034: fix double JSON serialization in AsyncAPIClient.request

Root cause: AsyncAPIClient.request() passes its  parameter directly
to httpx.AsyncClient.request(json=data). When callers pass a pre-serialized
JSON string (data=json.dumps(dict)), httpx re-encodes it via json.dumps(),
resulting in a double-encoded JSON string body instead of a JSON object.

This caused ALL POST/PUT requests with string data to fail — Superset received
a JSON string instead of a JSON object, returning GENERIC_BACKEND_ERROR
('dictionary update sequence element #0 has length 1; 2 is required').

Fix: if data is a string, pass it via httpx  parameter (raw body);
if it's a dict/list, pass via  for automatic encoding.

Affected callers (6 files) now correctly send JSON objects:
  - preview_executor.py: chart data requests
  - superset_executor.py
  - _run_source.py
  - _datasets.py: update_dataset
  - _datasets_preview.py: compile_dataset_preview
  - _dashboards_write.py

Also simplified preview_executor.fetch_sample_rows back to single-strategy
(chart data API only) since the root cause is now fixed.

backend/src/core/utils/async_network.py

@@ -201,15 +201,23 @@ class AsyncAPIClient:
                 auth_headers = await self.get_headers()
                 req_headers.update(auth_headers)
 
-                response = await self._client.request(
-                    method=method,
-                    url=url,
-                    params=params,
-                    json=data,
-                    headers=req_headers,
-                    timeout=httpx.Timeout(timeout or self.request_settings.get("timeout", self.DEFAULT_TIMEOUT)),
-                    follow_redirects=allow_redirects,
-                )
+                # Support both pre-serialized JSON strings (legacy convention)
+                # and Python dicts/list. Strings are sent as raw body via `content`
+                # to avoid double-encoding by httpx's `json` parameter.
+                request_kwargs: dict[str, Any] = {
+                    "method": method,
+                    "url": url,
+                    "params": params,
+                    "headers": req_headers,
+                    "timeout": httpx.Timeout(timeout or self.request_settings.get("timeout", self.DEFAULT_TIMEOUT)),
+                    "follow_redirects": allow_redirects,
+                }
+                if isinstance(data, str):
+                    request_kwargs["content"] = data.encode("utf-8")
+                elif data is not None:
+                    request_kwargs["json"] = data
+
+                response = await self._client.request(**request_kwargs)
 
                 if response.status_code == 401:
                     if method.upper() in ("GET", "HEAD", "OPTIONS"):
@@ -217,12 +225,8 @@ class AsyncAPIClient:
                         self._authenticated = False
                         auth_headers = await self.get_headers()
                         req_headers.update(auth_headers)
-                        response = await self._client.request(
-                            method=method, url=url, params=params, json=data,
-                            headers=req_headers,
-                            timeout=httpx.Timeout(timeout or self.request_settings.get("timeout", self.DEFAULT_TIMEOUT)),
-                            follow_redirects=allow_redirects,
-                        )
+                        retry_kwargs = {**request_kwargs, "headers": req_headers}
+                        response = await self._client.request(**retry_kwargs)
                     else:
                         raise AuthenticationError()
 

backend/src/plugins/translate/preview_executor.py

@@ -37,7 +37,7 @@ class PreviewExecutor:
 
     # region fetch_sample_rows [TYPE Function]
     # @PURPOSE: Fetch sample rows from Superset dataset for preview.
-    # @SIDE_EFFECT  Calls Superset chart data endpoint.
+    # @SIDE_EFFECT  Calls Superset chart data API.
     async def fetch_sample_rows(
         self,
         job: TranslationJob,
@@ -60,13 +60,20 @@ class PreviewExecutor:
                 dataset_id=int(job.source_datasource_id), dataset_record=dataset_detail,
                 template_params={}, effective_filters=[],
             )
+
+            # Build column list from dataset schema for explicit column projection
+            column_names = [
+                col["name"] for col in dataset_detail.get("columns", [])
+                if col.get("name") and col.get("is_active", True)
+            ]
+
             queries = query_context.get("queries", [])
             if queries:
                 queries[0]["row_limit"] = sample_size
-                queries[0].pop("result_type", None)
-                queries[0].pop("columns", None)
+                queries[0]["columns"] = column_names or []
                 queries[0]["metrics"] = []
-            query_context["result_type"] = "samples"
+                queries[0].pop("result_type", None)
+            query_context["result_type"] = "query"
             form_data = query_context.get("form_data", {})
             form_data.pop("query_mode", None)