Optimizing Memory Usage in Python for Large Dataset Processing

I'm working on a data processing project in Python where I need to handle a large dataset containing millions of records. I've noticed that my program's memory usage keeps increasing as I process the data, and eventually, it leads to MemoryError. I've tried using generators and iterating over the data in chunks, but it doesn't seem to solve the problem entirely.

I suspect that there might be some memory overhead from the libraries I'm using or the way I'm storing intermediate results. I want to know if there are any best practices or techniques to optimize memory usage in Python for processing large datasets.

Here's a simplified version of my code:

# Example: Processing a Large Dataset
def process_data():
    # Assuming data_source is a generator or an iterator
    data_source = get_large_dataset()  # Some function that provides the data source

    # Initializing empty lists to store intermediate results
    intermediate_results = []

    for data in data_source:
        # Some processing on the data
        result = perform_computation(data)

        # Storing intermediate results in a list
        intermediate_results.append(result)

    # Further processing on intermediate results
    final_result = aggregate_results(intermediate_results)

    return final_result

def get_large_dataset():
    # In a real scenario, this function would fetch data from a file, database, or other sources.
    # For this example, we'll generate sample data.
    num_records = 1000000  # One million records
    for i in range(num_records):
        yield i

def perform_computation(data):
    # Some computation on each data point
    result = data * 2  # For example purposes, let's just multiply the data by 2
    return result

def aggregate_results(results):
    # Some aggregation function to process intermediate results
    return sum(results)

if __name__ == "__main__":
    final_result = process_data()
    print("Final Result:", final_result)

I'd appreciate any insights, tips, or code examples that can help me efficiently handle large datasets without running into memory issues. Thank you in advance!

The only thing in that code that grows significant memory is the list of intermediate results. You could make that a generator.

Change

    intermediate_results = []
    for data in data_source:
        result = perform_computation(data)
        intermediate_results.append(result)
    final_result = aggregate_results(intermediate_results)

to:

    def intermediate_results():
        for data in data_source:
            result = perform_computation(data)
            yield result
    final_result = aggregate_results(intermediate_results())

Or if you actually have such functions that do all the work, you could just map:

def process_data():
    return aggregate_results(map(perform_computation, get_large_dataset()))

Use generators and iterators to process data in chunks.
Avoid unnecessary intermediate results to minimize memory consumption.
Choose memory-efficient data structures for your specific needs.
Consider streaming data from disk instead of loading it all into memory.
Profile and optimize memory-intensive sections of your code.