Getting data to debug your code while running in production, without stopping or redeploying it. Whatever you may be running, wherever you may be running it. And now, with support for open source machine learning frameworks Apache Spark and Tensorflow added to the mix. This is what startup Rookout promises.

Software bugs are a pain: buggy software can drop anything from your sales to aircraft in mid-flight. Debugging software is hard, tedious, and costs a fortune. A multitude of frameworks and processes have been created to facilitate software testing and ensure fewer bugs make it to production, and invariably they all fail from time to time.

When this happens, the pain for software developers and the nail-biting for businesses starts. Developers have to find the source code that caused the bug, and execute this in a test environment that resembles the production one as closely as possible. The situation that caused the bug has to be recreated, too.

The way this usually works is by adding logging statements and breakpoints in the code, and retracing execution in the code and its dependencies until the bug is located and can be fixed. Then the new code has to be rebuilt and redeployed in production.

Frankly, it’s a pain just thinking about it, let alone having to go through this. Or Weis and Liran Haimovitch are two software engineers who have gone through this time after time, and felt the pain, so they decided to do something about it.

Weis and Haimovitch founded Rookout in 2017, based on a seemingly simple premise: Adding non-breaking breakpoints into live code, and getting data in real time from its real-life environment, without stopping applications. It sounds a bit like software development black magic, so we had to wonder how it works.

As CEO Weis told ZDNet, Rookout’s framework works in two steps. First, the framework is integrated into a running application via an SDK. When done, instructions are received on-demand from Rookout’s management system, and bytecode or opcode manipulation is applied on the fly to augment the running service(s) as if it was deployed with the additional logging/data-collection code in the first place.

So, not exactly magic: there is an SDK integration via needed. But this is something that would have to be done once, and Weis said it’s “a simple dependency, 2-minute setup”. What can you do once this is done? Collect data as you please, and send it where you please, according to Weis.

Rookout’s breakpoints can collect any data that could be collected by adding an extra log line in the code: local variables, global variables, stack traces, thread-local storage, metrics, and anything else your code has access to. The only exception is the return values for method invocations, which is not enabled for security reasons.

As for performance, Weis said that adding a log-line with Rookout, or doing so by coding and re-deploying creates the same basic end result in memory, hence there is no overhead. He went on to add that Rookout makes sure to only apply read-only effects, and always remain within the resource limitation bounds the admin has set.

Rookout supports JVM programming languages, such as Java, Scala, or Groovy, Node.js, and Python, in all clouds, containers, Kubernetes, and serverless. Rookout also works on external and third party libraries, as long as a file name and a line number can be provided. The collected data points can then be sent wherever they need to go: an application performance monitoring solution, alerting and logging tools, business intelligence tools and more.

There is a catch though: at this point, it’s not certain that your favorite IDE coding environment is supported by Rookout. You may have to switch to Rookout’s own web IDE to use Rookout. Weis said Rookout is an API driven infrastructure platform, meaning you can connect to it through any interface of your choice.

Rookout is working to tailor multiple interfaces on top of its API as well as part of other products, for example Sentry.io + Rookout, Circle-CI. Its current focus, however, is its web IDE. Weis noted that:

Read the full article on ZDNet