# grafana-mimir

Published articles for grafana-mimir.

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## Grafana Mimir Compaction: From Bottleneck to Savings

DevFeed: [Grafana Mimir Compaction: From Bottleneck to Savings](<https://devfeed.tech/articles/grafana-mimir-compaction-from-bottleneck-to-savings-15452.md>)

Original publisher: [Read original article](<https://medium.com/wise-engineering/grafana-mimir-compaction-from-bottleneck-to-savings-b26c6b0125a6?source=rss----f2565bbe9c46---4>)

Author: Milon James

Published: 2025-01-15T11:55:57Z

Content type: article

Language: en

Sources: [Wise Engineering - Medium](<https://devfeed.tech/sources/wise-engineering-medium.md>)

Topics: [Grafana](<https://devfeed.tech/topics/grafana.md>), [observability](<https://devfeed.tech/topics/observability.md>), [Scalability](<https://devfeed.tech/topics/scalability.md>), [Multi-tenancy](<https://devfeed.tech/topics/multi-tenancy.md>), [Architecture & Design](<https://devfeed.tech/topics/architecture-design.md>), [Latency](<https://devfeed.tech/topics/latency.md>), [Disk image](<https://devfeed.tech/topics/disk-image.md>)

Tags: [architecture](<https://devfeed.tech/tags/architecture.md>), [grafana](<https://devfeed.tech/tags/grafana.md>), [grafana-mimir](<https://devfeed.tech/tags/grafana-mimir.md>), [metrics](<https://devfeed.tech/tags/metrics.md>), [multi-tenancy](<https://devfeed.tech/tags/multi-tenancy.md>), [observability](<https://devfeed.tech/tags/observability.md>), [performance](<https://devfeed.tech/tags/performance.md>), [platform](<https://devfeed.tech/tags/platform.md>), [platform-engineering](<https://devfeed.tech/tags/platform-engineering.md>), [s3](<https://devfeed.tech/tags/s3.md>), [scalability](<https://devfeed.tech/tags/scalability.md>)

### AI overview

This technical article describes Wise's migration from Thanos to Grafana Mimir, the compaction bottleneck that emerged in its metrics infrastructure, and the resulting improvements after the issue was resolved. The reported benefits include S3 storage savings, reduced cross-AZ network transfer, and lower query latency.

### Source excerpt

Intro This technical blog article dives deep into a compaction bottleneck that was silently impacting our metrics infrastructure after we migrated from Thanos to Grafana Mimir. Follow along as we detail the investigation, solution, and the remarkable performance and cost improvements that transformed our metrics pipeline. To give readers some context on the scale of our operations at Wise, our production Mimir stack is currently ingesting approximately a total of 6 million metric samples per second across different tenants at the time of writing -- a testament to the critical role it plays in our observability infrastructure. Write throughput and latency In the world of observability, managing large-scale metric storage systems can be a challenging endeavour. Recently, Wise made a significant shift from Thanos to Grafana Mimir, a decision driven by our growing need for better scalability, feature richness, and operational simplicity. The switch has already yielded several benefits, but a critical issue with Mimir compaction brought new challenges to light. Once resolved, this fix not only improved system reliability but also delivered substantial cost benefits in terms of S3 storage savings, cross-AZ network transfer reductions, and query latency improvements. Here's an overview of our journey -- from the switch to Mimir to resolving compaction issues and reaping the rewards. Why We Switched from Thanos to Mimir Thanos had served us well as a metrics storage and query system, but as our use case evolved, we began to encounter limitations: Scaling Challenges: Managing Thanos components at scale -- especially with the growth in the volume of metrics data -- became operationally complex. Retention and Compaction Flexibility: Thanos's approach to compaction and retention felt rigid for our growing requirements. Feature Set: Mimir provided robust multi-tenancy capabilities, advanced query performance optimisations, and seamless integration with our existing workflows. Benefi