[ML] rename frequent_items to frequent_item_sets and make it GA (#93421)

rename frequent_items to frequent_item_sets and remove the experimental batch

docs/changelog/93421.yaml

@@ -0,0 +1,10 @@
+pr: 93421
+summary: Make `frequent_item_sets` aggregation GA
+area: Machine Learning
+type: feature
+issues: []
+highlight:
+  title: Make `frequent_item_sets` aggregation GA
+  body: The `frequent_item_sets` aggregation has been moved from
+        technical preview to general availability.
+  notable: true

docs/reference/aggregations/bucket.asciidoc

@@ -36,7 +36,7 @@ include::bucket/filter-aggregation.asciidoc[]
 
 include::bucket/filters-aggregation.asciidoc[]
 
-include::bucket/frequent-items-aggregation.asciidoc[]
+include::bucket/frequent-item-sets-aggregation.asciidoc[]
 
 include::bucket/geodistance-aggregation.asciidoc[]
 

docs/reference/aggregations/bucket/frequent-items-aggregation.asciidoc → docs/reference/aggregations/bucket/frequent-item-sets-aggregation.asciidoc

@@ -1,40 +1,38 @@
-[[search-aggregations-bucket-frequent-items-aggregation]]
-=== Frequent items aggregation
+[[search-aggregations-bucket-frequent-item-sets-aggregation]]
+=== Frequent item sets aggregation
 ++++
-<titleabbrev>Frequent items</titleabbrev>
+<titleabbrev>Frequent item sets</titleabbrev>
 ++++
 
-experimental::[]
-
-A bucket aggregation which finds frequent item sets. It is a form of association 
-rules mining that identifies items that often occur together. Items that are 
-frequently purchased together or log events that tend to co-occur are examples 
-of frequent item sets. Finding frequent item sets helps to discover 
+A bucket aggregation which finds frequent item sets. It is a form of association
+rules mining that identifies items that often occur together. Items that are
+frequently purchased together or log events that tend to co-occur are examples
+of frequent item sets. Finding frequent item sets helps to discover
 relationships between different data points (items).
 
-The aggregation reports closed item sets. A frequent item set is called closed 
-if no superset exists with the same ratio of documents (also known as its 
-<<frequent-items-minimum-support,support value>>). For example, we have the two 
+The aggregation reports closed item sets. A frequent item set is called closed
+if no superset exists with the same ratio of documents (also known as its
+<<frequent-item-sets-minimum-support,support value>>). For example, we have the two
 following candidates for a frequent item set, which have the same support value:
 1. `apple, orange, banana`
 2. `apple, orange, banana, tomato`.
-Only the second item set (`apple, orange, banana, tomato`) is returned, and the 
-first set – which is a subset of the second one – is skipped. Both item sets 
+Only the second item set (`apple, orange, banana, tomato`) is returned, and the
+first set – which is a subset of the second one – is skipped. Both item sets
 might be returned if their support values are different.
 
-The runtime of the aggregation depends on the data and the provided parameters. 
-It might take a significant time for the aggregation to complete. For this 
-reason, it is recommended to use <<async-search,async search>> to run your 
+The runtime of the aggregation depends on the data and the provided parameters.
+It might take a significant time for the aggregation to complete. For this
+reason, it is recommended to use <<async-search,async search>> to run your
 requests asynchronously.
 
 
 ==== Syntax
 
-A `frequent_items` aggregation looks like this in isolation:
+A `frequent_item_sets` aggregation looks like this in isolation:
 
 [source,js]
 --------------------------------------------------
-"frequent_items": {
+"frequent_item_sets": {
   "minimum_set_size": 3,
   "fields": [
     {"field": "my_field_1"},
@@ -44,74 +42,74 @@ A `frequent_items` aggregation looks like this in isolation:
 --------------------------------------------------
 // NOTCONSOLE
 
-.`frequent_items` Parameters
+.`frequent_item_sets` Parameters
 |===
 |Parameter Name |Description |Required |Default Value
 |`fields` |(array) Fields to analyze. | Required |
-|`minimum_set_size` | (integer) The <<frequent-items-minimum-set-size,minimum size>> of one item set. | Optional | `1`
-|`minimum_support` | (integer) The <<frequent-items-minimum-support,minimum support>> of one item set. | Optional | `0.1`
+|`minimum_set_size` | (integer) The <<frequent-item-sets-minimum-set-size,minimum size>> of one item set. | Optional | `1`
+|`minimum_support` | (integer) The <<frequent-item-sets-minimum-support,minimum support>> of one item set. | Optional | `0.1`
 |`size` | (integer) The number of top item sets to return. | Optional | `10`
 |`filter` | (object) Query that filters documents from the analysis | Optional | `match_all`
 |===
 
 
 [discrete]
-[[frequent-items-fields]]
+[[frequent-item-sets-fields]]
 ==== Fields
 
-Supported field types for the analyzed fields are keyword, numeric, ip, date, 
-and arrays of these types. You can also add runtime fields to your analyzed 
+Supported field types for the analyzed fields are keyword, numeric, ip, date,
+and arrays of these types. You can also add runtime fields to your analyzed
 fields.
 
-If the combined cardinality of the analyzed fields are high, the aggregation 
+If the combined cardinality of the analyzed fields are high, the aggregation
 might require a significant amount of system resources.
 
-You can filter the values for each field by using the `include` and `exclude` 
-parameters. The parameters can be regular expression strings or arrays of 
-strings of exact terms. The filtered values are removed from the analysis and 
-therefore reduce the runtime. If both `include` and `exclude` are defined, 
-`exclude` takes precedence; it means `include` is evaluated first and then 
+You can filter the values for each field by using the `include` and `exclude`
+parameters. The parameters can be regular expression strings or arrays of
+strings of exact terms. The filtered values are removed from the analysis and
+therefore reduce the runtime. If both `include` and `exclude` are defined,
+`exclude` takes precedence; it means `include` is evaluated first and then
 `exclude`.
 
 [discrete]
-[[frequent-items-minimum-set-size]]
+[[frequent-item-sets-minimum-set-size]]
 ==== Minimum set size
 
-The minimum set size is the minimum number of items the set needs to contain. A 
-value of 1 returns the frequency of single items. Only item sets that contain at 
-least the number of `minimum_set_size` items are returned. For example, the item 
-set `orange, banana, apple` is returned only if the minimum set size is 3 or 
+The minimum set size is the minimum number of items the set needs to contain. A
+value of 1 returns the frequency of single items. Only item sets that contain at
+least the number of `minimum_set_size` items are returned. For example, the item
+set `orange, banana, apple` is returned only if the minimum set size is 3 or
 lower.
 
 [discrete]
-[[frequent-items-minimum-support]]
+[[frequent-item-sets-minimum-support]]
 ==== Minimum support
 
-The minimum support value is the ratio of documents that an item set must exist 
-in to be considered "frequent". In particular, it is a normalized value between 
-0 and 1. It is calculated by dividing the number of documents containing the 
+The minimum support value is the ratio of documents that an item set must exist
+in to be considered "frequent". In particular, it is a normalized value between
+0 and 1. It is calculated by dividing the number of documents containing the
 item set by the total number of documents.
 
-For example, if a given item set is contained by five documents and the total 
-number of documents is 20, then the support of the item set is 5/20 = 0.25. 
-Therefore, this set is returned only if the minimum support is 0.25 or lower. 
-As a higher minimum support prunes more items, the calculation is less resource 
-intensive. The `minimum_support` parameter has an effect on the required memory 
+For example, if a given item set is contained by five documents and the total
+number of documents is 20, then the support of the item set is 5/20 = 0.25.
+Therefore, this set is returned only if the minimum support is 0.25 or lower.
+As a higher minimum support prunes more items, the calculation is less resource
+intensive. The `minimum_support` parameter has an effect on the required memory
 and the runtime of the aggregation.
 
 
 [discrete]
-[[frequent-items-size]]
+[[frequent-item-sets-size]]
 ==== Size
 
-This parameter defines the maximum number of item sets to return. The result 
-contains top-k item sets; the item sets with the highest support values. This 
-parameter has a significant effect on the required memory and the runtime of the 
+This parameter defines the maximum number of item sets to return. The result
+contains top-k item sets; the item sets with the highest support values. This
+parameter has a significant effect on the required memory and the runtime of the
 aggregation.
 
 
 [discrete]
-[[frequent-items-filter]]
+[[frequent-item-sets-filter]]
 ==== Filter
 
 A query to filter documents to use as part of the analysis. Documents that
@@ -123,7 +121,7 @@ Use a top-level query to filter the data set.
 
 
 [discrete]
-[[frequent-items-example]]
+[[frequent-item-sets-example]]
 ==== Examples
 
 In the following examples, we use the e-commerce {kib} sample data set.
@@ -132,14 +130,14 @@ In the following examples, we use the e-commerce {kib} sample data set.
 [discrete]
 ==== Aggregation with two analyzed fields and an `exclude` parameter
 
-In the first example, the goal is to find out based on transaction data (1.) 
-from what product categories the customers purchase products frequently together 
-and (2.) from which cities they make those purchases. We want to exclude results 
-where location information is not available (where the city name is `other`). 
-Finally, we are interested in sets with three or more items, and want to see the 
+In the first example, the goal is to find out based on transaction data (1.)
+from what product categories the customers purchase products frequently together
+and (2.) from which cities they make those purchases. We want to exclude results
+where location information is not available (where the city name is `other`).
+Finally, we are interested in sets with three or more items, and want to see the
 first three frequent item sets with the highest support.
 
-Note that we use the <<async-search,async search>> endpoint in this first 
+Note that we use the <<async-search,async search>> endpoint in this first
 example.
 
 [source,console]
@@ -149,7 +147,7 @@ POST /kibana_sample_data_ecommerce/_async_search
    "size":0,
    "aggs":{
       "my_agg":{
-         "frequent_items":{
+         "frequent_item_sets":{
             "minimum_set_size":3,
             "fields":[
                {
@@ -168,7 +166,7 @@ POST /kibana_sample_data_ecommerce/_async_search
 -------------------------------------------------
 // TEST[skip:setup kibana sample data]
 
-The response of the API call above contains an identifier (`id`) of the async 
+The response of the API call above contains an identifier (`id`) of the async
 search request. You can use the identifier to retrieve the search results:
 
 [source,console]
@@ -225,27 +223,27 @@ The API returns a response similar to the following one:
           "support" : 0.026310160427807486
         }
       ],
-    (...) 
+    (...)
   }
 }
 -------------------------------------------------
 // TEST[skip:setup kibana sample data]
 
 <1> The array of returned item sets.
-<2> The `key` object contains one item set. In this case, it consists of two 
+<2> The `key` object contains one item set. In this case, it consists of two
 values of the `category.keyword` field and one value of the `geoip.city_name`.
-<3> The number of documents that contain the item set. 
-<4> The support value of the item set. It is calculated by dividing the number 
-of documents containing the item set by the total number of documents. 
-
-The response shows that the categories customers purchase from most frequently 
-together are `Women's Clothing` and `Women's Shoes` and customers from New York 
-tend to buy items from these categories frequently together. In other words, 
-customers who buy products labelled `Women's Clothing` more likely buy products 
-also from the `Women's Shoes` category and customers from New York most likely 
-buy products from these categories together. The item set with the second 
-highest support is `Women's Clothing` and `Women's Accessories` with customers 
-mostly from New York. Finally, the item set with the third highest support is 
+<3> The number of documents that contain the item set.
+<4> The support value of the item set. It is calculated by dividing the number
+of documents containing the item set by the total number of documents.
+
+The response shows that the categories customers purchase from most frequently
+together are `Women's Clothing` and `Women's Shoes` and customers from New York
+tend to buy items from these categories frequently together. In other words,
+customers who buy products labelled `Women's Clothing` more likely buy products
+also from the `Women's Shoes` category and customers from New York most likely
+buy products from these categories together. The item set with the second
+highest support is `Women's Clothing` and `Women's Accessories` with customers
+mostly from New York. Finally, the item set with the third highest support is
 `Men's Clothing` and `Men's Shoes` with customers mostly from Cairo.
 
 
@@ -262,7 +260,7 @@ POST /kibana_sample_data_ecommerce/_async_search
   "size": 0,
   "aggs": {
     "my_agg": {
-      "frequent_items": {
+      "frequent_item_sets": {
         "minimum_set_size": 3,
         "fields": [
           { "field": "category.keyword" },
@@ -282,20 +280,20 @@ POST /kibana_sample_data_ecommerce/_async_search
 // TEST[skip:setup kibana sample data]
 
 The result will only show item sets that created from documents matching the
-filter, namely purchases in Europe. Using `filter`, the calculated `support` 
-still takes all purchases into acount. That's different than specifying a query 
-at the top-level, in which case `support` gets calculated only from purchases in 
+filter, namely purchases in Europe. Using `filter`, the calculated `support`
+still takes all purchases into acount. That's different than specifying a query
+at the top-level, in which case `support` gets calculated only from purchases in
 Europe.
 
 
 [discrete]
 ==== Analyzing numeric values by using a runtime field
 
-The frequent items aggregation enables you to bucket numeric values by using 
-<<runtime,runtime fields>>. The next example demonstrates how to use a script to 
-add a runtime field to your documents called `price_range`, which is 
-calculated from the taxful total price of the individual transactions. The 
-runtime field then can be used in the frequent items aggregation as a field to 
+The frequent items aggregation enables you to bucket numeric values by using
+<<runtime,runtime fields>>. The next example demonstrates how to use a script to
+add a runtime field to your documents called `price_range`, which is
+calculated from the taxful total price of the individual transactions. The
+runtime field then can be used in the frequent items aggregation as a field to
 analyze.
 
 
@@ -318,7 +316,7 @@ GET kibana_sample_data_ecommerce/_search
   "size": 0,
   "aggs": {
     "my_agg": {
-      "frequent_items": {
+      "frequent_item_sets": {
         "minimum_set_size": 4,
         "fields": [
           {
@@ -402,6 +400,6 @@ The API returns a response similar to the following one:
 -------------------------------------------------
 // TEST[skip:setup kibana sample data]
 
-The response shows the categories that customers purchase from most frequently 
-together, the location of the customers who tend to buy items from these 
+The response shows the categories that customers purchase from most frequently
+together, the location of the customers who tend to buy items from these
 categories, and the most frequent price ranges of these purchases.

x-pack/plugin/ml/src/main/java/org/elasticsearch/xpack/ml/MachineLearning.java

@@ -1573,7 +1573,7 @@ public class MachineLearning extends Plugin
             ).addResultReader(InternalCategorizationAggregation::new)
                 .setAggregatorRegistrar(s -> s.registerUsage(CategorizeTextAggregationBuilder.NAME)),
             new AggregationSpec(
-                FrequentItemSetsAggregationBuilder.NAME,
+                new ParseField(FrequentItemSetsAggregationBuilder.NAME, FrequentItemSetsAggregationBuilder.DEPRECATED_NAME),
                 FrequentItemSetsAggregationBuilder::new,
                 checkAggLicense(FrequentItemSetsAggregationBuilder.PARSER, FREQUENT_ITEM_SETS_AGG_FEATURE)
             ).addResultReader(FrequentItemSetsAggregatorFactory.getResultReader())

x-pack/plugin/ml/src/main/java/org/elasticsearch/xpack/ml/aggs/frequentitemsets/EclatMapReducer.java

@@ -120,6 +120,7 @@ public final class EclatMapReducer extends AbstractItemSetMapReducer<
     private static final Logger logger = LogManager.getLogger(EclatMapReducer.class);
     private static final int VERSION = 1;
 
+    // named writable for this implementation
     public static final String NAME = "frequent_items-eclat-" + VERSION;
 
     // cache for marking transactions visited, memory usage: ((BITSET_CACHE_TRAVERSAL_DEPTH -2) * BITSET_CACHE_NUMBER_OF_TRANSACTIONS) / 8

x-pack/plugin/ml/src/main/java/org/elasticsearch/xpack/ml/aggs/frequentitemsets/FrequentItemSetsAggregationBuilder.java

@@ -37,7 +37,10 @@ import static org.elasticsearch.common.Strings.format;
 
 public final class FrequentItemSetsAggregationBuilder extends AbstractAggregationBuilder<FrequentItemSetsAggregationBuilder> {
 
-    public static final String NAME = "frequent_items";
+    public static final String NAME = "frequent_item_sets";
+
+    // name used between 8.4 - 8.6, kept for backwards compatibility until 9.0
+    public static final String DEPRECATED_NAME = "frequent_items";
 
     public static final double DEFAULT_MINIMUM_SUPPORT = 0.01;
     public static final int DEFAULT_MINIMUM_SET_SIZE = 1;

x-pack/plugin/ml/src/test/java/org/elasticsearch/xpack/ml/aggs/frequentitemsets/FrequentItemSetsAggregationBuilderTests.java

@@ -184,7 +184,7 @@ public class FrequentItemSetsAggregationBuilderTests extends AbstractXContentSer
             randomFrom(EXECUTION_HINT_ALLOWED_MODES)
         ).subAggregation(AggregationBuilders.avg("fieldA")));
 
-        assertEquals("Aggregator [fi] of type [frequent_items] cannot accept sub-aggregations", e.getMessage());
+        assertEquals("Aggregator [fi] of type [frequent_item_sets] cannot accept sub-aggregations", e.getMessage());
 
         e = expectThrows(
             IllegalArgumentException.class,
@@ -202,7 +202,7 @@ public class FrequentItemSetsAggregationBuilderTests extends AbstractXContentSer
             ).subAggregations(new AggregatorFactories.Builder().addAggregator(AggregationBuilders.avg("fieldA")))
         );
 
-        assertEquals("Aggregator [fi] of type [frequent_items] cannot accept sub-aggregations", e.getMessage());
+        assertEquals("Aggregator [fi] of type [frequent_item_sets] cannot accept sub-aggregations", e.getMessage());
 
         e = expectThrows(
             IllegalArgumentException.class,

x-pack/plugin/src/yamlRestTest/resources/rest-api-spec/test/ml/frequent_items_agg.yml → x-pack/plugin/src/yamlRestTest/resources/rest-api-spec/test/ml/frequent_item_sets_agg.yml

@@ -93,7 +93,7 @@ setup:
 
 
 ---
-"Test frequent items array fields":
+"Test frequent item sets array fields":
 
   - do:
       search:
@@ -103,7 +103,7 @@ setup:
             "size": 0,
             "aggs": {
               "fi": {
-                "frequent_items": {
+                "frequent_item_sets": {
                   "minimum_set_size": 3,
                   "minimum_support": 0.3,
                   "fields": [
@@ -123,7 +123,7 @@ setup:
   - match: { aggregations.fi.buckets.1.key.error_message: ["engine overheated"] }
 
 ---
-"Test frequent items date format":
+"Test frequent item sets date format":
 
   - do:
       search:
@@ -141,7 +141,7 @@ setup:
             "size": 0,
             "aggs": {
               "fi": {
-                "frequent_items": {
+                "frequent_item_sets": {
                   "minimum_set_size": 3,
                   "minimum_support": 0.3,
                   "fields": [
@@ -159,7 +159,7 @@ setup:
 
 
 ---
-"Test frequent items date format 2":
+"Test frequent item sets date format 2":
 
   - do:
       search:
@@ -177,7 +177,7 @@ setup:
             "size": 0,
             "aggs": {
               "fi": {
-                "frequent_items": {
+                "frequent_item_sets": {
                   "minimum_set_size": 2,
                   "minimum_support": 0.3,
                   "fields": [
@@ -195,7 +195,7 @@ setup:
   - match: { aggregations.fi.buckets.0.key.error_message: ["engine overheated"] }
 
 ---
-"Test frequent items array fields profile":
+"Test frequent item sets array fields profile":
 
   - do:
       search:
@@ -206,7 +206,7 @@ setup:
             "size": 0,
             "aggs": {
               "fi": {
-                "frequent_items": {
+                "frequent_item_sets": {
                   "minimum_set_size": 3,
                   "minimum_support": 0.2,
                   "fields": [
@@ -229,7 +229,7 @@ setup:
   - match: { aggregations.fi.profile.unique_items_after_prune: 11 }
 
 ---
-"Test frequent items flattened fields":
+"Test frequent item sets flattened fields":
 
   - do:
       search:
@@ -239,7 +239,7 @@ setup:
             "size": 0,
             "aggs": {
               "fi": {
-                "frequent_items": {
+                "frequent_item_sets": {
                   "minimum_set_size": 3,
                   "minimum_support": 0.3,
                   "fields": [
@@ -259,7 +259,7 @@ setup:
   - match: { aggregations.fi.buckets.1.key.data\.error_message: ["engine overheated"] }
 
 ---
-"Test frequent items as subagg":
+"Test frequent item sets as subagg":
 
   - do:
       search:
@@ -276,7 +276,7 @@ setup:
                 },
                 "aggs": {
                   "fi": {
-                    "frequent_items": {
+                    "frequent_item_sets": {
                       "minimum_set_size": 3,
                       "minimum_support": 0.3,
                       "fields": [
@@ -298,7 +298,7 @@ setup:
   - match: { aggregations.filter_error.fi.buckets.0.key.error_message: ["compressor low pressure"] }
 
 ---
-"Test frequent items as multi-bucket subagg":
+"Test frequent item sets as multi-bucket subagg":
 
   - do:
       search:
@@ -314,7 +314,7 @@ setup:
                 },
                 "aggs": {
                   "fi": {
-                    "frequent_items": {
+                    "frequent_item_sets": {
                       "minimum_set_size": 3,
                       "minimum_support": 0.3,
                       "fields": [
@@ -335,7 +335,7 @@ setup:
   - match: { aggregations.weekly.buckets.2.fi.buckets.0.doc_count: 1 }
 
 ---
-"Test frequent items filter":
+"Test frequent item sets filter":
 
   - do:
       search:
@@ -345,7 +345,7 @@ setup:
             "size": 0,
             "aggs": {
               "fi": {
-                "frequent_items": {
+                "frequent_item_sets": {
                   "minimum_set_size": 3,
                   "minimum_support": 0.3,
                   "fields": [
@@ -369,7 +369,7 @@ setup:
   - match: { aggregations.fi.buckets.0.key.error_message: ["compressor low pressure"] }
 
 ---
-"Test frequent items exclude":
+"Test frequent item sets exclude":
 
   - do:
       search:
@@ -379,7 +379,7 @@ setup:
             "size": 0,
             "aggs": {
               "fi": {
-                "frequent_items": {
+                "frequent_item_sets": {
                   "minimum_set_size": 3,
                   "minimum_support": 0.3,
                   "fields": [
@@ -401,7 +401,7 @@ setup:
   - match: { aggregations.fi.buckets.1.support: 0.3 }
 
 ---
-"Test frequent items include":
+"Test frequent item sets include":
 
   - do:
       search:
@@ -411,7 +411,7 @@ setup:
             "size": 0,
             "aggs": {
               "fi": {
-                "frequent_items": {
+                "frequent_item_sets": {
                   "minimum_set_size": 3,
                   "minimum_support": 0.3,
                   "fields": [
@@ -431,9 +431,9 @@ setup:
   - match: { aggregations.fi.buckets.0.key.error_message: ["engine overheated"] }
 
 ---
-"Test frequent items unsupported types":
+"Test frequent item sets unsupported types":
   - do:
-      catch: /Field \[geo_point\] of type \[geo_point\] is not supported for aggregation \[frequent_items\]/
+      catch: /Field \[geo_point\] of type \[geo_point\] is not supported for aggregation \[frequent_item_sets\]/
       search:
         index: store
         body: >
@@ -441,7 +441,7 @@ setup:
             "size": 0,
             "aggs": {
               "fi": {
-                "frequent_items": {
+                "frequent_item_sets": {
                   "minimum_set_size": 3,
                   "minimum_support": 0.3,
                   "fields": [
@@ -454,7 +454,7 @@ setup:
             }
           }
   - do:
-      catch: /Field \[histogram\] of type \[histogram\] is not supported for aggregation \[frequent_items\]/
+      catch: /Field \[histogram\] of type \[histogram\] is not supported for aggregation \[frequent_item_sets\]/
       search:
         index: store
         body: >
@@ -462,7 +462,7 @@ setup:
             "size": 0,
             "aggs": {
               "fi": {
-                "frequent_items": {
+                "frequent_item_sets": {
                   "minimum_set_size": 3,
                   "minimum_support": 0.3,
                   "fields": [
@@ -476,9 +476,9 @@ setup:
           }
 
 ---
-"Test frequent items unsupported subaggs":
+"Test frequent item sets unsupported subaggs":
   - do:
-      catch: /Aggregator \[fi\] of type \[frequent_items\] cannot accept sub-aggregations/
+      catch: /Aggregator \[fi\] of type \[frequent_item_sets\] cannot accept sub-aggregations/
       search:
         index: store
         body: >
@@ -486,7 +486,7 @@ setup:
             "size": 0,
             "aggs": {
               "fi": {
-                "frequent_items": {
+                "frequent_item_sets": {
                   "minimum_set_size": 3,
                   "minimum_support": 0.3,
                   "fields": [
@@ -504,3 +504,39 @@ setup:
             }
             }
           }
+
+---
+"Test deprecated frequent items":
+  - skip:
+      features:
+        - "allowed_warnings"
+
+  - do:
+      allowed_warnings:
+        - 'Deprecated field [frequent_items] used, expected [frequent_item_sets] instead'
+
+      search:
+        index: store
+        body: >
+          {
+            "size": 0,
+            "aggs": {
+              "fi": {
+                "frequent_items": {
+                  "minimum_set_size": 3,
+                  "minimum_support": 0.3,
+                  "fields": [
+                    {"field": "features"},
+                    {"field": "error_message"}
+                  ]
+                }
+              }
+            }
+          }
+  - length: { aggregations.fi.buckets: 4 }
+  - match: { aggregations.fi.buckets.0.doc_count: 5 }
+  - match: { aggregations.fi.buckets.0.support: 0.5 }
+  - match: { aggregations.fi.buckets.0.key.error_message: ["compressor low pressure"] }
+  - match: { aggregations.fi.buckets.1.doc_count: 4 }
+  - match: { aggregations.fi.buckets.1.support: 0.4 }
+  - match: { aggregations.fi.buckets.1.key.error_message: ["engine overheated"] }