README
¶
Jet
Jet is a complete solution for efficient and high performance database access, consisting of type-safe SQL builder
with code generation and automatic query result data mapping.
Jet currently supports PostgreSQL, MySQL, CockroachDB, MariaDB and SQLite. Future releases will add support for additional databases.
Jet is the easiest, and the fastest way to write complex type-safe SQL queries as a Go code and map database query result
into complex object composition. It is not an ORM.
Motivation
https://medium.com/@go.jet/jet-5f3667efa0cc
Contents
Features
-
Auto-generated type-safe SQL Builder. Statements supported:
- SELECT
(DISTINCT, FROM, WHERE, GROUP BY, HAVING, ORDER BY, LIMIT, OFFSET, FOR, LOCK_IN_SHARE_MODE, UNION, INTERSECT, EXCEPT, WINDOW, sub-queries) - INSERT
(VALUES, MODEL, MODELS, QUERY, ON_CONFLICT/ON_DUPLICATE_KEY_UPDATE, RETURNING), - UPDATE
(SET, MODEL, WHERE, RETURNING), - DELETE
(WHERE, ORDER_BY, LIMIT, RETURNING), - LOCK
(IN, NOWAIT),(READ, WRITE) - WITH
- SELECT
-
Auto-generated Data Model types - Go types mapped to database type (table, view or enum), used to store result of database queries. Can be combined to create complex query result destination.
-
Query execution with result mapping to arbitrary destination.
Getting Started
Prerequisites
To install Jet package, you need to install Go and set your Go workspace first.
Go version 1.9+ is required
Installation
Use the command bellow to add jet as a dependency into go.mod project:
$ go get -u github.com/go-jet/jet/v2
Jet generator can be installed in one of the following ways:
- (Go1.16+) Install jet generator using go install:
go install github.com/go-jet/jet/v2/cmd/jet@latest
Jet generator is installed to the directory named by the GOBIN environment variable, which defaults to $GOPATH/bin or $HOME/go/bin if the GOPATH environment variable is not set.
- Install jet generator to specific folder:
git clone https://github.com/go-jet/jet.git
cd jet && go build -o dir_path ./cmd/jet
Make sure dir_path folder is added to the PATH environment variable.
Quick Start
For this quick start example we will use PostgreSQL sample 'dvd rental' database. Full database dump can be found in ./tests/testdata/init/postgres/dvds.sql. Schema diagram of interest can be found here.
Generate SQL Builder and Model types
To generate jet SQL Builder and Data Model types from running postgres database, we need to call jet generator with postgres
connection parameters and destination folder path.
Assuming we are running local postgres database, with user user, user password pass, database jetdb and
schema dvds we will use this command:
jet -dsn=postgresql://user:pass@localhost:5432/jetdb?sslmode=disable -schema=dvds -path=./.gen
Connecting to postgres database: postgresql://user:pass@localhost:5432/jetdb?sslmode=disable
Retrieving schema information...
FOUND 15 table(s), 7 view(s), 1 enum(s)
Cleaning up destination directory...
Generating table sql builder files...
Generating view sql builder files...
Generating enum sql builder files...
Generating table model files...
Generating view model files...
Generating enum model files...
Done
Procedure is similar for MySQL, CockroachDB, MariaDB and SQLite. For example:
jet -source=mysql -dsn="user:pass@tcp(localhost:3306)/dbname" -path=./.gen
jet -dsn=postgres://user:pass@localhost:26257/jetdb?sslmode=disable -schema=dvds -path=./.gen #cockroachdb
jet -dsn="mariadb://user:pass@tcp(localhost:3306)/dvds" -path=./.gen # source flag can be omitted if data source appears in dsn
jet -source=sqlite -dsn="/path/to/sqlite/database/file" -schema=dvds -path=./.gen
jet -dsn="file:///path/to/sqlite/database/file" -schema=dvds -path=./.gen # sqlite database assumed for 'file' data sources
*User has to have a permission to read information schema tables.
As command output suggest, Jet will:
- connect to postgres database and retrieve information about the tables, views and enums of
dvdsschema - delete everything in schema destination folder -
./.gen/jetdb/dvds, - and finally generate SQL Builder and Model types for each schema table, view and enum.
Generated files folder structure will look like this:
|-- .gen # -path
| `-- jetdb # database name
| `-- dvds # schema name
| |-- enum # sql builder package for enums
| | |-- mpaa_rating.go
| |-- table # sql builder package for tables
| |-- actor.go
| |-- address.go
| |-- category.go
| ...
| |-- view # sql builder package for views
| |-- actor_info.go
| |-- film_list.go
| ...
| |-- model # data model types for each table, view and enum
| | |-- actor.go
| | |-- address.go
| | |-- mpaa_rating.go
| | ...
Types from table, view and enum are used to write type safe SQL in Go, and model types are combined to store
results of the SQL queries.
Let's write some SQL queries in Go
First we need to import postgres SQLBuilder and generated packages from the previous step:
import (
// dot import so go code would resemble as much as native SQL
// dot import is not mandatory
. "github.com/go-jet/jet/v2/examples/quick-start/.gen/jetdb/dvds/table"
. "github.com/go-jet/jet/v2/postgres"
"github.com/go-jet/jet/v2/examples/quick-start/.gen/jetdb/dvds/model"
)
Let's say we want to retrieve the list of all actors that acted in films longer than 180 minutes, film language is 'English' and film category is not 'Action'.
stmt := SELECT(
Actor.ActorID, Actor.FirstName, Actor.LastName, Actor.LastUpdate, // or just Actor.AllColumns
Film.AllColumns,
Language.AllColumns.Except(Language.LastUpdate), // all language columns except last_update
Category.AllColumns,
).FROM(
Actor.
INNER_JOIN(FilmActor, Actor.ActorID.EQ(FilmActor.ActorID)).
INNER_JOIN(Film, Film.FilmID.EQ(FilmActor.FilmID)).
INNER_JOIN(Language, Language.LanguageID.EQ(Film.LanguageID)).
INNER_JOIN(FilmCategory, FilmCategory.FilmID.EQ(Film.FilmID)).
INNER_JOIN(Category, Category.CategoryID.EQ(FilmCategory.CategoryID)),
).WHERE(
Language.Name.EQ(String("English")).
AND(Category.Name.NOT_EQ(String("Action"))).
AND(Film.Length.GT(Int(180))),
).ORDER_BY(
Actor.ActorID.ASC(),
Film.FilmID.ASC(),
)
Package(dot) import is used, so the statements look as close as possible to the native SQL.
Note that every column has a type. String column Language.Name and Category.Name can be compared only with
string columns and expressions. Actor.ActorID, FilmActor.ActorID, Film.Length are integer columns
and can be compared only with integer columns and expressions.
How to get a parametrized SQL query from the statement?
query, args := stmt.Sql()
query - parametrized query
args - query parameters
Click to see `query` and `args`
SELECT actor.actor_id AS "actor.actor_id",
actor.first_name AS "actor.first_name",
actor.last_name AS "actor.last_name",
actor.last_update AS "actor.last_update",
film.film_id AS "film.film_id",
film.title AS "film.title",
film.description AS "film.description",
film.release_year AS "film.release_year",
film.language_id AS "film.language_id",
film.rental_duration AS "film.rental_duration",
film.rental_rate AS "film.rental_rate",
film.length AS "film.length",
film.replacement_cost AS "film.replacement_cost",
film.rating AS "film.rating",
film.last_update AS "film.last_update",
film.special_features AS "film.special_features",
film.fulltext AS "film.fulltext",
language.language_id AS "language.language_id",
language.name AS "language.name",
language.last_update AS "language.last_update",
category.category_id AS "category.category_id",
category.name AS "category.name",
category.last_update AS "category.last_update"
FROM dvds.actor
INNER JOIN dvds.film_actor ON (actor.actor_id = film_actor.actor_id)
INNER JOIN dvds.film ON (film.film_id = film_actor.film_id)
INNER JOIN dvds.language ON (language.language_id = film.language_id)
INNER JOIN dvds.film_category ON (film_category.film_id = film.film_id)
INNER JOIN dvds.category ON (category.category_id = film_category.category_id)
WHERE ((language.name = $1) AND (category.name != $2)) AND (film.length > $3)
ORDER BY actor.actor_id ASC, film.film_id ASC;
[English Action 180]
How to get debug SQL from statement?
debugSql := stmt.DebugSql()
debugSql - this query string can be copy-pasted to sql editor and executed. It is not intended to be used in production. For debug purposes only!!!
Click to see debug sql
SELECT actor.actor_id AS "actor.actor_id",
actor.first_name AS "actor.first_name",
actor.last_name AS "actor.last_name",
actor.last_update AS "actor.last_update",
film.film_id AS "film.film_id",
film.title AS "film.title",
film.description AS "film.description",
film.release_year AS "film.release_year",
film.language_id AS "film.language_id",
film.rental_duration AS "film.rental_duration",
film.rental_rate AS "film.rental_rate",
film.length AS "film.length",
film.replacement_cost AS "film.replacement_cost",
film.rating AS "film.rating",
film.last_update AS "film.last_update",
film.special_features AS "film.special_features",
film.fulltext AS "film.fulltext",
language.language_id AS "language.language_id",
language.name AS "language.name",
language.last_update AS "language.last_update",
category.category_id AS "category.category_id",
category.name AS "category.name",
category.last_update AS "category.last_update"
FROM dvds.actor
INNER JOIN dvds.film_actor ON (actor.actor_id = film_actor.actor_id)
INNER JOIN dvds.film ON (film.film_id = film_actor.film_id)
INNER JOIN dvds.language ON (language.language_id = film.language_id)
INNER JOIN dvds.film_category ON (film_category.film_id = film.film_id)
INNER JOIN dvds.category ON (category.category_id = film_category.category_id)
WHERE ((language.name = 'English') AND (category.name != 'Action')) AND (film.length > 180)
ORDER BY actor.actor_id ASC, film.film_id ASC;
Execute query and store result
Well-formed SQL is just a first half of the job. Let's see how can we make some sense of result set returned executing above statement. Usually this is the most complex and tedious work, but with Jet it is the easiest.
First we have to create desired structure to store query result. This is done be combining autogenerated model types, or it can be done by combining custom model types(see wiki for more information).
Note that it's possible to overwrite default jet generator behavior. All the aspects of generated model and SQLBuilder types can be tailor-made(wiki).
Let's say this is our desired structure made of autogenerated types:
var dest []struct {
model.Actor
Films []struct {
model.Film
Language model.Language
Categories []model.Category
}
}
Films field is a slice because one actor can act in multiple films, and because each film belongs to one language
Langauge field is just a single model struct. Film can belong to multiple categories.
*There is no limitation of how big or nested destination can be.
Now let's execute above statement on open database connection (or transaction) db and store result into dest.
err := stmt.Query(db, &dest)
handleError(err)
And that's it.
dest now contains the list of all actors(with list of films acted, where each film has information about language and list of belonging categories) that acted in films longer than 180 minutes, film language is 'English'
and film category is not 'Action'.
Lets print dest as a json to see:
jsonText, _ := json.MarshalIndent(dest, "", "\t")
fmt.Println(string(jsonText))
[
{
"ActorID": 1,
"FirstName": "Penelope",
"LastName": "Guiness",
"LastUpdate": "2013-05-26T14:47:57.62Z",
"Films": [
{
"FilmID": 499,
"Title": "King Evolution",
"Description": "A Action-Packed Tale of a Boy And a Lumberjack who must Chase a Madman in A Baloon",
"ReleaseYear": 2006,
"LanguageID": 1,
"RentalDuration": 3,
"RentalRate": 4.99,
"Length": 184,
"ReplacementCost": 24.99,
"Rating": "NC-17",
"LastUpdate": "2013-05-26T14:50:58.951Z",
"SpecialFeatures": "{Trailers,\"Deleted Scenes\",\"Behind the Scenes\"}",
"Fulltext": "'action':5 'action-pack':4 'baloon':21 'boy':10 'chase':16 'evolut':2 'king':1 'lumberjack':13 'madman':18 'must':15 'pack':6 'tale':7",
"Language": {
"LanguageID": 1,
"Name": "English ",
"LastUpdate": "0001-01-01T00:00:00Z"
},
"Categories": [
{
"CategoryID": 8,
"Name": "Family",
"LastUpdate": "2006-02-15T09:46:27Z"
}
]
}
]
},
{
"ActorID": 3,
"FirstName": "Ed",
"LastName": "Chase",
"LastUpdate": "2013-05-26T14:47:57.62Z",
"Films": [
{
"FilmID": 996,
"Title": "Young Language",
"Description": "A Unbelieveable Yarn of a Boat And a Database Administrator who must Meet a Boy in The First Manned Space Station",
"ReleaseYear": 2006,
"LanguageID": 1,
"RentalDuration": 6,
"RentalRate": 0.99,
"Length": 183,
"ReplacementCost": 9.99,
"Rating": "G",
"LastUpdate": "2013-05-26T14:50:58.951Z",
"SpecialFeatures": "{Trailers,\"Behind the Scenes\"}",
"Fulltext": "'administr':12 'boat':8 'boy':17 'databas':11 'first':20 'languag':2 'man':21 'meet':15 'must':14 'space':22 'station':23 'unbeliev':4 'yarn':5 'young':1",
"Language": {
"LanguageID": 1,
"Name": "English ",
"LastUpdate": "0001-01-01T00:00:00Z"
},
"Categories": [
{
"CategoryID": 6,
"Name": "Documentary",
"LastUpdate": "2006-02-15T09:46:27Z"
}
]
}
]
},
//...(125 more items)
]
What if, we also want to have list of films per category and actors per category, where films are longer than 180 minutes, film language is 'English'
and film category is not 'Action'.
In that case we can reuse above statement stmt, and just change our destination:
var dest2 []struct {
model.Category
Films []model.Film
Actors []model.Actor
}
err = stmt.Query(db, &dest2)
handleError(err)
Click to see `dest2` json
[
{
"CategoryID": 8,
"Name": "Family",
"LastUpdate": "2006-02-15T09:46:27Z",
"Films": [
{
"FilmID": 499,
"Title": "King Evolution",
"Description": "A Action-Packed Tale of a Boy And a Lumberjack who must Chase a Madman in A Baloon",
"ReleaseYear": 2006,
"LanguageID": 1,
"RentalDuration": 3,
"RentalRate": 4.99,
"Length": 184,
"ReplacementCost": 24.99,
"Rating": "NC-17",
"LastUpdate": "2013-05-26T14:50:58.951Z",
"SpecialFeatures": "{Trailers,\"Deleted Scenes\",\"Behind the Scenes\"}",
"Fulltext": "'action':5 'action-pack':4 'baloon':21 'boy':10 'chase':16 'evolut':2 'king':1 'lumberjack':13 'madman':18 'must':15 'pack':6 'tale':7"
},
{
"FilmID": 50,
"Title": "Baked Cleopatra",
"Description": "A Stunning Drama of a Forensic Psychologist And a Husband who must Overcome a Waitress in A Monastery",
"ReleaseYear": 2006,
"LanguageID": 1,
"RentalDuration": 3,
"RentalRate": 2.99,
"Length": 182,
"ReplacementCost": 20.99,
"Rating": "G",
"LastUpdate": "2013-05-26T14:50:58.951Z",
"SpecialFeatures": "{Commentaries,\"Behind the Scenes\"}",
"Fulltext": "'bake':1 'cleopatra':2 'drama':5 'forens':8 'husband':12 'monasteri':20 'must':14 'overcom':15 'psychologist':9 'stun':4 'waitress':17"
}
],
"Actors": [
{
"ActorID": 1,
"FirstName": "Penelope",
"LastName": "Guiness",
"LastUpdate": "2013-05-26T14:47:57.62Z"
},
{
"ActorID": 20,
"FirstName": "Lucille",
"LastName": "Tracy",
"LastUpdate": "2013-05-26T14:47:57.62Z"
},
{
"ActorID": 36,
"FirstName": "Burt",
"LastName": "Dukakis",
"LastUpdate": "2013-05-26T14:47:57.62Z"
},
{
"ActorID": 70,
"FirstName": "Michelle",
"LastName": "Mcconaughey",
"LastUpdate": "2013-05-26T14:47:57.62Z"
},
{
"ActorID": 118,
"FirstName": "Cuba",
"LastName": "Allen",
"LastUpdate": "2013-05-26T14:47:57.62Z"
},
{
"ActorID": 187,
"FirstName": "Renee",
"LastName": "Ball",
"LastUpdate": "2013-05-26T14:47:57.62Z"
},
{
"ActorID": 198,
"FirstName": "Mary",
"LastName": "Keitel",
"LastUpdate": "2013-05-26T14:47:57.62Z"
}
]
},
//...
]
Complete code example can be found at ./examples/quick-start/quick-start.go
This example represent probably the most common use case. Detail info about additional statements, features and use cases can be found at project Wiki page.
Benefits
What are the benefits of writing SQL in Go using Jet?
The biggest benefit is speed. Speed is being improved in 3 major areas:
Writing SQL queries is faster and easier, as developers will have help of SQL code completion and SQL type safety directly from Go code. Automatic scan to arbitrary structure removes a lot of headache and boilerplate code needed to structure database query result.
While ORM libraries can introduce significant performance penalties due to number of round-trips to the database, Jet will always perform better as developers can write complex query and retrieve result with a single database call. Thus handler time lost on latency between server and database can be constant. Handler execution will be proportional only to the query complexity and the number of rows returned from database.
With Jet, it is even possible to join the whole database and store the whole structured result in one database call. This is exactly what is being done in one of the tests: TestJoinEverything. The whole test database is joined and query result(~10,000 rows) is stored in a structured variable in less than 0.5s.
The most expensive bugs are the one discovered on the production, and the least expensive are those found during development.
With automatically generated type safe SQL, not only queries are written faster but bugs are found sooner.
Let's return to quick start example, and take closer look at a line:
AND(Film.Length.GT(Int(180))),
Let's say someone changes column length to duration from film table. The next go build will fail at that line, and
the bug will be caught at compile time.
Let's say someone changes the type of length column to some non integer type. Build will also fail at the same line
because integer columns and expressions can be only compared to other integer columns and expressions.
Build will also fail if someone removes length column from film table. Film field will be omitted from SQL Builder and Model types,
next time jet generator is run.
Without Jet these bugs will have to be either caught by some test or by manual testing.
Dependencies
At the moment Jet dependence only of:
github.com/lib/pq(Used by jet generator to readPostgreSQLdatabase information)github.com/go-sql-driver/mysql(Used by jet generator to readMySQLandMariaDBdatabase information)github.com/mattn/go-sqlite3(Used by jet generator to readSQLitedatabase information)github.com/google/uuid(Used in data model files and for debug purposes)
To run the tests, additional dependencies are required:
github.com/pkg/profilegithub.com/stretchr/testifygithub.com/google/go-cmpgithub.com/jackc/pgx/v4github.com/shopspring/decimalgithub.com/volatiletech/null/v8
Versioning
SemVer is used for versioning. For the versions available, take a look at the releases.
License
Copyright 2019-2023 Goran Bjelanovic
Licensed under the Apache License, Version 2.0.
Documentation
¶
Overview ¶
Package jet is a complete solution for efficient and high performance database access, consisting of type-safe SQL builder with code generation and automatic query result data mapping. Jet currently supports PostgreSQL, MySQL, MariaDB and SQLite. Future releases will add support for additional databases.
Installation ¶
Use the command bellow to add jet as a dependency into go.mod project:
$ go get -u github.com/go-jet/jet/v2
Jet generator can be installed in one of the following ways:
(Go1.16+) Install jet generator using go install: go install github.com/go-jet/jet/v2/cmd/jet@latest
Install jet generator to GOPATH/bin folder: cd $GOPATH/src/ && GO111MODULE=off go get -u github.com/go-jet/jet/cmd/jet
Install jet generator into specific folder: git clone https://github.com/go-jet/jet.git cd jet && go build -o dir_path ./cmd/jet
Make sure that the destination folder is added to the PATH environment variable.
Usage ¶
Jet requires already defined database schema(with tables, enums etc), so that jet generator can generate SQL Builder and Model files. File generation is very fast, and can be added as every pre-build step. Sample command:
jet -dsn=postgresql://user:pass@localhost:5432/jetdb -schema=dvds -path=./.gen
Before we can write SQL queries in Go, we need to import generated SQL builder and model types:
import . "some_path/.gen/jetdb/dvds/table" import "some_path/.gen/jetdb/dvds/model"
To write postgres SQL queries we import:
. "github.com/go-jet/jet/v2/postgres" // Dot import is used so that Go code resemble as much as native SQL. It is not mandatory.
Then we can write the SQL query:
// sub-query
rRatingFilms :=
SELECT(
Film.FilmID,
Film.Title,
Film.Rating,
).FROM(
Film,
).WHERE(
Film.Rating.EQ(enum.FilmRating.R),
).AsTable("rFilms")
// export column from sub-query
rFilmID := Film.FilmID.From(rRatingFilms)
// main-query
stmt :=
SELECT(
Actor.AllColumns,
FilmActor.AllColumns,
rRatingFilms.AllColumns(),
).FROM(
rRatingFilms.
INNER_JOIN(FilmActor, FilmActor.FilmID.EQ(rFilmID)).
INNER_JOIN(Actor, Actor.ActorID.EQ(FilmActor.ActorID)
).ORDER_BY(
rFilmID,
Actor.ActorID,
)
Now we can run the statement and store the result into desired destination:
var dest []struct {
model.Film
Actors []model.Actor
}
err := stmt.Query(db, &dest)
We can print a statement to see SQL query and arguments sent to postgres server:
fmt.Println(stmt.Sql())
Output:
SELECT "rFilms"."film.film_id" AS "film.film_id", "rFilms"."film.title" AS "film.title", "rFilms"."film.rating" AS "film.rating", actor.actor_id AS "actor.actor_id", actor.first_name AS "actor.first_name", actor.last_name AS "actor.last_name", actor.last_update AS "actor.last_update", film_actor.actor_id AS "film_actor.actor_id", film_actor.film_id AS "film_actor.film_id", film_actor.last_update AS "film_actor.last_update" FROM ( SELECT film.film_id AS "film.film_id", film.title AS "film.title", film.rating AS "film.rating" FROM dvds.film WHERE film.rating = 'R' ) AS "rFilms" INNER JOIN dvds.film_actor ON (film_actor.film_id = "rFilms"."film.film_id") INNER JOIN dvds.actor ON (film_actor.actor_id = actor.actor_id) WHERE "rFilms"."film.film_id" < $1 ORDER BY "rFilms"."film.film_id" ASC, actor.actor_id ASC; [50]
If we print destination as json, we'll get:
[
{
"FilmID": 8,
"Title": "Airport Pollock",
"Rating": "R",
"Actors": [
{
"ActorID": 55,
"FirstName": "Fay",
"LastName": "Kilmer",
"LastUpdate": "2013-05-26T14:47:57.62Z"
},
{
"ActorID": 96,
"FirstName": "Gene",
"LastName": "Willis",
"LastUpdate": "2013-05-26T14:47:57.62Z"
},
...
]
},
{
"FilmID": 17,
"Title": "Alone Trip",
"Actors": [
{
"ActorID": 3,
"FirstName": "Ed",
"LastName": "Chase",
"LastUpdate": "2013-05-26T14:47:57.62Z"
},
{
"ActorID": 12,
"FirstName": "Karl",
"LastName": "Berry",
"LastUpdate": "2013-05-26T14:47:57.62Z"
},
...
...
]
Detail info about all statements, features and use cases can be found at project wiki page - https://github.com/go-jet/jet/wiki.
Source Files
Directories
