/*
 * Copyright (c) 2015, 2019 Oracle and/or its affiliates. All rights reserved.
 *
 * This program and the accompanying materials are made available under the
 * terms of the Eclipse Public License v. 2.0, which is available at
 * http://www.eclipse.org/legal/epl-2.0.
 *
 * This Source Code may also be made available under the following Secondary
 * Licenses when the conditions for such availability set forth in the
 * Eclipse Public License v. 2.0 are satisfied: GNU General Public License,
 * version 2 with the GNU Classpath Exception, which is available at
 * https://www.gnu.org/software/classpath/license.html.
 *
 * SPDX-License-Identifier: EPL-2.0 OR GPL-2.0 WITH Classpath-exception-2.0
 */

package org.glassfish.jersey.server.internal.monitoring;

import org.glassfish.jersey.server.internal.monitoring.core.AbstractSlidingWindowTimeReservoir;
import org.glassfish.jersey.server.internal.monitoring.core.UniformTimeSnapshot;

import java.util.Collection;
import java.util.concurrent.TimeUnit;

Aggregated sliding window time reservoir stores aggregated measurements in a time window of given size. The resulting snapshot
provides precise data as far as the granularity of aggregating trimmer is not concerned. The granularity of the trimmer
determines the granularity of the data the snapshot provides. In other words, the aggregated value object is either included in
the resulting measurements or not depending whether it was trimmed or not.
Author:
Stepan Vavra/**
 * Aggregated sliding window time reservoir stores aggregated measurements in a time window of given size. The resulting snapshot
 * provides precise data as far as the granularity of aggregating trimmer is not concerned. The granularity of the trimmer
 * determines the granularity of the data the snapshot provides. In other words, the aggregated value object is either included in
 * the resulting measurements or not depending whether it was trimmed or not.
 *
 * @author Stepan Vavra
 */
class AggregatedSlidingWindowTimeReservoir extends AbstractSlidingWindowTimeReservoir<AggregatedValueObject> {

    private final AggregatingTrimmer notifier;

    
Creates an aggregated sliding window reservoir.
Params:
window – The time size of the window
windowUnit – The unit of the window size
startTime – The start time from when to calculate the statistics
startTimeUnit – The unit of the start time
notifier – The aggregating trimmer that produces the aggregated data/**
     * Creates an aggregated sliding window reservoir.
     *
     * @param window The time size of the window
     * @param windowUnit The unit of the window size
     * @param startTime The start time from when to calculate the statistics
     * @param startTimeUnit The unit of the start time
     * @param notifier The aggregating trimmer that produces the aggregated data
     */
    public AggregatedSlidingWindowTimeReservoir(
            final long window,
            final TimeUnit windowUnit,
            final long startTime,
            final TimeUnit startTimeUnit, final AggregatingTrimmer notifier) {
        super(window, windowUnit, startTime, startTimeUnit);
        this.notifier = notifier;
        notifier.register(this);
    }

    @Override
    protected UniformTimeSnapshot snapshot(final Collection<AggregatedValueObject> values,
                                           final long timeInterval,
                                           final TimeUnit timeIntervalUnit,
                                           final long time,
                                           final TimeUnit timeUnit) {
        final UniformTimeSnapshot notTrimmedMeasurementsSnapshot = notifier.getTimeReservoirNotifier()
                .getSnapshot(time, timeUnit);

        AggregatedValueObject[] arrayValues = new AggregatedValueObject[values.size()];
        arrayValues = values.toArray(arrayValues);
        long min = Long.MAX_VALUE;
        long max = Long.MIN_VALUE;
        long count = 0;
        double meanNumerator = 0;

        for (AggregatedValueObject value : arrayValues) {
            min = Math.min(min, value.getMin());
            max = Math.max(max, value.getMax());
            count += value.getCount();
            meanNumerator += value.getCount() * value.getMean();
        }
        if (notTrimmedMeasurementsSnapshot.size() > 0) {
            min = Math.min(min, notTrimmedMeasurementsSnapshot.getMin());
            max = Math.max(max, notTrimmedMeasurementsSnapshot.getMax());
            count += notTrimmedMeasurementsSnapshot.size();
            meanNumerator += notTrimmedMeasurementsSnapshot.size() * notTrimmedMeasurementsSnapshot.getMean();
        }

        if (count == 0) {
            return new UniformTimeSimpleSnapshot(0, 0, 0, 0, timeInterval, timeIntervalUnit);
        } else {
            return new UniformTimeSimpleSnapshot(max, min, meanNumerator / count, count, timeInterval, timeIntervalUnit);
        }
    }

}