We want to export performance traces from the pageserver in OTEL format.
End goal is to see them in Grafana.
To this end, there are two changes here:
1. Update the `tracing-utils` crate to allow for explicitly specifying
the export configuration. Pageserver configuration is loaded from a file
on start-up. This allows us to use the same flow for export configs
there.
2. Update the `utils::logging::init` common entry point to set up OTEL
tracing infrastructure if requested. Note that an entirely different
tracing subscriber is used. This is to avoid interference with the
existing tracing set-up. For now, no service uses this functionality.
PR to plug this into the pageserver is
[here](https://github.com/neondatabase/neon/pull/11140).
Related https://github.com/neondatabase/neon/issues/9873
Move most of the code to compute.rs, so that all the major startup steps
are visible in one place. You can now get a pretty good picture of what
happens in the latency-critical path at compute startup by reading
ComputeNode::start_compute().
This also clarifies the error handling in start_compute. Previously, the
start_postgres function sometimes returned an Err, and sometimes Ok but
with the compute status already set to Failed. Now the start_compute
function always returns Err on failure, and it's the caller's
responsibility to change the compute status to Failed. Separately from
that, it returns a handle to the Postgres process via a `&mut` reference
if it had already started Postgres (i.e. on success, or if the failure
happens after launching the Postgres process).
---------
Co-authored-by: Alexey Kondratov <kondratov.aleksey@gmail.com>
compute_ctl is mostly written in synchronous fashion, intended to run in
a single thread. However various parts had become async, and they
launched their own tokio runtimes to run the async code. For example, VM
monitor ran in its own multi-threaded runtime, and apply_spec_sql()
launched another multi-threaded runtime to run the per-database SQL
commands in parallel. In addition to that, a few places used a
current-thread runtime to run async code in the main thread, or launched
a current-thread runtime in a *different* thread to run background
tasks.
Unify the runtimes so that there is only one tokio runtime. It's created
very early at process startup, and the main thread "enters" the runtime,
so that it's always available for tokio::spawn() and runtime.block_on()
calls. All code that needs to run async code uses the same runtime.
The main thread still mostly runs in a synchronous fashion. When it
needs to run async code, it uses rt.block_on().
Spawn fewer additional threads, prefer to spawn tokio tasks instead.
Convert some code that ran synchronously in background threads into
async. I didn't go all the way, though, some background threads are
still spawned.
* tracing-utils now returns a `Layer` impl. Removes the need for crates
to
import OTel crates.
* Drop the /v1/traces URI check. Verified that the code does the right
thing.
* Leave a TODO to hook in an error handler for OTel to log errors to
when it
assumes the regular pipeline cannot be used/is broken.
Allure does not support ansi colored logs, yet `compute_ctl` has them.
Upgrade criterion to get rid of atty dependency, disable ansi colors,
remove atty dependency and disable ansi feature of tracing-subscriber.
This is a heavy-handed approach. I am not aware of a workflow where
you'd want to connect a terminal directly to for example `compute_ctl`,
usually you find the logs in a file. If someone had been using colors,
they will now need to:
- turn the `tracing-subscriber.default-features` to `true`
- edit their wanted project to have colors
I decided to explicitly disable ansi colors in case we would have in
future a dependency accidentally enabling the feature on
`tracing-subscriber`, which would be quite surprising but not
unimagineable.
By getting rid of `atty` from dependencies we get rid of
<https://github.com/advisories/GHSA-g98v-hv3f-hcfr>.
compute_ctl can panic, but `tracing` is used for logging. panic stderr
output can interleave with messages from normal logging. The fix is to
use the established way (pageserver, safekeeper, storage_broker) of using
`tracing` to report panics.
This allows tracing the startup actions e.g. with Jaeger
(https://www.jaegertracing.io/). We use the "tracing-opentelemetry"
crate, which turns tracing spans into OpenTelemetry spans, so you can
use the usual "#[instrument]" directives to add tracing.
I put the tracing initialization code to a separate crate,
`tracing-utils`, so that we can reuse it in other programs. We
probably want to set up tracing in the same way in all our programs.
Co-authored-by: Joonas Koivunen <joonas@neon.tech>
Refactors Compute::prepare_and_run. It's split into subroutines
differently, to make it easier to attach tracing spans to the
different stages. The high-level logic for waiting for Postgres to
exit is moved to the caller.
Replace 'env_logger' with 'tracing', and add `#instrument` directives
to different stages fo the startup process. This is a fairly
mechanical change, except for the changes in 'spec.rs'. 'spec.rs'
contained some complicated formatting, where parts of log messages
were printed directly to stdout with `print`s. That was a bit messed
up because the log normally goes to stderr, but those lines were
printed to stdout. In our docker images, stderr and stdout both go to
the same place so you wouldn't notice, but I don't think it was
intentional.
This changes the log format to the default
'tracing_subscriber::format' format. It's different from the Postgres
log format, however, and because both compute_tools and Postgres print
to the same log, it's now a mix of two different formats. I'm not
sure how the Grafana log parsing pipeline can handle that. If it's a
problem, we can build custom formatter to change the compute_tools log
format to be the same as Postgres's, like it was before this commit,
or we can change the Postgres log format to match tracing_formatter's,
or we can start printing compute_tool's log output to a different
destination than Postgres
Currently it's included with minimal changes and lives aside of the main
workspace. Later we may re-use and combine common parts with zenith
control_plane.
This change is mostly needed to unify cloud deployment pipeline:
1.1. build compute-tools image
1.2. build compute-node image based on the freshly built compute-tools
2. build zenith image
So we can roll new compute image and new storage required by it to
operate properly. Also it becomes easier to test console against some
specific version of compute-node/-tools.
