Exploring Monte Carlo Simulations With DuckDB ft. James McNeill скачать в хорошем качестве

Exploring Monte Carlo Simulations With DuckDB ft. James McNeill

Talk from the DuckDB meetup that happened in Dublin on 23 January 2024! Future events: https://motherduck.com/events/ ☁️🦆 Start using DuckDB in the Cloud for FREE with MotherDuck : https://hubs.la/Q02QnFR40 📓 Resources Slides : https://docs.google.com/presentation/... James's Linkedin :   / james-mcneill-63a85014a   ➡️ Follow Us LinkedIn:   / motherduck   Twitter :   / motherduck   Blog: https://motherduck.com/blog/ #datascience #dataengineering #duckdb #montecarlosimulation -------------------------------------- This video explores a crucial question for data engineers and analysts: Is DuckDB a viable tool for running complex Monte Carlo simulations? We'll start with the basics: what is a Monte Carlo simulation? It's a powerful technique using random sampling to solve difficult mathematical problems, widely used in finance, physics, and even for weather forecasting. We'll explain why performance efficiency, both execution time and memory footprint, is critical for achieving accurate results, especially when scaling up sample sizes for your data analysis. This deep dive compares DuckDB's performance against common Python tools. To determine if DuckDB is a good fit for this type of data simulation, we benchmark its performance against standard Python libraries like NumPy, Numba, and Polars. The comparison focuses on two distinct use cases: a simple calculation of Pi and a more complex roulette game simulation. We'll analyze the execution time and memory consumption of each library across varying sample sizes, providing a clear answer to the "DuckDB vs NumPy performance" debate for simulation workloads. This isn't just about picking a winner, but understanding where DuckDB fits into the data engineering toolkit for computationally intensive tasks. The benchmark results are revealing. For simple, array-based operations, DuckDB initially appears slower. However, as we increase the "path complexity" in the roulette simulation, DuckDB's performance becomes highly competitive and even surpasses NumPy, especially in longer-running scenarios. We investigate why, highlighting DuckDB's incredibly stable memory profile and efficient aggregation engine, which prevents the aggressive memory scaling seen with NumPy. This analysis provides key insights into DuckDB's memory usage and how its architecture is optimized for complex queries and large-scale data processing without overwhelming system resources. In conclusion, DuckDB is a powerful and viable option for Monte Carlo simulations in Python, particularly for models with higher complexity. We share practical learnings from the benchmark, such as performance differences when writing results to Arrow vs. DataFrames and how to optimize your SQL queries using techniques like recursive CTEs over window functions for better performance. This video demonstrates that for many data simulation and financial modeling tasks, leveraging DuckDB can lead to faster execution, lower memory usage, and more scalable data pipelines.