I am a beginner in Big Data and I’m confused about when to use RDDs versus DataFrames. With Spark 3.5 being the current standard, is there still a reason to use the lower-level RDD API, or should we strictly stick to the strongly-typed Datasets for our production machine learning and ETL pipelines?
3 answers
For 99% of use cases, DataFrames are the way to go. They benefit from the Catalyst Optimizer and the Tungsten execution engine, which makes them significantly faster than RDDs. RDDs (Resilient Distributed Datasets) are lower-level and don't understand the schema of your data, meaning Spark can't optimize the execution plan. You should only drop down to the RDD API if you need to perform very specific, low-level functional transformations that aren't available in the SQL or DataFrame API. Datasets are a middle ground, offering the type-safety of RDDs with the optimization of DataFrames, but they are primarily used in Scala/Java rather than PySpark.
Melissa, that’s a very clean breakdown! However, doesn't using Datasets in Scala come with a performance hit due to the serialization overhead of Java objects? I’ve heard that DataFrames are actually faster because they stay in the highly optimized off-heap memory. What's your take?
Stick to DataFrames. The Spark community has moved almost entirely to the DataFrame API because it allows for much easier integration with Spark SQL and better overall execution speed.
I agree. Even for Machine Learning via MLlib, most of the newer APIs are now built around DataFrames. Learning RDDs is good for understanding the history, but it's not a daily requirement for a modern Data Scientist.
Christopher, you hit the nail on the head. Datasets involve more "garbage collection" because they deal with JVM objects. For performance-critical applications where you don't absolutely need compile-time type checking, DataFrames are actually the faster choice because they leverage the Tungsten binary format directly without the overhead of object serialization.