diff --git a/docs/website/docs/reference/performance.md b/docs/website/docs/reference/performance.md
index f1a405684f..af2d791324 100644
--- a/docs/website/docs/reference/performance.md
+++ b/docs/website/docs/reference/performance.md
@@ -139,9 +139,9 @@ PROGRESS=log python pipeline_script.py
 You can create pipelines that extract, normalize and load data in parallel.
 
 ### Extract
-You can extract data concurrently if you write your pipelines to yield callables or awaitables that can be then evaluated in a thread or futures pool respectively.
+You can extract data concurrently if you write your pipelines to yield callables or awaitables or use async generators for your resources that can be then evaluated in a thread or futures pool respectively.
 
-Example below simulates a typical situation where a dlt resource is used to fetch a page of items and then details of individual items are fetched separately in the transformer. The `@dlt.defer` decorator wraps the `get_details` function in another callable that will be executed in the thread pool.
+The example below simulates a typical situation where a dlt resource is used to fetch a page of items and then details of individual items are fetched separately in the transformer. The `@dlt.defer` decorator wraps the `get_details` function in another callable that will be executed in the thread pool.
 <!--@@@DLT_SNIPPET_START ./performance_snippets/performance-snippets.py::parallel_extract_callables-->
 ```py
 import dlt
@@ -185,11 +185,20 @@ workers=4
 <!--@@@DLT_SNIPPET_END ./performance_snippets/toml-snippets.toml::extract_workers_toml-->
 
 
-The example below does the same but using an async/await and futures pool:
+The example below does the same but using an async generator as the main resource and async/await and futures pool for the transformer:
 <!--@@@DLT_SNIPPET_START ./performance_snippets/performance-snippets.py::parallel_extract_awaitables-->
 ```py
 import asyncio
 
+@dlt.resource
+async def a_list_items(start, limit):
+    # simulate a slow REST API where you wait 0.3 sec for each item
+    index = start
+    while index < start + limit:
+        await asyncio.sleep(0.3)
+        yield index
+        index += 1
+
 @dlt.transformer
 async def a_get_details(item_id):
     # simulate a slow REST API where you wait 0.3 sec for each item
@@ -198,7 +207,7 @@ async def a_get_details(item_id):
     # just return the results, if you yield, generator will be evaluated in main thread
     return {"row": item_id}
 
-print(list(list_items(0, 10) | a_get_details))
+print(list(a_list_items(0, 10) | a_get_details))
 ```
 <!--@@@DLT_SNIPPET_END ./performance_snippets/performance-snippets.py::parallel_extract_awaitables-->
 
diff --git a/docs/website/docs/reference/performance_snippets/performance-snippets.py b/docs/website/docs/reference/performance_snippets/performance-snippets.py
index a6ad2f2618..a2ebd102a6 100644
--- a/docs/website/docs/reference/performance_snippets/performance-snippets.py
+++ b/docs/website/docs/reference/performance_snippets/performance-snippets.py
@@ -68,6 +68,15 @@ def get_details(item_id):
     # @@@DLT_SNIPPET_START parallel_extract_awaitables
     import asyncio
 
+    @dlt.resource
+    async def a_list_items(start, limit):
+        # simulate a slow REST API where you wait 0.3 sec for each item
+        index = start
+        while index < start + limit:
+            await asyncio.sleep(0.3)
+            yield index
+            index += 1
+
     @dlt.transformer
     async def a_get_details(item_id):
         # simulate a slow REST API where you wait 0.3 sec for each item
@@ -76,7 +85,7 @@ async def a_get_details(item_id):
         # just return the results, if you yield, generator will be evaluated in main thread
         return {"row": item_id}
 
-    print(list(list_items(0, 10) | a_get_details))
+    print(list(a_list_items(0, 10) | a_get_details))
     # @@@DLT_SNIPPET_END parallel_extract_awaitables