netdata/docs/netdata-agent/sizing-netdata-agents/cpu-requirements.md

CPU

Netdata's CPU usage depends on the features you enable. For details, see resource utilization.

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.

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.