Force computation of a lazy duckspatial_df — ddbs

Executes the accumulated query and stores the result in a DuckDB temporary table. The result remains lazy (a duckspatial_df) but points to the materialized data, avoiding repeated computation of complex query plans.

Usage

ddbs_compute(x, ..., name = NULL, temporary = TRUE)

Arguments

x: A duckspatial_df object
...: Additional arguments passed to dplyr::compute
name: Optional name for the result table. If NULL, a unique temporary name is generated.
temporary: If TRUE (default), creates a temporary table that is automatically cleaned up when the connection closes.

Value

A new duckspatial_df pointing to the materialized table

Details

This is useful when you want to:

Cache intermediate results for reuse across multiple subsequent operations
Simplify complex query plans before heavy operations like spatial joins
Force execution at a specific point without pulling data into R memory

Examples

if (FALSE) { # \dontrun{
library(duckspatial)
library(dplyr)

# Load lazy spatial data
countries <- ddbs_open_dataset(
  system.file("spatial/countries.geojson", package = "duckspatial")
)

# Complex pipeline - ddbs_compute() caches intermediate result
cached <- countries |>
  filter(CNTR_ID %in% c("DE", "FR", "IT")) |>
  ddbs_compute()  # Execute and store in temp table

# Check query plan - should reference temp table
show_query(cached)

# Further operations continue from cached result
result <- cached |>
  ddbs_filter(other_layer, predicate = "intersects") |>
  st_as_sf()
} # }