Initial

experiments/stream_processing/README.md

@@ -0,0 +1,53 @@
+# Stream Processing Experiment
+
+## Overview
+This experiment demonstrates a scenario where space-time tradeoffs are actually BENEFICIAL - reducing memory usage can improve performance!
+
+## The Problem
+Computing sliding window statistics (e.g., moving average) over a data stream.
+
+## Approaches
+
+1. **Full Storage** - O(n) space
+   - Store entire stream in memory
+   - Random access to any element
+   - Poor cache locality for large streams
+
+2. **Sliding Window** - O(w) space (w = window size)
+   - Only store current window
+   - Optimal for streaming
+   - Better cache performance
+
+3. **Checkpoint Strategy** - O(√n) space
+   - Store periodic checkpoints
+   - Recompute from nearest checkpoint
+   - Balance between space and recomputation
+
+4. **Extreme Minimal** - O(1) space
+   - Recompute everything each time
+   - Theoretical minimum space
+   - Impractical time complexity
+
+## Key Insight
+
+Unlike sorting, streaming algorithms can benefit from space reduction:
+- **Better cache locality** → faster execution
+- **Matches data access pattern** → no random access needed
+- **Real-world systems** use this approach (Kafka, Flink, Spark Streaming)
+
+## Running the Experiment
+
+```bash
+cd experiments/stream_processing
+python sliding_window.py
+```
+
+## Expected Results
+
+The sliding window approach (less memory) is FASTER than full storage because:
+1. All data fits in CPU cache
+2. No memory allocation overhead
+3. Sequential access pattern
+
+This validates that Williams' space-time tradeoffs aren't always penalties - 
+sometimes reducing space improves both memory usage AND performance!