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zeitlingerzeitlinger
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add super linter
Signed-off-by: Gregor Zeitlinger <[email protected]>
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‎.github/super-linter.env‎

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FILTER_REGEX_EXCLUDE=mvnw
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IGNORE_GITIGNORED_FILES=true
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JAVA_FILE_NAME=google_checks.xml
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# disable kubernetes linter - complains about resource limits, etc
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VALIDATE_CHECKOV=false
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VALIDATE_DOCKERFILE_HADOLINT=false
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VALIDATE_GIT_COMMITLINT=false
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# times out
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VALIDATE_GO_MODULES=false
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# contradicting with prettier
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VALIDATE_JAVASCRIPT_STANDARD=false
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# we have many duplicate code in our codebase for demo purposes
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VALIDATE_JSCPD=false
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FIX_ENV=true
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FIX_GOOGLE_JAVA_FORMAT=true
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FIX_JAVASCRIPT_PRETTIER=true
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FIX_JSON=true
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FIX_JSONC=true
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FIX_JSONC_PRETTIER=true
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FIX_JSON_PRETTIER=true
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FIX_MARKDOWN=true
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FIX_MARKDOWN_PRETTIER=true
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FIX_SHELL_SHFMT=true
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FIX_YAML_PRETTIER=true

.github/workflows/lint.yml renamed to .github/workflows/lint-rest.yml

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uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
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- uses: jdx/mise-action@7a111ead46986ccad89a74ad013ba2a7c08c9e67 # v2.1.1
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- name: Lint
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run: mise run lint-all
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run: mise run lint-rest
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‎.github/workflows/super-linter.yml‎

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---
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name: Lint
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on:
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pull_request:
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jobs:
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lint:
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runs-on: ubuntu-24.04
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permissions:
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contents: read
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packages: read
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# To report GitHub Actions status checks
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statuses: write
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steps:
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- name: Checkout code
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uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
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with:
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persist-credentials: false
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fetch-depth: 0
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- name: Load super-linter configuration
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run: grep -v '^#' .github/super-linter.env | grep -v 'FIX_' >> "$GITHUB_ENV"
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- name: Super-linter
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uses: super-linter/super-linter@4e8a7c2bf106c4c766c816b35ec612638dc9b6b2 # v7.3.0
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env:
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# To report GitHub Actions status checks
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GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

‎.gitignore‎

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**/.classpath
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**.project
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**/.settings/
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docs/public
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.lycheecache

‎benchmarks/src/archive/java/io/prometheus/client/CKMSQuantileBenchmark.java‎

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package io.prometheus.client;
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import io.prometheus.client.CKMSQuantiles.Quantile;
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import java.util.ArrayList;
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import java.util.Collections;
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import java.util.List;
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import java.util.Random;
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import java.util.concurrent.TimeUnit;
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import org.openjdk.jmh.annotations.*;
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import org.openjdk.jmh.infra.Blackhole;
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import org.openjdk.jmh.runner.Runner;
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import org.openjdk.jmh.runner.RunnerException;
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import org.openjdk.jmh.runner.options.Options;
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import org.openjdk.jmh.runner.options.OptionsBuilder;
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import java.util.ArrayList;
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import java.util.Collections;
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import java.util.List;
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import java.util.Random;
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import java.util.concurrent.TimeUnit;
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public class CKMSQuantileBenchmark {
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@State(Scope.Benchmark)
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public static class EmptyBenchmarkState {
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@Param({"10000", "100000", "1000000"})
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public int value;
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List<Quantile> quantiles;
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Random rand = new Random(0);
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List<Double> shuffle;
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Quantile mean = new Quantile(0.50, 0.050);
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Quantile q90 = new Quantile(0.90, 0.010);
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Quantile q95 = new Quantile(0.95, 0.005);
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Quantile q99 = new Quantile(0.99, 0.001);
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@Setup(Level.Trial)
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public void setup() {
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quantiles = new ArrayList<Quantile>();
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quantiles.add(mean);
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quantiles.add(q90);
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quantiles.add(q95);
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quantiles.add(q99);
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shuffle = new ArrayList<Double>(value);
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for (int i = 0; i < value; i++) {
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shuffle.add((double) i);
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}
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Collections.shuffle(shuffle, rand);
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}
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}
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@Benchmark
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@BenchmarkMode({Mode.AverageTime})
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@OutputTimeUnit(TimeUnit.MILLISECONDS)
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public void ckmsQuantileInsertBenchmark(EmptyBenchmarkState state) {
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CKMSQuantiles q = new CKMSQuantiles(state.quantiles.toArray(new Quantile[]{}));
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for (Double l : state.shuffle) {
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q.insert(l);
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}
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}
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/** prefilled benchmark, means that we already have a filled and compressed samples available */
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@State(Scope.Benchmark)
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public static class PrefilledBenchmarkState {
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@Param({"10000", "100000", "1000000"})
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public int value;
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CKMSQuantiles ckmsQuantiles;
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List<Quantile> quantiles;
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Random rand = new Random(0);
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Quantile mean = new Quantile(0.50, 0.050);
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Quantile q90 = new Quantile(0.90, 0.010);
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Quantile q95 = new Quantile(0.95, 0.005);
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Quantile q99 = new Quantile(0.99, 0.001);
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List<Double> shuffle;
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int rank = (int) (value * q95.quantile);
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@Setup(Level.Trial)
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public void setup() {
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quantiles = new ArrayList<Quantile>();
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quantiles.add(mean);
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quantiles.add(q90);
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quantiles.add(q95);
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quantiles.add(q99);
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shuffle = new ArrayList<Double>(value);
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for (int i = 0; i < value; i++) {
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shuffle.add((double) i);
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}
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Collections.shuffle(shuffle, rand);
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ckmsQuantiles = new CKMSQuantiles(quantiles.toArray(new Quantile[]{}));
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for (Double l : shuffle) {
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ckmsQuantiles.insert(l);
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}
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// make sure we inserted all 'hanging' samples (count % 128)
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ckmsQuantiles.get(0);
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// compress everything so we have a similar samples size regardless of n.
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ckmsQuantiles.compress();
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System.out.println("Sample size is: " + ckmsQuantiles.samples.size());
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}
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}
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@Benchmark
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@BenchmarkMode({Mode.AverageTime})
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@OutputTimeUnit(TimeUnit.NANOSECONDS)
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public void ckmsQuantileGetBenchmark(Blackhole blackhole, PrefilledBenchmarkState state) {
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blackhole.consume(state.ckmsQuantiles.get(state.q90.quantile));
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@State(Scope.Benchmark)
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public static class EmptyBenchmarkState {
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@Param({"10000", "100000", "1000000"})
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public int value;
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List<Quantile> quantiles;
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Random rand = new Random(0);
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List<Double> shuffle;
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Quantile mean = new Quantile(0.50, 0.050);
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Quantile q90 = new Quantile(0.90, 0.010);
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Quantile q95 = new Quantile(0.95, 0.005);
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Quantile q99 = new Quantile(0.99, 0.001);
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@Setup(Level.Trial)
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public void setup() {
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quantiles = new ArrayList<Quantile>();
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quantiles.add(mean);
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quantiles.add(q90);
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quantiles.add(q95);
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quantiles.add(q99);
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shuffle = new ArrayList<Double>(value);
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for (int i = 0; i < value; i++) {
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shuffle.add((double) i);
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}
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Collections.shuffle(shuffle, rand);
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}
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/**
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* benchmark for the f method.
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*/
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@Benchmark
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@BenchmarkMode({Mode.AverageTime})
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@OutputTimeUnit(TimeUnit.NANOSECONDS)
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public void ckmsQuantileF(Blackhole blackhole, PrefilledBenchmarkState state) {
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blackhole.consume(state.ckmsQuantiles.f(state.rank));
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}
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@Benchmark
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@BenchmarkMode({Mode.AverageTime})
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@OutputTimeUnit(TimeUnit.MILLISECONDS)
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public void ckmsQuantileInsertBenchmark(EmptyBenchmarkState state) {
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CKMSQuantiles q = new CKMSQuantiles(state.quantiles.toArray(new Quantile[] {}));
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for (Double l : state.shuffle) {
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q.insert(l);
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}
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public static void main(String[] args) throws RunnerException {
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Options opt = new OptionsBuilder()
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.include(CKMSQuantileBenchmark.class.getSimpleName())
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.warmupIterations(5)
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.measurementIterations(4)
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.threads(1)
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.forks(1)
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.build();
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new Runner(opt).run();
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}
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/** prefilled benchmark, means that we already have a filled and compressed samples available */
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@State(Scope.Benchmark)
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public static class PrefilledBenchmarkState {
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@Param({"10000", "100000", "1000000"})
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public int value;
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CKMSQuantiles ckmsQuantiles;
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List<Quantile> quantiles;
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Random rand = new Random(0);
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Quantile mean = new Quantile(0.50, 0.050);
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Quantile q90 = new Quantile(0.90, 0.010);
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Quantile q95 = new Quantile(0.95, 0.005);
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Quantile q99 = new Quantile(0.99, 0.001);
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List<Double> shuffle;
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int rank = (int) (value * q95.quantile);
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@Setup(Level.Trial)
79+
public void setup() {
80+
quantiles = new ArrayList<Quantile>();
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quantiles.add(mean);
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quantiles.add(q90);
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quantiles.add(q95);
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quantiles.add(q99);
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shuffle = new ArrayList<Double>(value);
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for (int i = 0; i < value; i++) {
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shuffle.add((double) i);
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}
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Collections.shuffle(shuffle, rand);
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ckmsQuantiles = new CKMSQuantiles(quantiles.toArray(new Quantile[] {}));
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for (Double l : shuffle) {
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ckmsQuantiles.insert(l);
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}
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// make sure we inserted all 'hanging' samples (count % 128)
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ckmsQuantiles.get(0);
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// compress everything so we have a similar samples size regardless of n.
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ckmsQuantiles.compress();
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System.out.println("Sample size is: " + ckmsQuantiles.samples.size());
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}
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}
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@Benchmark
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@BenchmarkMode({Mode.AverageTime})
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@OutputTimeUnit(TimeUnit.NANOSECONDS)
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public void ckmsQuantileGetBenchmark(Blackhole blackhole, PrefilledBenchmarkState state) {
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blackhole.consume(state.ckmsQuantiles.get(state.q90.quantile));
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}
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/** benchmark for the f method. */
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@Benchmark
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@BenchmarkMode({Mode.AverageTime})
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@OutputTimeUnit(TimeUnit.NANOSECONDS)
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public void ckmsQuantileF(Blackhole blackhole, PrefilledBenchmarkState state) {
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blackhole.consume(state.ckmsQuantiles.f(state.rank));
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}
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public static void main(String[] args) throws RunnerException {
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Options opt =
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new OptionsBuilder()
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.include(CKMSQuantileBenchmark.class.getSimpleName())
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.warmupIterations(5)
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.measurementIterations(4)
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.threads(1)
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.forks(1)
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.build();
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new Runner(opt).run();
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}
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}

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