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Posted on January 20, 2017

Abstract

Sometimes, it's actually best to ditch your mainstream web framework when starting new development or refactoring an existing project. If you try to make an opinionated tool do something that it wasn't designed to do, then prepare for a bloated budget and missed deadlines. It's important to understand what mainstream web frameworks are good at, and where they fall short.

All rules have exceptions. There are definitely situations where web frameworks such as Rails and Django may not be the first choices for use in developing custom web software.

Here are some known categories of exceptions, and several examples per category:

Low-latency and/or high-throughput

Think about how often someone will access your service. How many clicks will they make in an hour, and how soon do they expect a response? Examples of good candidate applications for web frameworks are social networks, p2p marketplaces, and ecommerce stores. All have pretty lax requirements on these metrics per user.

In order to make it simple and fast to model your business processes, web frameworks add several layers of abstraction that, as a side effect, slow down the processing of user requests. In most applications, this isn’t a problem and you can just throw more servers and resources to achieve a speedy request handling. However, some businesses processes with huge performance demands per user are just not well-suited to web frameworks.

Online Games

Have you ever played or watched someone play a first-person shooter such as Halo, Counter-Strike, Overwatch? What about other online multiplayer games like World of Warcraft, Minecraft, or Farmville? The common thread for these online games is that as soon as someone has to wait longer than 30-250 milliseconds to get a response to their click, keypress, or tap, they start having a horrible experience. If you’re ambitious enough to tackle game development, then you’re probably already knowledgeable enough to know that most web frameworks aren’t for you. The software architecture for networked games is vastly different than those of typical enterprise applications, and they are notoriously difficult to implement and scale. The exceptions to this may be page-based games such as Neopets.

Messaging Platforms

Whatsapp is a messaging platform that was acquired by Facebook for $19 billion. Its core focus was to deliver an amazing chat experience to billions of users. It was not built on Django or Rails for very good reasons: it would not have been a good return on investment as many of the components would have been pretty useless to the core strategy of fast, reliable, and high-volume message passing. In 2014, it processed 50 billion messages every day. By leveraging Erlang, the fifty engineers at Whatsapp were able to handle over 2.5 million connections per server, resulting in massive cost savings in employees and equipment.

The WhatsApp Architecture Facebook Bought For $19 Billion

Video Livestreaming

Video Livestreaming services like Twitch and Periscope that support thousands of concurrent users use video processing backends such as the Red5, Wowza Media Server, or custom and proprietary implementations. This Amazon AWS page lists some popular backends for video processing, encoding, and distribution. Django and Rails were not meant for high data throughput media streams, and therefore don’t come stock with any facilities to handle them. This is a different enough problem where it’s best to go with a solution that knows about media streaming. However, the websites and mobile apps for these services can definitely be backed by a Django or Rails instance to handle logins, page rendering, and more; just not the streaming itself. It’s possible to have these web frameworks hand off to specialized media processing engines through an interface.

What Technology Powers Periscope’s Live Video Stream?

Twitch Engineering: An Introduction and Overview

Web/IoT Analytics Collection

Web analytic companies like Google Analytics, Chartbeat, or Heap Analytics are technically challenging to implement because every single click for any of your tracked websites creates a data entry that must be processed. For a portfolio of busy websites, this can easily result in hundreds of thousands to millions of requests per second that must be handled efficiently as not to break the service. These screaming speeds usually demand pretty custom distributed systems based on technologies such as Kafka and Cassandra.

Chartbeat Backend Engineering Blog

Heavy data processing

More and more organizations want to move lots of data around and put them to good use use. Web frameworks alone simply aren’t enough for this.

Data Warehousing

Traditional data warehousing involves throwing all of a large company’s business data into a large relational database, and then expecting the database to quickly field questions across about metrics like averages, correlations, etc. For example, a grocery store chain might ask “how many apples were sold in our northeast region in 2016?” The data warehouse would contain all the transaction information across all the locations and crunch the numbers to spit out the answer. On modern systems, this could reasonably take only a few seconds. Cloud technologies such as Amazon Redshift or on-premise technologies such as SAP Business Warehouse are engineered to quickly comb through large amounts of entries in a very efficient way using specialized data storage methodologies such as columnar storage. Web frameworks do not have this ability. However, web frameworks can be used in tandem with these systems in an integrated business intelligence solution to keep track of queries, display dashboards, and provide user accounts.

“Big Data Analytics” & “Machine Learning”

“Big Data” and “Machine Learning” may be among the most overloaded terms ever. I will specify “Big Data” to mean operations on datasets that cannot fit on a single computer, and “Machine Learning” to mean a combination of statistical and probabilistic techniques that allow for predictive capabilities using less human programming. These have significant overlap with data warehousing. For example, a manufacturing company might have tens of thousands of equipment installations that all need maintenance from a few centralized teams. A good use of machine learning would be to first take measurements of many different characteristics of these machines: total widgets produced since last maintenance, temperature readings, historic failure rate, etc. These characteristics can then be used to train a machine learning model to accurately predict which machines need maintenance and when, saving the company significant replacement costs through maintenance at the right time. As mentioned above, web frameworks can be part of this solution, but the number crunching and data processing are better left to other technologies meant for this kind of use case such as Apache Spark, RStudio, and TensorFlow.

Situations with Expensive Failure Modes

The cost of mishandling a website request is pretty low. The user can just refresh the page, and most of the time it’s back. Maybe the application is down for a few minutes–usually not a huge deal. But sometimes it is. You wouldn’t want an F-16 fighter plane to freeze up on its pilot in a dogfight, and you wouldn’t want a heart monitor to crash during a surgery. The stakes are far higher in certain industries, and web software is incredibly complex and error-prone compared to the standards that are necessary. These industries include:

Companies in higher-stakes territories start to lean towards much smaller code bases with far fewer dependencies than web software. This can be more easily audited. They are also partial to statically-typed programming languages that lead to far more predictable results than dynamically-typed languages like Ruby and Python, and they have compilers that perform series of checks to ensure correct behavior before producing a running program. If you’re trying to break into these industries, be prepared to shell out a lot of money for very good programmers to write software that is correct to a very certain degree, and expensive lawyers to pass regulations.

With that stern warning in mind, web frameworks can be and have been used to great effect in healthcare systems, legal departments, and industrial control analytic platforms.

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