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

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title	weight	menu	tags	aliases
Reader	2	docs parent weight vmanomaly-components 2	metrics enterprise	/anomaly-detection/components/reader.html

VictoriaMetrics Anomaly Detection (vmanomaly) primarily uses VmReader to ingest data. This reader focuses on fetching time-series data directly from VictoriaMetrics with the help of powerful 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

There is backward-compatible change{{% available_from "v1.13.0" anomaly %}} of queries arg of VmReader. 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 section for the details.

Old format like

# 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:

# 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
      tz: 'UTC'  # by default, tz-free data is used throughout the model lifecycle
      # new query-level arguments will be added in backward-compatible way in future releases

Per-query parameters

There is change{{% available_from "v1.13.0" anomaly %}} of queries arg format. Now each query alias supports the next (sub)fields, which override reader-level parameters, if set:

• 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 to run on.

  If not set explicitly (or if older config style prior to v1.13.0) is used, then it is set to reader-level sampling_period arg.

  Having different individual step args for queries (i.e. 30s for q1 and 2m for q2) is not yet supported for multivariate model if you want to run it on several queries simultaneously (i.e. setting queries arg of a model to [q1, q2]).

• data_range{{% available_from "v1.15.1" anomaly %}} (list[float | string]): 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 range constraint violation (e.g. improperly configured metricsQL query, sensor malfunction, overflows in underlying metrics, etc.). Anomaly scores can be set to a specific value, like 5, to indicate a strong violation, using the anomaly_score_outside_data_range arg of a respective model this query is used in.
  • Lowest anomaly scores (=0) when the model's predictions (yhat) fall outside the expected range, meaning uncertain predictions that does not really aligh with the data.

  Works together with anomaly_score_outside_data_range arg of a model to determine the anomaly score for such cases as well as with clip_predictions arg of a model to clip the predictions to the expected range.

  If not set explicitly (or if older config style prior to v1.13.0) is used, then it is set to reader-level data_range arg{{% available_from "v1.18.1" anomaly %}}

• max_points_per_query{{% available_from "v1.17.0" anomaly %}} (int): Optional arg, overrides how search.maxPointsPerTimeseries flag{{% available_from "v1.14.1" anomaly %}} impacts vmanomaly on splitting long fit_window queries 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).

• tz{{% available_from "v1.18.0" anomaly %}} (string): this optional argument enables timezone specification per query, overriding the reader’s default tz. This setting helps to account for local timezone shifts, such as DST, in models that are sensitive to seasonal variations (e.g., ProphetModel or OnlineQuantileModel).

• tenant_id {{% available_from "v1.19.0" anomaly %}} (string): this optional argument enables tenant-level separation for queries (e.g. query1 to get the data from tenant "0:0", query2 - from tenant "1:0"). It works as follows:

  • if not set, inherits reader-level tenant_id
  • if set, overrides reader-level tenant_id
  • raises config validation error, if reader-level is not set and query-level is found (mixing of VictoriaMetrics single-node and cluster is prohibited in a single config)
  • raises config validation warning, if writer.tenant_id is not explicitly set to multitenant when reader uses tenants, meaning VictoriaMetrics cluster will be used for data querying.
  • also raises config validation error if a set of reader.queries for multivariate models has different tenant_ids (meaning tenant data is mixed, and special labels like vm_project_id, vm_account_id will have ambiguous values)

  The recommended approach for using per-query tenant_ids is to set both reader.tenant_id and writer.tenant_id to multitenant. See this section for more details. Configurations where reader.tenant_id equals writer.tenant_id and is not multitenant are also considered safe, provided there is a single, DISTINCT tenant_id defined in the reader (either at the reader level or the query level, if set).

Per-query config example

reader:
  class: 'vm'
  sampling_period: '1m'
  max_points_per_query: 10000
  data_range: [0, 'inf']
  tenant_id: 'multitenant'
  # other reader params ...
  queries:
    ingestion_rate_t1:
      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
      tz: 'America/New_York'  # to override reader-wise `tz`
      tenant_id: '1:0'  # overriding tenant_id to isolate data
    ingestion_rate_t2:
      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
      tz: 'America/New_York'  # to override reader-wise `tz`
      tenant_id: '2:0'  # overriding tenant_id to isolate data

Config parameters

Parameter	Example	Description
class	reader.vm.VmReader (or vm{{% available_from "v1.13.0" anomaly %}})	Name of the class needed to enable reading from VictoriaMetrics or Prometheus. VmReader is the default option, if not specified.
queries	See per-query config example above	See per-query config section above
datasource_url	http://localhost:8481/	Datasource URL address
tenant_id	0:0, multitenant	For VictoriaMetrics Cluster version only, tenants are identified by accountID or accountID:projectID. Starting from v1.16.2, multitenant endpoint is supported, to execute queries over multiple tenants. See VictoriaMetrics Cluster multitenancy docs
sampling_period	1h	Frequency of the points returned. Will be converted to /query_range?step=%s param (in seconds). Required since v1.9.0.
query_range_path	/api/v1/query_range	Performs PromQL/MetricsQL range query
health_path	health	Absolute or relative URL address where to check availability of the datasource.
user	USERNAME	BasicAuth username
password	PASSWORD	BasicAuth password
timeout	30s	Timeout for the requests, passed as a string
verify_tls	false	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.
tls_cert_file	path/to/cert.crt	Path to a file with the client certificate, i.e. client.crt{{% available_from "v1.16.3" anomaly %}}.
tls_key_file	path/to/key.crt	Path to a file with the client certificate key, i.e. client.key{{% available_from "v1.16.3" anomaly %}}.
bearer_token	token	Token is passed in the standard format with header: Authorization: bearer {token}
bearer_token_file	path_to_file	Path to a file, which contains token, that is passed in the standard format with header: Authorization: bearer {token}{{% available_from "v1.15.9" anomaly %}}.
extra_filters	[]	List of strings with series selector. See: Prometheus querying API enhancements
query_from_last_seen_timestamp	False	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.
latency_offset	1ms	It allows overriding the default -search.latencyOffset{{% available_from "v1.15.1" anomaly %}} flag of VictoriaMetrics (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. 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.
max_points_per_query	10000	Optional arg{{% available_from "v1.17.0" anomaly %}} overrides how search.maxPointsPerTimeseries flag{{% available_from "v1.14.1" anomaly %}} impacts vmanomaly on splitting long fit_window queries 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 basis to override this global one.
tz	UTC	Optional argument{{% available_from "v1.18.0" anomaly %}} specifies the IANA timezone to account for local shifts, like DST, in models sensitive to seasonal patterns (e.g., ProphetModel or OnlineQuantileModel). Defaults to UTC if not set and can be overridden on a per-query basis.
data_range	["-inf", "inf"]	Optional argument{{% available_from "v1.18.1" anomaly %}} allows defining valid data ranges for input of all the queries in queries. Defaults to ["-inf", "inf"] if not set and can be overridden on a per-query basis.

Config file example:

reader:
  class: "vm"  # or "reader.vm.VmReader" until v1.13.0
  datasource_url: "https://play.victoriametrics.com/"
  tenant_id: '0:0'
  tz: 'America/New_York'
  data_range: [1, 'inf']  # reader-level
  queries:
    ingestion_rate:
      expr: 'sum(rate(vm_rows_inserted_total[5m])) by (type) > 0'
      step: '1m' # can override reader-level `sampling_period` on per-query level
      data_range: [0, 'inf']  # if set, overrides reader-level data_range
      tz: 'Australia/Sydney'  # if set, overrides reader-level tz
      # tenant_id: '1:0'  # if set, overrides reader-level tenant_id
  sampling_period: '1m'
  query_from_last_seen_timestamp: True  # false by default
  latency_offset: '1ms'

mTLS protection

vmanomaly supports mutual TLS (mTLS){{% available_from "v1.16.3" anomaly %}} for secure communication across its components, including VmReader, VmWriter, and Monitoring/Push. This allows for mutual authentication between the client and server when querying or writing data to VictoriaMetrics Enterprise, configured for mTLS.

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:

• 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:

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.