Be Cautious of Batch API Calls in PySpark
In the realm of PySpark, where data manipulation and processing occur on distributed clusters, the lazy evaluation process is a powerful feature that enhances efficiency. However, when making multiple API calls in sequence, it’s crucial to tread carefully to avoid unexpected pitfalls. In this blog, we’ll unravel the intricacies of PySpark’s lazy execution and explore why a cautious approach is necessary, backed by clear examples.
Understanding PySpark’s Lazy Evaluation, follow link for detailed explanation
PySpark’s lazy evaluation is a mechanism that delays the execution of operations until the result is genuinely needed. Instead of immediately executing transformations(whats a tranformation? follow the link), PySpark builds up a logical plan, optimizing the sequence of operations. This deferred execution offers performance benefits by allowing the framework to skip unnecessary computations.
The Pitfalls of Sequential API Calls, click on the link to master requests module
Imagine you’re crafting a PySpark script that involves a series of API calls, perhaps chaining DataFrame transformations or actions. It’s tempting to assume that each call is executed sequentially, similar to how code runs in a Python script. However, due to PySpark’s lazy evaluation, this is not the case.
