1. PySpark at scale
Welcome back! Let's talk about what happens when we use PySpark at scale.
2. Leveraging scale
As our data grows, optimizing PySpark jobs becomes essential for managing performance, resource usage, and execution speed. In this video, we’ll focus on techniques to scale PySpark workflows effectively. First, we’ll explore how to interpret Spark execution plans to identify performance bottlenecks. Next, we’ll discuss caching and persisting DataFrames, which can significantly speed up iterative queries. Finally, we’ll cover best practices for optimizing PySpark jobs, ensuring we make the most of Spark’s distributed computing power.
Scaling PySpark is not just about faster results — it’s about building workflows that are efficient, maintainable, and capable of handling large datasets with ease.
Methods like `broadcast()` will load a smaller dataset across the cluster, using all available compute. As we work more with PySpark, the more we will need to think in moderately abstract ways.
3. Execution plans
A key tool for optimizing PySpark jobs is understanding Spark execution plans. Whenever we run an operation on a DataFrame, Spark constructs a logical plan, which it then optimizes into a physical plan. This physical plan outlines how Spark will execute the task on its distributed cluster.
We can inspect this process using the `.explain()` method. It gives us a breakdown of the query’s execution plan, showing the operations performed at each stage.
This outputs a logical and physical plan. By analyzing these plans, we can spot inefficiencies, like redundant shuffles or unoptimized joins, and address them before running the job.
The `explain()` method details the logical and physical plans for optimization, showing what the code ran and what hardware it used. This highlights how to improve it.
4. Caching and persisting DataFrames
When working with large datasets, we often reuse intermediate results across multiple operations. Recomputing these results can be costly, especially when reading from disk. To avoid this, we have two tools that keeps data readily available, caching and persisting.
The `.cache()` method stores a DataFrame in memory for fast access, while the `.persist()` method offers more flexibility by letting us choose storage levels.
Here, the dataset is read once, and subsequent operations reuse the cached version.
5. Persisting DataFrames with different storage levels
The second option is using the `.persist()` method, which allows us greater control of storage levels. We can use disk when memory is insufficient. This is especially useful for long-running jobs on large clusters.
However, caching consumes memory, so it’s important to uncache data when it’s no longer needed, using `.unpersist()` method.
In this example, if the DataFrame doesn’t fit in memory, Spark writes the overflow to disk. This ensures our operations can still proceed without crashing due to resource constraints.
This concept is difficult for us to demonstrate in this course but you will encounter it as you work with PySpark in the real world.
6. Optimizing PySpark
Beyond caching and persistance, here are a few best practices to optimize PySpark jobs:
Use Small Subsections: Favor targeted functions and methods like map() over whole dataset tools like groupBy() that require shuffles.
Broadcast Joins: For small datasets, use broadcast() to load the dataset onto all nodes and avoid shuffles.
Avoid Repeated Actions: Operations like count() or show() trigger jobs. Store intermediate results to prevent recomputation.
7. Let's practice!
Let's go look at some of these ideas in practice!