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Project 1, Part 3

  • Assigned: 10/26
  • Due: 11/14 11:59PM EST
  • (worth 50% of overall Project 1 grade)

In this part of the project, you will complete the web application by building the front end on top of the Flask Python webserver.

Requirements

Implement the application you described in Part 1 in Python using Flask.

  • Your application must execute SQL query strings on the provided staff database. (Note: This means you cannot use an ORM. Part of the goal of this project is to practice writing and debugging SQL queries. Tools that attempt to make this "easier" are not permitted.)
  • Your application must provide a way to view or interact with all the entities and relationships in your final ER diagram.
  • It does not need to be beautiful or sophisticated. Plain text pages are acceptable! However, if you want to make it fancy, you can do that (there will be a few "bonus" prizes for sophisticted applications).
  • In general, you can probably use whatever third-party libraries you want, except for ORMs or other libraries that simplify database access. If you are unsure if a library is permitted, ask a staff member or on Piazza.
  • Your application should prevent forms of SQL injection described in lecture.

Note: if you anticipate doing a huge number of queries (say hundreds of queries per second), or will have a huge database (more than 10k rows), please let the staff know so we can allocate resources appropriately.

Grading

  • How well your application matches the Part 1 submission, and how well you incorporated the mentor's feedback?
  • Does it let a user access all the entities and relationships on the ER diagram?
  • Your grade will not be based on how sophisticated the user interface is (though it may mildly help)
  • Your grade will suffer if it doesn't work, requires the user typing SQL, crashes or locks up on bad inputs, is vulnerable to the SQL injection described in lecture, and otherwise does not work as you described in part 1.

Submission

Please leave your virtual machine running so the IP address does not change, then fill out the Google submission form

Students will present to their project mentor between 11/14 and 11/18.

Similar to part 1, the mentors will email you to schedule a 15 minute meeting by 11/11.

Contact your mentor immediately if you have not been contacted by the end of 11/13.

You will show off your project using the mentor's web browser:

  1. Give your mentor the app's URL so they can run it in Chrome or Firefox -- make sure you tested in those browsers!

    • your grade will suffer considerably if this step doesn't work
  2. Your mentor will interact with your application and test the functionality described in Part 1

    • Have a number of example interactions prepared ahead of time to show your mentor.
      The more you impress your mentor, the better your grade is likely to be.
  3. Your mentor may ask to look at your code, so have it available.
    If the code is on GitHub, then it makes life much easier.

  4. The web interface doesn't need to be fancy, however users should not need to type anything resembling SQL.

References

The following documentation may be helpful for both learning Python and Flask:

Getting Started

Your job is to implement your proposed web application. To help you out, we have provided a bare-bones Flask web application in ./webserver/. It provides code that connects to a database url, and a default index page. Take a look at the comments in server.py to see how to use modify the server. You will need to connect to your database used for part 2.

Please read all these directions, and get the example server we provide running. Once you get it running you should edit it to talk to your own database and start working on your custom logic.

A Short Introduction to SQLAlchemy

We use a python package called SQLAlchemy to simplify our work for connecting to the database. For example, server.py contains the following code to load useful functions from the package:

    # import useful functions from the package
    from sqlalchemy import *

SQLAlchemy is able to connect to many different types of DBMSes such as SQLite, PostgreSQL, MySQL, Oracle and other databases. Each such DBMS is called an "engine". The create_engine() function sets up the configuration to specify which type of DBMS we want to connect to, and what their parameters are.

    engine = create_engine(DATABASEURI)

Given an engine, we can then connect to it (this is similar to how psql connects to the staff database).

    conn = engine.connect()

At this point, the conn connection object can be used to execute queries to the database. This is basically what psql is doing under the covers!

    cursor = conn.execute("select 1")

The execute function takes a SQL query string as input, and returns a cursor object. You can think of this as an iterator over the result relation. This means you can run select * on a million row table, and not run out of memory. Instead of sending the entire result at once. Instead, this object lets you treat the result as an iterator and call .next() on it, or loop through it. See the documentation for a detailed description.

    # this fetches the first row if called right after
    # the execute function above.  It also moves the
    # iterator to the next result row.
    record = cursor.fetchone()

    # this will fetch the next record, or None if
    # there are no more results.
    second_record = cursor.fetchone()

    # this loops through the results of the cursor one by one
    for row in cursor:
      print list(row)

The above description is a way to directly write and run SQL queries as strings, and directly manipulate the result relations. SQLAlchemy is also an Object Relational Mapper that provides an interface that hides SQL query strings and result sets from you. Instead you access and manipulate tables in the database as if they were normal Python objects.

In this project, you will directly write and run SQL queries, and will not use any ORM functionality.

Working with GitHub

  • Fork this repository so you have your own copy that you can edit. You will submit a link to the repository. (click the Fork button on the top right corner of this page)

  • Clone it to your local machine (if you have Python installed and want to run locally) or your VM: git clone git@github.com:[YOUR USERNAME]/project1.git

  • Edit your files

  • Use the following commands to add and checkpoint (commit) your changes locally

      git add --help
      git add <new files to store in git>
      git commit -m "a sentence describing your changes"
    
  • When everything has been committed you can push all the committed changes so GitHub.com has a copy

      git push
    
  • If you cloned the repository on another machine (say the VM), then you can download and apply those changes from GitHub.com

      git pull
    

Some notes

  • Your life will be easier by setting up SSH keys and cloning the git://.... versions of repositories. That way GitHub won't keep asking for your password when running git commands
  • Most errors you will encounter can be solved by consulting a search engine.

Running on the virtual machine

Once you have made some changes to your web application, you will deploy it to your Google App Engine virtual machine.

  1. Write down the IP of your virtual machine.

  2. Perform some default installations and scaffolding for the web-app. Setup Instructions.

  3. Copy your code to the Google App Engine virtual machine as per instructions above or on GitHub's help pages.

  4. Click on the SSH button on the Google App Engine dashboard to access your virtual machine and enter the "test" virtualenv

  5. Run the python server with the defaults, which will listen for requests on port 8111. Run with --help if you need help

     cd project1/webserver
     python server.py --debug
    
  6. Go to http://<IP ADDRESS>:8111/ in your browser to check that it worked.
    You will need this URL when presenting the project to your mentor.

(Optional) Running locally

It is much more convenient to be able to test your application on your laptop or local computer, and run it on the Google App Engine VM when you are happy with the code. You can do this by following the virtualenv setup commands from HW0 on your own computer. Once you have the correct virtualenv set up, you can run the the web server with:

To run the webserver, go into the webserver/ directory and run (make sure you have enabled the virtualenv environment)

    python server.py --debug

It should print something like:

    running on 0.0.0.0:8111
    * Running on http://0.0.0.0:8111/

The 0.0.0.0 listens to any IPv4 address on the machine. The 8111 after the : is the port number. So if this is running on your laptop, you can open you web browser to http://localhost:8111.

You can specify a custom port by passing a host and port as arguments:

    python server.py --debug 0.0.0.0 8888

To see its command line options, use the --help flag

    python server.py --help

If you run the server with the --debug flag, it will automatically pick up changes when you reload the page, which is more convenient than restarting the server each time. It additionally will display detailed errors in the web browser, instead of only on the console.

note: This is just a suggestion. Since it is impossible to support setting up Python on everyone's personal computers, we can't really help debug issues that aren't happening on an Google App Engine VM. Your best bet is google, office hours, or asking your fellow students on piazza.

(optional) Longer Term Running

The following are optional instructions on how to keep servers running

There are several ways to keep the server running after you have logged out of the VM. Note that these are all poor man's techniques.

  1. nohup. the HUP signal is how the terminal warns a process of user logout. the nohup command ensures that the process ignores this signal, allowing it to continue running. the "&" character at the end of the command tells the terminal to detach this process from the terminal.

     nohup python server.py 0.0.0.0 8008 &
    

    You can kill the process explicitly by getting the process ID and using the kill command:

     ps -A | grep python
     kill <the ID of the python process>
    
  2. tmux is a remote terminal manager. You can think of the terminal as two parts --
    the client that you interact with by typing characters and pressing ENTER, and a server that actually reads those commands and runs processes in response.
    Usually when you login to a VM, the client and server are tied together in a single process, so that when you logout the client and server both die. TMUX on the other hand explicitly starts two processes -- the server process that continues to run after you log out, and a client process that connects to the server process. This way, even if you disconnect, only the client dies. When you re-connect, you can re-attach to the server process and resume your terminal session! This is what I do.

     # install tmux
     sudo apt-get install tmux
    
     # run tmux
     tmux
    
     # it will open a terminal
     python server.py 0.0.0.0 8008
    
     # don't press ctrl-c, just close your window.
    

Tmux is quite powerful -- come ask me directly or post to piazza if you are curious about its other functionalities. GNU Screen is an alternative to tmux.

(optional) Copy Remote Database to Local

These are optional instructions on how to copy the remote database to local for testing.