The first stage in every data project is collecting information and routing it to a storage system for later analysis. For operational data this typically means collecting log messages and system metrics. Often a different tool is used for each class of data, increasing the overall complexity and number of moving parts. The engineers at Timber.io decided to build a new tool in the form of Vector that allows for processing both of these data types in a single framework that is reliable and performant. In this episode Ben Johnson and Luke Steensen explain how the project got started, how it compares to other tools in this space, and how you can get involved in making it even better.
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- Your host is Tobias Macey and today I’m interviewing Ben Johnson and Luke Steensen about Vector, a high-performance, open-source observability data router
- How did you get involved in the area of data management?
- Can you start by explaining what the Vector project is and your reason for creating it?
- What are some of the comparable tools that are available and what were they lacking that prompted you to start a new project?
- What strategy are you using for project governance and sustainability?
- What are the main use cases that Vector enables?
- Can you explain how Vector is implemented and how the system design has evolved since you began working on it?
- How did your experience building the business and products for Timber influence and inform your work on Vector?
- When you were planning the implementation, what were your criteria for the runtime implementation and why did you decide to use Rust?
- What led you to choose Lua as the embedded scripting environment?
- What data format does Vector use internally?
- Is there any support for defining and enforcing schemas?
- In the event of a malformed message is there any capacity for a dead letter queue?
- Is there any support for defining and enforcing schemas?
- What are some strategies for formatting source data to improve the effectiveness of the information that is gathered and the ability of Vector to parse it into useful data?
- When designing an event flow in Vector what are the available mechanisms for testing the overall delivery and any transformations?
- What options are available to operators to support visibility into the running system?
- In terms of deployment topologies, what capabilities does Vector have to support high availability and/or data redundancy?
- What are some of the other considerations that operators and administrators of Vector should be considering?
- You have a fairly well defined roadmap for the different point versions of Vector. How did you determine what the priority ordering was and how quickly are you progressing on your roadmap?
- What is the available interface for adding and extending the capabilities of Vector? (source/transform/sink)
- What are some of the most interesting/innovative/unexpected ways that you have seen Vector used?
- What are some of the challenges that you have faced in building/publicizing Vector?
- For someone who is interested in using Vector, how would you characterize the overall maturity of the project currently?
- What is missing that you would consider necessary for production readiness?
- When is Vector the wrong choice?
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
- Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used.
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