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