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# data.table continuous integration and deployment
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On each Pull Request opened in GitHub we run GitHub Actions test jobs to provide prompt feedback about the status of PR. Our main CI pipeline runs on GitLab CI nightly. GitLab repository automatically mirrors our GitHub repository and runs pipeline on `master` branch every night. It tests more environments and different configurations. It publish variety of artifacts.
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On each Pull Request opened in GitHub we run GitHub Actions test jobs to provide prompt feedback about the status of PR. Our more thorough main CI pipeline runs nightly on GitLab CI. GitLab repository automatically mirrors our GitHub repository and runs pipeline on `master` branch every night. It tests more environments and different configurations. It publishes a variety of artifacts such as our [homepage](https://rdatatable.gitlab.io/data.table/) and [CRAN-like website for dev version](https://rdatatable.gitlab.io/data.table/web/packages/data.table/index.html), including windows binaries for the dev version.
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## Environments
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### [`publish.R`](./publish.R)
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Base R implemented helper script to orchestrate generation of most artifacts and to arrange them nicely. It is being used only in [_integration_ stage in GitLab CI pipeline](./../.gitlab-ci.yml).
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## GitLab Open Source Program
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We are currently part of the [GitLab for Open Source Program](https://about.gitlab.com/solutions/open-source/). This gives us 50,000 compute minutes per month for our GitLab CI. Our license needs to be renewed yearly (around July) and is currently managed by @ben-schwen.
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## Updating CI pipeline
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Basic CI checks are also run on every push to the GitLab repository. This can **and should** be used for PRs changing the CI pipeline before merging them to master.
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```shell
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# fetch changes from remote (GitHub) and push them to GitLab
"forderv(retGrp=%s) improved in #4386", retGrp_chr
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)]] <-list(
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setup= quote({
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dt<- data.table(group= rep(1:2, l=N))
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}),
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expr= substitute({
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old.opt<- options(datatable.forder.auto.index=TRUE) # required for test, un-documented, comments in forder.c say it is for debugging only.
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data.table:::forderv(dt, "group", retGrp=RETGRP)
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options(old.opt) # so the option does not affect other tests.
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}, list(RETGRP=eval(str2lang(retGrp_chr)))),
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## From ?bench::mark, "Each expression will always run at least twice,
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## once to measure the memory allocation and store results
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## and one or more times to measure timing."
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## So for atime(times=10) that means 11 times total.
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## First time for memory allocation measurement,
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## (also sets the index of dt in this example),
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## then 10 more times for time measurement.
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## Timings should be constant if the cached index is used (Fast),
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## and (log-)linear if the index is re-computed (Slow).
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Slow="b1b1832b0d2d4032b46477d9fe6efb15006664f4", # Parent of the first commit (https://github.com/Rdatatable/data.table/commit/b0efcf59442a7d086c6df17fa6a45c81b082322e) in the PR (https://github.com/Rdatatable/data.table/pull/4386/commits) where the performance was improved.
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Fast="ffe431fbc1fe2d52ed9499f78e7e16eae4d71a93"# Last commit of the PR (https://github.com/Rdatatable/data.table/pull/4386/commits) where the performance was improved.
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)
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# A list of performance tests.
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#
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# See documentation in https://github.com/Rdatatable/data.table/wiki/Performance-testing for best practices.
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