perf(grafana): add user_id filter to direct cml_data queries - #60
Merged
Conversation
The four queries in cml-realtime.json that hit cml_data directly
(RSL/TSL × auto/raw) filtered only by cml_id. The PostgreSQL planner
therefore scanned data for all users matching that CML id, taking
12-16 s for a 2-day window on a hypertable with ~1 B rows.
Adding AND user_id = '${__user.login}' supplies user_id as a literal
constant (Grafana substitutes the variable before sending SQL to
PostgreSQL), enabling the composite index on (user_id, cml_id, time DESC)
to restrict the scan to only the authenticated user's data. Measured
speedup: ~1.4 s vs ~16 s (~11×) for a 2-day window.
Note: Row-Level Security cannot be used here because TimescaleDB
rejects ENABLE ROW LEVEL SECURITY when compression is active on the
hypertable. The composite index with an explicit user_id predicate
achieves the equivalent performance benefit.
The ${__user.login} Grafana variable matches the PostgreSQL role name
because Grafana and PostgreSQL user provisioning are kept in sync via
users.yml / generate_config.py.
Codecov Report✅ All modified and coverable lines are covered by tests. Additional details and impacted files@@ Coverage Diff @@
## main #60 +/- ##
==========================================
+ Coverage 86.10% 87.59% +1.48%
==========================================
Files 39 35 -4
Lines 3418 3038 -380
==========================================
- Hits 2943 2661 -282
+ Misses 475 377 -98
Flags with carried forward coverage won't be shown. Click here to find out more. ☔ View full report in Codecov by Harness. 🚀 New features to boost your workflow:
|
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
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
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 four queries in cml-realtime.json that hit cml_data directly (RSL/TSL × auto/raw) filtered only by cml_id. The PostgreSQL planner therefore scanned data for all users matching that CML id, taking 12-16 s for a 2-day window on a hypertable with ~1 B rows.
Adding AND user_id = '${__user.login}' supplies user_id as a literal constant (Grafana substitutes the variable before sending SQL to PostgreSQL), enabling the composite index on (user_id, cml_id, time DESC) to restrict the scan to only the authenticated user's data. Measured speedup: ~1.4 s vs ~16 s (~11×) for a 2-day window.
Note: Row-Level Security cannot be used here because TimescaleDB rejects ENABLE ROW LEVEL SECURITY when compression is active on the hypertable. The composite index with an explicit user_id predicate achieves the equivalent performance benefit.
The ${__user.login} Grafana variable matches the PostgreSQL role name because Grafana and PostgreSQL user provisioning are kept in sync via users.yml / generate_config.py.