Monday, December 5, 2011 - Twitter analytics for the masses

Our post today comes from Stefan and Niklas of, a nifty site that recommends the best time of day to tweet based on your followers’ habits.

Twitter handles an amazing number of Tweets - over 200 million tweets are sent per day.

We saw that many Twitter users were tweeting interesting content but much of it was lost in the constant stream of tweets. is born

While there were many tools for corporate Twitter users that performed deep analytics and provided insight into their tweets, there were none that answered the most basic question: what time of the day are my followers actually using Twitter?

And so the idea behind Whentotweet was born. In its current form, Whentotweet analyzes when your followers tweet and gives you a personalized recommendation of the best time of day to tweet to reach as many as possible.

Given the massive amount of data we needed to analyze, we knew it would be a huge engineering challenge to build what we wanted using the tools we had used previously. We also wanted to make sure we could offer at least a basic product for free. Not only did we need to process massive amounts of data - we also needed a way to do it without a second mortgage on our houses!

The Technology Used

As we went over the alternatives we started to sketch different ways of hosting our application. We had previous experience building web sites and knew that traditional cloud hosting would be expensive and difficult to manage for the kind of computing that we needed. After some quick back-of-the-envelope calculations it seemed clear that Google App Engine would give us both the kind of pricing we needed and a way to scale. We decided to write a quick test application to test our assumptions.

The test application blew our minds. Apart from proving our initial assumptions around pricing and scale we started appreciating the quick deploys. On previous projects we were used to one deploy per month. Almost immediately we shifted our schedule to one or sometimes several deploys per day to push new code to customers.

The main APIs that Whentotweet relies on are Google App Engine's task queues and Datastore. Whenever a new user requests a report it is added as a task. A typical report requires a huge number of interactions with external sites. By breaking down each external interaction into separate tasks in different queues it became easy to make sure we kept a steady rate of API calls to external sites without risking that a huge influx of users would break our API limits.

The initial task then spawns new tasks until finally one of the tasks decides that the report is complete and tweets a summary of the result and a link to a more detailed report. Whentotweet uses a "fail fast" technique so whenever any request fails, internal or external, the task terminates and puts itself back on the queue.

The Datastore saves a finished or ongoing analysis. Sometimes a single analysis will be updated several times a second by tasks as they finish and store their results.

The Result

After a few weeks of intense coding, we were ready to test our code on a small sized Twitter account with less than three hundred followers. The results came back in just a few minutes.

After verifying that everything had actually worked as well as we thought, we decided to try another account. This time one of the largest Twitter accounts on the planet: @techcrunch. Handling a Twitter account with over a million followers took the application one week. But after the analysis started, Whentotweet would quietly work in the background without us having to lift a finger.

Whentotweet got off to a better start than we imagined. During the initial launch thousands of people tested it on their Twitter accounts.

After a while blog posts appeared, recommending Whentotweet as an invaluable Twitter tool. Each post would generate a sudden huge spike in traffic. Sometimes, a blog owner would mail us and ask if we were ready for the sudden increase in traffic this would bring. But Whentotweet was built to scale and even massive sites such as didn't slow it down. The most amazing thing is that we didn't need to write a single extra line of code to handle these massive variations in load. Instead, as soon as we wrapped our head around the tools in the App Engine toolbox we knew that Whentotweet would easily scale. App Engine forced us to think outside the box and avoid the fallacies of traditional hosting that create bottlenecks.

Currently, over 38,000 people have tried Whentotweet and we see from the user feedback that they love it. Give it a try at:

- Niklas Agevik (@niklas_a) and Stefan Ă…lund (@stefan_alund) of

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