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Grafana Labs has released Tempo 3.1, the first minor update to the 3.0 architecture. It adds TLS and additional SASL mechanisms to the Kafka client, lets operators redact traces with a TraceQL query instead of a list of trace IDs, and introduces an experimental query time hint that corrects TraceQL metrics for…
By Oleksii Tkachuk , Rajiv Shringi , Chris Lohfink , Abe Ratnofsky , Kartik Sathyanarayanan Introduction In an earlier post , we introduced the TimeSeries Abstraction, our high throughput, low latency platform designed to store and query temporal event data at scale. It has since become the operational backbone for…
Some numbers are hard to picture until you sit with them for a moment. Last week we published the Prime Day 2026 metrics, and the one that stuck with me is 2.3 quadrillion: the daily requests Amazon ElastiCache peaked at during the event. Prime Day ran back in June, so this post always lands as […]
AWS has introduced an enhanced Custom Event Bus for Amazon EventBridge, targeting a problem that often emerges when event driven architectures move beyond a single team or AWS account: the event infrastructure itself becomes fragmented. By Craig Risi
Discover how Uber is performing vertical CPU autoscaling for stateful workloads across a server fleet with large CPU performance differences. Correctly handling these differences ensures we have a both efficient and reliable platform at Uber.
Building on the major release of Tempo 3.0 , Tempo 3.1 is here, delivering community contributed Kafka client improvements, query based trace redaction, sampling aware TraceQL metrics, and more. Together, the updates in 3.1 make it easier to operate Tempo, get accurate insights from your trace data, and investigate…
Discover how Uber engineered a highly scalable feature logging pipeline to ensure data consistency for training/serving and boost performance for large scale models. MachineLearning MLOps UberEngineering DataEngineering Scaling
