use of org.elasticsearch.search.aggregations.bucket.MultiBucketsAggregation 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);
}
use of org.elasticsearch.search.aggregations.bucket.MultiBucketsAggregation 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);
}
use of org.elasticsearch.search.aggregations.bucket.MultiBucketsAggregation 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);
}
use of org.elasticsearch.search.aggregations.bucket.MultiBucketsAggregation in project metasfresh-webui-api by metasfresh.
the class KPIDataLoader method loadData.
private void loadData(final KPIDataResult.Builder data, final TimeRange timeRange) {
logger.trace("Loading data for {}", timeRange);
//
// Create query evaluation context
final Evaluatee evalCtx = Evaluatees.mapBuilder().put("MainFromMillis", data.getRange().getFromMillis()).put("MainToMillis", data.getRange().getToMillis()).put("FromMillis", timeRange.getFromMillis()).put("ToMillis", timeRange.getToMillis()).build().andComposeWith(Evaluatees.ofCtx(Env.getCtx()));
//
// Resolve esQuery's variables
final IStringExpression esQuery = kpi.getESQuery();
final String esQueryParsed = esQuery.evaluate(evalCtx, OnVariableNotFound.Preserve);
//
// Execute the query
final SearchResponse response;
try {
logger.trace("Executing: \n{}", esQueryParsed);
response = elasticsearchClient.prepareSearch(kpi.getESSearchIndex()).setTypes(kpi.getESSearchTypes()).setSource(esQueryParsed).get();
logger.trace("Got response: \n{}", response);
} catch (final NoNodeAvailableException e) {
// elastic search transport error => nothing to do about it
throw e;
} catch (final Exception e) {
throw new AdempiereException("Failed executing query for " + this + ": " + e.getLocalizedMessage() + "\nQuery: " + esQueryParsed, e);
}
// Fetch data
try {
final List<Aggregation> aggregations = response.getAggregations().asList();
for (final Aggregation agg : aggregations) {
if (agg instanceof MultiBucketsAggregation) {
final String aggName = agg.getName();
final MultiBucketsAggregation multiBucketsAggregation = (MultiBucketsAggregation) agg;
for (final Bucket bucket : multiBucketsAggregation.getBuckets()) {
final Object key = dataSetValueKeyExtractor.apply(bucket, timeRange);
for (final KPIField field : kpi.getFields()) {
final Object value = field.getBucketValueExtractor().extractValue(aggName, bucket);
final Object jsonValue = formatValue(field, value);
if (jsonValue == null) {
continue;
}
final String fieldName = fieldNameExtractor.apply(field, timeRange);
data.putValue(aggName, key, fieldName, jsonValue);
}
//
// Make sure the groupByField's value is present in our dataSet value.
// If not exist, we can use the key as it's value.
final KPIField groupByField = kpi.getGroupByFieldOrNull();
if (groupByField != null) {
data.putValueIfAbsent(aggName, key, groupByField.getFieldName(), key);
}
}
} else if (agg instanceof NumericMetricsAggregation.SingleValue) {
final NumericMetricsAggregation.SingleValue singleValueAggregation = (NumericMetricsAggregation.SingleValue) agg;
// N/A
final String key = "NO_KEY";
for (final KPIField field : kpi.getFields()) {
final Object value;
if ("value".equals(field.getESPathAsString())) {
value = singleValueAggregation.value();
} else {
throw new IllegalStateException("Only ES path ending with 'value' allowed for field: " + field);
}
final Object jsonValue = field.convertValueToJson(value);
data.putValue(agg.getName(), key, field.getFieldName(), jsonValue);
}
} else {
new AdempiereException("Aggregation type not supported: " + agg.getClass()).throwIfDeveloperModeOrLogWarningElse(logger);
}
}
} catch (final Exception e) {
throw new AdempiereException(e.getLocalizedMessage() + "\n KPI: " + this + "\n Query: " + esQueryParsed + "\n Response: " + response, e);
}
}
use of org.elasticsearch.search.aggregations.bucket.MultiBucketsAggregation in project vertigo by KleeGroup.
the class ESFacetedQueryResultBuilder method createCluster.
private Map<FacetValue, DtList<I>> createCluster(final Map<String, I> dtcIndex, final Map<I, Map<DtField, String>> resultHighlights) {
final Map<FacetValue, DtList<I>> resultCluster = new LinkedHashMap<>();
final FacetDefinition facetDefinition = searchQuery.getClusteringFacetDefinition();
final Aggregation facetAggregation = queryResponse.getAggregations().get(facetDefinition.getName());
if (facetDefinition.isRangeFacet()) {
// Cas des facettes par 'range'
final MultiBucketsAggregation multiBuckets = (MultiBucketsAggregation) facetAggregation;
for (final FacetValue facetRange : facetDefinition.getFacetRanges()) {
final Bucket value = getBucketByKey(multiBuckets, facetRange.getListFilter().getFilterValue());
populateCluster(value, facetRange, resultCluster, dtcIndex, resultHighlights);
}
} else {
// Cas des facettes par 'term'
final MultiBucketsAggregation multiBuckets = (MultiBucketsAggregation) facetAggregation;
FacetValue facetValue;
for (final Bucket bucket : multiBuckets.getBuckets()) {
final String term = bucket.getKeyAsString();
final String query = facetDefinition.getDtField().getName() + ":\"" + term + "\"";
final MessageText label = MessageText.of(term);
facetValue = new FacetValue(term, ListFilter.of(query), label);
populateCluster(bucket, facetValue, resultCluster, dtcIndex, resultHighlights);
}
}
return resultCluster;
}
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