resettable stream

[j143]

Yes, that is an excellent and advanced concept. Your intuition is spot on. What you're describing is a powerful technique known as stream checkpointing. It's a way to combine the ideas of resetting and streaming without needing to hold the entire dataset in memory or on disk.

This approach is perfectly suited for the continuous, iterative nature of many machine learning algorithms.

---

## The Concept: Stream Checkpointing

Instead of caching the entire stream from beginning to end, you create periodic "save points," or checkpoints. Once a checkpoint is created, you can safely discard the cached data that came before it, freeing up resources.

Analogy 🎮: Think of it like a video game.

• Full Caching: This is like having a video recording of your entire gameplay. To replay a section, you must have the whole recording.
• Checkpointing: This is the game's save point system. Once you reach a save point, you no longer need the history of how you got there. If you need to "reset," you can restart from the last save point, not from the very beginning of the game.

---

## How It Works in an Iterative Algorithm

This is ideal for algorithms where the computation happens continuously in passes or iterations (like gradient descent, K-Means, etc.).

Let's imagine an algorithm that needs to make two passes over your large dataset X.

The Process:

1. First Pass Begins: The first consumer starts reading from a ResettableStream that is configured for checkpointing. The stream processes chunks and caches them.

2. Checkpoint Created: After the last chunk of the first pass is processed, the system calls a stream.checkpoint() method. This creates a "save point" (e.g., Checkpoint_1).

   • At this moment, the cache contains the full dataset.

3. Second Pass Begins: The second consumer needs to re-read the data. It calls stream.reset(Checkpoint_1).

   • The stream is "rewound" to the beginning of the cache.
   • As the second consumer reads chunks from the cache, the stream can now intelligently discard the chunks from the cache after they've been read.

The Memory Benefit: The cache only needs to hold the data for the current pass. You never need to hold the data for all passes at once. The memory is continuously recycled, keeping the footprint low.

---

## Combining with the tee Operator

Now, to your point about using this "along with a tee kind of operation." These two concepts—tee and checkpointing—are both advanced streaming techniques, but they solve different problems:

• tee is for PARALLEL Consumption (within a single pass):
  Use tee when two different operations need to consume the same stream at the same time. Our t(X) %*% X example is perfect: t() and *%* are running in parallel and both need access to X's data as it flows by.

• Checkpointing is for SEQUENTIAL Re-Consumption (across multiple passes):
  Use checkpointing when one operation needs to consume a stream, and then later, another operation (or the same one in the next iteration) needs to consume that same stream all over again from the beginning.

So, while you wouldn't typically use both on the exact same stream at the same time, a complex algorithm might use a tee for one part of its computation and a checkpointed stream for its main iterative loop. Knowing when to use which pattern is key to designing highly efficient data flow programs.