HBASE-19450 Addendum Limit logging of chore execution time at INFO to once per 5 minutes.
* Ensure MovingAverage related classes are IA.Private * Move trace logging into MovingAverage class Signed-off-by: Duo Zhang <zhangduo@apache.org>
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@ -81,7 +81,9 @@ public abstract class ScheduledChore implements Runnable {
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*/
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private final Stoppable stopper;
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private final MovingAverage<Void> timeMeasurement = new WindowMovingAverage();
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private final MovingAverage<Void> timeMeasurement;
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private static final long FIVE_MINUTES_IN_NANOS = TimeUnit.MINUTES.toNanos(5L);
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private long lastLog = System.nanoTime();
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interface ChoreServicer {
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/**
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@ -122,11 +124,7 @@ public abstract class ScheduledChore implements Runnable {
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@InterfaceAudience.Private
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@VisibleForTesting
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protected ScheduledChore() {
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this.name = null;
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this.stopper = null;
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this.period = 0;
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this.initialDelay = DEFAULT_INITIAL_DELAY;
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this.timeUnit = DEFAULT_TIME_UNIT;
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this("TestChore", null, 0, DEFAULT_INITIAL_DELAY, DEFAULT_TIME_UNIT);
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}
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/**
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@ -167,6 +165,7 @@ public abstract class ScheduledChore implements Runnable {
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this.period = period;
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this.initialDelay = initialDelay < 0 ? 0 : initialDelay;
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this.timeUnit = unit;
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this.timeMeasurement = new WindowMovingAverage(name);
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}
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/**
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@ -191,8 +190,11 @@ public abstract class ScheduledChore implements Runnable {
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chore();
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return null;
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});
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LOG.info(String.format("%s average execution time: %.2f ns.", getName(),
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timeMeasurement.getAverageTime()));
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if (LOG.isInfoEnabled() && (System.nanoTime() - lastLog > FIVE_MINUTES_IN_NANOS)) {
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LOG.info("{} average execution time: {} ns.", getName(),
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(long)(timeMeasurement.getAverageTime()));
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lastLog = System.nanoTime();
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}
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}
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} catch (Throwable t) {
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if (LOG.isErrorEnabled()) LOG.error("Caught error", t);
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@ -24,26 +24,26 @@ import org.apache.yetus.audience.InterfaceAudience;
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* EMA is similar to {@link WeightedMovingAverage} in weighted, but the weighting factor decrease
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* exponentially. It brings benefits that it is more sensitive, and can see the trends easily.
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*/
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@InterfaceAudience.Public
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@InterfaceAudience.Private
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public class ExponentialMovingAverage extends WindowMovingAverage {
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private double alpha;
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private double previousAverage;
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private double currentAverage;
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public ExponentialMovingAverage() {
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this(DEFAULT_SIZE);
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public ExponentialMovingAverage(String label) {
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this(label, DEFAULT_SIZE);
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}
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public ExponentialMovingAverage(double alpha) {
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this(DEFAULT_SIZE, alpha);
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public ExponentialMovingAverage(String label, double alpha) {
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this(label, DEFAULT_SIZE, alpha);
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}
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public ExponentialMovingAverage(int size) {
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this(size, (double) 2 / (1 + size));
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public ExponentialMovingAverage(String label, int size) {
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this(label, size, (double) 2 / (1 + size));
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}
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public ExponentialMovingAverage(int size, double alpha) {
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super(size);
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public ExponentialMovingAverage(String label, int size, double alpha) {
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super(label, size);
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this.previousAverage = -1.0;
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this.currentAverage = 0.0;
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this.alpha = alpha;
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@ -29,10 +29,16 @@ import org.slf4j.LoggerFactory;
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* <br>
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* In different situation, different {@link MovingAverage} algorithm can be used based on needs.
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*/
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@InterfaceAudience.Public
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@InterfaceAudience.Private
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public abstract class MovingAverage<T> {
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private final static Logger LOG = LoggerFactory.getLogger(MovingAverage.class);
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protected final String label;
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protected MovingAverage(String label) {
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this.label = label;
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}
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/**
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* Mark start time of an execution.
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* @return time in ns.
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@ -57,12 +63,12 @@ public abstract class MovingAverage<T> {
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*/
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public T measure(TimeMeasurable<T> measurable) {
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long startTime = start();
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LOG.debug("Start to measure at: {} ns.", startTime);
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LOG.trace("{} - start to measure at: {} ns.", label, startTime);
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// Here may throw exceptions which should be taken care by caller, not here.
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// If exception occurs, this time wouldn't count.
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T result = measurable.measure();
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long elapsed = stop(startTime);
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LOG.debug("Elapse: {} ns.", elapsed);
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LOG.trace("{} - elapse: {} ns.", label, elapsed);
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updateMostRecentTime(elapsed);
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return result;
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}
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@ -23,12 +23,13 @@ import org.apache.yetus.audience.InterfaceAudience;
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/**
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* SMA measure the overall average execution time of a specific method.
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*/
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@InterfaceAudience.Public
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@InterfaceAudience.Private
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public class SimpleMovingAverage extends MovingAverage {
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private double averageTime = 0.0;
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protected long count = 0;
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public SimpleMovingAverage() {
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public SimpleMovingAverage(String label) {
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super(label);
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this.averageTime = 0.0;
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this.count = 0;
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}
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@ -24,7 +24,7 @@ import org.apache.yetus.audience.InterfaceAudience;
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* Methods that implement this interface can be measured elapsed time.
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* It works together with {@link MovingAverage} to take effect.
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*/
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@InterfaceAudience.Public
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@InterfaceAudience.Private
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public interface TimeMeasurable<T> {
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/**
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@ -24,17 +24,17 @@ import org.apache.yetus.audience.InterfaceAudience;
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* Different from SMA {@link SimpleMovingAverage}, WeightedMovingAverage gives each data different
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* weight. And it is based on {@link WindowMovingAverage}, such that it only focus on the last N.
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*/
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@InterfaceAudience.Public
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@InterfaceAudience.Private
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public class WeightedMovingAverage extends WindowMovingAverage {
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private int[] coefficient;
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private int denominator;
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public WeightedMovingAverage() {
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this(DEFAULT_SIZE);
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public WeightedMovingAverage(String label) {
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this(label, DEFAULT_SIZE);
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}
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public WeightedMovingAverage(int size) {
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super(size);
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public WeightedMovingAverage(String label, int size) {
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super(label, size);
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int length = getNumberOfStatistics();
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denominator = length * (length + 1) / 2;
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coefficient = new int[length];
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@ -24,7 +24,7 @@ import org.apache.yetus.audience.InterfaceAudience;
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* Instead of calculate a whole time average, this class focus on the last N.
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* The last N is stored in a circle array.
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*/
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@InterfaceAudience.Public
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@InterfaceAudience.Private
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public class WindowMovingAverage extends MovingAverage {
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protected final static int DEFAULT_SIZE = 5;
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@ -35,11 +35,12 @@ public class WindowMovingAverage extends MovingAverage {
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// If it travels a round.
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protected boolean oneRound;
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public WindowMovingAverage() {
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this(DEFAULT_SIZE);
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public WindowMovingAverage(String label) {
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this(label, DEFAULT_SIZE);
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}
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public WindowMovingAverage(int size) {
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public WindowMovingAverage(String label, int size) {
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super(label);
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this.lastN = new long[size <= 0 ? DEFAULT_SIZE : size];
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this.mostRecent = -1;
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this.oneRound = false;
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@ -22,8 +22,10 @@ import org.apache.hadoop.hbase.HBaseClassTestRule;
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import org.apache.hadoop.hbase.testclassification.SmallTests;
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import org.junit.Assert;
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import org.junit.ClassRule;
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import org.junit.Rule;
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import org.junit.Test;
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import org.junit.experimental.categories.Category;
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import org.junit.rules.TestName;
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@Category(SmallTests.class)
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public class TestMovingAverage {
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@ -32,12 +34,15 @@ public class TestMovingAverage {
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public static final HBaseClassTestRule CLASS_RULE =
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HBaseClassTestRule.forClass(TestMovingAverage.class);
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@Rule
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public TestName name = new TestName();
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private long[] data = {1, 12, 13, 24, 25, 26, 37, 38, 39, 40};
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private double delta = 0.1;
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@Test
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public void testSimpleMovingAverage() throws Exception {
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MovingAverage<?> algorithm = new SimpleMovingAverage();
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MovingAverage<?> algorithm = new SimpleMovingAverage(name.getMethodName());
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int index = 0;
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// [1, 12, 13, 24]
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int bound = 4;
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@ -67,7 +72,7 @@ public class TestMovingAverage {
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@Test
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public void testWindowMovingAverage() throws Exception {
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// Default size is 5.
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MovingAverage<?> algorithm = new WindowMovingAverage();
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MovingAverage<?> algorithm = new WindowMovingAverage(name.getMethodName());
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int index = 0;
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// [1, 12, 13, 24]
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int bound = 4;
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@ -97,7 +102,7 @@ public class TestMovingAverage {
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@Test
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public void testWeightedMovingAverage() throws Exception {
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// Default size is 5.
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MovingAverage<?> algorithm = new WeightedMovingAverage();
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MovingAverage<?> algorithm = new WeightedMovingAverage(name.getMethodName());
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int index = 0;
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// [1, 12, 13, 24]
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int bound = 4;
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@ -127,7 +132,7 @@ public class TestMovingAverage {
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@Test
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public void testExponentialMovingAverage() throws Exception {
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// [1, 12, 13, 24, 25, 26, 37, 38, 39, 40]
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MovingAverage<?> algorithm = new ExponentialMovingAverage();
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MovingAverage<?> algorithm = new ExponentialMovingAverage(name.getMethodName());
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int index = 0;
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int bound = 5;
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for (; index < bound; index++) {
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