Prometheus Storage Backend
A prometheus storage backend stores time-series metrics on local disks or in remote systems. Teams set up local storage for fast queries and add remote storage for long retention. Most engineering teams start by writing all metrics straight to a single local disk. As metric volume grows, teams attach external object storage or shared metric databases. Overly broad roles on those storage systems let any user read or delete metric data. How a prometheus storage backend works The native promethe
Prometheus Security: the complete guide
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A prometheus storage backend stores time-series metrics on local disks or in remote systems. Teams set up local storage for fast queries and add remote storage for long retention. Most engineering teams start by writing all metrics straight to a single local disk. As metric volume grows, teams attach external object storage or shared metric databases. Overly broad roles on those storage systems let any user read or delete metric data.
How a prometheus storage backend works
The native prometheus storage backend runs as a local time-series database on the server disk. It groups incoming metric samples into two-hour blocks to keep disk writes fast. Each block holds index files, metric chunks, and a metadata file for quick lookups. While the server collects new metrics, it holds recent samples in memory first. A write-ahead log on the disk protects those in-memory samples if the server crashes. When the two-hour window ends, the system flushes the memory data to a disk block. Background tasks later compact smaller blocks into larger blocks to save disk space. Old blocks get deleted once they pass the retention time limit set by the team. For long-term retention, teams use remote write rules to send samples to external systems. Remote read rules then let the server query those external systems when users check old data.
What native storage setups solve and what they miss
This native storage design solves fast metric collection and short-term queries on one machine. Remote storage integrations also solve capacity limits by moving old blocks to cloud buckets. Still, the native storage engine does not include built-in user roles or fine-grained permissions. Anyone who can reach the query port can read every metric in the database. If the admin endpoints are open, any user can also delete time-series blocks. Teams often protect remote storage with static credentials stored in plain text on the server. Those shared keys grant full read and write permissions across the whole metric store. Native setups decide which server logs in, not what individual queries or commands do in flight. Also, metric labels sometimes contain sensitive data like emails, phone numbers, or national IDs. Without query controls, broad metric queries return those sensitive label values in plain text.
Securing metric queries and storage roles
To reduce risk, teams should separate read roles from write roles on external storage systems. The collector server only needs write permissions to push metric blocks to remote buckets. Query nodes only need read permissions to fetch old blocks during dashboard loads. Teams should also disable admin delete endpoints unless an operator needs to run a cleanup task. Regular checks of metric labels help teams spot personal data before it hits long-term storage. Limiting who can run broad queries prevents both accidental server slowdowns and data leaks. Clear audit logs of every query and admin request help teams track who checked which metrics.
Add runtime guardrails and masking beside your databases and APIs
Login rules, role grants, and CI checks govern who connects to your stack. Yet any allowed script, engineer, or AI agent still runs commands with the full scope of its role. For workloads on Postgres, MySQL, SQL Server, MongoDB, ClickHouse, Kubernetes, SSH, or internal HTTP services, hoop.dev sits beside the service as an open-source proxy, a sidecar for your database or API.
Prometheus Security: architecture patterns and best practices
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Before a query runs, hoop.dev guardrails check the statement against your rules. An AI Analyzer you configure can score intent or hold risky calls for human review. Next, hoop.dev data masking rewrites sensitive fields in responses, and each verdict is logged per listener. Explore the MIT-licensed project on GitHub.
Frequently asked questions
How long does local Prometheus storage keep metric data?
Local storage keeps metrics until they pass the time or disk size limit you set. Teams use local disks for short retention and remote backends for multi-year storage.
Can Prometheus use object storage directly without extra tools?
No, the core server writes to a local file system and sends data out via remote write. You need a remote storage adapter or compatible metric database to use object storage.
How do teams prevent sensitive data from leaking in metric labels?
Teams drop sensitive labels before storage and restrict who can run raw queries. Data masking rules can also rewrite matched fields like emails or card numbers in results.
Short role checks help teams work fast. Simple rules make each review clear. Good logs keep every team safe. Scoped keys stop bad changes early.