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# SqrtSpace SpaceTime for Python
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[](https://badge.fury.io/py/sqrtspace-spacetime)
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[](https://pypi.org/project/sqrtspace-spacetime/)
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[](https://github.com/sqrtspace/sqrtspace-python/blob/main/LICENSE)
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[](https://sqrtspace-spacetime.readthedocs.io/en/latest/?badge=latest)
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Memory-efficient algorithms and data structures for Python using Williams' √n space-time tradeoffs.
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## Installation
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```bash
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pip install sqrtspace-spacetime
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```
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For ML features:
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```bash
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pip install sqrtspace-spacetime[ml]
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```
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For all features:
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```bash
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pip install sqrtspace-spacetime[all]
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```
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## Core Concepts
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SpaceTime implements theoretical computer science results showing that many algorithms can achieve better memory usage by accepting slightly slower runtime. The key insight is using √n memory instead of n memory, where n is the input size.
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### Key Features
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- **Memory-Efficient Collections**: Arrays and dictionaries that automatically spill to disk
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- **External Algorithms**: Sort and group large datasets using minimal memory
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- **Streaming Operations**: Process files larger than RAM with elegant API
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- **Auto-Checkpointing**: Resume long computations from where they left off
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- **Memory Profiling**: Identify optimization opportunities in your code
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- **ML Optimizations**: Reduce neural network training memory by up to 90%
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## Quick Start
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### Basic Usage
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```python
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from sqrtspace_spacetime import SpaceTimeArray, external_sort, Stream
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# Memory-efficient array that spills to disk
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array = SpaceTimeArray(threshold=10000)
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for i in range(1000000):
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array.append(i)
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# Sort large datasets with minimal memory
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huge_list = list(range(10000000, 0, -1))
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sorted_data = external_sort(huge_list) # Uses only √n memory
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# Stream processing
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Stream.from_csv('huge_file.csv') \
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.filter(lambda row: row['value'] > 100) \
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.map(lambda row: row['value'] * 1.1) \
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.group_by(lambda row: row['category']) \
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.to_csv('processed.csv')
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```
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## Examples
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### Basic Examples
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See [`examples/basic_usage.py`](examples/basic_usage.py) for comprehensive examples of:
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- SpaceTimeArray and SpaceTimeDict usage
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- External sorting and grouping
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- Stream processing
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- Memory profiling
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- Auto-checkpointing
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### FastAPI Web Application
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Check out [`examples/fastapi-app/`](examples/fastapi-app/) for a production-ready web application featuring:
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- Streaming endpoints for large datasets
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- Server-Sent Events (SSE) for real-time data
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- Memory-efficient CSV exports
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- Checkpointed background tasks
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- ML model serving with memory constraints
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See the [FastAPI example README](examples/fastapi-app/README.md) for detailed documentation.
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### Machine Learning Pipeline
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Explore [`examples/ml-pipeline/`](examples/ml-pipeline/) for ML-specific patterns:
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- Training models on datasets larger than RAM
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- Memory-efficient feature extraction
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- Checkpointed training loops
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- Streaming predictions
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- Integration with PyTorch and TensorFlow
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See the [ML Pipeline README](examples/ml-pipeline/README.md) for complete documentation.
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### Memory-Efficient Collections
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```python
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from sqrtspace_spacetime import SpaceTimeArray, SpaceTimeDict
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# Array that automatically manages memory
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array = SpaceTimeArray(threshold=1000) # Keep 1000 items in memory
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for i in range(1000000):
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array.append(f"item_{i}")
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# Dictionary with LRU eviction to disk
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cache = SpaceTimeDict(threshold=10000)
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for key, value in huge_dataset:
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cache[key] = expensive_computation(value)
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```
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### External Algorithms
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```python
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from sqrtspace_spacetime import external_sort, external_groupby
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# Sort 100M items using only ~10K memory
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data = list(range(100_000_000, 0, -1))
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sorted_data = external_sort(data)
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# Group by with aggregation
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sales = [
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{'store': 'A', 'amount': 100},
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{'store': 'B', 'amount': 200},
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# ... millions more
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]
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by_store = external_groupby(
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sales,
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key_func=lambda x: x['store']
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)
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# Aggregate with minimal memory
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from sqrtspace_spacetime.algorithms import groupby_sum
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totals = groupby_sum(
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sales,
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key_func=lambda x: x['store'],
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value_func=lambda x: x['amount']
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)
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```
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### Streaming Operations
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```python
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from sqrtspace_spacetime import Stream
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# Process large files efficiently
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stream = Stream.from_csv('sales_2023.csv')
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.filter(lambda row: row['amount'] > 0)
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.map(lambda row: {
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'month': row['date'][:7],
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'amount': float(row['amount'])
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})
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.group_by(lambda row: row['month'])
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.to_csv('monthly_summary.csv')
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# Chain operations
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top_products = Stream.from_jsonl('products.jsonl') \
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.filter(lambda p: p['in_stock']) \
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.sort(key=lambda p: p['revenue'], reverse=True) \
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.take(100) \
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.collect()
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```
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### Auto-Checkpointing
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```python
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from sqrtspace_spacetime.checkpoint import auto_checkpoint
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@auto_checkpoint(total_iterations=1000000)
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def process_large_dataset(data):
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results = []
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for i, item in enumerate(data):
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# Process item
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result = expensive_computation(item)
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results.append(result)
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# Yield state for checkpointing
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yield {'i': i, 'results': results}
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return results
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# Automatically resumes from checkpoint if interrupted
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results = process_large_dataset(huge_dataset)
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```
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### Memory Profiling
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```python
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from sqrtspace_spacetime.profiler import profile, profile_memory
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@profile(output_file="profile.json")
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def my_algorithm(data):
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# Process data
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return results
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# Get detailed memory analysis
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result, report = my_algorithm(data)
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print(report.summary)
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# Simple memory tracking
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@profile_memory(threshold_mb=100)
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def memory_heavy_function():
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# Alerts if memory usage exceeds threshold
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large_list = list(range(10000000))
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return sum(large_list)
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```
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### ML Memory Optimization
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```python
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from sqrtspace_spacetime.ml import MLMemoryOptimizer
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import torch.nn as nn
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# Analyze model memory usage
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model = nn.Sequential(
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nn.Linear(784, 256),
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nn.ReLU(),
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nn.Linear(256, 128),
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nn.ReLU(),
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nn.Linear(128, 10)
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)
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optimizer = MLMemoryOptimizer()
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profile = optimizer.analyze_model(model, input_shape=(784,), batch_size=32)
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# Get optimization plan
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plan = optimizer.optimize(profile, target_batch_size=128)
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print(plan.explanation)
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# Apply optimizations
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config = optimizer.get_training_config(plan, profile)
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```
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## Advanced Features
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### Memory Pressure Handling
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```python
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from sqrtspace_spacetime.memory import MemoryMonitor, LoggingHandler
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# Monitor memory pressure
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monitor = MemoryMonitor()
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monitor.add_handler(LoggingHandler())
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# Your arrays automatically respond to memory pressure
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array = SpaceTimeArray()
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# Arrays spill to disk when memory is low
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```
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### Configuration
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```python
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from sqrtspace_spacetime import SpaceTimeConfig
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# Global configuration
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SpaceTimeConfig.set_defaults(
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memory_limit=2 * 1024**3, # 2GB
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chunk_strategy='sqrt_n',
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compression='gzip',
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external_storage_path='/fast/ssd/temp'
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)
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```
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### Parallel Processing
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```python
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from sqrtspace_spacetime.batch import BatchProcessor
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processor = BatchProcessor(
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memory_threshold=0.8,
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checkpoint_enabled=True
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)
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# Process in memory-efficient batches
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result = processor.process(
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huge_list,
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lambda batch: [transform(item) for item in batch]
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)
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print(f"Processed {result.get_success_count()} items")
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```
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## Real-World Examples
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### Processing Large CSV Files
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```python
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from sqrtspace_spacetime import Stream
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from sqrtspace_spacetime.profiler import profile_memory
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@profile_memory(threshold_mb=500)
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def analyze_sales_data(filename):
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# Stream process to stay under memory limit
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return Stream.from_csv(filename) \
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.filter(lambda row: row['status'] == 'completed') \
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.map(lambda row: {
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'product': row['product_id'],
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'revenue': float(row['price']) * int(row['quantity'])
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}) \
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.group_by(lambda row: row['product']) \
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.sort(key=lambda group: sum(r['revenue'] for r in group[1]), reverse=True) \
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.take(10) \
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.collect()
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top_products = analyze_sales_data('sales_2023.csv')
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```
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### Training Large Neural Networks
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```python
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from sqrtspace_spacetime.ml import MLMemoryOptimizer, GradientCheckpointer
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import torch.nn as nn
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# Memory-efficient training
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def train_large_model(model, train_loader, epochs=10):
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# Analyze memory requirements
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optimizer = MLMemoryOptimizer()
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profile = optimizer.analyze_model(model, input_shape=(3, 224, 224), batch_size=32)
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# Get optimization plan
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plan = optimizer.optimize(profile, target_batch_size=128)
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# Apply gradient checkpointing
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checkpointer = GradientCheckpointer()
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model = checkpointer.apply_checkpointing(model, plan.checkpoint_layers)
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# Train with optimized settings
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for epoch in range(epochs):
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for batch in train_loader:
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# Training loop with automatic memory management
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pass
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```
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### Data Pipeline with Checkpoints
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```python
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from sqrtspace_spacetime import Stream
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from sqrtspace_spacetime.checkpoint import auto_checkpoint
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@auto_checkpoint(total_iterations=1000000)
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def process_user_events(event_file):
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processed = 0
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for event in Stream.from_jsonl(event_file):
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# Complex processing
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user_profile = enhance_profile(event)
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recommendations = generate_recommendations(user_profile)
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save_to_database(recommendations)
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processed += 1
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# Checkpoint state
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yield {'processed': processed, 'last_event': event['id']}
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return processed
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# Automatically resumes if interrupted
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total = process_user_events('events.jsonl')
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```
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## Performance Benchmarks
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| Operation | Standard Python | SpaceTime | Memory Reduction | Time Overhead |
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|-----------|----------------|-----------|------------------|---------------|
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| Sort 10M integers | 400MB | 20MB | 95% | 40% |
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| Process 1GB CSV | 1GB | 32MB | 97% | 20% |
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| Group by on 1M rows | 200MB | 14MB | 93% | 30% |
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| Neural network training | 8GB | 2GB | 75% | 15% |
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## API Reference
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### Collections
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- `SpaceTimeArray`: Memory-efficient list with disk spillover
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- `SpaceTimeDict`: Memory-efficient dictionary with LRU eviction
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### Algorithms
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- `external_sort()`: Sort large datasets with √n memory
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- `external_groupby()`: Group large datasets with √n memory
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- `external_join()`: Join large datasets efficiently
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### Streaming
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- `Stream`: Lazy evaluation stream processing
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- `FileStream`: Stream lines from files
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- `CSVStream`: Stream CSV rows
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- `JSONLStream`: Stream JSON Lines
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### Memory Management
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- `MemoryMonitor`: Monitor memory pressure
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- `MemoryPressureHandler`: Custom pressure handlers
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### Checkpointing
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- `@auto_checkpoint`: Automatic checkpointing decorator
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- `CheckpointManager`: Manual checkpoint control
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### ML Optimization
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- `MLMemoryOptimizer`: Analyze and optimize models
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- `GradientCheckpointer`: Apply gradient checkpointing
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### Profiling
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- `@profile`: Full profiling decorator
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- `@profile_memory`: Memory-only profiling
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- `SpaceTimeProfiler`: Programmatic profiling
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## Contributing
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We welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details.
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## License
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Apache License 2.0. See [LICENSE](LICENSE) for details.
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## Citation
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If you use SpaceTime in your research, please cite:
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```bibtex
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@software{sqrtspace_spacetime,
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title = {SqrtSpace SpaceTime: Memory-Efficient Python Library},
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author={Friedel Jr., David H.},
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year = {2025},
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url = {https://github.com/sqrtspace/sqrtspace-python}
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}
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```
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## Links
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- [Documentation](https://sqrtspace-spacetime.readthedocs.io)
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- [PyPI Package](https://pypi.org/project/sqrtspace-spacetime/)
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- [GitHub Repository](https://github.com/sqrtspace/sqrtspace-python)
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- [Issue Tracker](https://github.com/sqrtspace/sqrtspace-python/issues)
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