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docs: remove extra erroneous backtick #1482

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Nov 9, 2024
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docs: remove extra erroneous backtick #1482

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2 changes: 1 addition & 1 deletion docs/index.md
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Expand Up @@ -4948,7 +4948,7 @@ When you spin up a process yourself you should generally have it pipe its output

Go's built-in `testing` package provides support for running `Benchmark`s. Earlier versions of Ginkgo subject-node variants that were able to mimic Go's `Benchmark` tests. As of Ginkgo 2.0 these nodes are no longer available. Instead, Ginkgo users can benchmark their code using Gomega's substantially more flexible `gmeasure` package. If you're interested, check out the `gmeasure` [docs](https://onsi.github.io/gomega/#gmeasure-benchmarking-code). Here we'll just provide a quick example to show how `gmeasure` integrates into Ginkgo's reporting infrastructure.

`gmeasure` is structured around the metaphor of Experiments. With `gmeasure` you create ``Experiments` that can record multiple named `Measurements`. Each named `Measurement` can record multiple values (either `float64` or `duration`). `Experiments` can then produce reports to show the statistical distribution of their `Measurements` and different `Measurements`, potentially from different `Experiments` can be ranked and compared. `Experiments` can also be cached using an `ExperimentCache` - this can be helpful to avoid rerunning expensive experiments _and_ to save off "gold-master" experiments to compare against to identify potential regressions in performance - orchestrating all that is left to the user.
`gmeasure` is structured around the metaphor of Experiments. With `gmeasure` you create `Experiments` that can record multiple named `Measurements`. Each named `Measurement` can record multiple values (either `float64` or `duration`). `Experiments` can then produce reports to show the statistical distribution of their `Measurements` and different `Measurements`, potentially from different `Experiments` can be ranked and compared. `Experiments` can also be cached using an `ExperimentCache` - this can be helpful to avoid rerunning expensive experiments _and_ to save off "gold-master" experiments to compare against to identify potential regressions in performance - orchestrating all that is left to the user.

Here's an example where we profile how long it takes to repaginate books:

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