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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
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.
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.
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().