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feat(telemetry): own every histogram ladder in one tested header

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The bucket edges for the OTel histograms lived as file-local `namespace {}`
constants, unreachable from any test, and they drifted from the collector's
spanmetrics ladder they were specified to match. The millisecond ladder
stayed capped at 5 s after the collector side was extended to 30 s, so any
quantile above 5 s read back as a flat 5000 -- Prometheus returns the
second-highest edge for a quantile in the `+Inf` bucket, which looks like a
measurement rather than an error.

Adds include/xrpl/telemetry/HistogramBuckets.h as the single owner of the
ladders, with a constexpr validator plus static_asserts so a descending or
duplicated edge cannot compile, and gtest coverage that pins the floor and
ceiling against the measured distributions:

- kMillisecondBuckets carries every representable collector edge and extends
  to 120 s, because the updatepaths job type averages ~60 s and a 30 s
  ceiling would censor it exactly as 5 s does today. Sub-millisecond
  collector edges are omitted: beast::insight::Event rounds durations up to
  whole milliseconds, so they would collect nothing.
- kByteBuckets is new, for Events whose samples are sizes rather than
  durations. Edges follow the measured RPC response distribution (mean
  2131 B, half under 1 kB, tail mean bounded at 7538 B) rather than a guess,
  so the resolution sits between 512 B and 64 kB.

No behaviour change yet -- nothing consumes the header until the views are
rewired.
This commit is contained in:
Pratik Mankawde
2026-08-21 11:51:22 +01:00
parent d8035a35aa
commit cbfbea67f2
2 changed files with 369 additions and 0 deletions
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179
include/xrpl/telemetry/HistogramBuckets.h Normal file
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#pragma once
#include <array>
#include <cstddef>
#include <span>
#include <vector>
namespace xrpl::telemetry::buckets {
/**
* @file HistogramBuckets.h
* @brief Explicit histogram bucket edges for xrpld's OTel instruments.
*
* One header owns every ladder so a reviewer sees all of them at once and a
* test can assert their invariants. Before this existed the edges lived as
* file-local `namespace {}` constants, unreachable from any test, and they
* drifted apart.
*
* Why a ladder is worth this much care: when a quantile falls in the `+Inf`
* bucket, Prometheus returns the *second-highest* edge, not `+Inf`. A
* saturated histogram therefore reports a believable constant instead of an
* obvious error. The same trap exists at the bottom -- if nearly every
* sample lands in bucket 0, `histogram_quantile` interpolates inside it and
* invents a value. A ladder is correct only when its floor sits below the
* mass of the distribution and its ceiling above the tail.
*
* sample --> [ SDK lower_bound over edges ] --> per-bucket counter
* | |
* edges come from v
* THIS header OTLP export
* |
* v
* histogram_quantile() in Grafana
*
* Ladders are `std::array<double, N>` so they are constant-initialised and
* usable in a `static_assert`. The OTel SDK wants `std::vector<double>` in
* its aggregation config, so call toVector() at the registration site
* rather than storing vectors here.
*
* Example -- register a view with the millisecond ladder:
* @code
* auto config = std::make_shared<HistogramAggregationConfig>();
* config->boundaries_ = buckets::toVector(buckets::kMillisecondBuckets);
* @endcode
*
* Example -- the edge case that motivated a second ladder. An Event whose
* samples are sizes rather than durations must not borrow a latency ladder,
* or a quarter of its samples land in `+Inf` and every quantile reads back
* as the top edge:
* @code
* config->boundaries_ = buckets::toVector(buckets::kByteBuckets);
* @endcode
*
* @note Thread safety: every member is `constexpr` and immutable, so
* reading them from any thread is safe. toVector() allocates and is
* meant for start-up registration paths, never for a record path.
* @note Limitation: changing a ladder changes the exported series count and
* ends bucket comparability across the change -- existing series keep
* their old `le` values, so panels show a break at restart. Grafana
* Cloud bills per series, so re-measure the series count after any
* edit here.
*/
/**
* Bucket edges, in milliseconds, for whole-millisecond `beast::insight`
* Events: job queue wait and run times, io latency, RPC time, pathfinding.
*
* **This list must contain every representable edge of the collector's
* spanmetrics ladder, and may extend above it.** Agreement over the shared
* range is deliberate: it lets a span-derived latency panel and a native
* histogram panel be read on the same scale. It was specified that way
* originally, then silently broken when the collector ladder alone was
* extended, which left this side capped at 5 s while spans reached 30 s and
* censored every quantile above 5 s. `check_bucket_parity.py` now enforces
* the containment -- add a collector edge, add it here too.
*
* The sub-millisecond edges the collector carries (0.01 to 0.5 ms) are
* deliberately absent. `beast::insight::Event` rounds every duration up to
* a whole millisecond before it reaches the histogram, so those edges would
* collect nothing. Metrics that genuinely need finer resolution belong on
* the microsecond ladder, on the OTel-native path.
*
* The 60 s and 120 s edges exceed the collector's 30 s top on purpose,
* because jobs outlive spans: the updatepaths job type was measured
* averaging about 60 s, so a 30 s ceiling would censor its quantiles just
* as 5 s censors them today. All these Events share one ladder, so its
* ceiling has to cover the slowest member rather than the typical one.
*
* The 2, 3 and 4 s edges resolve second-scale work that previously had to
* interpolate across a single four-second-wide bucket.
*/
inline constexpr std::array kMillisecondBuckets{
1.0,
5.0,
10.0,
25.0,
50.0,
100.0,
250.0,
500.0,
1'000.0,
2'000.0,
3'000.0,
4'000.0,
5'000.0,
10'000.0,
30'000.0,
60'000.0,
120'000.0};
/**
* Bucket edges, in bytes, for `beast::insight` Events whose samples are
* sizes rather than durations. Currently only the RPC response size.
*
* Placed from the measured distribution rather than from a guess about how
* large a response could theoretically be. Measured over 24 h: mean 2131 B,
* half of all responses under 1 kB, three quarters under 5 kB. The tail
* above 5 kB has a mean of at most 7538 B, which bounds p99 near 80 kB and
* p99.75 below 256 kB.
*
* So the resolution belongs between 512 B and 64 kB, where the
* distribution actually turns, and two further edges are ample headroom.
* Spending edges at the megabyte scale would cost cardinality on a range
* nothing measured occupies. If a genuinely multi-megabyte response ever
* shows up in the top bucket, extend this -- but extend it on evidence.
*/
inline constexpr std::array kByteBuckets{
512.0,
1'024.0,
2'048.0,
4'096.0,
8'192.0,
16'384.0,
32'768.0,
65'536.0,
262'144.0,
1'048'576.0};
/**
* @brief Check that a ladder is strictly ascending and non-negative.
*
* The SDK places a sample with `std::lower_bound` over the edges, which
* silently misbuckets when edges repeat or descend. Checking at compile
* time makes that class of typo impossible to ship.
*
* @param ladder Bucket upper bounds to check.
* @return true when the ladder is non-empty, starts at or above zero, and
* every later edge is strictly greater than its predecessor.
*/
constexpr bool
isAscendingNonNegative(std::span<double const> ladder) noexcept
{
if (ladder.empty() || ladder.front() < 0.0)
return false;
for (std::size_t i = 1; i < ladder.size(); ++i)
{
if (!(ladder[i] > ladder[i - 1]))
return false;
}
return true;
}
static_assert(isAscendingNonNegative(kMillisecondBuckets));
static_assert(isAscendingNonNegative(kByteBuckets));
/**
* @brief Copy a ladder into the `std::vector<double>` the OTel SDK wants.
*
* @param ladder Bucket upper bounds.
* @return A vector holding the same edges in the same order.
*/
inline std::vector<double>
toVector(std::span<double const> ladder)
{
return std::vector<double>(ladder.begin(), ladder.end());
}
} // namespace xrpl::telemetry::buckets

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src/tests/libxrpl/telemetry/HistogramBuckets.cpp Normal file
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/**
* GTest unit tests for the histogram bucket ladders.
*
* These ladders decide whether a Grafana percentile panel reports a
* measurement or an artefact, and neither failure mode is visible in the
* panel itself: a quantile that falls in the `+Inf` bucket reads back as the
* second-highest edge, and one that falls inside bucket 0 is interpolated.
* Both look like plausible numbers. So the invariants are asserted here
* rather than left to review.
*
* The ladders are `constexpr`, so most of this could be `static_assert`.
* They are runtime tests as well so that a failure names which edge is
* wrong instead of only failing the compile.
*/
#include <xrpl/telemetry/HistogramBuckets.h>
#include <gtest/gtest.h>
#include <algorithm>
#include <array>
#include <cmath>
#include <cstddef>
#include <span>
#include <vector>
namespace xrpl::telemetry::buckets {
// Every ladder must be strictly ascending and non-negative. The SDK places a
// sample with std::lower_bound over the edges, so a duplicated or
// out-of-order edge silently sends samples to the wrong bucket.
class HistogramBucketsTest : public ::testing::TestWithParam<std::span<double const>>
{
};
TEST_P(HistogramBucketsTest, isStrictlyAscending)
{
auto const ladder = GetParam();
ASSERT_FALSE(ladder.empty());
for (std::size_t i = 1; i < ladder.size(); ++i)
EXPECT_LT(ladder[i - 1], ladder[i]) << "edge index " << i << " does not ascend";
}
TEST_P(HistogramBucketsTest, isNonNegativeAndFinite)
{
for (double const edge : GetParam())
{
EXPECT_GE(edge, 0.0);
EXPECT_TRUE(std::isfinite(edge)) << "edge " << edge << " is not finite";
}
}
TEST_P(HistogramBucketsTest, passesTheCompileTimeValidator)
{
EXPECT_TRUE(isAscendingNonNegative(GetParam()));
}
INSTANTIATE_TEST_SUITE_P(
AllLadders,
HistogramBucketsTest,
::testing::Values(
std::span<double const>{kMillisecondBuckets},
std::span<double const>{kByteBuckets}));
// The validator must also REJECT. A predicate that only ever returns true
// would let every ladder above pass while proving nothing.
TEST(HistogramBucketsValidator, rejectsEmptyDescendingDuplicateAndNegative)
{
EXPECT_FALSE(isAscendingNonNegative(std::span<double const>{}));
constexpr std::array descending{5.0, 1.0};
EXPECT_FALSE(isAscendingNonNegative(descending));
constexpr std::array duplicated{1.0, 1.0, 2.0};
EXPECT_FALSE(isAscendingNonNegative(duplicated));
constexpr std::array negative{-1.0, 1.0};
EXPECT_FALSE(isAscendingNonNegative(negative));
}
TEST(HistogramBucketsValidator, acceptsASingleEdgeAndALeadingZero)
{
constexpr std::array single{1.0};
EXPECT_TRUE(isAscendingNonNegative(single));
// A leading zero is legal: the GetObject charge ladder starts at 0 to
// separate the free tier from everything else.
constexpr std::array leadingZero{0.0, 100.0};
EXPECT_TRUE(isAscendingNonNegative(leadingZero));
}
TEST(HistogramBucketsRange, millisecondFloorIsOneAndCeilingCoversTheSlowestJob)
{
// beast::insight::Event rounds durations up to whole milliseconds, so 1
// is the smallest edge that can ever collect a sample.
EXPECT_EQ(kMillisecondBuckets.front(), 1.0);
// The updatepaths job type was measured averaging 59,956 ms. A 30 s
// ceiling -- the collector's top edge -- would censor it just as the old
// 5 s ceiling does, so this ladder has to reach further.
EXPECT_GE(kMillisecondBuckets.back(), 120'000.0);
}
TEST(HistogramBucketsRange, millisecondLadderClearsTheMeasuredCensoringPoint)
{
// rpc_size had 24.9% of samples above the old 5000 ceiling and
// jobq_updatepaths had 100%. A ceiling at or below 5000 reintroduces the
// exact defect this ladder exists to fix.
EXPECT_GT(kMillisecondBuckets.back(), 5'000.0);
}
TEST(HistogramBucketsRange, millisecondLadderContainsEveryRepresentableCollectorEdge)
{
// Agreement with the collector's spanmetrics ladder over the shared
// range is the invariant; edges above its 30 s top are allowed because
// jobs outlive spans. Sub-millisecond collector edges are excluded
// because Event cannot represent them. check_bucket_parity.py enforces
// this against the YAML; this test pins it for the C++ side alone so a
// local edit fails fast.
constexpr std::array collectorEdges{
1.0,
5.0,
10.0,
25.0,
50.0,
100.0,
250.0,
500.0,
1'000.0,
2'000.0,
3'000.0,
4'000.0,
5'000.0,
10'000.0,
30'000.0};
for (double const edge : collectorEdges)
{
EXPECT_NE(std::ranges::find(kMillisecondBuckets, edge), kMillisecondBuckets.end())
<< edge << " ms is a collector spanmetrics edge and must be present";
}
}
TEST(HistogramBucketsRange, millisecondLadderResolvesTheOneToFiveSecondBand)
{
// Without these the 1 s to 5 s span was one four-second-wide bucket, so
// any quantile landing inside it was interpolated across four seconds.
for (double const edge : {2'000.0, 3'000.0, 4'000.0})
{
EXPECT_NE(std::ranges::find(kMillisecondBuckets, edge), kMillisecondBuckets.end())
<< edge << " ms edge missing";
}
}
TEST(HistogramBucketsRange, byteLadderBracketsTheMeasuredResponseDistribution)
{
// Measured: mean 2131 B, half under 1 kB, three quarters under 5 kB, and
// the tail above 5 kB has a mean of at most 7538 B -- which puts p99
// near 80 kB. The floor must sit at or below the measured median region
// and the ceiling well past the p99 bound.
EXPECT_LE(kByteBuckets.front(), 512.0);
EXPECT_GE(kByteBuckets.back(), 1'048'576.0);
// Most of the resolution belongs where the distribution actually turns.
auto const withinWorkingRange =
std::ranges::count_if(kByteBuckets, [](double e) { return e >= 512.0 && e <= 65'536.0; });
EXPECT_GE(withinWorkingRange, 6) << "too little resolution between 512 B and 64 kB";
}
TEST(HistogramBucketsRange, byteAndMillisecondLaddersAreDistinct)
{
// A single shared ladder is what put a byte count on a latency scale and
// censored a quarter of its samples.
EXPECT_NE(kByteBuckets.size(), kMillisecondBuckets.size());
EXPECT_GT(kByteBuckets.back(), kMillisecondBuckets.back());
}
TEST(HistogramBucketsConvert, toVectorPreservesOrderAndSize)
{
auto const converted = toVector(kByteBuckets);
ASSERT_EQ(converted.size(), kByteBuckets.size());
EXPECT_TRUE(std::ranges::equal(converted, kByteBuckets));
}
TEST(HistogramBucketsConvert, toVectorHandlesAnEmptyLadder)
{
EXPECT_TRUE(toVector(std::span<double const>{}).empty());
}
} // namespace xrpl::telemetry::buckets