FetchReport::elapsed was milliseconds, so every nodestore read rounded to
zero: a warm store answers in single-digit microseconds and a cold one in
low hundreds, and both became 0 ms. That difference is the whole signal
separating a cold-read stall from a healthy node, and it was being
discarded at the type. Database::fetchNodeObject now measures once and
uses that one value for both the cumulative counter and the report, so
the two can never disagree. The job-queue call still takes milliseconds
and now casts explicitly.
BatchWriteReport::elapsed stays milliseconds and is documented as such:
a batch write covers many objects and reaches the disk, so it belongs in
that range.
Also adds a sub-millisecond histogram ladder, because the existing bucket
edges start at 100 microseconds and put the entire warm range in bucket
0. It is not wired to a view yet: no sub-millisecond instrument exists to
name, so the edges wait for the instrument that records read latency.
The new test captures what the nodestore reports and asserts the reported
total equals the internal microsecond accumulator exactly, plus that at
least one report is not a whole number of milliseconds -- which a
millisecond-typed field can never satisfy on any hardware.
The nodestore namespace became xrpl::node_store when develop was merged
in, but one mock override still named the old spelling, so it did not
match the ServiceRegistry signature it overrides. The sibling mock in
TestServiceRegistry.h was already correct.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Adds the derived read and write means, the NuDB writer depth and insert
timings, and the seven acquisition counters to the existing
nodestore_state gauge. Every value multiplexes onto that one instrument
through its `metric` label, so no new instrument is created.
Means are omitted rather than reported as zero when their denominator is
zero, so a dashboard shows a gap instead of a plausible wrong number. All
four go through one new scaledMean() helper so the guard cannot be
forgotten at a future call site; it also saturates instead of wrapping,
because a wrapped gauge reads as a healthy-looking dip. A zero total over
real samples still reports zero, since a store fast enough to truncate
every sample must not look dead.
The NuDB write-path block is skipped entirely when getWriteStats() is
nullopt, which is every backend but NuDB, so absent labels distinguish
"not measured" from "measured, and idle". Writer depth is scaled by 100
and named accordingly, because it sits just above 1.0 and an integral
gauge would truncate the whole signal away.
The gauge callback body is split into four static helpers to stay inside
the per-function line budget and to make each domain testable with a
recording sink.
Also corrects nudb_bytes, which called getStoreSize() exactly as
node_written_bytes does, so the obvious write-amplification ratio was a
constant 1.0 and the old "on-disk size" comment was wrong. No file-size
accessor exists on Backend or Database, so the value is unchanged and the
comment now states what it really is.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
clang-tidy had been skipped on this branch while CMake configure was
failing, so these findings in makeStageSpan surfaced only now:
- brace the three single-statement if bodies (readability-braces-around-statements)
- compare the pointer parameter explicitly against nullptr
(readability-implicit-bool-conversion)
- include xrpl/protocol/Protocol.h for LedgerIndex (misc-include-cleaner)
A saturated ledgerData lane makes TimeoutCounter re-arm its timer
without running the timer body, so timeouts_ never advances and the
six-timeout give-up can never fire. Acquisitions then neither finish
nor fail until the one-minute sweep destroys their partial maps, and
the work restarts. Every step of that chain was debug-log-only, so a
node at warning level could not be diagnosed after the fact.
The counters are separate on purpose: deferrals rising while timeouts
stay flat is the signature, and no single counter shows it.
Completions are recorded in done() rather than at the "Done: complete"
log line, because that line also fires for failures and misses the
checkLocal and receiveNode paths; done() is the one funnel every
outcome passes through and its signaled_ guard makes it idempotent.
AcquireStats is only forward-declared in ServiceRegistry so libxrpl
still includes nothing from xrpld. The src/ include path for the test
binary moves out of the telemetry guard, since a header-only type
under src/xrpld/ is testable in every build.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
The consensus headers moved into the isolated xrpl/consensus module and
took a dependency on xrpl/telemetry for the tracing span constants, but
two things were left behind:
- Four includes still pointed at the old src/xrpld/consensus/ location,
which no longer exists, so the build failed with
"fatal error: 'xrpld/consensus/ConsensusParms.h' file not found".
- xrpl.libxrpl.consensus never linked xrpl.libxrpl.telemetry. add_module
isolates each module's headers, so xrpl/telemetry/SpanNames.h was not
on the include path even once the include was repointed.
Repoint the stale includes at xrpl/consensus/, and declare the telemetry
module before consensus so consensus can link it. Regenerate ordering.txt
for the resulting edge.
NuDB serializes every insert behind one global mutex held for the whole
call, so a caller cannot see how long it waited. Record instead the
writer depth joined at and the wall time spent; with mean depth L and
mean insert time W, Little's Law gives service time W/L and queueing
W - W/L. That distinguishes a serialized write path from a saturated
disk: measured on a dev box the device sat 89 percent idle while
throughput stayed flat at 42k inserts per second.
The accounting runs from a ScopeExit guard because the insert can
allocate and therefore throw; leaking the depth would strand the gauge
above zero for the life of the process.
getWriteLoad also stops returning a hardcoded zero. It now reports
writer depth, which is bounded by the writing-thread count and so stays
far below the kMaxWriteLoadAcquire cutoff that gates history
acquisition, where returning bytes or microseconds would have silently
suppressed it.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
The phase-2 merge re-added the pre-telemetry `add_module(xrpl tx)` block
without removing it, leaving the module declared twice. CMake's
add_library rejects a repeated target name, so configure failed before
any compilation:
add_library cannot create target "xrpl.libxrpl.tx" because another
target with the same name already exists.
Drop the stale pre-telemetry block and keep the one that follows
add_module(xrpl telemetry), which links both ledger and telemetry.
libxrpl/tx needs the telemetry link for the tx.transactor span, and
levelization already records `libxrpl.tx > xrpl.telemetry`.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
fetchDurationUs_ had no getter, so telemetry reached it by building a JSON
object, stringifying a uint64 and parsing it back with stoll on every
collect tick. storeDurationUs_ was declared and never written or read at
all, along with the jss::node_writes_duration_us key.
Both now have accessors, both production store paths time their backend
call, and the registry reads them without the round trip. get_counts also
reports the write duration, so the RPC and the metric agree. Mean read
latency is the signal that separates a cold store from a warm one: warm
reads are single-digit microseconds, cold ones low hundreds.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Both were uint32_t while every sibling counter was uint64_t. On a
multi-day node node_read_bytes read 1,481,244,491 for 1,894,924,394
reads, i.e. 0.8 bytes per read, which is impossible: the counter had
wrapped about 350 times. Read hit rate is used to tell a cold-read
stall from a write-lock ceiling, so a wrapping numerator makes that
diagnosis wrong rather than merely imprecise.
getFetchTotalCount() was already backed by a uint64_t member, so its
32-bit return type truncated a correct value at the accessor. All
three getters now return uint64_t.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Post-deploy verification against Grafana Cloud found two problems on the
job-queue dashboard.
Every aggregating panel's displayName names four labels, but the queries
aggregate with `sum by (...)` lists that omit `xrpl_work_item`, so that
part of the legend rendered as an unresolved reference. Pre-existing, but
splitting the wait and execution panels by job type made the legend
load-bearing, so it is fixed here: `xrpl_work_item` is added to the ten
affected sum-by lists.
The two panels split by job type in the previous commit also kept their
seven-section descriptions. They now carry the same ten sections as the
rest, and their glossary links point at the real `job-queue-job-type`
anchor rather than a `job-queue` anchor that does not exist.
Verified no dashboard links a glossary anchor that is absent.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
The eight panels added for the GetObject and job-queue metrics did not
follow the conventions the rest of the dashboards use.
- Descriptions go from seven sections to the ten used by the other 159
panels, adding Keywords, Computation boundary and References. The
existing diagnostic guidance is kept; only the format changed.
- Six glossary entries added for the terms those References link to
(concurrency limit, deferred job, handler label, NodeStore lookup
hit/miss, resource charge), so no link is dead.
- displayName becomes `${series} ${xrpl_ident}`, the form 122 of 143
panels use. This needs the label_join wrapper that builds xrpl_ident,
which these queries lacked, so it is added to ten targets; without it
the legend would render an unresolved label.
- Legend blocks now match each dashboard's own convention rather than
being split three-with and four-without across the new panels.
Job Queue Wait Time and Job Execution Time dropped job_type from their
aggregation, so every queue collapsed into one line and the legend could
not say which queue was slow. Both now split by type, capped with topk
to stay readable, matching the per-type panel already on that dashboard.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Resolved five files. In each case both sides had content worth keeping,
so nothing was taken wholesale:
- MetricsRegistry.cpp: kept the incoming `handler` label on the job
instruments and re-applied this branch's `queuedDurUs >= 0` guard,
which the incoming side does not have.
- telemetry-runbook.md: took the incoming gauge table, which adds the
three per-job-type rows, and re-applied this branch's corrected
`jobq_job_count` name.
- 09-data-collection-reference.md: kept this branch's validation
inventory (newer counts, extra Config File column) and inserted the
incoming call-site and per-job-type gauge rows plus their explanation.
- node-health.json: merged structurally rather than by text. This
branch's panels are authoritative; only the two incoming job-queue
panels were appended, below the existing layout. The
`Validated Ledger Seq` panels added directly in Grafana are preserved.
- job-queue.json, ledger-data-sync.json: panel sets were identical, so
took the incoming side for its `$handler` variable, the handler filter
on existing queries, and the new panels. Verified no panel was lost.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Resolved OpenTelemetryPlan/09-data-collection-reference.md: both sides
document the same new metrics, reached by different routes. The incoming
sections came from the phase-6 edit merged forward; this branch already
documents the same surface in its own layout, with the job lifecycle
metrics and the GetObject request path under the MetricsRegistry
section rather than a separate 2a.
Kept this branch's sections to avoid duplicate entries for every metric.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Resolved OpenTelemetryPlan/09-data-collection-reference.md: this branch
replaced the StatsD-shaped Section 2 with OTel-native naming, so the
incoming StatsD tables and the StatsD per-job timer section do not apply
here. Kept this branch's names throughout.
The incoming per-job-type saturation gauge section is new content and is
retained, renumbered to 2.5 and rewritten for the OTel naming: bare
lowercase `jobq_<jobtype>_{waiting,running,deferred}` derived through
OTelCollector rather than the case-preserving `xrpld_`-prefixed StatsD
form.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Slowness on the peer object-fetch path could be observed but not
attributed. Job duration metrics carry only `job_type`, and both
`RcvGetLedger` and `RcvGetObjByHash` report as `ledgerRequest`, so a
queue-wait spike could not be traced to a handler. Nothing measured
NodeStore cost, request size, or the differential charge.
Latency now decomposes into three additive parts, each separately
measurable:
end-to-end = queue wait + NodeStore lookup + everything else
- `handler` label on job_queued_total/_started_total/_finished_total and
job_queued_us/job_running_us. The value is sanitised: a name passes
through only if non-empty and all ASCII letters, else "other". Two job
names embed a ledger sequence, so a raw label would mint one series
per ledger; the rule bounds the domain at 43 names plus "other".
- getobject_lookup_us, _request_objects, _lookups_total{result},
_rejected_total{reason} and _charge, recorded at their call sites.
All three histograms get explicit bucket views: the SDK default stops
at 10,000, which every one of them exceeds.
- Per-job-type waiting/running/deferred gauges for the 35 non-special
job types. `deferred` is the leading indicator, since addJob never
rejects -- it defers, so backpressure otherwise shows up only as
latency after the fact.
`JobQueue::collect()` snapshots the counters under the queue lock and
publishes gauges after releasing it. Writing them while holding the lock
would invert a lock order against the collector's own lock, which the
collector's flush thread already holds when it calls this hook.
Tests assert exact values, including that the charge is priced on the
requested count rather than the capped iteration count.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Add reference entries for the observability surface introduced on
phase-9: the `handler` label on the job instruments, the five
`getobject_*` request metrics, and the per-job-type queue saturation
gauges.
Names here follow this branch's StatsD pipeline, which preserves case
and carries the `xrpld_` prefix, so they differ from the lowercased
OTel-native names used from phase-7 onward. The sections state where
the implementing code lives, since it is introduced downstream.
Also correct pre-existing entries: `job_count` exports as
`jobq_job_count` via the collector group prefix, the non-special job
type count is 35 (not 36), and `JtLedgerData` has five producers.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
