Mediates between the domain and data mapping layers using a collection-like interface for accessing domain objects.
With Repository, in-memory objects do not need to know whether there is a database present or absent, they need no SQL interface code, and certainly no knowledge of the database schema.
Repository isolates domain objects from details of the database access code;
Repository concentrates code of query construction; and
Repository helps to minimize duplicate query logic.
In R, the simplest form of Repository encapsulates data.frame
entries persisted in a data store and the operations performed over
them, providing a more object-oriented view of the persistence layer.
From the caller point of view, the location (locally or remotely), the
technology and the interface of the data store are obscured.
In situations with multiple data sources.
In situations where the real data store, the one that is used in production, is remote. This allows you to implement a Repository mock with identical queries that runs locally. Then, the mock could be used during development and testing. The mock itself may comprise a sample of the real data store or just fake data.
In situations where the real data store doesn’t exist. Implementing a mock Repository allows you to defer immature decisions about the database technology and/or defer its deployment. In this way, the temporary solution allows you to focus the development effort on the core functionality of the application.
In situations where using SQL queries can be represented by
meaningful names. For example Repository$get_efficient_cars() = SELECT * FROM mtcars WHERE mpg > 20
When building stateless microservices.
R6P::Singleton -> Repository
Inherited methods
new()Instantiate an object
AbstractRepository$new()
add()Add an element to the Repository.
AbstractRepository$add(key, value)
key(character) Name of the element.
value(?) Value of the element. Note: The values in the
Repository are not necessarily of the same type. That depends on the
implementation of AbstractRepository.
del()Delete an element from the Repository.
AbstractRepository$del(key)
key(character) Name of the element.
get()Retrieve an element from the Repository.
AbstractRepository$get(key)
key(character) Name of the element.
# See more examples at <https://tidylab.github.io/R6P/articles> # The following implementation is a Repository of car models with their # specifications. # First, we define the class constructor, initialize, to establish a # transient data storage. # In this case we use a dictionary from the collections package # <https://randy3k.github.io/collections/reference/dict.html> # Second, we define the add, del and get functions that operate on the dictionary. # As an optional step, we define the NULL object. In this case, rather then # the reserved word NULL, the NULL object is a data.frame with 0 rows and # predefined column. TransientRepository <- R6::R6Class( classname = "Repository", inherit = R6P::AbstractRepository, public = list( initialize = function() {private$cars <- collections::dict()}, add = function(key, value){private$cars$set(key, value); invisible(self)}, del = function(key){private$cars$remove(key); invisible(self)}, get = function(key){return(private$cars$get(key, default = private$NULL_car))} ), private = list( NULL_car = cbind(uid = NA_character_, datasets::mtcars)[0,], cars = NULL )) # Adding customised operations is also possible via the R6 set function. # The following example, adds a query that returns all the objects in the database TransientRepository$set("public", "get_all_cars", overwrite = TRUE, function(){ result <- private$cars$values() %>% dplyr::bind_rows() if(nrow(result) == 0) return(private$NULL_car) else return(result) }) # In this example, we use the mtcars dataset with a uid column that uniquely # identifies the different cars in the Repository: mtcars <- datasets::mtcars %>% tibble::rownames_to_column("uid") head(mtcars, 2)#> uid mpg cyl disp hp drat wt qsec vs am gear carb #> 1 Mazda RX4 21 6 160 110 3.9 2.620 16.46 0 1 4 4 #> 2 Mazda RX4 Wag 21 6 160 110 3.9 2.875 17.02 0 1 4 4# Here is how the caller uses the Repository: ## Instantiate a repository object repository <- TransientRepository$new() ## Add two different cars specification to the repository repository$add(key = "Mazda RX4", value = dplyr::filter(mtcars, uid == "Mazda RX4")) repository$add(key = "Mazda RX4 Wag", value = dplyr::filter(mtcars, uid == "Mazda RX4 Wag")) ## Get "Mazda RX4" specification repository$get(key = "Mazda RX4")#> uid mpg cyl disp hp drat wt qsec vs am gear carb #> 1 Mazda RX4 21 6 160 110 3.9 2.62 16.46 0 1 4 4## Get all the specifications in the repository repository$get_all_cars()#> uid mpg cyl disp hp drat wt qsec vs am gear carb #> 1 Mazda RX4 21 6 160 110 3.9 2.620 16.46 0 1 4 4 #> 2 Mazda RX4 Wag 21 6 160 110 3.9 2.875 17.02 0 1 4 4## Delete "Mazda RX4" specification repository$del(key = "Mazda RX4") ## Get "Mazda RX4" specification repository$get(key = "Mazda RX4")#> [1] uid mpg cyl disp hp drat wt qsec vs am gear carb #> <0 rows> (or 0-length row.names)