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, common_subplan_elimination: 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.
- common_subplan_elimination
Will try to cache branching subplans that occur on self-joins or unions.
- 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.groupby("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 │ │ groupby_partitioned(a) ┆ 5 ┆ 470 │ │ sort(a) ┆ 475 ┆ 1964 │ └────────────────────────┴───────┴──────┘)