Executing SQL

sqlhelper’s main purpose is the execution of files of SQL, with options for controlling the execution of individual SQL queries within each file. The function run_files() executes .SQL files. Internally, run_files() calls the functions read_sql(), prepare_sql() and run_queries() and these functions can be used to read and prepare SQL files without executing them, and to execute SQL query strings.

Executing SQL files

Executing SQL code requires a connection to a database, and sqlhelper provides ways to automate creating and managing connections. These are described in vignette("connections").

Once connections are configured, the run_files() command can be used to execute files of SQL code.

connect("examples/sqlhelper_db_conf.yml", exclusive = TRUE)
DBI::dbWriteTable( default_conn(), name = "IRIS", iris)

readLines("examples/example.sql") |>
#> -- qname = how_many_irises
#> SELECT count(*) as N FROM IRIS;
#> -- qname = short_petal_setosa
#> select Species, `Petal.Length`
#> WHERE Species = "setosa"
#> AND `Petal.Length` < {petal_length}

petal_length <- 1.3

results <- run_files("examples/example.sql")

#> $how_many_irises
#>     N
#> 1 150
#> $short_petal_setosa
#>   Species Petal.Length
#> 1  setosa          1.1
#> 2  setosa          1.2
#> 3  setosa          1.0
#> 4  setosa          1.2

As well as individual file names, run_files() accepts a vector of file names.

Accessing results of queries

run_files() returns a list of results of the same length as the number of queries. In the above example, names are assigned to queries with interpreted comments, of the form -- qname = my_query_name. If queries are named, individual results can be accessed by the same name:

#>   Species Petal.Length
#> 1  setosa          1.1
#> 2  setosa          1.2
#> 3  setosa          1.0
#> 4  setosa          1.2

Results returned as list may also be accessed by index, of course. However, if a file contains a single query, the result of that query will be returned as is, (i.e. an object, not a single element list).

Beware of the usual gotchas around list names. sqlhelper will not complain if you give two queries the same name, but if you then try to access the results by name, you will only get the result of the first query with that name. This is particularly relevant if your project executes queries from multiple files and those files are developed by different people. Similarly, be careful not to use anything in query names that that R will interpret as an operator or special character. In the above example, naming the query short-petal-setosa will cause an error because R will interpret - as a subtraction.

Controlling execution of individual queries

As well as naming queries, interpreted comments can be used to control aspects of execution on a per-query basis. For example, queries are executed with DBI::dbGetQuery() by default, but sqlite will complain if you use this to send a statement to the database. You can control the execution function with the execmethod keyword:

-- qname = bobby_tables
-- execmethod = sendq
DROP TABLE Students;

All interpreted comments follow the form -- <keyword> = <value>.

Interpretable keywords are:

## add combined standard/spatial example

Cascaded execution parameters

All interpreted comments except qname are cascaded within their file, meaning that if you want to use the same values throughout, you need only set them for the first query. See also read_sql() for details.

If you want to change the execution parameters for the first query only and retain the defaults for the remainder you will need to either:

  • use interpreted comments to explicitly reset the defaults for the second query; or
  • put the second and subsequent queries in a different file.

You can prevent cascading by passing cascade = FALSE.

## cascaded comments example


By default, sqlhelper will attempt to parameterize SQL queries with glue::glue_sql() using values from the current environment. This means that values from R can be easily inserted in your SQL code, or calculated in situ:

readLines("examples/petal_length_params.sql") |>
#> -- qname = short_petal_setosa
#> select Species, `Petal.Length`
#> WHERE Species = "setosa"
#> AND `Petal.Length` < {petal_length};
#> -- qname = setosa_petal_1sd_below
#> select Species, `Petal.Length`
#> WHERE Species = "setosa"
#> AND `Petal.Length` < {
#>   mean(iris$Petal.Length[iris$Species == "setosa"])
#>     -  sd(iris$Petal.Length[iris$Species == "setosa"])
#> }

petal_length = 1.2

#> $short_petal_setosa
#>   Species Petal.Length
#> 1  setosa          1.1
#> 2  setosa          1.0
#> $setosa_petal_1sd_below
#>   Species Petal.Length
#> 1  setosa          1.1
#> 2  setosa          1.2
#> 3  setosa          1.0
#> 4  setosa          1.2

Interpolation behaviour can be controlled using the keywords interpolate and quotesql, and the values parameter of prepare_sql() (which can be also passed to run_files() and run_queries()).

The default behaviour is to quote SQL strings (i.e. interpolate with glue::glue_sql()); if this is not desired it can be avoided with -- quotesql = no (for an individual query in a file; but see the ‘cascaded comments’ example, above) or by passing quotesql = "no" as a parameter to run_files() (for all queries). If strings are not quoted they will be inserted bare; whilst this is occasionally useful, great care should be taken to sanitize any interpolated values.

If you want to skip interpolation for an individual query, precede it with -- interplate = no. If you want to skip interpolation altogether, pass interpolate = "no" as a parameter and see also the ‘cascaded comments’ example, above.

Passing parameter values

Sometimes you may need to parameterize your SQL with values that are not in the calling environment. This is particularly important if your are executing SQL code from within a package: you cannot rely on, and should not risk writing to, your users’ globalenv(). To supply interpolation values to run_files() and run_queries(), pass a populated environment as the values parameter.

# reusing the petal length parameter example
# A user may have a petal_length parameter in the globalenv already
#> [1] 1.2
result_from_globalenv <-
#>   Species Petal.Length
#> 1  setosa          1.1
#> 2  setosa          1.0

# a bespoke environment can provide a specific set of values for interpolation
my_values <- new.env()
my_values$petal_length <- 1.4
result_from_my_values <-
  run_files("examples/petal_length_params.sql", values = my_values)
#>    Species Petal.Length
#> 1   setosa          1.3
#> 2   setosa          1.1
#> 3   setosa          1.2
#> 4   setosa          1.3
#> 5   setosa          1.0
#> 6   setosa          1.2
#> 7   setosa          1.3
#> 8   setosa          1.3
#> 9   setosa          1.3
#> 10  setosa          1.3
#> 11  setosa          1.3


Binding can be performed alongside interpolation. Queries and statements are first interpolated and then should then be executed with DBI::dbSendQuery() or DBI::dbSendStatement(). They may then be bound and the result fetched.

readLines("examples/binding.SQL") |>
#> -- execmethod = sendq
#> -- qname = binding_example
#> SELECT species, [Petal.Width]
#> [Petal.Width] < {petal_width};

petal_width <- 0.2

result <- run_files("examples/binding.SQL")

DBI::dbBind(result$binding_example, list("setosa"))

#>   Species Petal.Width
#> 1  setosa         0.1
#> 2  setosa         0.1
#> 3  setosa         0.1
#> 4  setosa         0.1
#> 5  setosa         0.1


Reading and preparing SQL files

SQL files and strings can be read and prepared without being executed by the read_sql() and prepare_sql() functions. These functions return tibbles containing the prepared SQL, associated metadata (e.g. filename), and execution parameters. These functions enable both inspection of prepared SQL and parameters for debugging, and further manipulation of SQL queries prior to execution.

Executing SQL strings

One of the main objectives of sqlhelper is to reduce the incidence of SQL written as strings in R code. However, it is occasionally convenient for interactive exploratory work to type a query as a string. For this you may use run_queries(). This function can also be used to execute queries that have been read from files (e.g. with read_sql()) and then manipulated programmatically before execution.

Passing ad-hoc connections to functions

It may not always be possible or desirable to have sqlhelper manage your database connections. For example, the use of secrets is not yet supported in sqlhelper connections. In these cases, connections created outside sqlhelper may be passed to to run_files() or run_queries().

con <- DBI::dbConnect(RSQLite::SQLite(), ":memory:")

cars <- mtcars
cars$model <- row.names(mtcars)
DBI::dbWriteTable(con, "cars", cars)

minmpg = 30

run_queries("SELECT model, mpg, cyl FROM CARS WHERE mpg >= {minmpg}",
            default.conn = con)
#>            model  mpg cyl
#> 1       Fiat 128 32.4   4
#> 2    Honda Civic 30.4   4
#> 3 Toyota Corolla 33.9   4
#> 4   Lotus Europa 30.4   4