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https://github.com/gandresto/mongodb-m220p

Archivos del proyecto mflix, del curso M220P de MongoDB University
https://github.com/gandresto/mongodb-m220p

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Archivos del proyecto mflix, del curso M220P de MongoDB University

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README 

        
=====

mflix

=====



This is a short guide on setting up the system and environment dependencies

required for the MFlix application to run.



Project Structure

-----------------



Everything you will implement is located in the ``mflix/db.py`` file, which

contains all database interfacing methods. The API will make calls to ``db.py``

to interact with MongoDB.



The unit tests in ``tests`` will test these database access methods directly,

without going through the API. The UI will run these methods in integration

tests, and therefore requires the full application to be running.



The API layer is fully implemented, as is the UI. If you need to run on a port

other than 5000, you can edit the ``index.html`` file in the build directory to

modify the value of **window.host**.



Please do not modify the API layer in any way, ``movies.py`` and ``user.py``

under the **mflix/api** directory. Doing so will most likely result in the

frontend application failing to validate some of the labs.



Local Development Environment Configuration

-------------------------------------------



Anaconda

~~~~~~~~



We're going to use `Anaconda `_ to install Python 3 and

to manage our Python 3 environment.



**Installing Anaconda for Mac**



You can download Anaconda from their `MacOS download site

`_. The installer will give you

the option to "Change Install Location", so you can choose the path where the

``anaconda3`` folder will be placed. Remember this location, because you will

need it to activate the environment.



Once installed, you will have to create and activate a ``conda`` environment:



.. code-block:: sh



  # navigate to the mflix-python directory

  cd mflix-python



  # enable the "conda" command in Terminal

  echo ". /anaconda3/etc/profile.d/conda.sh" >> ~/.bash_profile

  source ~/.bash_profile



  # create a new environment for MFlix

  conda create --name mflix



  # activate the environment

  conda activate mflix



You can deactivate the environment with the following command:



.. code-block:: sh



  conda deactivate



**Installing Anaconda for Windows**



You can download Anaconda from their `Download site

`_. Please be careful to select ``Windows Tab`` before downloading.



The Anaconda installer will prompt you to *Add Anaconda to your PATH*. Select

this option to use ``conda`` commands from the Command Prompt.



If you forget to select this option before installing, no worries. The installer

will let you choose an "Install Location" for Anaconda, which is the directory

where the ``Anaconda3`` folder will be placed.



Using your machine's location of ``Anaconda3`` as ````, run

the following commands to activate ``conda`` commands from the Command Prompt::



  set PATH=%PATH%;;\Scripts\



Once installed, you will have to create and enable a ``conda`` environment.



.. code-block:: sh



  # enter mflix-python folder

  cd mflix-python



  # create a new environment for MFlix

  conda create --name mflix



  # activate the environment

  activate mflix



You can deactivate the environment with the following command:



.. code-block:: sh



  deactivate



Virtualenv

~~~~~~~~~~



*Note: If you installed Anaconda instead, skip this step.*



As an alternative to Anaconda, you can also use ``virtualenv``, to define your

Python 3 environment. You are required to have a Python 3 installed in your

workstation.



You can find the `virtualenv installation procedure`_ on the PyPA website.



Once you've installed Python 3 and ``virtualenv``, you will have to setup a

``virtualenv`` environment:



.. code-block:: sh



  # navigate to the mflix-python directory

  cd mflix-python



  # create the virtual environment for MFlix

  virtualenv -p YOUR_LOCAL_PYTHON3_PATH mflix_venv



  # activate the virtual environment

  source mflix_venv/bin/activate



You can deactivate the virtual environment with the following command:



.. code-block:: sh



  deactivate



.. _`virtualenv installation procedure`: https://virtualenv.pypa.io/en/stable/installation/



Please remember that you may have to reactivate the virtual environment if you

open a new Terminal or Command Prompt window, or restart your system.



Python Library Dependencies

~~~~~~~~~~~~~~~~~~~~~~~~~~~



Once the Python 3 environment is activated, we need to install our python

dependencies. These dependencies are defined in the ``requirements.txt`` file,

and can be installed with the following command:



.. code-block:: sh



  pip install -r requirements.txt



MongoDB Installation

~~~~~~~~~~~~~~~~~~~~



It is recommended to connect MFlix with MongoDB Atlas, so you do not need to

have a MongoDB server running on your host machine. The lectures and labs in

this course will assume that you are using an Atlas cluster instead of a local

instance.



That said, you are still required to have the MongoDB server installed, in order

to be able to use two server tool dependencies:



- ``mongorestore``



  - A utility for importing binary data into MongoDB.



- ``mongo``



  - The MongoDB shell



To download these command line tools, please visit the

`MongoDB download center `_

and choose the appropriate platform.



MongoDB Atlas Cluster

---------------------



MFlix uses MongoDB to persist all of its data.



One of easiest ways to get up and running with MongoDB is to use MongoDB Atlas,

a hosted and fully-managed database solution.



If you have taken other MongoDB University courses like M001 or M121, you may

already have an account - feel free to reuse that cluster for this course.



*Note: Be advised that some of the UI aspects of Atlas may have changed since

the inception of this README, therefore some of the screenshots in this file may

be different from the actual Atlas UI interface.*



Using an existing MongoDB Atlas Account:

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~



If you already have a previous Atlas account created, perhaps because you've

taken one of our other MongoDB university courses, you can repurpose it for

M220P.



Log-in to your Atlas account and create a new project named **M220** by clicking

on the *Context* dropdown menu:



.. image:: https://s3.amazonaws.com/university-courses/m220/cluster_create_project.png



After creating a new project, you need to create a **mflix** free tier cluster.



Creating a new MongoDB Atlas Account:

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~



If you do not have an existing Atlas account, go ahead and `create an Atlas

Account `_ by filling in the

required fields:



.. image:: https://s3.amazonaws.com/university-courses/m220/atlas_registration.png



Creating a free tier cluster called "mflix":

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~



*Note: You will need to do this step even if you are reusing an Atlas account.*



1. After creating a new project, you will be prompted to create the first

   cluster in that project:



.. image:: https://s3.amazonaws.com/university-courses/m220/cluster_create.png



2. Choose AWS as the cloud provider, in a Region that has the label

   ``FREE TIER AVAILABLE``:



.. image:: https://s3.amazonaws.com/university-courses/m220/cluster_provider.png



3. Select *Cluster Tier* M0:



.. image:: https://s3.amazonaws.com/university-courses/m220/cluster_tier.png



4. Set *Cluster Name* to **mflix** and click "Create Cluster":



.. image:: https://s3.amazonaws.com/university-courses/m220/cluster_name.png



5. Once you press *Create Cluster*, you will be redirected to the account

   dashboard. In this dashboard, make sure you set your project name to **M220**.

   Go to ``Settings`` menu item and change the project name from the default

   **Project 0** to **M220**:



.. image:: https://s3.amazonaws.com/university-courses/m220/cluster_project.png



6. Configure the network settings of this cluster in the **Network Access** tab,

   so you can connect from your IP address. When you select ``ADD IP ADDRESS``,

   the menu that appears may give you the option to add ``ADD CURRENT IP

   ADDRESS``. This is preferable, but if this option does not appear, please

   select ``ALLOW ACCESS FROM ANYWHERE``:



.. image:: https://s3.amazonaws.com/university-courses/m220/whitelist_an_ip_address.png



.. image:: https://s3.amazonaws.com/university-courses/m220/add_whitelist_entry.png



7. Then create the application MongoDB database user required for this course:



  - username: **m220student**

  - password: **m220password**



You can create new users through *Security* -> *Add New User*.



Allow this user the privilege to ``Read and write to any database``:



.. image:: https://s3.amazonaws.com/university-courses/m220/cluster_application_user.png



8. When the user is created, and the cluster deployed, you have the option to

   ``Load Sample Dataset``. This will load the Atlas sample dataset, containing

   the MFlix database, into your cluster:



.. image:: https://s3.amazonaws.com/university-courses/m220/load_sample_dataset.png



**Note: The MFlix database in the Sample Dataset is called "sample_mflix".**



9. Now you can test the setup by

   connecting via ``mongo`` shell. You can find instructions to connect in the

   "Connect" section of the cluster dashboard:



.. image:: https://s3.amazonaws.com/university-courses/m220/cluster_connect_application.png



Go to your cluster *Overview* -> *Connect* -> *Connect Your Application*. Select

the option corresponding to your local MongoDB version and copy the ``mongo``

connection string.



The below example connects to Atlas as the user you created before, with

username ``m220student`` and password ``m220password``. You can run this command

from your command line:



.. code-block:: sh



  mongo "mongodb+srv://m220student:m220password@"



By connecting to the server from your host machine, you have validated that the

cluster is configured and reachable from your local workstation.



The connection string you used to connect will be used in your MFlix application

as well.



Importing Data (Optional)

-------------------------



**Note: if you used Load Sample Dataset, you can skip this step.**



**Note: if you used Load Sample Dataset, you can skip this step.**



The ``mongorestore`` command necessary to import the data is located below. Copy

and paste the command, and replace ```` with your Atlas SRV

string:



.. code-block:: sh



  # navigate to mflix-python directory

  cd mflix-python



  # import data into Atlas

  mongorestore --drop --gzip --uri  data



Running the Application

-----------------------



In the ``mflix-python`` directory there are two files, called ``dotini_unix``

and ``dotini_win``.



Rename this file to ``.ini`` with the following command:



.. code-block:: sh



  mv dotini_unix .ini  # on Unix

  ren dotini_win .ini # on Windows



Once the file has been renamed, open it, and enter your Atlas SRV connection

string as directed in the comment. This is the information the driver will use

to connect!



To start MFlix, run the following command:



.. code-block:: sh



  python run.py



And then point your browser to `http://localhost:5000/`_.



Running the Unit Tests

----------------------



To run the unit tests for this course, you will use ``pytest``. Each course lab

contains a module of unit tests that you can call individually with a command

like the following:



.. code-block:: sh



  pytest -m LAB_UNIT_TEST_NAME



Each ticket will contain the command to run that ticket's specific unit tests.