library(ducklake)
library(dplyr)
# Setup for examples
install_ducklake()
attach_ducklake("transactions_lake", lake_path = vignette_temp_dir)Introduction
Transactions are essential for maintaining data integrity when making multiple related changes to your data lake. DuckLake provides full ACID (Atomicity, Consistency, Isolation, Durability) transaction support, ensuring that either all operations succeed or none do.
DuckLake offers two approaches for working with transactions:
-
with_transaction()(Recommended): A modern, R-idiomatic approach that automatically handles errors and rollbacks -
begin_transaction()/commit_transaction()/rollback_transaction(): Manual transaction control for advanced use cases
This vignette demonstrates both approaches and explains when to use each one.
Setup: Loading Initial Data
We’ll use the mtcars dataset throughout this vignette to
demonstrate transaction workflows.
# Load initial data
with_transaction(
create_table(mtcars, "cars"),
author = "Tutorial",
commit_message = "Initial load of mtcars dataset"
)
#> Transaction started.
#> Transaction committed.
# View the data
get_ducklake_table("cars") |>
select(mpg, cyl, hp, wt) |>
head()
#> # A query: ?? x 4
#> # Database: DuckDB 1.5.4 [unknown@Linux 6.17.0-1018-azure:R 4.6.1//tmp/RtmpS449Ht/ducklake/ducklake22092ab2af60.duckdb]
#> mpg cyl hp wt
#> <dbl> <dbl> <dbl> <dbl>
#> 1 21 6 110 2.62
#> 2 21 6 110 2.88
#> 3 22.8 4 93 2.32
#> 4 21.4 6 110 3.22
#> 5 18.7 8 175 3.44
#> 6 18.1 6 105 3.46Approach 1: with_transaction() (Recommended)
The with_transaction() function provides automatic error
handling and cleanup, similar to the withr::with_*()
pattern used throughout the R ecosystem. This is the recommended
approach for most use cases.
Why use with_transaction()?
- Automatic rollback on error: If any operation fails, all changes are automatically rolled back
-
Cleaner code: No need to manually call
begin_transaction()andcommit_transaction() - Built-in metadata support: Easily add author and commit messages
- Safer: Prevents accidentally leaving transactions open
-
R-idiomatic: Follows familiar patterns from
packages like
withr
Single Operation with Metadata
# Add a new column with automatic metadata tracking
with_transaction(
get_ducklake_table("cars") |>
mutate(kpl = mpg * 0.425144) |>
replace_table("cars"),
author = "Data Team",
commit_message = "Add kilometers per liter column"
)
#> Transaction started.
#> Transaction committed.
# Verify the change
get_ducklake_table("cars") |>
select(mpg, kpl) |>
head()
#> # A query: ?? x 2
#> # Database: DuckDB 1.5.4 [unknown@Linux 6.17.0-1018-azure:R 4.6.1//tmp/RtmpS449Ht/ducklake/ducklake22092ab2af60.duckdb]
#> mpg kpl
#> <dbl> <dbl>
#> 1 21 8.93
#> 2 21 8.93
#> 3 22.8 9.69
#> 4 21.4 9.10
#> 5 18.7 7.95
#> 6 18.1 7.70Multiple Operations in a Single Transaction
You can group multiple operations together by wrapping them in curly braces:
# Multiple related changes in one atomic transaction
with_transaction({
# Add efficiency rating
get_ducklake_table("cars") |>
mutate(
efficiency = case_when(
mpg >= 25 ~ "high",
mpg >= 20 ~ "medium",
TRUE ~ "low"
)
) |>
replace_table("cars")
# Create a summary table
get_ducklake_table("cars") |>
group_by(cyl) |>
summarize(
avg_mpg = mean(mpg, na.rm = TRUE),
avg_hp = mean(hp, na.rm = TRUE),
count = n()
) |>
create_table("cars_summary")
}, author = "Data Team", commit_message = "Add efficiency ratings and summary table")
#> Transaction started.
#> Transaction committed.
# View results
get_ducklake_table("cars") |>
select(mpg, cyl, efficiency) |>
head()
#> # A query: ?? x 3
#> # Database: DuckDB 1.5.4 [unknown@Linux 6.17.0-1018-azure:R 4.6.1//tmp/RtmpS449Ht/ducklake/ducklake22092ab2af60.duckdb]
#> mpg cyl efficiency
#> <dbl> <dbl> <chr>
#> 1 21 6 medium
#> 2 21 6 medium
#> 3 22.8 4 medium
#> 4 21.4 6 medium
#> 5 18.7 8 low
#> 6 18.1 6 low
get_ducklake_table("cars_summary") |>
collect()
#> # A tibble: 3 × 4
#> cyl avg_mpg avg_hp count
#> <dbl> <dbl> <dbl> <dbl>
#> 1 6 19.7 122. 7
#> 2 8 15.1 209. 14
#> 3 4 26.7 82.6 11Automatic Rollback on Error
One of the key benefits of with_transaction() is
automatic error handling:
# This transaction will fail and automatically rollback
tryCatch(
with_transaction({
# This will succeed
get_ducklake_table("cars") |>
mutate(test_column = "temporary") |>
replace_table("cars")
# This will fail
stop("Simulated error - something went wrong!")
}, author = "Data Team", commit_message = "This will be rolled back"),
error = function(e) {
message("Transaction automatically rolled back: ", e$message)
}
)
#> Transaction started.
#> Transaction rolled back.
#> Transaction automatically rolled back: Transaction rolled back due to error: Simulated error - something went wrong!
# Verify that test_column was NOT added (transaction was rolled back)
get_ducklake_table("cars") |>
colnames()
#> [1] "mpg" "cyl" "disp" "hp" "drat"
#> [6] "wt" "qsec" "vs" "am" "gear"
#> [11] "carb" "kpl" "efficiency"
# View all versioned changes
list_table_snapshots("cars")
#> snapshot_id snapshot_time schema_version
#> 1 1 2026-07-08 05:41:22 1
#> 2 2 2026-07-08 05:41:22 2
#> 3 3 2026-07-08 05:41:22 3
#> changes
#> 1 tables_created, tables_inserted_into, main.cars, 1
#> 2 tables_created, tables_dropped, tables_inserted_into, main.cars, 1, 2
#> 3 tables_created, tables_dropped, tables_inserted_into, inlined_insert, main.cars, main.cars_summary, 2, 4, 3
#> author commit_message commit_extra_info
#> 1 Tutorial Initial load of mtcars dataset <NA>
#> 2 Data Team Add kilometers per liter column <NA>
#> 3 Data Team Add efficiency ratings and summary table <NA>Approach 2: Manual Transaction Control
For advanced use cases where you need explicit control over transaction boundaries, DuckLake provides manual transaction functions.
When to use manual transactions?
- Interactive workflows: When you want to inspect data between operations before committing
- Conditional commits: When commit logic depends on runtime conditions
- Long-running transactions: When you need fine-grained control over transaction lifecycle
- Legacy code: When migrating from other transaction systems
Basic Manual Transaction Workflow
# Start a transaction
begin_transaction()
#> Transaction started.
# Make changes
get_ducklake_table("cars") |>
mutate(weight_kg = wt * 453.592) |>
replace_table("cars")
# Commit the changes with metadata
commit_transaction(
author = "Data Team",
commit_message = "Add weight in kg"
)
#> Transaction committed.
# Verify changes
get_ducklake_table("cars") |>
filter(cyl == 4) |>
select(wt, weight_kg) |>
head()
#> # A query: ?? x 2
#> # Database: DuckDB 1.5.4 [unknown@Linux 6.17.0-1018-azure:R 4.6.1//tmp/RtmpS449Ht/ducklake/ducklake22092ab2af60.duckdb]
#> wt weight_kg
#> <dbl> <dbl>
#> 1 2.32 1052.
#> 2 3.19 1447.
#> 3 3.15 1429.
#> 4 2.2 998.
#> 5 1.62 733.
#> 6 1.84 832.Manual Rollback
Sometimes you may want to inspect data before deciding whether to commit:
# Start a transaction
begin_transaction()
#> Transaction started.
# Make a test change
get_ducklake_table("cars") |>
mutate(test_flag = TRUE) |>
replace_table("cars")
# Check the result
test_result <- get_ducklake_table("cars") |>
select(mpg, test_flag) |>
head() |>
collect()
print(test_result)
#> # A tibble: 6 × 2
#> mpg test_flag
#> <dbl> <lgl>
#> 1 21 TRUE
#> 2 21 TRUE
#> 3 22.8 TRUE
#> 4 21.4 TRUE
#> 5 18.7 TRUE
#> 6 18.1 TRUE
# Decide to rollback
rollback_transaction()
#> Transaction rolled back.
# Verify the change was NOT applied
"test_flag" %in% colnames(get_ducklake_table("cars"))
#> [1] FALSE
# View all versioned changes
list_table_snapshots("cars")
#> snapshot_id snapshot_time schema_version
#> 1 1 2026-07-08 05:41:22 1
#> 2 2 2026-07-08 05:41:22 2
#> 3 3 2026-07-08 05:41:22 3
#> 4 4 2026-07-08 05:41:23 4
#> changes
#> 1 tables_created, tables_inserted_into, main.cars, 1
#> 2 tables_created, tables_dropped, tables_inserted_into, main.cars, 1, 2
#> 3 tables_created, tables_dropped, tables_inserted_into, inlined_insert, main.cars, main.cars_summary, 2, 4, 3
#> 4 tables_created, tables_dropped, tables_inserted_into, main.cars, 4, 5
#> author commit_message commit_extra_info
#> 1 Tutorial Initial load of mtcars dataset <NA>
#> 2 Data Team Add kilometers per liter column <NA>
#> 3 Data Team Add efficiency ratings and summary table <NA>
#> 4 Data Team Add weight in kg <NA>Snapshot Metadata: At Commit Time vs After the Fact
DuckLake supports two ways to attach metadata (author, commit message, and optional extra info) to a snapshot.
At commit time (recommended): Pass
author, commit_message, and/or
commit_extra_info to commit_transaction() or
with_transaction(). This uses the DuckLake v1.0
set_commit_message() API to record metadata as part of the
transaction itself, before the commit is finalized.
# Metadata set at commit time (preferred approach)
begin_transaction()
#> Transaction started.
get_ducklake_table("cars") |>
mutate(hp_per_liter = hp / (cyl * 0.5)) |>
replace_table("cars")
commit_transaction(
author = "Performance Team",
commit_message = "Add horsepower per liter metric",
commit_extra_info = '{"ticket": "DATA-123"}'
)
#> Transaction committed.
get_ducklake_table("cars") |>
select(hp, cyl, hp_per_liter) |>
head()
#> # A query: ?? x 3
#> # Database: DuckDB 1.5.4 [unknown@Linux 6.17.0-1018-azure:R 4.6.1//tmp/RtmpS449Ht/ducklake/ducklake22092ab2af60.duckdb]
#> hp cyl hp_per_liter
#> <dbl> <dbl> <dbl>
#> 1 110 6 36.7
#> 2 110 6 36.7
#> 3 93 4 46.5
#> 4 110 6 36.7
#> 5 175 8 43.8
#> 6 105 6 35After the fact: Use
set_snapshot_metadata() to retroactively update the
metadata on the most recent snapshot. This directly updates the
ducklake_snapshot_changes metadata table.
# Retrospectively update metadata on the last snapshot
set_snapshot_metadata(
ducklake_name = "transactions_lake",
author = "Performance Team (reviewed)",
commit_message = "Add horsepower per liter metric (approved)"
)
#> Snapshot metadata updated.Both approaches result in the same metadata fields being populated — the choice is about workflow. Use at-commit-time metadata for automated pipelines where metadata is known upfront. Use after-the-fact metadata for interactive workflows where you want to annotate a snapshot after reviewing the results.
Viewing Transaction History
Regardless of which approach you use, all transactions are tracked with complete metadata:
# View recent transaction history
list_table_snapshots("cars") |>
select(snapshot_id, snapshot_time, author, commit_message) |>
tail(5)
#> snapshot_id snapshot_time author
#> 1 1 2026-07-08 05:41:22 Tutorial
#> 2 2 2026-07-08 05:41:22 Data Team
#> 3 3 2026-07-08 05:41:22 Data Team
#> 4 4 2026-07-08 05:41:23 Data Team
#> 5 5 2026-07-08 05:41:23 Performance Team (reviewed)
#> commit_message
#> 1 Initial load of mtcars dataset
#> 2 Add kilometers per liter column
#> 3 Add efficiency ratings and summary table
#> 4 Add weight in kg
#> 5 Add horsepower per liter metric (approved)Comparison: with_transaction() vs Manual Control
| Feature | with_transaction() |
Manual (begin/commit/rollback) |
|---|---|---|
| Ease of use | ✅ Simple, one function | ❌ Requires multiple function calls |
| Error handling | ✅ Automatic rollback | ❌ Must handle manually |
| Metadata | ✅ Inline with transaction | ✅ Inline via parameter, or retroactive via
set_snapshot_metadata()
|
| Safety | ✅ Can’t forget to commit | ❌ Risk of open transactions |
| Use case | Most production workflows | Interactive/conditional workflows |
| Code clarity | ✅ Clear transaction scope | ⚠️ Scope can be unclear |
Best Practices
-
Default to
with_transaction(): Use it for all standard workflows -
Always add metadata: Include
authorandcommit_messagefor audit trails - Keep transactions focused: Group related changes, but avoid overly long transactions
-
Handle errors gracefully: When using manual
transactions, always use
tryCatch()to ensure rollback - Test rollback behavior: Verify that your error handling works correctly
Key Concepts Summary
-
with_transaction(expr, author, commit_message, commit_extra_info): Modern, automatic transaction handling (recommended) -
begin_transaction(): Start a manual transaction -
commit_transaction(author, commit_message, commit_extra_info): Apply changes from a manual transaction, with optional metadata set at commit time via the DuckLake v1.0 API -
rollback_transaction(): Discard changes from a manual transaction -
set_snapshot_metadata(): Retroactively update metadata on the most recent snapshot
Transactions ensure data integrity and provide complete audit trails for all changes in your DuckLake.