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235 lines
10 KiB
Markdown
235 lines
10 KiB
Markdown
---
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sort: 3
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weight: 1
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title: Presets
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menu:
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docs:
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parent: "anomaly-detection"
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weight: 1
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title: Presets
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---
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> Please check the [Quick Start Guide](./QuickStart.md) to install and run `vmanomaly`
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> Presets are available starting from [v1.13.0](./CHANGELOG.md#v1130)
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**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.
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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.
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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.
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Available presets:
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- [Node-Exporter](#node-exporter)
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Here is an example config file to enable [Node-Exporter](#node-exporter) preset:
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```yaml
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preset: "node-exporter"
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reader:
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datasource_url: "http://victoriametrics:8428/" # your datasource url
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# tenant_id: '0:0' # specify for cluster version
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writer:
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datasource_url: "http://victoriametrics:8428/" # your datasource url
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# tenant_id: '0:0' # specify for cluster version
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```
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Run a service using config file with one of the [available options](./QuickStart.md#how-to-install-and-run-vmanomaly).
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After you run `vmanomaly` with `preset` arg specified, available assets can be viewed, copied and downloaded at `http://localhost:8490/presets/` endpoint.
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## Node-Exporter
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> **Note: Preset assets can be also found [here](https://github.com/VictoriaMetrics/VictoriaMetrics/tree/master/deployment/docker/vmanomaly/vmanomaly-node-exporter-preset/)**
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For enabling Node-Exporter in config file use `preset` parameter:
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```yaml
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preset: "node-exporter"
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```
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### Generated anomaly scores
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Machine learning models will be fit for each timeseries, returned by underlying [MetricsQL](../MetricsQL.md) queries.
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Anomaly score metric labels will also contain [model classes](./components/models.md) and [schedulers](./components/scheduler.md) for labelset uniqueness.
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Here's an example of produced metrics:
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```promtextmetric
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anomaly_score{for="cpu_seconds_total", instance="node-exporter:9100", preset="node-exporter", mode="system", model_alias="prophet", scheduler_alias="1d_1m"} 0.23451242720277776
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anomaly_score{for="cpu_seconds_total", instance="node-exporter:9100", preset="node-exporter", mode="user", model_alias="prophet", scheduler_alias="1d_1m"} 0.2637952255694444
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anomaly_score{for="page_faults", instance="node-exporter:9100", job="node-exporter", preset="node-exporter", model_alias="prophet", scheduler_alias="1d_1m"} 0.00593712535
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anomaly_score{for="read_latency", instance="node-exporter:9100", preset="node-exporter", model_alias="mad", scheduler_alias="1d_1m"} 0.27773362795333334
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anomaly_score{for="receive_bytes", instance="node-exporter:9100", preset="node-exporter", model_alias="mad", scheduler_alias="1d_1m"} 0.037753486136666674
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anomaly_score{for="transmit_bytes", instance="node-exporter:9100", preset="node-exporter", model_alias="mad", scheduler_alias="1d_1m"} 0.17633085235
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anomaly_score{for="write_latency", instance="node-exporter:9100", preset="node-exporter", model_alias="mad", scheduler_alias="1d_1m"} 0.019314370926666668
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anomaly_score{for="cpu_seconds_total", instance="node-exporter:9100", preset="node-exporter", mode="idle", model_alias="mad", scheduler_alias="1d_1m"} 4.2323617935
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anomaly_score{for="cpu_seconds_total", instance="node-exporter:9100", preset="node-exporter", mode="idle", model_alias="mad", scheduler_alias="2w_1m"} 1.5261359215
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anomaly_score{for="cpu_seconds_total", instance="node-exporter:9100", preset="node-exporter", mode="idle", model_alias="prophet", scheduler_alias="2w_1m"} 0.5850743651
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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
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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
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anomaly_score{for="cpu_seconds_total", instance="node-exporter:9100", preset="node-exporter", mode="iowait", model_alias="mad", scheduler_alias="1d_1m"} 0.8571428657
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...
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```
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### Alerts
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> 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.
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> Provided `vmanomaly` alerts are set to fire only if *all anomaly detection models* vote that the datapoint is anomalous.
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You can find corresponding alerting rules here:
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- `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`
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- [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`
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#### Awesome Alerts replaced by Machine Learning alerts
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- HostMemoryUnderMemoryPressure
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- HostContextSwitching
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- HostHighCpuLoad
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- HostCpuIsUnderutilized
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- HostCpuStealNoisyNeighbor
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- HostCpuHighIowait
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- HostNetworkReceiveErrors
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- HostNetworkTransmitErrors
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- HostUnusualNetworkThroughputIn
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- HostUnusualNetworkThroughputOut
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### Grafana dashboard
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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`
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### Indicators monitored by preset
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The produced anomaly scores will have a label `for` containing the name of corresponding indicator.
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<table class="params">
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<thead>
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<tr>
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<th>Indicator</th>
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<th>Based on metrics</th>
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<th>Description</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td>
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`page_faults`
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</td>
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<td>
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`node_vmstat_pgmajfault`
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</td>
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<td>
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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.
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</td>
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</tr>
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<tr>
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<td>
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`context_switch`
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</td>
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<td>
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`node_context_switches_total`
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</td>
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<td>
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This metric represents the total number of context switches across all CPUs.
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</td>
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</tr>
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<tr>
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<td>
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`cpu_seconds_total`
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</td>
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<td>
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`node_cpu_seconds_total`
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</td>
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<td>
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Total amount of CPU time consumed by the system in seconds by CPU processing mode (e.g., user, system, idle).
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</td>
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</tr>
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<tr>
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<td>
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`host_network_receive_errors` & `host_network_transmit_errors`
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</td>
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<td>
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`node_network_receive_errs_total`,
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`node_network_receive_packets_total`,
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`node_network_transmit_errs_total`,
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`node_network_transmit_packets_total`
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<td>
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Total number of errors encountered while receiving/transmitting packets on the network interfaces of a node.
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</td>
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</tr>
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<tr>
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<td>
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`receive_bytes` & `transmit_bytes`
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</td>
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<td>
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`node_network_receive_bytes_total`,
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`node_network_transmit_bytes_total`
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</td>
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<td>
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Total number of bytes received/transmitted on network interfaces of a node.
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</td>
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</tr>
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<tr>
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<td>
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`read_latency` & `write_latency`
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</td>
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<td>
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`node_disk_read_time_seconds_total`,
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`node_disk_reads_completed_total`,
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`node_disk_write_time_seconds_total`,
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`node_disk_writes_completed_total`
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</td>
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<td>
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Disk latency. The total read/write time spent in seconds. / The total number of reads/writes completed successfully.
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</td>
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</tr>
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</tbody>
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</table>
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## Example
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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:
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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.
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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`
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Now you can select anomalous node to drill down further (local):
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For this node from the timestamp `2024-06-03 10:35:00` CPU time spent handling software interrupts started to grow.
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(`cpu_seconds_total{mode="softirq"}`)
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At the same time `cpu_seconds_total` for `steal` mode started to grow as well.
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