Description
Provides the memdb package that implements a simple in-memory database
built on immutable radix trees. The database provides Atomicity, Consistency
and Isolation from ACID. Being that it is in-memory, it does not provide durability.
The database is instantiated with a schema that specifies the tables and indices
that exist and allows transactions to be executed.
The database provides the following:
- Multi-Version Concurrency Control (MVCC)
- Transaction Support
- Rich Indexing
- Watches
go-memdb alternatives and similar packages
Based on the "Database" category.
Alternatively, view go-memdb alternatives based on common mentions on social networks and blogs.
-
prometheus
The Prometheus monitoring system and time series database. -
influxdb
Scalable datastore for metrics, events, and real-time analytics -
tidb
TiDB is an open-source, cloud-native, distributed, MySQL-Compatible database for elastic scale and real-time analytics. Try AI-powered Chat2Query free at : https://tidbcloud.com/free-trial -
cockroach
CockroachDB - the open source, cloud-native distributed SQL database. -
vitess
Vitess is a database clustering system for horizontal scaling of MySQL. -
Milvus
A cloud-native vector database with high-performance and high scalability. -
TinyGo
Go compiler for small places. Microcontrollers, WebAssembly (WASM/WASI), and command-line tools. Based on LLVM. -
groupcache
groupcache is a caching and cache-filling library, intended as a replacement for memcached in many cases. -
VictoriaMetrics
VictoriaMetrics: fast, cost-effective monitoring solution and time series 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 and Key-Value, tamperproof, data change history -
go-mysql-elasticsearch
Sync MySQL data into elasticsearch -
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 -
rosedb
🚀 A high performance NoSQL database based on bitcask, supports string, list, hash, set, and sorted set. -
xo
Command line tool to generate idiomatic Go code for SQL databases supporting PostgreSQL, MySQL, SQLite, Oracle, and Microsoft SQL Server -
dbmate
:rocket: A lightweight, framework-agnostic database migration tool. -
tiedot
A rudimentary implementation of a basic document (NoSQL) database in Go -
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. -
cache2go
Concurrency-safe Go caching library with expiration capabilities and access counters -
GCache
An in-memory cache library for golang. It supports multiple eviction policies: LRU, LFU, ARC -
fastcache
Fast thread-safe inmemory cache for big number of entries in Go. Minimizes GC overhead -
gocraft/dbr (database records)
Additions to Go's database/sql for super fast performance and convenience. -
CovenantSQL
A decentralized, trusted, high performance, SQL database with blockchain features
Access the most powerful time series database as a service
Do you think we are missing an alternative of go-memdb or a related project?
Popular Comparisons
README
go-memdb 
Provides the memdb
package that implements a simple in-memory database
built on immutable radix trees. The database provides Atomicity, Consistency
and Isolation from ACID. Being that it is in-memory, it does not provide durability.
The database is instantiated with a schema that specifies the tables and indices
that exist and allows transactions to be executed.
The database provides the following:
Multi-Version Concurrency Control (MVCC) - By leveraging immutable radix trees the database is able to support any number of concurrent readers without locking, and allows a writer to make progress.
Transaction Support - The database allows for rich transactions, in which multiple objects are inserted, updated or deleted. The transactions can span multiple tables, and are applied atomically. The database provides atomicity and isolation in ACID terminology, such that until commit the updates are not visible.
Rich Indexing - Tables can support any number of indexes, which can be simple like a single field index, or more advanced compound field indexes. Certain types like UUID can be efficiently compressed from strings into byte indexes for reduced storage requirements.
Watches - Callers can populate a watch set as part of a query, which can be used to detect when a modification has been made to the database which affects the query results. This lets callers easily watch for changes in the database in a very general way.
For the underlying immutable radix trees, see go-immutable-radix.
Documentation
The full documentation is available on Godoc.
Example
Below is a simple example of usage
// Create a sample struct
type Person struct {
Email string
Name string
Age int
}
// Create the DB schema
schema := &memdb.DBSchema{
Tables: map[string]*memdb.TableSchema{
"person": &memdb.TableSchema{
Name: "person",
Indexes: map[string]*memdb.IndexSchema{
"id": &memdb.IndexSchema{
Name: "id",
Unique: true,
Indexer: &memdb.StringFieldIndex{Field: "Email"},
},
"age": &memdb.IndexSchema{
Name: "age",
Unique: false,
Indexer: &memdb.IntFieldIndex{Field: "Age"},
},
},
},
},
}
// Create a new data base
db, err := memdb.NewMemDB(schema)
if err != nil {
panic(err)
}
// Create a write transaction
txn := db.Txn(true)
// Insert some people
people := []*Person{
&Person{"[email protected]", "Joe", 30},
&Person{"[email protected]", "Lucy", 35},
&Person{"[email protected]", "Tariq", 21},
&Person{"[email protected]", "Dorothy", 53},
}
for _, p := range people {
if err := txn.Insert("person", p); err != nil {
panic(err)
}
}
// Commit the transaction
txn.Commit()
// Create read-only transaction
txn = db.Txn(false)
defer txn.Abort()
// Lookup by email
raw, err := txn.First("person", "id", "[email protected]")
if err != nil {
panic(err)
}
// Say hi!
fmt.Printf("Hello %s!\n", raw.(*Person).Name)
// List all the people
it, err := txn.Get("person", "id")
if err != nil {
panic(err)
}
fmt.Println("All the people:")
for obj := it.Next(); obj != nil; obj = it.Next() {
p := obj.(*Person)
fmt.Printf(" %s\n", p.Name)
}
// Range scan over people with ages between 25 and 35 inclusive
it, err = txn.LowerBound("person", "age", 25)
if err != nil {
panic(err)
}
fmt.Println("People aged 25 - 35:")
for obj := it.Next(); obj != nil; obj = it.Next() {
p := obj.(*Person)
if p.Age > 35 {
break
}
fmt.Printf(" %s is aged %d\n", p.Name, p.Age)
}
// Output:
// Hello Joe!
// All the people:
// Dorothy
// Joe
// Lucy
// Tariq
// People aged 25 - 35:
// Joe is aged 30
// Lucy is aged 35