diff --git a/benchmarks/perf-tool/README.md b/benchmarks/perf-tool/README.md
index 2d84df819..52590d22b 100644
--- a/benchmarks/perf-tool/README.md
+++ b/benchmarks/perf-tool/README.md
@@ -270,6 +270,26 @@ Ingests a dataset of multiple context types into the cluster.
 | ----------- | ----------- | ----------- |
 | took | Total time to ingest the dataset into the index.| ms |
 
+#### ingest_nested_field
+
+Ingests a dataset with nested field into the cluster.
+
+##### Parameters
+
+| Parameter Name | Description                                                                                                                                                                                                      | Default |  
+| ----------- |------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| ----------- |
+| index_name | Name of index to ingest into                                                                                                                                                                                     | No default |
+| field_name | Name of field to ingest into                                                                                                                                                                                     | No default |
+| dataset_path | Path to data-set                                                                                                                                                                                                 | No default |
+| attributes_dataset_name | Name of dataset with additional attributes inside the main dataset                                                                                                                                               | No default |
+| attribute_spec | Definition of attributes, format is: [{ name: [name_val], type: [type_val]}] Order is important and must match order of attributes column in dataset file. It should contains { name: 'parent_id', type: 'int'}  | No default |
+
+##### Metrics
+
+| Metric Name | Description | Unit |  
+| ----------- | ----------- | ----------- |
+| took | Total time to ingest the dataset into the index.| ms |
+
 #### query
 
 Runs a set of queries against an index.
@@ -330,6 +350,36 @@ Runs a set of queries with filter against an index.
 | recall@R | ratio of top R results from the ground truth neighbors that are in the K results returned by the plugin | float 0.0-1.0 |
 | recall@K | ratio of results returned that were ground truth nearest neighbors  | float 0.0-1.0 |
 
+
+#### query_nested_field
+
+Runs a set of queries with nested field against an index.
+
+##### Parameters
+
+| Parameter Name | Description                                                                                                                                                                                                                               | Default              |  
+| ----------- |-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------|
+| k | Number of neighbors to return on search                                                                                                                                                                                                   | 100                  |
+| r | r value in Recall@R                                                                                                                                                                                                                       | 1                    |
+| index_name | Name of index to search                                                                                                                                                                                                                   | No default           |
+| field_name | Name field to search                                                                                                                                                                                                                      | No default           |
+| calculate_recall | Whether to calculate recall values                                                                                                                                                                                                        | False                |
+| dataset_format | Format the dataset is in. Currently hdf5 and bigann is supported. The hdf5 file must be organized in the same way that the ann-benchmarks organizes theirs.                                                                               | 'hdf5'               |
+| dataset_path | Path to dataset                                                                                                                                                                                                                           | No default           |
+| neighbors_format | Format the neighbors dataset is in. Currently hdf5 and bigann is supported. The hdf5 file must be organized in the same way that the ann-benchmarks organizes theirs.                                                                     | 'hdf5'               |
+| neighbors_path | Path to neighbors dataset                                                                                                                                                                                                                 | No default           |
+| neighbors_dataset | Name of filter dataset inside the neighbors dataset                                                                                                                                                                                       | No default           |
+| query_count | Number of queries to create from data-set                                                                                                                                                                                                 | Size of the data-set |
+
+##### Metrics
+
+| Metric Name | Description | Unit |  
+| ----------- | ----------- | ----------- |
+| took | Took times returned per query aggregated as total, p50, p90 and p99 (when applicable) | ms |
+| memory_kb | Native memory k-NN is using at the end of the query workload | KB |
+| recall@R | ratio of top R results from the ground truth neighbors that are in the K results returned by the plugin | float 0.0-1.0 |
+| recall@K | ratio of results returned that were ground truth nearest neighbors  | float 0.0-1.0 |
+
 #### get_stats
 
 Gets the index stats.
diff --git a/benchmarks/perf-tool/add-parent-doc-id-to-dataset.py b/benchmarks/perf-tool/add-parent-doc-id-to-dataset.py
index aed838f6b..54a40b281 100644
--- a/benchmarks/perf-tool/add-parent-doc-id-to-dataset.py
+++ b/benchmarks/perf-tool/add-parent-doc-id-to-dataset.py
@@ -152,18 +152,18 @@ def run(self, source_path, target_path) -> None:
         cpus = multiprocessing.cpu_count()
         total_clients = min(8, cpus)  # 1  # 10
         hdf5Data_train = HDF5DataSet(target_path, "train")
-        train_vectors = hdf5Data_train.read(0, 1000000)
+        train_vectors = hdf5Data_train.read(0, hdf5Data_train.size())
         hdf5Data_train.close()
         print(f'Train vector size: {len(train_vectors)}')
 
         hdf5Data_test = HDF5DataSet(target_path, "test")
-        total_queries = 10000  # 10000
+        total_queries = hdf5Data_test.size()  # 10000
         dis = [] * total_queries
 
         for i in range(total_queries):
             dis.insert(i, [])
 
-        queries_per_client = int(total_queries / total_clients)
+        queries_per_client = int(total_queries / total_clients + 0.5)
         if queries_per_client == 0:
             queries_per_client = total_queries
 
@@ -176,7 +176,7 @@ def run(self, source_path, target_path) -> None:
             if start_index + queries_per_client <= total_queries:
                 end_index = int(start_index + queries_per_client)
             else:
-                end_index = total_queries - start_index
+                end_index = total_queries
 
             print(f'Start Index: {start_index}, end Index: {end_index}')
             print(f'client is  : {client}')
@@ -199,7 +199,6 @@ def run(self, source_path, target_path) -> None:
                 i = 0
                 for d in calculatedDis:
                     if d:
-                        print("Dis is not null")
                         dis[i] = d
                         j = j + 1
                     i = i + 1
@@ -238,14 +237,12 @@ def queryTask(train_vectors, test_vectors, startIndex, endIndex, process_number,
         i = startIndex
         for test in test_vectors:
             distances = []
-            parent_ids = {}
             values = {}
             for value in train_vectors:
-                parent_ids[value.id] = value.parent_id
                 values[value.id] = value
                 distances.append({
                     "dis": calculateL2Distance(test.vector, value.vector),
-                    "id": value.id
+                    "id": value.parent_id
                 })
 
             distances.sort(key=lambda vector: vector['dis'])
@@ -258,15 +255,15 @@ def queryTask(train_vectors, test_vectors, startIndex, endIndex, process_number,
             for sub_i in range(len(distances)):
                 id = distances[sub_i]['id']
                 # Check if the number has been seen before
-                if len(nested) < 1000 and parent_ids[id] not in seen_set_nested:
+                if len(nested) < 1000 and id not in seen_set_nested:
                     # If not seen before, mark it as seen
-                    seen_set_nested.add(parent_ids[id])
+                    seen_set_nested.add(id)
                     nested.append(distances[sub_i])
-                if len(restricted) < 1000 and parent_ids[id] not in seen_set_restricted and values[id].apply_restricted_filter():
-                    seen_set_restricted.add(parent_ids[id])
+                if len(restricted) < 1000 and id not in seen_set_restricted and values[id].apply_restricted_filter():
+                    seen_set_restricted.add(id)
                     restricted.append(distances[sub_i])
-                if len(relaxed) < 1000 and parent_ids[id] not in seen_set_relaxed and values[id].apply_relaxed_filter():
-                    seen_set_relaxed.add(parent_ids[id])
+                if len(relaxed) < 1000 and id not in seen_set_relaxed and values[id].apply_relaxed_filter():
+                    seen_set_relaxed.add(id)
                     relaxed.append(distances[sub_i])
 
             all_distances[i]['nested'] = nested
diff --git a/benchmarks/perf-tool/dataset/data-nested.hdf5 b/benchmarks/perf-tool/dataset/data-nested.hdf5
new file mode 100644
index 000000000..4223d7281
Binary files /dev/null and b/benchmarks/perf-tool/dataset/data-nested.hdf5 differ
diff --git a/benchmarks/perf-tool/dataset/sift-128-euclidean-nested.hdf5 b/benchmarks/perf-tool/dataset/sift-128-euclidean-nested.hdf5
new file mode 100644
index 000000000..4223d7281
Binary files /dev/null and b/benchmarks/perf-tool/dataset/sift-128-euclidean-nested.hdf5 differ
diff --git a/benchmarks/perf-tool/okpt/test/steps/factory.py b/benchmarks/perf-tool/okpt/test/steps/factory.py
index 77e963921..2033f2672 100644
--- a/benchmarks/perf-tool/okpt/test/steps/factory.py
+++ b/benchmarks/perf-tool/okpt/test/steps/factory.py
@@ -10,7 +10,7 @@
 
 from okpt.test.steps.steps import CreateIndexStep, DisableRefreshStep, RefreshIndexStep, DeleteIndexStep, \
     TrainModelStep, DeleteModelStep, ForceMergeStep, ClearCacheStep, IngestStep, IngestMultiFieldStep, \
-    QueryStep, QueryWithFilterStep, GetStatsStep, WarmupStep
+    IngestNestedFieldStep, QueryStep, QueryWithFilterStep, QueryNestedFieldStep, GetStatsStep, WarmupStep
 
 
 def create_step(step_config: StepConfig) -> Step:
@@ -30,10 +30,14 @@ def create_step(step_config: StepConfig) -> Step:
         return IngestStep(step_config)
     elif step_config.step_name == IngestMultiFieldStep.label:
         return IngestMultiFieldStep(step_config)
+    elif step_config.step_name == IngestNestedFieldStep.label:
+        return IngestNestedFieldStep(step_config)
     elif step_config.step_name == QueryStep.label:
         return QueryStep(step_config)
     elif step_config.step_name == QueryWithFilterStep.label:
         return QueryWithFilterStep(step_config)
+    elif step_config.step_name == QueryNestedFieldStep.label:
+        return QueryNestedFieldStep(step_config)
     elif step_config.step_name == ForceMergeStep.label:
         return ForceMergeStep(step_config)
     elif step_config.step_name == ClearCacheStep.label:
diff --git a/benchmarks/perf-tool/okpt/test/steps/steps.py b/benchmarks/perf-tool/okpt/test/steps/steps.py
index 6ec164d9d..c0adba62a 100644
--- a/benchmarks/perf-tool/okpt/test/steps/steps.py
+++ b/benchmarks/perf-tool/okpt/test/steps/steps.py
@@ -333,7 +333,6 @@ def action(doc_id):
         for i in range(0, self.doc_count, self.bulk_size):
             partition = self.dataset.read(self.bulk_size)
             self._handle_data_bulk(partition, action, i)
-
         self.dataset.reset()
 
         return {}
@@ -379,6 +378,7 @@ def __init__(self, step_config: StepConfig):
                                                step_config.config, {}, [])
 
         self.partition_attr = self.attributes_dataset.read(self.doc_count)
+        self.action_buffer = None
 
     def _handle_data_bulk(self, partition, action, i):
         if partition is None:
@@ -429,6 +429,118 @@ def bulk_transform_with_attributes(self, partition: np.ndarray, partition_attr,
         return actions
 
 
+class IngestNestedFieldStep(BaseIngestStep):
+    """See base class."""
+
+    label = 'ingest_nested_field'
+
+    def __init__(self, step_config: StepConfig):
+        super().__init__(step_config)
+
+        dataset_path = parse_string_param('dataset_path', step_config.config,
+                                          {}, None)
+
+        self.attributes_dataset_name = parse_string_param('attributes_dataset_name',
+                                                          step_config.config, {}, None)
+
+        self.attributes_dataset = parse_dataset('hdf5', dataset_path,
+                                                Context.CUSTOM, self.attributes_dataset_name)
+
+        self.attribute_spec = parse_list_param('attribute_spec',
+                                               step_config.config, {}, [])
+
+        self.partition_attr = self.attributes_dataset.read(self.doc_count)
+
+        if self.dataset.size() != self.doc_count:
+            raise ValueError("custom doc_count is not supported for nested field")
+        self.action_buffer = None
+        self.action_parent_id = None
+        self.count = 0
+
+    def _handle_data_bulk(self, partition, action, i):
+        if partition is None:
+            return
+        body = self.bulk_transform_with_nested(partition, self.partition_attr, self.field_name,
+                                               action, i, self.attribute_spec)
+        if len(body) > 0:
+            bulk_index(self.opensearch, self.index_name, body)
+
+    def bulk_transform_with_nested(self, partition: np.ndarray, partition_attr, field_name: str,
+                                   action, offset: int, attributes_def) -> List[Dict[str, Any]]:
+        """Partitions and transforms a list of vectors into OpenSearch's bulk
+        injection format.
+        Args:
+            partition: An array of vectors to transform.
+            partition_attr: dictionary of additional data to transform
+            field_name: field name for action
+            action: Bulk API action.
+            offset: to start counting from
+            attributes_def: definition of additional doc fields
+        Returns:
+            An array of transformed vectors in bulk format.
+        """
+        # offset is index of start row. We need number of parent doc - 1.
+        # The number of parent document can be calculated by using partition_attr data.
+        # We need to keep the last parent doc aside so that additional data can be added later.
+        parent_id_idx = next((index for (index, d) in enumerate(attributes_def) if d.get('name') == 'parent_id'), None)
+        if parent_id_idx is None:
+            raise ValueError("parent_id should be provided as attribute spec")
+        if attributes_def[parent_id_idx]['type'] != 'int':
+            raise ValueError("parent_id should be int type")
+
+        first_index = offset
+        last_index = offset + len(partition) - 1
+        num_of_actions = int(partition_attr[last_index][parent_id_idx].decode()) - int(partition_attr[first_index][parent_id_idx].decode())
+        if self.action_buffer is None:
+            self.action_buffer = {"nested_field": []}
+            self.action_parent_id = int(partition_attr[first_index][parent_id_idx].decode())
+
+        actions = []
+        _ = [
+            actions.extend([action(i + self.action_parent_id), None])
+            for i in range(num_of_actions)
+        ]
+
+        idx = 1
+        part_list = partition.tolist()
+        for i in range(len(partition)):
+            self.count += 1
+            nested = {field_name: part_list[i]}
+            attr_idx = i + offset
+            attr_def_idx = 0
+            current_parent_id = None
+            for attribute in attributes_def:
+                attr_def_name = attribute['name']
+                attr_def_type = attribute['type']
+                if attr_def_name == "parent_id":
+                    current_parent_id = int(partition_attr[attr_idx][attr_def_idx].decode())
+                    attr_def_idx += 1
+                    continue
+
+                if attr_def_type == 'str':
+                    val = partition_attr[attr_idx][attr_def_idx].decode()
+                    if val != 'None':
+                        nested[attr_def_name] = val
+                elif attr_def_type == 'int':
+                    val = int(partition_attr[attr_idx][attr_def_idx].decode())
+                    nested[attr_def_name] = val
+                attr_def_idx += 1
+
+            if self.action_parent_id == current_parent_id:
+                self.action_buffer["nested_field"].append(nested)
+            else:
+                actions.extend([action(self.action_parent_id), self.action_buffer])
+                self.action_buffer = {"nested_field": []}
+                self.action_buffer["nested_field"].append(nested)
+                self.action_parent_id = current_parent_id
+                idx += 2
+
+        if self.count == self.doc_count:
+            actions.extend([action(self.action_parent_id), self.action_buffer])
+
+        return actions
+
+
 class BaseQueryStep(OpenSearchStep):
     """See base class."""
 
@@ -469,7 +581,7 @@ def _action(self):
                 break
             query_responses.append(
                 query_index(self.opensearch, self.index_name,
-                            self.get_body(query[0]) , [self.field_name]))
+                            self.get_body(query[0]) , self.get_exclude_fields()))
 
         results['took'] = [
             float(query_response['took']) for query_response in query_responses
@@ -506,6 +618,8 @@ def _get_measures(self) -> List[str]:
     def get_body(self, vec):
         pass
 
+    def get_exclude_fields(self):
+        return [self.field_name]
 
 class QueryStep(BaseQueryStep):
     """See base class."""
@@ -611,6 +725,43 @@ def get_body(self, vec):
         else:
             raise ConfigurationError('Not supported filter type {}'.format(self.filter_type))
 
+class QueryNestedFieldStep(BaseQueryStep):
+    """See base class."""
+
+    label = 'query_nested_field'
+
+    def __init__(self, step_config: StepConfig):
+        super().__init__(step_config)
+
+        neighbors_dataset = parse_string_param('neighbors_dataset',
+                                               step_config.config, {}, None)
+
+        self.neighbors = parse_dataset(self.neighbors_format, self.neighbors_path,
+                                       Context.CUSTOM, neighbors_dataset)
+
+        self.implicit_config = step_config.implicit_config
+
+    def get_body(self, vec):
+        return {
+            'size': self.k,
+            'query': {
+                'nested': {
+                    'path': 'nested_field',
+                    'query': {
+                        'knn': {
+                            'nested_field.' + self.field_name: {
+                                'vector': vec,
+                                'k': self.k
+                            }
+                        }
+                    }
+                }
+            }
+        }
+
+    def get_exclude_fields(self):
+        return ['nested_field.' + self.field_name]
+
 class GetStatsStep(OpenSearchStep):
     """See base class."""
 
diff --git a/benchmarks/perf-tool/release-configs/faiss-hnsw/nested/simple/index.json b/benchmarks/perf-tool/release-configs/faiss-hnsw/nested/simple/index.json
new file mode 100644
index 000000000..a982afc81
--- /dev/null
+++ b/benchmarks/perf-tool/release-configs/faiss-hnsw/nested/simple/index.json
@@ -0,0 +1,32 @@
+{
+  "settings": {
+    "index": {
+      "knn": true,
+      "number_of_shards": 24,
+      "number_of_replicas": 1,
+      "knn.algo_param.ef_search": 100
+    }
+  },
+  "mappings": {
+    "properties": {
+      "nested_field": {
+        "type": "nested",
+        "properties": {
+          "target_field": {
+            "type": "knn_vector",
+            "dimension": 128,
+            "method": {
+              "name": "hnsw",
+              "space_type": "l2",
+              "engine": "faiss",
+              "parameters": {
+                "ef_construction": 256,
+                "m": 16
+              }
+            }
+          }
+        }
+      }
+    }
+  }
+}
diff --git a/benchmarks/perf-tool/release-configs/faiss-hnsw/nested/simple/simple-nested-test.yml b/benchmarks/perf-tool/release-configs/faiss-hnsw/nested/simple/simple-nested-test.yml
new file mode 100644
index 000000000..151b2014d
--- /dev/null
+++ b/benchmarks/perf-tool/release-configs/faiss-hnsw/nested/simple/simple-nested-test.yml
@@ -0,0 +1,37 @@
+endpoint: [ENDPOINT]
+port: [PORT]
+test_name: "Faiss HNSW Nested Field Test"
+test_id: "Faiss HNSW Nested Field Test"
+num_runs: 3
+show_runs: false
+steps:
+  - name: delete_index
+    index_name: target_index
+  - name: create_index
+    index_name: target_index
+    index_spec: release-configs/faiss-hnsw/nested/simple/index.json
+  - name: ingest_nested_field
+    index_name: target_index
+    field_name: target_field
+    dataset_format: hdf5
+    dataset_path: dataset/sift-128-euclidean-nested.hdf5
+    attributes_dataset_name: attributes
+    attribute_spec: [ { name: 'color', type: 'str' }, { name: 'taste', type: 'str' }, { name: 'age', type: 'int' }, { name: 'parent_id', type: 'int'} ]
+  - name: refresh_index
+    index_name: target_index
+  - name: force_merge
+    index_name: target_index
+    max_num_segments: 1
+  - name: warmup_operation
+    index_name: target_index
+  - name: query_nested_field
+    k: 100
+    r: 1
+    calculate_recall: true
+    index_name: target_index
+    field_name: target_field
+    dataset_format: hdf5
+    dataset_path: dataset/sift-128-euclidean-nested.hdf5
+    neighbors_format: hdf5
+    neighbors_path: dataset/sift-128-euclidean-nested.hdf5
+    neighbors_dataset: neighbour_nested
\ No newline at end of file
diff --git a/benchmarks/perf-tool/release-configs/lucene-hnsw/nested/simple/index.json b/benchmarks/perf-tool/release-configs/lucene-hnsw/nested/simple/index.json
new file mode 100644
index 000000000..8dc749c39
--- /dev/null
+++ b/benchmarks/perf-tool/release-configs/lucene-hnsw/nested/simple/index.json
@@ -0,0 +1,31 @@
+{
+  "settings": {
+    "index": {
+      "knn": true,
+      "number_of_shards": 24,
+      "number_of_replicas": 1
+    }
+  },
+  "mappings": {
+    "properties": {
+      "nested_field": {
+        "type": "nested",
+        "properties": {
+          "target_field": {
+            "type": "knn_vector",
+            "dimension": 128,
+            "method": {
+              "name": "hnsw",
+              "space_type": "l2",
+              "engine": "lucene",
+              "parameters": {
+                "ef_construction": 256,
+                "m": 16
+              }
+            }
+          }
+        }
+      }
+    }
+  }
+}
diff --git a/benchmarks/perf-tool/release-configs/lucene-hnsw/nested/simple/simple-nested-test.yml b/benchmarks/perf-tool/release-configs/lucene-hnsw/nested/simple/simple-nested-test.yml
new file mode 100644
index 000000000..cf1e4edc4
--- /dev/null
+++ b/benchmarks/perf-tool/release-configs/lucene-hnsw/nested/simple/simple-nested-test.yml
@@ -0,0 +1,37 @@
+endpoint: [ENDPOINT]
+port: [PORT]
+test_name: "Lucene HNSW Nested Field Test"
+test_id: "Lucene HNSW Nested Field Test"
+num_runs: 3
+show_runs: false
+steps:
+  - name: delete_index
+    index_name: target_index
+  - name: create_index
+    index_name: target_index
+    index_spec: release-configs/faiss-hnsw/nested/simple/index.json
+  - name: ingest_nested_field
+    index_name: target_index
+    field_name: target_field
+    dataset_format: hdf5
+    dataset_path: dataset/sift-128-euclidean-nested.hdf5
+    attributes_dataset_name: attributes
+    attribute_spec: [ { name: 'color', type: 'str' }, { name: 'taste', type: 'str' }, { name: 'age', type: 'int' }, { name: 'parent_id', type: 'int'} ]
+  - name: refresh_index
+    index_name: target_index
+  - name: force_merge
+    index_name: target_index
+    max_num_segments: 1
+  - name: warmup_operation
+    index_name: target_index
+  - name: query_nested_field
+    k: 100
+    r: 1
+    calculate_recall: true
+    index_name: target_index
+    field_name: target_field
+    dataset_format: hdf5
+    dataset_path: dataset/sift-128-euclidean-nested.hdf5
+    neighbors_format: hdf5
+    neighbors_path: dataset/sift-128-euclidean-nested.hdf5
+    neighbors_dataset: neighbour_nested
\ No newline at end of file