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/*
* Copyright 2016-2020 47 Degrees Open Source <https://www.47deg.com>
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package doobielib
import cats.effect.IO
import cats.implicits._
import DoobieUtils._
import doobie.implicits._
import doobie._
import org.scalaexercises.definitions.Section
import org.scalatest.matchers.should.Matchers
import org.scalatest.flatspec.AnyFlatSpec
/**
* ==Introduction==
* doobie is a monadic API that provides a number of data types that all work the same way
* but describe computations in different contexts.
*
* In the doobie high level API the most common types we will deal with have the form
* ConnectionIO[A], specifying computations that take place in a context where a
* java.sql.Connection is available, ultimately producing a value of type A.
*
* doobie programs are values. You can compose small programs to build larger programs. Once you
* have constructed a program you wish to run, you interpret it into an effectful target monad of
* your choice (Task or IO for example) and drop it into your main application wherever you like.
*
* ==First programs==
*
* Before we can use doobie we need to import some symbols. We will use package imports here as a
* convenience; this will give us the most commonly-used symbols when working with the high-level
* API.
*
* {{{
* import doobie._
* import doobie.implicits._
* }}}
*
* Let’s also bring in Cats.
*
* {{{
* import cats._
* import cats.effect._
* import cats.implicits._
* }}}
*
* In the doobie high level API the most common types we will deal with have the form `ConnectionIO[A]`,
* specifying computations that take place in a context where a `java.sql.Connection` is available,
* ultimately producing a value of type `A`.
*
* So let’s start with a `ConnectionIO` program that simply returns a constant.
*
* {{{
* val program1 = 42.pure[ConnectionIO]
* // program1: ConnectionIO[Int] = Pure(42)
* }}}
*
* This is a perfectly respectable doobie program, but we can’t run it as-is; we need a `Connection`
* first. There are several ways to do this, but here let’s use a `Transactor`.
*
* '''Note''': DriverManagerTransactors have the advantage of no connection pooling and configuration, so
* are perfect for testing. The main disadvantage is that it is slower than pooling connection managers,
* no provides upper bound for concurrent connections and executes blocking operations in an unbounded
* pool of threads. The `doobie-hikari` add-on provides a `Transactor` implementation backed by a HikariCP
* connection pool. The connection pool is a lifetime-managed object that must be shut down cleanly, so
* it is managed as a Resource.
*
* {{{
* import doobie.hikari._
*
* // Resource yielding a transactor configured with a bounded connect EC and an unbounded
* // transaction EC. Everything will be closed and shut down cleanly after use.
* val transactor: Resource[IO, HikariTransactor[IO]] =
* for {
* ce <- ExecutionContexts.fixedThreadPool[IO] (32) // our connect EC
* be <- Blocker[IO] // our blocking EC
* xa <- HikariTransactor.newHikariTransactor[IO] (
* "org.h2.Driver", // driver classname
* "jdbc:h2:mem:test;DB_CLOSE_DELAY=-1", // connect URL
* "sa", // username
* "", // password
* ce, // await connection here
* be // execute JDBC operations here
* )
* } yield xa
* }}}
*
* A `Transactor` is a data type that knows how to connect to a database, hand out connections, and
* clean them up; and with this knowledge it can transform `ConnectionIO ~> IO`, which gives us a
* program we can run. Specifically it gives us an `IO` that, when run, will connect to the database
* and execute single transaction.
*
* We are using `cats.effect.IO` as our final effect type, but you can use any monad `M[_]` given
* `cats.effect.Async[M]`. See Using Your Own Target Monad at the end of this chapter for more details.
*
* @param name connecting_to_database
*/
object ConnectingToDatabaseSection extends AnyFlatSpec with Matchers with Section {
/**
* And here we go.
*/
def constantValue(res0: Int) =
transactor.use(42.pure[ConnectionIO].transact[IO]).unsafeRunSync() should be(res0)
/**
* We have computed a constant. It’s not very interesting because we never ask the database to
* perform any work, but it’s a first step
*
* We are gonna connect to a database to compute a constant.
* Let’s use the sql string interpolator to construct a query that asks the database to compute
* a constant. The meaning of this program is “run the query, interpret the resultset as
* a stream of Int values, and yield its one and only element.”
*/
def constantValueFromDatabase(res0: Int) =
transactor.use(sql"select 42".query[Int].unique.transact[IO]).unsafeRunSync() should be(res0)
/**
* What if we want to do more than one thing in a transaction? Easy! `ConnectionIO` is a monad,
* so we can use a for comprehension to compose two smaller programs into one larger program.
*/
def combineTwoPrograms(res0: (Int, Int)) = {
val largerProgram = for {
a <- sql"select 42".query[Int].unique
b <- sql"select power(5, 2)".query[Int].unique
} yield (a, b)
transactor.use(largerProgram.transact[IO]).unsafeRunSync() should be(res0)
}
/**
* The astute among you will note that we don’t actually need a monad to do this; an applicative
* functor is all we need here. So we could also write the above program as:
*/
def combineTwoProgramsWithApplicative(res0: Int) = {
val oneProgram = sql"select 42".query[Int].unique
val anotherProgram = sql"select power(5, 2)".query[Int].unique
transactor.use((oneProgram, anotherProgram).mapN(_ + _).transact[IO]).unsafeRunSync() should be(
res0
)
}
}