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add hash range scaled with scale factor parameter

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This commit is contained in:
Jonathan Shook 2021-02-24 13:02:15 -06:00
parent 69ad2b54e4
commit c3910d69a4
4 changed files with 88 additions and 10 deletions
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16
virtdata-lib-basics/src/main/java/io/nosqlbench/virtdata/library/basics/shared/from_long/to_int/HashRangeScaled.java
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@ -9,11 +9,23 @@ import java.util.function.LongToIntFunction;
public class HashRangeScaled implements LongToIntFunction {
private final Hash hash = new Hash();
private final double scalefactor;
public HashRangeScaled(double scalefactor) {
this.scalefactor = scalefactor;
}
public HashRangeScaled() {
this.scalefactor = 1.0D;
}
@Override
public int applyAsInt(long operand) {
if (operand==0) { return 0; }
if (operand == 0) {
return 0;
}
long l = hash.applyAsLong(operand);
return (int) ((l % operand) % Integer.MAX_VALUE);
return (int) (((l % operand) * scalefactor) % Integer.MAX_VALUE);
}
}

17
virtdata-lib-basics/src/main/java/io/nosqlbench/virtdata/library/basics/shared/from_long/to_long/HashRangeScaled.java
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@ -5,20 +5,29 @@ import io.nosqlbench.virtdata.api.annotations.ThreadSafeMapper;
import java.util.function.LongUnaryOperator;
/**
* Return a pseudo-random value which can only be as large as the input.
* Return a pseudo-random value which can only be as large as the input times
* a scale factor, with a default scale factor of 1.0d
*/
@ThreadSafeMapper
public class HashRangeScaled implements LongUnaryOperator {
private Hash hash = new Hash();
private final double scalefactor;
private final Hash hash = new Hash();
public HashRangeScaled(double scalefactor) {
this.scalefactor = scalefactor;
}
public HashRangeScaled() {
this.scalefactor = 1.0D;
}
@Override
public long applyAsLong(long operand) {
if (operand==0) { return 0; }
if (operand == 0) {
return 0;
}
long hashed = hash.applyAsLong(operand);
return hashed % operand;
return (long) ((hashed % operand) * scalefactor);
}
}

15
virtdata-lib-basics/src/main/java/io/nosqlbench/virtdata/library/basics/shared/unary_int/HashRangeScaled.java
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@ -8,10 +8,21 @@ import java.util.function.IntUnaryOperator;
public class HashRangeScaled implements IntUnaryOperator {
private final Hash hash = new Hash();
private final double scalefactor;
public HashRangeScaled(double scalefactor) {
this.scalefactor = scalefactor;
}
public HashRangeScaled() {
this.scalefactor = 1.0D;
}
@Override
public int applyAsInt(int operand) {
if (operand==0) { return 0; }
return hash.applyAsInt(operand) % operand;
if (operand == 0) {
return 0;
}
return (int) ((hash.applyAsInt(operand) % operand) * scalefactor) % Integer.MAX_VALUE;
}
}

50
virtdata-lib-basics/src/test/java/io/nosqlbench/virtdata/library/basics/shared/from_long/to_long/HashRangeScaledTest.java
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@ -1,7 +1,10 @@
package io.nosqlbench.virtdata.library.basics.shared.from_long.to_long;
import org.assertj.core.data.Percentage;
import org.junit.Test;
import java.util.LongSummaryStatistics;
import static org.assertj.core.api.Assertions.assertThat;
public class HashRangeScaledTest {
@ -11,8 +14,51 @@ public class HashRangeScaledTest {
HashRangeScaled hrs = new HashRangeScaled();
for (long i = 0; i < 100; i++) {
long l = hrs.applyAsLong(i);
assertThat(l).isBetween(0L,i);
assertThat(l).isBetween(0L, i);
}
}
}
@Test
public void testHashRangeScaledLongs() {
// This presumes a sliding triangular distribution in the data
HashRangeScaled hrs = new HashRangeScaled();
LongSummaryStatistics lss = new LongSummaryStatistics();
long top = 1000000;
for (long i = 0; i < top; i++) {
lss.accept(hrs.applyAsLong(i));
}
System.out.println(lss);
assertThat(lss.getAverage()).isCloseTo(top / 4d, Percentage.withPercentage(1d));
}
@Test
public void testHashRangeScaledLongsHalf() {
// This presumes a sliding triangular distribution in the data
HashRangeScaled hrs = new HashRangeScaled(0.5d);
LongSummaryStatistics lss = new LongSummaryStatistics();
long top = 1000000;
for (long i = 0; i < top; i++) {
lss.accept(hrs.applyAsLong(i));
}
System.out.println(lss);
assertThat(lss.getAverage()).isCloseTo(top / 8d, Percentage.withPercentage(1d));
}
@Test
public void testHashRangeScaledLongsDoubled() {
// This presumes a sliding triangular distribution in the data
HashRangeScaled hrs = new HashRangeScaled(2d);
LongSummaryStatistics lss = new LongSummaryStatistics();
long top = 1000000;
for (long i = 0; i < top; i++) {
lss.accept(hrs.applyAsLong(i));
}
System.out.println(lss);
assertThat(lss.getAverage()).isCloseTo(top / 2d, Percentage.withPercentage(1d));
}
}