⚡️ Speed up function is_file_used by 32% in PR #10819 (s3-file-size-and-associations-to-flows)#10822
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The optimized code achieves a **32% speedup** by implementing several key micro-optimizations that reduce dictionary operations and improve control flow:
**Key optimizations:**
1. **Early null checks with explicit continues**: Instead of chained `.get(..., {})` calls that create temporary empty dictionaries, the optimized version checks for `None` values early and uses `continue` statements to skip unnecessary processing. This eliminates the overhead of creating temporary objects.
2. **Extracted dictionary lookup**: `nodes = flow_data["nodes"]` is extracted once rather than accessed repeatedly in the loop, reducing dictionary lookups.
3. **Flattened dictionary access chain**: The original chained call `node.get("data", {}).get("node", {})` is broken into separate checks with early exits, avoiding the creation of intermediate empty dictionaries when keys are missing.
4. **Restructured condition logic**: The `isinstance(field, dict) and "value" in field` check is split into separate conditions with early `continue`, and `field.get("value")` replaces direct key access, making the code more defensive against missing keys.
5. **Simplified string vs list handling**: Uses `elif` for the list case instead of separate `if` statements, providing clearer control flow.
**Why this is faster:**
- Reduces temporary object creation (empty dicts from `.get(..., {})`)
- Minimizes dictionary lookups through early exits
- Eliminates redundant type checks and key existence checks
- Better CPU branch prediction with explicit control flow
**Performance characteristics:**
The optimization is most effective for flows with many nodes that have missing or incomplete data structures, where early exits prevent unnecessary processing. Based on the test results, it provides consistent speedups across both small-scale cases and large-scale scenarios with hundreds of nodes and fields.
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Codecov Report✅ All modified and coverable lines are covered by tests. ❌ Your project status has failed because the head coverage (40.04%) is below the target coverage (60.00%). You can increase the head coverage or adjust the target coverage. Additional details and impacted files@@ Coverage Diff @@
## s3-file-size-and-associations-to-flows #10822 +/- ##
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- Coverage 32.39% 32.38% -0.01%
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Files 1367 1367
Lines 63235 63225 -10
Branches 9358 9357 -1
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- Hits 20482 20478 -4
+ Misses 41720 41714 -6
Partials 1033 1033
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⚡️ This pull request contains optimizations for PR #10819
If you approve this dependent PR, these changes will be merged into the original PR branch
s3-file-size-and-associations-to-flows.📄 32% (0.32x) speedup for
is_file_usedinsrc/backend/base/langflow/api/v1/files.py⏱️ Runtime :
4.53 milliseconds→3.43 milliseconds(best of96runs)📝 Explanation and details
The optimized code achieves a 32% speedup by implementing several key micro-optimizations that reduce dictionary operations and improve control flow:
Key optimizations:
Early null checks with explicit continues: Instead of chained
.get(..., {})calls that create temporary empty dictionaries, the optimized version checks forNonevalues early and usescontinuestatements to skip unnecessary processing. This eliminates the overhead of creating temporary objects.Extracted dictionary lookup:
nodes = flow_data["nodes"]is extracted once rather than accessed repeatedly in the loop, reducing dictionary lookups.Flattened dictionary access chain: The original chained call
node.get("data", {}).get("node", {})is broken into separate checks with early exits, avoiding the creation of intermediate empty dictionaries when keys are missing.Restructured condition logic: The
isinstance(field, dict) and "value" in fieldcheck is split into separate conditions with earlycontinue, andfield.get("value")replaces direct key access, making the code more defensive against missing keys.Simplified string vs list handling: Uses
eliffor the list case instead of separateifstatements, providing clearer control flow.Why this is faster:
.get(..., {}))Performance characteristics:
The optimization is most effective for flows with many nodes that have missing or incomplete data structures, where early exits prevent unnecessary processing. Based on the test results, it provides consistent speedups across both small-scale cases and large-scale scenarios with hundreds of nodes and fields.
✅ Correctness verification report:
🌀 Generated Regression Tests and Runtime
To edit these changes
git checkout codeflash/optimize-pr10819-2025-12-01T20.10.17and push.