# SPDX-License-Identifier: Apache-2.0
#
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# this file be licensed under the Apache-2.0 license or a
# compatible open source license.
#
# Modifications Copyright OpenSearch Contributors. See
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# license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright
# ownership. Elasticsearch B.V. licenses this file to you under
# the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
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# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
import collections.abc as collections_abc
from typing import Any, Optional
from .response.aggs import AggResponse, BucketData, FieldBucketData, TopHitsData
from .utils import DslBase
def A( # pylint: disable=invalid-name
name_or_agg: Any, filter: Any = None, **params: Any
) -> Any:
if filter is not None:
if name_or_agg != "filter":
raise ValueError(
"Aggregation %r doesn't accept positional argument 'filter'."
% name_or_agg
)
params["filter"] = filter
# {"terms": {"field": "tags"}, "aggs": {...}}
if isinstance(name_or_agg, collections_abc.Mapping):
if params:
raise ValueError("A() cannot accept parameters when passing in a dict.")
# copy to avoid modifying in-place
agg = name_or_agg.copy() # type: ignore
# pop out nested aggs
aggs = agg.pop("aggs", None)
# pop out meta data
meta = agg.pop("meta", None)
# should be {"terms": {"field": "tags"}}
if len(agg) != 1:
raise ValueError(
'A() can only accept dict with an aggregation ({"terms": {...}}). '
"Instead it got (%r)" % name_or_agg
)
agg_type, params = agg.popitem()
if aggs:
params = params.copy()
params["aggs"] = aggs
if meta:
params = params.copy()
params["meta"] = meta
return Agg.get_dsl_class(agg_type)(_expand__to_dot=False, **params)
# Terms(...) just return the nested agg
elif isinstance(name_or_agg, Agg):
if params:
raise ValueError(
"A() cannot accept parameters when passing in an Agg object."
)
return name_or_agg
# "terms", field="tags"
return Agg.get_dsl_class(name_or_agg)(**params)
[docs]class Agg(DslBase):
_type_name: str = "agg"
_type_shortcut = staticmethod(A)
name: Optional[str] = None
def __contains__(self, key: Any) -> bool:
return False
[docs] def to_dict(self) -> Any:
d = super(Agg, self).to_dict()
if "meta" in d[self.name]:
d["meta"] = d[self.name].pop("meta")
return d
def result(self, search: Any, data: Any) -> Any:
return AggResponse(self, search, data)
class AggBase(object):
_param_defs = {
"aggs": {"type": "agg", "hash": True},
}
def __contains__(self: Any, key: Any) -> bool:
return key in self._params.get("aggs", {})
def __getitem__(self: Any, agg_name: Any) -> Any:
agg = self._params.setdefault("aggs", {})[agg_name] # propagate KeyError
# make sure we're not mutating a shared state - whenever accessing a
# bucket, return a shallow copy of it to be safe
if isinstance(agg, Bucket):
agg = A(agg.name, **agg._params)
# be sure to store the copy so any modifications to it will affect us
self._params["aggs"][agg_name] = agg
return agg
def __setitem__(self: Any, agg_name: str, agg: Any) -> None:
self.aggs[agg_name] = A(agg)
def __iter__(self: Any) -> Any:
return iter(self.aggs)
def _agg(
self: Any, bucket: Any, name: Any, agg_type: Any, *args: Any, **params: Any
) -> Any:
agg = self[name] = A(agg_type, *args, **params)
# For chaining - when creating new buckets return them...
if bucket:
return agg
# otherwise return self._base so we can keep chaining
else:
return self._base
def metric(self: Any, name: Any, agg_type: Any, *args: Any, **params: Any) -> Any:
return self._agg(False, name, agg_type, *args, **params)
def bucket(self: Any, name: Any, agg_type: Any, *args: Any, **params: Any) -> Any:
return self._agg(True, name, agg_type, *args, **params)
def pipeline(self: Any, name: Any, agg_type: Any, *args: Any, **params: Any) -> Any:
return self._agg(False, name, agg_type, *args, **params)
def result(self: Any, search: Any, data: Any) -> Any:
return BucketData(self, search, data)
class Bucket(AggBase, Agg):
def __init__(self, **params: Any) -> None:
super(Bucket, self).__init__(**params)
# remember self for chaining
self._base = self
def to_dict(self) -> Any:
d = super(AggBase, self).to_dict()
if "aggs" in d[self.name]:
d["aggs"] = d[self.name].pop("aggs")
return d
class Filter(Bucket):
name: Optional[str] = "filter"
_param_defs = {
"filter": {"type": "query"},
"aggs": {"type": "agg", "hash": True},
}
def __init__(self, filter: Any = None, **params: Any) -> None:
if filter is not None:
params["filter"] = filter
super(Filter, self).__init__(**params)
def to_dict(self) -> Any:
d = super(Filter, self).to_dict()
d[self.name].update(d[self.name].pop("filter", {}))
return d
class Pipeline(Agg):
pass
# bucket aggregations
class Filters(Bucket):
name: str = "filters"
_param_defs = {
"filters": {"type": "query", "hash": True},
"aggs": {"type": "agg", "hash": True},
}
class Children(Bucket):
name = "children"
class Parent(Bucket):
name = "parent"
class DateHistogram(Bucket):
name = "date_histogram"
def result(self, search: Any, data: Any) -> Any:
return FieldBucketData(self, search, data)
class AutoDateHistogram(DateHistogram):
name = "auto_date_histogram"
class DateRange(Bucket):
name = "date_range"
class GeoDistance(Bucket):
name = "geo_distance"
class GeohashGrid(Bucket):
name = "geohash_grid"
class GeotileGrid(Bucket):
name = "geotile_grid"
class GeoCentroid(Bucket):
name = "geo_centroid"
class Global(Bucket):
name = "global"
class Histogram(Bucket):
name = "histogram"
def result(self, search: Any, data: Any) -> Any:
return FieldBucketData(self, search, data)
class IPRange(Bucket):
name = "ip_range"
class Missing(Bucket):
name = "missing"
class Nested(Bucket):
name = "nested"
class Range(Bucket):
name = "range"
class RareTerms(Bucket):
name = "rare_terms"
def result(self, search: Any, data: Any) -> Any:
return FieldBucketData(self, search, data)
class ReverseNested(Bucket):
name = "reverse_nested"
class SignificantTerms(Bucket):
name = "significant_terms"
class SignificantText(Bucket):
name = "significant_text"
class Terms(Bucket):
name = "terms"
def result(self, search: Any, data: Any) -> Any:
return FieldBucketData(self, search, data)
class Sampler(Bucket):
name = "sampler"
class DiversifiedSampler(Bucket):
name = "diversified_sampler"
class Composite(Bucket):
name = "composite"
_param_defs = {
"sources": {"type": "agg", "hash": True, "multi": True},
"aggs": {"type": "agg", "hash": True},
}
class VariableWidthHistogram(Bucket):
name = "variable_width_histogram"
def result(self, search: Any, data: Any) -> Any:
return FieldBucketData(self, search, data)
# metric aggregations
class TopHits(Agg):
name = "top_hits"
def result(self, search: Any, data: Any) -> Any:
return TopHitsData(self, search, data)
class Avg(Agg):
name = "avg"
class WeightedAvg(Agg):
name = "weighted_avg"
class Cardinality(Agg):
name = "cardinality"
class ExtendedStats(Agg):
name = "extended_stats"
class Boxplot(Agg):
name = "boxplot"
class GeoBounds(Agg):
name = "geo_bounds"
class Max(Agg):
name = "max"
class MedianAbsoluteDeviation(Agg):
name = "median_absolute_deviation"
class Min(Agg):
name = "min"
class Percentiles(Agg):
name = "percentiles"
class PercentileRanks(Agg):
name = "percentile_ranks"
class ScriptedMetric(Agg):
name = "scripted_metric"
class Stats(Agg):
name = "stats"
class Sum(Agg):
name = "sum"
class TTest(Agg):
name = "t_test"
class ValueCount(Agg):
name = "value_count"
# pipeline aggregations
class AvgBucket(Pipeline):
name = "avg_bucket"
class BucketScript(Pipeline):
name = "bucket_script"
class BucketSelector(Pipeline):
name = "bucket_selector"
class CumulativeSum(Pipeline):
name = "cumulative_sum"
class CumulativeCardinality(Pipeline):
name = "cumulative_cardinality"
class Derivative(Pipeline):
name = "derivative"
class ExtendedStatsBucket(Pipeline):
name = "extended_stats_bucket"
class Inference(Pipeline):
name = "inference"
class MaxBucket(Pipeline):
name = "max_bucket"
class MinBucket(Pipeline):
name = "min_bucket"
class MovingFn(Pipeline):
name = "moving_fn"
class MovingAvg(Pipeline):
name = "moving_avg"
class MovingPercentiles(Pipeline):
name = "moving_percentiles"
class Normalize(Pipeline):
name = "normalize"
class PercentilesBucket(Pipeline):
name = "percentiles_bucket"
class SerialDiff(Pipeline):
name = "serial_diff"
class StatsBucket(Pipeline):
name = "stats_bucket"
class SumBucket(Pipeline):
name = "sum_bucket"
class BucketSort(Pipeline):
name = "bucket_sort"