[DRAFT] refactor query graph to make it more useful #140
+427
−90
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
|
The problem I'm facing now is that the profiling exposed by the API doesn't seem to have the float precision in CPU Time and overall Latency, and has some differences: {
"query_name": "",
"total_bytes_written": 0,
"latency": 0.0,
"total_bytes_read": 0,
"cumulative_rows_scanned": 0,
"system_peak_buffer_memory": 0,
"cumulative_cardinality": 0,
"rows_returned": 0,
"cpu_time": 0.0,
"result_set_size": 0,
"extra_info": {},
"system_peak_temp_dir_size": 0,
"blocked_thread_time": 0.0,
"children": [
{
"operator_timing": 0.00003275,
"operator_rows_scanned": 0,
"operator_name": "PROJECTION",
"operator_cardinality": 1,
"cumulative_rows_scanned": 0,
"cumulative_cardinality": 0,
"cpu_time": 0.0,
"result_set_size": 4,
"operator_type": "PROJECTION",
"extra_info": {
"Projections": "42",
"Estimated Cardinality": "1"
},
"children": [
{
"operator_type": "DUMMY_SCAN",
"extra_info": {},
"cumulative_cardinality": 0,
"cumulative_rows_scanned": 0,
"operator_cardinality": 1,
"operator_rows_scanned": 0,
"operator_timing": 0.000005333,
"result_set_size": 4,
"cpu_time": 0.0,
"operator_name": "DUMMY_SCAN",
"children": []
}
]
}
]
}Versus the same query using the PRAGMA approach within SQL: {
"total_bytes_written": 0,
"total_bytes_read": 0,
"rows_returned": 1,
"latency": 0.004957334,
"result_set_size": 4,
"cumulative_rows_scanned": 0,
"cpu_time": 0.000059875000000000005,
"extra_info": {},
"system_peak_buffer_memory": 0,
"blocked_thread_time": 0.0,
"cumulative_cardinality": 2,
"system_peak_temp_dir_size": 0,
"query_name": "select 42;",
"children": [
{
"total_bytes_written": 0,
"result_set_size": 4,
"operator_timing": 0.000030667,
"operator_rows_scanned": 0,
"cumulative_rows_scanned": 0,
"operator_cardinality": 1,
"operator_type": "PROJECTION",
"total_bytes_read": 0,
"operator_name": "PROJECTION",
"cpu_time": 0.000059875000000000005,
"extra_info": {
"Projections": "42",
"Estimated Cardinality": "1"
},
"cumulative_cardinality": 2,
"children": [
{
"cumulative_cardinality": 1,
"extra_info": {},
"operator_name": "DUMMY_SCAN",
"cpu_time": 0.000029208,
"cumulative_rows_scanned": 0,
"operator_rows_scanned": 0,
"operator_timing": 0.000029208,
"result_set_size": 4,
"total_bytes_read": 0,
"operator_type": "DUMMY_SCAN",
"total_bytes_written": 0,
"operator_cardinality": 1,
"children": []
}
]
}
]
} |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
1 participant
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
The idea of this PR is to make this tool a bit more useful (and visually appealing). I think generally it is good to have some better profiling tools to explore queries.
EDIT: Initially it was only about the query graph visuals, but I decided to expose the profiling functions enabled in the C++ API (similar to what the Go client does with the C API).
To use the tool:
If you want to use the profiling within the client:
WIP: Now you can expose
translate_json_to_htmlfrom duckdb.query_graph and do everything within the python process natively:Which should yield something like the following screenshot:
