November 04, 2016

Tal Weiss on Observability, Instrumentation and Bytecode Manipulation on the JVM

28 minutes

In this week's podcast, QCon chair Wesley Reisz talks to Tal Weiss, CEO of OverOps, recently re-branded from Takipi. The conversation covers how the OverOps product works, explores the difference between instrumentation and observability, discusses bytecode manipulation approaches and common errors in Java based applications.

A keen blogger, Weiss has been designing scalable, real-time Java and C++ applications for the past 15 years. He was co-founder and CEO at VisualTao which was acquired by Autodesk in 2009, and also worked as a software architect at IAI Space Industries focusing on distributed, real-time satellite tracking and control systems.

Why listen to this podcast:

- OverOps uses a mixture of machine code instrumentation and static code analysis at deployment time to build up an index of the code

- Observability is how you architect your code to be able to capture information from its outputs. Instrumentation is where you come in from the outside and use bytecode or machine code manipulation techniques to capture information after the system has been designed and built.

- Bytecode instrumentation is a technique that most companies can benefit from learning a bit more about. Bytecode isn’t machine code - it is a high-level programming language. Being able to read it really helps you understand how the JVM works.

- There are a number of bytecode manipulation tools you can use to work with bytecode - ASM is probably the most well known.

- A fairly small number of events within an application’s life-cycle generate the majority of the log volume. A good practice is to regularly review your log files to consider if what is being logged is the right thing.

Notes and links can be found on http://bit.ly/2fInGsW

SaaS vs On-Premise

5:43 - OverOps started as a SaaS product, but given that a lot of the data it collects is potentially sensitive, they introduced a new product called Hybrid. Hybrid separates the data into two independent streams: data and metadata.

6:42 - The data stream is the raw data that is captured which is then privately encrypted using 256 bit AES encryption keys which are only stored on the production machine and by the user when they need to decrypt it. The metadata stream is not sensitive since it is just an abstract mathematical graph.

7:18 - Because the data stream is already privately encrypted, that stream can be stored behind a firewall and never needs to leave a company’s network.