Flask+SQLAlchemy+graphene+docker示例

搭建一個利用docker啓動服務的Flask的小demo

定義數據庫

# -*- coding: utf-8 -*-


from sqlalchemy import *
from sqlalchemy.orm import (
    scoped_session, sessionmaker, relationship, backref
)
from sqlalchemy.ext.declarative import declarative_base


# mysql+mysqlconnector://<user>:<password>@<host>[:<port>]/<dbname>
engine = create_engine("mysql+mysqlconnector://root:@localhost:3306/demo", convert_unicode=True)
session = scoped_session(sessionmaker(
    autocommit=False, autoflush=False, bind=engine
))


Base = declarative_base()
Base.query = session.query_property()


class Department(Base):

    __tablename__ = "department"

    id = Column(Integer, primary_key=True)
    name = Column(String(50))


class Employee(Base):

    __tablename__ = "employee"

    id = Column(Integer, primary_key=True)
    name = Column(String(50))
    hired_on = Column(DateTime, default=func.now())
    department_id = Column(Integer, ForeignKey("department.id"))
    department = relationship(
        Department,
        backref=backref(
            "employee",
            uselist=True,
            cascade="delete,all"
        )
    )

利用SQLAlchemy定義了兩個表，其中Department經過relationship能夠關聯多個Employee，而後經過python console建立表和數據：

>>> from models import *
>>>
>>>
>>> Base.metadata.create_all(bind=engine)
>>>
>>>
>>> engineering = Department(name="Engineering")
>>> session.add(engineering)
>>> hr = Department(name="Human")
>>> session.add(hr)
>>>
>>>
>>> peter = Employee(name="Peter", department=engine)
engine              engine_from_config( engineering
>>> peter = Employee(name="Peter", department=engineering)
>>>
>>> session.add(peter)
>>>
>>>
>>>
>>> roy = Employee(name="Roy", department=engineering)
>>>
>>> session.add(roy)
>>>
>>>
>>> tracy = Employee(name="Tracy", department=hr)
>>>
>>> session.add(tracy)

定義graphql的的Query

# -*- coding: utf-8 -*-


from graphene import relay, ObjectType, Schema
from graphene_sqlalchemy import (
    SQLAlchemyConnectionField, SQLAlchemyObjectType
)

from models import (
    Department as DepartmentModel,
    Employee as EmployeeModel
)


class Department(SQLAlchemyObjectType):

    class Meta:
        model = DepartmentModel
        interfaces = (relay.Node, )


class DepartmentConnections(relay.Connection):

    class Meta:
        node = Department


class Employee(SQLAlchemyObjectType):

    class Meta:
        model = EmployeeModel
        interfaces = (relay.Node, )


class EmployeeConnections(relay.Connection):

    class Meta:
        node = Employee


class Query(ObjectType):

    node = relay.Node.Field()
    all_employees = SQLAlchemyConnectionField(EmployeeConnections)
    all_departments = SQLAlchemyConnectionField(DepartmentConnections, sort=None)


schema = Schema(query=Query)

首先經過繼承SQLAlchemyObjectType類來定義新的查詢的類，而後經過relay.Connection來鏈接所定義的查詢類，而且在Query中進行申明，其中我在Connection後面加了一個s是由於在github上看issue的時候發如今構造類的過程當中會出現重名的狀況致使申明Query的時候會報錯，因此加一個s用來避免這個錯誤。
其中有關graphene的部分我本身也還不是特別熟悉，因此只能是大概說一下本身的思路，若是有錯誤的地方會在後續中及時的進行修改，避免誤人子弟。
最終達到的效果是指定來一個schema，其中包含了我所定義的查詢。

本地啓動

# -*- coding: utf-8 -*-


from flask import Flask
from flask_graphql import GraphQLView

from models import session
from schema import schema


app = Flask(__name__)
app.debug = True


app.add_url_rule(
    "/graphql",
    view_func=GraphQLView.as_view(
        "graphql",
        schema=schema,
        graphiql=True
    )
)


@app.teardown_appcontext
def shutdown_session(exception=None):
    session.remove()


if __name__ == "__main__":
    app.run()

經過Flask的add_url_rule將graph的視圖定義成經過路由可訪問，而後啓動就能夠進行訪問了,點擊http://127.0.0.1:5000/graphql就能夠本地訪問了。

經過docker啓動

• 創建鏡像
  

# run.docker
FROM python:3.6

COPY . /app

WORKDIR /app

RUN pip install -r requirements.txt

CMD ["python", "app.py"]

這個是個人Dockerfile，經過Dockerfile，我指定了這個鏡像是來自於python:3.6這個鏡像，而後把我當前目錄下的全部內容經過COPY . /app複製到了docker鏡像中的/app目錄下，接着我指定了WORKDIR爲/app,這樣我就能夠在/app目錄下進行操做了，首先是安裝全部須要的依賴包，由於我是從python3.6拉的鏡像，因此能夠不用再去安裝pip，直接就能夠安裝了，若是是其餘鏡像可能還要同構apt去安裝pip再進行依賴包的安裝，最後就是用CMD來運行文件了。

docker build -t flask_sqlalchemy:core -f run.docker .
# 其中的.是爲了指明上下文路徑，其實Dockerfile中的命令並非對本地文件進行操做，而是經過指定上下文路徑將這些文件傳到docker搭建鏡像的環境中再進行操做。

鏡像創建以後就能夠run了

docker run -d -p 5000:5000 --name flask-core flask_sqlalchemy:lastest

而後就啓動了。