Proteus

A simple tool for generating an application's data access layer.

Purpose

Quick Start

1. Define a struct that contains function fields and tags to indicate the query and the parameter names:

type ProductDao struct {
	FindByID                      func(e proteus.Querier, id int) (Product, error)                                     `proq:"select * from Product where id = :id:" prop:"id"`
	Update                        func(e proteus.Executor, p Product) (int64, error)                                   `proq:"update Product set name = :p.Name:, cost = :p.Cost: where id = :p.Id:" prop:"p"`
	FindByNameAndCost             func(e proteus.Querier, name string, cost float64) ([]Product, error)                `proq:"select * from Product where name=:name: and cost=:cost:" prop:"name,cost"`
	FindByIDMap                   func(e proteus.Querier, id int) (map[string]interface{}, error)                      `proq:"select * from Product where id = :id:" prop:"id"`
	UpdateMap                     func(e proteus.Executor, p map[string]interface{}) (int64, error)                    `proq:"update Product set name = :p.Name:, cost = :p.Cost: where id = :p.Id:" prop:"p"`
	FindByNameAndCostMap          func(e proteus.Querier, name string, cost float64) ([]map[string]interface{}, error) `proq:"select * from Product where name=:name: and cost=:cost:" prop:"name,cost"`
	Insert                        func(e proteus.Executor, id int, name string, cost *float64) (int64, error)          `proq:"insert into product(id, name, cost) values(:id:, :name:, :cost:)" prop:"id,name,cost"`
	FindByIDSlice                 func(e proteus.Querier, ids []int) ([]Product, error)                                `proq:"select * from Product where id in (:ids:)" prop:"ids"`
	FindByIDSliceAndName          func(e proteus.Querier, ids []int, name string) ([]Product, error)                   `proq:"select * from Product where name = :name: and id in (:ids:)" prop:"ids,name"`
	FindByIDSliceNameAndCost      func(e proteus.Querier, ids []int, name string, cost *float64) ([]Product, error)    `proq:"select * from Product where name = :name: and id in (:ids:) and (cost is null or cost = :cost:)" prop:"ids,name,cost"`
	FindByIDSliceCostAndNameSlice func(e proteus.Querier, ids []int, names []string, cost *float64) ([]Product, error) `proq:"select * from Product where id in (:ids:) and (cost is null or cost = :cost:) and name in (:names:)" prop:"ids,names,cost"`
	FindByNameAndCostUnlabeled    func(e proteus.Querier, name string, cost float64) ([]Product, error)                `proq:"select * from Product where name=:$1: and cost=:$2:"`
}

The first input parameter is either of type proteus.Executor or proteus.Querier:

// Executor runs queries that modify the data store.
type Executor interface {
	// Exec executes a query without returning any rows.
	// The args are for any placeholder parameters in the query.
	Exec(query string, args ...interface{}) (sql.Result, error)
}

// Querier runs queries that return Rows from the data store
type Querier interface {
	// Query executes a query that returns rows, typically a SELECT.
	// The args are for any placeholder parameters in the query.
	Query(query string, args ...interface{}) (Rows, error)
}

The remaining input parameters can be primitives, structs, maps of string to interface{}, or slices.

For queries, return types can be:

• empty
• a single value being returned (a primitive, struct, or map of string to interface{})
• a single value that's a slice of primitive, struct, or a map of string to interface{}
• a primitive, struct, or map of string to interface{} and an error
• a slice of primitive, struct, or a map of string to interface{} and an error

For insert/updates, return types can be:

• empty
• an int64 that indicates the number of rows affected
• an int64 that indicates the number of rows affected and an error

2. If you want to map response fields to a struct, define a struct with struct tags to indicate the mapping:

type Product struct {
	Id   int     `prof:"id"`
	Name string  `prof:"name"`
	Cost float64 `prof:"cost"`
}

3. Pass an instance of the Dao struct to the proteus.Build function:

var productDao = ProductDao{}

func init() {
	err := proteus.Build(&productDao, proteus.Sqlite)
	if err != nil {
		panic(err)
	}
}

The proteus.Build factory function only returns errors if the wrong data type is passed in for the first parameter. If you want errors returned when there is a failure to generate a function field, use proteus.ShouldBuild instead.

4. Open a connection to a data store that meets the Proteus interface:

	db := setupDb()
	defer db.Close()
	exec := proteus.Wrap(db)

5. Make calls to the function fields in your Proteus-populated struct:

	fmt.Println(productDao.FindById(gExec, 10))
	p := Product{10, "Thingie", 56.23}
	fmt.Println(productDao.Update(gExec, p))
	fmt.Println(productDao.FindById(gExec, 10))
	fmt.Println(productDao.FindByNameAndCost(gExec, "fred", 54.10))
	fmt.Println(productDao.FindByNameAndCost(gExec, "Thingie", 56.23))

	//using a map of [string]interface{} works too!
	fmt.Println(productDao.FindByIdMap(gExec, 10))
	fmt.Println(productDao.FindByNameAndCostMap(gExec, "Thingie", 56.23))

	fmt.Println(productDao.FindById(gExec, 11))
	m := map[string]interface{}{
		"Id":   11,
		"Name": "bobbo",
		"Cost": 12.94,
	}
	fmt.Println(productDao.UpdateMap(gExec, m))
	fmt.Println(productDao.FindById(gExec, 11))

	fmt.Println(productDao.FindByIDSlice(pExec, []int{1, 3, 5}))
	fmt.Println(productDao.FindByIDSliceAndName(pExec, []int{1, 3, 5}, "person1"))
	fmt.Println(productDao.FindByIDSliceNameAndCost(pExec, []int{1, 3, 5}, "person3", nil))
	fmt.Println(productDao.FindByIDSliceCostAndNameSlice(pExec, []int{1, 3, 5}, []string{"person3", "person5"}, nil))

Struct Tags

Proteus generates implementations of DAO functions by examining struct tags and parameters types on function fields in a struct. The following are the recognized struct tags:

• proq - The query. Returns single entity or list of entities
• prop - The parameter names. Should be in the order of the function parameters (skipping over the first Executor or Querier parameter)

The prop struct tag is optional. If it is not supplied, the query must contain positional parameters ($1, $2, etc.) instead of named parameters. For example:

type ProductDaoS struct {
	FindById             func(e proteus.Querier, id int) (Product, error)                                     `proq:"select * from Product where id = :$1:"`
	Update               func(e proteus.Executor, p Product) (int64, error)                                   `proq:"update Product set name = :$1.Name:, cost = :$1.Cost: where id = :$1.Id:"`
	FindByNameAndCost    func(e proteus.Querier, name string, cost float64) ([]Product, error)                `proq:"select * from Product where name=:$1: and cost=:$2:"`
}

If you want to map the output of a DAO with a proq tag to a struct, then create a struct and put the following struct tag on each field that you want to map to a value in the output:

• prof - The fields on the dto that are mapped to select parameters in a query

Storing queries outside of struct tags

Struct tags are cumbersome for all but the shortest queries. In order to allow a more natural way to store longer queries, one or more instances of the proteus.QueryMapper interface can be passed into the proteus.Build function. In order to reference a query stored in an proteus.QueryMapper, you should put q:name as the value of the proq struct tag, where name is the name for the query.

For example:

	m := proteus.MapMapper{
		"q1": "select * from foo where id = :id:",
		"q2": "update foo set x=:x: where id = :id:",
	}

	type s struct {
		GetF func(e proteus.Querier, id string) (f, error) `proq:"q:q1" prop:"id"`
		Update func(e proteus.Executor, id string, x string) (int64, error) `proq:"q:q2" prop:"id,x"`
	}
	
	sImpl := s{}
	err := proteus.Build(&sImpl, proteus.Sqlite, m)

Out of the box, you can use either a map[string]string or a unix properties file to store your queries. In order to use a map[string]string, cast your map to proteus.MapMapper (or just declare your variable to be of type proteus.MapMapper). To use a properties file, call the method proteus.PropFileToQueryMapper with the name of the property file that contains your queries.

Valid function signatures

API

FAQ

1. Why doesn't Proteus generate a struct that meets an interface?

Go has some surprising limitations. One of them is that there is no way to use reflection to build a implementation of a method; you can only build implementations of functions. The difference is subtle, but the net result is that you cannot supply an interface to the reflection API and get back something that implements that interface.

Another go limitation is that there is also no way to attach metadata to an interface's method. only struct fields can have associated metadata.

A third limitation is that a go interface is not satisfied by a struct that has fields of function type, even if the names of the functions and the types of the function parameters match an interface.

Given these limitations, Proteus uses structs to hold the generated functions. If you want an interface that describes the functionality provided by the struct, you can do something like this:

type ProductDaoS struct {
	FindById             func(e proteus.Executor, id int) (Product, error)                                     `proq:"select * from Product where id = :id:" prop:"id"`
	Update               func(e proteus.Executor, p Product) (int64, error)                                    `proq:"update Product set name = :p.Name:, cost = :p.Cost: where id = :p.Id:" prop:"p"`
	FindByNameAndCost    func(e proteus.Executor, name string, cost float64) ([]Product, error)                `proq:"select * from Product where name=:name: and cost=:cost:" prop:"name,cost"`
}

type ProductDao interface {
    FindById(proteus.Executor, int) (Product, error)
    Update(proteus.Executor, Product) (int64, error)
    FindByNameAndCost(proteus.Executor, string, float64) ([]Product, error)
}

type productWrapper struct {
    ProductDaoS
}

func (pw productWrapper) FindById(exec proteus.Executor, id int) (Product, error) {
    return pw.ProductDaoS.FindById(exec, id)
}

func (pw productWrapper) Update(exec proteus.Executor, p Product) (int64, error) {
    return pw.ProductDaoS.Update(exec, p)
}

func (pw productWrapper) FindByNameAndCost(exec proteus.Executor, n string, c float64) ([]Product, error) {
    return pw.ProductDaoS.FindByNameAndCost(exec, n, c)
}

func NewProductDao() ProductDao {
    p := ProductDaoS{}
    proteus.Build(&p,proteus.Sqlite)
    return productWrapper{p}
}

A future version of Proteus may include a tool that can be used with go generate to automatically create the wrapper and interface.

2. Why do I have to specify the parameter names with a struct tag?

This is another limitation of go's reflection API. The names of parameters are not available at runtime to be inspected, and must be supplied by another way in order to be referenced in a query. If you do not want to use a prop struct tag, you can use positional parameters ($1, $2, etc.) instead.

3. Why do I need to use the proteus.Wrap function to wrap a sql.DB or sql.Tx from the standard library?

The Query method defined on sql.DB and sql.Tx returns a *sql.Rows type. Unfortunately, sql.Rows is a struct, not an interface. While Proteus takes advantage of interfaces in the sql packages, it is not tied to SQL and cannot depend on the concrete sql.Rows type. To replace it, an interface, proteus.Rows, is defined within Proteus. This has the side-effect of making the method signature of a standard SQL Query incompatible with the proteus.Querier interface definition. The proteus.Wrap function fixes this incompatibility.

Logging

In order to avoid tying the client code to one particular implementation, Proteus includes its own logger that can be bridged to any other Go logger.

By default, Proteus will log nothing. The simplest way to change the amount of information logged by Proteus is by calling the function proteus.SetLogLevel. This function takes in a value of type logger.Level.

If you are using the proteus.ShouldBuild function to generate your DAOs, you can supply a logging level by passing in the context returned by the function logger.WithLevel . This value is overridden by the logging level set by proteus.SetLogLevel.

You can also include additional values in the logs by passing in the context returned by the function logger.WithValues. This function adds one or more logger.Pair values to the context.

All Proteus logging output include 3 default fields (in order):

• time (value is a time.Time in UTC)
• level (the level of the log as a logger.Level)
• message (the message supplied with the log

The default logger provided with the Proteus logging package outputs to os.Stdout in a JSON format. The default logger implementation is configurable; you can specify a different io.Writer by using the call:

    logger.Config(logger.DefaultLogger{
        Writer: myWriterImpl,
    })

The format of the output of the DefaultLogger can be configured by supplying a logger.Formatter:

    logger.Config(logger.DefaultLogger{
        Writer: myWriterImpl,
        Formatter: myFormatter,
    })

A helper type logger.FormatterFunc will turn any function with the signature func(vals ...interface{}) string into a logger.Formatter.

The first 6 values passed to the Format method are:

position	value
0	"time"
1	a time.Time in UTC
2	"level"
3	a logger.Level
4	"message"
5	the message for the log

If you want to supply your own logger implementation, pass an implementation of logger.Logger into logger.Config. This interface matches the definition used by go-kit. The default parameters are in the order specified above. There is a logger.LoggerFunc helper type to convert any function with the signature of func(vals ...interface{}) error into a logger.Logger.

Feel free to use this logger within your own code. If this logger proves to be useful, it might be broken into its own top-level package.

Future Directions

There are more interesting features coming to Proteus. They are (in likely order of implementation):

• support for the context SQL calls and Proteus-generated function with a context as a first parameter

• more expansive performance measurement support and per-request logging control

• go generate tool to create a wrapper struct and interface for a Proteus DAO

• API implementations for nonSQL data stores and HTTP requests

• Additional struct tags to generate basic CRUD queries automatically. This is about as far as I'd go in implementing ORM-like features. The goal for Proteus is to be an adapter between a data source and business logic, not to define your entire programming model.