The `Logger` interface and its related classes are general purpose
and could be used by other tooling. Moving it into ngtsc is a more
suitable place from which to share it - similar to the FileSystem stuff.
PR Close#37114
When running in parallel mode, worker processes forward errors thrown
during task processing to the master process, which in turn exits with
an error.
However, there are cases where the error is not directly related to
processing the entry-point. One such case is when there is not enough
memory (for example, due to all the other tasks being processed
simultaneously).
Previously, an `ENOMEM` error thrown on a worker process would propagate
to the master process, eventually causing ngcc to exit with an error.
Example failure: https://circleci.com/gh/angular/angular/682198
This commit improves handling of these low-memory situations by
detecting `ENOMEM` errors and killing the worker process, thus allowing
the master process to decide how to handle that. The master process will
put the task back into the tasks queue and continue processing tasks
with the rest of the worker processes (and thus with lower memory
pressure).
PR Close#36626
Previously, when running in parallel mode and a worker process crashed
while processing a task, it was not possible for ngcc to continue
without risking ending up with a corrupted entry-point and therefore it
exited with an error. This, for example, could happen when a worker
process received a `SIGKILL` signal, which was frequently observed in CI
environments. This was probably the result of Docker killing processes
due to increased memory pressure.
One factor that amplifies the problem under Docker (which is often used
in CI) is that it is not possible to distinguish between the available
CPU cores on the host machine and the ones made available to Docker
containers, thus resulting in ngcc spawning too many worker processes.
This commit addresses these issues in the following ways:
1. We take advantage of the fact that files are written to disk only
after an entry-point has been fully analyzed/compiled. The master
process can now determine whether a worker process has not yet
started writing files to disk (even if it was in the middle of
processing a task) and just put the task back into the tasks queue if
the worker process crashes.
2. The master process keeps track of the transformed files that a worker
process will attempt to write to disk. If the worker process crashes
while writing files, the master process can revert any changes and
put the task back into the tasks queue (without risking corruption).
3. When a worker process crashes while processing a task (which can be a
result of increased memory pressure or too many worker processes),
the master process will not try to re-spawn it. This way the number
or worker processes is gradually adjusted to a level that can be
accomodated by the system's resources.
Examples of ngcc being able to recover after a worker process crashed:
- While idling: https://circleci.com/gh/angular/angular/682197
- While compiling: https://circleci.com/gh/angular/angular/682209
- While writing files: https://circleci.com/gh/angular/angular/682267
Jira issue: [FW-2008](https://angular-team.atlassian.net/browse/FW-2008)
Fixes#36278
PR Close#36626
With this commit, worker processes will notify the master process about
the transformed files they are about to write to disk before starting
writing them.
In a subsequent commit, this will be used to allow ngcc to recover when
a worker process crashes in the middle of processing a task.
PR Close#36626
This commit enhances the `CompileFn`, which is used to process each
entry-point, to support running a passed-in callback (and wait for it to
complete) before proceeding with writing the transformed files to disk.
This functionality is currently not used. In a subsequent commit, it
will be used for passing info from worker processes to the master
process that will allow ngcc to recover when a worker process crashes in
the middle of processing a task.
PR Close#36626
The cached file-system was implemented to speed up ngcc
processing, but in reality most files are not accessed many times
and there is no noticeable degradation in speed by removing it.
Benchmarking `ngcc -l debug` for AIO on a local machine
gave a range of 196-236 seconds with the cache and 197-224
seconds without the cache.
Moreover, when running in parallel mode, ngcc has a separate
file cache for each process. This results in excess memory usage.
Notably the master process, which only does analysis of entry-points
holds on to up to 500Mb for AIO when using the cache compared to
only around 30Mb when not using the cache.
Finally, the file-system cache being incorrectly primed with file
contents before being processed has been the cause of a number
of bugs. For example https://github.com/angular/angular-cli/issues/16860#issuecomment-614694269.
PR Close#36687
Moving the definition of the `onTaskCompleted` callback into `mainNgcc()`
allows it to be configured based on options passed in there more easily.
This will be the case when we want to configure whether to log or throw
an error for tasks that failed to be processed successfully.
This commit also creates two new folders and moves the code around a bit
to make it easier to navigate the code§:
* `execution/tasks`: specific helpers such as task completion handlers
* `execution/tasks/queues`: the `TaskQueue` implementations and helpers
PR Close#36083
`ngcc` supports both synchronous and asynchronous execution. The default
mode when using `ngcc` programmatically (which is how `@angular/cli` is
using it) is synchronous. When running `ngcc` from the command line
(i.e. via the `ivy-ngcc` script), it runs in async mode.
Previously, the work would be executed in the same way in both modes.
This commit improves the performance of `ngcc` in async mode by
processing tasks in parallel on multiple processes. It uses the Node.js
built-in [`cluster` module](https://nodejs.org/api/cluster.html) to
launch a cluster of Node.js processes and take advantage of multi-core
systems.
Preliminary comparisons indicate a 1.8x to 2.6x speed improvement when
processing the angular.io app (apparently depending on the OS, number of
available cores, system load, etc.). Further investigation is needed to
better understand these numbers and identify potential areas of
improvement.
Inspired by/Based on @alxhub's prototype: alxhub/angular@cb631bdb1
Original design doc: https://hackmd.io/uYG9CJrFQZ-6FtKqpnYJAA?view
Jira issue: [FW-1460](https://angular-team.atlassian.net/browse/FW-1460)
PR Close#32427
