- Best Practices
Explore key best practices for writing efficient PySpark code in batch model pipelines. Understand how to avoid using non-distributable Python types, limit memory-heavy operations like toPandas, prefer functional approaches over loops, minimize eager operations, and leverage SQL to create scalable, clean, and maintainable data pipelines in cloud-based production environments.
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While PySpark provides a familiar environment for Python programmers, it’s good to follow a few best practices to make sure you are using Spark efficiently. Here are a set of recommendations I’ve compiled based on my experience porting a few projects from Python to PySpark.
Avoid dictionaries
Using Python data types such as dictionaries means that the code might not be executable in a distributed mode. Instead of using keys to index ...