Deploying Web Applications

Once you've written your next-big-thing web application you need to deploy it to the web so that the waiting hoards of users can get access and start earning you money. This chapter looks at the things you'll need to do to deploy a Python based web application and some of the issues related to scaling to larger numbers of users when you get successful.

Hosting Services

The first requirement for deploying a web application is a server connected to the internet that can run the different components of your application. Web hosting providers have developed a number of products that can fulfil this need from fully dedicated rack-space hosting of your own server to a shared allocation within an application hosting environment.

The obvious thing to do to host an application is to buy a computer and connect it to the internet. Most home ISP terms of service forbid running servers from home and so the solution is to house your machine with a service provider. Many of these will offer pre-packaged servers in their shared machine room that you can buy or rent for your exclusive use. The advantage of this is that you get to choose all the options for how your server will run and manage the whole thing yourself; but these can also be the disadvantages since this adds significant work to the job of running a web application. This is also often the most expensive options since it takes up physical space in the machine room that can't be shared with anyone else. For some applications (and people) this is still the right thing to do but increasingly the majority of web applications are being hosted in some kind of shared hosting environment.

A shared hosting environment offers to run your web application on a server that might also be running applications for other people. You will usually get an allocation of memory, disk space and CPU usage that your application can use as well as for the network bandwidth that you use per month. Hosting providers will support different environments, so you need to find one that supports eg. Python and WSGI based applications. The provider will often give you a number of databases as part of a subscription and these can be used to support your application. The major advantage of this arrangement is that it is low cost and you don't need to do anything about configuring or maintaining the hosting environment.

The third family of providers take a more fine-grained approach, offering specific hosting for e.g. Python WSGI based applications. This is often called Platform as a Service (PAAS). One example in this space is Google AppEngine which supports applications written in Python, Java or PHP or Gondor wihc supports Python only. These services host your application directly and often provide services like data storage via their own APIs; for example, Google AppEngine supports a NoSQL database via its own Object Relational Mapping module. These services provide no control whatsoever over the execution environment but are highly tuned to running this kind of web application. They can be an excellent choice for deploying small boutique web applications since the overhead in setting them up can be very small. In some cases they can also scale very well so that when your application becomes popular, the resources are there to support the larger user based that you have developed.

Web Servers

The main requirement for deploying an application is a web server that will listen for HTTP requests and forward them to your WSGI based application. During development we used the Python webserver provided by the wsgiref module. This server did the job of listening for HTTP requests, building the WSGI environ dictionary and calling our application, then returning the headers and content back to the client when our application procedure returns. This simple web server could be used in production, however it is not really suitable as it is not designed to cope with large numbers of requests arriving in quick succession.

A production web server must be able to serve a large number of requests arriving at a very fast rate. Obviously the volume of traffic will depend on the popularity of the site, but you should plan for when this happens rather than assuming there won't be much traffic for a while.

To make our WSGI based web application available we need a web server that can understand the WSGI protocol and call our application with the right input. Many web servers are able to do this either natively or via a plugin module. If this is not available, there are adaptors that can run WSGI applications via other protocols such as CGI (Common Gateway Interface), FastCGI or SCGI. The general configuration to enable this is to associate a URL prefix with a particular WSGI application defined in a Python module. Other configuration such as setting up Python paths and modules might also be needed.


The most common web server is Apache, an Open Source product that has been around since the very early days of the web. It is popular because it is free and included as a standard component in many server environments. It works and is reliable and fast. As a web developer, you should get to know how to configure Apache because you will probably be doing it a lot in the years to come.

To handle large amounts of traffic, Apache uses either multiple threads or processes for handling each request. The basic idea is that each request is sent to a thread or process so that it can run independently of any others that arrive. A main supervisor process/thread listens for HTTP requests and then sends the work to a subprocess/thread to generate the response.

Apache has a large number of plugin modules that extend its functionality. For example, there are modules for authentication, caching, running PHP, Python and Perl applications etc. Many of these are distributed as standard and many more are available from third parties. The ubiquitous nature of Apache means that many new server side web technologies are made available first as Apache modules.

To run WSGI applications via apache there are a number of options, the most common would be the mod_wsgi module which extends the server to understand the WSGI protocol. mod_wsgi can run Python scripts either as part of the Apache process itself or by running the application as a separate process. Both options have their strengths in different contexts.

The main criticism of Apache is that it is big and has too many configuration and plugin options. These can certainly be confusing when all you want is a simple fast web server. Building Apache from source is confusing because you need to make sure you have the right options turned on etc. Configuring Apache can be confusing because of all of the different options available. The process/thread based model for dealing with large amounts of traffic is also less efficient for some tasks than a number of the newer web servers (below).

Asynchronous Web Servers

Nginx (prodounced 'engine X') is a lightweight web server with fewer configuration options than Apache that is optimised for handling large volumes of traffic. In particular it is used for serving static content, as opposed to content generated by server side scripts. Nginx is often used as a reverse proxy where it acts as the front end accepting HTTP requests and the forwarding them on to another server like Apache that deals with the request. This configuration allows Nginx to be used for serving static content while Apache deals with generated content from things like our Python WSGI script. The disconnect between the network and the Apache processes turns out to give a big performance win for high traffic sites.

The model used by Nginx to process many requests is an event driven model rather than a process based model. This means that the server uses operating system level event handling and asynchronous input/output facilities to be able to handle requests quickly. The consequence of this is that the memory use is much lower for Nginx than it would be for Apache given a large number of requests per second. This is because for each request, Apache must allocate a new thread or process which must contain a copy of the execution context. Nginx can just handle the request in an existing thread and move on. This model wouldn't work if processing each request took a long time since one request would block all of the others; hence slower page-generation code (like our Python application) is usually done in another process (eg. via an Apache server) leaving Nginx to do what it does best.

Lighttpd (pronounced 'lighty') is another lightweight, asynchronous web server very similar to Nginx in many ways. Whereas nginx started life as a load balancing proxy and only later became a full web server, lighttpd was designed as a web server from the start. The performance of the two servers is similar. Lighttpd is perhaps a little easier to configure and comes with support for FastCGI based applications out of the box - this can be used to run our WSGI based Python applications.


As mentioned above, it is common to run slower web application scripts in a separate process so as not to slow down the main HTTP server process. One common way to do this is via the FastCGI protocol. This protocol is just a way for a web server to forward requests to a server side application, similar to the way that our Python server called our WSGI application procedure. FastCGI is language neutral though, but sends more or less the same information as is contained in the environ dictionary passed to a WSGI procedure.

The FastCGI protocol requires that there is another process running on the server that is able to respond to requests sent by the HTTP server. This is like having another web server that listens only to the main server, but the protocol used isn't HTTP, it's FastCGI (we don't need to know the details). The FastCGI process would be written in Python using a module such as flup and might use multiple threads or processes to cope with large numbers of requests per second.

There is a useful guide to deploying a WSGI application via FastCGI on the Flask website (Flask is a Python web framework).

WSGI Based Servers

There are a small number of web servers that support WSGI natively they are designed specifically for running Python based web applications. The server we use for development from the wsgiref module is one example, but that is not suitable for a production deployment. Perhaps the most well known production WSGI server is Gnuicorn; while Gnuicorn could be used as a sandalone web server, the documentation recommends that it is placed behind a reverse proxy such as Nginx.


SQLite is a fully featured implementation of an SQL database system that is lightweight and runs without requiring installation of a large software system. As such it is ideal for development of web applications because we can work with it on a laptop in a development environment. However, SQLite isn't designed as a production database engine, the most common mode of use is as a single-user embedded database. In a production environment, we need to be able to process many simultaneus requests that might query and update the database at the same time. We need guarantees of consistency of data and we need to be able to scale the size of data stored to be very large if our application is successful.

There are a number of production relational database systems and this is a very old and well established market so their capabilities are well known in the IT industry. This means that when looking for web hosting providers, you will find standard database servers as part of the offering and these can be expected to run efficiently and scale to whatever size of data you are willing to pay for. The most common Open Source choices are MySQL (actually owned by Oracle these days) and PostgreSQL. In the commercial space you will probably choose between Oracle and Microsoft SQL Server. Each of these has strengths and weaknesses but to a first approximation they will all be capable of providing a reliable data store for a web application.

When writing our WSGI application we used the sqlite3 module as the interface between our code and the relational database. If you were to want to deploy your application on a MySQL or Oracle server, this code wouldn't work. Fortunately, the SQLite interface provided by the sqlite3 module conforms to the Python DB-API standard which is also implemented by other modules that interface to other database systems. In most cases, the only change you need to make to your code is when the database connection is created. So, for example, to connect to a MySQL database using the mysql-python module you would write:


Note that compared with creating a connection to an SQLite database we need to provide a little more information. A MySQL database might run on a different server (hence the host name) and will require a username and password. Once this connection is made however, it can be used to create cursors and execute queries in the same way as with SQLite. In some cases, the variant of the SQL language understood by the database system is different to that used by SQLite. This means that you might need to manage two variants of a query - one for development with SQLite and one for production with MySQL. Usually by the time you get queries as complex as this you've moved to a higher level of abstraction using a database Object Relational Model package such as SQLAlchemy. In this case, the interface module will take care of the database differences for you and you can go back to worrying about the right way to model your data.

Non-SQL Data Stores

A very recent development is a number of alternatives to the traditional SQL relational database, often grouped together as NoSQL databases. These are special purpose data stores that are optimised for particular tasks or kinds of data. The general idea is that rather than the general purpose relational model, these stores provide a simpler data model that is useful for a particular task and can be implemented very efficiently. In some cases these are being developed to serve parts of the data storage needs of web applications.

Perhaps the simplest of these databases are the key-value stores that provide a very large and fast version of the Python dictionary data structure - associating unique keys with arbitary data items. One example of this kind of product is Redis which implements an in-memory key-value store that provides very fast access to data via keys. If you are building something like a cache which stores web page contents for a given URL then something like Redis will provide a very fast store without the overhead of relational tables or complex query processing.

Another family of databases are the document stores that store whole documents again using a key to identify them, but also supporting query via the content of the document. An example of this kind of store is CouchDB which presents itself as "a database for the web" that "uses JSON for documents, JavaScript for MapReduce queries, and regular HTTP for an API". CouchDB stores JSON documents but other products are tuned for storing larger traditional PDF or Word documents and support queries over these via an API. These products can be ideal if you have a document oriented web application, eg. something like Evernote that stores notes written by users.

Other kinds of NoSQL database include those that are tuned to store particular data structures. So, there are graph data stores that provide optimised access to graph based data structures such as the network of relations between people in the Facebook Social Graph. Another family is the Object Store which stores object based data structures directly; these are less widely used perhaps because the Object Relational Mapping libraries available for various languages provide just the right abstraction over traditional relational databases for these to offer any advantage.

In summary there are now a number of competing data models in the kind of data store that we might use for a web application. The default position is still a traditional relational database but these other options are starting to be attractive for some parts of an application and are worth knowing about.

User Management

It is increasingly common to see the option to login to a web application with your Facebook or Google account credentials either as the only option or as an alternative to a login for that particular site. This has the advantage for the user that they only need to remember one username and password to access many services. For the developer, the advantages can be much greater.

The primary advantage to using an authentication service like Facebook or Google is that you may not need to manage user data yourself. This then means that you can have as many users as you want without any more overhead in storing user details and with no risk (to yourself) of private information being leaked. The disadvantage here is that you might not know as much about your users. A more common case is that you allow people to authenticate via one of these services but still create a local account for them so that you can store preferences etc. This means that you will need to store information about users in your database but that you don't need to manage usernames and passwords or provide support to users who have lost their password (password resets make up 20-30% of all help desk calls).

Further advantages of using third party authentication come from being able to leverage the services provided by the third party provide. This might include embedding lists of friends in your pages from Facebook to linking to other personalised services from Google or Facebook etc. Some of these things can be done without using these services for authentication but much more is possible if you know who the user is.

Content Delivery Networks

A significant problem for a large scale web appliation is that of providing content to users spread around the globe as fast as possible. If I choose to host my application in Australia, that might provide good service to Australian users but is likely to be slower for US and European users than it might be. If I host in the US then I risk providing a slower service for Australian users. One solution to this problem is the Content Delivery Network (CDN) which provides a distributed delivery service with servers spread around the world providing content to users nearby.

The CDN is basically a kind of distributed cache which sits between your web application and users. It is typically configured to work with only the static parts of your content but could also cache generated pages if they changed infrequently. While a normal cache runs on a single server machine, the CDN has a network of servers around the world which duplicate your content at each site. A central server receives each request and routes it to the server closest to the client for delivery. This has two effects: your content is delivered by the CDN so it will place no load on your server; and the content will be delivered from somewhere close to the client giving them a faster page-load response.

There is a good discussion of the technical background to CDNs on the Wikipedia page which includes links to some notable examples of this kind of service.

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License\ Python Web Programming by Steve Cassidy is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.