Pereira Braga
Observability Technical Steward
I'm the Chief Architect (Über Technical Leader) of a group of 100+ Engineers, who develop observability (monitoring, alerting, performance and risk) solutions for Google/Alphabet -> P2020 Monitoring. I also lead the technical group for overall production service management at Google (Production 2020) - a group of ~300 engineers.
I have 18+ years of experience in the industry and 5 years in Academia and Research.
I'm the Chief Architect (Über Technical Leader) of a group of 100+ Engineers, who develop observability (monitoring, alerting, performance and risk) solutions for Google/Alphabet -> P2020 Monitoring. I also lead the technical group for overall production service management at Google (Production 2020) - a group of ~300 engineers.
I have 18+ years of experience in the industry and 5 years in Academia and Research.
Authored Publications
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Unifying query languages is key in reducing toil for app developers and end users to query and analyze observability data. A common query language that can leverage all observability data such as metrics, traces, profiles, events, logs to facilitate correlation, support trend analytics and provide end-to-end observability for AI applications. The Observability TAG QLS workgroup is finalizing a semantic query language spec in 2025 and is recommending SQL as a basis with further experimentation on syntaxes. This talk will explore the design principles, user research and challenges of creating a query language to support observability goals. It will delve into the core concepts, syntax, and semantics of SQL operators and its needed syntactic sugar, while addressing the unique requirements of observability data. It will also explore the trade-offs between simplicity, expressiveness, and performance. This query language convergence for end-to-end analytics could enhance reliability and operational efficiency for SREs and your app developers. A win-win for all.
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Preview abstract
Unifying query languages is key in reducing toil for app developers and end users to query and analyze observability data. A common query language that can leverage all observability data such as metrics, traces, profiles, events, logs to facilitate correlation, support trend analytics and provide end-to-end observability for AI applications. The Observability TAG QLS workgroup is finalizing a semantic query language spec in 2025 and is recommending SQL as a basis with further experimentation on syntaxes. This talk will explore the design principles, user research and challenges of creating a query language to support observability goals. It will delve into the core concepts, syntax, and semantics of SQL operators and its needed syntactic sugar, while addressing the unique requirements of observability data. It will also explore the trade-offs between simplicity, expressiveness, and performance. This query language convergence for end-to-end analytics could enhance reliability and operational efficiency for SREs and your app developers. A win-win for all.
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Observability Query Language at Google - How Google analyzed the approach to observability data to decide on query engine to cover telemetry (real-time and analytics), logs and traces.
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