Apache NiFi: Forwarding HTTP headers

Apache NiFi can be used to expose various flavors of webservices. Using NiFi in such a way provides benefits like quick development using a GUI and of course data provenance. You know who called you with which data and where the data went. The NiFi is very scalable, delivery can be guaranteed and NiFi can help with features like back-pressure if a backend system cannot handle requests as quickly as they are offered. Exposing webservices by using NiFi, can have additional benefits such as service virtualization (decoupling). When exposing HTTP(S) webservices, a regular requirement is to pass through HTTP headers. This blog post is about how you can do that using the NiFi processors ListenHTTP, InvokeHTTP, HandleHttpRequest and HandleHttpResponse. I've used the environment which is described here.

Python: A Google Translate service using Playwright

There are a lot of use-cases in which you might want to automate a web-browser. For example to automate tedious repetitive tasks or to perform automated tests of front-end applications. There are also several tools available to do this such as Selenium, Cypress and Puppeteer. Several blog posts and presentations by Lucas Jellema picked my interest in Playwright so I decided to give it a try. I'm not a great fan of JavaScript so I decided to go with Python for this one. I also did some tests with wrk, a simple yet powerful HTTP bench-marking tool, to get an indication about how Playwright would handle concurrency and was not disappointed.

HTTP benchmarking using wrk. Parsing output to CSV or JSON using Python

wrk is a modern HTTP benchmarking tool. Using a simple CLI interface you can put simple load on HTTP services and determine latency, response times and the number of successfully processed requests. It has a LuaJIT scripting interface which provides extensibility. A distinguishing feature of wrk compared to for example ab (Apache Bench) is that it requires far less CPU at higher concurrency (it uses threads very efficiently). It does have less CLI features when compared to ab. You need to do scripting to achieve specific functionality. Also you need to compile wrk for yourself since no binaries are provided, which might be a barrier to people who are not used to compiling code.

Parsing the wrk output is a challenge. It would be nice to have a feature to output the results in the same units as CSV or JSON file. More people asked this question and the answer was: do some LuaJIT scripting to achieve that. Since I'm no Lua expert and to be honest, I don't have any people in my vicinity that are, I decided to parse the output using Python (my favorite language for data processing and visualization) and provide you with the code so you don't have to repeat this exercise.

You can see example Python code of this here.