ShreyeshArangath opened a new pull request, #1381:
URL: https://github.com/apache/datafusion-python/pull/1381
# Which issue does this PR close?
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Closes #1379
# Rationale for this change
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the issue then this section is not needed.
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Today, DataFusion Python only exposes execution metrics through formatted
console output via `explain(analyze=True)`.
This makes it difficult to programmatically inspect execution behavior.
There is currently no structured API to access per-operator metrics such as
output_rows, elapsed_compute, spill_count and other runtime metrics collected
during execution.
This PR introduces a structured Python interface for execution metrics,
mirroring the Rust API in `datafusion::physical_plan::metrics`.
# What changes are included in this PR?
- Added plan caching to `PyDataFrame` so the physical plan used during
execution is retained and available for metrics access.
- Kept the `metrics()` method and added `collect_metrics()` helper to walk
the execution plan tree and aggregate metrics from all operators.
# Are there any user-facing changes?
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updated before approving the PR.
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Users can now programmatically access execution metrics
```python
df = ctx.sql("SELECT * FROM t WHERE x > 1")
df.collect()
plan = df.execution_plan()
metrics = plan.collect_metrics()
for operator_name, metrics_set in metrics:
print(f"{operator_name}: {metrics_set.output_rows} rows")
```
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