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 goal of KSQL is not to offer new features that are unavailable on other libraries (although we do have some), but to offer a well-thought and well-planned API so that users have an easier time, learning, debugging, and avoiding common pitfalls.

KSQL is also decoupled from its backend so that the actual communication with the database is performed by well-known and trusted technologies, namely: pgx and database/sql. You can even create your 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.

Outstanding Features

Every operation returns errors a single time, so its easier to handle them
Helper functions for everyday operations, namely: Insert, Patch and Delete
Generic and powerful functions for Querying and Scanning data into structs
Works on top of existing battle-tested libraries such as database/sql and pgx
Supports sql.Scanner and sql.Valuer and also all pgx special types (when using kpgx)
And many other features designed to make your life easier

Let's start with some Code:

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

You will find more complete examples in the sections below.

This example is available on ./examples/overview/main.go if you want to run it

package main

import (
	"context"
	"errors"
	"fmt"
	"log"

	"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"`
	Posts []Post
}

// Post have a many to one relationship with User
var PostsTable = ksql.NewTable("posts", "post_id")

type Post struct {
	ID     int    `ksql:"post_id"`
	UserID int    `ksql:"user_id"`
	Title  string `ksql:"title"`
	Text   string `ksql:"text"`
}

// Address have a one to one relationship with User
var AddressesTable = ksql.NewTable("addresses", "id")

type Address struct {
	ID       int    `ksql:"id"`
	UserID   int    `ksql:"user_id"`
	FullAddr string `ksql:"full_addr"`
}

func main() {
	ctx := context.Background()
	dbURL, closeDB := startExampleDB(ctx)
	defer closeDB()

	db, err := kpgx.New(ctx, dbURL, 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 int    `ksql:"count"`
		Type  string `ksql:"type"`
	}
	err = db.Query(ctx, &count, "SELECT type, count(*) as count FROM users GROUP BY type")
	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 adminUsers []User
	err = db.Query(ctx, &adminUsers, "FROM users WHERE type = $1", "admin")
	if err != nil {
		log.Fatalf("unable to query admin users: %s", err)
	}

	fmt.Println("admin users:", adminUsers)

	// A nice way of loading the posts of a user might be like this:
	var user User
	err = errors.Join(
		db.QueryOne(ctx, &user, "FROM users WHERE user_id = $1", 42),
		db.Query(ctx, &user.Posts, "FROM posts WHERE user_id = $1", user.ID),
	)
	if err != nil {
		log.Fatalf("unable to query users: %s", err)
	}

	fmt.Println("user with posts:", user)

	// You can retrieve data from joined tables like this
	// (notice you can either use the name of the table or the alias you choose for it in the query):
	var rows []struct {
		OneUser    User    `tablename:"users"`
		OneAddress Address `tablename:"addr"`
	}
	err = db.Query(ctx, &rows,
		`FROM users
		JOIN addresses addr
		  ON users.user_id = addr.user_id`,
	)
	if err != nil {
		log.Fatalf("unable to query users: %s", err)
	}

	fmt.Println("rows of joined tables:", rows)
}

Note: In the example above we are using the $1, $2 and $3 as placeholders on the query because this example is meant to run on top of Postgres.

If you are running on top of MySQL or SQLite use ? instead, and if you are running on top of SQLServer use @p1, @p2 and @p3 instead.

Supported Adapters:

We support a few different adapters, one of them is illustrated above (kpgx), the other ones have the exact same signature but work on different databases or driver versions, they are:

kpgx.New(ctx, os.Getenv("DATABASE_URL"), ksql.Config{}) for Postgres, it works on top of pgxpool and pgx version 4, download it with:
```
go get github.com/vingarcia/ksql/adapters/kpgx
```
kpgx5.New(ctx, os.Getenv("DATABASE_URL"), ksql.Config{}) for Postgres, it works on top of pgxpool and pgx version 5, download it with:
```
go get github.com/vingarcia/ksql/adapters/kpgx5
```
kmysql.New(ctx, os.Getenv("DATABASE_URL"), ksql.Config{}) for MySQL, it works on top of database/sql, download it with:
```
go get github.com/vingarcia/ksql/adapters/kmysql
```
ksqlserver.New(ctx, os.Getenv("DATABASE_URL"), ksql.Config{}) for SQLServer, it works on top of database/sql, download it with:
```
go get github.com/vingarcia/ksql/adapters/ksqlserver
```
ksqlite3.New(ctx, os.Getenv("DATBAASE_PATH"), ksql.Config{}) for SQLite3, it works on top of database/sql and mattn/go-sqlite3 which relies on CGO, download it with:
```
go get github.com/vingarcia/ksql/adapters/ksqlite3
```
ksqlite.New(ctx, os.Getenv("DATABASE_PATH"), ksql.Config{}) for SQLite, it works on top of database/sql and modernc.org/sqlite which does not require CGO, download it with:
```
go get github.com/vingarcia/ksql/adapters/modernc-ksqlite
```

For more detailed examples see:

./examples/all_adapters/all_adapters.go

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:

// 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{}) (Result, 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:

Inserting records
Updating records
Deleting records
Querying one or many records
Making transactions

More advanced use cases are illustrated on their own pages on our Wiki:

Querying in Chunks for Big Queries
Avoiding Code Duplication with the Select Builder
Reusing Existing Structs on Queries with JOINs
Testing Tools and ksql.Mock

For the more common use cases please read the example below, which is also available here if you want to compile it yourself.

package main

import (
	"context"
	"fmt"
	"time"

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

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

	// The following attributes are making use of the KSQL Modifiers,
	// you can find more about them on our Wiki:
	//
	// - https://github.com/VinGarcia/ksql/wiki/Modifiers
	//

	// The `json` modifier will save the address as JSON in the database
	Address Address `ksql:"address,json"`

	// The timeNowUTC modifier will set this field to `time.Now().UTC()` before saving it:
	UpdatedAt time.Time `ksql:"updated_at,timeNowUTC"`

	// The timeNowUTC/skipUpdates modifier will set this field to `time.Now().UTC()` only
	// when first creating it and ignore it during updates.
	CreatedAt time.Time `ksql:"created_at,timeNowUTC/skipUpdates"`
}

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

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()

	// 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,
		created_at DATETIME,
		updated_at DATETIME
	)`)
	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())
	}

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

	// 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 KSQL library
			panic(err.Error())
		}

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

Benchmark Comparison

The results of the benchmark are good for KSQL, but not flawless.

The next section summarizes the results so its more comprehensible, but if you prefer to read the raw benchmark data just scroll down to the Benchmark Results section.

Summary

For transparency purposes this summary will focus at the benchmark showing the worst results for KSQL which is querying multiple lines, this is the summary:

Comparing KSQL running on top of database/sql with sqlx, sqlx is 5% faster than KSQL, which is in practical terms an insignificant difference. And if KSQL is running on top of pgx then KSQL becomes 42% faster because pgx is significantly faster than sqlx. Finally if you are using sqlx with prepared statements everytime then sqlx is 7.5% faster than KSQL on top of pgx.

So between KSQL vs sqlx the performance difference is very small, and if you are using Postgres odds are KSQL will be much faster.

Comparing KSQL running on top of pgx with pgx itself, KSQL is 13.66% slower (on average), which is not insignificant but isn't much either.

Comparing KSQL running on top pgx with gorm, KSQL is 11.87% faster than gorm or inversely gorm is 13.4% slower.

It is worth noting that KSQL is only caching of prepared statements when using postgres, because this is performed by pgx, and this means that when using MySQL, SQLServer or SQLite, if you plan on also using prepared statements other libaries such as sqlx will be significantly faster than KSQL.

We are working on adding support for cached prepared statements for these other databases in the future.

Benchmark Results

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
sqlc
sqlboiler

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

The insert-one query looks like this:

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

The single-row query looks like this:

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

The multiple-rows query looks like this:

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:

$ make bench TIME=5s
sqlc generate
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         	    9711	    618727 ns/op
BenchmarkInsert/ksql/pgx-adapter/insert-one-12         	   10000	    555967 ns/op
BenchmarkInsert/sql/insert-one-12                      	    9450	    624334 ns/op
BenchmarkInsert/sql/prep-stmt/insert-one-12            	   10000	    555119 ns/op
BenchmarkInsert/sqlx/insert-one-12                     	    9552	    632986 ns/op
BenchmarkInsert/sqlx/prep-stmt/insert-one-12           	   10000	    560244 ns/op
BenchmarkInsert/pgxpool/insert-one-12                  	   10000	    553535 ns/op
BenchmarkInsert/gorm/insert-one-12                     	    9231	    668423 ns/op
BenchmarkInsert/sqlc/insert-one-12                     	    9589	    632277 ns/op
BenchmarkInsert/sqlc/prep-stmt/insert-one-12           	   10803	    560301 ns/op
BenchmarkInsert/sqlboiler/insert-one-12                	    9790	    631464 ns/op
BenchmarkQuery/ksql/sql-adapter/single-row-12          	   44436	    131191 ns/op
BenchmarkQuery/ksql/sql-adapter/multiple-rows-12       	   42087	    143795 ns/op
BenchmarkQuery/ksql/pgx-adapter/single-row-12          	   86192	     65447 ns/op
BenchmarkQuery/ksql/pgx-adapter/multiple-rows-12       	   74106	     79004 ns/op
BenchmarkQuery/sql/single-row-12                       	   44719	    134491 ns/op
BenchmarkQuery/sql/multiple-rows-12                    	   43218	    138309 ns/op
BenchmarkQuery/sql/prep-stmt/single-row-12             	   89328	     64162 ns/op
BenchmarkQuery/sql/prep-stmt/multiple-rows-12          	   84282	     71454 ns/op
BenchmarkQuery/sqlx/single-row-12                      	   44118	    132928 ns/op
BenchmarkQuery/sqlx/multiple-rows-12                   	   43824	    137235 ns/op
BenchmarkQuery/sqlx/prep-stmt/single-row-12            	   87570	     66610 ns/op
BenchmarkQuery/sqlx/prep-stmt/multiple-rows-12         	   82202	     72660 ns/op
BenchmarkQuery/pgxpool/single-row-12                   	   94034	     63373 ns/op
BenchmarkQuery/pgxpool/multiple-rows-12                	   86275	     70275 ns/op
BenchmarkQuery/gorm/single-row-12                      	   83052	     71539 ns/op
BenchmarkQuery/gorm/multiple-rows-12                   	   62636	     89652 ns/op
BenchmarkQuery/sqlc/single-row-12                      	   44329	    132659 ns/op
BenchmarkQuery/sqlc/multiple-rows-12                   	   44440	    139026 ns/op
BenchmarkQuery/sqlc/prep-stmt/single-row-12            	   91486	     66679 ns/op
BenchmarkQuery/sqlc/prep-stmt/multiple-rows-12         	   78583	     72583 ns/op
BenchmarkQuery/sqlboiler/single-row-12                 	   70030	     87089 ns/op
BenchmarkQuery/sqlboiler/multiple-rows-12              	   69961	     84376 ns/op
PASS
ok  	github.com/vingarcia/ksql/benchmarks	221.596s
Benchmark executed at: 2023-10-22
Benchmark executed on commit: 35b6882317e82de7773fb3908332e8ac3d127010

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.:
```
$ sudo usermod <your_username> -aG docker
```
And then restart your login session (or just reboot)
Finally run make pre-download-all-images only once so your tests don't timeout downloading the database images.

After that, you can just run the tests by using:

make test

TODO List

Add an Upsert helper method
Try to implement an automatic prepared statements cache like pgx does.
Update ksqltest.FillStructWith to work with ksql:"..,json" tagged attributes
Improve error messages (ongoing)
Finish the kbuilder package

Optimization Opportunities

Test if using a pointer on the field info is faster or not
Consider passing the cached structInfo as an 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()
Use prepared statements for the helper functions, Update, Insert and Delete.

Features for a possible V2

Change the .Transaction(db ksql.Provider) to a .Transaction(ctx context.Context)
Make the .Query() method to return a type Query interface { One(); All(); Chunks(); }
Have an Update() method that updates without ignoring NULLs as Patch() does
- Have a new Modifier skipNullUpdates so that the Update function will do the job of the Patch
- Remove the Patch function.
Rename NewTable() to just Table() so it feels right to declare it inline when convenient

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README.md

README.md

KSQL the Keep it Simple SQL library

Outstanding Features

Let's start with some Code:

Supported Adapters:

The KSQL Interface

Using KSQL

Benchmark Comparison

Summary

Benchmark Results

Running the KSQL tests (for contributors)

TODO List

Optimization Opportunities

Features for a possible V2

Files

README.md

Latest commit

History

README.md

File metadata and controls

KSQL the Keep it Simple SQL library

Outstanding Features

Let's start with some Code:

Supported Adapters:

The KSQL Interface

Using KSQL

Benchmark Comparison

Summary

Benchmark Results

Running the KSQL tests (for contributors)

TODO List

Optimization Opportunities

Features for a possible V2