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1 # CPU Utilization
2
3 Netdata's CPU usage depends on the features you enable. For details, see [resource utilization](/docs/netdata-agent/sizing-netdata-agents/README.md).
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5 ## Children
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7 With default settings on Children, CPU utilization typically falls within the range of 1% to 5% of a single core. This includes the combined resource usage of:
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9 - Three Database Tiers for storage
10 - ML for Anomaly Detection
11 - Per-second data collection
12 - Alerts
13 - Streaming to a [Parent Agent](/docs/observability-centralization-points/metrics-centralization-points/README.md)
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15 ## Parents
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17 For Parents, we estimate the following CPU utilization:
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19 | Feature | Depends On | Expected Utilization (CPU cores per million) | Key Reasons |
20 |:--------------------:|:---------------------------------------------------:|:--------------------------------------------:|:------------------------------------------------------------------------:|
21 | Metrics Ingest | Number of samples received per second | 2 | Decompress and decode received messages, update database |
22 | Metrics re-streaming | Number of samples resent per second | 2 | Encode and compress messages towards another Parent |
23 | Machine Learning | Number of unique time-series concurrently collected | 2 | Train machine learning models, query existing models to detect anomalies |
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25 To ensure optimal performance, keep total CPU utilization below 60% when the Parent is actively processing metrics, training models, and running health checks.
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27 ## Increased CPU consumption on Parent startup
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29 When a Parent starts up, it undergoes a series of initialization tasks that can temporarily increase CPU, network, and disk I/O usage:
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31 1. **Backfilling Higher Tiers**: The Parent calculates aggregated metrics for missing data points, ensuring consistency across different time resolutions.
32 2. **Metadata Synchronization**: The Parent and Children exchange metadata information about collected metrics.
33 3. **Data Replication**: Missing data is transferred from Children to the Parent.
34 4. **Normal Streaming**: Regular streaming of new metrics begins.
35 5. **Machine Learning Initialization**: ML models are loaded and prepared for Anomaly Detection.
36 6. **Health Check Initialization**: The health engine starts monitoring metrics and triggering alerts.
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38 Additional considerations:
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40 - **Compression Optimization**: The compression algorithm learns data patterns to optimize compression ratios.
41 - **Database Optimization**: The Database engine adjusts page sizes for efficient disk I/O.
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43 These initial tasks can temporarily increase resource usage, but the impact typically diminishes as the Parent stabilizes and enters a steady-state operation.