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:
- Backfilling Higher Tiers: The Parent calculates aggregated metrics for missing data points, ensuring consistency across different time resolutions.
- Metadata Synchronization: The Parent and Children exchange metadata information about collected metrics.
- Data Replication: Missing data is transferred from Children to the Parent.
- Normal Streaming: Regular streaming of new metrics begins.
- Machine Learning Initialization: Machine learning models are loaded and prepared for anomaly detection.
- 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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