FireDucks
Compiler Accelerated DataFrame Library for Python with fully-compatible pandas API
Get Startedimport fireducks.pandas as pd
Updated TPC-H Benchmark: 125x average speedup over pandas, 2.2x average speedup over polars (Dec 06, 2024)
"How to run polars-tpch benchmark with FireDucks" is now available(blog) (Dec 06, 2024)
"Unveiling the Optimization Benefit of FireDucks Lazy Execution: Part #2" is now available(blog) (Dec 05, 2024)
"Unveiling the Optimization Benefit of FireDucks Lazy Execution: Part #1" is now available(blog) (Dec 05, 2024)
"What to do when FireDucks is slow" is now available(blog) (Nov 15, 2024)
Do you have a pandas-based program that is slow? FireDucks can speed-up your programs without any manual code changes. You can accelerate your data analysis without worrying about slow performance due to single-threaded execution in pandas.
Concerned about the cost and environmental impact of cloud computing? Our acceleration technology reduces cloud usage fees, while minimizing the CO2 emissions at the same time, making FireDucks an environment-friendly and wallet-friendly choice.
FireDucks is developed by infusing the essence of supercomputers that NEC has refined over the years. Made in Japan, high-quality FireDucks promises reliability and high performance.
Features
FireDucks is multi-threaded, enabling higher speeds on multi-core CPUs. More
A runtime compiler embedded in the library optimizes your code. More
FireDucks is fully compatible with pandas API. The only difference is the import statement. No additional learning is required to start with FireDucks.Get started
You can run your pandas program directly with FireDucks. Its import-hook functionality will automatically replace the import statement for pandas with the import statement for FireDucks. More
FireDucks shows high performance gain while executing various queries included in the TPC-H and TPCx-BB benchmarks. More.
Use Cases
From Users
From Developers
How to run polars-tpch benchmark with FireDucks
By Kazuhisa Ishizaka | 2024-12-06
Recently we have updated the result of polars-tpch benchmark with FireDucks. This post describes how to reproduce it.
ReadFireDucks optimization helps you process larger-than-memory-dataset
By Sourav Saha | 2024-12-05
This article explains how FireDucks can efficienly optimize data loading when only a few columns are to be processed in a query
ReadEfficient caching in FireDucks Lazy Execution
By Sourav Saha | 2024-12-05
This article explains how FireDucks lazy-execution caches intermediate results to be used at later stage
ReadWhat to do when FireDucks is slow
By Masashi Kotera | 2024-11-11
This article describes possible causes and remedies for slow programs using FireDucks.
Read