Spark MLlib模型训练：是否需调用trainingData.cache？fit内部是否缓存？

Great questions—caching is a key optimization in Spark, especially when training ML models, so let’s break these down clearly:

1. Does calling trainingData.cache() improve training performance, and is it necessary? #

Short answer: It often does improve performance, and while not strictly "necessary" in all cases, it’s highly recommended for most iterative ML algorithms.

Here’s why:

• Most Spark MLlib algorithms (like gradient-boosted trees, random forests, ALS for collaborative filtering, or iterative linear models) require multiple passes over the training data during the .fit() process. If you don’t cache the data, Spark will re-read and re-process the original source (like HDFS, S3, or a database) every single time it needs to access the data for an iteration. This adds massive I/O overhead, which slows down training significantly.
• When you call trainingData.cache(), Spark stores the processed DataFrame/Dataset in memory (and spills to disk if memory is limited, using the default MEMORY_ONLY storage level) after the first pass. Subsequent iterations can access this cached data directly, cutting out expensive re-reads.
• The only scenarios where caching might not give a noticeable boost are when your training data is extremely small (fits entirely in memory and is processed in a single pass) or if you’re using an algorithm that only traverses the data once. Even then, caching doesn’t hurt—it just won’t provide a big performance gain.

So while it’s not mandatory, skipping caching for iterative models is usually a missed optimization that leads to slower training times.

2. Does the .fit(...) method of ML algorithms automatically call cache or unpersist? #

This depends on the specific algorithm, but here’s the general breakdown:

• Some algorithms do automatically cache the training data under the hood. For example, tree-based models (RandomForestClassifier, GBTRegressor) and ALS are designed to cache training data because they require many iterations. Spark’s implementation handles this to save users from having to remember to cache manually.
• Not all algorithms auto-cache, though. Simpler algorithms that only make a single pass over the data (like some basic linear regression variants in older Spark versions, or certain clustering algorithms) might not.
• For unpersist: Almost no MLlib algorithms automatically unpersist the training data after .fit() completes. Spark uses an LRU (Least Recently Used) cache eviction policy to automatically free up cache space when needed, but if you know you won’t be using the training DataFrame again after training, it’s a good practice to call trainingData.unpersist() manually to free up memory immediately, especially if you’re working with large datasets or running multiple jobs in the same cluster.

One quick note: If you manually cache the data before calling .fit(), Spark will detect that it’s already cached and won’t duplicate the cache—so you don’t have to worry about redundant caching.

内容的提问来源于stack exchange，提问作者Marsellus Wallace