Typechecking Queries

In this chapter we learn how to use YOLO mode to validate queries against the database schema and ensure that our type mappings are correct (and if not, get some hints on how to fix them).

Setting Up

Our setup here is the same as last chapter, so if you’re still running from last chapter you can skip this section. Otherwise: imports, Transactor, and YOLO mode.

import doobie._
import doobie.implicits._
import doobie.util.ExecutionContexts
import cats._
import cats.data._
import cats.effect._
import cats.implicits._

// This is just for testing. Consider using cats.effect.IOApp instead of calling
// unsafe methods directly.
import cats.effect.unsafe.implicits.global

// A transactor that gets connections from java.sql.DriverManager and executes blocking operations
// on an our synchronous EC. See the chapter on connection handling for more info.
val xa = Transactor.fromDriverManager[IO](
  "org.postgresql.Driver",     // driver classname
  "jdbc:postgresql:world",     // connect URL (driver-specific)
  "postgres",                  // user
  ""                           // password

val y = xa.yolo
import y._

And again, we’re playing with the country table, shown here for reference.

CREATE TABLE country (
  code        character(3)  NOT NULL,
  name        text          NOT NULL,
  population  integer NOT NULL,
  gnp         numeric(10,2),
  indepyear   smallint
  -- more columns, but we won't use them here

Checking a Query

In order to create a query that’s not quite right, let’s redefine our Country class with slightly different types.

case class Country(code: Int, name: String, pop: Int, gnp: Double)

Here’s our parameterized query from last chapter, but with the new Country definition and the minPop parameter changed to a Short.

def biggerThan(minPop: Short) =
    select code, name, population, gnp, indepyear
    from country
    where population > $minPop

Now let’s try the check method provided by YOLO and see what happens.


Yikes, there are quite a few problems, in several categories. In this case doobie found

  • a parameter coercion that should always work but is not required to be supported by compliant drivers;
  • two column coercions that are supported by JDBC but are not recommended and can fail in some cases;
  • a column nullability mismatch, where a column that is provably nullable is read into a non-Option type;
  • and an unused column.

If we fix all of these problems and try again, we get a clean bill of health.

case class Country2(code: String, name: String, pop: Int, gnp: Option[BigDecimal])

def biggerThan2(minPop: Int) =
    select code, name, population, gnp
    from country
    where population > $minPop

doobie supports check for queries and updates in four ways: programmatically, via YOLO mode in the REPL, and via the doobie-specs2, doobie-scalatest and doobie-munit packages, which allow checking to become part of your unit test suite. We will investigate this in the chapter on testing.

Working Around Bad Metadata

Some drivers do not implement the JDBC metadata specification very well, which limits the usefulness of the query checking feature. MySQL and MS-SQL do a particularly rotten job in this department. In some cases queries simply cannot be checked because no metadata is available for the prepared statement (manifested as an exception) or the returned metadata is obviously inaccurate.

However a common case is that parameter metadata is unavailable but output column metadata is. And in these cases there is a workaround: use checkOutput rather than check. This instructs doobie to punt on the input parameters and only check output columns. Unsatisfying but better than nothing.


Diving Deeper

The check logic requires both a database connection and concrete Get and Put instances that define column-level JDBC mappings.

The way this works is that a Query value has enough type information to describe all parameter and column mappings, as well as the SQL literal itself (with interpolated parameters erased into ?). From here it is straightforward to prepare the statement, pull the ResultsetMetaData and DatabaseMetaData and work out whether things are aligned correctly (and if not, determine how misalignments might be fixed). The Anaylsis class consumes this metadata and is able to provide the following diagnostics:

  • SQL validity. The query must compile, which means it must be consistent with the schema.
  • Parameter and column arity. All query inputs and outputs must map 1:1 with parameters and columns.
  • Nullability. A parameter or column that is provably nullable must be mapped to a Scala Option. Note that this is a weak guarantee; columns introduced by an outer join might be nullable but JDBC will tend to report them as “might not be nullable” which isn’t useful information.
  • Coercibility of types. Mapping of Scala types to JDBC types and JDBC types to vendor types, is asymmetric with respect to reading and writing, and the specification is quite terrible. doobie encodes the JDBC spec and combines this with vendor-specific metadata to determine whether a given asserted mapping is sensible or not, and if not, will suggest a fix via changing the Scala type, and another via changing the schema type.
The source code for this page can be found here.