Our friends at GitHub show the programming languages for a long time,
and inspired by that this commit means to create about the same
functionality.

Language detection is done through Linguist, as before, where the
difference is that we cache the result in the database. Also, Gitaly can
incrementaly scan a repository. This is done through a shell out, which
creates overhead of about 3s each run. For now this won't be improved.

Scans are triggered by pushed to the default branch, usually `master`.
However, one exception to this rule the charts page. If we're requesting
this expensive data anyway, we just cache it in the database.

Edge cases where there is no repository, or its empty are caught in the
Repository model. This makes use of Redis caching, which is probably
already loaded.

The added model is called RepositoryLanguage, which will make it harder
if/when GitLab supports multiple repositories per project. However, for
now I think this shouldn't be a concern. Also, Language could be
confused with the i18n languages and felt like the current name was
suiteable too.

Design of the Project#Show page is done with help from @dimitrieh. This
change is not visible to the end user unless detections are done.

We request Gitaly in a N+1 manner to build discussion diffs. Once the diffs are from different revisions, it's hard to make a single request to the service in order to build the whole response.
With this change we solve this problem and simplify a lot fetching this piece of info.

Also, refactor the mail sending slightly: instead of one worker sending all
emails, create a worker per project with issues due, which will send all emails
for that project.

Integrate two workers into one ArchiveTraceWorker with pipeline_background queue. This queue takes loqer precedence than pipeline_default.

Can migrate LFS objects to S3 style object storage

Closes #2841

See merge request !2760

Signed-off-by: Dmitriy Zaporozhets <dmitriy.zaporozhets@gmail.com>

Fix https://gitlab.com/gitlab-org/gitlab-ce/issues/38962

Prior to this MR there were two GitHub related importers:

* Github::Import: the main importer used for GitHub projects
* Gitlab::GithubImport: importer that's somewhat confusingly used for
  importing Gitea projects (apparently they have a compatible API)

This MR renames the Gitea importer to Gitlab::LegacyGithubImport and
introduces a new GitHub importer in the Gitlab::GithubImport namespace.
This new GitHub importer uses Sidekiq for importing multiple resources
in parallel, though it also has the ability to import data sequentially
should this be necessary.

The new code is spread across the following directories:

* lib/gitlab/github_import: this directory contains most of the importer
  code such as the classes used for importing resources.
* app/workers/gitlab/github_import: this directory contains the Sidekiq
  workers, most of which simply use the code from the directory above.
* app/workers/concerns/gitlab/github_import: this directory provides a
  few modules that are included in every GitHub importer worker.

== Stages

The import work is divided into separate stages, with each stage
importing a specific set of data. Stages will schedule the work that
needs to be performed, followed by scheduling a job for the
"AdvanceStageWorker" worker. This worker will periodically check if all
work is completed and schedule the next stage if this is the case. If
work is not yet completed this worker will reschedule itself.

Using this approach we don't have to block threads by calling `sleep()`,
as doing so for large projects could block the thread from doing any
work for many hours.

== Retrying Work

Workers will reschedule themselves whenever necessary. For example,
hitting the GitHub API's rate limit will result in jobs rescheduling
themselves. These jobs are not processed until the rate limit has been
reset.

== User Lookups

Part of the importing process involves looking up user details in the
GitHub API so we can map them to GitLab users. The old importer used
an in-memory cache, but this obviously doesn't work when the work is
spread across different threads.

The new importer uses a Redis cache and makes sure we only perform
API/database calls if absolutely necessary.  Frequently used keys are
refreshed, and lookup misses are also cached; removing the need for
performing API/database calls if we know we don't have the data we're
looking for.

== Performance & Models

The new importer in various places uses raw INSERT statements (as
generated by `Gitlab::Database.bulk_insert`) instead of using Rails
models. This allows us to bypass any validations and callbacks,
drastically reducing the number of SQL queries and Gitaly RPC calls
necessary to import projects.

To ensure the code produces valid data the corresponding tests check if
the produced rows are valid according to the model validation rules.

This makes things simpler as no scheduling is involved. Further we
remove the need for running a SELECT + UPDATE just to get the key and
update it, whereas we only need an UPDATE when setting last_used_at
directly in a request.

The added service class takes care of updating Key#last_used_at without
using Sidekiq. Further it makes sure we only try to obtain a Redis lease
if we're confident that we actually need to do so, instead of always
obtaining it. We also make sure to _only_ update last_used_at instead of
also updating updated_at.

Fixes https://gitlab.com/gitlab-org/gitlab-ce/issues/36663

Background migrations can be used to perform long running data
migrations without these blocking a deployment procedure.

See MR https://gitlab.com/gitlab-org/gitlab-ce/merge_requests/11854 for
more information.

* implemented logging of project and system web hooks
* implemented UI for user area (project hooks)
* implemented UI for admin area (system hooks)
* implemented retry of logged webhook
* NOT imeplemented log remover

Destroying projects can be very time consuming. So instead of destroying them in
the post-deploy, just schedule them and make Sidekiq do the hard work.

They are scheduled in batches of 5000 records. This way the number of database
requests is limited while also the amount data read to memory is limited.