FireDucks Speeds Pandas Workloads 20.77x on 10 Million Rows
Updated
Updated · KDnuggets · Sep 2
FireDucks Speeds Pandas Workloads 20.77x on 10 Million Rows
1 articles · Updated · KDnuggets · Sep 2
Summary
Benchmarks on a 10-million-row dataset showed FireDucks beat pandas in all seven tested workloads, with the biggest gain in full-dataset sorting at 20.77x and a geometric-mean speedup of 7.28x.
FireDucks gets those gains through lazy execution, compiler optimization and multithreaded CPU processing, while keeping a largely pandas-compatible API that often works by changing the import statement.
Median runtimes fell sharply across common tasks: filtering was 11.63x faster, low-cardinality groupby 15.44x, high-cardinality groupby 5.75x, joins 3.20x and a chained pipeline 5.94x faster.
The test used a 2-million-row lookup table, five measured runs after warm-up, alternating execution order and explicit materialization of FireDucks results to capture actual runtime.
The report says FireDucks is not a full drop-in replacement, and real-world gains will vary by workload, data and hardware even though outside tests also reported sizable time reductions.