It’s a recurring task in software development to forecast the amount of time something will take. There are Roadmap Meetings, Sprint Planning Meetings or even the spontaneous estimates you must provide in the hallway. All of them require you to pull out your divining rod and predict the future.
Imagine you are asked how long it will take to create an applicatio which responds with the word
four whenever somebody enters the number
4 . it is dead simple, it will take about four seconds:
#!/bin/bash echo "four"
There you go, a simple application that will always respond with the word
four when you pass
4 as a parameter.
But it is pretty obvious there is more we need to take care of. What if somebody passed
5 as parameter? Should the application respond with
four as well? I think you can guess the answer: No.
The reason why estimates on software projects are so complicated is, they are the opposite of your normal planning habits. As a human being we tend to plan for the happy path. That’s why we assume everything will be fine, start to gamble or do only have the basic insurances for our health and housing.
But there are obstacles which will cross your happy path. To plan project timings always comes down to forecast possible obstacles on your way to the end zone and how this delay will affect your happy path.
Take Scrum for example: During the Sprint Planning Meeting the team commits itself to the tasks for an iteration of most often two weeks. The length of two weeks is not chosen randomly nor because
2 is a beautiful number. It is because two weeks are enough time to start, finish, and deploy something and it’s a time window you feel comfortable to predict possible obstacles within. What could possibly go wrong, right?
The longer your iterations are, the more can go wrong. The more can go wrong and the less accurate your predictions will be.
AWS Lambda functions together with an Amazon Kinesis Stream offer a great way to process continuous information. I created an example project called Serverless Analytics to demonstrate this. You can use this as the starting point to create your very own Google Analytics clone and run it serverless and hopefully maintenance-free on Amazon.617 words, posted on August 23
Since a few days, Amazon provides a native way to enable Auto Scaling for DynamoDB tables! Luckily the settings can be configured using CloudFormation templates, and so I wrote a plugin for serverless to easily configure Auto Scaling without having to write the whole CloudFormation configuration.154 words, posted on July 19
When you use a serverless environment for your service (and you should!), chances are high you might be using the Serverless framework and may end up in a situation like me with the need to process the AWS CloudFormation Stack Output after deploying the service.268 words, posted on July 1