Add Python bindings for accessing ExecutionMetrics

python/datafusion/__init__.py

@@ -55,7 +55,7 @@
 from .expr import Expr, WindowFrame
 from .io import read_avro, read_csv, read_json, read_parquet
 from .options import CsvReadOptions
-from .plan import ExecutionPlan, LogicalPlan
+from .plan import ExecutionPlan, LogicalPlan, Metric, MetricsSet
 from .record_batch import RecordBatch, RecordBatchStream
 from .user_defined import (
     Accumulator,
@@ -85,6 +85,8 @@
     "Expr",
     "InsertOp",
     "LogicalPlan",
+    "Metric",
+    "MetricsSet",
     "ParquetColumnOptions",
     "ParquetWriterOptions",
     "RecordBatch",

python/datafusion/plan.py

@@ -29,6 +29,8 @@
 __all__ = [
     "ExecutionPlan",
     "LogicalPlan",
+    "Metric",
+    "MetricsSet",
 ]
 
 
@@ -151,3 +153,107 @@ def to_proto(self) -> bytes:
         Tables created in memory from record batches are currently not supported.
         """
         return self._raw_plan.to_proto()
+
+    def metrics(self) -> MetricsSet | None:
+        """Return metrics for this plan node after execution, or None if unavailable."""
+        raw = self._raw_plan.metrics()
+        if raw is None:
+            return None
+        return MetricsSet(raw)
+
+    def collect_metrics(self) -> list[tuple[str, MetricsSet]]:
+        """Walk the plan tree and collect metrics from all operators.
+
+        Returns a list of (operator_name, MetricsSet) tuples.
+        """
+        result: list[tuple[str, MetricsSet]] = []
+
+        def _walk(node: ExecutionPlan) -> None:
+            ms = node.metrics()
+            if ms is not None:
+                result.append((node.display(), ms))
+            for child in node.children():
+                _walk(child)
+
+        _walk(self)
+        return result
+
+
+class MetricsSet:
+    """A set of metrics for a single execution plan operator.
+
+    Provides both individual metric access and convenience aggregations
+    across partitions.
+    """
+
+    def __init__(self, raw: df_internal.MetricsSet) -> None:
+        """This constructor should not be called by the end user."""
+        self._raw = raw
+
+    def metrics(self) -> list[Metric]:
+        """Return all individual metrics in this set."""
+        return [Metric(m) for m in self._raw.metrics()]
+
+    @property
+    def output_rows(self) -> int | None:
+        """Sum of output_rows across all partitions."""
+        return self._raw.output_rows()
+
+    @property
+    def elapsed_compute(self) -> int | None:
+        """Sum of elapsed_compute across all partitions, in nanoseconds."""
+        return self._raw.elapsed_compute()
+
+    @property
+    def spill_count(self) -> int | None:
+        """Sum of spill_count across all partitions."""
+        return self._raw.spill_count()
+
+    @property
+    def spilled_bytes(self) -> int | None:
+        """Sum of spilled_bytes across all partitions."""
+        return self._raw.spilled_bytes()
+
+    @property
+    def spilled_rows(self) -> int | None:
+        """Sum of spilled_rows across all partitions."""
+        return self._raw.spilled_rows()
+
+    def sum_by_name(self, name: str) -> int | None:
+        """Return the sum of metrics matching the given name."""
+        return self._raw.sum_by_name(name)
+
+    def __repr__(self) -> str:
+        """Return a string representation of the metrics set."""
+        return repr(self._raw)
+
+
+class Metric:
+    """A single execution metric with name, value, partition, and labels."""
+
+    def __init__(self, raw: df_internal.Metric) -> None:
+        """This constructor should not be called by the end user."""
+        self._raw = raw
+
+    @property
+    def name(self) -> str:
+        """The name of this metric (e.g. ``output_rows``)."""
+        return self._raw.name
+
+    @property
+    def value(self) -> int | None:
+        """The numeric value of this metric, or None for non-numeric types."""
+        return self._raw.value
+
+    @property
+    def partition(self) -> int | None:
+        """The partition this metric applies to, or None if global."""
+        return self._raw.partition
+
+    def labels(self) -> dict[str, str]:
+        """Return the labels associated with this metric."""
+        return self._raw.labels()
+
+    def __repr__(self) -> str:
+        """Return a string representation of the metric."""
+        return repr(self._raw)

python/tests/test_plans.py

@@ -16,7 +16,13 @@
 # under the License.
 
 import pytest
-from datafusion import ExecutionPlan, LogicalPlan, SessionContext
+from datafusion import (
+    ExecutionPlan,
+    LogicalPlan,
+    Metric,
+    MetricsSet,
+    SessionContext,
+)
 
 
 # Note: We must use CSV because memory tables are currently not supported for
@@ -40,3 +46,101 @@ def test_logical_plan_to_proto(ctx, df) -> None:
     execution_plan = ExecutionPlan.from_proto(ctx, execution_plan_bytes)
 
     assert str(original_execution_plan) == str(execution_plan)
+
+
+def test_metrics_tree_walk() -> None:
+    ctx = SessionContext()
+    ctx.sql("CREATE TABLE t AS VALUES (1, 'a'), (2, 'b'), (3, 'c')")
+    df = ctx.sql("SELECT * FROM t WHERE column1 > 1")
+    df.collect()
+    plan = df.execution_plan()
+
+    results = plan.collect_metrics()
+    assert len(results) >= 1
+    found_metrics = False
+    for name, ms in results:
+        assert isinstance(name, str)
+        assert isinstance(ms, MetricsSet)
+        if ms.output_rows is not None and ms.output_rows > 0:
+            found_metrics = True
+    assert found_metrics
+
+
+def test_metric_properties() -> None:
+    ctx = SessionContext()
+    ctx.sql("CREATE TABLE t AS VALUES (1, 'a'), (2, 'b'), (3, 'c')")
+    df = ctx.sql("SELECT * FROM t WHERE column1 > 1")
+    df.collect()
+    plan = df.execution_plan()
+
+    for _, ms in plan.collect_metrics():
+        r = repr(ms)
+        assert isinstance(r, str)
+        for metric in ms.metrics():
+            assert isinstance(metric, Metric)
+            assert isinstance(metric.name, str)
+            assert len(metric.name) > 0
+            assert metric.partition is None or isinstance(metric.partition, int)
+            assert isinstance(metric.labels(), dict)
+            mr = repr(metric)
+            assert isinstance(mr, str)
+            assert len(mr) > 0
+            return
+    pytest.skip("No metrics found")
+
+
+def test_no_metrics_before_execution() -> None:
+    ctx = SessionContext()
+    ctx.sql("CREATE TABLE t AS VALUES (1), (2), (3)")
+    df = ctx.sql("SELECT * FROM t")
+    plan = df.execution_plan()
+    ms = plan.metrics()
+    assert ms is None or ms.output_rows is None or ms.output_rows == 0
+
+
+def test_collect_partitioned_metrics() -> None:
+    ctx = SessionContext()
+    ctx.sql("CREATE TABLE t AS VALUES (1, 'a'), (2, 'b'), (3, 'c')")
+    df = ctx.sql("SELECT * FROM t WHERE column1 > 1")
+
+    df.collect_partitioned()
+    plan = df.execution_plan()
+
+    found_metrics = False
+    for _, ms in plan.collect_metrics():
+        if ms.output_rows is not None and ms.output_rows > 0:
+            found_metrics = True
+    assert found_metrics
+
+
+def test_execute_stream_metrics() -> None:
+    ctx = SessionContext()
+    ctx.sql("CREATE TABLE t AS VALUES (1, 'a'), (2, 'b'), (3, 'c')")
+    df = ctx.sql("SELECT * FROM t WHERE column1 > 1")
+
+    for _ in df.execute_stream():
+        pass
+
+    plan = df.execution_plan()
+    found_metrics = False
+    for _, ms in plan.collect_metrics():
+        if ms.output_rows is not None and ms.output_rows > 0:
+            found_metrics = True
+    assert found_metrics
+
+
+def test_execute_stream_partitioned_metrics() -> None:
+    ctx = SessionContext()
+    ctx.sql("CREATE TABLE t AS VALUES (1, 'a'), (2, 'b'), (3, 'c')")
+    df = ctx.sql("SELECT * FROM t WHERE column1 > 1")
+
+    for stream in df.execute_stream_partitioned():
+        for _ in stream:
+            pass
+
+    plan = df.execution_plan()
+    found_metrics = False
+    for _, ms in plan.collect_metrics():
+        if ms.output_rows is not None and ms.output_rows > 0:
+            found_metrics = True
+    assert found_metrics

src/dataframe.rs

@@ -48,6 +48,14 @@ use pyo3::prelude::*;
 use pyo3::pybacked::PyBackedStr;
 use pyo3::types::{PyCapsule, PyList, PyTuple, PyTupleMethods};
 
+use datafusion::physical_plan::{
+    ExecutionPlan as DFExecutionPlan,
+    collect as df_collect,
+    collect_partitioned as df_collect_partitioned,
+    execute_stream as df_execute_stream,
+    execute_stream_partitioned as df_execute_stream_partitioned,
+};
+
 use crate::errors::{PyDataFusionError, PyDataFusionResult, py_datafusion_err};
 use crate::expr::PyExpr;
 use crate::expr::sort_expr::{PySortExpr, to_sort_expressions};
@@ -290,6 +298,9 @@ pub struct PyDataFrame {
 
     // In IPython environment cache batches between __repr__ and _repr_html_ calls.
     batches: SharedCachedBatches,
+
+    // Cache the last physical plan so that metrics are available after execution.
+    last_plan: Arc<Mutex<Option<Arc<dyn DFExecutionPlan>>>>,
 }
 
 impl PyDataFrame {
@@ -298,6 +309,7 @@ impl PyDataFrame {
         Self {
             df: Arc::new(df),
             batches: Arc::new(Mutex::new(None)),
+            last_plan: Arc::new(Mutex::new(None)),
         }
     }
 
@@ -627,7 +639,12 @@ impl PyDataFrame {
     /// Unless some order is specified in the plan, there is no
     /// guarantee of the order of the result.
     fn collect<'py>(&self, py: Python<'py>) -> PyResult<Vec<Bound<'py, PyAny>>> {
-        let batches = wait_for_future(py, self.df.as_ref().clone().collect())?
+        let df = self.df.as_ref().clone();
+        let plan = wait_for_future(py, df.create_physical_plan())?
+            .map_err(PyDataFusionError::from)?;
+        *self.last_plan.lock() = Some(Arc::clone(&plan));
+        let task_ctx = Arc::new(self.df.as_ref().task_ctx());
+        let batches = wait_for_future(py, df_collect(plan, task_ctx))?
             .map_err(PyDataFusionError::from)?;
         // cannot use PyResult<Vec<RecordBatch>> return type due to
         // https://github.com/PyO3/pyo3/issues/1813
@@ -643,7 +660,12 @@ impl PyDataFrame {
     /// Executes this DataFrame and collects all results into a vector of vector of RecordBatch
     /// maintaining the input partitioning.
     fn collect_partitioned<'py>(&self, py: Python<'py>) -> PyResult<Vec<Vec<Bound<'py, PyAny>>>> {
-        let batches = wait_for_future(py, self.df.as_ref().clone().collect_partitioned())?
+        let df = self.df.as_ref().clone();
+        let plan = wait_for_future(py, df.create_physical_plan())?
+            .map_err(PyDataFusionError::from)?;
+        *self.last_plan.lock() = Some(Arc::clone(&plan));
+        let task_ctx = Arc::new(self.df.as_ref().task_ctx());
+        let batches = wait_for_future(py, df_collect_partitioned(plan, task_ctx))?
             .map_err(PyDataFusionError::from)?;
 
         batches
@@ -803,7 +825,13 @@ impl PyDataFrame {
     }
 
     /// Get the execution plan for this `DataFrame`
+    ///
+    /// If the DataFrame has already been executed (e.g. via `collect()`),
+    /// returns the cached plan which includes populated metrics.
     fn execution_plan(&self, py: Python) -> PyDataFusionResult<PyExecutionPlan> {
+        if let Some(plan) = self.last_plan.lock().as_ref() {
+            return Ok(PyExecutionPlan::new(Arc::clone(plan)));
+        }
         let plan = wait_for_future(py, self.df.as_ref().clone().create_physical_plan())??;
         Ok(plan.into())
     }
@@ -1128,13 +1156,22 @@ impl PyDataFrame {
 
     fn execute_stream(&self, py: Python) -> PyDataFusionResult<PyRecordBatchStream> {
         let df = self.df.as_ref().clone();
-        let stream = spawn_future(py, async move { df.execute_stream().await })?;
+        let plan = wait_for_future(py, df.create_physical_plan())??;
+        *self.last_plan.lock() = Some(Arc::clone(&plan));
+        let task_ctx = Arc::new(self.df.as_ref().task_ctx());
+        let stream = spawn_future(py, async move { df_execute_stream(plan, task_ctx) })?;
         Ok(PyRecordBatchStream::new(stream))
     }
 
     fn execute_stream_partitioned(&self, py: Python) -> PyResult<Vec<PyRecordBatchStream>> {
         let df = self.df.as_ref().clone();
-        let streams = spawn_future(py, async move { df.execute_stream_partitioned().await })?;
+        let plan = wait_for_future(py, df.create_physical_plan())?
+            .map_err(PyDataFusionError::from)?;
+        *self.last_plan.lock() = Some(Arc::clone(&plan));
+        let task_ctx = Arc::new(self.df.as_ref().task_ctx());
+        let streams = spawn_future(py, async move {
+            df_execute_stream_partitioned(plan, task_ctx)
+        })?;
         Ok(streams.into_iter().map(PyRecordBatchStream::new).collect())
     }
 

src/lib.rs

@@ -43,6 +43,7 @@ pub mod errors;
 pub mod expr;
 #[allow(clippy::borrow_deref_ref)]
 mod functions;
+pub mod metrics;
 mod options;
 pub mod physical_plan;
 mod pyarrow_filter_expression;
@@ -96,6 +97,8 @@ fn _internal(py: Python, m: Bound<'_, PyModule>) -> PyResult<()> {
     m.add_class::<udtf::PyTableFunction>()?;
     m.add_class::<config::PyConfig>()?;
     m.add_class::<sql::logical::PyLogicalPlan>()?;
+    m.add_class::<metrics::PyMetricsSet>()?;
+    m.add_class::<metrics::PyMetric>()?;
     m.add_class::<physical_plan::PyExecutionPlan>()?;
     m.add_class::<record_batch::PyRecordBatch>()?;
     m.add_class::<record_batch::PyRecordBatchStream>()?;