AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |
Back to Blog
Fake user data generator10/23/2023 Women are known to be capable of multitasking. That means at least, the outcome is 200 hundred files, 19 GB of test data. My use case is that I need 20 million users, divided into CSV files with 10 thousand users in each file. But for a full integration test, you should use nearly the same amount + 10% as like you have later in production. To measure changes in the performance between commit, I suggest using so many items that your test does not run longer than 10 minutes on average. We also need a fast hard drive, because we will work with files and that produces a lot of IO processes. Why these languages? To be honest, I am not an expert with these languages, but these languages are known for being able to work on parallel processes and that's what we need! Because faker needs a lot of power for calculations and memory. In this article, we will try out the faker implementations in NodeJS, Python and Ruby. Or should I say created a concept? Because it is implemented in various programming languages. Thankfully, there is a group of developers out there which have created a creation tool called faker. ![]() ![]() There is a need for, because you wanna test then the performance of your stack, and you want to omit to use real data because of data privacy and licence restrictions. :) Sometimes we need millions, I mean gigabytes of test data, which are really close to the data we are using later for our new hyper big data application. In 2019 (and still in 2022), we probably do not need to create data, we just need to print out one of the famous Twitter channels.
0 Comments
Read More
Leave a Reply. |