Merge branch 'main' into 2025/10/31/resize-action-cleanup

Author: DaveCTurner

docs/changelog/135231.yaml

@@ -0,0 +1,5 @@
+pr: 135231
+summary: Improve retrying PIT contexts for read-only indices
+area: Search
+type: enhancement
+issues: []

docs/changelog/137023.yaml

@@ -0,0 +1,5 @@
+pr: 137023
+summary: Support choosing the downsampling method in data stream lifecycle
+area: "Data streams"
+type: enhancement
+issues: []

docs/changelog/137220.yaml

@@ -0,0 +1,5 @@
+pr: 137220
+summary: Skip dataframes when disabled
+area: Machine Learning
+type: bug
+issues: []

docs/changelog/137302.yaml

@@ -0,0 +1,5 @@
+pr: 137302
+summary: Add audit log testing for cert-based cross-cluster authentication
+area: Security
+type: enhancement
+issues: []

docs/changelog/137331.yaml

@@ -0,0 +1,5 @@
+pr: 137331
+summary: Add ES93BloomFilterStoredFieldsFormat for efficient field existence checks
+area: TSDB
+type: enhancement
+issues: []

docs/changelog/137442.yaml

@@ -0,0 +1,5 @@
+pr: 137442
+summary: Handle ._original stored fields with fls
+area: "Authorization"
+type: bug
+issues: []

docs/reference/elasticsearch/mapping-reference/semantic-text.md

@@ -25,9 +25,12 @@ type make it simpler to perform semantic search on your data. The
 with [match](/reference/query-languages/query-dsl/query-dsl-match-query.md), [sparse_vector](/reference/query-languages/query-dsl/query-dsl-sparse-vector-query.md)
 or [knn](/reference/query-languages/query-dsl/query-dsl-knn-query.md) queries.
 
-If you don’t specify an inference endpoint, the `inference_id` field defaults to
-`.elser-2-elasticsearch`, a preconfigured endpoint for the elasticsearch
-service.
+{applies_to}`serverless: ga` If you don’t specify an {{infer}} endpoint, the `inference_id` field defaults to
+`.elser-v2-elastic`, a preconfigured endpoint for the `elasticsearch` service.
+This endpoint uses the [Elastic {{infer-cap}} Service (EIS)](docs-content://explore-analyze/elastic-inference/eis.md#elser-on-eis).
+
+{applies_to}`stack: ga 9.0` If you don’t specify an {{infer}} endpoint, the `inference_id` field defaults to
+`.elser-2-elasticsearch`, a preconfigured endpoint for the `elasticsearch` service.
 
 Using `semantic_text`, you won’t need to specify how to generate embeddings for
 your data, or how to index it. The {{infer}} endpoint automatically determines
@@ -43,10 +46,15 @@ You can use either preconfigured endpoints in your `semantic_text` fields which
 are ideal for most use cases or create custom endpoints and reference them in
 the field mappings.
 
-### Using the default ELSER endpoint
+:::::::{tab-set}
+
+::::::{tab-item} Using the default ELSER on EIS endpoint on Serverless
+
+```{applies_to}
+serverless: ga
+```
 
-If you use the preconfigured `.elser-2-elasticsearch` endpoint, you can set up
-`semantic_text` with the following API request:
+If you use the default `.elser-v2-elastic` endpoint that runs on EIS, you can set up `semantic_text` with the following API request:
 
 ```console
 PUT my-index-000001
@@ -62,10 +70,58 @@ PUT my-index-000001
 ```
 % TEST[skip:Requires inference endpoint]
 
+::::::
+
+::::::{tab-item} Using the preconfigured ELSER on EIS endpoint in Cloud
+
+```{applies_to}
+stack: ga 9.2
+deployment:
+  self: unavailable
+```
+
+If you use the preconfigured `.elser-v2-elastic` endpoint that runs on EIS, you can set up `semantic_text` with the following API request:
+
+```console
+PUT my-index-000001
+{
+  "mappings": {
+    "properties": {
+      "inference_field": {
+        "type": "semantic_text",
+        "inference_id": ".elser-v2-elastic"
+      }
+    }
+  }
+}
+```
+
+::::::
+
+::::::{tab-item} Using the default ELSER endpoint
+
+If you use the preconfigured `.elser-2-elasticsearch` endpoint, you can set up `semantic_text` with the following API request:
+
+```console
+PUT my-index-000001
+{
+  "mappings": {
+    "properties": {
+      "inference_field": {
+        "type": "semantic_text"
+      }
+    }
+  }
+}
+```
+
+::::::
+
+:::::::
+
 ### Using a custom endpoint
 
-To use a custom {{infer}} endpoint instead of the default
-`.elser-2-elasticsearch`, you
+To use a custom {{infer}} endpoint instead of the default, you
 must [Create {{infer}} API](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-inference-put)
 and specify its `inference_id` when setting up the `semantic_text` field type.
 
@@ -110,14 +166,48 @@ PUT my-index-000003
 % TEST[skip:Requires inference endpoint]
 
 ### Using ELSER on EIS
+
 ```{applies_to}
-stack: preview 9.1
-serverless: preview
+stack: preview 9.1 ga 9.2
+deployment:
+  self: unavailable
+serverless: ga
 ```
 
 If you use the preconfigured `.elser-2-elastic` endpoint that utilizes the ELSER model as a service through the Elastic Inference Service ([ELSER on EIS](docs-content://explore-analyze/elastic-inference/eis.md#elser-on-eis)), you can
 set up `semantic_text` with the following API request:
 
+:::::::{tab-set}
+
+::::::{tab-item} Using ELSER on EIS on Serverless
+
+```{applies_to}
+serverless: ga
+```
+
+```console
+PUT my-index-000001
+{
+  "mappings": {
+    "properties": {
+      "inference_field": {
+        "type": "semantic_text"
+      }
+    }
+  }
+}
+```
+
+::::::
+
+::::::{tab-item} Using ELSER on EIS in Cloud
+
+```{applies_to}
+stack: ga 9.2
+deployment:
+  self: unavailable
+```
+
 ```console
 PUT my-index-000001
 {
@@ -133,10 +223,9 @@ PUT my-index-000001
 ```
 % TEST[skip:Requires inference endpoint]
 
-::::{note}
-While we do encourage experimentation, we do not recommend implementing production use cases on top of this feature while it is in Technical Preview.
+::::::
 
-::::
+:::::::
 
 ## Parameters for `semantic_text` fields [semantic-text-params]
 

docs/reference/query-languages/esql/_snippets/commands/layout/fork.md

@@ -17,6 +17,8 @@ FORK ( <processing_commands> ) ( <processing_commands> ) ... ( <processing_comma
 The `FORK` processing command creates multiple execution branches to operate
 on the same input data and combines the results in a single output table. A discriminator column (`_fork`) is added to identify which branch each row came from.
 
+Together with the [`FUSE`](/reference/query-languages/esql/commands/fuse.md) command, `FORK` enables hybrid search to combine and score results from multiple queries. To learn more about using {{esql}} for search, refer to [ES|QL for search](docs-content://solutions/search/esql-for-search.md).
+
 **Branch identification:**
 - The `_fork` column identifies each branch with values like `fork1`, `fork2`, `fork3`
 - Values correspond to the order branches are defined

libs/simdvec/src/main21/java/org/elasticsearch/simdvec/internal/vectorization/MSBitToInt4ESNextOSQVectorsScorer.java

@@ -14,7 +14,6 @@
 import jdk.incubator.vector.LongVector;
 import jdk.incubator.vector.ShortVector;
 import jdk.incubator.vector.VectorOperators;
-import jdk.incubator.vector.VectorSpecies;
 
 import org.apache.lucene.index.VectorSimilarityFunction;
 import org.apache.lucene.store.IndexInput;
@@ -31,23 +30,6 @@
 /** Panamized scorer for quantized vectors stored as a {@link MemorySegment}. */
 final class MSBitToInt4ESNextOSQVectorsScorer extends MemorySegmentESNextOSQVectorsScorer.MemorySegmentScorer {
 
-    private static final int BULK_SIZE = MemorySegmentESNextOSQVectorsScorer.BULK_SIZE;
-    private static final float FOUR_BIT_SCALE = 1f / ((1 << 4) - 1);
-
-    private static final VectorSpecies<Integer> INT_SPECIES_128 = IntVector.SPECIES_128;
-
-    private static final VectorSpecies<Long> LONG_SPECIES_128 = LongVector.SPECIES_128;
-    private static final VectorSpecies<Long> LONG_SPECIES_256 = LongVector.SPECIES_256;
-
-    private static final VectorSpecies<Byte> BYTE_SPECIES_128 = ByteVector.SPECIES_128;
-    private static final VectorSpecies<Byte> BYTE_SPECIES_256 = ByteVector.SPECIES_256;
-
-    private static final VectorSpecies<Short> SHORT_SPECIES_128 = ShortVector.SPECIES_128;
-    private static final VectorSpecies<Short> SHORT_SPECIES_256 = ShortVector.SPECIES_256;
-
-    private static final VectorSpecies<Float> FLOAT_SPECIES_128 = FloatVector.SPECIES_128;
-    private static final VectorSpecies<Float> FLOAT_SPECIES_256 = FloatVector.SPECIES_256;
-
     MSBitToInt4ESNextOSQVectorsScorer(IndexInput in, int dimensions, int dataLength, MemorySegment memorySegment) {
         super(in, dimensions, dataLength, memorySegment);
     }