Replace a table with modified data and create a new snapshot

Replace a table with modified data and create a new snapshot

Usage

replace_table(.data, table_name, .quiet = TRUE)

Arguments

.data

A dplyr query object (tbl_lazy) with transformations

table_name

Table name to replace

.quiet

Logical, whether to suppress messages (default TRUE)

Value

Invisibly returns NULL

Details

This function is designed for schema changes or bulk transformations that should create a new versioned snapshot. It:

1. Collects the transformed data

2. Drops the existing table

3. Creates a new table with the updated schema/data

All operations happen within the current transaction context. Use begin_transaction() and commit_transaction() to ensure proper versioning.

When to use replace_table():

• Adding new columns - DuckLake UPDATE cannot add columns; use replace_table()

• Removing columns - Restructure schema with select()

• Versioning needed - Creates snapshots via DROP + CREATE for time travel

• Complex transformations - Apply full dplyr pipelines naturally

When to use update_table() instead:

• Modifying existing column values only (no schema changes)

• Performance critical and versioning not needed

• Making targeted corrections to specific rows

Examples

if (FALSE) { # \dontrun{
# Add new derived columns with versioning
begin_transaction()
get_ducklake_table("adsl") |>
  mutate(
    AGE65FL = if_else(AGE >= 65, "Y", "N"),
    AGECAT = case_when(
      AGE < 65 ~ "<65",
      AGE >= 65 & AGE < 75 ~ "65-74",
      AGE >= 75 ~ ">=75"
    )
  ) |>
  replace_table("adsl")
commit_transaction()

# Remove columns and create new snapshot
begin_transaction()
get_ducklake_table("adsl") |>
  select(-AGE65FL, -AGECAT) |>
  replace_table("adsl")
commit_transaction()
} # }