Dotsql alternatives and similar packages
Based on the "Database" category.
Alternatively, view Dotsql alternatives based on common mentions on social networks and blogs.
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vitess
vitess provides servers and tools which facilitate scaling of MySQL databases for large scale web services. -
groupcache
Groupcache is a caching and cache-filling library, intended as a replacement for memcached in many cases. -
TinyGo
Go compiler for small places. Microcontrollers, WebAssembly, and command-line tools. Based on LLVM. -
go-cache
An in-memory key:value store/cache (similar to Memcached) library for Go, suitable for single-machine applications. -
VictoriaMetrics
fast, resource-effective and scalable open source time series database. May be used as long-term remote storage for Prometheus. Supports PromQL. -
buntdb
A fast, embeddable, in-memory key/value database for Go with custom indexing and spatial support. -
xo
Generate idiomatic Go code for databases based on existing schema definitions or custom queries supporting PostgreSQL, MySQL, SQLite, Oracle, and Microsoft SQL Server. -
sql-migrate
Database migration tool. Allows embedding migrations into the application using go-bindata. -
immudb
immudb is a lightweight, high-speed immutable database for systems and applications written in Go. -
nutsdb
Nutsdb is a simple, fast, embeddable, persistent key/value store written in pure Go. It supports fully serializable transactions and many data structures such as list, set, sorted set. -
skeema
Pure-SQL schema management system for MySQL, with support for sharding and external online schema change tools. -
Bitcask
Bitcask is an embeddable, persistent and fast key-value (KV) database written in pure Go with predictable read/write performance, low latency and high throughput thanks to the bitcask on-disk layout (LSM+WAL).
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README
dotsql
A Golang library for using SQL.
It is not an ORM, it is not a query builder. Dotsql is a library that helps you keep sql files in one place and use it with ease.
Dotsql is heavily inspired by yesql.
Installation
$ go get github.com/gchaincl/dotsql
Usage
First of all, you need to define queries inside your sql file:
-- name: create-users-table
CREATE TABLE users (
id INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL,
name VARCHAR(255),
email VARCHAR(255)
);
-- name: create-user
INSERT INTO users (name, email) VALUES(?, ?)
-- name: find-users-by-email
SELECT id,name,email FROM users WHERE email = ?
-- name: find-one-user-by-email
SELECT id,name,email FROM users WHERE email = ? LIMIT 1
--name: drop-users-table
DROP TABLE users
Notice that every query has a name tag (--name:<some name>),
this is needed to be able to uniquely identify each query
inside dotsql.
With your sql file prepared, you can load it up and start utilizing your queries:
// Get a database handle
db, err := sql.Open("sqlite3", ":memory:")
// Loads queries from file
dot, err := dotsql.LoadFromFile("queries.sql")
// Run queries
res, err := dot.Exec(db, "create-users-table")
res, err := dot.Exec(db, "create-user", "User Name", "[email protected]")
rows, err := dot.Query(db, "find-users-by-email", "[email protected]")
row, err := dot.QueryRow(db, "find-one-user-by-email", "[email protected]")
stmt, err := dot.Prepare(db, "drop-users-table")
result, err := stmt.Exec()
You can also merge multiple dotsql instances created from different sql file inputs:
dot1, err := dotsql.LoadFromFile("queries1.sql")
dot2, err := dotsql.LoadFromFile("queries2.sql")
dot := dotsql.Merge(dot1, dot2)
Embeding
To avoid distributing sql files alongside the binary file, you will need to use tools like
gotic to embed / pack everything into one file.
TODO
- [ ] Enable text interpolation inside queries using
text/template