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Speed benchmark (same-runner A/B)

head d577ed0297e4e31760f118a26401a661421026b7 · base cb47f70cd3a4c52225c16d2fe8fe1945adbe31bf · Python 3.12.15 · NumPy 2.1.1 · GitHub Actions 1000039411

base rows 46/46

method / sink rows rows/µs base s head s ratio (modifier: overhead ÷ unmodified column) peak MiB
integers 1,000,000 195.561 0.005 0.005 1.02 15.3
int_zfilled 1,000,000 30.732 0.274 0.033 0.12 45.8
floats 1,000,000 202.227 0.006 0.005 0.76 15.3
floats_normal 1,000,000 69.233 0.016 0.014 0.89 15.3
exponential 1,000,000 122.338 0.010 0.008 0.85 15.3
lognormal 1,000,000 48.381 0.022 0.021 0.93 15.3
poisson 1,000,000 69.345 0.015 0.014 0.94 23.8
zipf 1,000,000 62.982 0.017 0.016 0.93 23.9
constant 1,000,000 194.787 0.065 0.003 0.05 0.0
distincts 1,000,000 109.638 0.017 0.009 0.55 7.6
distincts_prop 1,000,000 109.589 0.069 0.009 0.13 7.6
unix_timestamps 1,000,000 230.787 0.004 0.004 0.99 15.3
uuid4 1,000,000 12.247 2.709 0.081 0.03 83.9
booleans 1,000,000 70.862 0.013 0.014 1.09 16.2
dates 1,000,000 73.478 0.248 0.012 0.05 22.9
distincts_map 1,000,000 52.100 0.225 0.018 0.08 7.6
distincts_multi_map 1,000,000 45.219 0.239 0.020 0.08 7.6
distincts_map_prop 1,000,000 42.080 0.201 0.024 0.12 7.6
complex_distincts 1,000,000 7.340 1.112 0.134 0.12 19.1
pk 1,000,000 512.401 0.002 0.002 0.98 22.9
fk 1,000,000 59.448 0.021 0.016 0.74 45.8
integers 10,000,000 257.619 0.039 0.039 1.00 152.6
int_zfilled 10,000,000 36.050 2.754 0.277 0.10 457.8
floats 10,000,000 183.043 0.055 0.055 0.99 152.7
floats_normal 10,000,000 64.173 0.156 0.156 1.00 152.6
exponential 10,000,000 109.176 0.092 0.092 1.00 152.6
lognormal 10,000,000 45.933 0.217 0.218 1.00 152.6
poisson 10,000,000 66.298 0.151 0.151 1.00 238.4
zipf 10,000,000 59.581 0.168 0.168 1.00 238.5
constant 10,000,000 289.116 0.633 0.035 0.06 0.0
distincts 10,000,000 114.534 0.173 0.086 0.50 76.3
distincts_prop 10,000,000 99.993 0.693 0.100 0.14 76.3
unix_timestamps 10,000,000 220.616 0.045 0.045 1.00 152.6
uuid4 10,000,000 11.637 26.962 0.859 0.03 839.2
booleans 10,000,000 71.614 0.140 0.140 1.00 162.1
dates 10,000,000 73.729 2.573 0.136 0.05 228.9
distincts_map 10,000,000 52.062 2.177 0.192 0.09 76.3
distincts_multi_map 10,000,000 48.248 2.326 0.207 0.09 76.3
distincts_map_prop 10,000,000 40.398 1.939 0.247 0.13 76.3
complex_distincts 10,000,000 7.841 10.855 1.282 0.12 190.8
pk 10,000,000 290.059 0.035 0.034 0.97 228.9
fk 10,000,000 37.254 0.272 0.269 0.99 457.8
csv 1,000,000 3.032 3.555 0.330 0.09 —
parquet 1,000,000 2.421 3.628 0.412 0.11 —
json 1,000,000 0.506 4.995 1.983 0.40 —
stream_dict 1,000,000 0.170 6.498 5.876 0.90 —
null_rate 1,000,000 116.463 — 0.008 0.55 × column —
anomaly_rate 1,000,000 101.267 — 0.010 0.94 × column —
null_rate 10,000,000 108.221 — 0.092 1.38 × column —
anomaly_rate 10,000,000 87.732 — 0.113 1.93 × column —