What problem it solves

Organizations need to store, ingest, and query growing volumes of time series data while supporting the protocols and query languages used by existing monitoring systems. They also need deployment options and operational features that support long-term storage, backups, and centralized querying across data sources.

Key features

• Long-term time series storage — Stores time series data for long-term use and can retain Prometheus data or serve as a drop-in replacement for Prometheus and Graphite in Grafana.

  Source: README

• Stream aggregation — Provides stream aggregation for metrics and can be used as a StatsD alternative.

  Source: README

• Flexible query languages — Supports PromQL and MetricsQL for querying stored metrics.

  Source: README

• Simple deployment and operations — Runs as a dependency-free binary configured through command-line flags, with default settings and instant snapshot support for backup and restore.

  Source: README

• Broad ingestion protocol support — Accepts metrics through scraping, remote write, line protocols, OpenTSDB requests, JSON lines, CSV, native binary data, agent formats, and OpenTelemetry metrics.

  Source: README

Who it may suit

Inference: This project may suit operators and developers who need to store and query Prometheus, APM, Kubernetes, IoT, industrial telemetry, or other time series workloads. It may also suit teams seeking single-node or clustered deployment options and integrations with common metric ingestion protocols.

Topics to discuss

• Which deployment type is the best fit for different monitoring environments?

• How should teams plan retention, downsampling, and storage growth for changing time series workloads?

• Which protocol and query-language integrations would be most valuable to add or improve?

Written by ReadmeX AI from the project's README, source and public GitHub data; it may contain mistakes.

Read the original README https://github.com/VictoriaMetrics/VictoriaMetrics#readme

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