Pivot is a library used to access, query, and aggregate data across a variety of database systems, written in Golang.
Pivot is organized into multiple sub-packages that perform various functions:
Package | What it does... |
---|---|
pivot |
Entry point for the package. Connect to a database from here. |
pivot/dal |
Data Abstraction Layer; provides a database-agnostic view of collections (i.e: tables), records (i.e.: rows), and the fields that make up those records (i.e.: columns). |
pivot/filter |
A database-agnostic representation of queries that return some subset of the data from a collection. |
pivot/mapper |
A more user-friendly data mapping layer that provides more traditional ODM/ORM semantics for working with collections. |
pivot/backends |
Where all the database and search index adapters live. |
pivot/utils |
Utilities largely used within the rest of this library. |
Below is a table describing which data systems are currently supported. For systems with Backend support, Pivot can create and delete collections, and create/retrieve/update/delete records by their primary key / ID. If a system has Indexer support, Pivot can perform arbitrary queries against collections using a standard filter syntax, and return the results as a set of standard Record objects.
Product | Backend | Indexer |
---|---|---|
MySQL / MariaDB | X | X |
PostgreSQL | X | X |
SQLite 3.x | X | X |
Filesystem | X | X |
MongoDB | X | X |
Amazon DynamoDB | X | |
Elasticsearch | X |
Here is a simple example for connecting to a SQLite database, creating a table; and inserting, retrieving, and deleting a record.
package main
import (
"fmt"
"time"
"github.com/ghetzel/pivot"
"github.com/ghetzel/pivot/dal"
"github.com/ghetzel/pivot/mapper"
)
var Widgets mapper.Mapper
var WidgetsSchema = &dal.Collection{
Name: `widgets`,
IdentityFieldType: dal.StringType,
IdentityFieldFormatter: dal.GenerateUUID,
Fields: []dal.Field{
{
Name: `type`,
Description: `The type of widget.`,
Type: dal.StringType,
Validator: dal.ValidateIsOneOf(`foo`, `bar`, `baz`),
Required: true,
}, {
Name: `usage`,
Description: `Short description on how to use this widget.`,
Type: dal.StringType,
}, {
Name: `created_at`,
Description: `When the widget was created.`,
Type: dal.TimeType,
Formatter: dal.CurrentTimeIfUnset,
}, {
Name: `updated_at`,
Description: `Last time the widget was updated.`,
Type: dal.TimeType,
Formatter: dal.CurrentTime,
},
},
}
type Widget struct {
ID string `pivot:"id,identity"`
Type string `pivot:"type"`
Usage string `pivot:"usage"`
CreatedAt time.Time `pivot:"created_at"`
UpdatedAt time.Time `pivot:"updated_at"`
}
func main() {
// setup a new backend instance based on the supplied connection string
if backend, err := pivot.NewDatabase(`sqlite:///./test.db`); err == nil {
// initialize the backend (connect to/open it)
if err := backend.Initialize(); err == nil {
// register models to this database backend
Widgets = mapper.NewModel(backend, WidgetsSchema)
// create the model tables if they don't exist
if err := Widgets.Migrate(); err != nil {
fmt.Printf("failed to create widget table: %v\n", err)
return
}
// make a new Widget instance, containing the data we want to see
// the ID field will be populated after creation with the auto-
// generated UUID.
newWidget := Widget{
Type: `foo`,
Usage: `A fooable widget.`,
}
// insert a widget (ID will be auto-generated because of dal.GenerateUUID)
if err := Widgets.Create(&newWidget); err != nil {
fmt.Printf("failed to insert widget: %v\n", err)
return
}
// retrieve the widget using the ID we just got back
var gotWidget Widget
if err := Widgets.Get(newWidget.ID, &gotWidget); err != nil {
fmt.Printf("failed to retrieve widget: %v\n", err)
return
}
fmt.Printf("Got Widget: %#+v", gotWidget)
// delete the widget
if err := Widgets.Delete(newWidget.ID); err != nil {
fmt.Printf("failed to delete widget: %v\n", err)
return
}
} else {
fmt.Printf("failed to initialize backend: %v\n", err)
return
}
} else {
fmt.Printf("failed to create backend: %v\n", err)
return
}
}
The ability to mix and match persistent structured data storage and retrieval mechanisms with various indexing strategies is a powerful one. The idea here is to provide a common interface for systems to integrate with in a way that doesn't tightly couple those systems to specific databases, query languages, and infrastructures. It's an attempt to deliver on the promises of traditional ORM/ODM libraries in a platform- and language-agnostic way.