A single "Annotate perf-iac runs" layer matched only `perf-iac`, so a Locust
load window was indistinguishable from a JMeter one. perf-iac now tags every
region with its load driver, so each driver can have its own layer and colour.
- Replace that layer with `Perf Runs (JMeter)` and `Perf Runs (Locust)`, each
matching ["perf-iac", "<driver>"] with matchAny:false, on 12 dashboards.
- job-queue, ledger-data-sync and log-derived-insights had an empty annotations
list and drew no perf regions at all; they now carry the builtIn layer plus
both driver layers.
- Grafana tag matching is a superset AND with no negation, so a generic
`perf-iac` layer also matches every driver region. Keeping one alongside the
driver layers would draw each load window twice, so it is replaced, not kept.
- Document the layers in the telemetry runbook, including two rendering limits:
annotations draw only on timeseries, state-timeline and candlestick panels,
and the shaded fill is 10% opacity so the region edges carry the colour.
- Add `jmeter` to the cspell dictionary; the hook rejects the bare word.
JLOG is defined in xrpl/basics/Log.h, and libxrpl.beast cannot include
xrpl.basics -- basics depends on beast, not the reverse. Use the journal
stream idiom the rest of the file already uses.
detachCallbacks() flips a flag that each observable callback checks on entry,
which leaves a callback already past that check running while the twelve
service stops below it tear down the state it reads. Stop the provider at the
same point instead: that joins the reader thread, so once it returns no
callback is running and none can start. Metrics recorded during the remaining
shutdown steps are no longer exported, which is the cost of the guarantee.
Build metricsRegistry_ in the member-init list rather than assigning it in
setup(). getMetricsRegistry() is read from the job queue and io threads, which
are running by then, so the later assignment was an unsynchronised write to the
handle those reads follow.
beast::insight instruments are created during ApplicationImp's member-init
list, and opentelemetry-cpp 1.28 never rebinds an already-vended Meter, so an
instrument created before the MeterProvider is published records nothing for
the rest of the process. Observable instruments carry the opposite constraint:
registering one arms the SDK reader thread, and its callbacks run hook handlers
that read services which do not exist that early.
Publish the provider in Telemetry's constructor, ahead of every producer, and
defer only the observables. Collector gains onCollectionReady() and
onCollectionStopping(); OTelCollector arms and disarms its gauges in response.
StatsDCollector starts its polling thread in its own constructor and had the
same hazard, so it uses the pair to gate that thread.
The metrics resource carries service.instance.id and is immutable once built,
so the node public key is resolved in Main.cpp, where a config error can still
be reported, and passed to makeApplication(). getNodeIdentity() remains
authoritative; both paths now share readNodeIdentity(), so telemetry cannot
report a key the node has abandoned.
An explicit ~ApplicationImp stops observing and stops telemetry, covering the
setup() failure paths that never reach run(). Telemetry::stop() is once-only
and no longer clears another instance's global pointer. The histogram view's
meter selector now matches the meter actually in use, so its bucket boundaries
apply for the first time.
makeTelemetrySetup() rejects a contradictory [telemetry] mutual-TLS
setup by throwing, but it is called from ApplicationImp's
member-initializer list. A try/catch in the constructor body cannot
reach a throw from there, and nothing further up the stack caught it
either, so a config mistake reached std::terminate: the default handler
printed a terminate dump and raised SIGABRT, leaving a core file
instead of a startup error.
Catch std::exception around makeApplication() in run(), report the
reason on stderr and return -1, so the failure is a clean non-zero exit
with a message an operator can act on. Only the construction is
wrapped. setup() starts subsystems whose shutdown order is delicate and
is left outside deliberately, because unwinding a half-started
Application would skip the normal stop sequence.
Gate both validation guards on enabled. A node with telemetry switched
off previously refused to start over certificate paths that nothing
would read.
Document both throws on makeTelemetrySetup(), state in
cfg/xrpld-example.cfg and the configuration reference that a partial
mutual-TLS setup is fatal and that the checks apply only when
enabled=1, and add a runbook troubleshooting entry keyed on the two
error messages.
Tests cover both guards with the message asserted so the two are told
apart, both enabled=0 paths, and the default plaintext configuration.
develop moved TempDir from beast:: to xrpl::, deleting
include/xrpl/beast/utility/temp_dir.h in favour of
include/xrpl/basics/FileUtilities.h. The merge kept this branch's
references to the old API, so the tree no longer compiled: the missing
header is a fatal include, and because DatabaseConfig_test.cpp lands in
the xrpld unity blob it broke the xrpld target itself, not just the tests.
Swap the include and drop the stale beast:: qualifier on 17 uses. The
three src/tests/libxrpl/nodestore files already include FileUtilities.h
and already spell TempDir unqualified elsewhere, so only the qualifier
was wrong there. DatabaseConfig_test.cpp needed the include as well; it
sits in namespace xrpl::node_store, so unqualified TempDir resolves to
xrpl::TempDir through the enclosing namespace.
Two further uses exist only on the sync-diagnostics tip and are fixed
there rather than here.
Node identity reached the OTel resource only as service.instance.id, which is
config-overridable and carries a deployment-chosen label rather than the node's
own identity. Add xrpl.node.id, set unconditionally from the node public key
(base58, TokenType::NodePublic), so traces and metrics share a stable per-node
key independent of [telemetry] service_instance_id.
Set on the tracer resource via Telemetry::setNodeId(), called from
ApplicationImp::setup() once nodeIdentity_ is known, and on the MetricsRegistry
resource via an added start() parameter. The beast::insight meter provider is
built in TelemetryImpl's constructor, before the wallet DB exists, so its
resource cannot carry the value; that path is left for later and the attribute
is omitted rather than stamped blank.
Also drops the transform/spanidentity collector processor added in
4a361a496d: per-node identity belongs on the resource, not copied onto every
span.
Consensus spans share one deterministic, ledger-derived trace_id, so a
single trace holds spans from every node and the resource-level node id is
not a reliable per-span discriminator in stored traces.
Add transform/spanidentity to both collector configs, copying
service.instance.id onto every span as service_instance_id so TraceQL can
filter per node with the same value the $node dashboard variable already
uses on the metrics side. Wired into the traces pipeline locally and into
traces/store (after tail_sampling) on the Grafana Cloud variant.
The transitions panel used increase(...[$__rate_interval]). $__rate_interval is
defined as max($__interval + scrape, 4 * scrape), i.e. deliberately one scrape
longer than the step so rate() windows overlap and lose no counter increase.
That overlap is harmless for rate(), but this panel reads the value as a count
of discrete events, and the overlap counts each event in more than one bucket.
Measured against a log-derived ground truth of 106 syncing transitions on
devnet-otel-usw2-01 over 2026-08-11T11:05Z..2026-08-12T23:04Z, the old query
reported 111.3 at a 300s step and 133.7 at a 60s step -- the error grew to +26%
as you zoomed in, because the overlap is a larger fraction of a smaller step.
Switch to $__interval so the buckets tile exactly, and wrap in round() because
increase() extrapolates to the window edges and so reports fractional counts for
an integer counter. The same measurement now gives 106 at 300s, 105 at 60s and
107 at 900s. Every state and both nodes land within a few counts of truth at any
zoom, and the legend Total is now a meaningful figure.
Pin Min step to 1m: the real scrape interval is 60s while the datasource
declares 15s, so without a floor $__interval can fall below one sample.
Draw as bars with 0 decimals -- the value is a discrete count per bucket, and a
line implies interpolation between counts that does not exist.
