<LazyFrame>$fetch()

NEWS.md

@@ -57,6 +57,7 @@ features. Unrelated breaking changes and new features are put in separate sectio
 - Stream query to file with `pl$sink_ipc()` and `pl$sink_parquet()` (#343)
 - New method `$explode()` for `DataFrame` and `LazyFrame` (#314).
 - New method `$clone()` for `LazyFrame` (#347).
+- New method `$fetch()` for `LazyFrame` (#319).
 - New methods `$optimization_toggle()` and `$profile()` for `LazyFrame` (#323).
 - `$with_column()` is now deprecated (following upstream `polars`). It will be
   removed in 0.9.0. It should be replaced with `$with_columns()` (#313).

R/error__trait.R

@@ -32,6 +32,7 @@ call_to_string = function(call) paste(capture.output(print(call)), collapse = "\
 #' @param err any type which impl as.character
 #' @param context calling context
 #' @keywords internal
+#' @noRd
 #' @return err as string
 #' @examples
 #' #

R/extendr-wrappers.R

@@ -1011,6 +1011,8 @@ LazyFrame$rename <- function(existing, new) .Call(wrap__LazyFrame__rename, self,
 
 LazyFrame$schema <- function() .Call(wrap__LazyFrame__schema, self)
 
+LazyFrame$fetch <- function(n_rows) .Call(wrap__LazyFrame__fetch, self, n_rows)
+
 LazyFrame$optimization_toggle <- function(type_coercion, predicate_pushdown, projection_pushdown, simplify_expr, slice_pushdown, comm_subplan_elim, comm_subexpr_elim, streaming) .Call(wrap__LazyFrame__optimization_toggle, self, type_coercion, predicate_pushdown, projection_pushdown, simplify_expr, slice_pushdown, comm_subplan_elim, comm_subexpr_elim, streaming)
 
 LazyFrame$profile <- function() .Call(wrap__LazyFrame__profile, self)

R/lazyframe__lazy.R

@@ -269,16 +269,17 @@ LazyFrame_filter = "use_extendr_wrapper"
 #' run on minimal required memory.
 #' @param predicate_pushdown Boolean. Applies filters as early as possible at
 #' scan level.
-#' @param projection_pushdown Boolean. Select only the columns that are needed at the scan level.
-#' @param simplify_expression Boolean. Various optimizations, such as constant folding
-#' and replacing expensive operations with faster alternatives.
+#' @param projection_pushdown Boolean. Select only the columns that are needed
+#' at the scan level.
+#' @param simplify_expression Boolean. Various optimizations, such as constant
+#' folding and replacing expensive operations with faster alternatives.
 #' @param slice_pushdown Boolean. Only load the required slice from the scan
 #' Don't materialize sliced outputs
 #' level. Don't materialize sliced outputs (e.g. `join$head(10)`).
-#' @param comm_subplan_elim Boolean. Will try to cache branching subplans that occur on self-joins
-#' or unions.
-#' @param comm_subexpr_elim Boolean. Common subexpressions will be cached and reused.
-#' or unions.
+#' @param comm_subplan_elim Boolean. Will try to cache branching subplans that
+#'  occur on self-joins or unions.
+#' @param comm_subexpr_elim Boolean. Common subexpressions will be cached and
+#' reused.
 #' @param no_optimization  Boolean. Turn off the following optimizations:
 #'  predicate_pushdown = FALSE
 #'  projection_pushdown = FALSE
@@ -297,6 +298,11 @@ LazyFrame_filter = "use_extendr_wrapper"
 #' @return A `DataFrame`
 #' @examples pl$LazyFrame(iris)$filter(pl$col("Species") == "setosa")$collect()
 #' @seealso
+#'  - [`$fetch()`][LazyFrame_fetch] - fast limited query check
+#'  - [`$profile()`][LazyFrame_profile] - returns as `$collect()` but also table with each operation
+#'  profiled.
+#'  - [`$collect_in_background()`][LazyFrame_collect_in_background] - non-blocking collect returns
+#'  a future handle. Can also just be used via `$collect(collect_in_background = TRUE)`.
 #'  - [`$sink_parquet()`][LazyFrame_sink_parquet()] stream query to a parquet file.
 #'  - [`$sink_ipc()`][LazyFrame_sink_ipc()] stream query to a arrow file.
 LazyFrame_collect = function(
@@ -1183,13 +1189,108 @@ LazyFrame_dtypes = method_as_property(function() {
     unwrap("in $dtypes()")
 })
 
+
+#' Fetch `n` rows of a LazyFrame
+#'
+#' This is similar to `$collect()` but limit the number of rows to collect. It
+#' is mostly useful to check that a query works as expected.
+#'
+#' @keywords LazyFrame
+#' @details
+#' `$fetch()` does not guarantee the final number of rows in the DataFrame output.
+#' It only guarantees that `n` rows are used at the beginning of the query.
+#' Filters, join operations and a lower number of rows available in the scanned
+#' file influence the final number of rows.
+#'
+#' @param n_rows Integer. Maximum number of rows to fetch.
+#' @param type_coercion Boolean. Coerce types such that operations succeed and
+#' run on minimal required memory.
+#' @param predicate_pushdown  Boolean. Applies filters as early as possible / at
+#' scan level.
+#' @param projection_pushdown  Boolean. Applies filters as early as possible / at
+#' scan level.
+#' @param simplify_expression  Boolean. Cache subtrees/file scans that are used
+#' by multiple subtrees in the query plan.
+#' @param slice_pushdown  Boolean. Only load the required slice from the scan
+#' level. Don't materialize sliced outputs (e.g. `join$head(10)`).
+#' @param comm_subplan_elim Boolean. Will try to cache branching subplans that
+#' occur on self-joins or unions.
+#' @param comm_subexpr_elim Boolean. Common subexpressions will be cached and
+#' reused.
+#' @param no_optimization  Boolean. Turn off the following optimizations:
+#'  predicate_pushdown = FALSE
+#'  projection_pushdown = FALSE
+#'  slice_pushdown = FALSE
+#'  common_subplan_elimination = FALSE
+#' @param streaming  Boolean. Run parts of the query in a streaming fashion
+#' (this is in an alpha state).
+#' @return A DataFrame of maximum n_rows
+#' @seealso
+#'  - [`$collect()`][LazyFrame_collect] - regular collect.
+#'  - [`$profile()`][LazyFrame_profile] - returns as `$collect()` but also table with each operation
+#'  profiled.
+#'  - [`$collect_in_background()`][LazyFrame_collect_in_background] - non-blocking collect returns
+#'  a future handle. Can also just be used via `$collect(collect_in_background = TRUE)`.
+#' @examples
+#'
+#' # fetch 3 rows
+#' pl$LazyFrame(iris)$fetch(3)
+#'
+#' # this fetch-query returns 4 rows, because we started with 3 and appended one
+#' # row in the query (see section 'Details')
+#' pl$LazyFrame(iris)$select(pl$col("Species")$append("flora gigantica, alien"))$fetch(3)
+LazyFrame_fetch = function(
+    n_rows = 500,
+    type_coercion = TRUE,
+    predicate_pushdown = TRUE,
+    projection_pushdown = TRUE,
+    simplify_expression = TRUE,
+    slice_pushdown = TRUE,
+    comm_subplan_elim = TRUE,
+    comm_subexpr_elim = TRUE,
+    no_optimization = FALSE,
+    streaming = FALSE) {
+
+  if (isTRUE(no_optimization)) {
+    predicate_pushdown = FALSE
+    projection_pushdown = FALSE
+    slice_pushdown = FALSE
+    comm_subplan_elim = FALSE
+    comm_subexpr_elim = FALSE
+  }
+
+  if (isTRUE(streaming)) {
+    comm_subplan_elim = FALSE
+  }
+
+  self |>
+    .pr$LazyFrame$optimization_toggle(
+      type_coercion,
+      predicate_pushdown,
+      projection_pushdown,
+      simplify_expression,
+      slice_pushdown,
+      comm_subplan_elim,
+      comm_subexpr_elim,
+      streaming
+    ) |>
+    and_then(\(self) .pr$LazyFrame$fetch(self, n_rows)) |>
+    unwrap("in $fetch()")
+}
+
 #' @title Collect and profile a lazy query.
 #' @description This will run the query and return a list containing the materialized DataFrame and
 #'  a DataFrame that contains profiling information of each node that is executed.
 #' @details The units of the timings are microseconds.
 #'
 #' @keywords LazyFrame
-#' @return List of two `DataFrame`s: one with the collected result, the other with the timings of each step.
+#' @return List of two `DataFrame`s: one with the collected result, the other with the timings of
+#' each step.
+#' @seealso
+#'  - [`$collect()`][LazyFrame_collect] - regular collect.
+#'  - [`$fetch()`][LazyFrame_fetch] - fast limited query check
+#'  - [`$collect_in_background()`][LazyFrame_collect_in_background] - non-blocking collect returns
+#'  a future handle. Can also just be used via `$collect(collect_in_background = TRUE)`.
 #' @examples
 #'
 #' ## Simplest use case

src/rust/src/concurrent.rs

@@ -75,3 +75,19 @@ pub fn profile_with_r_func_support(lazy_df: pl::LazyFrame) -> RResult<(DataFrame
     .map_err(polars_to_rpolars_err)
     .map(|(result_df, profile_df)| (DataFrame(result_df), DataFrame(profile_df)))
 }
+
+pub fn fetch_with_r_func_support(lazy_df: pl::LazyFrame, n_rows: usize) -> RResult<DataFrame> {
+    concurrent_handler(
+        move |tc| {
+            let retval = lazy_df.fetch(n_rows);
+            ThreadCom::kill_global(&CONFIG);
+            drop(tc);
+            retval
+        },
+        serve_r,
+        &CONFIG,
+    )
+    .map_err(|err| RPolarsErr::new().plain(err.to_string()))?
+    .map_err(polars_to_rpolars_err)
+    .map(DataFrame)
+}

src/rust/src/lazy/dataframe.rs

@@ -1,5 +1,8 @@
-use crate::concurrent::{collect_with_r_func_support, profile_with_r_func_support};
+use crate::concurrent::{
+    collect_with_r_func_support, fetch_with_r_func_support, profile_with_r_func_support,
+};
 use crate::conversion::strings_to_smartstrings;
+
 use crate::lazy::dsl::*;
 
 use crate::rdataframe::DataFrame as RDF;
@@ -428,6 +431,10 @@ impl LazyFrame {
         ))
     }
 
+    fn fetch(&self, n_rows: Robj) -> RResult<RDF> {
+        fetch_with_r_func_support(self.0.clone(), robj_to!(usize, n_rows)?)
+    }
+
     #[allow(clippy::too_many_arguments)]
     fn optimization_toggle(
         &self,