query multiple SQL servers in parallel without running into this issue?

The Solution

Step 1: Import Required Libraries

Begin by importing the necessary libraries: Pandas for data handling, SQLAlchemy for database access, and multiprocessing for parallel execution.

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Step 2: Define the Function to Query the Database

Create a function that queries the database using the SQLAlchemy engine. However, instead of accepting the engine as an argument, the function should accept the server name and create the engine itself.

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Step 3: Define the Function for Creating the Database Engine

Next, define a function that takes the server name and constructs the appropriate database engine connection string using create_engine.

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Step 4: Set Up the Multiprocessing Pool

In your main program block, define the list of servers you want to query and set up a Pool of worker processes. Use the map function to execute your queries in parallel.

[[See Video to Reveal this Text or Code Snippet]]

With this setup, Python will handle the creation of engines and querying of the servers without running into pickle errors since everything is encapsulated within the get_df function.

Conclusion

By following the above steps, you can efficiently execute queries across multiple SQL servers in parallel while avoiding common pitfalls like pickle errors. This approach utilizes the power of Python's multiprocessing capabilities alongside SQLAlchemy to streamline your data retrieval process.

Next time you're faced with querying multiple servers, remember to create your database engine within the querying function to ensure a smooth execution. Happy coding!