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### Describe Your Changes - change links from relative to absolute under Anomaly Detection section ### Checklist The following checks are **mandatory**: - [x] My change adheres [VictoriaMetrics contributing guidelines](https://docs.victoriametrics.com/contributing/).
90 lines
5.2 KiB
Markdown
90 lines
5.2 KiB
Markdown
This chapter describes different components, that correspond to respective sections of a config to launch VictoriaMetrics Anomaly Detection (or simply [`vmanomaly`](https://docs.victoriametrics.com/anomaly-detection/overview/)) service:
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- [Model(s) section](https://docs.victoriametrics.com/anomaly-detection/components/models/) - Required
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- [Reader section](https://docs.victoriametrics.com/anomaly-detection/components/reader/) - Required
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- [Scheduler(s) section](https://docs.victoriametrics.com/anomaly-detection/components/scheduler/) - Required
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- [Writer section](https://docs.victoriametrics.com/anomaly-detection/components/writer/) - Required
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- [Monitoring section](https://docs.victoriametrics.com/anomaly-detection/components/monitoring/) - Optional
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> **Note**: starting from [v1.7.0](https://docs.victoriametrics.com/anomaly-detection/changelog/#v172), once the service starts, automated config validation is performed. Please see container logs for errors that need to be fixed to create fully valid config, visiting sections above for examples and documentation.
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> **Note**: starting from [v1.13.0](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1130), components' class can be referenced by a short alias instead of a full class path - i.e. `model.zscore.ZscoreModel` becomes `zscore`, `reader.vm.VmReader` becomes `vm`, `scheduler.periodic.PeriodicScheduler` becomes `periodic`, etc. Please see according sections for the details.
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> **Note:** Starting from [v1.13.0](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1130) `preset` modes are available for `vmanomaly`. Please find the guide [here](https://docs.victoriametrics.com/anomaly-detection/presets/).
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Below, you will find an example illustrating how the components of `vmanomaly` interact with each other and with a single-node VictoriaMetrics setup.
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> **Note**: [Reader](https://docs.victoriametrics.com/anomaly-detection/components/reader/#vm-reader) and [Writer](https://docs.victoriametrics.com/anomaly-detection/components/writer/#vm-writer) also support [multitenancy](https://docs.victoriametrics.com/cluster-victoriametrics/#multitenancy), so you can read/write from/to different locations - see `tenant_id` param description.
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Here's a minimalistic full config example, demonstrating many-to-many configuration (actual for [latest version](https://docs.victoriametrics.com/anomaly-detection/changelog/)):
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```yaml
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# how and when to run the models is defined by schedulers
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# https://docs.victoriametrics.com/anomaly-detection/components/scheduler/
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schedulers:
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periodic_1d: # alias
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class: 'periodic' # scheduler class
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infer_every: "30s"
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fit_every: "10m"
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fit_window: "24h"
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periodic_1w:
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class: 'periodic'
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infer_every: "15m"
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fit_every: "1h"
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fit_window: "7d"
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# what model types and with what hyperparams to run on your data
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# https://docs.victoriametrics.com/anomaly-detection/components/models/
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models:
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zscore: # we can set up alias for model
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class: 'zscore' # model class
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z_threshold: 3.5
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provide_series: ['anomaly_score'] # what series to produce
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queries: ['host_network_receive_errors'] # what queries to run particular model on
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schedulers: ['periodic_1d'] # will be attached to 1-day schedule, fit every 10m and infer every 30s
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min_dev_from_expected: 0.0 # turned off. if |y - yhat| < min_dev_from_expected, anomaly score will be 0
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detection_direction: 'above_expected' # detect anomalies only when y > yhat, "peaks"
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prophet: # we can set up alias for model
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class: 'prophet'
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provide_series: ['anomaly_score', 'yhat', 'yhat_lower', 'yhat_upper']
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queries: ['cpu_seconds_total']
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schedulers: ['periodic_1w'] # will be attached to 1-week schedule, fit every 1h and infer every 15m
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min_dev_from_expected: 0.01 # if |y - yhat| < 0.01, anomaly score will be 0
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detection_direction: 'above_expected'
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args: # model-specific arguments
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interval_width: 0.98
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# where to read data from
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# https://docs.victoriametrics.com/anomaly-detection/components/reader/#vm-reader
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reader:
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datasource_url: "https://play.victoriametrics.com/"
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tenant_id: "0:0"
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class: 'vm'
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sampling_period: "30s" # what data resolution to fetch from VictoriaMetrics' /query_range endpoint
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latency_offset: '1ms'
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query_from_last_seen_timestamp: False
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queries: # aliases to MetricsQL expressions
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cpu_seconds_total:
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expr: 'avg(rate(node_cpu_seconds_total[5m])) by (mode)'
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# step: '30s' # if not set, will be equal to sampling_period
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data_range: [0, 'inf'] # expected value range, anomaly_score > 1 if y (real value) is outside
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host_network_receive_errors:
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expr: 'rate(node_network_receive_errs_total[3m]) / rate(node_network_receive_packets_total[3m])'
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step: '15m' # here we override per-query `sampling_period` to request way less data from VM TSDB
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data_range: [0, 'inf']
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# where to write data to
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# https://docs.victoriametrics.com/anomaly-detection/components/writer/
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writer:
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datasource_url: "http://victoriametrics:8428/"
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# enable self-monitoring in pull and/or push mode
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# https://docs.victoriametrics.com/anomaly-detection/components/monitoring/
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monitoring:
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pull: # Enable /metrics endpoint.
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addr: "0.0.0.0"
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port: 8490
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```
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