--- sort: 3 weight: 1 title: Presets menu: docs: parent: "anomaly-detection" weight: 1 title: Presets --- > Please check the [Quick Start Guide](./QuickStart.md) to install and run `vmanomaly` > Presets are available starting from [v1.13.0](./CHANGELOG.md#v1130) **Preset** mode allows for simpler configuration and anomaly detection with `vmanomaly` on widely-recognized metrics, such as those generated by [node_exporter](https://github.com/prometheus/node_exporter), which are typically challenging to monitor using static threshold-based alerting rules. This approach represents a paradigm shift from traditional [static threshold-based alerting rules](https://victoriametrics.com/blog/victoriametrics-anomaly-detection-handbook-chapter-1/#rule-based-alerting), focused on *raw metric values*, to *static* rules based on [`anomaly_scores`](./FAQ.md#what-is-anomaly-score). These scores offer a consistent, default threshold that remains stable over time, being adjusted for trends, seasonality, data scale, thus, reducing the engineering effort required for maintenance. Anomaly scores are produced by [machine learning models](./components/models.md), which are regularly retrained on varying time frames, ensuring alerts remain current and responsive to evolving data patterns. Additionally, **preset mode** minimizes user input needed to run the service. You can configure `vmanomaly` by specifying only the preset name and data sources in the [`reader`](./components/reader.md) and [`writer`](./components/writer.md) sections of the configuration file. All other parameters are already preconfigured. Available presets: - [Node-Exporter](#node-exporter) Here is an example config file to enable [Node-Exporter](#node-exporter) preset: ```yaml preset: "node-exporter" reader: datasource_url: "http://victoriametrics:8428/" # your datasource url # tenant_id: '0:0' # specify for cluster version writer: datasource_url: "http://victoriametrics:8428/" # your datasource url # tenant_id: '0:0' # specify for cluster version ``` Run a service using config file with one of the [available options](./QuickStart.md#how-to-install-and-run-vmanomaly). After you run `vmanomaly` with `preset` arg specified, available assets can be viewed, copied and downloaded at `http://localhost:8490/presets/` endpoint. ![preset-localhost](presets-localhost.webp) ## Node-Exporter > **Note: Preset assets can be also found [here](https://github.com/VictoriaMetrics/VictoriaMetrics/tree/master/deployment/docker/vmanomaly/vmanomaly-node-exporter-preset/)** For enabling Node-Exporter in config file use `preset` parameter: ```yaml preset: "node-exporter" ``` ### Generated anomaly scores Machine learning models will be fit for each timeseries, returned by underlying [MetricsQL](../MetricsQL.md) queries. Anomaly score metric labels will also contain [model classes](./components/models.md) and [schedulers](./components/scheduler.md) for labelset uniqueness. Here's an example of produced metrics: ```promtextmetric anomaly_score{for="cpu_seconds_total", instance="node-exporter:9100", preset="node-exporter", mode="system", model_alias="prophet", scheduler_alias="1d_1m"} 0.23451242720277776 anomaly_score{for="cpu_seconds_total", instance="node-exporter:9100", preset="node-exporter", mode="user", model_alias="prophet", scheduler_alias="1d_1m"} 0.2637952255694444 anomaly_score{for="page_faults", instance="node-exporter:9100", job="node-exporter", preset="node-exporter", model_alias="prophet", scheduler_alias="1d_1m"} 0.00593712535 anomaly_score{for="read_latency", instance="node-exporter:9100", preset="node-exporter", model_alias="mad", scheduler_alias="1d_1m"} 0.27773362795333334 anomaly_score{for="receive_bytes", instance="node-exporter:9100", preset="node-exporter", model_alias="mad", scheduler_alias="1d_1m"} 0.037753486136666674 anomaly_score{for="transmit_bytes", instance="node-exporter:9100", preset="node-exporter", model_alias="mad", scheduler_alias="1d_1m"} 0.17633085235 anomaly_score{for="write_latency", instance="node-exporter:9100", preset="node-exporter", model_alias="mad", scheduler_alias="1d_1m"} 0.019314370926666668 anomaly_score{for="cpu_seconds_total", instance="node-exporter:9100", preset="node-exporter", mode="idle", model_alias="mad", scheduler_alias="1d_1m"} 4.2323617935 anomaly_score{for="cpu_seconds_total", instance="node-exporter:9100", preset="node-exporter", mode="idle", model_alias="mad", scheduler_alias="2w_1m"} 1.5261359215 anomaly_score{for="cpu_seconds_total", instance="node-exporter:9100", preset="node-exporter", mode="idle", model_alias="prophet", scheduler_alias="2w_1m"} 0.5850743651 anomaly_score{for="cpu_seconds_total", instance="node-exporter:9100", preset="node-exporter", mode="idle", model_alias="z-score", scheduler_alias="1d_1m"} 1.6496064663 anomaly_score{for="cpu_seconds_total", instance="node-exporter:9100", preset="node-exporter", mode="idle", model_alias="z-score", scheduler_alias="2w_1m"} 0.924392581 anomaly_score{for="cpu_seconds_total", instance="node-exporter:9100", preset="node-exporter", mode="iowait", model_alias="mad", scheduler_alias="1d_1m"} 0.8571428657 ... ``` ### Alerts > For optimal alerting experience, we include [Awesome alerts](https://github.com/samber/awesome-prometheus-alerts) to cover indicators not addressed by the preset, as static thresholds can effectively complement our machine learning approach. > Provided `vmanomaly` alerts are set to fire only if *all anomaly detection models* vote that the datapoint is anomalous. You can find corresponding alerting rules here: - `vmanomaly` [Anomaly Detection alerts](https://github.com/VictoriaMetrics/VictoriaMetrics/tree/master/deployment/docker/vmanomaly/vmanomaly-node-exporter-preset/vmanomaly_alerts.yml): `http://localhost:8490/presets/vmanomaly_alerts.yml` - [Modified Awesome Alerts](https://github.com/VictoriaMetrics/VictoriaMetrics/tree/master/deployment/docker/vmanomaly/vmanomaly-node-exporter-preset/awesome_alerts.yml): `http://localhost:8490/presets/awesome_alerts.yml` #### Awesome Alerts replaced by Machine Learning alerts - HostMemoryUnderMemoryPressure - HostContextSwitching - HostHighCpuLoad - HostCpuIsUnderutilized - HostCpuStealNoisyNeighbor - HostCpuHighIowait - HostNetworkReceiveErrors - HostNetworkTransmitErrors - HostUnusualNetworkThroughputIn - HostUnusualNetworkThroughputOut ### Grafana dashboard Grafana dashboard `.json` file can be found [here](https://github.com/VictoriaMetrics/VictoriaMetrics/tree/master/deployment/docker/vmanomaly/vmanomaly-node-exporter-preset/dashboard.json): `http://localhost:8490/presets/dashboard.json` ### Indicators monitored by preset The produced anomaly scores will have a label `for` containing the name of corresponding indicator.
Indicator Based on metrics Description
`page_faults` `node_vmstat_pgmajfault` Number of major faults that have occurred since the last update. Major faults occur when a process tries to access a page in memory that is not currently mapped in the process's address space, and it requires loading data from the disk.
`context_switch` `node_context_switches_total` This metric represents the total number of context switches across all CPUs.
`cpu_seconds_total` `node_cpu_seconds_total` Total amount of CPU time consumed by the system in seconds by CPU processing mode (e.g., user, system, idle).
`host_network_receive_errors` & `host_network_transmit_errors` `node_network_receive_errs_total`, `node_network_receive_packets_total`, `node_network_transmit_errs_total`, `node_network_transmit_packets_total` Total number of errors encountered while receiving/transmitting packets on the network interfaces of a node.
`receive_bytes` & `transmit_bytes` `node_network_receive_bytes_total`, `node_network_transmit_bytes_total` Total number of bytes received/transmitted on network interfaces of a node.
`read_latency` & `write_latency` `node_disk_read_time_seconds_total`, `node_disk_reads_completed_total`, `node_disk_write_time_seconds_total`, `node_disk_writes_completed_total` Disk latency. The total read/write time spent in seconds. / The total number of reads/writes completed successfully.
## Example Here's how attached [Grafana dashboard](https://github.com/VictoriaMetrics/VictoriaMetrics/tree/master/deployment/docker/vmanomaly/vmanomaly-node-exporter-preset/dashboard.json) can be used to drill down anomalies: On the (global) graph **'Percentage of Anomalies'**, you can see a spike 8.75% of anomalies at the timestamp '2024-06-03 10:35:00'. The (global) graph **'Anomalies per Indicator'** shows the indicators that were anomalous at the corresponding time. ![global](presets_global_percentage.webp) At this timestamp on the **'Number of Anomalous Indicators by Node'** graph we can identify the node that had the most anomalies: `10.142.0.27` ![by_node](presets_anomalies_by_node.webp) Now you can select anomalous node to drill down further (local): ![anomalous_node_selection](presets_anomalous_node_selection.webp) For this node from the timestamp `2024-06-03 10:35:00` CPU time spent handling software interrupts started to grow. (`cpu_seconds_total{mode="softirq"}`) ![irq](presets_cpu_seconds_softirq.webp) At the same time `cpu_seconds_total` for `steal` mode started to grow as well. ![steal](presets_cpu_seconds_steal.webp)