The project is supported by API Foundation
We started in 2018 with few concepts but one idea: fastest development. Now, in 2020 we are giving solutions:
- APIexec - executor library for shell scripts
- APIcra - shell scripts libraries
- APIunit - definition of application, CI, CD
- APIbuild - build process definition, focused on quality, versioning
- APIsql - data bases, queries, models
- APIfunc - FaaS solutions
This tools is able to create meta data from SQL statement and SQL schema The way from SQL to API is possbile with one step, just for configuration
In preparation is a data connector and model data with API REST server for many formats and types of data
Doumentation, Tutorial, Information, Comparasion to another tools
https://cloud-elements.com/elements/sql-server-api/
One API Integration to connect you to all the leading database services: MySQL, SQL Server and PostreSQL. HubSpot API
Using Element Mapper, our drag-and-drop UI, easily map and normalize data objects and fields between leading cloud services. Marketing Cloud Services
We even manage user access and authentication, API updates, logging and monitoring, all from a consistent platform.
https://nordicapis.com/making-fast-apis-lessons-learned-from-40-years-of-sql/
https://carto.com/developers/sql-api/reference/#operation/postSQLStatement
This is tool, which is the first step to reach in practical some parts of goals for UnitApi which will be later the main standard to build any API
urlQuery -> sqlQuery
apisql.yaml
version: 1.1.0
name: Import data from localhost application
import:
file:
dump:
db:
host:
password
export:
file:
The sql file
The Code, sdk
Source
Collection
Object
Param
Collection
List: 1,2,9,11
Range: 1-11, A-M
Filter:
Object,Param
Object:
Create
Read
Update
Delete
Param
Get
Set
many items Collection as model,view (one or more models)
Schema:
source-output-model-command
Path:
{source}/{output}/{model}/{command}
Condition:
where: id/1/2/3/4/5
between: id/10/20
where: name/to
Command
Create - json can many create
read - can many list
Filter - json
Range - json
Param: -list of param - Which attributes show
db/collection/user/read - json
db/collection/user/delete - json
db/collection/user/update - json
db/collection/user/create - json
one item
Schema:
source-output-model-method-command-param-value
Path:
{source}/{output}/{model}/{command}/{condition}/{param}/{value}/
Condition:
where
Command:
ReadBy
UpdateBy -json
DeleteBy
db/model/user/ReadBy/id/10
db/model/user/DeleteBy/login/tom
one param from one item
Schema:
source-output-model-param-param-value
Path:
{source}/{output}/{model}/{param}/{command}/{condition}/{param}/{value}/
Condition:
where
Command:
Get - Read
Set - Update
db/param/user/login/Read/id/10
db/param/user/password/read/where/login/tom
db/param/user/login/update/where/login/tom
Model: User
Structure:
User:
id
login
password
created_at
{Param} = id
{items} = 1,2,3
https://domain/{source}/User/Collection/List/{Param}/{items}
https://domain/{source}/User/Collection/Range/{Param}/{items}
https://domain/{source}/User/Object/Update/{Param}/{items}
Pattern:
https://domain/{source}/{model}/param/get/{param}/{name}/
Example:
https://domain/source/{source}/model/{model}/param/{param}/get/id/10/
GET https://domain/{source}/{model}/{param}/{value} /param/ login /get/ where /
GET https://domain/ db / user / id / 10 /param/ login /get/ where /
GET https://domain/source/ db /model/ user /param/ login /get/ where / id / 10 /
PUT https://domain/source/ db /model/ user /param/ password /set/ id / 10 { "param": }
Creating from Tree of model-name-type The SQL Statements for:
- Collection
- Object
- Param
- Create metada from SQL schema
- Create REST API based on SWAGGER From Metadata
- Generate sdk for SWAGGER API
zamiast pliku - zmienne w array dla modelu i konfiguracji petla parsowania wygenerowane 9 zapytan
plik konfiguracyjny czytanie yaml pliku i zamiana na array plik z modelami danych i relacjami czytanie pliku z modelami petla parsowania wygenerowane wielu modeli i 9 zapytan
plik konfiguracyjny czytanie yaml pliku i zamiana na array plik z modelami danych i relacjami czytanie pliku z modelami generowanie 9 zapytan do kazdego modelu dodatkowe rozpoznawanie