criterion performance measurements

overview

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hasql

lower bound estimate upper bound
Mean execution time 8.759362637723486e-2 8.868805587018526e-2 9.016613900388283e-2
Standard deviation 5.093082197066026e-3 6.467029372029632e-3 8.496233923139163e-3

Outlying measurements have severe (0.6664119087641515%) effect on estimated standard deviation.

postgresql-simple

lower bound estimate upper bound
Mean execution time 0.14940416037763168 0.15029469430173442 0.1519652694436411
Standard deviation 3.7142063935288864e-3 6.035820782712763e-3 9.63362068156103e-3

Outlying measurements have moderate (0.3752645342719205%) effect on estimated standard deviation.

HDBC

lower bound estimate upper bound
Mean execution time 0.2437508887025217 0.2481936448785166 0.2544463914605479
Standard deviation 2.051051647740825e-2 2.668160444728132e-2 3.5485682381104736e-2

Outlying measurements have severe (0.8206860555857204%) effect on estimated standard deviation.

understanding this report

In this report, each function benchmarked by criterion is assigned a section of its own. In each section, we display two charts, each with an x axis that represents measured execution time. These charts are active; if you hover your mouse over data points and annotations, you will see more details.

Under the charts is a small table displaying the mean and standard deviation of the measurements. We use a statistical technique called the bootstrap to provide confidence intervals on our estimates of these values. The bootstrap-derived upper and lower bounds on the mean and standard deviation let you see how accurate we believe those estimates to be. (Hover the mouse over the table headers to see the confidence levels.)

A noisy benchmarking environment can cause some or many measurements to fall far from the mean. These outlying measurements can have a significant inflationary effect on the estimate of the standard deviation. We calculate and display an estimate of the extent to which the standard deviation has been inflated by outliers.