{ "snapshot_id": "4b351b50-d95b-4a72-b184-3b5101880166", "name": "fix-eval-report-v2", "description": "End-to-end /fix evaluation across 4 error scenarios", "workspace_id": "89663e7e-fd1a-4303-8912-1b10ee21676a", "artifact_id": "967ec516-01d0-48f4-95c3-0a6421d72312", "spark_session_id": "68687916-33f8-4327-9e95-cc4caa12bf93", "kernel_id": "35fcdaba-ab70-4d9f-bceb-6867c31370d0", "cases": [ { "case_id": "fix-python-nameerror", "title": "/fix - Python NameError on undefined variable", "description": "Simulates clicking \"Diagnose with Copilot\" on a Python cell that references an undefined variable. Triggers a plain Python NameError (no Spark statement-meta). The runner captures the real outputs + traceback and sends /fix to the spark-diagnostics agent to verify how the agent handles a non-Spark error.", "category": "fix", "tags": [ "fix", "copilot-button", "p1", "spark-diagnostics", "python-error" ], "model": "gpt-4.1", "input": { "code": "result = totally_undefined_variable_xyz + 1\nprint(result)\n", "cell_type": "code", "cell_index": 0 }, "execution": { "status": "error", "outputs": [ { "output_type": "display_data", "data": { "application/vnd.livy.statement-meta+json": { "spark_pool": null, "statement_id": -1, "statement_ids": null, "state": "waiting", "livy_statement_state": null, "spark_jobs_updating": true, "spark_jobs": null, "session_id": null, "normalized_state": "waiting", "queued_time": "2026-05-19T13:50:03.1614074Z", "session_start_time": null, "execution_start_time": null, "execution_finish_time": null, "parent_msg_id": "a4730f3b-a6ec-44ea-89aa-7432182d4b2c" }, "text/plain": "StatementMeta(, , -1, Waiting, , Waiting, True)" } }, { "output_type": "error", "ename": "NameError", "evalue": "name 'totally_undefined_variable_xyz' is not defined", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[20], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m result \u001b[38;5;241m=\u001b[39m totally_undefined_variable_xyz \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28mprint\u001b[39m(result)\n", "\u001b[0;31mNameError\u001b[0m: name 'totally_undefined_variable_xyz' is not defined" ] } ] }, "wire_payload": { "question": "/fix", "agent": "spark-diagnostics", "location": "cellOutput", "intents": [ { "type": "/", "value": "fix" } ], "context": { "current_notebook_context": { "editor_context": { "user_focused_cell_id": "62526e39-0c0d-4d1f-84d4-5b34f2bf5f7c" }, "run_context": { "run_failed_cell_ids": [ "62526e39-0c0d-4d1f-84d4-5b34f2bf5f7c" ] } }, "fabric_context": { "notebook_artifact_info": { "artifact_id": "967ec516-01d0-48f4-95c3-0a6421d72312", "workspace_id": "89663e7e-fd1a-4303-8912-1b10ee21676a" } }, "additional_context": { "data": [ { "type": "cell", "data": { "cell_id": "62526e39-0c0d-4d1f-84d4-5b34f2bf5f7c", "cell_index": 0, "cell_type": "code", "notebook_artifact_id": "967ec516-01d0-48f4-95c3-0a6421d72312", "notebook_name": "diagnose-with-copilot-eval-target", "cell_outputs": [ { "output_type": "display_data", "data": { "application/vnd.livy.statement-meta+json": { "spark_pool": null, "statement_id": -1, "statement_ids": null, "state": "waiting", "livy_statement_state": null, "spark_jobs_updating": true, "spark_jobs": null, "session_id": null, "normalized_state": "waiting", "queued_time": "2026-05-19T13:50:03.1614074Z", "session_start_time": null, "execution_start_time": null, "execution_finish_time": null, "parent_msg_id": "a4730f3b-a6ec-44ea-89aa-7432182d4b2c" }, "text/plain": "StatementMeta(, , -1, Waiting, , Waiting, True)" } }, { "output_type": "error", "ename": "NameError", "evalue": "name 'totally_undefined_variable_xyz' is not defined", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[20], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m result \u001b[38;5;241m=\u001b[39m totally_undefined_variable_xyz \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28mprint\u001b[39m(result)\n", "\u001b[0;31mNameError\u001b[0m: name 'totally_undefined_variable_xyz' is not defined" ] } ] } } ] } } }, "llm_response": { "markdown": "### 2. Error Summary\n\n**Error Type**: NameError (Undefined Variable)\n\n**Root Cause**: The code in the cell attempts to use the variable `totally_undefined_variable_xyz`, which has not been defined anywhere above or in the current notebook context. This results in a standard Python `NameError`.\n\n---\n\n### 3. Suggested Code Fix\n\n```python\n# Key Changes:\n# - Removed usage of undefined variable.\n# - Added a defined variable as an example for successful execution.\n\ndefined_variable = 10 # Example definition\nresult = defined_variable + 1\nprint(result)\n```\n*Replace `defined_variable = 10` with your intended value or reference, as needed.*\n\n---\n\nSuggested code fix was not applied. You can copy the code above and apply it manually.\n\n**Reason:** The cell failure was due to a NameError, not a Spark/job failure. No running Spark session context is available for programmatic cell replacement and there is no evidence of this cell containing necessary non-demo logic. The provided fix corrects the undefined variable usage. Please update the cell with the code above and re-run.", "char_count": 1077, "tool_calls": [], "chunk_count": 230 }, "duration_s": 11.64, "error": null }, { "case_id": "fix-spark-column-not-found", "title": "/fix - DataFrame column not found", "description": "Simulates clicking \"Diagnose with Copilot\" on a PySpark cell that selects a column that doesn't exist on the DataFrame. Triggers an AnalysisException about an unresolved column reference. The runner captures the real outputs + traceback and sends /fix to the spark-diagnostics agent.", "category": "fix", "tags": [ "fix", "copilot-button", "p1", "spark-diagnostics", "column-error" ], "model": "gpt-4.1", "input": { "code": "from pyspark.sql import Row\ndf = spark.createDataFrame([Row(name=\"alice\", age=30), Row(name=\"bob\", age=25)])\ndf.select(\"nonexistent_column_zzz\").show()\n", "cell_type": "code", "cell_index": 0 }, "execution": { "status": "error", "outputs": [ { "output_type": "display_data", "data": { "application/vnd.livy.statement-meta+json": { "spark_pool": null, "statement_id": -1, "statement_ids": null, "state": "waiting", "livy_statement_state": null, "spark_jobs_updating": true, "spark_jobs": null, "session_id": null, "normalized_state": "waiting", "queued_time": "2026-05-19T13:50:13.7211536Z", "session_start_time": null, "execution_start_time": null, "execution_finish_time": null, "parent_msg_id": "65bbecc4-fe33-4855-90ad-fa2a515d9184" }, "text/plain": "StatementMeta(, , -1, Waiting, , Waiting, True)" } }, { "output_type": "error", "ename": "AnalysisException", "evalue": "[UNRESOLVED_COLUMN.WITH_SUGGESTION] A column or function parameter with name `nonexistent_column_zzz` cannot be resolved. Did you mean one of the following? [`name`, `age`].;\n'Project ['nonexistent_column_zzz]\n+- LogicalRDD [name#730, age#731L], false\n", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAnalysisException\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[23], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpyspark\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msql\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Row\n\u001b[1;32m 2\u001b[0m df \u001b[38;5;241m=\u001b[39m spark\u001b[38;5;241m.\u001b[39mcreateDataFrame([Row(name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124malice\u001b[39m\u001b[38;5;124m\"\u001b[39m, age\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m30\u001b[39m), Row(name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbob\u001b[39m\u001b[38;5;124m\"\u001b[39m, age\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m25\u001b[39m)])\n\u001b[0;32m----> 3\u001b[0m df\u001b[38;5;241m.\u001b[39mselect(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnonexistent_column_zzz\u001b[39m\u001b[38;5;124m\"\u001b[39m)\u001b[38;5;241m.\u001b[39mshow()\n", "File \u001b[0;32m/opt/spark/python/lib/pyspark.zip/pyspark/sql/dataframe.py:3229\u001b[0m, in \u001b[0;36mDataFrame.select\u001b[0;34m(self, *cols)\u001b[0m\n\u001b[1;32m 3184\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mselect\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39mcols: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mColumnOrName\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDataFrame\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 3185\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Projects a set of expressions and returns a new :class:`DataFrame`.\u001b[39;00m\n\u001b[1;32m 3186\u001b[0m \n\u001b[1;32m 3187\u001b[0m \u001b[38;5;124;03m .. versionadded:: 1.3.0\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3227\u001b[0m \u001b[38;5;124;03m +-----+---+\u001b[39;00m\n\u001b[1;32m 3228\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 3229\u001b[0m jdf \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jdf\u001b[38;5;241m.\u001b[39mselect(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jcols(\u001b[38;5;241m*\u001b[39mcols))\n\u001b[1;32m 3230\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m DataFrame(jdf, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msparkSession)\n", "File \u001b[0;32m~/cluster-env/trident_env/lib/python3.11/site-packages/py4j/java_gateway.py:1322\u001b[0m, in \u001b[0;36mJavaMember.__call__\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m 1316\u001b[0m command \u001b[38;5;241m=\u001b[39m proto\u001b[38;5;241m.\u001b[39mCALL_COMMAND_NAME \u001b[38;5;241m+\u001b[39m\\\n\u001b[1;32m 1317\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcommand_header \u001b[38;5;241m+\u001b[39m\\\n\u001b[1;32m 1318\u001b[0m args_command \u001b[38;5;241m+\u001b[39m\\\n\u001b[1;32m 1319\u001b[0m proto\u001b[38;5;241m.\u001b[39mEND_COMMAND_PART\n\u001b[1;32m 1321\u001b[0m answer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgateway_client\u001b[38;5;241m.\u001b[39msend_command(command)\n\u001b[0;32m-> 1322\u001b[0m return_value \u001b[38;5;241m=\u001b[39m get_return_value(\n\u001b[1;32m 1323\u001b[0m answer, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgateway_client, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtarget_id, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname)\n\u001b[1;32m 1325\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m temp_arg \u001b[38;5;129;01min\u001b[39;00m temp_args:\n\u001b[1;32m 1326\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(temp_arg, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_detach\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n", "File \u001b[0;32m/opt/spark/python/lib/pyspark.zip/pyspark/errors/exceptions/captured.py:185\u001b[0m, in \u001b[0;36mcapture_sql_exception..deco\u001b[0;34m(*a, **kw)\u001b[0m\n\u001b[1;32m 181\u001b[0m converted \u001b[38;5;241m=\u001b[39m convert_exception(e\u001b[38;5;241m.\u001b[39mjava_exception)\n\u001b[1;32m 182\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(converted, UnknownException):\n\u001b[1;32m 183\u001b[0m \u001b[38;5;66;03m# Hide where the exception came from that shows a non-Pythonic\u001b[39;00m\n\u001b[1;32m 184\u001b[0m \u001b[38;5;66;03m# JVM exception message.\u001b[39;00m\n\u001b[0;32m--> 185\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m converted \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 186\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 187\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m\n", "\u001b[0;31mAnalysisException\u001b[0m: [UNRESOLVED_COLUMN.WITH_SUGGESTION] A column or function parameter with name `nonexistent_column_zzz` cannot be resolved. Did you mean one of the following? [`name`, `age`].;\n'Project ['nonexistent_column_zzz]\n+- LogicalRDD [name#730, age#731L], false\n" ] } ] }, "wire_payload": { "question": "/fix", "agent": "spark-diagnostics", "location": "cellOutput", "intents": [ { "type": "/", "value": "fix" } ], "context": { "current_notebook_context": { "editor_context": { "user_focused_cell_id": "e93ac37c-50c4-4460-8adf-a26d15e056b3" }, "run_context": { "run_failed_cell_ids": [ "e93ac37c-50c4-4460-8adf-a26d15e056b3" ] } }, "fabric_context": { "notebook_artifact_info": { "artifact_id": "967ec516-01d0-48f4-95c3-0a6421d72312", "workspace_id": "89663e7e-fd1a-4303-8912-1b10ee21676a" } }, "additional_context": { "data": [ { "type": "cell", "data": { "cell_id": "e93ac37c-50c4-4460-8adf-a26d15e056b3", "cell_index": 0, "cell_type": "code", "notebook_artifact_id": "967ec516-01d0-48f4-95c3-0a6421d72312", "notebook_name": "diagnose-with-copilot-eval-target", "cell_outputs": [ { "output_type": "display_data", "data": { "application/vnd.livy.statement-meta+json": { "spark_pool": null, "statement_id": -1, "statement_ids": null, "state": "waiting", "livy_statement_state": null, "spark_jobs_updating": true, "spark_jobs": null, "session_id": null, "normalized_state": "waiting", "queued_time": "2026-05-19T13:50:13.7211536Z", "session_start_time": null, "execution_start_time": null, "execution_finish_time": null, "parent_msg_id": "65bbecc4-fe33-4855-90ad-fa2a515d9184" }, "text/plain": "StatementMeta(, , -1, Waiting, , Waiting, True)" } }, { "output_type": "error", "ename": "AnalysisException", "evalue": "[UNRESOLVED_COLUMN.WITH_SUGGESTION] A column or function parameter with name `nonexistent_column_zzz` cannot be resolved. Did you mean one of the following? [`name`, `age`].;\n'Project ['nonexistent_column_zzz]\n+- LogicalRDD [name#730, age#731L], false\n", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAnalysisException\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[23], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpyspark\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msql\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Row\n\u001b[1;32m 2\u001b[0m df \u001b[38;5;241m=\u001b[39m spark\u001b[38;5;241m.\u001b[39mcreateDataFrame([Row(name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124malice\u001b[39m\u001b[38;5;124m\"\u001b[39m, age\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m30\u001b[39m), Row(name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbob\u001b[39m\u001b[38;5;124m\"\u001b[39m, age\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m25\u001b[39m)])\n\u001b[0;32m----> 3\u001b[0m df\u001b[38;5;241m.\u001b[39mselect(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnonexistent_column_zzz\u001b[39m\u001b[38;5;124m\"\u001b[39m)\u001b[38;5;241m.\u001b[39mshow()\n", "File \u001b[0;32m/opt/spark/python/lib/pyspark.zip/pyspark/sql/dataframe.py:3229\u001b[0m, in \u001b[0;36mDataFrame.select\u001b[0;34m(self, *cols)\u001b[0m\n\u001b[1;32m 3184\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mselect\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39mcols: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mColumnOrName\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDataFrame\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 3185\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Projects a set of expressions and returns a new :class:`DataFrame`.\u001b[39;00m\n\u001b[1;32m 3186\u001b[0m \n\u001b[1;32m 3187\u001b[0m \u001b[38;5;124;03m .. versionadded:: 1.3.0\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3227\u001b[0m \u001b[38;5;124;03m +-----+---+\u001b[39;00m\n\u001b[1;32m 3228\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 3229\u001b[0m jdf \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jdf\u001b[38;5;241m.\u001b[39mselect(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jcols(\u001b[38;5;241m*\u001b[39mcols))\n\u001b[1;32m 3230\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m DataFrame(jdf, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msparkSession)\n", "File \u001b[0;32m~/cluster-env/trident_env/lib/python3.11/site-packages/py4j/java_gateway.py:1322\u001b[0m, in \u001b[0;36mJavaMember.__call__\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m 1316\u001b[0m command \u001b[38;5;241m=\u001b[39m proto\u001b[38;5;241m.\u001b[39mCALL_COMMAND_NAME \u001b[38;5;241m+\u001b[39m\\\n\u001b[1;32m 1317\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcommand_header \u001b[38;5;241m+\u001b[39m\\\n\u001b[1;32m 1318\u001b[0m args_command \u001b[38;5;241m+\u001b[39m\\\n\u001b[1;32m 1319\u001b[0m proto\u001b[38;5;241m.\u001b[39mEND_COMMAND_PART\n\u001b[1;32m 1321\u001b[0m answer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgateway_client\u001b[38;5;241m.\u001b[39msend_command(command)\n\u001b[0;32m-> 1322\u001b[0m return_value \u001b[38;5;241m=\u001b[39m get_return_value(\n\u001b[1;32m 1323\u001b[0m answer, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgateway_client, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtarget_id, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname)\n\u001b[1;32m 1325\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m temp_arg \u001b[38;5;129;01min\u001b[39;00m temp_args:\n\u001b[1;32m 1326\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(temp_arg, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_detach\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n", "File \u001b[0;32m/opt/spark/python/lib/pyspark.zip/pyspark/errors/exceptions/captured.py:185\u001b[0m, in \u001b[0;36mcapture_sql_exception..deco\u001b[0;34m(*a, **kw)\u001b[0m\n\u001b[1;32m 181\u001b[0m converted \u001b[38;5;241m=\u001b[39m convert_exception(e\u001b[38;5;241m.\u001b[39mjava_exception)\n\u001b[1;32m 182\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(converted, UnknownException):\n\u001b[1;32m 183\u001b[0m \u001b[38;5;66;03m# Hide where the exception came from that shows a non-Pythonic\u001b[39;00m\n\u001b[1;32m 184\u001b[0m \u001b[38;5;66;03m# JVM exception message.\u001b[39;00m\n\u001b[0;32m--> 185\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m converted \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 186\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 187\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m\n", "\u001b[0;31mAnalysisException\u001b[0m: [UNRESOLVED_COLUMN.WITH_SUGGESTION] A column or function parameter with name `nonexistent_column_zzz` cannot be resolved. Did you mean one of the following? [`name`, `age`].;\n'Project ['nonexistent_column_zzz]\n+- LogicalRDD [name#730, age#731L], false\n" ] } ] } } ] } } }, "llm_response": { "markdown": "### 2. Error Summary\n\n**Error Type**: Column Not Found (AnalysisException)\n\n**Root Cause**: The code attempts to select a column named `nonexistent_column_zzz` from the DataFrame, but this column does not exist. The DataFrame only contains the columns `name` and `age`, as shown in the exception’s suggestion.\n\n---\n\n### 3. Suggested Code Fix\n\n```python\n# Import Row to create structured data for DataFrame\nfrom pyspark.sql import Row\n\n# Create a DataFrame with two columns: 'name' and 'age'\ndf = spark.createDataFrame([\n Row(name=\"alice\", age=30),\n Row(name=\"bob\", age=25)\n])\n\n# Select an EXISTING column — options: 'name' or 'age'\n# Example: Selecting the 'name' column (as 'nonexistent_column_zzz' does not exist)\ndf.select(\"name\").show()\n```\n**Key Changes:**\n- Replaced `df.select(\"nonexistent_column_zzz\")` with `df.select(\"name\")`, as `name` exists in the DataFrame. Alternatively, you could use `age`.\n\n---\n\n### 4. Confirmation\n\nSuggested code fix was not applied automatically due to Spark session/context issues. You can copy the code above and apply it manually.\n\n---\n\n**Note:** The direct diagnostics for the Spark session could not be retrieved due to missing or inactive context, but the error is fully explained and solved by correcting the column name in the `select()` statement.", "char_count": 1300, "tool_calls": [], "chunk_count": 313 }, "duration_s": 11.44, "error": null }, { "case_id": "fix-spark-read-bad-path", "title": "/fix - Spark read from non-existent lakehouse path", "description": "Simulates clicking \"Diagnose with Copilot\" on a PySpark cell that tries to read a parquet file from a path that doesn't exist in any attached lakehouse. Triggers an AnalysisException / PathNotFoundException from the Spark catalyst. The runner captures the real outputs + traceback and sends /fix to the spark-diagnostics agent.", "category": "fix", "tags": [ "fix", "copilot-button", "p1", "spark-diagnostics", "io-error" ], "model": "gpt-4.1", "input": { "code": "df = spark.read.parquet(\"abfss://nonexistent_workspace@onelake.dfs.fabric.microsoft.com/bogus.lakehouse/Tables/missing_table\")\ndf.show()\n", "cell_type": "code", "cell_index": 0 }, "execution": { "status": "error", "outputs": [ { "output_type": "display_data", "data": { "application/vnd.livy.statement-meta+json": { "spark_pool": null, "statement_id": -1, "statement_ids": null, "state": "waiting", "livy_statement_state": null, "spark_jobs_updating": true, "spark_jobs": null, "session_id": null, "normalized_state": "waiting", "queued_time": "2026-05-19T13:50:25.1360898Z", "session_start_time": null, "execution_start_time": null, "execution_finish_time": null, "parent_msg_id": "2c400593-c18b-463f-808e-2ca290fe8da7" }, "text/plain": "StatementMeta(, , -1, Waiting, , Waiting, True)" } }, { "output_type": "error", "ename": "AnalysisException", "evalue": "[PATH_NOT_FOUND] Path does not exist: abfss://nonexistent_workspace@onelake.dfs.fabric.microsoft.com/bogus.lakehouse/Tables/missing_table.", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAnalysisException\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[26], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m df \u001b[38;5;241m=\u001b[39m spark\u001b[38;5;241m.\u001b[39mread\u001b[38;5;241m.\u001b[39mparquet(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mabfss://nonexistent_workspace@onelake.dfs.fabric.microsoft.com/bogus.lakehouse/Tables/missing_table\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 2\u001b[0m df\u001b[38;5;241m.\u001b[39mshow()\n", "File \u001b[0;32m/opt/spark/python/lib/pyspark.zip/pyspark/sql/readwriter.py:544\u001b[0m, in \u001b[0;36mDataFrameReader.parquet\u001b[0;34m(self, *paths, **options)\u001b[0m\n\u001b[1;32m 533\u001b[0m int96RebaseMode \u001b[38;5;241m=\u001b[39m options\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mint96RebaseMode\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m 534\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_set_opts(\n\u001b[1;32m 535\u001b[0m mergeSchema\u001b[38;5;241m=\u001b[39mmergeSchema,\n\u001b[1;32m 536\u001b[0m pathGlobFilter\u001b[38;5;241m=\u001b[39mpathGlobFilter,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 541\u001b[0m int96RebaseMode\u001b[38;5;241m=\u001b[39mint96RebaseMode,\n\u001b[1;32m 542\u001b[0m )\n\u001b[0;32m--> 544\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_df(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jreader\u001b[38;5;241m.\u001b[39mparquet(_to_seq(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_spark\u001b[38;5;241m.\u001b[39m_sc, paths)))\n", "File \u001b[0;32m~/cluster-env/trident_env/lib/python3.11/site-packages/py4j/java_gateway.py:1322\u001b[0m, in \u001b[0;36mJavaMember.__call__\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m 1316\u001b[0m command \u001b[38;5;241m=\u001b[39m proto\u001b[38;5;241m.\u001b[39mCALL_COMMAND_NAME \u001b[38;5;241m+\u001b[39m\\\n\u001b[1;32m 1317\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcommand_header \u001b[38;5;241m+\u001b[39m\\\n\u001b[1;32m 1318\u001b[0m args_command \u001b[38;5;241m+\u001b[39m\\\n\u001b[1;32m 1319\u001b[0m proto\u001b[38;5;241m.\u001b[39mEND_COMMAND_PART\n\u001b[1;32m 1321\u001b[0m answer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgateway_client\u001b[38;5;241m.\u001b[39msend_command(command)\n\u001b[0;32m-> 1322\u001b[0m return_value \u001b[38;5;241m=\u001b[39m get_return_value(\n\u001b[1;32m 1323\u001b[0m answer, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgateway_client, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtarget_id, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname)\n\u001b[1;32m 1325\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m temp_arg \u001b[38;5;129;01min\u001b[39;00m temp_args:\n\u001b[1;32m 1326\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(temp_arg, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_detach\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n", "File \u001b[0;32m/opt/spark/python/lib/pyspark.zip/pyspark/errors/exceptions/captured.py:185\u001b[0m, in \u001b[0;36mcapture_sql_exception..deco\u001b[0;34m(*a, **kw)\u001b[0m\n\u001b[1;32m 181\u001b[0m converted \u001b[38;5;241m=\u001b[39m convert_exception(e\u001b[38;5;241m.\u001b[39mjava_exception)\n\u001b[1;32m 182\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(converted, UnknownException):\n\u001b[1;32m 183\u001b[0m \u001b[38;5;66;03m# Hide where the exception came from that shows a non-Pythonic\u001b[39;00m\n\u001b[1;32m 184\u001b[0m \u001b[38;5;66;03m# JVM exception message.\u001b[39;00m\n\u001b[0;32m--> 185\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m converted \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 186\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 187\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m\n", "\u001b[0;31mAnalysisException\u001b[0m: [PATH_NOT_FOUND] Path does not exist: abfss://nonexistent_workspace@onelake.dfs.fabric.microsoft.com/bogus.lakehouse/Tables/missing_table." ] } ] }, "wire_payload": { "question": "/fix", "agent": "spark-diagnostics", "location": "cellOutput", "intents": [ { "type": "/", "value": "fix" } ], "context": { "current_notebook_context": { "editor_context": { "user_focused_cell_id": "e0f0bf3d-ae4c-4a62-8f3d-2d6ecaded458" }, "run_context": { "run_failed_cell_ids": [ "e0f0bf3d-ae4c-4a62-8f3d-2d6ecaded458" ] } }, "fabric_context": { "notebook_artifact_info": { "artifact_id": "967ec516-01d0-48f4-95c3-0a6421d72312", "workspace_id": "89663e7e-fd1a-4303-8912-1b10ee21676a" } }, "additional_context": { "data": [ { "type": "cell", "data": { "cell_id": "e0f0bf3d-ae4c-4a62-8f3d-2d6ecaded458", "cell_index": 0, "cell_type": "code", "notebook_artifact_id": "967ec516-01d0-48f4-95c3-0a6421d72312", "notebook_name": "diagnose-with-copilot-eval-target", "cell_outputs": [ { "output_type": "display_data", "data": { "application/vnd.livy.statement-meta+json": { "spark_pool": null, "statement_id": -1, "statement_ids": null, "state": "waiting", "livy_statement_state": null, "spark_jobs_updating": true, "spark_jobs": null, "session_id": null, "normalized_state": "waiting", "queued_time": "2026-05-19T13:50:25.1360898Z", "session_start_time": null, "execution_start_time": null, "execution_finish_time": null, "parent_msg_id": "2c400593-c18b-463f-808e-2ca290fe8da7" }, "text/plain": "StatementMeta(, , -1, Waiting, , Waiting, True)" } }, { "output_type": "error", "ename": "AnalysisException", "evalue": "[PATH_NOT_FOUND] Path does not exist: abfss://nonexistent_workspace@onelake.dfs.fabric.microsoft.com/bogus.lakehouse/Tables/missing_table.", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAnalysisException\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[26], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m df \u001b[38;5;241m=\u001b[39m spark\u001b[38;5;241m.\u001b[39mread\u001b[38;5;241m.\u001b[39mparquet(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mabfss://nonexistent_workspace@onelake.dfs.fabric.microsoft.com/bogus.lakehouse/Tables/missing_table\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 2\u001b[0m df\u001b[38;5;241m.\u001b[39mshow()\n", "File \u001b[0;32m/opt/spark/python/lib/pyspark.zip/pyspark/sql/readwriter.py:544\u001b[0m, in \u001b[0;36mDataFrameReader.parquet\u001b[0;34m(self, *paths, **options)\u001b[0m\n\u001b[1;32m 533\u001b[0m int96RebaseMode \u001b[38;5;241m=\u001b[39m options\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mint96RebaseMode\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m 534\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_set_opts(\n\u001b[1;32m 535\u001b[0m mergeSchema\u001b[38;5;241m=\u001b[39mmergeSchema,\n\u001b[1;32m 536\u001b[0m pathGlobFilter\u001b[38;5;241m=\u001b[39mpathGlobFilter,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 541\u001b[0m int96RebaseMode\u001b[38;5;241m=\u001b[39mint96RebaseMode,\n\u001b[1;32m 542\u001b[0m )\n\u001b[0;32m--> 544\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_df(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jreader\u001b[38;5;241m.\u001b[39mparquet(_to_seq(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_spark\u001b[38;5;241m.\u001b[39m_sc, paths)))\n", "File \u001b[0;32m~/cluster-env/trident_env/lib/python3.11/site-packages/py4j/java_gateway.py:1322\u001b[0m, in \u001b[0;36mJavaMember.__call__\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m 1316\u001b[0m command \u001b[38;5;241m=\u001b[39m proto\u001b[38;5;241m.\u001b[39mCALL_COMMAND_NAME \u001b[38;5;241m+\u001b[39m\\\n\u001b[1;32m 1317\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcommand_header \u001b[38;5;241m+\u001b[39m\\\n\u001b[1;32m 1318\u001b[0m args_command \u001b[38;5;241m+\u001b[39m\\\n\u001b[1;32m 1319\u001b[0m proto\u001b[38;5;241m.\u001b[39mEND_COMMAND_PART\n\u001b[1;32m 1321\u001b[0m answer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgateway_client\u001b[38;5;241m.\u001b[39msend_command(command)\n\u001b[0;32m-> 1322\u001b[0m return_value \u001b[38;5;241m=\u001b[39m get_return_value(\n\u001b[1;32m 1323\u001b[0m answer, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgateway_client, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtarget_id, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname)\n\u001b[1;32m 1325\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m temp_arg \u001b[38;5;129;01min\u001b[39;00m temp_args:\n\u001b[1;32m 1326\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(temp_arg, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_detach\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n", "File \u001b[0;32m/opt/spark/python/lib/pyspark.zip/pyspark/errors/exceptions/captured.py:185\u001b[0m, in \u001b[0;36mcapture_sql_exception..deco\u001b[0;34m(*a, **kw)\u001b[0m\n\u001b[1;32m 181\u001b[0m converted \u001b[38;5;241m=\u001b[39m convert_exception(e\u001b[38;5;241m.\u001b[39mjava_exception)\n\u001b[1;32m 182\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(converted, UnknownException):\n\u001b[1;32m 183\u001b[0m \u001b[38;5;66;03m# Hide where the exception came from that shows a non-Pythonic\u001b[39;00m\n\u001b[1;32m 184\u001b[0m \u001b[38;5;66;03m# JVM exception message.\u001b[39;00m\n\u001b[0;32m--> 185\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m converted \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 186\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 187\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m\n", "\u001b[0;31mAnalysisException\u001b[0m: [PATH_NOT_FOUND] Path does not exist: abfss://nonexistent_workspace@onelake.dfs.fabric.microsoft.com/bogus.lakehouse/Tables/missing_table." ] } ] } } ] } } }, "llm_response": { "markdown": "---\n\n## Error Summary\n\n**Error Type**: AnalysisException – Path Not Found\n\n**Root Cause**: The code attempts to read a Parquet file from the following path, which does not exist: \n`abfss://nonexistent_workspace@onelake.dfs.fabric.microsoft.com/bogus.lakehouse/Tables/missing_table` \nThis is either a placeholder, an incorrect workspace/lakehouse/table, or the file/table has not been created.\n\n---\n\n## Suggested Code Fix\n\n```python\n# Key Changes:\n# - The original path referenced a non-existent workspace and table.\n# - You must update this path to a valid table or file that exists in your current Lakehouse.\n# - If you want to read from a real table, use the Files/ or Tables/ location from your default Lakehouse.\n# - Here is a template example. Replace 'your_table' and optionally 'dbo' (for schema-enabled) with your existing Lakehouse table name.\n\n# Example: Read from an existing default Lakehouse table 'your_table' (schema 'dbo')\ndf = spark.read.table(\"Tables/dbo/your_table\")\n\n# To discover existing tables in your Lakehouse, use:\n# tables = spark.sql(\"SHOW TABLES\")\n# display(tables)\n\n# Replace 'your_table' with a table you verified exists.\ndisplay(df)\n```\n\n---\n\nSuggested code fix was not applied. You need to manually update the path with a real table or file that exists in your environment. Use the \"SHOW TABLES\" command or the Lakehouse UI to find an available table, then re-run the cell with the correct table name.", "char_count": 1436, "tool_calls": [], "chunk_count": 334 }, "duration_s": 10.22, "error": null }, { "case_id": "fix-spark-sql-nonexistent-table", "title": "/fix - SQL referring to nonexistent table", "description": "Simulates clicking the \"Diagnose with Copilot\" (/fix) button after a Spark SQL cell fails because it queries a table that does not exist. The runner executes the failing query on the live Spark kernel, captures the real ename/evalue/traceback + statement-meta, then sends a /fix message to the spark-diagnostics agent with the captured outputs in additional_context.data (matching the trident UX button payload).", "category": "fix", "tags": [ "fix", "copilot-button", "p0", "spark-diagnostics" ], "model": "gpt-4.1", "input": { "code": "spark.sql(\"SELECT * FROM nonexistent_table_xyz_nbeval_fix_test LIMIT 1\").show()\n", "cell_type": "code", "cell_index": 0 }, "execution": { "status": "error", "outputs": [ { "output_type": "display_data", "data": { "application/vnd.livy.statement-meta+json": { "spark_pool": null, "statement_id": -1, "statement_ids": null, "state": "waiting", "livy_statement_state": null, "spark_jobs_updating": true, "spark_jobs": null, "session_id": null, "normalized_state": "waiting", "queued_time": "2026-05-19T13:50:35.3762825Z", "session_start_time": null, "execution_start_time": null, "execution_finish_time": null, "parent_msg_id": "29ae78e0-d3d3-48e9-be3d-aaf7f1a640d5" }, "text/plain": "StatementMeta(, , -1, Waiting, , Waiting, True)" } }, { "output_type": "error", "ename": "UnsupportedOperationException", "evalue": "No default context found, please attach a lakehouse before running spark sql queries with partial namespaces.", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mUnsupportedOperationException\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[29], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m spark\u001b[38;5;241m.\u001b[39msql(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSELECT * FROM nonexistent_table_xyz_nbeval_fix_test LIMIT 1\u001b[39m\u001b[38;5;124m\"\u001b[39m)\u001b[38;5;241m.\u001b[39mshow()\n", "File \u001b[0;32m/opt/spark/python/lib/pyspark.zip/pyspark/sql/session.py:1631\u001b[0m, in \u001b[0;36mSparkSession.sql\u001b[0;34m(self, sqlQuery, args, **kwargs)\u001b[0m\n\u001b[1;32m 1627\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jvm \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 1628\u001b[0m litArgs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jvm\u001b[38;5;241m.\u001b[39mPythonUtils\u001b[38;5;241m.\u001b[39mtoArray(\n\u001b[1;32m 1629\u001b[0m [_to_java_column(lit(v)) \u001b[38;5;28;01mfor\u001b[39;00m v \u001b[38;5;129;01min\u001b[39;00m (args \u001b[38;5;129;01mor\u001b[39;00m [])]\n\u001b[1;32m 1630\u001b[0m )\n\u001b[0;32m-> 1631\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m DataFrame(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jsparkSession\u001b[38;5;241m.\u001b[39msql(sqlQuery, litArgs), \u001b[38;5;28mself\u001b[39m)\n\u001b[1;32m 1632\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 1633\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(kwargs) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n", "File \u001b[0;32m~/cluster-env/trident_env/lib/python3.11/site-packages/py4j/java_gateway.py:1322\u001b[0m, in \u001b[0;36mJavaMember.__call__\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m 1316\u001b[0m command \u001b[38;5;241m=\u001b[39m proto\u001b[38;5;241m.\u001b[39mCALL_COMMAND_NAME \u001b[38;5;241m+\u001b[39m\\\n\u001b[1;32m 1317\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcommand_header \u001b[38;5;241m+\u001b[39m\\\n\u001b[1;32m 1318\u001b[0m args_command \u001b[38;5;241m+\u001b[39m\\\n\u001b[1;32m 1319\u001b[0m proto\u001b[38;5;241m.\u001b[39mEND_COMMAND_PART\n\u001b[1;32m 1321\u001b[0m answer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgateway_client\u001b[38;5;241m.\u001b[39msend_command(command)\n\u001b[0;32m-> 1322\u001b[0m return_value \u001b[38;5;241m=\u001b[39m get_return_value(\n\u001b[1;32m 1323\u001b[0m answer, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgateway_client, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtarget_id, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname)\n\u001b[1;32m 1325\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m temp_arg \u001b[38;5;129;01min\u001b[39;00m temp_args:\n\u001b[1;32m 1326\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(temp_arg, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_detach\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n", "File \u001b[0;32m/opt/spark/python/lib/pyspark.zip/pyspark/errors/exceptions/captured.py:185\u001b[0m, in \u001b[0;36mcapture_sql_exception..deco\u001b[0;34m(*a, **kw)\u001b[0m\n\u001b[1;32m 181\u001b[0m converted \u001b[38;5;241m=\u001b[39m convert_exception(e\u001b[38;5;241m.\u001b[39mjava_exception)\n\u001b[1;32m 182\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(converted, UnknownException):\n\u001b[1;32m 183\u001b[0m \u001b[38;5;66;03m# Hide where the exception came from that shows a non-Pythonic\u001b[39;00m\n\u001b[1;32m 184\u001b[0m \u001b[38;5;66;03m# JVM exception message.\u001b[39;00m\n\u001b[0;32m--> 185\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m converted \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 186\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 187\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m\n", "\u001b[0;31mUnsupportedOperationException\u001b[0m: No default context found, please attach a lakehouse before running spark sql queries with partial namespaces." ] } ] }, "wire_payload": { "question": "/fix", "agent": "spark-diagnostics", "location": "cellOutput", "intents": [ { "type": "/", "value": "fix" } ], "context": { "current_notebook_context": { "editor_context": { "user_focused_cell_id": "5fe02731-3d59-466b-9673-a5e5afdb6268" }, "run_context": { "run_failed_cell_ids": [ "5fe02731-3d59-466b-9673-a5e5afdb6268" ] } }, "fabric_context": { "notebook_artifact_info": { "artifact_id": "967ec516-01d0-48f4-95c3-0a6421d72312", "workspace_id": "89663e7e-fd1a-4303-8912-1b10ee21676a" } }, "additional_context": { "data": [ { "type": "cell", "data": { "cell_id": "5fe02731-3d59-466b-9673-a5e5afdb6268", "cell_index": 0, "cell_type": "code", "notebook_artifact_id": "967ec516-01d0-48f4-95c3-0a6421d72312", "notebook_name": "diagnose-with-copilot-eval-target", "cell_outputs": [ { "output_type": "display_data", "data": { "application/vnd.livy.statement-meta+json": { "spark_pool": null, "statement_id": -1, "statement_ids": null, "state": "waiting", "livy_statement_state": null, "spark_jobs_updating": true, "spark_jobs": null, "session_id": null, "normalized_state": "waiting", "queued_time": "2026-05-19T13:50:35.3762825Z", "session_start_time": null, "execution_start_time": null, "execution_finish_time": null, "parent_msg_id": "29ae78e0-d3d3-48e9-be3d-aaf7f1a640d5" }, "text/plain": "StatementMeta(, , -1, Waiting, , Waiting, True)" } }, { "output_type": "error", "ename": "UnsupportedOperationException", "evalue": "No default context found, please attach a lakehouse before running spark sql queries with partial namespaces.", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mUnsupportedOperationException\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[29], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m spark\u001b[38;5;241m.\u001b[39msql(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSELECT * FROM nonexistent_table_xyz_nbeval_fix_test LIMIT 1\u001b[39m\u001b[38;5;124m\"\u001b[39m)\u001b[38;5;241m.\u001b[39mshow()\n", "File \u001b[0;32m/opt/spark/python/lib/pyspark.zip/pyspark/sql/session.py:1631\u001b[0m, in \u001b[0;36mSparkSession.sql\u001b[0;34m(self, sqlQuery, args, **kwargs)\u001b[0m\n\u001b[1;32m 1627\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jvm \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 1628\u001b[0m litArgs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jvm\u001b[38;5;241m.\u001b[39mPythonUtils\u001b[38;5;241m.\u001b[39mtoArray(\n\u001b[1;32m 1629\u001b[0m [_to_java_column(lit(v)) \u001b[38;5;28;01mfor\u001b[39;00m v \u001b[38;5;129;01min\u001b[39;00m (args \u001b[38;5;129;01mor\u001b[39;00m [])]\n\u001b[1;32m 1630\u001b[0m )\n\u001b[0;32m-> 1631\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m DataFrame(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jsparkSession\u001b[38;5;241m.\u001b[39msql(sqlQuery, litArgs), \u001b[38;5;28mself\u001b[39m)\n\u001b[1;32m 1632\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 1633\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(kwargs) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n", "File \u001b[0;32m~/cluster-env/trident_env/lib/python3.11/site-packages/py4j/java_gateway.py:1322\u001b[0m, in \u001b[0;36mJavaMember.__call__\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m 1316\u001b[0m command \u001b[38;5;241m=\u001b[39m proto\u001b[38;5;241m.\u001b[39mCALL_COMMAND_NAME \u001b[38;5;241m+\u001b[39m\\\n\u001b[1;32m 1317\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcommand_header \u001b[38;5;241m+\u001b[39m\\\n\u001b[1;32m 1318\u001b[0m args_command \u001b[38;5;241m+\u001b[39m\\\n\u001b[1;32m 1319\u001b[0m proto\u001b[38;5;241m.\u001b[39mEND_COMMAND_PART\n\u001b[1;32m 1321\u001b[0m answer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgateway_client\u001b[38;5;241m.\u001b[39msend_command(command)\n\u001b[0;32m-> 1322\u001b[0m return_value \u001b[38;5;241m=\u001b[39m get_return_value(\n\u001b[1;32m 1323\u001b[0m answer, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgateway_client, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtarget_id, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname)\n\u001b[1;32m 1325\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m temp_arg \u001b[38;5;129;01min\u001b[39;00m temp_args:\n\u001b[1;32m 1326\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(temp_arg, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_detach\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n", "File \u001b[0;32m/opt/spark/python/lib/pyspark.zip/pyspark/errors/exceptions/captured.py:185\u001b[0m, in \u001b[0;36mcapture_sql_exception..deco\u001b[0;34m(*a, **kw)\u001b[0m\n\u001b[1;32m 181\u001b[0m converted \u001b[38;5;241m=\u001b[39m convert_exception(e\u001b[38;5;241m.\u001b[39mjava_exception)\n\u001b[1;32m 182\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(converted, UnknownException):\n\u001b[1;32m 183\u001b[0m \u001b[38;5;66;03m# Hide where the exception came from that shows a non-Pythonic\u001b[39;00m\n\u001b[1;32m 184\u001b[0m \u001b[38;5;66;03m# JVM exception message.\u001b[39;00m\n\u001b[0;32m--> 185\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m converted \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 186\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 187\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m\n", "\u001b[0;31mUnsupportedOperationException\u001b[0m: No default context found, please attach a lakehouse before running spark sql queries with partial namespaces." ] } ] } } ] } } }, "llm_response": { "markdown": "---\n\n## Error Summary\n\n**Error Type**: Lakehouse Context Not Attached\n\n**Root Cause**: SparkSQL cannot execute queries without an attached lakehouse context. Your notebook has no default lakehouse attached, which is required for running SQL queries with partial namespaces.\n\n---\n\n## Resolution (User Action Required)\n\n**No code changes needed.** This error requires you to perform the following action:\n\n1. In the Fabric notebook interface, click the \"Add Lakehouse\" button at the top. \n2. Select or create a Lakehouse to attach to your notebook.\n3. Once your lakehouse is attached, re-run this cell.\n\n---\n\nSuggested code fix was not applied. You can copy the code above and apply it manually.", "char_count": 694, "tool_calls": [], "chunk_count": 146 }, "duration_s": 7.31, "error": null } ] }