Auto-generated API code

Author: elasticmachine

elasticsearch/_async/client/logstash.py

@@ -141,7 +141,9 @@ async def put_pipeline(
 
         `<https://www.elastic.co/docs/api/doc/elasticsearch/v9/operation/operation-logstash-put-pipeline>`_
 
-        :param id: An identifier for the pipeline.
+        :param id: An identifier for the pipeline. Pipeline IDs must begin with a letter
+            or underscore and contain only letters, underscores, dashes, hyphens and
+            numbers.
         :param pipeline:
         """
         if id in SKIP_IN_PATH:

elasticsearch/_sync/client/logstash.py

@@ -141,7 +141,9 @@ def put_pipeline(
 
         `<https://www.elastic.co/docs/api/doc/elasticsearch/v9/operation/operation-logstash-put-pipeline>`_
 
-        :param id: An identifier for the pipeline.
+        :param id: An identifier for the pipeline. Pipeline IDs must begin with a letter
+            or underscore and contain only letters, underscores, dashes, hyphens and
+            numbers.
         :param pipeline:
         """
         if id in SKIP_IN_PATH:

elasticsearch/dsl/aggs.py

@@ -643,6 +643,54 @@ def __init__(
         )
 
 
+class CartesianBounds(Agg[_R]):
+    """
+    A metric aggregation that computes the spatial bounding box containing
+    all values for a Point or Shape field.
+
+    :arg field: The field on which to run the aggregation.
+    :arg missing: The value to apply to documents that do not have a
+        value. By default, documents without a value are ignored.
+    :arg script:
+    """
+
+    name = "cartesian_bounds"
+
+    def __init__(
+        self,
+        *,
+        field: Union[str, "InstrumentedField", "DefaultType"] = DEFAULT,
+        missing: Union[str, int, float, bool, "DefaultType"] = DEFAULT,
+        script: Union["types.Script", Dict[str, Any], "DefaultType"] = DEFAULT,
+        **kwargs: Any,
+    ):
+        super().__init__(field=field, missing=missing, script=script, **kwargs)
+
+
+class CartesianCentroid(Agg[_R]):
+    """
+    A metric aggregation that computes the weighted centroid from all
+    coordinate values for point and shape fields.
+
+    :arg field: The field on which to run the aggregation.
+    :arg missing: The value to apply to documents that do not have a
+        value. By default, documents without a value are ignored.
+    :arg script:
+    """
+
+    name = "cartesian_centroid"
+
+    def __init__(
+        self,
+        *,
+        field: Union[str, "InstrumentedField", "DefaultType"] = DEFAULT,
+        missing: Union[str, int, float, bool, "DefaultType"] = DEFAULT,
+        script: Union["types.Script", Dict[str, Any], "DefaultType"] = DEFAULT,
+        **kwargs: Any,
+    ):
+        super().__init__(field=field, missing=missing, script=script, **kwargs)
+
+
 class CategorizeText(Bucket[_R]):
     """
     A multi-bucket aggregation that groups semi-structured text into
@@ -725,6 +773,43 @@ def __init__(
         )
 
 
+class ChangePoint(Pipeline[_R]):
+    """
+    A sibling pipeline that detects, spikes, dips, and change points in a
+    metric. Given a distribution of values provided by the sibling multi-
+    bucket aggregation, this aggregation indicates the bucket of any spike
+    or dip and/or the bucket at which the largest change in the
+    distribution of values, if they are statistically significant. There
+    must be at least 22 bucketed values. Fewer than 1,000 is preferred.
+
+    :arg format: `DecimalFormat` pattern for the output value. If
+        specified, the formatted value is returned in the aggregation’s
+        `value_as_string` property.
+    :arg gap_policy: Policy to apply when gaps are found in the data.
+        Defaults to `skip` if omitted.
+    :arg buckets_path: Path to the buckets that contain one set of values
+        to correlate.
+    """
+
+    name = "change_point"
+
+    def __init__(
+        self,
+        *,
+        format: Union[str, "DefaultType"] = DEFAULT,
+        gap_policy: Union[
+            Literal["skip", "insert_zeros", "keep_values"], "DefaultType"
+        ] = DEFAULT,
+        buckets_path: Union[
+            str, Sequence[str], Mapping[str, str], "DefaultType"
+        ] = DEFAULT,
+        **kwargs: Any,
+    ):
+        super().__init__(
+            format=format, gap_policy=gap_policy, buckets_path=buckets_path, **kwargs
+        )
+
+
 class Children(Bucket[_R]):
     """
     A single bucket aggregation that selects child documents that have the
@@ -2960,6 +3045,14 @@ class SignificantTerms(Bucket[_R]):
         the foreground sample with a term divided by the number of
         documents in the background with the term.
     :arg script_heuristic: Customized score, implemented via a script.
+    :arg p_value: Significant terms heuristic that calculates the p-value
+        between the term existing in foreground and background sets.  The
+        p-value is the probability of obtaining test results at least as
+        extreme as the results actually observed, under the assumption
+        that the null hypothesis is correct. The p-value is calculated
+        assuming that the foreground set and the background set are
+        independent https://en.wikipedia.org/wiki/Bernoulli_trial, with
+        the null hypothesis that the probabilities are the same.
     :arg shard_min_doc_count: Regulates the certainty a shard has if the
         term should actually be added to the candidate list or not with
         respect to the `min_doc_count`. Terms will only be considered if
@@ -3013,6 +3106,9 @@ def __init__(
         script_heuristic: Union[
             "types.ScriptedHeuristic", Dict[str, Any], "DefaultType"
         ] = DEFAULT,
+        p_value: Union[
+            "types.PValueHeuristic", Dict[str, Any], "DefaultType"
+        ] = DEFAULT,
         shard_min_doc_count: Union[int, "DefaultType"] = DEFAULT,
         shard_size: Union[int, "DefaultType"] = DEFAULT,
         size: Union[int, "DefaultType"] = DEFAULT,
@@ -3031,6 +3127,7 @@ def __init__(
             mutual_information=mutual_information,
             percentage=percentage,
             script_heuristic=script_heuristic,
+            p_value=p_value,
             shard_min_doc_count=shard_min_doc_count,
             shard_size=shard_size,
             size=size,

elasticsearch/dsl/response/__init__.py

@@ -233,10 +233,13 @@ def search_after(self) -> "SearchBase[_R]":
     "types.SimpleValueAggregate",
     "types.DerivativeAggregate",
     "types.BucketMetricValueAggregate",
+    "types.ChangePointAggregate",
     "types.StatsAggregate",
     "types.StatsBucketAggregate",
     "types.ExtendedStatsAggregate",
     "types.ExtendedStatsBucketAggregate",
+    "types.CartesianBoundsAggregate",
+    "types.CartesianCentroidAggregate",
     "types.GeoBoundsAggregate",
     "types.GeoCentroidAggregate",
     "types.HistogramAggregate",