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* remove stale docs, and update links and optimization documentation * typos * simplify --------- Co-authored-by: ilyam8 <ilya@netdata.cloud>
71 lines
4.6 KiB
Markdown
71 lines
4.6 KiB
Markdown
# Agent Performance Optimization Guide
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While Netdata Agents prioritize simplicity and out-of-the-box functionality, their default configuration focuses on comprehensive monitoring rather than performance optimization.
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By default, Agents provide:
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- **Automatic Application Discovery**: Continuously detects and monitors applications running on your node without manual configuration.
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- **Real-time Metric Collection**: Collects metrics with one-second granularity.
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- **Health Monitoring**: Actively tracks the health status of your applications and system components with built-in alerting.
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- **Machine Learning**: Trains models for each metric to detect anomalies and unusual patterns in your system's behavior ([Anomaly Detection](/src/ml/README.md)).
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> **Note**
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>
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> For details about Agent resource requirements, see [Resource Utilization](/docs/netdata-agent/sizing-netdata-agents/README.md).
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This document describes various strategies to optimize Netdata's performance for your specific monitoring needs.
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## Summary of performance optimizations
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The following table summarizes the effect of each optimization on the CPU, RAM and Disk IO utilization in production.
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| Optimization | CPU | RAM | Disk IO |
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|---------------------------------------------------------------------------|--------------------|--------------------|--------------------|
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| [Implement Centralization Points](#implement-centralization-points) | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
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| [Disable Plugins or Collectors](#disable-plugins-or-collectors) | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
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| [Adjust data collection frequency](#adjust-data-collection-frequency) | :heavy_check_mark: | | :heavy_check_mark: |
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| [Optimize metric retention settings](#optimize-metric-retention-settings) | | :heavy_check_mark: | :heavy_check_mark: |
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| [Select appropriate Database Mode](#select-appropriate-database-mode) | | :heavy_check_mark: | :heavy_check_mark: |
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| [Disable ML on Children](#disable-machine-learning-on-children) | :heavy_check_mark: | | |
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## Implement Centralization Points
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In production environments, use Parent nodes as centralization points to collect and aggregate data from Child nodes across your infrastructure. This architecture follows our recommended [Centralization Points](/docs/observability-centralization-points/README.md) pattern.
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## Disable Plugins or Collectors
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You can improve Agent performance by selectively disabling [Plugins or Collectors](/src/collectors/README.md) that you don't need for your monitoring requirements.
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> **Note**
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> Inactive Plugins and Collectors automatically shut down and don't consume system resources. Performance benefits come only from disabling those that are actively collecting metrics.
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For detailed instructions on managing Plugins and Collectors, see [configuration guide](/src/collectors/REFERENCE.md).
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## Adjust Data Collection Frequency
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One of the most effective ways to reduce the Agent's resource consumption is to modify its data collection frequency.
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If you don't require per-second precision, or if your Agent is consuming excessive CPU during periods of low dashboard activity, you can [reduce the collection frequency](/src/collectors/REFERENCE.md).
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This adjustment can significantly improve CPU utilization while maintaining meaningful monitoring capabilities.
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## Optimize Metric Retention Settings
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You can reduce memory and disk usage by adjusting [how long the Agent stores metrics](/src/database/CONFIGURATION.md).
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## Select Appropriate Database Mode
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For IoT devices and Child nodes in [Centralization Point setups](/docs/observability-centralization-points/README.md), you can optimize performance by [switching to RAM mode](/src/database/CONFIGURATION.md). This can significantly reduce resource usage while maintaining essential monitoring capabilities.
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## Disable Machine Learning on Children
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For optimal resource allocation, we recommend running Machine Learning only on Parent nodes, or on systems with sufficient CPU and memory capacity.
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To reduce resource usage on Child nodes or less powerful systems, you can disable ML by modifying `netdata.conf` using [`edit-config`](/docs/netdata-agent/configuration/README.md#edit-a-configuration-file-using-edit-config):
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```text
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[ml]
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enabled = no
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```
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This configuration is particularly beneficial for Child nodes since their primary role is to collect and stream metrics to Parent nodes, where ML analysis can be performed centrally.
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