In rare cases it could happen that an MR was created, but creating the
MR diffs somehow failed (e.g. due to an error). This commit adds an
additional check to make sure MR diffs are always present when importing
GitHub pull requests.
When importing a GitHub pull request we would perform all work in a
single database transaction. This is less than ideal, because we perform
various slow Git operations when creating a merge request. This in turn
can lead to many DB connections being used, while just waiting for an IO
operation to complete.
To work around this, we now move most of the heavy lifting out of the
database transaction. Some extra error handling is added to ensure we
can resume importing a partially imported pull request, instead of just
throwing an error.
This commit also changes the specs for IssueImporter so they don't rely
on deprecated RSpec methods.
When a repository does not exist on a remote, Gitaly won't be able to
clone it. This is correct behaviour, but from the clients perspective a
change in behaviour.
This change implements the client side changes that allows Gitaly to
execute a `git ls-remote <remote-url> HEAD`. This way the client has no
need to shell out to Git.
In the situation where multiple Gitalies are available, one is chosen at
random.
This commit closes https://gitlab.com/gitlab-org/gitlab-ce/issues/43929,
while its also a part of https://gitlab.com/gitlab-org/gitaly/issues/1084
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.
