polars.LazyFrame.profile#

LazyFrame.profile(
*,
type_coercion: bool = True,
_type_check: 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,
collapse_joins: bool = True,
show_plot: bool = False,
truncate_nodes: int = 0,
figsize: tuple[int, int] = (18, 8),
engine: EngineType = 'auto',
_check_order: bool = True,
**_kwargs: Any,
) → 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

collapse_joins

Collapse a join and filters into a faster join

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

engine

Select the engine used to process the query, optional. At the moment, if set to "auto" (default), the query is run using the polars in-memory engine. If set to "gpu", the GPU engine is used. Fine-grained control over the GPU engine, for example which device to use on a system with multiple devices, is possible by providing a GPUEngine object with configuration options.

Note

GPU mode is considered unstable. Not all queries will run successfully on the GPU, however, they should fall back transparently to the default engine if execution is not supported.

Running with POLARS_VERBOSE=1 will provide information if a query falls back (and why).

Note

The GPU engine does not support streaming, if streaming is enabled then GPU execution is switched off.

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 │
 └─────────────────────────┴───────┴──────┘)
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