Collecting and processing metrics for monitoring use cases is an interesting data problem. It is eminently possible to generate millions or billions of data points per second, the information needs to be propagated to a central location, processed, and analyzed in timeframes on the order of milliseconds or single-digit seconds, and the consumers of the data need to be able to query the information quickly and flexibly. As the systems that we build continue to grow in scale and complexity the need for reliable and manageable monitoring platforms increases proportionately. In this episode Rob Skillington, CTO of Chronosphere, shares his experiences building metrics systems that provide observability to companies that are operating at extreme scale. He describes how the M3DB storage engine is designed to manage the pressures of a critical system component, the inherent complexities of working with telemetry data, and the motivating factors that are contributing to the growing need for flexibility in querying the collected metrics. This is a fascinating conversation about an area of data management that is often taken for granted.
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- Hello and welcome to the Data Engineering Podcast, the show about modern data management
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- Your host is Tobias Macey and today I’m interviewing Rob Skillington about Chronosphere, a scalable, reliable and customizable monitoring-as-a-service purpose built for cloud-native applications.
- How did you get involved in the area of data management?
- Can you start by describing what you are building at Chronosphere and your motivation for turning it into a business?
- What are the biggest challenges inherent to monitoring use cases?
- How does the advent of cloud native environments complicate things further?
- While you were at Uber you helped to create the M3 storage engine. There are a wide array of time series databases available, including many purpose built for metrics use cases. What were the missing pieces that made it necessary to create a new system?
- How do you handle schema design/data modeling for metrics storage?
- How do the usage patterns of metrics systems contribute to the complexity of building a storage layer to support them?
- What are the optimizations that need to be made for the read and write paths in M3?
- How do you handle high cardinality of metrics and ad-hoc queries to understand system behaviors?
- What are the scaling factors for M3?
- Can you describe how you have architected the Chronosphere platform?
- What are the convenience features built on top of M3 that you are creating at Chronosphere?
- How do you handle deployment and scaling of your infrastructure given the scale of the businesses that you are working with?
- Beyond just server infrastructure and application behavior, what are some of the other sources of metrics that you and your users are sending into Chronosphere?
- How do those alternative metrics sources complicate the work of generating useful insights from the data?
- In addition to the read and write loads, metrics systems also need to be able to identify patterns, thresholds, and anomalies in the data to alert on it with minimal latency. How do you handle that in the Chronosphere platform?
- What are some of the most interesting, innovative, or unexpected ways that you have seen Chronosphere/M3 used?
- What are some of the most interesting, unexpected, or challenging lessons that you have learned while building Chronosphere?
- When is Chronosphere the wrong choice?
- What do you have planned for the future of the platform and business?
- 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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