VictoriaMetrics/docs/anomaly-detection/components/reader.md

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---
title: Reader
weight: 2
menu:
docs:
parent: "vmanomaly-components"
weight: 2
aliases:
- /anomaly-detection/components/reader.html
---
<!--
There are 4 sources available to read data into VM Anomaly Detection from: VictoriaMetrics, (ND)JSON file, QueryRange, or CSV file. Depending on the data source, different parameters should be specified in the config file in the `reader` section.
-->
VictoriaMetrics Anomaly Detection (`vmanomaly`) primarily uses [VmReader](#vm-reader) to ingest data. This reader focuses on fetching time-series data directly from VictoriaMetrics with the help of powerful [MetricsQL](https://docs.victoriametrics.com/metricsql/) expressions for aggregating, filtering and grouping your data, ensuring seamless integration and efficient data handling.
Future updates will introduce additional readers, expanding the range of data sources `vmanomaly` can work with.
## VM reader
> **Note**: Starting from [v1.13.0](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1130) there is backward-compatible change of [`queries`](https://docs.victoriametrics.com/anomaly-detection/components/reader?highlight=queries#vm-reader) arg of [VmReader](#vm-reader). New format allows to specify per-query parameters, like `step` to reduce amount of data read from VictoriaMetrics TSDB and to allow config flexibility. Please see [per-query parameters](#per-query-parameters) section for the details.
Old format like
```yaml
# other config sections ...
reader:
class: 'vm'
datasource_url: 'http://localhost:8428' # source victoriametrics/prometheus
sampling_period: "10s" # set it <= min(infer_every) in schedulers section
queries:
# old format {query_alias: query_expr}, prior to 1.13, will be converted to a new format automatically
vmb: 'avg(vm_blocks)'
```
will be converted to a new one with a warning raised in logs:
```yaml
# other config sections ...
reader:
class: 'vm'
datasource_url: 'http://localhost:8428' # source victoriametrics/prometheus
sampling_period: '10s'
queries:
# old format {query_alias: query_expr}, prior to 1.13, will be converted to a new format automatically
vmb:
expr: 'avg(vm_blocks)' # initial MetricsQL expression
step: '10s' # individual step for this query, will be filled with `sampling_period` from the root level
data_range: ['-inf', 'inf'] # by default, no constraints applied on data range
# new query-level arguments will be added in backward-compatible way in future releases
```
### Per-query parameters
Starting from [v1.13.0](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1130) there is change of [`queries`](https://docs.victoriametrics.com/anomaly-detection/components/reader?highlight=queries#vm-reader) arg format. Now each query alias supports the next (sub)fields:
- `expr` (string): MetricsQL/PromQL expression that defines an input for VmReader. As accepted by `/query_range?query=%s`. i.e. `avg(vm_blocks)`
- `step` (string): query-level frequency of the points returned, i.e. `30s`. Will be converted to `/query_range?step=%s` param (in seconds). Useful to optimize total amount of data read from VictoriaMetrics, where different queries may have **different frequencies for different [machine learning models](https://docs.victoriametrics.com/anomaly-detection/components/models)** to run on.
> **Note**: if not set explicitly (or if older config style prior to [v1.13.0](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1130)) is used, then it is set to reader-level `sampling_period` arg.
> **Note**: having **different** individual `step` args for queries (i.e. `30s` for `q1` and `2m` for `q2`) is not yet supported for [multivariate model](https://docs.victoriametrics.com/anomaly-detection/components/models/#multivariate-models) if you want to run it on several queries simultaneously (i.e. setting [`queries`](https://docs.victoriametrics.com/anomaly-detection/components/models/#queries) arg of a model to [`q1`, `q2`]).
- `data_range` (list[float | string]): Introduced in [v1.15.1](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1151), it allows defining **valid** data ranges for input per individual query in `queries`, resulting in:
- **High anomaly scores** (>1) when the *data falls outside the expected range*, indicating a data constraint violation.
- **Lowest anomaly scores** (=0) when the *model's predictions (`yhat`) fall outside the expected range*, meaning uncertain predictions.
- `max_points_per_query` (int): Introduced in [v1.17.0](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1170), optional arg overrides how `search.maxPointsPerTimeseries` flag (available since [v1.14.1](#v1141)) impacts `vmanomaly` on splitting long `fit_window` [queries](https://docs.victoriametrics.com/anomaly-detection/components/reader/?highlight=queries#vm-reader) into smaller sub-intervals. This helps users avoid hitting the `search.maxQueryDuration` limit for individual queries by distributing initial query across multiple subquery requests with minimal overhead. Set less than `search.maxPointsPerTimeseries` if hitting `maxQueryDuration` limits. If set on a query-level, it overrides the global `max_points_per_query` (reader-level).
### Per-query config example
```yaml
reader:
class: 'vm'
sampling_period: '1m'
max_points_per_query: 10000
# other reader params ...
queries:
ingestion_rate:
expr: 'sum(rate(vm_rows_inserted_total[5m])) by (type) > 0'
step: '2m' # overrides global `sampling_period` of 1m
data_range: [10, 'inf'] # meaning only positive values > 10 are expected, i.e. a value `y` < 10 will trigger anomaly score > 1
max_points_per_query: 5000 # overrides reader-level value of 10000 for `ingestion_rate` query
```
### Config parameters
<table class="params">
<thead>
<tr>
<th>Parameter</th>
<th>Example</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td>
`class`
</td>
<td>
`reader.vm.VmReader` (or `vm` starting from [v1.13.0](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1130))
</td>
<td>
Name of the class needed to enable reading from VictoriaMetrics or Prometheus. VmReader is the default option, if not specified.
</td>
</tr>
<tr>
<td>
`queries`
</td>
<td>
See [per-query config example](#per-query-config-example) above
</td>
<td>
See [per-query config section](#per-query-parameters) above
</td>
</tr>
<tr>
<td>
`datasource_url`
</td>
<td>
`http://localhost:8481/`
</td>
<td>
Datasource URL address
</td>
</tr>
<tr>
<td>
`tenant_id`
</td>
<td>
`0:0`, `multitenant`
</td>
<td>
For VictoriaMetrics Cluster version only, tenants are identified by `accountID` or `accountID:projectID`. Starting from [v1.16.2](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1162), `multitenant` [endpoint](https://docs.victoriametrics.com/cluster-victoriametrics/?highlight=reads#multitenancy-via-labels) is supported, to execute queries over multiple [tenants](https://docs.victoriametrics.com/cluster-victoriametrics/#multitenancy). See VictoriaMetrics Cluster [multitenancy docs](https://docs.victoriametrics.com/cluster-victoriametrics/#multitenancy)
</td>
</tr>
<tr>
<td>
`sampling_period`
</td>
<td>
`1h`
</td>
<td>
Frequency of the points returned. Will be converted to `/query_range?step=%s` param (in seconds). **Required** since [v1.9.0](https://docs.victoriametrics.com/anomaly-detection/changelog/#v190).
</td>
</tr>
<tr>
<td>
`query_range_path`
</td>
<td>
`/api/v1/query_range`
</td>
<td>
Performs PromQL/MetricsQL range query
</td>
</tr>
<tr>
<td>
`health_path`
</td>
<td>
`health`
</td>
<td>
Absolute or relative URL address where to check availability of the datasource.
</td>
</tr>
<tr>
<td>
`user`
</td>
<td>
`USERNAME`
</td>
<td>
BasicAuth username
</td>
</tr>
<tr>
<td>
`password`
</td>
<td>
`PASSWORD`
</td>
<td>
BasicAuth password
</td>
</tr>
<tr>
<td>
`timeout`
</td>
<td>
`30s`
</td>
<td>
Timeout for the requests, passed as a string
</td>
</tr>
<tr>
<td>
`verify_tls`
</td>
<td>
`false`
</td>
<td>
Verify TLS certificate. If `False`, it will not verify the TLS certificate.
If `True`, it will verify the certificate using the system's CA store.
If a path to a CA bundle file (like `ca.crt`), it will verify the certificate using the provided CA bundle.
</td>
</tr>
<tr>
<td>
`tls_cert_file`
</td>
<td>
`path/to/cert.crt`
</td>
<td>
Path to a file with the client certificate, i.e. `client.crt`. Available since [v1.16.3](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1163).
</td>
</tr>
<tr>
<td>
`tls_key_file`
</td>
<td>
`path/to/key.crt`
</td>
<td>
Path to a file with the client certificate key, i.e. `client.key`. Available since [v1.16.3](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1163).
</td>
</tr>
<tr>
<td>
`bearer_token`
</td>
<td>
`token`
</td>
<td>
Token is passed in the standard format with header: `Authorization: bearer {token}`
</td>
</tr>
<tr>
<td>
`bearer_token_file`
</td>
<td>
`path_to_file`
</td>
<td>
Path to a file, which contains token, that is passed in the standard format with header: `Authorization: bearer {token}`. Available since [v1.15.9](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1159)
</td>
</tr>
<tr>
<td>
`extra_filters`
</td>
<td>
`[]`
</td>
<td>
List of strings with series selector. See: [Prometheus querying API enhancements](https://docs.victoriametrics.com/##prometheus-querying-api-enhancements)
</td>
</tr>
<tr>
<td>
`query_from_last_seen_timestamp`
</td>
<td>
`False`
</td>
<td>
If True, then query will be performed from the last seen timestamp for a given series. If False, then query will be performed from the start timestamp, based on a schedule period. Defaults to `False`. Useful for `infer` stages in case there were skipped `infer` calls prior to given.
</td>
</tr>
<tr>
<td>
`latency_offset`
</td>
<td>
`1ms`
</td>
<td>
Introduced in [v1.15.1](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1151), it allows overriding the default `-search.latencyOffset` [flag of VictoriaMetrics](https://docs.victoriametrics.com/?highlight=search.latencyOffset#list-of-command-line-flags) (30s). The default value is set to 1ms, which should help in cases where `sampling_frequency` is low (10-60s) and `sampling_frequency` equals `infer_every` in the [PeriodicScheduler](https://docs.victoriametrics.com/anomaly-detection/components/scheduler/?highlight=infer_every#periodic-scheduler). This prevents users from receiving `service - WARNING - [Scheduler [scheduler_alias]] No data available for inference.` warnings in logs and allows for consecutive `infer` calls without gaps. To restore the old behavior, set it equal to your `-search.latencyOffset` [flag value]((https://docs.victoriametrics.com/?highlight=search.latencyOffset#list-of-command-line-flags)).
</td>
</tr>
<tr>
<td>
`max_points_per_query`
</td>
<td>
`10000`
</td>
<td>
Introduced in [v1.17.0](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1170), optional arg overrides how `search.maxPointsPerTimeseries` flag (available since [v1.14.1](#v1141)) impacts `vmanomaly` on splitting long `fit_window` [queries](https://docs.victoriametrics.com/anomaly-detection/components/reader/?highlight=queries#vm-reader) into smaller sub-intervals. This helps users avoid hitting the `search.maxQueryDuration` limit for individual queries by distributing initial query across multiple subquery requests with minimal overhead. Set less than `search.maxPointsPerTimeseries` if hitting `maxQueryDuration` limits. You can also set it on [per-query](#per-query-parameters) basis to override this global one.
</td>
</tr>
</tbody>
</table>
Config file example:
```yaml
reader:
class: "vm" # or "reader.vm.VmReader" until v1.13.0
datasource_url: "https://play.victoriametrics.com/"
tenant_id: "0:0"
queries:
ingestion_rate:
expr: 'sum(rate(vm_rows_inserted_total[5m])) by (type) > 0'
step: '1m' # can override global `sampling_period` on per-query level
data_range: [0, 'inf']
sampling_period: '1m'
query_from_last_seen_timestamp: True # false by default
latency_offset: '1ms'
```
### mTLS protection
As of [v1.16.3](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1163), `vmanomaly` supports [mutual TLS (mTLS)](https://en.wikipedia.org/wiki/Mutual_authentication) for secure communication across its components, including [VmReader](https://docs.victoriametrics.com/anomaly-detection/components/reader/#vm-reader), [VmWriter](https://docs.victoriametrics.com/anomaly-detection/components/writer/#vm-writer), and [Monitoring/Push](https://docs.victoriametrics.com/anomaly-detection/components/monitoring/#push-config-parameters). This allows for mutual authentication between the client and server when querying or writing data to [VictoriaMetrics Enterprise, configured for mTLS](https://docs.victoriametrics.com/#mtls-protection).
mTLS ensures that both the client and server verify each other's identity using certificates, which enhances security by preventing unauthorized access.
To configure mTLS, the following parameters can be set in the [config](#config-parameters):
- `verify_tls`: If set to a string, it functions like the `-mtlsCAFile` command-line argument of VictoriaMetrics, specifying the CA bundle to use. Set to `True` to use the system's default certificate store.
- `tls_cert_file`: Specifies the path to the client certificate, analogous to the `-tlsCertFile` argument of VictoriaMetrics.
- `tls_key_file`: Specifies the path to the client certificate key, similar to the `-tlsKeyFile` argument of VictoriaMetrics.
These options allow you to securely interact with mTLS-enabled VictoriaMetrics endpoints.
Example configuration to enable mTLS with custom certificates:
```yaml
reader:
class: "vm"
datasource_url: "https://your-victoriametrics-instance-with-mtls"
# tenant_id: "0:0" uncomment and set for cluster version
queries:
vm_blocks_example:
expr: 'avg(rate(vm_blocks[5m]))'
step: 30s
sampling_period: 30s
verify_tls: "path/to/ca.crt" # path to CA bundle for TLS verification
tls_cert_file: "path/to/client.crt" # path to the client certificate
tls_key_file: "path/to/client.key" # path to the client certificate key
# additional reader parameters ...
# other config sections, like models, schedulers, writer, ...
```
### Healthcheck metrics
`VmReader` exposes [several healthchecks metrics](https://docs.victoriametrics.com/anomaly-detection/components/monitoring/#reader-behaviour-metrics).