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docs/Single-server-VictoriaMetrics.md: mention that VictoriaMetrics works great with APM workloads (aka Application Performance Monitoring)
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@ -94,7 +94,7 @@ Alphabetically sorted links to case studies:
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* [Arbitrary CSV data](#how-to-import-csv-data).
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* Supports metrics' relabeling. See [these docs](#relabeling) for details.
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* Can deal with high cardinality and high churn rate issues using [series limiter](#cardinality-limiter).
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* Ideally works with big amounts of time series data from Kubernetes, IoT sensors, connected cars, industrial telemetry, financial data and various Enterprise workloads.
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* Ideally works with big amounts of time series data from APM, Kubernetes, IoT sensors, connected cars, industrial telemetry, financial data and various Enterprise workloads.
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* Has open source [cluster version](https://github.com/VictoriaMetrics/VictoriaMetrics/tree/cluster).
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* See also technical [Articles about VictoriaMetrics](https://docs.victoriametrics.com/Articles.html).
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@ -98,7 +98,7 @@ Alphabetically sorted links to case studies:
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* [Arbitrary CSV data](#how-to-import-csv-data).
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* Supports metrics' relabeling. See [these docs](#relabeling) for details.
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* Can deal with high cardinality and high churn rate issues using [series limiter](#cardinality-limiter).
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* Ideally works with big amounts of time series data from Kubernetes, IoT sensors, connected cars, industrial telemetry, financial data and various Enterprise workloads.
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* Ideally works with big amounts of time series data from APM, Kubernetes, IoT sensors, connected cars, industrial telemetry, financial data and various Enterprise workloads.
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* Has open source [cluster version](https://github.com/VictoriaMetrics/VictoriaMetrics/tree/cluster).
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* See also technical [Articles about VictoriaMetrics](https://docs.victoriametrics.com/Articles.html).
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