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### Describe Your Changes This is a follow-up PR containing workflow related part of the initial [PR#6362](https://github.com/VictoriaMetrics/VictoriaMetrics/pull/6362). It adds spell checking control based on [cspell](https://github.com/streetsidesoftware/cspell). The related configs are consolidated under `cspell` directory. You can add VictoriaMetrics specific terms into `cspell/custom-dict.txt` (it contains ~30 items atm). All other absent commonly used terms should be added directly to respective [cspell-dicts](https://github.com/streetsidesoftware/cspell-dicts/blob/main/CONTRIBUTING.md). ### Checklist The following checks are **mandatory**: - [x] My change adheres [VictoriaMetrics contributing guidelines](https://docs.victoriametrics.com/contributing/). --------- Signed-off-by: Arkadii Yakovets <ark@victoriametrics.com> |
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| guides | ||
| CHANGELOG.md | ||
| FAQ.md | ||
| Overview.md | ||
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| Presets.md | ||
| presets_anomalies_by_node.webp | ||
| presets_anomalous_node_selection.webp | ||
| presets_cpu_seconds_softirq.webp | ||
| presets_cpu_seconds_steal.webp | ||
| presets_global_percentage.webp | ||
| QuickStart.md | ||
| README.md | ||
| vmanomaly-holtwinters-example.webp | ||
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| VictoriaMetrics Anomaly Detection | 0 |
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VictoriaMetrics Anomaly Detection
In the dynamic and complex world of system monitoring, VictoriaMetrics Anomaly Detection, being a part of our Enterprise offering, stands as a pivotal tool for achieving advanced observability. It empowers SREs and DevOps teams by automating the intricate task of identifying abnormal behavior in time-series data. It goes beyond traditional threshold-based alerting, utilizing machine learning techniques to not only detect anomalies but also minimize false positives, thus reducing alert fatigue. By providing simplified alerting mechanisms atop of unified anomaly scores, it enables teams to spot and address potential issues faster, ensuring system reliability and operational efficiency.
Practical Guides and Installation
Begin your VictoriaMetrics Anomaly Detection journey with ease using our guides and installation instructions:
-
Quickstart: Check out how to get
vmanomalyup and running here. -
Overview: Find out how
vmanomalyservice operates here -
Integration: Integrate anomaly detection into your observability ecosystem. Get started here.
-
Anomaly Detection Presets: Enable anomaly detection on predefined set of indicators, that require frequently changing static thresholds for alerting. Find more information here.
-
Installation Options: Select the method that aligns with your technical requirements:
- Docker Installation: Suitable for containerized environments. See Docker guide.
- Helm Chart Installation: Appropriate for those using Kubernetes. See our Helm charts.
Note
: starting from v1.5.0
vmanomalyrequires a license key to run. You can obtain a trial license key here.
Key Components
Explore the integral components that configure VictoriaMetrics Anomaly Detection:
Deep Dive into Anomaly Detection
Enhance your knowledge with our handbook on Anomaly Detection & Root Cause Analysis and stay updated:
- Anomaly Detection Handbook
- Follow the
#anomaly-detectiontag in our blog
Frequently Asked Questions (FAQ)
Got questions about VictoriaMetrics Anomaly Detection? Chances are, we've got the answers ready for you.
Dive into our FAQ section to find responses to common questions.
Get in Touch
We are eager to connect with you and adapt our solutions to your specific needs. Here's how you can engage with us:
- Book a Demo to discover what our product can do.
- Interested in exploring our Enterprise features, including Anomaly Detection? Request your trial license today and take the first step towards advanced system observability.
Our CHANGELOG is just a click away, keeping you informed about the latest updates and enhancements.