grafana/mimir

Grafana Mimir provides horizontally scalable, highly available, multi-tenant, long-term storage for Prometheus.

View on GitHub ↗Jump to charts ↓Open shareable report

Summary Information

Updated 51 minutes ago
Added to GitGenius on April 10th, 2022
Created on July 13th, 2021
Open Issues & Pull Requests: 808 (+1)
Number of forks: 826
Total Stargazers: 5,214 (+0)
Total Subscribers: 154 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 4.0 days
Mean response time: 138.8 days
90th percentile: 578.8 days
Tracked items: 861

How this project is maintained

Around half of the issues opened in the past year never receive a reply. 93% of open issues come from outside the core team, so the backlog reflects real-world use rather than internal planning. Work labelled "flaky-test" is answered fastest, typically in about 30 hours, while "component/store-gateway" waits about 3 weeks. 62% of tracked open issues have had no activity in three months, so the open count overstates what is actively being worked. Only 4% of issues opened in the past year have been closed.

Charts & Analytics

Fetching additional details & charts...

Issue Activity (beta)

Open issues: 470
New in 7 days: 2
Closed in 7 days: 6
Avg open age: 669 days
Stale 30+ days: 446
Stale 90+ days: 412

Recent activity

Opened in 7 days: 2
Closed in 7 days: 4
Comments in 7 days: 23
Events in 7 days: 62

Top labels

  • bug (291)
  • enhancement (224)
  • type/docs (190)
  • helm (174)
  • good first issue (80)
  • type/tests (78)
  • component/store-gateway (51)
  • component/alertmanager (45)

Detailed Description

Grafana Mimir is an open source project written in Go that provides horizontally scalable, highly available, multi-tenant long-term storage for Prometheus metrics. It addresses the limitations of single Prometheus instances by enabling organizations to store and query massive volumes of time series data across distributed infrastructure. The project is designed to handle up to 1 billion active time series according to internal testing, making it suitable for large-scale observability deployments.

The core architecture emphasizes both operational simplicity and massive scalability. Mimir can run in monolithic mode with just a single binary and no additional dependencies, making initial deployment straightforward. However, it also supports a horizontally scalable distributed architecture that allows organizations to process orders of magnitude more time series than standalone Prometheus. The system uses object storage for long-term data retention, supporting multiple backends including AWS S3, Google Cloud Storage, Azure Blob Storage, OpenStack Swift, and any S3-compatible storage solutions. This approach provides cost-effective, durable storage while maintaining high availability through metric replication that prevents data loss during machine failures.

Mimir's query engine is built for performance, extensively parallelizing query execution to handle high-cardinality queries with speed. The system enables global views of metrics by allowing queries that aggregate series from multiple Prometheus instances, providing unified visibility across distributed systems. Its natively multi-tenant architecture isolates data and queries between independent teams or business units sharing the same cluster, with advanced limits and quality-of-service controls ensuring fair capacity distribution among tenants.

The repository shows active development and maintenance.

The project maintains comprehensive documentation covering deployment, configuration, and production operations. Migration paths are explicitly documented for users transitioning from Thanos, Prometheus, or Cortex. The repository is distributed under the AGPL-3.0-only license and maintains active community engagement through GitHub discussions, a monthly community call, and a dedicated Slack channel. The project's classification across multiple domains including time-series databases, distributed architecture, query processing, and data aggregation reflects its position as a comprehensive metrics storage and querying platform for modern observability infrastructure.