Simple and performant SQL database client

Main features are:

• Works with PostgreSQL, MySQL, SQLite.
• Selecting into a map, struct, slice of maps/structs/vars.
• Bulk inserts.
• Bulk updates using common table expression.
• Bulk deletes.
• Fixtures.
• Migrations.
• Soft deletes.

Resources:

• Examples
• Documentation
• Reference
• Starter kit
• RealWorld app

github.com/frederikhors/orm-benchmark results

  4000 times - Insert
  raw_stmt:     0.38s        94280 ns/op     718 B/op     14 allocs/op
       raw:     0.39s        96719 ns/op     718 B/op     13 allocs/op
 beego_orm:     0.48s       118994 ns/op    2411 B/op     56 allocs/op
       bun:     0.57s       142285 ns/op     918 B/op     12 allocs/op
        pg:     0.58s       145496 ns/op    1235 B/op     12 allocs/op
      gorm:     0.70s       175294 ns/op    6665 B/op     88 allocs/op
      xorm:     0.76s       189533 ns/op    3032 B/op     94 allocs/op

  4000 times - MultiInsert 100 row
       raw:     4.59s      1147385 ns/op  135155 B/op    916 allocs/op
  raw_stmt:     4.59s      1148137 ns/op  131076 B/op    916 allocs/op
 beego_orm:     5.50s      1375637 ns/op  179962 B/op   2747 allocs/op
       bun:     6.18s      1544648 ns/op    4265 B/op    214 allocs/op
        pg:     7.01s      1753495 ns/op    5039 B/op    114 allocs/op
      gorm:     9.52s      2379219 ns/op  293956 B/op   3729 allocs/op
      xorm:    11.66s      2915478 ns/op  286140 B/op   7422 allocs/op

  4000 times - Update
  raw_stmt:     0.26s        65781 ns/op     773 B/op     14 allocs/op
       raw:     0.31s        77209 ns/op     757 B/op     13 allocs/op
 beego_orm:     0.43s       107064 ns/op    1802 B/op     47 allocs/op
       bun:     0.56s       139839 ns/op     589 B/op      4 allocs/op
        pg:     0.60s       149608 ns/op     896 B/op     11 allocs/op
      gorm:     0.74s       185970 ns/op    6604 B/op     81 allocs/op
      xorm:     0.81s       203240 ns/op    2994 B/op    119 allocs/op

  4000 times - Read
       raw:     0.33s        81671 ns/op    2081 B/op     49 allocs/op
  raw_stmt:     0.34s        85847 ns/op    2112 B/op     50 allocs/op
 beego_orm:     0.38s        94777 ns/op    2106 B/op     75 allocs/op
        pg:     0.42s       106148 ns/op    1526 B/op     22 allocs/op
       bun:     0.43s       106904 ns/op    1319 B/op     18 allocs/op
      gorm:     0.65s       162221 ns/op    5240 B/op    108 allocs/op
      xorm:     1.13s       281738 ns/op    8326 B/op    237 allocs/op

  4000 times - MultiRead limit 100
       raw:     1.52s       380351 ns/op   38356 B/op   1037 allocs/op
  raw_stmt:     1.54s       385541 ns/op   38388 B/op   1038 allocs/op
        pg:     1.86s       465468 ns/op   24045 B/op    631 allocs/op
       bun:     2.58s       645354 ns/op   30009 B/op   1122 allocs/op
 beego_orm:     2.93s       732028 ns/op   55280 B/op   3077 allocs/op
      gorm:     4.97s      1241831 ns/op   71628 B/op   3877 allocs/op
      xorm:     doesn't work

Installation

go get github.com/uptrace/bun

You also need to install a database/sql driver and the corresponding Bun dialect.

Quickstart

First you need to create a sql.DB. Here we are using the sqliteshim driver which choses between modernc.org/sqlite and mattn/go-sqlite3 depending on your platform.

import "github.com/uptrace/bun/driver/sqliteshim"

sqldb, err := sql.Open(sqliteshim.ShimName, "file::memory:?cache=shared")
if err != nil {
	panic(err)
}

And then create a bun.DB on top of it using the corresponding SQLite dialect:

import (
	"github.com/uptrace/bun"
	"github.com/uptrace/bun/dialect/sqlitedialect"
)

db := bun.NewDB(sqldb, sqlitedialect.New())

Now you are ready to issue some queries:

type User struct {
	ID   int64
	Name string
}

user := new(User)
err := db.NewSelect().
	Model(user).
	Where("name != ?", "").
	OrderExpr("id ASC").
	Limit(1).
	Scan(ctx)

The code above is equivalent to:

query := "SELECT id, name FROM users AS user WHERE name != '' ORDER BY id ASC LIMIT 1"

rows, err := sqldb.QueryContext(ctx, query)
if err != nil {
	panic(err)
}

if !rows.Next() {
    panic(sql.ErrNoRows)
}

user := new(User)
if err := db.ScanRow(ctx, rows, user); err != nil {
	panic(err)
}

if err := rows.Err(); err != nil {
    panic(err)
}

Basic example

To provide initial data for our example, we will use Bun fixtures:

import "github.com/uptrace/bun/dbfixture"

// Register models for the fixture.
db.RegisterModel((*User)(nil), (*Story)(nil))

// WithRecreateTables tells Bun to drop existing tables and create new ones.
fixture := dbfixture.New(db, dbfixture.WithRecreateTables())

// Load fixture.yaml which contains data for User and Story models.
if err := fixture.Load(ctx, os.DirFS("."), "fixture.yaml"); err != nil {
	panic(err)
}

The fixture.yaml looks like this:

- model: User
  rows:
    - _id: admin
      name: admin
      emails: ['admin1@admin', 'admin2@admin']
    - _id: root
      name: root
      emails: ['root1@root', 'root2@root']

- model: Story
  rows:
    - title: Cool story
      author_id: '{{ $.User.admin.ID }}'

To select all users:

users := make([]User, 0)
if err := db.NewSelect().Model(&users).OrderExpr("id ASC").Scan(ctx); err != nil {
	panic(err)
}

To select a single user by id:

user1 := new(User)
if err := db.NewSelect().Model(user1).Where("id = ?", 1).Scan(ctx); err != nil {
	panic(err)
}

To select a story and the associated author in a single query:

story := new(Story)
if err := db.NewSelect().
	Model(story).
	Relation("Author").
	Limit(1).
	Scan(ctx); err != nil {
	panic(err)
}

To select a user into a map:

m := make(map[string]interface{})
if err := db.NewSelect().
	Model((*User)(nil)).
	Limit(1).
	Scan(ctx, &m); err != nil {
	panic(err)
}

To select all users scanning each column into a separate slice:

var ids []int64
var names []string
if err := db.NewSelect().
	ColumnExpr("id, name").
	Model((*User)(nil)).
	OrderExpr("id ASC").
	Scan(ctx, &ids, &names); err != nil {
	panic(err)
}

For more details, please consult docs and check examples.

Contributors

Thanks to all the people who already contributed!