Examples with HistogramFactory org.elasticsearch.search.aggregations.bucket.histogram.HistogramFactory used on opensource projects

Example 1 with HistogramFactory

use of org.elasticsearch.search.aggregations.bucket.histogram.HistogramFactory in project elasticsearch by elastic.

the class CumulativeSumPipelineAggregator method reduce.

@Override
public InternalAggregation reduce(InternalAggregation aggregation, ReduceContext reduceContext) {
    MultiBucketsAggregation histo = (MultiBucketsAggregation) aggregation;
    List<? extends Bucket> buckets = histo.getBuckets();
    HistogramFactory factory = (HistogramFactory) histo;
    List<Bucket> newBuckets = new ArrayList<>();
    double sum = 0;
    for (Bucket bucket : buckets) {
        Double thisBucketValue = resolveBucketValue(histo, bucket, bucketsPaths()[0], GapPolicy.INSERT_ZEROS);
        sum += thisBucketValue;
        List<InternalAggregation> aggs = StreamSupport.stream(bucket.getAggregations().spliterator(), false).map((p) -> {
            return (InternalAggregation) p;
        }).collect(Collectors.toList());
        aggs.add(new InternalSimpleValue(name(), sum, formatter, new ArrayList<PipelineAggregator>(), metaData()));
        Bucket newBucket = factory.createBucket(factory.getKey(bucket), bucket.getDocCount(), new InternalAggregations(aggs));
        newBuckets.add(newBucket);
    }
    return factory.createAggregation(newBuckets);
}

Example 2 with HistogramFactory

use of org.elasticsearch.search.aggregations.bucket.histogram.HistogramFactory in project elasticsearch by elastic.

the class DerivativePipelineAggregator method reduce.

@Override
public InternalAggregation reduce(InternalAggregation aggregation, ReduceContext reduceContext) {
    MultiBucketsAggregation histo = (MultiBucketsAggregation) aggregation;
    List<? extends Bucket> buckets = histo.getBuckets();
    HistogramFactory factory = (HistogramFactory) histo;
    List<Bucket> newBuckets = new ArrayList<>();
    Number lastBucketKey = null;
    Double lastBucketValue = null;
    for (Bucket bucket : buckets) {
        Number thisBucketKey = factory.getKey(bucket);
        Double thisBucketValue = resolveBucketValue(histo, bucket, bucketsPaths()[0], gapPolicy);
        if (lastBucketValue != null && thisBucketValue != null) {
            double gradient = thisBucketValue - lastBucketValue;
            double xDiff = -1;
            if (xAxisUnits != null) {
                xDiff = (thisBucketKey.doubleValue() - lastBucketKey.doubleValue()) / xAxisUnits;
            }
            final List<InternalAggregation> aggs = StreamSupport.stream(bucket.getAggregations().spliterator(), false).map((p) -> {
                return (InternalAggregation) p;
            }).collect(Collectors.toList());
            aggs.add(new InternalDerivative(name(), gradient, xDiff, formatter, new ArrayList<PipelineAggregator>(), metaData()));
            Bucket newBucket = factory.createBucket(factory.getKey(bucket), bucket.getDocCount(), new InternalAggregations(aggs));
            newBuckets.add(newBucket);
        } else {
            newBuckets.add(bucket);
        }
        lastBucketKey = thisBucketKey;
        lastBucketValue = thisBucketValue;
    }
    return factory.createAggregation(newBuckets);
}

Example 3 with HistogramFactory

use of org.elasticsearch.search.aggregations.bucket.histogram.HistogramFactory in project elasticsearch by elastic.

the class MovAvgPipelineAggregator method reduce.

@Override
public InternalAggregation reduce(InternalAggregation aggregation, ReduceContext reduceContext) {
    MultiBucketsAggregation histo = (MultiBucketsAggregation) aggregation;
    List<? extends Bucket> buckets = histo.getBuckets();
    HistogramFactory factory = (HistogramFactory) histo;
    List<Bucket> newBuckets = new ArrayList<>();
    EvictingQueue<Double> values = new EvictingQueue<>(this.window);
    Number lastValidKey = 0;
    int lastValidPosition = 0;
    int counter = 0;
    // Do we need to fit the model parameters to the data?
    if (minimize) {
        assert (model.canBeMinimized());
        model = minimize(buckets, histo, model);
    }
    for (Bucket bucket : buckets) {
        Double thisBucketValue = resolveBucketValue(histo, bucket, bucketsPaths()[0], gapPolicy);
        // Default is to reuse existing bucket.  Simplifies the rest of the logic,
        // since we only change newBucket if we can add to it
        Bucket newBucket = bucket;
        if (!(thisBucketValue == null || thisBucketValue.equals(Double.NaN))) {
            // Some models (e.g. HoltWinters) have certain preconditions that must be met
            if (model.hasValue(values.size())) {
                double movavg = model.next(values);
                List<InternalAggregation> aggs = StreamSupport.stream(bucket.getAggregations().spliterator(), false).map((p) -> {
                    return (InternalAggregation) p;
                }).collect(Collectors.toList());
                aggs.add(new InternalSimpleValue(name(), movavg, formatter, new ArrayList<PipelineAggregator>(), metaData()));
                newBucket = factory.createBucket(factory.getKey(bucket), bucket.getDocCount(), new InternalAggregations(aggs));
            }
            if (predict > 0) {
                lastValidKey = factory.getKey(bucket);
                lastValidPosition = counter;
            }
            values.offer(thisBucketValue);
        }
        counter += 1;
        newBuckets.add(newBucket);
    }
    if (buckets.size() > 0 && predict > 0) {
        double[] predictions = model.predict(values, predict);
        for (int i = 0; i < predictions.length; i++) {
            List<InternalAggregation> aggs;
            Number newKey = factory.nextKey(lastValidKey);
            if (lastValidPosition + i + 1 < newBuckets.size()) {
                Bucket bucket = newBuckets.get(lastValidPosition + i + 1);
                // Get the existing aggs in the bucket so we don't clobber data
                aggs = StreamSupport.stream(bucket.getAggregations().spliterator(), false).map((p) -> {
                    return (InternalAggregation) p;
                }).collect(Collectors.toList());
                aggs.add(new InternalSimpleValue(name(), predictions[i], formatter, new ArrayList<PipelineAggregator>(), metaData()));
                Bucket newBucket = factory.createBucket(newKey, 0, new InternalAggregations(aggs));
                // Overwrite the existing bucket with the new version
                newBuckets.set(lastValidPosition + i + 1, newBucket);
            } else {
                // Not seen before, create fresh
                aggs = new ArrayList<>();
                aggs.add(new InternalSimpleValue(name(), predictions[i], formatter, new ArrayList<PipelineAggregator>(), metaData()));
                Bucket newBucket = factory.createBucket(newKey, 0, new InternalAggregations(aggs));
                // Since this is a new bucket, simply append it
                newBuckets.add(newBucket);
            }
            lastValidKey = newKey;
        }
    }
    return factory.createAggregation(newBuckets);
}

Example 4 with HistogramFactory

use of org.elasticsearch.search.aggregations.bucket.histogram.HistogramFactory in project elasticsearch by elastic.

the class SerialDiffPipelineAggregator method reduce.

@Override
public InternalAggregation reduce(InternalAggregation aggregation, ReduceContext reduceContext) {
    MultiBucketsAggregation histo = (MultiBucketsAggregation) aggregation;
    List<? extends Bucket> buckets = histo.getBuckets();
    HistogramFactory factory = (HistogramFactory) histo;
    List<Bucket> newBuckets = new ArrayList<>();
    EvictingQueue<Double> lagWindow = new EvictingQueue<>(lag);
    int counter = 0;
    for (Bucket bucket : buckets) {
        Double thisBucketValue = resolveBucketValue(histo, bucket, bucketsPaths()[0], gapPolicy);
        Bucket newBucket = bucket;
        counter += 1;
        // Still under the initial lag period, add nothing and move on
        Double lagValue;
        if (counter <= lag) {
            lagValue = Double.NaN;
        } else {
            // Peek here, because we rely on add'ing to always move the window
            lagValue = lagWindow.peek();
        }
        // Normalize null's to NaN
        if (thisBucketValue == null) {
            thisBucketValue = Double.NaN;
        }
        // Both have values, calculate diff and replace the "empty" bucket
        if (!Double.isNaN(thisBucketValue) && !Double.isNaN(lagValue)) {
            double diff = thisBucketValue - lagValue;
            List<InternalAggregation> aggs = StreamSupport.stream(bucket.getAggregations().spliterator(), false).map((p) -> {
                return (InternalAggregation) p;
            }).collect(Collectors.toList());
            aggs.add(new InternalSimpleValue(name(), diff, formatter, new ArrayList<PipelineAggregator>(), metaData()));
            newBucket = factory.createBucket(factory.getKey(bucket), bucket.getDocCount(), new InternalAggregations(aggs));
        }
        newBuckets.add(newBucket);
        lagWindow.add(thisBucketValue);
    }
    return factory.createAggregation(newBuckets);
}