Examples with AggregationIterator net.opentsdb.core.AggregationIterator used on opensource projects

Example 1 with AggregationIterator

use of net.opentsdb.core.AggregationIterator in project opentsdb by OpenTSDB.

the class HighestCurrent method evaluate.

@Override
public DataPoints[] evaluate(final TSQuery data_query, final List<DataPoints[]> query_results, final List<String> params) {
    if (data_query == null) {
        throw new IllegalArgumentException("Missing time series query");
    }
    if (query_results == null || query_results.isEmpty()) {
        return new DataPoints[] {};
    }
    // TODO(cl) - allow for empty top-n maybe? Just sort the results by max?
    if (params == null || params.isEmpty()) {
        throw new IllegalArgumentException("Need aggregation window for moving average");
    }
    String param = params.get(0);
    if (param == null || param.length() == 0) {
        throw new IllegalArgumentException("Missing top n value " + "(number of series to return)");
    }
    int topn = 0;
    if (param.matches("^[0-9]+$")) {
        try {
            topn = Integer.parseInt(param);
        } catch (NumberFormatException nfe) {
            throw new IllegalArgumentException("Invalid parameter, must be an integer", nfe);
        }
    } else {
        throw new IllegalArgumentException("Unparseable top n value: " + param);
    }
    if (topn < 1) {
        throw new IllegalArgumentException("Top n value must be greater " + "than zero: " + topn);
    }
    int num_results = 0;
    for (DataPoints[] results : query_results) {
        num_results += results.length;
    }
    final PostAggregatedDataPoints[] post_agg_results = new PostAggregatedDataPoints[num_results];
    int ix = 0;
    // one or more sub queries (m=...&m=...&m=...)
    for (final DataPoints[] sub_query_result : query_results) {
        // group bys (m=sum:foo{host=*})
        for (final DataPoints dps : sub_query_result) {
            // TODO(cl) - Avoid iterating and copying if we can help it. We should
            // be able to pass the original DataPoints object to the seekable view
            // and then iterate through it.
            final List<DataPoint> mutable_points = new ArrayList<DataPoint>();
            for (final DataPoint point : dps) {
                mutable_points.add(point.isInteger() ? MutableDataPoint.ofLongValue(point.timestamp(), point.longValue()) : MutableDataPoint.ofDoubleValue(point.timestamp(), point.doubleValue()));
            }
            post_agg_results[ix++] = new PostAggregatedDataPoints(dps, mutable_points.toArray(new DataPoint[mutable_points.size()]));
        }
    }
    final SeekableView[] views = new SeekableView[num_results];
    for (int i = 0; i < num_results; i++) {
        views[i] = post_agg_results[i].iterator();
    }
    final MaxLatestAggregator aggregator = new MaxLatestAggregator(Aggregators.Interpolation.LERP, "maxLatest", num_results, data_query.startTime(), data_query.endTime());
    final SeekableView view = (new AggregationIterator(views, data_query.startTime(), data_query.endTime(), aggregator, Aggregators.Interpolation.LERP, false));
    // slurp all the points even though we aren't using them at this stage
    while (view.hasNext()) {
        final DataPoint mdp = view.next();
        @SuppressWarnings("unused") final Object o = mdp.isInteger() ? mdp.longValue() : mdp.doubleValue();
    }
    final long[] max_longs = aggregator.getLongMaxes();
    final double[] max_doubles = aggregator.getDoubleMaxes();
    final TopNSortingEntry[] max_by_ts = new TopNSortingEntry[num_results];
    if (aggregator.hasDoubles() && aggregator.hasLongs()) {
        for (int i = 0; i < num_results; i++) {
            max_by_ts[i] = new TopNSortingEntry(Math.max((double) max_longs[i], max_doubles[i]), i);
        }
    } else if (aggregator.hasLongs() && !aggregator.hasDoubles()) {
        for (int i = 0; i < num_results; i++) {
            max_by_ts[i] = new TopNSortingEntry((double) max_longs[i], i);
        }
    } else if (aggregator.hasDoubles() && !aggregator.hasLongs()) {
        for (int i = 0; i < num_results; i++) {
            max_by_ts[i] = new TopNSortingEntry(max_doubles[i], i);
        }
    }
    Arrays.sort(max_by_ts);
    final int result_count = Math.min(topn, num_results);
    final DataPoints[] results = new DataPoints[result_count];
    for (int i = 0; i < result_count; i++) {
        results[i] = post_agg_results[max_by_ts[i].pos];
    }
    return results;
}

Example 2 with AggregationIterator

use of net.opentsdb.core.AggregationIterator in project opentsdb by OpenTSDB.

the class HighestMax method evaluate.

@Override
public DataPoints[] evaluate(final TSQuery data_query, final List<DataPoints[]> query_results, final List<String> params) {
    if (data_query == null) {
        throw new IllegalArgumentException("Missing time series query");
    }
    if (query_results == null || query_results.isEmpty()) {
        return new DataPoints[] {};
    }
    // TODO(cl) - allow for empty top-n maybe? Just sort the results by max?
    if (params == null || params.isEmpty()) {
        throw new IllegalArgumentException("Need aggregation window for moving average");
    }
    String param = params.get(0);
    if (param == null || param.length() == 0) {
        throw new IllegalArgumentException("Missing top n value " + "(number of series to return)");
    }
    int topn = 0;
    if (param.matches("^[0-9]+$")) {
        try {
            topn = Integer.parseInt(param);
        } catch (NumberFormatException nfe) {
            throw new IllegalArgumentException("Invalid parameter, must be an integer", nfe);
        }
    } else {
        throw new IllegalArgumentException("Unparseable top n value: " + param);
    }
    if (topn < 1) {
        throw new IllegalArgumentException("Top n value must be greater " + "than zero: " + topn);
    }
    int num_results = 0;
    for (DataPoints[] results : query_results) {
        num_results += results.length;
    }
    final PostAggregatedDataPoints[] post_agg_results = new PostAggregatedDataPoints[num_results];
    int ix = 0;
    // one or more sub queries (m=...&m=...&m=...)
    for (final DataPoints[] sub_query_result : query_results) {
        // group bys (m=sum:foo{host=*})
        for (final DataPoints dps : sub_query_result) {
            // TODO(cl) - Avoid iterating and copying if we can help it. We should
            // be able to pass the original DataPoints object to the seekable view
            // and then iterate through it.
            final List<DataPoint> mutable_points = new ArrayList<DataPoint>();
            for (final DataPoint point : dps) {
                mutable_points.add(point.isInteger() ? MutableDataPoint.ofLongValue(point.timestamp(), point.longValue()) : MutableDataPoint.ofDoubleValue(point.timestamp(), point.doubleValue()));
            }
            post_agg_results[ix++] = new PostAggregatedDataPoints(dps, mutable_points.toArray(new DataPoint[mutable_points.size()]));
        }
    }
    final SeekableView[] views = new SeekableView[num_results];
    for (int i = 0; i < num_results; i++) {
        views[i] = post_agg_results[i].iterator();
    }
    final MaxCacheAggregator aggregator = new MaxCacheAggregator(Aggregators.Interpolation.LERP, "maxCache", num_results, data_query.startTime(), data_query.endTime());
    final SeekableView view = (new AggregationIterator(views, data_query.startTime(), data_query.endTime(), aggregator, Aggregators.Interpolation.LERP, false));
    // slurp all the points even though we aren't using them at this stage
    while (view.hasNext()) {
        final DataPoint mdp = view.next();
        @SuppressWarnings("unused") final Object o = mdp.isInteger() ? mdp.longValue() : mdp.doubleValue();
    }
    final long[] max_longs = aggregator.getLongMaxes();
    final double[] max_doubles = aggregator.getDoubleMaxes();
    final TopNSortingEntry[] max_by_ts = new TopNSortingEntry[num_results];
    if (aggregator.hasDoubles() && aggregator.hasLongs()) {
        for (int i = 0; i < num_results; i++) {
            max_by_ts[i] = new TopNSortingEntry(Math.max((double) max_longs[i], max_doubles[i]), i);
        }
    } else if (aggregator.hasLongs() && !aggregator.hasDoubles()) {
        for (int i = 0; i < num_results; i++) {
            max_by_ts[i] = new TopNSortingEntry((double) max_longs[i], i);
        }
    } else if (aggregator.hasDoubles() && !aggregator.hasLongs()) {
        for (int i = 0; i < num_results; i++) {
            max_by_ts[i] = new TopNSortingEntry(max_doubles[i], i);
        }
    }
    Arrays.sort(max_by_ts);
    final int result_count = Math.min(topn, num_results);
    final DataPoints[] results = new DataPoints[result_count];
    for (int i = 0; i < result_count; i++) {
        results[i] = post_agg_results[max_by_ts[i].pos];
    }
    return results;
}

Example 3 with AggregationIterator

use of net.opentsdb.core.AggregationIterator in project opentsdb by OpenTSDB.

the class MovingAverage method evaluate.

@Override
public DataPoints[] evaluate(final TSQuery data_query, final List<DataPoints[]> query_results, final List<String> params) {
    if (data_query == null) {
        throw new IllegalArgumentException("Missing time series query");
    }
    if (query_results == null || query_results.isEmpty()) {
        return new DataPoints[] {};
    }
    if (params == null || params.isEmpty()) {
        throw new IllegalArgumentException("Missing moving average window size");
    }
    String param = params.get(0);
    if (param == null || param.isEmpty()) {
        throw new IllegalArgumentException("Missing moving average window size");
    }
    param = param.trim();
    long condition = -1;
    boolean is_time_unit = false;
    if (param.matches("^[0-9]+$")) {
        try {
            condition = Integer.parseInt(param);
        } catch (NumberFormatException nfe) {
            throw new IllegalArgumentException("Invalid parameter, must be an integer", nfe);
        }
    } else if (param.startsWith("'") && param.endsWith("'")) {
        condition = parseParam(param);
        is_time_unit = true;
    } else {
        throw new IllegalArgumentException("Unparseable window size: " + param);
    }
    if (condition <= 0) {
        throw new IllegalArgumentException("Moving average window must be an " + "integer greater than zero");
    }
    int num_results = 0;
    for (final DataPoints[] results : query_results) {
        num_results += results.length;
    }
    final PostAggregatedDataPoints[] post_agg_results = new PostAggregatedDataPoints[num_results];
    int ix = 0;
    // one or more queries (m=...&m=...&m=...)
    for (final DataPoints[] sub_query_result : query_results) {
        // group bys (m=sum:foo{host=*})
        for (final DataPoints dps : sub_query_result) {
            // TODO(cl) - Avoid iterating and copying if we can help it. We should
            // be able to pass the original DataPoints object to the seekable view
            // and then iterate through it.
            final List<DataPoint> mutable_points = new ArrayList<DataPoint>();
            for (final DataPoint point : dps) {
                // avoid flip-flopping between integers and floats, always use double
                // for average.
                mutable_points.add(MutableDataPoint.ofDoubleValue(point.timestamp(), point.toDouble()));
            }
            post_agg_results[ix++] = new PostAggregatedDataPoints(dps, mutable_points.toArray(new DataPoint[mutable_points.size()]));
        }
    }
    final DataPoints[] results = new DataPoints[num_results];
    for (int i = 0; i < num_results; i++) {
        final Aggregator moving_average = new MovingAverageAggregator(Aggregators.Interpolation.LERP, "movingAverage", condition, is_time_unit);
        final SeekableView[] metrics_groups = new SeekableView[] { post_agg_results[i].iterator() };
        final SeekableView view = new AggregationIterator(metrics_groups, data_query.startTime(), data_query.endTime(), moving_average, Aggregators.Interpolation.LERP, false);
        final List<DataPoint> points = new ArrayList<DataPoint>();
        while (view.hasNext()) {
            final DataPoint mdp = view.next();
            points.add(MutableDataPoint.ofDoubleValue(mdp.timestamp(), mdp.toDouble()));
        }
        results[i] = new PostAggregatedDataPoints(post_agg_results[i], points.toArray(new DataPoint[points.size()]));
    }
    return results;
}