diff --git a/src/promql/src/aggregate.rs b/src/promql/src/aggregate.rs
index 115c5ac4030..d164cf3bac5 100644
--- a/src/promql/src/aggregate.rs
+++ b/src/promql/src/aggregate.rs
@@ -28,6 +28,7 @@ use datafusion::error::{DataFusionError, Result};
 use datatypes::series::MetricName;
 use datatypes::series::samples::{SampleRef, Series, SeriesSchema};
 
+use crate::go_format::format_float_f;
 use crate::grid_series::{GridSeriesBuilder, external};
 use crate::map::{drop_empty_labels, validate_float_series, validate_scalar};
 use crate::range_eval::quantile;
@@ -316,7 +317,7 @@ pub fn count_values(
                     Entry::Vacant(entry) => {
                         let mut metric = series.metric.clone();
                         metric
-                            .set(label, Arc::from(format_value(value)))
+                            .set(label, Arc::from(format_float_f(value)))
                             .map_err(external)?;
                         let metric = grouping.apply(&metric)?;
                         let index = *group_index.entry(label_key(&metric)).or_insert_with(|| {
@@ -348,57 +349,6 @@ pub fn count_values(
     Ok(output)
 }
 
-/// Go's `strconv.FormatFloat(value, 'f', -1, 64)`: the fewest significant digits that round-trip,
-/// in positional notation. When two candidates of that length are equally close, Go takes the
-/// even one while Rust's shortest formatting rounds up, so the digits are recomputed with Rust's
-/// exact formatting, which rounds half to even. The recomputed digits only count when they still
-/// parse back to `value`: just above a power of two the lower neighbour is half as far away, so
-/// the even candidate can name that neighbour, and only the other one round-trips.
-fn format_value(value: f64) -> String {
-    match value {
-        f64::INFINITY => return "+Inf".to_string(),
-        f64::NEG_INFINITY => return "-Inf".to_string(),
-        _ if value.is_nan() => return "NaN".to_string(),
-        _ => {}
-    }
-    let shortest = format!("{value:e}");
-    let mantissa = shortest.split_once('e').expect("exponent format").0;
-    let significant = mantissa.chars().filter(char::is_ascii_digit).count();
-    let exact = format!("{value:.precision$e}", precision = significant - 1);
-    let digits = if exact.parse::<f64>() == Ok(value) {
-        &exact
-    } else {
-        &shortest
-    };
-    let (mantissa, exponent) = digits.split_once('e').expect("exponent format");
-    let exponent: i64 = exponent.parse().expect("integer exponent");
-    let (sign, mantissa) = match mantissa.strip_prefix('-') {
-        Some(unsigned) => ("-", unsigned),
-        None => ("", mantissa),
-    };
-    let digits: String = mantissa.chars().filter(char::is_ascii_digit).collect();
-    // The decimal point goes after `exponent + 1` digits.
-    let point = exponent + 1;
-    let mut out = String::from(sign);
-    if point <= 0 {
-        out.push_str("0.");
-        out.extend(std::iter::repeat_n('0', (-point) as usize));
-        out.push_str(digits.trim_end_matches('0'));
-    } else if point as usize >= digits.len() {
-        out.push_str(&digits);
-        out.extend(std::iter::repeat_n('0', point as usize - digits.len()));
-    } else {
-        let (whole, fraction) = digits.split_at(point as usize);
-        out.push_str(whole);
-        let fraction = fraction.trim_end_matches('0');
-        if !fraction.is_empty() {
-            out.push('.');
-            out.push_str(fraction);
-        }
-    }
-    out
-}
-
 /// Schema validation is repeated because Series exposes its fields publicly.
 fn validate_input(schema: &SeriesSchema, input: &[Series], grouping: Grouping<'_>) -> Result<()> {
     if schema.eval_grid.is_none() {
diff --git a/src/promql/src/aggregate/tests.rs b/src/promql/src/aggregate/tests.rs
index 6b20c4c6bf8..9d71cb0ca48 100644
--- a/src/promql/src/aggregate/tests.rs
+++ b/src/promql/src/aggregate/tests.rs
@@ -907,7 +907,7 @@ fn value_labels_format_like_go() {
     for line in include_str!("format_float_cases.txt").lines() {
         let (bits, expected) = line.split_once(' ').unwrap();
         let value = f64::from_bits(u64::from_str_radix(bits, 16).unwrap());
-        assert_eq!(format_value(value), expected, "{bits}");
+        assert_eq!(crate::go_format::format_float_f(value), expected, "{bits}");
         checked += 1;
     }
     assert!(checked > 2_400, "{checked}");
@@ -916,7 +916,7 @@ fn value_labels_format_like_go() {
         (f64::INFINITY, "+Inf"),
         (f64::NEG_INFINITY, "-Inf"),
     ] {
-        assert_eq!(format_value(value), expected);
+        assert_eq!(crate::go_format::format_float_f(value), expected);
     }
 }
 
diff --git a/src/promql/src/go_format.rs b/src/promql/src/go_format.rs
new file mode 100644
index 00000000000..bf19922df25
--- /dev/null
+++ b/src/promql/src/go_format.rs
@@ -0,0 +1,129 @@
+// Copyright 2023 Greptime Team
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+//! The shortest float formats of Go's `strconv`, which Prometheus writes label values in.
+
+#[cfg(test)]
+mod tests;
+
+/// Go's `strconv.FormatFloat(value, 'f', -1, 64)`, the form of `count_values` labels.
+pub(crate) fn format_float_f(value: f64) -> String {
+    match special(value) {
+        Some(text) => text.to_string(),
+        None => {
+            let (negative, digits, point) = shortest(value);
+            positional(negative, &digits, point)
+        }
+    }
+}
+
+/// Prometheus `labels.FormatOpenMetricsFloat`, the form of `histogram_quantiles` labels: Go's
+/// shortest `'g'` format, with `.0` appended to an integer and `0`, `1` and `-1` written as
+/// `0.0`, `1.0` and `-1.0`.
+pub(crate) fn format_open_metrics_float(value: f64) -> String {
+    if value == 1.0 {
+        return "1.0".to_string();
+    }
+    if value == 0.0 {
+        return "0.0".to_string();
+    }
+    if value == -1.0 {
+        return "-1.0".to_string();
+    }
+    if let Some(text) = special(value) {
+        return text.to_string();
+    }
+    let (negative, digits, point) = shortest(value);
+    // The shortest `'g'` format switches to an exponent below 1e-4 and from 1e6 on.
+    let exponent = point - 1;
+    let mut text = if !(-4..6).contains(&exponent) {
+        let mut text = String::from(if negative { "-" } else { "" });
+        text.push_str(&digits[..1]);
+        if digits.len() > 1 {
+            text.push('.');
+            text.push_str(&digits[1..]);
+        }
+        text.push_str(&format!(
+            "e{}{:02}",
+            if exponent < 0 { '-' } else { '+' },
+            exponent.abs()
+        ));
+        text
+    } else {
+        positional(negative, &digits, point)
+    };
+    if !text.contains(['e', '.']) {
+        text.push_str(".0");
+    }
+    text
+}
+
+fn special(value: f64) -> Option<&'static str> {
+    match value {
+        f64::INFINITY => Some("+Inf"),
+        f64::NEG_INFINITY => Some("-Inf"),
+        _ if value.is_nan() => Some("NaN"),
+        _ => None,
+    }
+}
+
+/// The fewest significant digits of a finite `value` that round-trip, without trailing zeros, and
+/// the position of the decimal point among them: `value = ±0.digits × 10^point`. Zero is `"0"` at
+/// point 1.
+///
+/// When two candidates of that length are equally close, Go takes the even one while Rust's
+/// shortest formatting rounds up, so the digits are recomputed with Rust's exact formatting,
+/// which rounds half to even. The recomputed digits only count when they still parse back to
+/// `value`: just above a power of two the lower neighbour is half as far away, so the even
+/// candidate can name that neighbour, and only the other one round-trips.
+fn shortest(value: f64) -> (bool, String, i64) {
+    let shortest = format!("{value:e}");
+    let mantissa = shortest.split_once('e').expect("exponent format").0;
+    let significant = mantissa.chars().filter(char::is_ascii_digit).count();
+    let exact = format!("{value:.precision$e}", precision = significant - 1);
+    let chosen = if exact.parse::<f64>() == Ok(value) {
+        &exact
+    } else {
+        &shortest
+    };
+    let (mantissa, exponent) = chosen.split_once('e').expect("exponent format");
+    let exponent: i64 = exponent.parse().expect("integer exponent");
+    let (negative, mantissa) = match mantissa.strip_prefix('-') {
+        Some(unsigned) => (true, unsigned),
+        None => (false, mantissa),
+    };
+    let digits: String = mantissa.chars().filter(char::is_ascii_digit).collect();
+    let trimmed = digits.trim_end_matches('0');
+    let digits = if trimmed.is_empty() { "0" } else { trimmed };
+    (negative, digits.to_string(), exponent + 1)
+}
+
+/// `±0.digits × 10^point` in positional notation without trailing fractional zeros.
+fn positional(negative: bool, digits: &str, point: i64) -> String {
+    let mut out = String::from(if negative { "-" } else { "" });
+    if point <= 0 {
+        out.push_str("0.");
+        out.extend(std::iter::repeat_n('0', (-point) as usize));
+        out.push_str(digits);
+    } else if point as usize >= digits.len() {
+        out.push_str(digits);
+        out.extend(std::iter::repeat_n('0', point as usize - digits.len()));
+    } else {
+        let (whole, fraction) = digits.split_at(point as usize);
+        out.push_str(whole);
+        out.push('.');
+        out.push_str(fraction);
+    }
+    out
+}
diff --git a/src/promql/src/go_format/open_metrics_cases.txt b/src/promql/src/go_format/open_metrics_cases.txt
new file mode 100644
index 00000000000..38bb7526cfe
--- /dev/null
+++ b/src/promql/src/go_format/open_metrics_cases.txt
@@ -0,0 +1,358 @@
+0 0.0
+8000000000000000 0.0
+3ff0000000000000 1.0
+bff0000000000000 -1.0
+3fe0000000000000 0.5
+3feccccccccccccd 0.9
+3fefae147ae147ae 0.99
+3feff7ced916872b 0.999
+3f1a36e2eb1c432d 0.0001
+3ee4f8b588e368f1 1e-05
+3f201f31f46ed246 0.000123
+40f869f000000000 99999.0
+40f86a0000000000 100000.0
+412e847e00000000 999999.0
+412e848000000000 1e+06
+4132d68700000000 1.234567e+06
+444b1ae4d6e2ef50 1e+21
+3e7ad7f29abcaf48 1e-07
+4000000000000000 2.0
+c004000000000000 -2.5
+1 5e-324
+7fefffffffffffff 1.7976931348623157e+308
+7ff8000000000001 NaN
+7ff0000000000000 +Inf
+fff0000000000000 -Inf
+3e0fffffffffffff 9.313225746154784e-10
+3e10000000000000 9.313225746154785e-10
+3e10000000000001 9.313225746154787e-10
+3e1fffffffffffff 1.8626451492309568e-09
+3e20000000000000 1.862645149230957e-09
+3e20000000000001 1.8626451492309574e-09
+3e2fffffffffffff 3.7252902984619136e-09
+3e30000000000000 3.725290298461914e-09
+3e30000000000001 3.725290298461915e-09
+3e3fffffffffffff 7.450580596923827e-09
+3e40000000000000 7.450580596923828e-09
+3e40000000000001 7.45058059692383e-09
+3e4fffffffffffff 1.4901161193847655e-08
+3e50000000000000 1.4901161193847656e-08
+3e50000000000001 1.490116119384766e-08
+3e5fffffffffffff 2.980232238769531e-08
+3e60000000000000 2.9802322387695312e-08
+3e60000000000001 2.980232238769532e-08
+3e6fffffffffffff 5.960464477539062e-08
+3e70000000000000 5.960464477539063e-08
+3e70000000000001 5.960464477539064e-08
+3e7fffffffffffff 1.1920928955078124e-07
+3e80000000000000 1.1920928955078125e-07
+3e80000000000001 1.1920928955078128e-07
+3e8fffffffffffff 2.3841857910156247e-07
+3e90000000000000 2.384185791015625e-07
+3e90000000000001 2.3841857910156255e-07
+3e9fffffffffffff 4.7683715820312495e-07
+3ea0000000000000 4.76837158203125e-07
+3ea0000000000001 4.768371582031251e-07
+3eafffffffffffff 9.536743164062499e-07
+3eb0000000000000 9.5367431640625e-07
+3eb0000000000001 9.536743164062502e-07
+3ebfffffffffffff 1.9073486328124998e-06
+3ec0000000000000 1.9073486328125e-06
+3ec0000000000001 1.9073486328125004e-06
+3ecfffffffffffff 3.8146972656249996e-06
+3ed0000000000000 3.814697265625e-06
+3ed0000000000001 3.814697265625001e-06
+3edfffffffffffff 7.629394531249999e-06
+3ee0000000000000 7.62939453125e-06
+3ee0000000000001 7.629394531250002e-06
+3eefffffffffffff 1.5258789062499998e-05
+3ef0000000000000 1.52587890625e-05
+3ef0000000000001 1.5258789062500003e-05
+3effffffffffffff 3.0517578124999997e-05
+3f00000000000000 3.0517578125e-05
+3f00000000000001 3.051757812500001e-05
+3f0fffffffffffff 6.103515624999999e-05
+3f10000000000000 6.103515625e-05
+3f10000000000001 6.103515625000001e-05
+3f1fffffffffffff 0.00012207031249999999
+3f20000000000000 0.0001220703125
+3f20000000000001 0.00012207031250000003
+3f2fffffffffffff 0.00024414062499999997
+3f30000000000000 0.000244140625
+3f30000000000001 0.00024414062500000005
+3f3fffffffffffff 0.00048828124999999995
+3f40000000000000 0.00048828125
+3f40000000000001 0.0004882812500000001
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+3f5fffffffffffff 0.0019531249999999998
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+3f6fffffffffffff 0.0039062499999999996
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+3fd796b83f190476 0.36857420122038975
+3f89c820f5c353cf 0.012588746547786215
+bf1c9b582d230176 -0.00010912633595961348
+3f0b626c3f1d7427 5.223172037347622e-05
+c1b98e40b01aa47d -4.287530721040724e+08
+bf40e7ac7417bf38 -0.0005158989007991504
+c0fa23eb55ece0ea -107070.70847785813
+c144a3c813d346b5 -2.705296154885138e+06
+420cfdbc6db30e37 1.556450859838194e+10
+bffaedf1a6e222f5 -1.6830917853241634
+bdaa7d712e166bdf -1.2046261072190693e-11
+bd9166a221898f34 -3.956509186266304e-12
+41f29c7719af53ba 4.995903898957941e+09
+c257d70a133c9673 -4.0956803813035077e+11
+3e03f10e0f212551 5.803775500632443e-10
+bdd09a3e6f241c57 -6.039959833086004e-11
+3def70ff3be70cb0 2.2876625416160384e-10
+3e0b4e8e20f31127 7.947341135001321e-10
+c101aa87289cbd2c -144720.89483020583
+c1a30cf840a79c3b -1.5980854432736382e+08
+c1f2cee10365790c -5.048766518342052e+09
+4241f57b0bb131e8 1.542658517463899e+11
+41644786139efcca 1.0632240613157649e+07
+c10c4694a54af747 -231634.5807093924
+3fb93fb09f6c47ac 0.09862808124697037
+410283b654043a66 151670.7910236895
+3f955397b4926431 0.020826692959051655
+c2770eb58f0d2558 -1.584496439506334e+12
+c2574b9d7f803594 -4.0021144320083716e+11
+c1798c4033fe2656 -2.678886724954828e+07
+bdda20af6b41292d -9.505193404385634e-11
+bd9d5ac882093298 -6.6744682973325806e-12
+c10fbff5b2d5a113 -260094.71232152786
+3ea8e96f5b8e56a9 7.42431146882456e-07
+3d98fa6e04f9ce92 5.679394522162092e-12
+bfb82947a67e52b1 -0.09437988104611851
+3f819e7792417fc3 0.008603033209681038
+3ef67230b027b7e0 2.1406226560154065e-05
+410bdd8f44c9ab40 228273.908587778
+bdaddb0efd7a84af -1.3576799338601639e-11
+bfde7f03e3abff4a -0.476502392135434
+be3c374d736e41cc -6.569555391781771e-09
+bd8fd8d37b3ccebc -3.6205821004411243e-12
+bf015dbd315ae6af -3.312333698830599e-05
+bef0a23119ea0f2f -1.58630006077541e-05
+4147e80684a6bfdc 3.1334530363387894e+06
+418ffa8aa44a03a4 6.706414853613976e+07
+41b39bb8b580c8ba 3.2897246950306284e+08
+c13b6a22b648e604 -1.7966427120498428e+06
+c235fcdd893a310f -9.443669023419164e+10
+421ec18437f5198b 3.3023921661274944e+10
+bf8ae37ae39d02db -0.013129196234900877
+bef95e8cca599392 -2.4194083968604218e-05
+3dcad1bdc176163d 4.8784025588835895e-11
+3f1df0dcf9f95ede 0.00011421536191258015
+c1757d0f8a928585 -2.2532344660771865e+07
+c0a5dcfd887953e8 -2798.4951818385052
+c1ad00c4e533e9a3 -2.4329483460139188e+08
+be189b251dba77ae -1.432259747359515e-09
+c0faea4753b428d7 -110244.45793548539
+3f1888203391860a 9.358116666782087e-05
+c07eccfcba9075b0 -492.81170135907723
+c1a9bf542b517f2b -2.1598466165917334e+08
+be6f489bc56ba9c3 -5.827029585742761e-08
+c07aeee5fdde63b1 -430.9311503111922
+bdbff35e51e90a1f -2.905895413133007e-11
+c08585e696d511e1 -688.7375923772889
+3e1584aa5403510d 1.2525254861511635e-09
+425175c897a14c51 2.9996209728519244e+11
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+3ece74d4b80f63d4 3.630682329430478e-06
+be56a526ee438107 -2.108991638348143e-08
+3e7365a824bf7034 7.225981696286206e-08
+405b24ce99aa99ff 108.57510987911517
+bd8f8f5522164965 -3.5879444088569995e-12
+c11bb2a14ccdd7af -453800.3250039769
+be24ed37e9a7b972 -2.4361808796334138e-09
+bec4827163240d09 -2.444928008606154e-06
+c1c39851da458093 -6.574990605429863e+08
+3e46091f323f491a 1.0261140888290476e-08
+bdaa570dd2d99eb9 -1.197807008879946e-11
+40f265b33b61e4f4 75355.2019976562
diff --git a/src/promql/src/go_format/tests.rs b/src/promql/src/go_format/tests.rs
new file mode 100644
index 00000000000..ef7a02591a4
--- /dev/null
+++ b/src/promql/src/go_format/tests.rs
@@ -0,0 +1,29 @@
+// Copyright 2023 Greptime Team
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+use crate::go_format::format_open_metrics_float;
+
+/// Prometheus `labels.FormatOpenMetricsFloat`; `open_metrics_cases.txt` comes from promql-compat
+/// `narthex-golden-formatfloat -openmetrics` as `<hex bits> <formatted>`.
+#[test]
+fn open_metrics_floats_format_like_prometheus() {
+    let mut checked = 0;
+    for line in include_str!("open_metrics_cases.txt").lines() {
+        let (bits, expected) = line.split_once(' ').unwrap();
+        let value = f64::from_bits(u64::from_str_radix(bits, 16).unwrap());
+        assert_eq!(format_open_metrics_float(value), expected, "{bits}");
+        checked += 1;
+    }
+    assert!(checked > 300, "{checked}");
+}
diff --git a/src/promql/src/histogram_eval.rs b/src/promql/src/histogram_eval.rs
index 4b7fdba6c54..4aa577fc54a 100644
--- a/src/promql/src/histogram_eval.rs
+++ b/src/promql/src/histogram_eval.rs
@@ -40,9 +40,10 @@ use datatypes::series::time::{TimeAxis, union_positions};
 pub(crate) use aggregate::sum_or_avg;
 pub use aggregate::{HistogramAggregate, aggregate_histograms};
 
+use crate::go_format::format_open_metrics_float;
 use crate::go_sort;
 use crate::grid_series::{
-    GridSeriesBuilder, LabelSet, METADATA_LABELS, label_set, merge_same_labelset,
+    GridSeriesBuilder, LabelSet, METADATA_LABELS, external, label_set, merge_same_labelset,
 };
 
 const BUCKET_LABEL: &str = "le";
@@ -56,6 +57,30 @@ pub enum HistogramFunction {
     Fraction { lower: f64, upper: f64 },
 }
 
+/// `histogram_quantiles(v, label, q...)`: [`evaluate_histograms`] with `Quantile(q)` for each
+/// `q`, each result labelled with `label` set to `q` as Prometheus' `FormatOpenMetricsFloat`
+/// writes it. Results that then share a label set merge as [`merge_same_labelset`] does, so a
+/// repeated `q`, or a `label` that overwrites the label telling two histograms apart, fails at a
+/// point both hold.
+pub fn histogram_quantiles(
+    label: &str,
+    quantiles: &[f64],
+    schema: &Arc<SeriesSchema>,
+    series: &[Series],
+) -> Result<Vec<Series>> {
+    let mut output = Vec::new();
+    for &quantile in quantiles {
+        let value: Arc<str> = Arc::from(format_open_metrics_float(quantile));
+        for mut result in
+            evaluate_histograms(HistogramFunction::Quantile(quantile), schema, series)?
+        {
+            result.metric.set(label, value.clone()).map_err(external)?;
+            output.push(result);
+        }
+    }
+    merge_same_labelset(schema, output)
+}
+
 /// Evaluates `function` over the classic and native histograms in `series`.
 ///
 /// Float series with the same labels other than `le` form a classic histogram; a float series
diff --git a/src/promql/src/histogram_eval/quantiles_cases.txt b/src/promql/src/histogram_eval/quantiles_cases.txt
new file mode 100644
index 00000000000..0e9e1501ff8
--- /dev/null
+++ b/src/promql/src/histogram_eval/quantiles_cases.txt
@@ -0,0 +1,425 @@
+# Generated by promql-compat cmd/narthex-golden-histogram built for amd64 (Prometheus
+# v3.15.0 engine, seed 1, 30 cases per function). Each case lists its operation and
+# tab-separated arguments, the grid, the input series and either the result series or
+# the error. Points are `grid index:hex bits`.
+case quantiles	quantile	3fd0000000000000
+grid 100000 10000 2
+series __name__=a_bucket|job=x ; 0:4026000000000000 1:4018000000000000
+series __name__=a_bucket|job=x|le=abc ; 0:404f000000000000 1:4057c00000000000
+series __name__=a_bucket|job=x|le=+Inf ; 0:403c000000000000 1:403c000000000000
+series __name__=a_bucket|job=y|le=0.1 ; 0:4051500000000000 1:4053b00000000000
+series __name__=a_bucket|job=y|le=0.5 ; 0:4058e00000000000
+series __name__=a_bucket|job=y|le=1 ; 0:405d300000000000 1:4069600000000000
+series __name__=a_bucket|job=y|le=5 ; 0:4065580000000000
+series __name__=a_bucket|job=y|le=+Inf ; 0:406c280000000000 1:4074240000000000
+series __name__=b_bucket|job=z|le=0.25 ; 0:404ba00000000000 1:4040200000000000
+series __name__=b_bucket|job=z|le=2.5 ; 0:4060580000000000 1:405a100000000000
+series __name__=b_bucket|job=z|le=inf ; 0:4065680000000000 1:4065180000000000
+result job=x|quantile=0.25 ; 0:7ff8000000000001 1:7ff8000000000001
+result job=y|quantile=0.25 ; 0:3fb4d13c711ede2d 1:3fbcf601dab51b18
+result job=z|quantile=0.25 ; 0:3fc8cbdf90cbdf91 1:3fe1f00000000000
+end
+case quantiles	quantile	bfe0000000000000,3fefae147ae147ae
+grid 100000 10000 5
+series __name__=a_bucket|job=x|le=1 ; 0:4024000000000000 1:4035000000000000 2:4000000000000000 3:4028000000000000 4:7ff0000000000000
+series __name__=a_bucket|job=x|le=abc ; 1:4047800000000000 4:7ff0000000000000
+series __name__=a_bucket|job=x|le=1e400 ; 0:0 1:4053800000000000 2:4052800000000000 3:7ff8000000000001 4:7ff0000000000000
+series __name__=a_bucket|job=x|le=+Inf ; 0:4044800000000000 1:4057800000000000 3:7ff8000000000001 4:7ff0000000000000
+series __name__=a_bucket|job=y ; 0:404a400000000000 1:4041400000000000 2:4017000000000000 3:4058600000000000 4:4050d00000000000
+series __name__=a_bucket|job=y|le=0.5 ; 0:405d800000000000 1:404de00000000000 2:405a700000000000 3:405db00000000000 4:405ad00000000000
+series __name__=a_bucket|job=y|le=5 ; 2:4063c80000000000 3:406af00000000000 4:4072440000000000
+series __name__=a_bucket|job=y|le=+Inf ; 0:406fd80000000000 1:4066280000000000 3:406c180000000000 4:4074440000000000
+series __name__=b_bucket|job=y ; 0:404f800000000000 2:4058300000000000
+series __name__=b_bucket|job=y|le=0 ; 0:4061480000000000 1:4064700000000000 2:4066800000000000 3:4058600000000000 4:4051900000000000
+series __name__=b_bucket|job=y|le=1 ; 0:4069300000000000 1:406ee00000000000 2:406a600000000000 3:4066180000000000 4:405a400000000000
+series __name__=b_bucket|job=y|le=+Inf ; 0:406a680000000000 2:4072380000000000 3:406b600000000000 4:4063680000000000
+series __name__=b_bucket|job=z|le=0.25 ; 0:4014000000000000 1:4020000000000000 2:4031000000000000 3:4043800000000000 4:4028000000000000
+series __name__=b_bucket|job=z|le=2.5 ; 0:4048000000000000 1:40200000000001c2 2:4030ffffffff4515 3:4055400000000000 4:4028000000000000
+series __name__=b_bucket|job=z|le=inf ; 1:40200000000001c2 2:4030ffffffff4336 3:405c400000000000 4:4028000000000000
+error vector cannot contain metrics with the same labelset
+end
+case quantiles	job	0,bfe0000000000000
+grid 100000 10000 3
+series __name__=a_bucket|job=x|le=0.1 ; 0:403e000000000000 1:4028000000000000 2:7ff0000000000000
+series __name__=a_bucket|job=x|le=0.5 ; 0:404c800000000000 1:4041000000000000 2:7ff0000000000000
+series __name__=a_bucket|job=x|le=1 ; 0:4055400000000000 1:4045000000000000 2:7ff0000000000000
+series __name__=a_bucket|job=x|le=5 ; 0:4060000000000000 1:4056000000000000 2:7ff0000000000000
+series __name__=a_bucket|job=x|le=+Inf ; 0:7ff8000000000001 1:7ff0000000000000 2:7ff0000000000000
+series __name__=b_bucket|job=y|le=0.1 ; 0:4041800000000000 1:4038000000000000 2:4048000000000000
+series __name__=b_bucket|job=y|le=0.5 ; 0:4043000000000000 1:4037fffffffffd5c
+series __name__=b_bucket|job=y|le=1 ; 0:4054000000000000 1:4037ffffffffffff
+series __name__=b_bucket|job=y|le=5 ; 0:4053ffffffff2419 1:40380000000107e1 2:40553fffffff165b
+series __name__=b_bucket|job=y|le=+Inf ; 0:4054000000000000 2:40557fffffff165b
+series __name__=b_bucket|job=z|le=1 ; 0:0 1:0 2:0
+series __name__=b_bucket|job=z|le=1.0 ; 0:0 2:0
+series __name__=b_bucket|job=z|le=2 ; 1:1 2:1a56e1fc2f8f359
+series __name__=b_bucket|job=z|le=+Inf ; 0:1 1:1 2:0
+error vector cannot contain metrics with the same labelset
+end
+case quantiles	quantile	bfe0000000000000,bfe0000000000000
+grid 100000 10000 6
+series __name__=a_bucket|job=y|le=1 ; 0:402a000000000000 2:4010000000000000 3:4033000000000000 4:401c000000000000
+series __name__=a_bucket|job=y|le=2 ; 0:4035000000000000 1:4040ffffffff4515 2:4010000000000000 3:403d000000000000 4:401c000000000000 5:4056800000000000
+series __name__=a_bucket|job=y|le=5 ; 0:4034ffffffff191a 1:404a7fffffff4515 2:4047000000000000 4:401c000000000000 5:4058400000000000
+series __name__=b_bucket|job=z|le=1 ; 0:4057c00000000000 1:4039800000000000 3:4028800000000000 4:404f600000000000 5:4044600000000000
+series __name__=b_bucket|job=z|le=2 ; 0:4065c00000000000 1:4055d00000000000 2:4060200000000000 3:4057500000000000 4:405f300000000000 5:4059600000000000
+series __name__=b_bucket|job=z|le=5 ; 0:4070e80000000000 1:405a900000000000 2:4069e80000000000 3:4062580000000000 4:4065500000000000 5:4062780000000000
+error vector cannot contain metrics with the same labelset
+end
+case quantiles	quantile	3fd0000000000000,3ff8000000000000,3fe0000000000000
+grid 100000 10000 8
+series __name__=b_bucket|job=z ; 0:4044c00000000000 1:402e000000000000 2:4021000000000000 3:4048e00000000000 4:404ea00000000000 5:4056c00000000000 6:404ce00000000000 7:4047e00000000000
+series __name__=b_bucket|job=z|le=2.5 ; 1:403dc00000000000 2:4049000000000000 3:4050800000000000 4:4055b00000000000 5:4061300000000000 6:4055d00000000000
+series __name__=b_bucket|job=z|le=inf ; 0:4064180000000000 1:4056c00000000000 2:4060400000000000 3:4064100000000000 5:406bf00000000000 6:4057d00000000000 7:4051e00000000000
+result job=z|quantile=0.25 ; 0:7ff8000000000001 1:3ffe969696969696 2:3ffa000000000000 3:3ff851745d1745d1 4:7ff8000000000001 5:3ff04129e4129e41 6:3fe5d574a1601779 7:7ff8000000000001
+result job=z|quantile=0.5 ; 0:7ff8000000000001 1:4004000000000000 2:4004000000000000 3:4004000000000000 4:7ff8000000000001 5:40004129e4129e41 6:3ff5d574a1601779 7:7ff8000000000001
+result job=z|quantile=1.5 ; 0:7ff0000000000000 1:7ff0000000000000 2:7ff0000000000000 3:7ff0000000000000 4:7ff0000000000000 5:7ff0000000000000 6:7ff0000000000000 7:7ff0000000000000
+end
+case quantiles	quantile	3ff8000000000000,3ff0000000000000
+grid 100000 10000 5
+series __name__=a_bucket|job=x|le=+Inf ; 1:4052600000000000 3:404b000000000000 4:404fe00000000000
+series __name__=a_bucket|job=y|le=+Inf ; 1:0 2:0 3:0
+series __name__=b_bucket|job=z|le=1 ; 0:404e800000000000 1:4052000000000000 2:4029800000000000 3:4054b00000000000 4:4052900000000000
+series __name__=b_bucket|job=z|le=abc ; 2:4056900000000000 3:4062380000000000 4:405f200000000000
+series __name__=b_bucket|job=z|le=1e400 ; 0:4053900000000000 1:4062f00000000000 2:405e800000000000 3:4064380000000000
+series __name__=b_bucket|job=z|le=+Inf ; 0:4063080000000000 1:4067680000000000 2:4064480000000000 4:406ee80000000000
+result job=x|quantile=1.0 ; 1:7ff8000000000001 3:7ff8000000000001 4:7ff8000000000001
+result job=x|quantile=1.5 ; 1:7ff0000000000000 3:7ff0000000000000 4:7ff0000000000000
+result job=y|quantile=1.0 ; 1:7ff8000000000001 2:7ff8000000000001 3:7ff8000000000001
+result job=y|quantile=1.5 ; 1:7ff0000000000000 2:7ff0000000000000 3:7ff0000000000000
+result job=z|quantile=1.0 ; 0:3ff0000000000000 1:3ff0000000000000 2:3ff0000000000000 3:7ff8000000000001 4:3ff0000000000000
+result job=z|quantile=1.5 ; 0:7ff0000000000000 1:7ff0000000000000 2:7ff0000000000000 3:7ff0000000000000 4:7ff0000000000000
+end
+case quantiles	quantile	3ff0000000000000
+grid 100000 10000 2
+series __name__=a_bucket|job=x ; 0:4026000000000000 1:4040800000000000
+series __name__=a_bucket|job=x|le=0.5 ; 0:7ff8000000000001 1:7ff0000000000000
+series __name__=a_bucket|job=x|le=1 ; 0:7ff8000000000001 1:7ff8000000000001
+series __name__=a_bucket|job=x|le=5 ; 0:7ff8000000000001 1:7ff8000000000001
+series __name__=a_bucket|job=x|le=+Inf ; 0:7ff8000000000001
+result job=x|quantile=1.0 ; 0:4014000000000000 1:7ff8000000000001
+end
+case quantiles	quantile	3feccccccccccccd,3feccccccccccccd,3fe0000000000000
+grid 100000 10000 2
+series __name__=a_bucket|job=x|le=1 ; 0:4024000000000000 1:4010000000000000
+series __name__=a_bucket|job=x|le=2 ; 0:4008000000000000 1:4058800000000000
+series __name__=a_bucket|job=x|le=5 ;
+error vector cannot contain metrics with the same labelset
+end
+case quantiles	quantile	3fefae147ae147ae,3fd0000000000000,3ff0000000000000
+grid 100000 10000 7
+series __name__=a_bucket|job=x|le=+Inf ; 1:4047000000000000 2:4010000000000000 3:4024000000000000 4:4022000000000000 5:4047800000000000 6:4033000000000000
+series __name__=b_bucket|job=y|le=1 ; 0:4053000000000000 1:404d000000000000 2:4026000000000000 3:4054800000000000 4:4055400000000000 5:4050000000000000 6:403d000000000000
+series __name__=b_bucket|job=y|le=2 ; 0:4054400000000000 1:4045000000000000 2:402a000000000000 3:4032000000000000 4:403d000000000000 5:4054800000000000 6:4055400000000000
+series __name__=b_bucket|job=y|le=5 ; 0:4057800000000000 1:4056800000000000 2:4038000000000000 3:403a000000000000 4:404f800000000000 5:404a000000000000 6:4055000000000000
+result job=x|quantile=0.25 ; 1:7ff8000000000001 2:7ff8000000000001 3:7ff8000000000001 4:7ff8000000000001 5:7ff8000000000001 6:7ff8000000000001
+result job=x|quantile=0.99 ; 1:7ff8000000000001 2:7ff8000000000001 3:7ff8000000000001 4:7ff8000000000001 5:7ff8000000000001 6:7ff8000000000001
+result job=x|quantile=1.0 ; 1:7ff8000000000001 2:7ff8000000000001 3:7ff8000000000001 4:7ff8000000000001 5:7ff8000000000001 6:7ff8000000000001
+result job=y|quantile=0.25 ; 0:7ff8000000000001 1:7ff8000000000001 2:7ff8000000000001 3:7ff8000000000001 4:7ff8000000000001 5:7ff8000000000001 6:7ff8000000000001
+result job=y|quantile=0.99 ; 0:7ff8000000000001 1:7ff8000000000001 2:7ff8000000000001 3:7ff8000000000001 4:7ff8000000000001 5:7ff8000000000001 6:7ff8000000000001
+result job=y|quantile=1.0 ; 0:7ff8000000000001 1:7ff8000000000001 2:7ff8000000000001 3:7ff8000000000001 4:7ff8000000000001 5:7ff8000000000001 6:7ff8000000000001
+end
+case quantiles	quantile	0,3ff0000000000000,0
+grid 100000 10000 7
+series __name__=a_bucket|job=x|le=1 ; 0:4045000000000000 1:4034000000000000 2:403a000000000000 3:4056000000000000 5:401c000000000000
+series __name__=a_bucket|job=x|le=1.0 ; 0:4024000000000000 1:4044000000000000 2:4008000000000000 3:4054400000000000 4:4039000000000000 5:4055800000000000 6:4052c00000000000
+series __name__=a_bucket|job=x|le=2 ; 0:404b800000000000 1:4042800000000000 3:4055c00000000000 4:4018000000000000 5:404b800000000000 6:4054c00000000000
+series __name__=a_bucket|job=x|le=+Inf ; 0:4045800000000000 1:4054800000000000 2:4050400000000000 3:4043000000000000 4:4050800000000000
+series __name__=a_bucket|job=y|le=0.1 ; 0:4056900000000000 1:403bc00000000000 2:4035800000000000 3:403b400000000000 4:402f000000000000 5:4052600000000000 6:4047600000000000
+series __name__=a_bucket|job=y|le=0.5 ; 0:4063f00000000000 1:4045800000000000 2:405a100000000000 3:4057d00000000000 5:4061080000000000
+series __name__=a_bucket|job=y|le=1 ; 0:4069e00000000000 1:405ab00000000000 3:4066400000000000 6:4053200000000000
+series __name__=a_bucket|job=y|le=5 ; 0:4072180000000000 2:406b780000000000 3:406d000000000000 4:4067080000000000 5:4070700000000000 6:405a500000000000
+series __name__=a_bucket|job=y|le=+Inf ; 0:4074b00000000000 1:4067680000000000 2:4073c80000000000 3:4072340000000000 4:4069000000000000 5:4070e80000000000 6:4062c80000000000
+error vector cannot contain metrics with the same labelset
+end
+case quantiles	job	0
+grid 100000 10000 2
+series __name__=b_bucket|job=z|le=1 ; 0:4055300000000000 1:4036400000000000
+series __name__=b_bucket|job=z|le=2 ; 0:4062280000000000 1:4047800000000000
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+case quantiles	quantile	3feccccccccccccd,bfe0000000000000
+grid 100000 10000 6
+series __name__=a_bucket|job=x|le=+Inf ; 0:0 1:0 4:0 5:0
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+result job=z|quantile=0.9 ; 0:7ff8000000000001 1:7ff8000000000001 2:7ff8000000000001 3:7ff8000000000001 4:7ff8000000000001 5:7ff8000000000001
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+case quantiles	job	3fefae147ae147ae,3fefae147ae147ae
+grid 100000 10000 8
+series __name__=a_bucket|job=x ; 0:4050f00000000000 1:4047000000000000 2:4057500000000000 3:404aa00000000000 4:4025000000000000 5:4050c00000000000 6:4055500000000000 7:402b800000000000
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+error vector cannot contain metrics with the same labelset
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+case quantiles	job	7ff8000000000001
+grid 100000 10000 6
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+error vector cannot contain metrics with the same labelset
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+case quantiles	quantile	3fd0000000000000
+grid 100000 10000 2
+series __name__=a_bucket|job=x|le=-1 ; 0:4048800000000000
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+case quantiles	job	3fefae147ae147ae,3fd0000000000000
+grid 100000 10000 7
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+error vector cannot contain metrics with the same labelset
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+grid 100000 10000 2
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+grid 100000 10000 4
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+case quantiles	quantile	3fefae147ae147ae
+grid 100000 10000 6
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+case quantiles	quantile	3fefae147ae147ae
+grid 100000 10000 6
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+end
+case quantiles	quantile	7ff8000000000001
+grid 100000 10000 7
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+result job=z|quantile=NaN ; 0:7ff8000000000001 2:7ff8000000000001 4:7ff8000000000001 5:7ff8000000000001 6:7ff8000000000001
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+case quantiles	quantile	3fefae147ae147ae,3fefae147ae147ae,7ff8000000000001
+grid 100000 10000 2
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+error vector cannot contain metrics with the same labelset
+end
+case quantiles	quantile	3ff0000000000000,7ff8000000000001,3ff8000000000000
+grid 100000 10000 1
+series __name__=a_bucket|job=x|le=0.1 ; 0:403d000000000000
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+series __name__=b_bucket|job=z|le=2 ;
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+result job=x|quantile=1.0 ; 0:4014000000000000
+result job=x|quantile=1.5 ; 0:7ff0000000000000
+result job=x|quantile=NaN ; 0:7ff8000000000001
+result job=y|quantile=1.0 ; 0:3ff0000000000000
+result job=y|quantile=1.5 ; 0:7ff0000000000000
+result job=y|quantile=NaN ; 0:7ff8000000000001
+result job=z|quantile=1.0 ; 0:3ff0000000000000
+result job=z|quantile=1.5 ; 0:7ff0000000000000
+result job=z|quantile=NaN ; 0:7ff8000000000001
+end
+case quantiles	quantile	3fd0000000000000,bfe0000000000000,3fefae147ae147ae
+grid 100000 10000 6
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+series __name__=b_bucket|job=z|le=+Inf ; 0:4061000000000000 1:406df00000000000 2:4070100000000000 3:4072040000000000 4:4065280000000000 5:4064880000000000
+error vector cannot contain metrics with the same labelset
+end
+case quantiles	job	3fe0000000000000,3ff0000000000000,bfe0000000000000
+grid 100000 10000 1
+series __name__=a_bucket|job=x|le=1 ; 0:4012000000000000
+series __name__=a_bucket|job=x ; 0:403d000000000000
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+series __name__=b_bucket|job=z|le=2 ; 0:404b000000000000
+series __name__=b_bucket|job=z|le=+Inf ; 0:404b000000000000
+error vector cannot contain metrics with the same labelset
+end
+case quantiles	job	3ff8000000000000,0
+grid 100000 10000 3
+series __name__=b_bucket|job=z|le=1 ; 0:4041800000000000 1:4024000000000000 2:4000000000000000
+series __name__=b_bucket|job=z|le=abc ; 0:40417fffffff3f96 1:402e000000000000 2:4000000000000000
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+series __name__=b_bucket|job=z|le=+Inf ; 0:405dbfffffff9fcb 1:4044ffffffff191a 2:400000000000b1ae
+result job=0.0 ; 0:0 1:0 2:0
+result job=1.5 ; 0:7ff0000000000000 1:7ff0000000000000 2:7ff0000000000000
+end
+case quantiles	quantile	3ff0000000000000,3fe0000000000000,3fefae147ae147ae
+grid 100000 10000 4
+series __name__=a_bucket|job=x|le=0.1 ; 0:c014000000000000 1:4026000000000000 2:4010000000000000 3:0
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+series __name__=a_bucket|job=x|le=+Inf ; 0:7ff8000000000001 1:4060400000000000 2:4055c00000000000 3:4061000000000000
+series __name__=a_bucket|job=y|le=0.1 ; 0:0 1:0 2:0 3:0
+series __name__=a_bucket|job=y|le=0.5 ; 0:1 1:0 2:0 3:1
+series __name__=a_bucket|job=y|le=1 ; 0:1 1:0 2:0 3:1
+series __name__=a_bucket|job=y|le=5 ; 0:0 2:0 3:1
+series __name__=a_bucket|job=y|le=+Inf ; 0:0 1:0 3:0
+result job=x|quantile=0.5 ; 0:3ff0000000000000 1:3fe9bd37a6f4de9c 2:3fe9d1745d1745d2 3:400a620ae4c415ca
+result job=x|quantile=0.99 ; 0:3ff0000000000000 1:4014000000000000 2:4014000000000000 3:4014000000000000
+result job=x|quantile=1.0 ; 0:3ff0000000000000 1:4014000000000000 2:4014000000000000 3:4014000000000000
+result job=y|quantile=0.5 ; 0:7ff8000000000001 1:7ff8000000000001 2:7ff8000000000001 3:7ff8000000000001
+result job=y|quantile=0.99 ; 0:7ff8000000000001 1:7ff8000000000001 2:7ff8000000000001 3:7ff8000000000001
+result job=y|quantile=1.0 ; 0:7ff8000000000001 1:7ff8000000000001 2:7ff8000000000001 3:7ff8000000000001
+end
+case quantiles	job	3fefae147ae147ae
+grid 100000 10000 1
+series __name__=a_bucket|job=x|le=0.25 ; 0:4055e00000000000
+series __name__=a_bucket|job=x|le=2.5 ; 0:4059200000000000
+series __name__=a_bucket|job=x|le=inf ; 0:405b200000000000
+series __name__=a_bucket|job=y|le=1 ; 0:4051300000000000
+series __name__=a_bucket|job=y|le=2 ; 0:4057800000000000
+series __name__=a_bucket|job=y|le=5 ; 0:4058b00000000000
+series __name__=b_bucket|job=z|le=-1 ; 0:4051c00000000000
+series __name__=b_bucket|job=z|le=0 ; 0:4060400000000000
+series __name__=b_bucket|job=z|le=1 ; 0:4065280000000000
+series __name__=b_bucket|job=z|le=+Inf ; 0:4066880000000000
+error vector cannot contain metrics with the same labelset
+end
+case quantiles	job	3fe0000000000000,3fe0000000000000
+grid 100000 10000 5
+series __name__=a_bucket|job=x|le=0.1 ; 0:4008000000000000 3:4022000000000000 4:403a000000000000
+series __name__=a_bucket|job=x|le=0.5 ; 0:4032000000000000 1:4053400000000000 3:40220000000001fa 4:4040000000000000
+series __name__=a_bucket|job=x|le=1 ; 0:40320000000001fa 1:40533fffffff2c58 2:404200000000c5ea 3:404c80000000007e
+series __name__=a_bucket|job=x|le=5 ; 0:40320000000003f4 2:4042000000018bd4 4:4054000000000000
+series __name__=a_bucket|job=x|le=+Inf ; 1:40533fffffff2c57 2:40420000000251be 3:404c7ffffffd8dc6 4:4053ffffffff2419
+series __name__=b_bucket|job=y|le=1 ; 0:4050c00000000000 2:4055500000000000 3:4044c00000000000 4:4047000000000000
+series __name__=b_bucket|job=y|le=1.0 ; 0:405b000000000000 1:405f400000000000 2:4066c00000000000 3:4054500000000000 4:404b800000000000
+series __name__=b_bucket|job=y|le=2 ; 0:405e400000000000 1:406a500000000000 2:406b980000000000 4:405d100000000000
+series __name__=b_bucket|job=y|le=+Inf ; 0:4060b80000000000 1:4072180000000000 2:406e400000000000 3:4067500000000000 4:4067580000000000
+series __name__=b_bucket|job=z|le=-1 ; 0:0 1:0 2:0 3:0 4:0
+series __name__=b_bucket|job=z|le=0 ; 0:1 1:0 3:1a56e1fc2f8f359 4:0
+series __name__=b_bucket|job=z ; 0:1 1:0 2:1a56e1fc2f8f359 3:0 4:0
+series __name__=b_bucket|job=z|le=+Inf ; 0:0 1:1 3:0
+error vector cannot contain metrics with the same labelset
+end
+case quantiles	quantile	bfe0000000000000,0
+grid 100000 10000 8
+series __name__=a_bucket|job=x|le=1 ; 0:0 1:0 5:0 6:0
+series __name__=a_bucket|job=x ; 0:1a56e1fc2f8f359 1:0 2:0 3:0 4:0 5:0 6:0 7:1
+series __name__=a_bucket|job=x|le=2 ; 0:1 1:1a56e1fc2f8f359 2:0 3:1 4:0 5:0 6:1a56e1fc2f8f359 7:0
+series __name__=a_bucket|job=x|le=+Inf ; 0:0 1:0 2:1 3:0 4:1a56e1fc2f8f359 5:0 6:0 7:0
+series __name__=a_bucket|job=y|le=0.1 ; 0:403b000000000000 1:4040000000000000 2:4018000000000000 3:4008000000000000 4:4042000000000000 5:403c000000000000 6:4045800000000000 7:4032000000000000
+series __name__=a_bucket|job=y|le=0.5 ; 0:4052000000000000 1:404b800000000000 2:4017fffffffffd5c 3:4008000000000000 5:403c000000000000 6:4045800000000000 7:4032000000000000
+series __name__=a_bucket|job=y ; 0:405bc00000000000 1:404b800000012e5e 2:4017fffffffef57a 3:4030000000000000 4:4051400000000000 7:4045000000000000
+series __name__=a_bucket|job=y|le=+Inf ; 0:4061c0000000c32a 1:404e000000012e5e 2:402ffffffffef6cc 3:402ffffffffea028 4:405ac00000000000 5:40587fffffffff3b 6:40458000000004ba
+series __name__=b_bucket|job=z ; 0:4000000000000000 1:4043000000000000 2:402a000000000000 3:403c000000000000 4:4043800000000000 5:4043800000000000 6:4020000000000000 7:0
+series __name__=b_bucket|job=z|le=1.0 ; 0:4014000000000000 1:4050400000000000 2:402a000000000000 3:4046000000000000 4:4049800000000000 5:4055c00000000000 6:4039000000000000 7:403c000000000000
+series __name__=b_bucket|job=z|le=2 ; 0:4018000000000000 1:4056400000000000 2:402a0000000002db 3:4046000000000000 4:40497ffffffee7a0 5:405f800000000000 6:4038fffffffeed1f 7:403c0000000133dd
+series __name__=b_bucket|job=z|le=+Inf ; 0:4047800000000000 1:405640000000f4a4 2:40418000000000b7 3:404600000000f1e4 4:40497ffffffee7a0 5:405f800000000376 6:4038fffffffeed1f 7:403c000000000000
+result job=x|quantile=-0.5 ; 0:fff0000000000000 1:fff0000000000000 2:fff0000000000000 3:fff0000000000000 4:fff0000000000000 5:fff0000000000000 6:fff0000000000000 7:fff0000000000000
+result job=x|quantile=0.0 ; 0:7ff8000000000001 1:fff8000000000000 2:7ff8000000000001 3:0 4:fff8000000000000 5:7ff8000000000001 6:fff8000000000000 7:7ff8000000000001
+result job=y|quantile=-0.5 ; 0:fff0000000000000 1:fff0000000000000 2:fff0000000000000 3:fff0000000000000 4:fff0000000000000 5:fff0000000000000 6:fff0000000000000 7:fff0000000000000
+result job=y|quantile=0.0 ; 0:0 1:0 2:0 3:0 4:0 5:0 6:0 7:7ff8000000000001
+result job=z|quantile=-0.5 ; 0:fff0000000000000 1:fff0000000000000 2:fff0000000000000 3:fff0000000000000 4:fff0000000000000 5:fff0000000000000 6:fff0000000000000 7:fff0000000000000
+result job=z|quantile=0.0 ; 0:0 1:0 2:0 3:0 4:0 5:0 6:0 7:0
+end
diff --git a/src/promql/src/histogram_eval/tests.rs b/src/promql/src/histogram_eval/tests.rs
index 8b815b0a49d..2452cffe5b7 100644
--- a/src/promql/src/histogram_eval/tests.rs
+++ b/src/promql/src/histogram_eval/tests.rs
@@ -22,7 +22,7 @@ use datatypes::series::time::{EvalGrid, GridRange, GridSpec, TimeAxis};
 use super::float_histogram::{AddOutcome, FloatHistogram};
 use super::{
     HistogramAggregate, HistogramFunction, NativeHistogramFunction, aggregate_histograms,
-    evaluate_histograms, map_native_histogram,
+    evaluate_histograms, histogram_quantiles, map_native_histogram,
 };
 use crate::grid_series::merge_same_labelset;
 use crate::grid_series::reference::{
@@ -127,6 +127,30 @@ fn classic_histograms_match_prometheus() {
     assert!(failed > 20, "{failed} failed");
 }
 
+/// `histogram_quantiles` over classic histograms, evaluated by the Prometheus v3.15.0 engine with
+/// experimental functions enabled (promql-compat `narthex-golden-histogram -quantiles`).
+#[test]
+fn histogram_quantiles_match_prometheus() {
+    let cases = golden_cases(include_str!("quantiles_cases.txt"));
+    assert_eq!(cases.len(), 30);
+    let mut failed = 0;
+    for case in &cases {
+        let schema = schema(case);
+        let input: Vec<_> = case
+            .inputs
+            .iter()
+            .filter_map(|(labels, points)| mixed_series(&schema, labels, points, &BTreeMap::new()))
+            .collect();
+        let quantiles: Vec<f64> = case.spec[2]
+            .split(',')
+            .map(|bits| f64::from_bits(u64::from_str_radix(bits, 16).unwrap()))
+            .collect();
+        let actual = histogram_quantiles(&case.spec[1], &quantiles, &schema, &input);
+        failed += usize::from(assert_outcome(case, actual, &case.outcome));
+    }
+    assert!(failed > 5, "{failed} failed");
+}
+
 #[test]
 fn native_histograms_match_prometheus() {
     let cases = golden_cases(include_str!("native_cases.txt"));
diff --git a/src/promql/src/lib.rs b/src/promql/src/lib.rs
index c9ee89ff62c..433717d5502 100644
--- a/src/promql/src/lib.rs
+++ b/src/promql/src/lib.rs
@@ -17,6 +17,7 @@ pub mod error;
 pub mod evaluated;
 pub mod extension_plan;
 pub mod functions;
+mod go_format;
 mod go_sort;
 #[cfg(test)]
 mod golden;