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# KSQL the Keep it Simple SQL library

KSQL was created to offer an actually simple and satisfactory
tool for interacting with SQL Databases in Golang.

The core idea on KSQL is to offer an easy to use interface,
the actual comunication with the database is decoupled so we can use
KSQL on top of `pgx`, `database/sql` and possibly other tools.
You can even create you own backend adapter for KSQL which is
useful in some situations.

In this README you will find examples for "Getting Started" with the library,
for more advanced use-cases [please read our Wiki](https://github.com/VinGarcia/ksql/wiki).

## Let's start with some Code:

This short example below is a TLDR version for illustrating how easy it is to use KSQL.

You will find more complete examples on the sections below.

```golang
package main

import (
	"context"
	"fmt"
	"log"
	"os"

	"github.com/vingarcia/ksql"
	"github.com/vingarcia/ksql/adapters/kpgx"
)

var UsersTable = ksql.NewTable("users", "user_id")

type User struct {
	ID   int    `ksql:"user_id"`
	Name string `ksql:"name"`
	Type string `ksql:"type"`
}

func main() {
	ctx := context.Background()
	db, err := kpgx.New(ctx, os.Getenv("POSTGRES_URL"), ksql.Config{})
	if err != nil {
		log.Fatalf("unable connect to database: %s", err)
	}
	defer db.Close()

	// For querying only some attributes you can
	// create a custom struct like this:
	var count []struct {
		Count string `ksql:"count"`
		Type string `ksql:"type"`
	}
	err = db.Query(ctx, &count, "SELECT type, count(*) as count FROM users WHERE type = $1 GROUP BY type", "admin")
	if err != nil {
		log.Fatalf("unable to query users: %s", err)
	}

	fmt.Println("number of users by type:", count)

	// For loading entities from the database KSQL can build
	// the SELECT part of the query for you if you omit it like this:
	var users []User
	err = db.Query(ctx, &users, "FROM users WHERE type = $1", "admin")
	if err != nil {
		log.Fatalf("unable to query users: %s", err)
	}

	fmt.Println("users:", users)
}
```

We currently have 4 constructors available,
one of them is illustrated above (`kpgx.New()`),
the other ones have the exact same signature
but work on different databases, they are:

- `kpgx.New(ctx, os.Getenv("POSTGRES_URL"), ksql.Config{})` for Postgres, it works on top of `pgxpool`
- `kmysql.New(ctx, os.Getenv("POSTGRES_URL"), ksql.Config{})` for MySQL, it works on top of `database/sql`
- `ksqlserver.New(ctx, os.Getenv("POSTGRES_URL"), ksql.Config{})` for SQLServer, it works on top of `database/sql`
- `ksqlite3.New(ctx, os.Getenv("POSTGRES_URL"), ksql.Config{})` for SQLite3, it works on top of `database/sql`

## The KSQL Interface

The current interface contains the methods the users are expected to use,
and it is also used for making it easy to mock the whole library if needed.

This interface is declared in the project as `ksql.Provider` and is displayed below.

We plan on keeping it very simple with a small number
of well thought functions that cover all use-cases,
so don't expect many additions:

```go
// Provider describes the KSQL public behavior
//
// The Insert, Patch, Delete and QueryOne functions return `ksql.ErrRecordNotFound`
// if no record was found or no rows were changed during the operation.
type Provider interface {
	Insert(ctx context.Context, table Table, record interface{}) error
	Patch(ctx context.Context, table Table, record interface{}) error
	Delete(ctx context.Context, table Table, idOrRecord interface{}) error

	Query(ctx context.Context, records interface{}, query string, params ...interface{}) error
	QueryOne(ctx context.Context, record interface{}, query string, params ...interface{}) error
	QueryChunks(ctx context.Context, parser ChunkParser) error

	Exec(ctx context.Context, query string, params ...interface{}) (rowsAffected int64, _ error)
	Transaction(ctx context.Context, fn func(Provider) error) error
}
```

## Using KSQL

In the example below we'll cover all the most common use-cases such as:

1. Inserting records
2. Updating records
3. Deleting records
4. Querying one or many records
5. Making transactions

More advanced use cases are illustrated on their own pages on [our Wiki](https://github.com/VinGarcia/ksql/wiki):

- [Querying in Chunks for Big Queries](https://github.com/VinGarcia/ksql/wiki/Querying-in-Chunks-for-Big-Queries)
- [Avoiding Code Duplication with the Select Builder](https://github.com/VinGarcia/ksql/wiki/Avoiding-Code-Duplication-with-the-Select-Builder)
- [Reusing Existing Structs on Queries with JOINs](https://github.com/VinGarcia/ksql/wiki/Reusing-Existing-Structs-on-Queries-with-JOINs)
- [Testing Tools and `ksql.Mock`](https://github.com/VinGarcia/ksql/wiki/Testing-Tools-and-ksql.Mock)

For the more common use-cases please read the example below,
which is also available [here](./examples/crud/crud.go)
if you want to compile it yourself.

```Go
package main

import (
	"context"
	"fmt"

	"github.com/vingarcia/ksql"
	"github.com/vingarcia/ksql/adapters/ksqlite3"
	"github.com/vingarcia/ksql/nullable"
)

// User ...
type User struct {
	ID   int    `ksql:"id"`
	Name string `ksql:"name"`
	Age  int    `ksql:"age"`

	// This field will be saved as JSON in the database
	Address Address `ksql:"address,json"`
}

// PartialUpdateUser ...
type PartialUpdateUser struct {
	ID      int      `ksql:"id"`
	Name    *string  `ksql:"name"`
	Age     *int     `ksql:"age"`
	Address *Address `ksql:"address,json"`
}

// Address ...
type Address struct {
	State string `json:"state"`
	City  string `json:"city"`
}

// UsersTable informs KSQL the name of the table and that it can
// use the default value for the primary key column name: "id"
var UsersTable = ksql.NewTable("users")

func main() {
	ctx := context.Background()

	// The available adapters are:
	// - kpgx.New(ctx, connURL, ksql.Config{})
	// - kmysql.New(ctx, connURL, ksql.Config{})
	// - ksqlserver.New(ctx, connURL, ksql.Config{})
	// - ksqlite3.New(ctx, connURL, ksql.Config{})
	//
	// For more detailed examples see:
	// - `./examples/all_adapters/all_adapters.go`
	//
	// In this example we'll use sqlite3:
	db, err := ksqlite3.New(ctx, "/tmp/hello.sqlite", ksql.Config{
		MaxOpenConns: 1,
	})
	if err != nil {
		panic(err.Error())
	}
	defer db.Close()

	// In the definition below, please note that BLOB is
	// the only type we can use in sqlite for storing JSON.
	_, err = db.Exec(ctx, `CREATE TABLE IF NOT EXISTS users (
	  id INTEGER PRIMARY KEY,
		age INTEGER,
		name TEXT,
		address BLOB
	)`)
	if err != nil {
		panic(err.Error())
	}

	var alison = User{
		Name: "Alison",
		Age:  22,
		Address: Address{
			State: "MG",
		},
	}
	err = db.Insert(ctx, UsersTable, &alison)
	if err != nil {
		panic(err.Error())
	}
	fmt.Println("Alison ID:", alison.ID)

	// Inserting inline:
	err = db.Insert(ctx, UsersTable, &User{
		Name: "Cristina",
		Age:  27,
		Address: Address{
			State: "SP",
		},
	})
	if err != nil {
		panic(err.Error())
	}

	// Deleting Alison:
	err = db.Delete(ctx, UsersTable, alison.ID)
	if err != nil {
		panic(err.Error())
	}

	// Retrieving Cristina, note that if you omit the SELECT part of the query
	// KSQL will build it for you (efficiently) based on the fields from the struct:
	var cris User
	err = db.QueryOne(ctx, &cris, "FROM users WHERE name = ? ORDER BY id", "Cristina")
	if err != nil {
		panic(err.Error())
	}
	fmt.Printf("Cristina: %#v\n", cris)

	// Updating all fields from Cristina:
	cris.Name = "Cris"
	err = db.Patch(ctx, UsersTable, cris)

	// Changing the age of Cristina but not touching any other fields:

	// Partial update technique 1:
	err = db.Patch(ctx, UsersTable, struct {
		ID  int `ksql:"id"`
		Age int `ksql:"age"`
	}{ID: cris.ID, Age: 28})
	if err != nil {
		panic(err.Error())
	}

	// Partial update technique 2:
	err = db.Patch(ctx, UsersTable, PartialUpdateUser{
		ID:  cris.ID,
		Age: nullable.Int(28), // (just a pointer to an int, if null it won't be updated)
	})
	if err != nil {
		panic(err.Error())
	}

	// Listing first 10 users from the database
	// (each time you run this example a new Cristina is created)
	//
	// Note: Using this function it is recommended to set a LIMIT, since
	// not doing so can load too many users on your computer's memory or
	// cause an Out Of Memory Kill.
	//
	// If you need to query very big numbers of users we recommend using
	// the `QueryChunks` function.
	var users []User
	err = db.Query(ctx, &users, "FROM users LIMIT 10")
	if err != nil {
		panic(err.Error())
	}

	// Making transactions:
	err = db.Transaction(ctx, func(db ksql.Provider) error {
		var cris2 User
		err = db.QueryOne(ctx, &cris2, "FROM users WHERE id = ?", cris.ID)
		if err != nil {
			// This will cause an automatic rollback:
			return err
		}

		err = db.Patch(ctx, UsersTable, PartialUpdateUser{
			ID:  cris2.ID,
			Age: nullable.Int(29),
		})
		if err != nil {
			// This will also cause an automatic rollback and then panic again
			// so that we don't hide the panic inside the KissSQL library
			panic(err.Error())
		}

		// Commits the transaction
		return nil
	})
	if err != nil {
		panic(err.Error())
	}

	fmt.Printf("Users: %#v\n", users)
}
```

## Benchmark Comparison

The results of the benchmark are good:
they show that KSQL is in practical terms,
as fast as sqlx which was our goal from the start.

To understand the benchmark below you must know
that all tests are performed using Postgres 12.1 and
that we are comparing the following tools:

- KSQL using the adapter that wraps `database/sql`
- KSQL using the adapter that wraps `pgx`
- `database/sql`
- `sqlx`
- `pgx` (with `pgxpool`)
- `gorm`

For each of these tools we are running 3 different queries:

The `insert-one` query looks like:

`INSERT INTO users (name, age) VALUES ($1, $2) RETURNING id`

The `single-row` query looks like:

`SELECT id, name, age FROM users OFFSET $1 LIMIT 1`

The `multiple-rows` query looks like:

`SELECT id, name, age FROM users OFFSET $1 LIMIT 10`

Keep in mind that some of the tools tested (like GORM) actually build
the queries internally so the actual code used for the benchmark
might differ a little bit from the example ones above.

Without further ado, here are the results:

```bash
$ make bench TIME=5s
cd benchmarks && go test -bench=. -benchtime=5s
goos: linux
goarch: amd64
pkg: github.com/vingarcia/ksql/benchmarks
cpu: Intel(R) Core(TM) i7-10750H CPU @ 2.60GHz
BenchmarkInsert/ksql/sql-adapter/insert-one-12         	    9181	    630987 ns/op
BenchmarkInsert/ksql/pgx-adapter/insert-one-12         	   10000	    554494 ns/op
BenchmarkInsert/sql/insert-one-12                      	    9130	    628353 ns/op
BenchmarkInsert/sql/prep-statements/insert-one-12      	   10000	    555724 ns/op
BenchmarkInsert/sqlx/insert-one-12                     	    9346	    630891 ns/op
BenchmarkInsert/pgxpool/insert-one-12                  	   10000	    558666 ns/op
BenchmarkInsert/gorm/insert-one-12                     	    8822	    670264 ns/op
BenchmarkQuery/ksql/sql-adapter/single-row-12          	   41002	    142869 ns/op
BenchmarkQuery/ksql/sql-adapter/multiple-rows-12       	   38989	    154036 ns/op
BenchmarkQuery/ksql/pgx-adapter/single-row-12          	   95594	     65885 ns/op
BenchmarkQuery/ksql/pgx-adapter/multiple-rows-12       	   74559	     78792 ns/op
BenchmarkQuery/sql/single-row-12                       	   42746	    139622 ns/op
BenchmarkQuery/sql/multiple-rows-12                    	   41036	    144600 ns/op
BenchmarkQuery/sql/prep-statements/single-row-12       	   95922	     64532 ns/op
BenchmarkQuery/sql/prep-statements/multiple-rows-12    	   84481	     71378 ns/op
BenchmarkQuery/sqlx/single-row-12                      	   42567	    142479 ns/op
BenchmarkQuery/sqlx/multiple-rows-12                   	   40503	    146865 ns/op
BenchmarkQuery/pgxpool/single-row-12                   	   91155	     63803 ns/op
BenchmarkQuery/pgxpool/multiple-rows-12                	   82599	     69758 ns/op
BenchmarkQuery/gorm/single-row-12                      	   85747	     69912 ns/op
BenchmarkQuery/gorm/multiple-rows-12                   	   67530	     87910 ns/op
PASS
ok  	github.com/vingarcia/ksql/benchmarks	139.589s
Benchmark executed at: 2022-07-04
Benchmark executed on commit: 589ad0a6934a7d4c2d89796203e55e76f5466d75
```

## Running the KSQL tests (for contributors)

The tests use `docker-test` for setting up all the supported databases,
which means that:

- You need to have `docker` installed
- You must be able to run docker without `sudo`, i.e.
  if you are not root you should add yourself to the docker group, e.g.:

  ```bash
  $ sudo usermod <your_username> -aG docker
  ```
  And then restart your login session (or just reboot)

After that you can just run the tests by using:

```bash
make test
```

But it is recommended to first download the required images using:

```bash
docker pull postgres:14.0
docker pull mysql:8.0.27
docker pull mcr.microsoft.com/mssql/server:2017-latest
```

Otherwise the first attempt to run the tests will
spend a long time downloading these images
and then fail because the `TestMain()` function
is configured to kill the containers after 20 seconds.

## TODO List

- Add tests for tables using composite keys (ongoing)
- Add support for serializing structs as other formats such as YAML
- Update `ksqltest.FillStructWith` to work with `ksql:"..,json"` tagged attributes
- Create a way for users to submit user defined dialects
- Improve error messages (ongoing)
- Add support for the Patch function to work with maps for partial updates
- Add support for the Insert function to work with maps
- Add support for a `ksql.Array(params ...interface{})` for allowing queries like this:
  `db.Query(ctx, &user, "SELECT * FROM user WHERE id in (?)", ksql.Array(1,2,3))`
- Improve docs about `ksql.Mock`

## Optimization Oportunities

- Test if using a pointer on the field info is faster or not
- Consider passing the cached structInfo as argument for all the functions that use it,
  so that we don't need to get it more than once in the same call.
- Use a cache to store often used queries (like pgx)
- Preload the insert method for all dialects inside `ksql.NewTable()`