compute() checks the query, optimizes it in the background, and runs it.
The output is a Polars DataFrame. collect() is
similar to compute() but converts the output to an R data.frame, which
consumes more memory.
Until tidypolars 0.7.0, there was only collect() and it was used to
collect a LazyFrame into a Polars DataFrame. This usage is still valid for
now but will change in 0.8.0 to automatically convert a DataFrame to a
data.frame. Use compute() to have a Polars DataFrame as output.
Usage
# S3 method for class 'RPolarsLazyFrame'
compute(
x,
...,
type_coercion = TRUE,
predicate_pushdown = TRUE,
projection_pushdown = TRUE,
simplify_expression = TRUE,
slice_pushdown = TRUE,
comm_subplan_elim = TRUE,
comm_subexpr_elim = TRUE,
cluster_with_columns = TRUE,
no_optimization = FALSE,
streaming = FALSE,
collect_in_background = FALSE
)
# S3 method for class 'RPolarsLazyFrame'
collect(
x,
...,
type_coercion = TRUE,
predicate_pushdown = TRUE,
projection_pushdown = TRUE,
simplify_expression = TRUE,
slice_pushdown = TRUE,
comm_subplan_elim = TRUE,
comm_subexpr_elim = TRUE,
cluster_with_columns = TRUE,
no_optimization = FALSE,
streaming = FALSE,
collect_in_background = FALSE
)Arguments
- x
A Polars LazyFrame
- ...
Dots which should be empty.
- type_coercion
Coerce types such that operations succeed and run on minimal required memory (default is
TRUE).- predicate_pushdown
Applies filters as early as possible at scan level (default is
TRUE).- projection_pushdown
Select only the columns that are needed at the scan level (default is
TRUE).- simplify_expression
Various optimizations, such as constant folding and replacing expensive operations with faster alternatives (default is
TRUE).- slice_pushdown
Only load the required slice from the scan. Don't materialize sliced outputs level. Don't materialize sliced outputs (default is
TRUE).- comm_subplan_elim
Cache branching subplans that occur on self-joins or unions (default is
TRUE).- comm_subexpr_elim
Cache common subexpressions (default is
TRUE).- cluster_with_columns
Combine sequential independent calls to
$with_columns().- no_optimization
Sets the following optimizations to
FALSE:predicate_pushdown,projection_pushdown,slice_pushdown,simplify_expression. Default isFALSE.- streaming
Run parts of the query in a streaming fashion (this is in an alpha state). Default is
FALSE.- collect_in_background
Detach this query from the R session. Computation will start in background. Get a handle which later can be converted into the resulting DataFrame. Useful in interactive mode to not lock R session (default is
FALSE).
See also
fetch() for applying a lazy query on a subset of the data.
Examples
dat_lazy <- polars::as_polars_df(iris)$lazy()
compute(dat_lazy)
#> shape: (150, 5)
#> ┌──────────────┬─────────────┬──────────────┬─────────────┬───────────┐
#> │ Sepal.Length ┆ Sepal.Width ┆ Petal.Length ┆ Petal.Width ┆ Species │
#> │ --- ┆ --- ┆ --- ┆ --- ┆ --- │
#> │ f64 ┆ f64 ┆ f64 ┆ f64 ┆ cat │
#> ╞══════════════╪═════════════╪══════════════╪═════════════╪═══════════╡
#> │ 5.1 ┆ 3.5 ┆ 1.4 ┆ 0.2 ┆ setosa │
#> │ 4.9 ┆ 3.0 ┆ 1.4 ┆ 0.2 ┆ setosa │
#> │ 4.7 ┆ 3.2 ┆ 1.3 ┆ 0.2 ┆ setosa │
#> │ 4.6 ┆ 3.1 ┆ 1.5 ┆ 0.2 ┆ setosa │
#> │ 5.0 ┆ 3.6 ┆ 1.4 ┆ 0.2 ┆ setosa │
#> │ … ┆ … ┆ … ┆ … ┆ … │
#> │ 6.7 ┆ 3.0 ┆ 5.2 ┆ 2.3 ┆ virginica │
#> │ 6.3 ┆ 2.5 ┆ 5.0 ┆ 1.9 ┆ virginica │
#> │ 6.5 ┆ 3.0 ┆ 5.2 ┆ 2.0 ┆ virginica │
#> │ 6.2 ┆ 3.4 ┆ 5.4 ┆ 2.3 ┆ virginica │
#> │ 5.9 ┆ 3.0 ┆ 5.1 ┆ 1.8 ┆ virginica │
#> └──────────────┴─────────────┴──────────────┴─────────────┴───────────┘
# you can build a query and add compute() as the last piece
dat_lazy |>
select(starts_with("Sepal")) |>
filter(between(Sepal.Length, 5, 6)) |>
compute()
#> shape: (67, 2)
#> ┌──────────────┬─────────────┐
#> │ Sepal.Length ┆ Sepal.Width │
#> │ --- ┆ --- │
#> │ f64 ┆ f64 │
#> ╞══════════════╪═════════════╡
#> │ 5.1 ┆ 3.5 │
#> │ 5.0 ┆ 3.6 │
#> │ 5.4 ┆ 3.9 │
#> │ 5.0 ┆ 3.4 │
#> │ 5.4 ┆ 3.7 │
#> │ … ┆ … │
#> │ 6.0 ┆ 2.2 │
#> │ 5.6 ┆ 2.8 │
#> │ 6.0 ┆ 3.0 │
#> │ 5.8 ┆ 2.7 │
#> │ 5.9 ┆ 3.0 │
#> └──────────────┴─────────────┘
# call collect() instead to return a data.frame (note that this is more
# expensive than compute())
dat_lazy |>
select(starts_with("Sepal")) |>
filter(between(Sepal.Length, 5, 6)) |>
collect()
#> Sepal.Length Sepal.Width
#> 1 5.1 3.5
#> 2 5.0 3.6
#> 3 5.4 3.9
#> 4 5.0 3.4
#> 5 5.4 3.7
#> 6 5.8 4.0
#> 7 5.7 4.4
#> 8 5.4 3.9
#> 9 5.1 3.5
#> 10 5.7 3.8
#> 11 5.1 3.8
#> 12 5.4 3.4
#> 13 5.1 3.7
#> 14 5.1 3.3
#> 15 5.0 3.0
#> 16 5.0 3.4
#> 17 5.2 3.5
#> 18 5.2 3.4
#> 19 5.4 3.4
#> 20 5.2 4.1
#> 21 5.5 4.2
#> 22 5.0 3.2
#> 23 5.5 3.5
#> 24 5.1 3.4
#> 25 5.0 3.5
#> 26 5.0 3.5
#> 27 5.1 3.8
#> 28 5.1 3.8
#> 29 5.3 3.7
#> 30 5.0 3.3
#> 31 5.5 2.3
#> 32 5.7 2.8
#> 33 5.2 2.7
#> 34 5.0 2.0
#> 35 5.9 3.0
#> 36 6.0 2.2
#> 37 5.6 2.9
#> 38 5.6 3.0
#> 39 5.8 2.7
#> 40 5.6 2.5
#> 41 5.9 3.2
#> 42 6.0 2.9
#> 43 5.7 2.6
#> 44 5.5 2.4
#> 45 5.5 2.4
#> 46 5.8 2.7
#> 47 6.0 2.7
#> 48 5.4 3.0
#> 49 6.0 3.4
#> 50 5.6 3.0
#> 51 5.5 2.5
#> 52 5.5 2.6
#> 53 5.8 2.6
#> 54 5.0 2.3
#> 55 5.6 2.7
#> 56 5.7 3.0
#> 57 5.7 2.9
#> 58 5.1 2.5
#> 59 5.7 2.8
#> 60 5.8 2.7
#> 61 5.7 2.5
#> 62 5.8 2.8
#> 63 6.0 2.2
#> 64 5.6 2.8
#> 65 6.0 3.0
#> 66 5.8 2.7
#> 67 5.9 3.0