Remove call to Math.floor in f()

Since we can be sure that the result is always positive, rounding down is faster than using Math.floor while getting the same result.

```
Benchmark                               Mode  Cnt   Score   Error  Units
CKMSQuantileBenchmark.ckmsQuantileF  avgt    4  37,188 ± 6,681  ns/op
CKMSQuantileBenchmark.ckmsQuantileF  avgt    4  20,500 ± 1,054  ns/op <= Without floor()
```

Signed-off-by: Jens <[email protected]>

Author: DieBauer

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

@@ -8,7 +8,10 @@
 import org.openjdk.jmh.runner.options.Options;
 import org.openjdk.jmh.runner.options.OptionsBuilder;
 
-import java.util.*;
+import java.util.ArrayList;
+import java.util.Collections;
+import java.util.List;
+import java.util.Random;
 import java.util.concurrent.TimeUnit;
 
 public class CKMSQuantileBenchmark {
@@ -18,79 +21,102 @@ public static class EmptyBenchmarkState {
         @Param({"10000", "100000", "1000000"})
         public int value;
 
-        CKMSQuantiles ckmsQuantiles;
-
         List<Quantile> quantiles;
         Random rand = new Random(0);
 
-        long[] shuffle;
+        List<Double> shuffle;
+
+        Quantile mean = new Quantile(0.50, 0.050);
+        Quantile q90 = new Quantile(0.90, 0.010);
+        Quantile q95 = new Quantile(0.95, 0.005);
+        Quantile q99 = new Quantile(0.99, 0.001);
 
         @Setup(Level.Trial)
         public void setup() {
             quantiles = new ArrayList<Quantile>();
-            quantiles.add(new Quantile(0.50, 0.050));
-            quantiles.add(new Quantile(0.90, 0.010));
-            quantiles.add(new Quantile(0.95, 0.005));
-            quantiles.add(new Quantile(0.99, 0.001));
-
-
-            shuffle = new long[value];
-            for (int i = 0; i < shuffle.length; i++) {
-                shuffle[i] = i;
+            quantiles.add(mean);
+            quantiles.add(q90);
+            quantiles.add(q95);
+            quantiles.add(q99);
+
+            shuffle = new ArrayList<Double>(value);
+            for (int i = 0; i < value; i++) {
+                shuffle.add((double) i);
             }
-            Collections.shuffle(Arrays.asList(shuffle), rand);
-
+            Collections.shuffle(shuffle, rand);
         }
     }
 
     @Benchmark
     @BenchmarkMode({Mode.AverageTime})
     @OutputTimeUnit(TimeUnit.MILLISECONDS)
-    public void ckmsQuantileInsertBenchmark(Blackhole blackhole, EmptyBenchmarkState state) {
+    public void ckmsQuantileInsertBenchmark(EmptyBenchmarkState state) {
         CKMSQuantiles q = new CKMSQuantiles(state.quantiles.toArray(new Quantile[]{}));
-        for (long l : state.shuffle) {
+        for (Double l : state.shuffle) {
             q.insert(l);
         }
     }
 
+    /** prefilled benchmark, means that we already have a filled and compressed samples available */
     @State(Scope.Benchmark)
     public static class PrefilledBenchmarkState {
-        public int value = 1000000;
+        @Param({"10000", "100000", "1000000"})
+        public int value;
+
 
         CKMSQuantiles ckmsQuantiles;
 
         List<Quantile> quantiles;
         Random rand = new Random(0);
 
-        long[] shuffle;
+        Quantile mean = new Quantile(0.50, 0.050);
+        Quantile q90 = new Quantile(0.90, 0.010);
+        Quantile q95 = new Quantile(0.95, 0.005);
+        Quantile q99 = new Quantile(0.99, 0.001);
+        List<Double> shuffle;
+
+        int rank = (int) (value * q95.quantile);
+
 
         @Setup(Level.Trial)
         public void setup() {
             quantiles = new ArrayList<Quantile>();
-            quantiles.add(new Quantile(0.50, 0.050));
-            quantiles.add(new Quantile(0.90, 0.010));
-            quantiles.add(new Quantile(0.95, 0.005));
-            quantiles.add(new Quantile(0.99, 0.001));
+            quantiles.add(mean);
+            quantiles.add(q90);
+            quantiles.add(q95);
+            quantiles.add(q99);
+
+            shuffle = new ArrayList<Double>(value);
+            for (int i = 0; i < value; i++) {
+                shuffle.add((double) i);
+            }
+            Collections.shuffle(shuffle, rand);
 
 
-            shuffle = new long[value];
-            for (int i = 0; i < shuffle.length; i++) {
-                shuffle[i] = i;
-            }
-            Collections.shuffle(Arrays.asList(shuffle), rand);
             ckmsQuantiles = new CKMSQuantiles(quantiles.toArray(new Quantile[]{}));
-            for (long l : shuffle) {
+            for (Double l : shuffle) {
                 ckmsQuantiles.insert(l);
             }
-
+            System.out.println("Sample size is: " + ckmsQuantiles.samples.size());
         }
+
     }
 
     @Benchmark
     @BenchmarkMode({Mode.AverageTime})
     @OutputTimeUnit(TimeUnit.NANOSECONDS)
     public void ckmsQuantileGetBenchmark(Blackhole blackhole, PrefilledBenchmarkState state) {
-        blackhole.consume(state.ckmsQuantiles.get(0.95));
+        blackhole.consume(state.ckmsQuantiles.get(state.q90.quantile));
+    }
+
+    /**
+     * benchmark for the f method.
+     */
+    @Benchmark
+    @BenchmarkMode({Mode.AverageTime})
+    @OutputTimeUnit(TimeUnit.NANOSECONDS)
+    public void ckmsQuantileF(Blackhole blackhole, PrefilledBenchmarkState state) {
+        blackhole.consume(state.ckmsQuantiles.f(state.rank));
     }
 
     public static void main(String[] args) throws RunnerException {

simpleclient/src/main/java/io/prometheus/client/CKMSQuantiles.java

@@ -185,9 +185,9 @@ int f(int r) {
             // The expected result of (2*0.01*30)/(1-0.95) is 12. The actual result is 11.99999999999999.
             // To avoid running into these types of error we add 0.00000000001 before rounding down.
             if (r >= q.quantile * n) {
-                result = (int) Math.floor(q.v * r + 0.00000000001);
+                result = (int) (q.v * r + 0.00000000001);
             } else {
-                result = (int) Math.floor(q.u * (n - r) + 0.00000000001);
+                result = (int) (q.u * (n - r) + 0.00000000001);
             }
             if (result < minResult) {
                 minResult = result;