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sql-translation skill

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This skill guides you through adding database-specific SQL translations for dbplyr backends, accelerating accurate R-to-SQL mappings.

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---
name: sql-translation
description: Guide for adding SQL function translations to dbplyr backends. Use when implementing new database-specific R-to-SQL translations for functions like string manipulation, date/time, aggregates, or window functions.
---

# SQL Translation Skill

Use this skill when adding new SQL function translations for a specific database backend.

## Overview

This skill guides you through adding SQL translations to dbplyr. SQL translations convert R functions to their SQL equivalents for different database backends.

## Workflow

### 1. Research SQL (CRITICAL - ALWAYS FIRST)

Before implementing any SQL translation, you MUST research the SQL syntax and behavior using the **sql-research** skill. See that skill for the complete research workflow.

**Quick summary:**
- Search official documentation for "{dialect} {function}"
- Document findings in `research/{dialect}-{function}.md`
- Include all source URLs
- Only proceed to implementation after completing research

### 2. Identify the backend file

SQL translations are defined in backend-specific files:
- `R/backend-sqlite.R` - SQLite
- `R/backend-postgres.R` - PostgreSQL
- `R/backend-mysql.R` - MySQL
- `R/backend-mssql.R` - MS SQL Server
- etc.

### 3. Add translation

Translations are added to the `sql_translation()` method for the connection class. This method returns a `sql_variant()` with three components:

**Scalar translations** (for mutate/filter):
```r
sql_translator(.parent = base_scalar,
  # Simple function name mapping
  log10 = function(x) sql_expr(log(!!x)),

  # Function with different arguments
  round = function(x, digits = 0L) {
    digits <- as.integer(digits)
    sql_expr(round(((!!x)) %::% numeric, !!digits))
  },

  # Infix operators
  paste0 = sql_paste(""),

  # Complex logic
  grepl = function(pattern, x, ignore.case = FALSE) {
    if (ignore.case) {
      sql_expr(((!!x)) %~*% ((!!pattern)))
    } else {
      sql_expr(((!!x)) %~% ((!!pattern)))
    }
  }
)
```

**Aggregate translations** (for summarise):
```r
sql_translator(.parent = base_agg,
  sd = sql_aggregate("STDEV", "sd"),
  median = sql_aggregate("MEDIAN"),
  quantile = sql_not_supported("quantile")
)
```

**Window translations** (for mutate with groups):
```r
sql_translator(.parent = base_win,
  sd = win_aggregate("STDEV"),
  median = win_absent("median"),
  quantile = sql_not_supported("quantile")
)
```

### 4. Helper functions

Common translation patterns:

- `sql_expr()` - Build SQL expressions with `!!` for interpolation
- `sql_cast(type)` - Type casting (e.g., `sql_cast("REAL")`)
- `sql_aggregate(sql_name, r_name)` - Simple aggregates
- `sql_paste(sep)` - String concatenation
- `sql_not_supported(name)` - Mark unsupported functions
- `win_aggregate(sql_name)` - Window aggregates
- `win_absent(name)` - Window functions not supported

### 5. Test the translation

**Interactive testing:**
```r
Rscript -e "devtools::load_all(); library(dplyr, warn.conflicts = FALSE);
  translate_sql(your_function(x), con = simulate_yourdb())"
```

**Write tests:**
- Tests for `R/{name}.R` go in `tests/testthat/test-{name}.R`
- Place new tests next to similar existing tests
- Keep tests minimal with few comments

Example test:
```r
test_that("backend_name translates function_name correctly", {
  lf <- lazy_frame(x = 1, con = simulate_backend())

  expect_snapshot(
    lf |> mutate(y = your_function(x))
  )
})
```

### 6. Document the translation

**Update backend documentation:**
- Edit the `@description` section in the backend file (e.g., `R/backend-postgres.R`)
- List key translation differences
- Add examples to `@examples` if helpful

**Example:**
```r
#' Backend: PostgreSQL
#'
#' @description
#' See `vignette("translation-function")` and `vignette("translation-verb")` for
#' details of overall translation technology. Key differences for this backend
#' are:
#'
#' * Many stringr functions
#' * lubridate date-time extraction functions
#' * Your new translation
```

### 7. Format and check

```bash
# Format code
air format .

# Run relevant tests
Rscript -e "devtools::test(filter = 'backend-name', reporter = 'llm')"

# Check documentation
Rscript -e "devtools::document()"
```

## Key concepts

**Parent translators:**
- `base_scalar` - Common scalar functions (math, string, logical)
- `base_agg` - Common aggregates (sum, mean, min, max)
- `base_win` - Common window functions

**SQL expression building:**
- Use `sql_expr()` to build SQL
- Use `!!` to interpolate R variables
- Use `%as%` for AS, `%::%` for ::, etc.

**Argument handling:**
- Check arguments with `check_bool()`, `check_unsupported_arg()`
- Convert R types appropriately (e.g., `as.integer()`)
- Handle optional arguments with defaults

## Resources

See also:
- `vignette("translation-function")` - Function translation overview
- `vignette("new-backend")` - Creating new backends
- Existing backend files for examples

## Checklist

Before completing a SQL translation:

- [ ] Researched SQL syntax in official documentation
- [ ] Created research file in `research/{dialect}-{function}.md`
- [ ] Added translation to appropriate `sql_translator()` section
- [ ] Tested translation interactively
- [ ] Added/updated tests
- [ ] Updated backend documentation
- [ ] Ran `air format .`
- [ ] Verified tests pass

Overview

This skill guides adding SQL function translations to dbplyr backend implementations. It focuses on mapping R functions to database-specific SQL for scalar, aggregate, and window use. Follow it to research syntax, implement translations, test, and document changes consistently.

How this skill works

Start by researching the target dialect and function behavior, then add entries to the backend's sql_translation() implementation. Implement scalar, aggregate, and window translators using helpers like sql_expr(), sql_aggregate(), sql_paste(), and win_aggregate(). Test translations interactively with translate_sql() and with unit tests, then update backend docs and run formatting and test checks.

When to use it

  • Implementing a new database backend for dbplyr
  • Adding or fixing translation for string, date/time, aggregate, or window functions
  • Porting R functions to SQL for a specific SQL dialect
  • Ensuring dbplyr verbs produce correct SQL for a target DB
  • Preparing translations before writing integration tests

Best practices

  • Always complete dialect research first and record sources in research/{dialect}-{function}.md
  • Add translations to the appropriate sql_translator() section: scalar, aggregate, or window
  • Prefer helper functions (sql_expr, sql_cast, sql_paste, sql_aggregate) to keep translations consistent
  • Write minimal focused tests next to related tests and use translate_sql() for interactive validation
  • Update backend documentation with examples and run code formatters and full test runs before submitting

Example use cases

  • Translate stringr pattern functions to dialect-specific regex operators in the scalar translator
  • Map R rounding or cast behavior to the database using sql_cast and explicit argument handling
  • Implement MEDIAN or STDEV variants in aggregate and window translators for a backend
  • Mark unsupported R functions with sql_not_supported() while documenting alternatives
  • Create tests using lazy_frame() and expect_snapshot() to assert generated SQL

FAQ

What should I do before writing code?

Research the SQL syntax and semantics for the target dialect and function, record findings and source URLs in research/{dialect}-{function}.md before implementation.

Where do I add a new translation?

Add it in the backend-specific file under the appropriate sql_translator() component: scalar (mutate/filter), aggregate (summarise), or window (mutate with groups).

How do I test a new translation?

Use translate_sql(your_function(...), con = simulate_yourdb()) for interactive checks and add unit tests in tests/testthat/ that use lazy_frame() and expect_snapshot().