# `{DBI}`/`{dbplyr}` backend

## Motivation

Connecting to Databricks SQL Warehouses typically involves using one of the methods below:

Method | Advantages | Disadvantages
--- | --- | ---
ODBC | Works with various authentication mechanisms, robust, acceptable performance. | Heavy install that can be difficult to install on corporate devices. Very slow with larger write operations.
JDBC | Not sure to be honest. | Slower than ODBC. Requires Java. Very slow with larger write operations.
Statement Execution API | Straightforward API that can handle data of all sizes. | No direct support for write operations.
Databricks SQL Connector (`databricks-sql-connector`) | Convenient to work with and good performance. | It’s in Python, requires `{reticulate}`.

That leaves a void for something that is:

- Native to R

- Easy to install

- Acceptable performance (or better)

- Handles bulk uploads efficiently

- Supports OAuth U2M (no tokens!)

`{brickster}` now provides both `{DBI}` and `{dbplyr}` backends for working with Databricks SQL warehouses.

It uses the Statement Execution API in combination with the Files API (the latter specifically for large data uploads).

## Connecting to a Warehouse

Connecting to a Databricks SQL warehouse requires creating a `{DBI}` connection:

    library(brickster)
    library(DBI)
    library(dplyr)
    library(dbplyr)

    # Create connection to SQL warehouse
    con <- dbConnect(
      drv = DatabricksSQL(),
      warehouse_id = "<your_warehouse_id>",
      catalog = "samples",
      show_progress = FALSE
    )

## `{DBI}` Backend

### Reading Data

Execute SQL directly and return results as data frames:

    trips <- dbGetQuery(con, "SELECT * FROM samples.nyctaxi.trips LIMIT 10")

Tables can be references either via `Id()`, `I()`, or using the name as-is:

    # List available tables
    tables <- dbListTables(con)

    # Check if specific table exists
    dbExistsTable(con, "samples.nyctaxi.trips")
    dbExistsTable(con, I("samples.nyctaxi.trips"))
    dbExistsTable(con, Id("samples", "nyctaxi", "trips"))

    # Get column information
    dbListFields(con, "samples.nyctaxi.trips")
    dbListFields(con, I("samples.nyctaxi.trips"))
    dbListFields(con, Id("samples", "nyctaxi", "trips"))

### Writing Data

When writing data (append, overwrite, etc) there are two possible behaviours:

1. In-line SQL statement write
2. Stage `.parquet` files to a Volume directory and `COPY INTO` or `CTAS`

For data larger than 50k rows `{brickster}` will only permit the use of method (2), which requires `staging_volume` to be specified when establishing the connection, or directly to `dbWriteQuery`.

Ensure that `staging_volume` is a valid Volume path and you have permission to write files.

    # small data (150 rows)
    # creates the table schema explicitly then inserts rows inline
    dbWriteTable(
      conn = con,
      name = Id(catalog = "<catalog>", schema = "<schema>", table = "<table>"),
      value = iris,
      overwrite = TRUE
    )

    # bigger data (4 million rows)
    # writes parquet files to volume then CTAS
    iris_big <- sample_n(iris, replace = TRUE, size = 4000000)

    dbWriteTable(
      conn = con,
      name = Id(catalog = "<catalog>", schema = "<schema>", table = "<table>"),
      value = iris_big,
      overwrite = TRUE,
      staging_volume = "/Volumes/<catalog>/<schema>/<volume>/...", # or inherited from connection
      show_progress = TRUE
    )

## `{dbplyr}` Backend

### Reading Data

As in the `{DBI}` backend tables can be referenced either via `Id()`, `I()`, or using the name as-is in `tbl()`:

    # Connect to existing tables
    tbl(con, "samples.nyctaxi.trips")
    tbl(con, I("samples.nyctaxi.trips"))
    tbl(con, Id("samples", "nyctaxi", "trips"))
    tbl(con, in_catalog("samples", "nyctaxi", "trips"))

Chain dplyr operations - they execute remotely on Databricks:

    # Filter and select (translated to SQL)
    long_trips <- tbl(con, "samples.nyctaxi.trips") |>
      filter(trip_distance > 10) |>
      select(
        tpep_pickup_datetime,
        tpep_dropoff_datetime,
        trip_distance,
        fare_amount
      )

    # View the generated SQL (without executing)
    show_query(long_trips)

    # Execute and collect results
    long_trips |> collect()

As a general reminder, call `collect()` at the latest point possible in your analysis to take reduce the required computation locally.

    # Customer summary statistics
    trips_summary <- tbl(con, "samples.nyctaxi.trips") |>
      group_by(pickup_zip) %>%
      summarise(
        trip_count = n(),
        total_fare_amount = sum(fare_amount, na.rm = TRUE),
        total_trip_distance = sum(trip_distance, na.rm = TRUE),
        avg_fare_amount = mean(fare_amount, na.rm = TRUE)
      ) |>
      arrange(desc(avg_fare_amount))

    # Execute to get the 20 most expensive pickip zip codes with more than 30 trips
    top_zipz <- trips_summary |>
      filter(trip_count > 20) |>
      head(20) |>
      collect()

## Writing Data

A key difference is that temporary tables are not supported - this makes functions like `copy_to` only usable when specifying `temporary` as `FALSE` which will use `dbWriteTable` to create a table.

    iris_remote <- copy_to(
      con,
      iris,
      "iris_table",
      temporary = FALSE,
      overwrite = TRUE
    )

## Connection Management

The connection for the `DatabricksSQL` driver is different to other `{DBI}` backends as it doesn’t have a persistent session (it’s all just API calls). This means calling `dbDisconnect` serves no purpose when it comes to freeing resources on the SQL warehouse.

## `{WebR}` Support

`{bricksters}` core dependencies are all `{WebR}` compatible. The backend uses `{nanoarrow}` as a fallback when `{arrow}` is unavailable (currently `{arrow}` is a `Suggests`).

It’s recommended to always have `{arrow}` installed to improve the performance of data loading.