polars.LazyFrame.profile#

LazyFrame.profile(
*,
type_coercion: bool = True,
predicate_pushdown: bool = True,
projection_pushdown: bool = True,
simplify_expression: bool = True,
no_optimization: bool = False,
slice_pushdown: bool = True,
comm_subplan_elim: bool = True,
comm_subexpr_elim: bool = True,
cluster_with_columns: bool = True,
show_plot: bool = False,
truncate_nodes: int = 0,
figsize: tuple[int, int] = (18, 8),
streaming: bool = False,
) tuple[DataFrame, DataFrame][source]#

Profile a LazyFrame.

This will run the query and return a tuple containing the materialized DataFrame and a DataFrame that contains profiling information of each node that is executed.

The units of the timings are microseconds.

Parameters:
type_coercion

Do type coercion optimization.

predicate_pushdown

Do predicate pushdown optimization.

projection_pushdown

Do projection pushdown optimization.

simplify_expression

Run simplify expressions optimization.

no_optimization

Turn off (certain) optimizations.

slice_pushdown

Slice pushdown optimization.

comm_subplan_elim

Will try to cache branching subplans that occur on self-joins or unions.

comm_subexpr_elim

Common subexpressions will be cached and reused.

cluster_with_columns

Combine sequential independent calls to with_columns

show_plot

Show a gantt chart of the profiling result

truncate_nodes

Truncate the label lengths in the gantt chart to this number of characters.

figsize

matplotlib figsize of the profiling plot

streaming

Run parts of the query in a streaming fashion (this is in an alpha state)

Examples

>>> lf = pl.LazyFrame(
...     {
...         "a": ["a", "b", "a", "b", "b", "c"],
...         "b": [1, 2, 3, 4, 5, 6],
...         "c": [6, 5, 4, 3, 2, 1],
...     }
... )
>>> lf.group_by("a", maintain_order=True).agg(pl.all().sum()).sort(
...     "a"
... ).profile()
(shape: (3, 3)
 ┌─────┬─────┬─────┐
 │ a   ┆ b   ┆ c   │
 │ --- ┆ --- ┆ --- │
 │ str ┆ i64 ┆ i64 │
 ╞═════╪═════╪═════╡
 │ a   ┆ 4   ┆ 10  │
 │ b   ┆ 11  ┆ 10  │
 │ c   ┆ 6   ┆ 1   │
 └─────┴─────┴─────┘,
 shape: (3, 3)
 ┌─────────────────────────┬───────┬──────┐
 │ node                    ┆ start ┆ end  │
 │ ---                     ┆ ---   ┆ ---  │
 │ str                     ┆ u64   ┆ u64  │
 ╞═════════════════════════╪═══════╪══════╡
 │ optimization            ┆ 0     ┆ 5    │
 │ group_by_partitioned(a) ┆ 5     ┆ 470  │
 │ sort(a)                 ┆ 475   ┆ 1964 │
 └─────────────────────────┴───────┴──────┘)