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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Milvus
Milvus is a high-performance, cloud-native vector database built for scalable vector ANN search -
tidb
TiDB is built for agentic workloads that grow unpredictably, with ACID guarantees and native support for transactions, analytics, and vector search. No data silos. No noisy neighbors. No infrastructure ceiling. -
cockroach
CockroachDB — the cloud native, distributed SQL database designed for high availability, effortless scale, and control over data placement. -
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 (WASM/WASI), and command-line tools. Based on LLVM. -
rqlite
The lightweight, fault-tolerant database built on SQLite. Designed to keep your data highly available with minimal effort. -
bytebase
Database governance built for humans and agents — controlling changes and access across every major database. -
go-cache
An in-memory key:value store/cache (similar to Memcached) library for Go, suitable for single-machine applications. -
immudb
immudb - immutable database based on zero trust, SQL/Key-Value/Document model, tamperproof, data change history -
buntdb
BuntDB is an embeddable, in-memory key/value database for Go with custom indexing and geospatial support -
pREST
PostgreSQL ➕ REST, low-code, simplify and accelerate development, ⚡ instant, realtime, high-performance on any Postgres application, existing or new, MCP server -
xo
Command line tool to generate idiomatic Go code for SQL databases supporting PostgreSQL, MySQL, SQLite, Oracle, and Microsoft SQL Server -
nutsdb
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. -
lotusdb
Most advanced key-value database written in Go, extremely fast, compatible with LSM tree and B+ tree.
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README
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", "main@example.com")
rows, err := dot.Query(db, "find-users-by-email", "main@example.com")
row, err := dot.QueryRow(db, "find-one-user-by-email", "user@example.com")
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