[pkg/translator/prometheusremotewrite] Improve performance of convers…

…ion to Prometheus remote write format (#24288) **Description:** This PR improves the performance of the conversion from OpenTelemetry to Prometheus remote write. It reduces latency and memory utilisation by up to 70% in some cases, particularly those involving histograms, summaries or series with many labels. <details> <summary>Benchmark results</summary> ``` goos: darwin goarch: arm64 pkg: github.com/open-telemetry/opentelemetry-collector-contrib/pkg/translator/prometheusremotewrite │ before.txt │ after.txt │ │ sec/op │ sec/op vs base │ FromMetrics/resource_attribute_count:_0/histogram_count:_0/non-histogram_count:_1000/labels_per_metric:_2/exemplars_per_series:_0-10 2.150m ± 1% 1.987m ± 1% -7.57% (p=0.002 n=6) FromMetrics/resource_attribute_count:_0/histogram_count:_0/non-histogram_count:_1000/labels_per_metric:_2/exemplars_per_series:_5-10 4.061m ± 1% 3.980m ± 2% -1.98% (p=0.002 n=6) FromMetrics/resource_attribute_count:_0/histogram_count:_0/non-histogram_count:_1000/labels_per_metric:_2/exemplars_per_series:_10-10 5.570m ± 4% 5.443m ± 1% -2.28% (p=0.002 n=6) FromMetrics/resource_attribute_count:_0/histogram_count:_0/non-histogram_count:_1000/labels_per_metric:_20/exemplars_per_series:_0-10 16.29m ± 2% 13.04m ± 1% -19.96% (p=0.002 n=6) FromMetrics/resource_attribute_count:_0/histogram_count:_0/non-histogram_count:_1000/labels_per_metric:_20/exemplars_per_series:_5-10 18.14m ± 1% 14.98m ± 1% -17.41% (p=0.002 n=6) FromMetrics/resource_attribute_count:_0/histogram_count:_0/non-histogram_count:_1000/labels_per_metric:_20/exemplars_per_series:_10-10 19.51m ± 1% 16.36m ± 1% -16.11% (p=0.002 n=6) FromMetrics/resource_attribute_count:_0/histogram_count:_1000/non-histogram_count:_0/labels_per_metric:_2/exemplars_per_series:_0-10 10.568m ± 1% 6.405m ± 1% -39.39% (p=0.002 n=6) FromMetrics/resource_attribute_count:_0/histogram_count:_1000/non-histogram_count:_0/labels_per_metric:_2/exemplars_per_series:_5-10 14.27m ± 1% 10.25m ± 0% -28.18% (p=0.002 n=6) FromMetrics/resource_attribute_count:_0/histogram_count:_1000/non-histogram_count:_0/labels_per_metric:_2/exemplars_per_series:_10-10 16.70m ± 1% 12.75m ± 1% -23.66% (p=0.002 n=6) FromMetrics/resource_attribute_count:_0/histogram_count:_1000/non-histogram_count:_0/labels_per_metric:_20/exemplars_per_series:_0-10 71.82m ± 0% 21.41m ± 1% -70.19% (p=0.002 n=6) FromMetrics/resource_attribute_count:_0/histogram_count:_1000/non-histogram_count:_0/labels_per_metric:_20/exemplars_per_series:_5-10 76.46m ± 1% 25.36m ± 1% -66.84% (p=0.002 n=6) FromMetrics/resource_attribute_count:_0/histogram_count:_1000/non-histogram_count:_0/labels_per_metric:_20/exemplars_per_series:_10-10 78.24m ± 0% 28.07m ± 0% -64.12% (p=0.002 n=6) FromMetrics/resource_attribute_count:_0/histogram_count:_1000/non-histogram_count:_1000/labels_per_metric:_2/exemplars_per_series:_0-10 12.180m ± 1% 7.912m ± 1% -35.04% (p=0.002 n=6) FromMetrics/resource_attribute_count:_0/histogram_count:_1000/non-histogram_count:_1000/labels_per_metric:_2/exemplars_per_series:_5-10 17.38m ± 0% 13.26m ± 0% -23.71% (p=0.002 n=6) FromMetrics/resource_attribute_count:_0/histogram_count:_1000/non-histogram_count:_1000/labels_per_metric:_2/exemplars_per_series:_10-10 21.14m ± 1% 17.07m ± 1% -19.25% (p=0.002 n=6) FromMetrics/resource_attribute_count:_0/histogram_count:_1000/non-histogram_count:_1000/labels_per_metric:_20/exemplars_per_series:_0-10 86.62m ± 3% 33.35m ± 2% -61.50% (p=0.002 n=6) FromMetrics/resource_attribute_count:_0/histogram_count:_1000/non-histogram_count:_1000/labels_per_metric:_20/exemplars_per_series:_5-10 91.72m ± 1% 38.95m ± 1% -57.53% (p=0.002 n=6) FromMetrics/resource_attribute_count:_0/histogram_count:_1000/non-histogram_count:_1000/labels_per_metric:_20/exemplars_per_series:_10-10 95.29m ± 1% 43.16m ± 36% -54.70% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_0/non-histogram_count:_0/labels_per_metric:_2/exemplars_per_series:_0-10 1.338µ ± 1% 1.205µ ± 1% -9.94% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_0/non-histogram_count:_0/labels_per_metric:_2/exemplars_per_series:_5-10 1.334µ ± 2% 1.225µ ± 2% -8.17% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_0/non-histogram_count:_0/labels_per_metric:_2/exemplars_per_series:_10-10 1.328µ ± 1% 1.218µ ± 8% -8.25% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_0/non-histogram_count:_0/labels_per_metric:_20/exemplars_per_series:_0-10 1.326µ ± 0% 1.240µ ± 4% -6.49% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_0/non-histogram_count:_0/labels_per_metric:_20/exemplars_per_series:_5-10 1.326µ ± 1% 1.205µ ± 1% -9.13% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_0/non-histogram_count:_0/labels_per_metric:_20/exemplars_per_series:_10-10 1.327µ ± 0% 1.215µ ± 2% -8.48% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_0/non-histogram_count:_1000/labels_per_metric:_2/exemplars_per_series:_0-10 2.144m ± 1% 2.008m ± 1% -6.37% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_0/non-histogram_count:_1000/labels_per_metric:_2/exemplars_per_series:_5-10 4.094m ± 1% 3.969m ± 1% -3.06% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_0/non-histogram_count:_1000/labels_per_metric:_2/exemplars_per_series:_10-10 5.595m ± 1% 5.465m ± 1% -2.33% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_0/non-histogram_count:_1000/labels_per_metric:_20/exemplars_per_series:_0-10 16.34m ± 1% 13.02m ± 0% -20.28% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_0/non-histogram_count:_1000/labels_per_metric:_20/exemplars_per_series:_5-10 18.27m ± 2% 14.99m ± 0% -17.95% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_0/non-histogram_count:_1000/labels_per_metric:_20/exemplars_per_series:_10-10 19.49m ± 1% 16.38m ± 0% -15.95% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_1000/non-histogram_count:_0/labels_per_metric:_2/exemplars_per_series:_0-10 10.624m ± 2% 6.389m ± 0% -39.86% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_1000/non-histogram_count:_0/labels_per_metric:_2/exemplars_per_series:_5-10 14.28m ± 1% 10.26m ± 0% -28.17% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_1000/non-histogram_count:_0/labels_per_metric:_2/exemplars_per_series:_10-10 16.80m ± 2% 12.75m ± 0% -24.11% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_1000/non-histogram_count:_0/labels_per_metric:_20/exemplars_per_series:_0-10 72.25m ± 1% 21.52m ± 1% -70.22% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_1000/non-histogram_count:_0/labels_per_metric:_20/exemplars_per_series:_5-10 76.34m ± 1% 25.29m ± 1% -66.87% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_1000/non-histogram_count:_0/labels_per_metric:_20/exemplars_per_series:_10-10 78.38m ± 1% 28.11m ± 1% -64.13% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_1000/non-histogram_count:_1000/labels_per_metric:_2/exemplars_per_series:_0-10 12.452m ± 1% 7.930m ± 1% -36.32% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_1000/non-histogram_count:_1000/labels_per_metric:_2/exemplars_per_series:_5-10 17.72m ± 3% 13.29m ± 2% -25.00% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_1000/non-histogram_count:_1000/labels_per_metric:_2/exemplars_per_series:_10-10 21.57m ± 2% 17.13m ± 2% -20.57% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_1000/non-histogram_count:_1000/labels_per_metric:_20/exemplars_per_series:_0-10 87.41m ± 1% 33.19m ± 1% -62.03% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_1000/non-histogram_count:_1000/labels_per_metric:_20/exemplars_per_series:_5-10 92.93m ± 1% 38.91m ± 1% -58.13% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_1000/non-histogram_count:_1000/labels_per_metric:_20/exemplars_per_series:_10-10 95.96m ± 0% 42.89m ± 1% -55.30% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_0/non-histogram_count:_0/labels_per_metric:_2/exemplars_per_series:_0-10 16.58µ ± 1% 13.71µ ± 1% -17.35% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_0/non-histogram_count:_0/labels_per_metric:_2/exemplars_per_series:_5-10 17.06µ ± 4% 13.69µ ± 0% -19.78% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_0/non-histogram_count:_0/labels_per_metric:_2/exemplars_per_series:_10-10 16.96µ ± 2% 13.71µ ± 0% -19.18% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_0/non-histogram_count:_0/labels_per_metric:_20/exemplars_per_series:_0-10 16.62µ ± 1% 13.71µ ± 0% -17.49% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_0/non-histogram_count:_0/labels_per_metric:_20/exemplars_per_series:_5-10 16.64µ ± 2% 13.72µ ± 6% -17.51% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_0/non-histogram_count:_0/labels_per_metric:_20/exemplars_per_series:_10-10 16.55µ ± 1% 13.73µ ± 0% -17.04% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_0/non-histogram_count:_1000/labels_per_metric:_2/exemplars_per_series:_0-10 2.220m ± 0% 2.148m ± 0% -3.23% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_0/non-histogram_count:_1000/labels_per_metric:_2/exemplars_per_series:_5-10 4.236m ± 1% 4.119m ± 1% -2.76% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_0/non-histogram_count:_1000/labels_per_metric:_2/exemplars_per_series:_10-10 5.705m ± 0% 5.605m ± 0% -1.74% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_0/non-histogram_count:_1000/labels_per_metric:_20/exemplars_per_series:_0-10 16.49m ± 0% 13.20m ± 0% -19.96% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_0/non-histogram_count:_1000/labels_per_metric:_20/exemplars_per_series:_5-10 18.43m ± 0% 15.13m ± 0% -17.88% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_0/non-histogram_count:_1000/labels_per_metric:_20/exemplars_per_series:_10-10 19.72m ± 1% 16.52m ± 1% -16.25% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_1000/non-histogram_count:_0/labels_per_metric:_2/exemplars_per_series:_0-10 11.039m ± 1% 6.469m ± 1% -41.40% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_1000/non-histogram_count:_0/labels_per_metric:_2/exemplars_per_series:_5-10 14.71m ± 1% 10.33m ± 0% -29.80% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_1000/non-histogram_count:_0/labels_per_metric:_2/exemplars_per_series:_10-10 17.18m ± 1% 12.82m ± 1% -25.37% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_1000/non-histogram_count:_0/labels_per_metric:_20/exemplars_per_series:_0-10 73.12m ± 1% 21.63m ± 1% -70.41% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_1000/non-histogram_count:_0/labels_per_metric:_20/exemplars_per_series:_5-10 77.44m ± 2% 25.49m ± 1% -67.08% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_1000/non-histogram_count:_0/labels_per_metric:_20/exemplars_per_series:_10-10 79.62m ± 1% 28.22m ± 0% -64.56% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_1000/non-histogram_count:_1000/labels_per_metric:_2/exemplars_per_series:_0-10 12.979m ± 1% 8.137m ± 1% -37.31% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_1000/non-histogram_count:_1000/labels_per_metric:_2/exemplars_per_series:_5-10 18.09m ± 1% 13.53m ± 1% -25.23% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_1000/non-histogram_count:_1000/labels_per_metric:_2/exemplars_per_series:_10-10 21.86m ± 1% 17.35m ± 0% -20.64% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_1000/non-histogram_count:_1000/labels_per_metric:_20/exemplars_per_series:_0-10 87.53m ± 2% 33.70m ± 1% -61.50% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_1000/non-histogram_count:_1000/labels_per_metric:_20/exemplars_per_series:_5-10 93.54m ± 0% 39.12m ± 0% -58.18% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_1000/non-histogram_count:_1000/labels_per_metric:_20/exemplars_per_series:_10-10 97.08m ± 0% 43.04m ± 1% -55.66% (p=0.002 n=6) geomean 4.704m 3.076m -34.61% │ before.txt │ after.txt │ │ B/op │ B/op vs base │ FromMetrics/resource_attribute_count:_0/histogram_count:_0/non-histogram_count:_1000/labels_per_metric:_2/exemplars_per_series:_0-10 1.797Mi ± 0% 1.583Mi ± 0% -11.90% (p=0.002 n=6) FromMetrics/resource_attribute_count:_0/histogram_count:_0/non-histogram_count:_1000/labels_per_metric:_2/exemplars_per_series:_5-10 4.583Mi ± 0% 4.369Mi ± 0% -4.67% (p=0.002 n=6) FromMetrics/resource_attribute_count:_0/histogram_count:_0/non-histogram_count:_1000/labels_per_metric:_2/exemplars_per_series:_10-10 7.436Mi ± 0% 7.221Mi ± 0% -2.88% (p=0.002 n=6) FromMetrics/resource_attribute_count:_0/histogram_count:_0/non-histogram_count:_1000/labels_per_metric:_20/exemplars_per_series:_0-10 17.26Mi ± 0% 10.15Mi ± 0% -41.21% (p=0.002 n=6) FromMetrics/resource_attribute_count:_0/histogram_count:_0/non-histogram_count:_1000/labels_per_metric:_20/exemplars_per_series:_5-10 20.08Mi ± 0% 12.96Mi ± 0% -35.46% (p=0.002 n=6) FromMetrics/resource_attribute_count:_0/histogram_count:_0/non-histogram_count:_1000/labels_per_metric:_20/exemplars_per_series:_10-10 22.97Mi ± 0% 15.84Mi ± 0% -31.02% (p=0.002 n=6) FromMetrics/resource_attribute_count:_0/histogram_count:_1000/non-histogram_count:_0/labels_per_metric:_2/exemplars_per_series:_0-10 9.241Mi ± 0% 6.583Mi ± 0% -28.76% (p=0.002 n=6) FromMetrics/resource_attribute_count:_0/histogram_count:_1000/non-histogram_count:_0/labels_per_metric:_2/exemplars_per_series:_5-10 12.73Mi ± 0% 10.07Mi ± 0% -20.90% (p=0.002 n=6) FromMetrics/resource_attribute_count:_0/histogram_count:_1000/non-histogram_count:_0/labels_per_metric:_2/exemplars_per_series:_10-10 16.32Mi ± 0% 13.66Mi ± 0% -16.32% (p=0.002 n=6) FromMetrics/resource_attribute_count:_0/histogram_count:_1000/non-histogram_count:_0/labels_per_metric:_20/exemplars_per_series:_0-10 78.03Mi ± 0% 23.99Mi ± 0% -69.26% (p=0.002 n=6) FromMetrics/resource_attribute_count:_0/histogram_count:_1000/non-histogram_count:_0/labels_per_metric:_20/exemplars_per_series:_5-10 81.57Mi ± 0% 27.49Mi ± 0% -66.30% (p=0.002 n=6) FromMetrics/resource_attribute_count:_0/histogram_count:_1000/non-histogram_count:_0/labels_per_metric:_20/exemplars_per_series:_10-10 85.26Mi ± 0% 31.10Mi ± 0% -63.52% (p=0.002 n=6) FromMetrics/resource_attribute_count:_0/histogram_count:_1000/non-histogram_count:_1000/labels_per_metric:_2/exemplars_per_series:_0-10 10.821Mi ± 0% 7.946Mi ± 0% -26.56% (p=0.002 n=6) FromMetrics/resource_attribute_count:_0/histogram_count:_1000/non-histogram_count:_1000/labels_per_metric:_2/exemplars_per_series:_5-10 17.12Mi ± 0% 14.24Mi ± 0% -16.85% (p=0.002 n=6) FromMetrics/resource_attribute_count:_0/histogram_count:_1000/non-histogram_count:_1000/labels_per_metric:_2/exemplars_per_series:_10-10 23.61Mi ± 0% 20.71Mi ± 0% -12.27% (p=0.002 n=6) FromMetrics/resource_attribute_count:_0/histogram_count:_1000/non-histogram_count:_1000/labels_per_metric:_20/exemplars_per_series:_0-10 95.50Mi ± 0% 34.07Mi ± 0% -64.33% (p=0.002 n=6) FromMetrics/resource_attribute_count:_0/histogram_count:_1000/non-histogram_count:_1000/labels_per_metric:_20/exemplars_per_series:_5-10 102.08Mi ± 0% 40.46Mi ± 0% -60.36% (p=0.002 n=6) FromMetrics/resource_attribute_count:_0/histogram_count:_1000/non-histogram_count:_1000/labels_per_metric:_20/exemplars_per_series:_10-10 108.76Mi ± 0% 47.06Mi ± 1% -56.73% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_0/non-histogram_count:_0/labels_per_metric:_2/exemplars_per_series:_0-10 1.922Ki ± 0% 1.586Ki ± 0% -17.48% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_0/non-histogram_count:_0/labels_per_metric:_2/exemplars_per_series:_5-10 1.922Ki ± 0% 1.586Ki ± 0% -17.48% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_0/non-histogram_count:_0/labels_per_metric:_2/exemplars_per_series:_10-10 1.922Ki ± 0% 1.586Ki ± 0% -17.48% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_0/non-histogram_count:_0/labels_per_metric:_20/exemplars_per_series:_0-10 1.922Ki ± 0% 1.586Ki ± 0% -17.48% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_0/non-histogram_count:_0/labels_per_metric:_20/exemplars_per_series:_5-10 1.922Ki ± 0% 1.586Ki ± 0% -17.48% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_0/non-histogram_count:_0/labels_per_metric:_20/exemplars_per_series:_10-10 1.922Ki ± 0% 1.586Ki ± 0% -17.48% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_0/non-histogram_count:_1000/labels_per_metric:_2/exemplars_per_series:_0-10 1.799Mi ± 0% 1.585Mi ± 0% -11.91% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_0/non-histogram_count:_1000/labels_per_metric:_2/exemplars_per_series:_5-10 4.585Mi ± 0% 4.370Mi ± 0% -4.68% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_0/non-histogram_count:_1000/labels_per_metric:_2/exemplars_per_series:_10-10 7.438Mi ± 0% 7.223Mi ± 0% -2.89% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_0/non-histogram_count:_1000/labels_per_metric:_20/exemplars_per_series:_0-10 17.26Mi ± 0% 10.15Mi ± 0% -41.20% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_0/non-histogram_count:_1000/labels_per_metric:_20/exemplars_per_series:_5-10 20.08Mi ± 0% 12.96Mi ± 0% -35.45% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_0/non-histogram_count:_1000/labels_per_metric:_20/exemplars_per_series:_10-10 22.97Mi ± 0% 15.84Mi ± 0% -31.02% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_1000/non-histogram_count:_0/labels_per_metric:_2/exemplars_per_series:_0-10 9.243Mi ± 0% 6.584Mi ± 0% -28.76% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_1000/non-histogram_count:_0/labels_per_metric:_2/exemplars_per_series:_5-10 12.73Mi ± 0% 10.07Mi ± 0% -20.90% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_1000/non-histogram_count:_0/labels_per_metric:_2/exemplars_per_series:_10-10 16.32Mi ± 0% 13.66Mi ± 0% -16.33% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_1000/non-histogram_count:_0/labels_per_metric:_20/exemplars_per_series:_0-10 78.03Mi ± 0% 23.99Mi ± 0% -69.26% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_1000/non-histogram_count:_0/labels_per_metric:_20/exemplars_per_series:_5-10 81.58Mi ± 0% 27.50Mi ± 0% -66.30% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_1000/non-histogram_count:_0/labels_per_metric:_20/exemplars_per_series:_10-10 85.27Mi ± 0% 31.10Mi ± 0% -63.52% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_1000/non-histogram_count:_1000/labels_per_metric:_2/exemplars_per_series:_0-10 10.825Mi ± 0% 7.948Mi ± 0% -26.58% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_1000/non-histogram_count:_1000/labels_per_metric:_2/exemplars_per_series:_5-10 17.13Mi ± 0% 14.24Mi ± 0% -16.86% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_1000/non-histogram_count:_1000/labels_per_metric:_2/exemplars_per_series:_10-10 23.61Mi ± 0% 20.71Mi ± 0% -12.28% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_1000/non-histogram_count:_1000/labels_per_metric:_20/exemplars_per_series:_0-10 95.48Mi ± 0% 34.07Mi ± 0% -64.32% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_1000/non-histogram_count:_1000/labels_per_metric:_20/exemplars_per_series:_5-10 102.08Mi ± 0% 40.46Mi ± 0% -60.36% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_1000/non-histogram_count:_1000/labels_per_metric:_20/exemplars_per_series:_10-10 108.78Mi ± 0% 47.05Mi ± 0% -56.75% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_0/non-histogram_count:_0/labels_per_metric:_2/exemplars_per_series:_0-10 24.01Ki ± 0% 13.91Ki ± 0% -42.06% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_0/non-histogram_count:_0/labels_per_metric:_2/exemplars_per_series:_5-10 24.02Ki ± 0% 13.91Ki ± 0% -42.08% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_0/non-histogram_count:_0/labels_per_metric:_2/exemplars_per_series:_10-10 24.01Ki ± 0% 13.91Ki ± 0% -42.07% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_0/non-histogram_count:_0/labels_per_metric:_20/exemplars_per_series:_0-10 24.02Ki ± 0% 13.91Ki ± 0% -42.07% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_0/non-histogram_count:_0/labels_per_metric:_20/exemplars_per_series:_5-10 24.02Ki ± 0% 13.91Ki ± 0% -42.07% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_0/non-histogram_count:_0/labels_per_metric:_20/exemplars_per_series:_10-10 24.02Ki ± 0% 13.91Ki ± 0% -42.08% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_0/non-histogram_count:_1000/labels_per_metric:_2/exemplars_per_series:_0-10 1.821Mi ± 0% 1.597Mi ± 0% -12.29% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_0/non-histogram_count:_1000/labels_per_metric:_2/exemplars_per_series:_5-10 4.607Mi ± 0% 4.383Mi ± 0% -4.86% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_0/non-histogram_count:_1000/labels_per_metric:_2/exemplars_per_series:_10-10 7.460Mi ± 0% 7.236Mi ± 0% -3.01% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_0/non-histogram_count:_1000/labels_per_metric:_20/exemplars_per_series:_0-10 17.28Mi ± 0% 10.16Mi ± 0% -41.21% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_0/non-histogram_count:_1000/labels_per_metric:_20/exemplars_per_series:_5-10 20.11Mi ± 0% 12.98Mi ± 0% -35.46% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_0/non-histogram_count:_1000/labels_per_metric:_20/exemplars_per_series:_10-10 22.99Mi ± 0% 15.86Mi ± 0% -31.02% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_1000/non-histogram_count:_0/labels_per_metric:_2/exemplars_per_series:_0-10 9.264Mi ± 0% 6.596Mi ± 0% -28.80% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_1000/non-histogram_count:_0/labels_per_metric:_2/exemplars_per_series:_5-10 12.75Mi ± 0% 10.08Mi ± 0% -20.94% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_1000/non-histogram_count:_0/labels_per_metric:_2/exemplars_per_series:_10-10 16.35Mi ± 0% 13.67Mi ± 0% -16.37% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_1000/non-histogram_count:_0/labels_per_metric:_20/exemplars_per_series:_0-10 78.05Mi ± 0% 24.00Mi ± 0% -69.25% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_1000/non-histogram_count:_0/labels_per_metric:_20/exemplars_per_series:_5-10 81.63Mi ± 0% 27.51Mi ± 0% -66.30% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_1000/non-histogram_count:_0/labels_per_metric:_20/exemplars_per_series:_10-10 85.29Mi ± 0% 31.11Mi ± 0% -63.52% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_1000/non-histogram_count:_1000/labels_per_metric:_2/exemplars_per_series:_0-10 10.847Mi ± 0% 7.960Mi ± 0% -26.61% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_1000/non-histogram_count:_1000/labels_per_metric:_2/exemplars_per_series:_5-10 17.15Mi ± 0% 14.25Mi ± 0% -16.89% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_1000/non-histogram_count:_1000/labels_per_metric:_2/exemplars_per_series:_10-10 23.64Mi ± 0% 20.73Mi ± 0% -12.31% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_1000/non-histogram_count:_1000/labels_per_metric:_20/exemplars_per_series:_0-10 95.52Mi ± 0% 34.08Mi ± 0% -64.32% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_1000/non-histogram_count:_1000/labels_per_metric:_20/exemplars_per_series:_5-10 102.10Mi ± 0% 40.48Mi ± 0% -60.36% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_1000/non-histogram_count:_1000/labels_per_metric:_20/exemplars_per_series:_10-10 108.78Mi ± 0% 47.07Mi ± 0% -56.73% (p=0.002 n=6) geomean 5.153Mi 3.202Mi -37.87% │ before.txt │ after.txt │ │ allocs/op │ allocs/op vs base │ FromMetrics/resource_attribute_count:_0/histogram_count:_0/non-histogram_count:_1000/labels_per_metric:_2/exemplars_per_series:_0-10 29.14k ± 0% 22.13k ± 0% -24.04% (p=0.002 n=6) FromMetrics/resource_attribute_count:_0/histogram_count:_0/non-histogram_count:_1000/labels_per_metric:_2/exemplars_per_series:_5-10 64.26k ± 0% 57.25k ± 0% -10.90% (p=0.002 n=6) FromMetrics/resource_attribute_count:_0/histogram_count:_0/non-histogram_count:_1000/labels_per_metric:_2/exemplars_per_series:_10-10 95.38k ± 0% 88.37k ± 0% -7.34% (p=0.002 n=6) FromMetrics/resource_attribute_count:_0/histogram_count:_0/non-histogram_count:_1000/labels_per_metric:_20/exemplars_per_series:_0-10 77.59k ± 0% 62.56k ± 0% -19.37% (p=0.002 n=6) FromMetrics/resource_attribute_count:_0/histogram_count:_0/non-histogram_count:_1000/labels_per_metric:_20/exemplars_per_series:_5-10 113.24k ± 0% 98.11k ± 0% -13.36% (p=0.002 n=6) FromMetrics/resource_attribute_count:_0/histogram_count:_0/non-histogram_count:_1000/labels_per_metric:_20/exemplars_per_series:_10-10 144.6k ± 0% 129.5k ± 0% -10.45% (p=0.002 n=6) FromMetrics/resource_attribute_count:_0/histogram_count:_1000/non-histogram_count:_0/labels_per_metric:_2/exemplars_per_series:_0-10 131.48k ± 0% 73.39k ± 0% -44.18% (p=0.002 n=6) FromMetrics/resource_attribute_count:_0/histogram_count:_1000/non-histogram_count:_0/labels_per_metric:_2/exemplars_per_series:_5-10 170.7k ± 0% 112.6k ± 0% -34.04% (p=0.002 n=6) FromMetrics/resource_attribute_count:_0/histogram_count:_1000/non-histogram_count:_0/labels_per_metric:_2/exemplars_per_series:_10-10 202.8k ± 0% 144.7k ± 0% -28.65% (p=0.002 n=6) FromMetrics/resource_attribute_count:_0/histogram_count:_1000/non-histogram_count:_0/labels_per_metric:_20/exemplars_per_series:_0-10 340.88k ± 0% 94.18k ± 0% -72.37% (p=0.002 n=6) FromMetrics/resource_attribute_count:_0/histogram_count:_1000/non-histogram_count:_0/labels_per_metric:_20/exemplars_per_series:_5-10 380.5k ± 0% 133.7k ± 0% -64.86% (p=0.002 n=6) FromMetrics/resource_attribute_count:_0/histogram_count:_1000/non-histogram_count:_0/labels_per_metric:_20/exemplars_per_series:_10-10 413.6k ± 0% 166.1k ± 0% -59.84% (p=0.002 n=6) FromMetrics/resource_attribute_count:_0/histogram_count:_1000/non-histogram_count:_1000/labels_per_metric:_2/exemplars_per_series:_0-10 160.89k ± 0% 95.69k ± 0% -40.53% (p=0.002 n=6) FromMetrics/resource_attribute_count:_0/histogram_count:_1000/non-histogram_count:_1000/labels_per_metric:_2/exemplars_per_series:_5-10 235.5k ± 0% 170.2k ± 0% -27.72% (p=0.002 n=6) FromMetrics/resource_attribute_count:_0/histogram_count:_1000/non-histogram_count:_1000/labels_per_metric:_2/exemplars_per_series:_10-10 299.1k ± 0% 233.8k ± 0% -21.85% (p=0.002 n=6) FromMetrics/resource_attribute_count:_0/histogram_count:_1000/non-histogram_count:_1000/labels_per_metric:_20/exemplars_per_series:_0-10 430.8k ± 0% 161.2k ± 0% -62.58% (p=0.002 n=6) FromMetrics/resource_attribute_count:_0/histogram_count:_1000/non-histogram_count:_1000/labels_per_metric:_20/exemplars_per_series:_5-10 508.9k ± 0% 237.2k ± 0% -53.38% (p=0.002 n=6) FromMetrics/resource_attribute_count:_0/histogram_count:_1000/non-histogram_count:_1000/labels_per_metric:_20/exemplars_per_series:_10-10 573.4k ± 0% 302.3k ± 2% -47.28% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_0/non-histogram_count:_0/labels_per_metric:_2/exemplars_per_series:_0-10 20.00 ± 0% 15.00 ± 0% -25.00% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_0/non-histogram_count:_0/labels_per_metric:_2/exemplars_per_series:_5-10 20.00 ± 0% 15.00 ± 0% -25.00% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_0/non-histogram_count:_0/labels_per_metric:_2/exemplars_per_series:_10-10 20.00 ± 0% 15.00 ± 0% -25.00% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_0/non-histogram_count:_0/labels_per_metric:_20/exemplars_per_series:_0-10 20.00 ± 0% 15.00 ± 0% -25.00% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_0/non-histogram_count:_0/labels_per_metric:_20/exemplars_per_series:_5-10 20.00 ± 0% 15.00 ± 0% -25.00% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_0/non-histogram_count:_0/labels_per_metric:_20/exemplars_per_series:_10-10 20.00 ± 0% 15.00 ± 0% -25.00% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_0/non-histogram_count:_1000/labels_per_metric:_2/exemplars_per_series:_0-10 29.16k ± 0% 22.15k ± 0% -24.04% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_0/non-histogram_count:_1000/labels_per_metric:_2/exemplars_per_series:_5-10 64.28k ± 0% 57.26k ± 0% -10.91% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_0/non-histogram_count:_1000/labels_per_metric:_2/exemplars_per_series:_10-10 95.40k ± 0% 88.39k ± 0% -7.35% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_0/non-histogram_count:_1000/labels_per_metric:_20/exemplars_per_series:_0-10 77.64k ± 0% 62.60k ± 0% -19.38% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_0/non-histogram_count:_1000/labels_per_metric:_20/exemplars_per_series:_5-10 113.25k ± 0% 98.12k ± 0% -13.36% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_0/non-histogram_count:_1000/labels_per_metric:_20/exemplars_per_series:_10-10 144.7k ± 0% 129.5k ± 0% -10.46% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_1000/non-histogram_count:_0/labels_per_metric:_2/exemplars_per_series:_0-10 131.50k ± 0% 73.40k ± 0% -44.18% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_1000/non-histogram_count:_0/labels_per_metric:_2/exemplars_per_series:_5-10 170.7k ± 0% 112.6k ± 0% -34.03% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_1000/non-histogram_count:_0/labels_per_metric:_2/exemplars_per_series:_10-10 202.9k ± 0% 144.7k ± 0% -28.65% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_1000/non-histogram_count:_0/labels_per_metric:_20/exemplars_per_series:_0-10 340.90k ± 0% 94.19k ± 0% -72.37% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_1000/non-histogram_count:_0/labels_per_metric:_20/exemplars_per_series:_5-10 380.6k ± 0% 133.7k ± 0% -64.86% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_1000/non-histogram_count:_0/labels_per_metric:_20/exemplars_per_series:_10-10 413.6k ± 0% 166.1k ± 0% -59.84% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_1000/non-histogram_count:_1000/labels_per_metric:_2/exemplars_per_series:_0-10 160.98k ± 0% 95.70k ± 0% -40.55% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_1000/non-histogram_count:_1000/labels_per_metric:_2/exemplars_per_series:_5-10 235.6k ± 0% 170.2k ± 0% -27.73% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_1000/non-histogram_count:_1000/labels_per_metric:_2/exemplars_per_series:_10-10 299.2k ± 0% 233.8k ± 0% -21.85% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_1000/non-histogram_count:_1000/labels_per_metric:_20/exemplars_per_series:_0-10 430.8k ± 0% 161.2k ± 0% -62.57% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_1000/non-histogram_count:_1000/labels_per_metric:_20/exemplars_per_series:_5-10 508.9k ± 0% 237.3k ± 0% -53.38% (p=0.002 n=6) FromMetrics/resource_attribute_count:_5/histogram_count:_1000/non-histogram_count:_1000/labels_per_metric:_20/exemplars_per_series:_10-10 573.4k ± 0% 302.1k ± 0% -47.32% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_0/non-histogram_count:_0/labels_per_metric:_2/exemplars_per_series:_0-10 73.00 ± 0% 62.00 ± 0% -15.07% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_0/non-histogram_count:_0/labels_per_metric:_2/exemplars_per_series:_5-10 73.00 ± 0% 62.00 ± 0% -15.07% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_0/non-histogram_count:_0/labels_per_metric:_2/exemplars_per_series:_10-10 73.00 ± 0% 62.00 ± 0% -15.07% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_0/non-histogram_count:_0/labels_per_metric:_20/exemplars_per_series:_0-10 73.00 ± 0% 62.00 ± 0% -15.07% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_0/non-histogram_count:_0/labels_per_metric:_20/exemplars_per_series:_5-10 73.00 ± 0% 62.00 ± 0% -15.07% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_0/non-histogram_count:_0/labels_per_metric:_20/exemplars_per_series:_10-10 73.00 ± 0% 62.00 ± 0% -15.07% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_0/non-histogram_count:_1000/labels_per_metric:_2/exemplars_per_series:_0-10 29.21k ± 0% 22.20k ± 0% -24.01% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_0/non-histogram_count:_1000/labels_per_metric:_2/exemplars_per_series:_5-10 64.34k ± 0% 57.32k ± 0% -10.91% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_0/non-histogram_count:_1000/labels_per_metric:_2/exemplars_per_series:_10-10 95.46k ± 0% 88.44k ± 0% -7.35% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_0/non-histogram_count:_1000/labels_per_metric:_20/exemplars_per_series:_0-10 77.70k ± 0% 62.65k ± 0% -19.37% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_0/non-histogram_count:_1000/labels_per_metric:_20/exemplars_per_series:_5-10 113.36k ± 0% 98.20k ± 0% -13.37% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_0/non-histogram_count:_1000/labels_per_metric:_20/exemplars_per_series:_10-10 144.7k ± 0% 129.6k ± 0% -10.43% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_1000/non-histogram_count:_0/labels_per_metric:_2/exemplars_per_series:_0-10 131.55k ± 0% 73.45k ± 0% -44.17% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_1000/non-histogram_count:_0/labels_per_metric:_2/exemplars_per_series:_5-10 170.7k ± 0% 112.6k ± 0% -34.03% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_1000/non-histogram_count:_0/labels_per_metric:_2/exemplars_per_series:_10-10 202.9k ± 0% 144.8k ± 0% -28.65% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_1000/non-histogram_count:_0/labels_per_metric:_20/exemplars_per_series:_0-10 340.97k ± 0% 94.24k ± 0% -72.36% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_1000/non-histogram_count:_0/labels_per_metric:_20/exemplars_per_series:_5-10 381.2k ± 0% 133.8k ± 0% -64.90% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_1000/non-histogram_count:_0/labels_per_metric:_20/exemplars_per_series:_10-10 413.7k ± 0% 166.1k ± 0% -59.84% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_1000/non-histogram_count:_1000/labels_per_metric:_2/exemplars_per_series:_0-10 161.05k ± 0% 95.76k ± 0% -40.54% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_1000/non-histogram_count:_1000/labels_per_metric:_2/exemplars_per_series:_5-10 235.7k ± 0% 170.3k ± 0% -27.72% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_1000/non-histogram_count:_1000/labels_per_metric:_2/exemplars_per_series:_10-10 299.3k ± 0% 233.9k ± 0% -21.85% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_1000/non-histogram_count:_1000/labels_per_metric:_20/exemplars_per_series:_0-10 430.9k ± 0% 161.3k ± 0% -62.57% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_1000/non-histogram_count:_1000/labels_per_metric:_20/exemplars_per_series:_5-10 509.0k ± 0% 237.3k ± 0% -53.37% (p=0.002 n=6) FromMetrics/resource_attribute_count:_50/histogram_count:_1000/non-histogram_count:_1000/labels_per_metric:_20/exemplars_per_series:_10-10 573.5k ± 0% 302.4k ± 0% -47.27% (p=0.002 n=6) geomean 39.93k 25.46k -36.23% ``` </details> **Link to tracking Issue:** (none) **Testing:** I've added a benchmark to verify the improvement, and run the existing tests to ensure these changes have not introduced any regressions. **Documentation:** (none required)
open-telemetry · Jul 28, 2023 · 1c292e8 · 1c292e8
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diff --git a/.chloggen/prometheus-remote-write-performance.yaml b/.chloggen/prometheus-remote-write-performance.yaml
@@ -0,0 +1,20 @@
+# Use this changelog template to create an entry for release notes.
+# If your change doesn't affect end users, such as a test fix or a tooling change,
+# you should instead start your pull request title with [chore] or use the "Skip Changelog" label.
+
+# One of 'breaking', 'deprecation', 'new_component', 'enhancement', 'bug_fix'
+change_type: enhancement
+
+# The name of the component, or a single word describing the area of concern, (e.g. filelogreceiver)
+component: prometheusremotewrite
+
+# A brief description of the change.  Surround your text with quotes ("") if it needs to start with a backtick (`).
+note: improve the latency and memory utilisation of the conversion from OpenTelemetry to Prometheus remote write
+
+# Mandatory: One or more tracking issues related to the change. You can use the PR number here if no issue exists.
+issues: [24288]
+
+# (Optional) One or more lines of additional information to render under the primary note.
+# These lines will be padded with 2 spaces and then inserted directly into the document.
+# Use pipe (|) for multiline entries.
+subtext:
diff --git a/pkg/translator/prometheusremotewrite/helper.go b/pkg/translator/prometheusremotewrite/helper.go
@@ -130,7 +130,14 @@ func addExemplar(tsMap map[string]*prompb.TimeSeries, bucketBounds []bucketBound
 // the label slice should not contain duplicate label names; this method sorts the slice by label name before creating
 // the signature.
 func timeSeriesSignature(datatype string, labels *[]prompb.Label) string {
+	length := len(datatype)
+
+	for _, lb := range *labels {
+		length += 2 + len(lb.GetName()) + len(lb.GetValue())
+	}
+
 	b := strings.Builder{}
+	b.Grow(length)
 	b.WriteString(datatype)
 
 	sort.Sort(ByLabelName(*labels))
@@ -149,8 +156,22 @@ func timeSeriesSignature(datatype string, labels *[]prompb.Label) string {
 // Unpaired string value is ignored. String pairs overwrites OTLP labels if collision happens, and the overwrite is
 // logged. Resultant label names are sanitized.
 func createAttributes(resource pcommon.Resource, attributes pcommon.Map, externalLabels map[string]string, extras ...string) []prompb.Label {
+	serviceName, haveServiceName := resource.Attributes().Get(conventions.AttributeServiceName)
+	instance, haveInstanceID := resource.Attributes().Get(conventions.AttributeServiceInstanceID)
+
+	// Calculate the maximum possible number of labels we could return so we can preallocate l
+	maxLabelCount := attributes.Len() + len(externalLabels) + len(extras)/2
+
+	if haveServiceName {
+		maxLabelCount++
+	}
+
+	if haveInstanceID {
+		maxLabelCount++
+	}
+
 	// map ensures no duplicate label name
-	l := map[string]prompb.Label{}
+	l := make(map[string]string, maxLabelCount)
 
 	// Ensure attributes are sorted by key for consistent merging of keys which
 	// collide when sanitized.
@@ -164,44 +185,31 @@ func createAttributes(resource pcommon.Resource, attributes pcommon.Map, externa
 	for _, label := range labels {
 		var finalKey = prometheustranslator.NormalizeLabel(label.Name)
 		if existingLabel, alreadyExists := l[finalKey]; alreadyExists {
-			existingLabel.Value = existingLabel.Value + ";" + label.Value
-			l[finalKey] = existingLabel
+			l[finalKey] = existingLabel + ";" + label.Value
 		} else {
-			l[finalKey] = prompb.Label{
-				Name:  finalKey,
-				Value: label.Value,
-			}
+			l[finalKey] = label.Value
 		}
 	}
 
 	// Map service.name + service.namespace to job
-	if serviceName, ok := resource.Attributes().Get(conventions.AttributeServiceName); ok {
+	if haveServiceName {
 		val := serviceName.AsString()
 		if serviceNamespace, ok := resource.Attributes().Get(conventions.AttributeServiceNamespace); ok {
 			val = fmt.Sprintf("%s/%s", serviceNamespace.AsString(), val)
 		}
-		l[model.JobLabel] = prompb.Label{
-			Name:  model.JobLabel,
-			Value: val,
-		}
+		l[model.JobLabel] = val
 	}
 	// Map service.instance.id to instance
-	if instance, ok := resource.Attributes().Get(conventions.AttributeServiceInstanceID); ok {
-		l[model.InstanceLabel] = prompb.Label{
-			Name:  model.InstanceLabel,
-			Value: instance.AsString(),
-		}
+	if haveInstanceID {
+		l[model.InstanceLabel] = instance.AsString()
 	}
 	for key, value := range externalLabels {
 		// External labels have already been sanitized
 		if _, alreadyExists := l[key]; alreadyExists {
 			// Skip external labels if they are overridden by metric attributes
 			continue
 		}
-		l[key] = prompb.Label{
-			Name:  key,
-			Value: value,
-		}
+		l[key] = value
 	}
 
 	for i := 0; i < len(extras); i += 2 {
@@ -217,15 +225,12 @@ func createAttributes(resource pcommon.Resource, attributes pcommon.Map, externa
 		if !(len(name) > 4 && name[:2] == "__" && name[len(name)-2:] == "__") {
 			name = prometheustranslator.NormalizeLabel(name)
 		}
-		l[name] = prompb.Label{
-			Name:  name,
-			Value: extras[i+1],
-		}
+		l[name] = extras[i+1]
 	}
 
 	s := make([]prompb.Label, 0, len(l))
-	for _, lb := range l {
-		s = append(s, lb)
+	for k, v := range l {
+		s = append(s, prompb.Label{Name: k, Value: v})
 	}
 
 	return s
@@ -253,6 +258,21 @@ func addSingleHistogramDataPoint(pt pmetric.HistogramDataPoint, resource pcommon
 	timestamp := convertTimeStamp(pt.Timestamp())
 	// sum, count, and buckets of the histogram should append suffix to baseName
 	baseName := prometheustranslator.BuildCompliantName(metric, settings.Namespace, settings.AddMetricSuffixes)
+	baseLabels := createAttributes(resource, pt.Attributes(), settings.ExternalLabels)
+
+	createLabels := func(nameSuffix string, extras ...string) []prompb.Label {
+		extraLabelCount := len(extras) / 2
+		labels := make([]prompb.Label, len(baseLabels), len(baseLabels)+extraLabelCount+1) // +1 for name
+		copy(labels, baseLabels)
+
+		for extrasIdx := 0; extrasIdx < extraLabelCount; extrasIdx++ {
+			labels = append(labels, prompb.Label{Name: extras[extrasIdx], Value: extras[extrasIdx+1]})
+		}
+
+		labels = append(labels, prompb.Label{Name: nameStr, Value: baseName + nameSuffix})
+
+		return labels
+	}
 
 	// If the sum is unset, it indicates the _sum metric point should be
 	// omitted
@@ -266,7 +286,7 @@ func addSingleHistogramDataPoint(pt pmetric.HistogramDataPoint, resource pcommon
 			sum.Value = math.Float64frombits(value.StaleNaN)
 		}
 
-		sumlabels := createAttributes(resource, pt.Attributes(), settings.ExternalLabels, nameStr, baseName+sumStr)
+		sumlabels := createLabels(sumStr)
 		addSample(tsMap, sum, sumlabels, metric.Type().String())
 
 	}
@@ -280,7 +300,7 @@ func addSingleHistogramDataPoint(pt pmetric.HistogramDataPoint, resource pcommon
 		count.Value = math.Float64frombits(value.StaleNaN)
 	}
 
-	countlabels := createAttributes(resource, pt.Attributes(), settings.ExternalLabels, nameStr, baseName+countStr)
+	countlabels := createLabels(countStr)
 	addSample(tsMap, count, countlabels, metric.Type().String())
 
 	// cumulative count for conversion to cumulative histogram
@@ -302,7 +322,7 @@ func addSingleHistogramDataPoint(pt pmetric.HistogramDataPoint, resource pcommon
 			bucket.Value = math.Float64frombits(value.StaleNaN)
 		}
 		boundStr := strconv.FormatFloat(bound, 'f', -1, 64)
-		labels := createAttributes(resource, pt.Attributes(), settings.ExternalLabels, nameStr, baseName+bucketStr, leStr, boundStr)
+		labels := createLabels(bucketStr, leStr, boundStr)
 		sig := addSample(tsMap, bucket, labels, metric.Type().String())
 
 		bucketBounds = append(bucketBounds, bucketBoundsData{sig: sig, bound: bound})
@@ -316,7 +336,7 @@ func addSingleHistogramDataPoint(pt pmetric.HistogramDataPoint, resource pcommon
 	} else {
 		infBucket.Value = float64(pt.Count())
 	}
-	infLabels := createAttributes(resource, pt.Attributes(), settings.ExternalLabels, nameStr, baseName+bucketStr, leStr, pInfStr)
+	infLabels := createLabels(bucketStr, leStr, pInfStr)
 	sig := addSample(tsMap, infBucket, infLabels, metric.Type().String())
 
 	bucketBounds = append(bucketBounds, bucketBoundsData{sig: sig, bound: math.Inf(1)})
@@ -325,14 +345,8 @@ func addSingleHistogramDataPoint(pt pmetric.HistogramDataPoint, resource pcommon
 	// add _created time series if needed
 	startTimestamp := pt.StartTimestamp()
 	if settings.ExportCreatedMetric && startTimestamp != 0 {
-		createdLabels := createAttributes(
-			resource,
-			pt.Attributes(),
-			settings.ExternalLabels,
-			nameStr,
-			baseName+createdSuffix,
-		)
-		addCreatedTimeSeriesIfNeeded(tsMap, createdLabels, startTimestamp, metric.Type().String())
+		labels := createLabels(createdSuffix)
+		addCreatedTimeSeriesIfNeeded(tsMap, labels, startTimestamp, metric.Type().String())
 	}
 }
 
@@ -443,6 +457,22 @@ func addSingleSummaryDataPoint(pt pmetric.SummaryDataPoint, resource pcommon.Res
 	timestamp := convertTimeStamp(pt.Timestamp())
 	// sum and count of the summary should append suffix to baseName
 	baseName := prometheustranslator.BuildCompliantName(metric, settings.Namespace, settings.AddMetricSuffixes)
+	baseLabels := createAttributes(resource, pt.Attributes(), settings.ExternalLabels)
+
+	createLabels := func(name string, extras ...string) []prompb.Label {
+		extraLabelCount := len(extras) / 2
+		labels := make([]prompb.Label, len(baseLabels), len(baseLabels)+extraLabelCount+1) // +1 for name
+		copy(labels, baseLabels)
+
+		for extrasIdx := 0; extrasIdx < extraLabelCount; extrasIdx++ {
+			labels = append(labels, prompb.Label{Name: extras[extrasIdx], Value: extras[extrasIdx+1]})
+		}
+
+		labels = append(labels, prompb.Label{Name: nameStr, Value: name})
+
+		return labels
+	}
+
 	// treat sum as a sample in an individual TimeSeries
 	sum := &prompb.Sample{
 		Value:     pt.Sum(),
@@ -451,7 +481,7 @@ func addSingleSummaryDataPoint(pt pmetric.SummaryDataPoint, resource pcommon.Res
 	if pt.Flags().NoRecordedValue() {
 		sum.Value = math.Float64frombits(value.StaleNaN)
 	}
-	sumlabels := createAttributes(resource, pt.Attributes(), settings.ExternalLabels, nameStr, baseName+sumStr)
+	sumlabels := createLabels(baseName + sumStr)
 	addSample(tsMap, sum, sumlabels, metric.Type().String())
 
 	// treat count as a sample in an individual TimeSeries
@@ -462,7 +492,7 @@ func addSingleSummaryDataPoint(pt pmetric.SummaryDataPoint, resource pcommon.Res
 	if pt.Flags().NoRecordedValue() {
 		count.Value = math.Float64frombits(value.StaleNaN)
 	}
-	countlabels := createAttributes(resource, pt.Attributes(), settings.ExternalLabels, nameStr, baseName+countStr)
+	countlabels := createLabels(baseName + countStr)
 	addSample(tsMap, count, countlabels, metric.Type().String())
 
 	// process each percentile/quantile
@@ -476,20 +506,14 @@ func addSingleSummaryDataPoint(pt pmetric.SummaryDataPoint, resource pcommon.Res
 			quantile.Value = math.Float64frombits(value.StaleNaN)
 		}
 		percentileStr := strconv.FormatFloat(qt.Quantile(), 'f', -1, 64)
-		qtlabels := createAttributes(resource, pt.Attributes(), settings.ExternalLabels, nameStr, baseName, quantileStr, percentileStr)
+		qtlabels := createLabels(baseName, quantileStr, percentileStr)
 		addSample(tsMap, quantile, qtlabels, metric.Type().String())
 	}
 
 	// add _created time series if needed
 	startTimestamp := pt.StartTimestamp()
 	if settings.ExportCreatedMetric && startTimestamp != 0 {
-		createdLabels := createAttributes(
-			resource,
-			pt.Attributes(),
-			settings.ExternalLabels,
-			nameStr,
-			baseName+createdSuffix,
-		)
+		createdLabels := createLabels(baseName + createdSuffix)
 		addCreatedTimeSeriesIfNeeded(tsMap, createdLabels, startTimestamp, metric.Type().String())
 	}
 }

diff --git a/pkg/translator/prometheusremotewrite/metrics_to_prw_test.go b/pkg/translator/prometheusremotewrite/metrics_to_prw_test.go
@@ -0,0 +1,123 @@
+// Copyright The OpenTelemetry Authors
+// SPDX-License-Identifier: Apache-2.0
+
+package prometheusremotewrite
+
+import (
+	"fmt"
+	"testing"
+	"time"
+
+	"github.com/stretchr/testify/require"
+	"go.opentelemetry.io/collector/pdata/pcommon"
+	"go.opentelemetry.io/collector/pdata/pmetric"
+	"go.opentelemetry.io/collector/pdata/pmetric/pmetricotlp"
+)
+
+func BenchmarkFromMetrics(b *testing.B) {
+	for _, resourceAttributeCount := range []int{0, 5, 50} {
+		b.Run(fmt.Sprintf("resource attribute count: %v", resourceAttributeCount), func(b *testing.B) {
+			for _, histogramCount := range []int{0, 1000} {
+				b.Run(fmt.Sprintf("histogram count: %v", histogramCount), func(b *testing.B) {
+					nonHistogramCounts := []int{0, 1000}
+
+					if resourceAttributeCount == 0 && histogramCount == 0 {
+						// Don't bother running a scenario where we'll generate no series.
+						nonHistogramCounts = []int{1000}
+					}
+
+					for _, nonHistogramCount := range nonHistogramCounts {
+						b.Run(fmt.Sprintf("non-histogram count: %v", nonHistogramCount), func(b *testing.B) {
+							for _, labelsPerMetric := range []int{2, 20} {
+								b.Run(fmt.Sprintf("labels per metric: %v", labelsPerMetric), func(b *testing.B) {
+									for _, exemplarsPerSeries := range []int{0, 5, 10} {
+										b.Run(fmt.Sprintf("exemplars per series: %v", exemplarsPerSeries), func(b *testing.B) {
+											payload := createExportRequest(resourceAttributeCount, histogramCount, nonHistogramCount, labelsPerMetric, exemplarsPerSeries)
+
+											for i := 0; i < b.N; i++ {
+												_, err := FromMetrics(payload.Metrics(), Settings{})
+
+												if err != nil {
+													require.NoError(b, err)
+												}
+											}
+										})
+									}
+								})
+							}
+						})
+					}
+				})
+			}
+		})
+	}
+}
+
+func createExportRequest(resourceAttributeCount int, histogramCount int, nonHistogramCount int, labelsPerMetric int, exemplarsPerSeries int) pmetricotlp.ExportRequest {
+	request := pmetricotlp.NewExportRequest()
+
+	rm := request.Metrics().ResourceMetrics().AppendEmpty()
+	generateAttributes(rm.Resource().Attributes(), "resource", resourceAttributeCount)
+
+	metrics := rm.ScopeMetrics().AppendEmpty().Metrics()
+	ts := pcommon.NewTimestampFromTime(time.Now())
+
+	for i := 1; i <= histogramCount; i++ {
+		m := metrics.AppendEmpty()
+		m.SetEmptyHistogram()
+		m.SetName(fmt.Sprintf("histogram-%v", i))
+		m.Histogram().SetAggregationTemporality(pmetric.AggregationTemporalityCumulative)
+		h := m.Histogram().DataPoints().AppendEmpty()
+		h.SetTimestamp(ts)
+
+		// Set 50 samples, 10 each with values 0.5, 1, 2, 4, and 8
+		h.SetCount(50)
+		h.SetSum(155)
+		h.BucketCounts().FromRaw([]uint64{10, 10, 10, 10, 10, 0})
+		h.ExplicitBounds().FromRaw([]float64{.5, 1, 2, 4, 8, 16}) // Bucket boundaries include the upper limit (ie. each sample is on the upper limit of its bucket)
+
+		generateAttributes(h.Attributes(), "series", labelsPerMetric)
+		generateExemplars(h.Exemplars(), exemplarsPerSeries, ts)
+	}
+
+	for i := 1; i <= nonHistogramCount; i++ {
+		m := metrics.AppendEmpty()
+		m.SetEmptySum()
+		m.SetName(fmt.Sprintf("sum-%v", i))
+		m.Sum().SetAggregationTemporality(pmetric.AggregationTemporalityCumulative)
+		point := m.Sum().DataPoints().AppendEmpty()
+		point.SetTimestamp(ts)
+		point.SetDoubleValue(1.23)
+		generateAttributes(point.Attributes(), "series", labelsPerMetric)
+		generateExemplars(point.Exemplars(), exemplarsPerSeries, ts)
+	}
+
+	for i := 1; i <= nonHistogramCount; i++ {
+		m := metrics.AppendEmpty()
+		m.SetEmptyGauge()
+		m.SetName(fmt.Sprintf("gauge-%v", i))
+		point := m.Gauge().DataPoints().AppendEmpty()
+		point.SetTimestamp(ts)
+		point.SetDoubleValue(1.23)
+		generateAttributes(point.Attributes(), "series", labelsPerMetric)
+		generateExemplars(point.Exemplars(), exemplarsPerSeries, ts)
+	}
+
+	return request
+}
+
+func generateAttributes(m pcommon.Map, prefix string, count int) {
+	for i := 1; i <= count; i++ {
+		m.PutStr(fmt.Sprintf("%v-name-%v", prefix, i), fmt.Sprintf("value-%v", i))
+	}
+}
+
+func generateExemplars(exemplars pmetric.ExemplarSlice, count int, ts pcommon.Timestamp) {
+	for i := 1; i <= count; i++ {
+		e := exemplars.AppendEmpty()
+		e.SetTimestamp(ts)
+		e.SetDoubleValue(2.22)
+		e.SetSpanID(pcommon.SpanID{0x01, 0x02, 0x03, 0x04, 0x05, 0x06, 0x07, 0x08})
+		e.SetTraceID(pcommon.TraceID{0x00, 0x01, 0x02, 0x03, 0x04, 0x05, 0x06, 0x07, 0x08, 0x09, 0x0a, 0x0b, 0x0c, 0x0d, 0x0e, 0x0f})
+	}
+}