Monitors as Code

Overview

Monte Carlo developed a YAML-based monitors configuration to help teams deploy monitors as part of their CI/CD process. The following guide explains how to get started with monitors as code.

Prerequisites

1. Install the CLI — https://docs.getmontecarlo.com/docs/using-the-cli
2. When running montecarlo configure, provide your API key and you may leave the AWS settings blank

Using code to define monitors

First, you will need to create a Monte Carlo project. A Monte Carlo project is simply a directory which contains a montecarlo.yml file, which contains project-level configuration options. If you are using DBT, we recommend placing montecarlo.yml in the same directory as dbt_project.yml.

The montecarlo.yml format:

version: 1
default_resource: <string>
include_file_patterns:
  - <string>
exclude_file_patterns:
  - <string>
namespace: <string - optional> 

Description of options inside the montecarlo.yml file:

• version: The version of MC configuration. Set to 1
• default_resource: The warehouse friendly name or UUID where YAML-defined monitors will be created.
  • If your account only has a single warehouse configured, MC will use this warehouse by default, and this option does not need to be defined.
  • If you have multiple warehouses configured, you will need to either (1) define default_resource, or (2) specify the warehouse friendly name or UUID for each monitor explicitly in the resource property (see YAML format for configuring monitors below).
• include_file_patterns: List of file patterns to include when searching for monitor configuration files. By default, this is set to **/*.yaml and **/*.yml . With these defaults, MC will search recursively for all directories nested within the project directory for any files with a yaml or yml extension.
• exclude_file_patterns: List of file patterns to exclude when searching for monitor configuration files.

Example montecarlo.yml configuration file, which should be sufficient for customers with a single warehouse:

version: 1

Example montecarlo.yml configuration file, for customers with multiple warehouses configured.

version: 1
default_resource: bigquery

Defining individual monitors

📘

Define monitors in separate YML files than montecarlo.yml

Your montecarlo.yml file should only be used to define project-level configuration options. Use separate YML files to define individual monitors.

Monitors are defined in YAML files within directories nested within the Monte Carlo project. Monitors can be configured in standalone YAML files, or embedded within DBT schema.yml files within the meta property of a DBT model definition.

Monitor definitions inside of yml files must conform to the expected format in order to be processed successfully by the CLI. Some example monitor configurations, defining the most basic options can be found below.

For an exhaustive list of configuration options and their definitions, refer to the Monitor configuration reference section below.

Example of a standalone monitor definition:

namespace: <string - optional>
montecarlo:
  field_health:
    - table: project:dataset.table_name
      timestamp_field: created
  dimension_tracking:
    - table: project:dataset.table_name
      timestamp_field: created
      field: order_status

Example of monitor embedded within a DBT schema.yml file:

version: 2

models:
  - name: table_name
    description: My table
    meta:
      montecarlo:
        field_health:
          - table: project:dataset.table_name
            timestamp_field: created
        dimension_tracking:
          - table: '[[ ref("table_name") ]]'
            name: dimension_tracking_for_table_name
            description: 'Tracks dimensions for [[ ref("table_name") ]]'
            timestamp_field: created
            field: order_status

dbt ref resolution

In the snippet above, you can see an example of resolving dbt ref("<model_name>") notation for any string field within Monte Carlo monitor configs. Just wrap the ref in [[ ]] and make sure to quote the string as in the example above. To resolve refs, you must pass the --dbt-manifest <path_to_dbt_manifest> argument to the CLI when applying the monitor config with the path to your dbt manifest.json file (by default created in the target/ directory after running dbt commands like compile or run). Each dbt ref will be looked up in the manifest and replaced with the full table name. To use this feature you must be on version 0.42.0 or newer of the Monte Carlo CLI.

📘

Tip: Using monitors as code with DBT

If your organization already uses DBT, you may find that embedding monitor configurations within DBT schema.yml files may make maintenance easier, as all configuration/metadata concerning a given table are maintained in the same location. For an example DBT repo with some basic monitor configuration, click here.

Shortcut to building monitor configuration

If you want to get a head start on setting up monitors as code, visit the monitor details page of an existing monitor where you can find the YAML definition for that monitor's configuration.

Below is a screenshot of the YAML definition for an existing monitor within Monte Carlo.

Developing and testing locally

Once the Monte Carlo project is setup with a montecarlo.yml and at least one monitor definition in a separate .yml file, you can use the Monte Carlo CLI to apply them.

To apply monitor configuration to MC run:

montecarlo monitors apply --namespace <namespace>

Note in the above command, a namespace parameter is supplied. Namespaces are required for all monitors configured via Monitors as Code as they make it easier to organize and manage monitors at scale. Namespaces can either be defined by passing a --namespace argument in the CLI command or defined within the .yml files (see above for more details).

The apply command behaves as follows:

1. MC will search for all monitor configuration elements in the project, both in standalone and embedded in DBT schema files. All monitor configuration elements will be concatenated into a single configuration template.
2. MC will apply the configuration template to your MC account:
   1. Any new monitors defined since last apply will be created
   2. Any previously defined monitors present in current configuration template will be updated if any attributes have changed
   3. Any previously defined monitors absent from current configuration template will be deleted

Namespaces

📘

You can think of namespaces like Cloudformation stack names. It’s a logical separation of a collection of resources that you can define. Monitors from different namespaces are isolated from each other.

Some examples of why this is useful -

1. You have multiple people (or teams) working on managing monitors and don’t want to conflict or override configurations.
2. You want to manage different groups monitors in different pipelines (e.g. dbt models in CI/CD x & non-dbt models in CI/CD y).

Namespaces can also be defined within the montecarlo.yml file or within individual monitor definitions.

Namespaces are currently limited to 2000 resources. Support for this starts in CLI version 0.52.0, so please upgrade your CLI version if you are running into timeouts while applying changes.

To delete (destroy) a namespace:

montecarlo monitors delete --namespace <namespace>

This will delete all monitors for a given namespace.

Dry Runs

👍

The apply command also supports a --dry-run argument which will dry run the configuration update and report each operation. Using this argument just shows planned changes but doesn't apply them.

Integrating into your CI/CD pipeline

Deploying monitors within a continuous integration pipeline is straightforward. Once changes are merged into your main production branch, configure your CI pipeline to install the montecarlodata CLI:

pip install montecarlodata

And run this command:

MCD_DEFAULT_API_ID=${MCD_DEFAULT_API_ID} \
    MCD_DEFAULT_API_TOKEN=${MCD_DEFAULT_API_TOKEN} \
    montecarlo monitors apply \
            --namespace ${MC_MONITORS_NAMESPACE} \
            --project-dir ${PROJECT_DIR} \
                         --auto-yes

📘

Skipping confirmations

The --auto-yes option will disable any confirmation prompts shown by the command and should only be used on CI, where there's no interaction. Otherwise we recommend not including the --auto-yes so you can review what changes will be applied before confirming.

These environment variables need to be populated:

• MCD_DEFAULT_API_ID: Your Monte Carlo API ID
• MCD_DEFAULT_API_TOKEN: Your Monte Carlo API Token
• MC_MONITORS_NAMESPACE: Namespace to apply monitor configuration to
• PROJECT_DIR: If the montecarlo command is not run within your Monte Carlo project directory, you can specify it here. Optional.

Example projects with CI/CD

For some examples of monitors as code projects with CI/CD setup, checkout these repositories:

• Standalone Monte Carlo project
• Basic DBT project with monitors (Gitlab)
• Basic DBT project with monitors (Github)

Monitor configuration reference

montecarlo:
  field_health:
    - table: <string>  # required
      name: <string> # optional -- by default it will be autogenerated
      description: <string>
      fields:
        - <string>
      use_important_fields: <bool> # optional -- by default, do not use important fields
      segmented_expressions:
        - <string> # Can be a field or a SQL expression
      timestamp_field: <string>
      timestamp_field_expression: <string>
      where_condition: <string>
      lookback_days: <int>
      aggregation_time_interval: <one of 'day' or 'hour'>
      schedule:  # optional -- by default, loose schedule with interval_minutes=720 (12h)
        type: <loose, fixed, or dynamic>  # required
        interval_minutes: <integer>  # required if loose or fixed
        start_time: <date as isoformatted string>  # required if fixed
        timezone: <timezone> # optional - select regional timezone for daylight savings ex. America/Los_Angeles
      labels:
        - <string>
  dimension_tracking:
    - table: <string>  # required
      name: <string> # optional -- by default it will be autogenerated
      description: <string>
      field: <string>  # required
      timestamp_field: <string>
      timestamp_field_expression: <string>
      where_condition: <string>
      lookback_days: <int>
      aggregation_time_interval: <one of 'day' or 'hour'>
      schedule:  # optional -- by default, loose schedule with interval_minutes=720 (12h)
        type: <loose, fixed, or dynamic>  # required
        interval_minutes: <integer>  # required if loose or fixed
        start_time: <date as isoformatted string>  # required if fixed
        timezone: <timezone> # optional - select regional timezone for daylight savings ex. America/Los_Angeles
      labels:
        - <string>
  json_schema:
    - table: <string>  # required
      name: <string> # optional -- by default it will be autogenerated
      description: <string>
      field: <string>  # required
      timestamp_field: <string>
      timestamp_field_expression: <string>
      where_condition: <string>
      schedule:  # optional -- by default, loose schedule with interval_minutes=720 (12h)
        type: <loose, fixed, or dynamic>  # required
        interval_minutes: <integer>  # required if loose or fixed
        start_time: <date as isoformatted string>  # required if fixed
        timezone: <timezone> # optional - select regional timezone for daylight savings ex. America/Los_Angeles
      labels:
        - <string>
  custom_sql:
    - sql: <string>  # required
      name: <string> # optional -- by default it will be autogenerated
      comparisons: <comparison>  # required
      variables: <variable values>
      description: <string>
      notes: <string>
      schedule:
        type: <string>  # must be 'fixed' or 'manual'
        start_time: <date as isoformatted string>
        interval_minutes: <integer> 
        interval_crontab: 
          - <string>
        timezone: <timezone> # optional - select regional timezone for daylight savings ex. America/Los_Angeles
          - <string>
      event_rollup_count: <integer> # optional - enable to only send notifications every X incidents
      event_rollup_until_changed: <boolean> # optional - enable to send subsequent notifications if breach changes, default: false
      labels:
        - <string>
      severity: <string> # optional - only use SEV-0 through SEV-4 - custom values will be rejected
  field_quality:
    - table: <string> / tables: <list> # required
      field: <string> / fields: <list> # required      
      metric_type: <metric_type> # required, see below
      comparisons: <comparison>  # required, see below
      filters: # optional, rows to consider when collecting the metric
        - field: <string> # required
          operator: <string> # required, one of EQ, NEQ, LT, LTE, GT, GTE
          value: <string> required
      description: <string>
      notes: <string>
      schedule:
        type: <string>  # must be 'fixed' or 'manual'
        start_time: <date as isoformatted string>
        interval_minutes: <integer> 
        interval_crontab: 
          - <string>
        timezone: <timezone> # optional - select regional timezone for daylight savings ex. America/Los_Angeles
          - <string>
      event_rollup_count: <integer> # optional - enable to only send notifications every X incidents
      event_rollup_until_changed: <boolean> # optional - enable to send subsequent notifications if breach changes, default: false
      labels:
        - <string>
      severity: <string> # optional - only use SEV-0 through SEV-4 - custom values will be rejected  
  freshness:
    - table: <string> / tables: <list> # required
      name: <string> # optional -- by default it will be autogenerated
      freshness_threshold: <integer>  # required
      description: <string>
      schedule:
        type: fixed  # must be fixed
        start_time: <date as isoformatted string>
        interval_minutes: <integer>
        interval_crontab: 
          - <string>
        timezone: <timezone> # optional - select regional timezone for daylight savings ex. America/Los_Angeles
      event_rollup_count: <integer> # optional - enable to only send notifications every X incidents
      event_rollup_until_changed: <boolean> # optional - enable to send subsequent notifications if breach changes, default: false
      labels:
        - <string>
      severity: <string> # optional - only use SEV-0 through SEV-4 - custom values will be rejected
  volume:
    - table: <string> / tables: <list>   # required
      name: <string> # optional -- by default it will be autogenerated
      comparisons: <comparison>  # required
      volume_metric: <row_count or byte_count>  # row_count by default
      description: <string>
      schedule:
        type: fixed  # must be fixed
        start_time: <date as isoformatted string>
        interval_minutes: <integer>
        interval_crontab: 
          - <string>
        timezone: <timezone> # optional - select regional timezone for daylight savings ex. America/Los_Angeles
      event_rollup_count: <integer> # optional - enable to only send notifications every X incidents
      event_rollup_until_changed: <boolean> # optional - enable to send subsequent notifications if breach changes, default: false
      labels:
        - <string>
      severity: <string> # optional - only use SEV-0 through SEV-4 - custom values will be rejected

🚧

Lookback Limits

Where we allow you to specify a longer lookback period on some monitors (in case the data in your table has historical timestamps), you cannot pick a number larger than 7. This is because for each day we "lookback", an additional query against your table is run. This is a safeguard to prevent specifying a very large period, like 90 days, and then having 90 queries run against your warehouse each time the monitor runs. If you need help with these windows, please feel free to reach out to [email protected] or the chat bot in the lower right hand corner.

Field Health Monitor

• table: MC global table ID (format <database>:<schema>.<table name>
• name: Optional name used to identify the monitor (to determine whether to create a new one or update an existing one for the namespace). By default, it will be autogenerated using table, timestamp_field and where_condition.
• description: Friendly description of rule
• fields: List of fields in table to monitor. Optional — by default all fields are monitored
• use_important_fields: Defaults to false. If true, use the table current important fields to build the monitor. You can use important fields and also provide a specific list of fields at the same time.
• segmented_expressions: List of fields or SQL expressions used to segment the field (must have exactly one field in fields). Enables Monitoring by Dimension.
• timestamp_field: Timestamp field
• timestamp_field_expression: Arbitrary SQL expression to be used as timestamp field, e.g. DATE(created). Must use either timestamp_field or timestamp_field_expression or neither.
• where_condition: SQL snippet of where condition to add to field health query
• lookback_days: Lookback period in days. Default: 3
• aggregation_time_interval: Aggregation bucket time interval, either hour (default) or day
• schedule
  • type: One of loose, fixed, or dynamic
  • interval_minutes: For loose or fixed, how frequently to run the monitor
  • start_time: For fixed, when to start the schedule
  • timezone: Optional - select regional timezone for daylight savings ex. America/Los_Angeles
• labels: Optional list of labels associated with the monitor.

The monitored fields cannot exceed 300 fields, including important fields and manually specified fields. If they do, we'll attempt to keep all the manually specified fields and as many important fields as possible (ordered by importance score) until we reach 300 fields.

Dimension Tracking Monitor

• table: MC global table ID (format <database>:<schema>.<table name>
• name: Optional name used to identify the monitor (to determine whether to create a new one or update an existing one for the namespace). By default, it will be autogenerated using table, field, timestamp_field and where_condition.
• description: Friendly description of rule
• field: Field in table to monitor or a valid SQL expression that returns the row's dimension value as a string
• timestamp_field: Timestamp field
• timestamp_field_expression: Arbitrary SQL expression to be used as timestamp field, e.g. DATE(created). Must use either timestamp_field or timestamp_field_expression or neither.
• where_condition: SQL snippet of where condition to add to field health query
• lookback_days: Lookback period in days. Default: 3
• aggregation_time_interval: Aggregation bucket time interval, either hour (default) or day
• schedule
  • type: One of loose, fixed, or dynamic
  • interval_minutes: For loose or fixed, how frequently to run the monitor
  • start_time: For fixed, when to start the schedule
  • timezone: Optional - select regional timezone for daylight savings ex. America/Los_Angeles
• labels: Optional list of labels associated with the monitor.

JSON Schema Monitor

• table: MC global table ID (format <database>:<schema>.<table name>
• name: Optional name used to identify the monitor (to determine whether to create a new one or update an existing one for the namespace). By default, it will be autogenerated using table, field, timestamp_field and where_condition.
• description: Friendly description of rule
• field: Field in table to monitor
• timestamp_field: Timestamp field
• timestamp_field_expression: Arbitrary SQL expression to be used as timestamp field, e.g. DATE(created). Must use either timestamp_field or timestamp_field_expression or neither.
• where_condition: SQL snippet of where condition to add to field health query
• schedule
  • type: One of loose, fixed, or dynamic
  • interval_minutes: For loose or fixed, how frequently to run the monitor
  • start_time: For fixed, when to start the schedule
  • timezone: Optional - select regional timezone for daylight savings ex. America/Los_Angeles
• labels: Optional list of labels associated with the monitor.

SQL Rule

• sql: SQL of rule
• query_result_type: Optional, can be set to SINGLE_NUMERIC to make the rule use a value-based threshold
• sampling_sql: Optional custom SQL query to be run on breach (results will be displayed in Incident IQ to help with investigation).
• name: Optional name used to identify the rule (to determine whether to create a new one or update an existing one for the namespace). By default, it will be autogenerated using sql.
• comparisons: See comparisons below
• variables: See variables below
• description: Friendly description of rule
• notes: Additional context for the rule
• schedule
  • type: Can be fixed or manual. Manual would be for SQL rules implemented during processes like Circuit Breakers.
  • interval_minutes: How frequently to run the monitor (in minutes).
  • interval_crontab: How frequently to run the monitor (using a list of CRON expressions, check example below).
  • start_time: When to start the schedule. Required for fixed
  • timezone: Optional - select regional timezone for daylight savings ex. America/Los_Angeles
  • event_rollup_count: Optional - a Reduce Noise option to only send notifications every X incidents
  • event_rollup_until_changed: Optional - a Reduce Noise option to send subsequent notifications if breach changes
• labels: Optional list of labels associated with the rule
• severity: Optional, pre-set the severity of incidents generated by this monitor. Only use SEV-0 through SEV-4 as custom values will be rejected.

SQL Rule comparisons

📘

comparisons are definitions of breaches, not expected return values. This section would be where you would define the logic for when to get alerted about anomalous behavior in your monitor. For example, if you make a custom SQL rule and pick:

• type: threshold
• operator: GT
• threshold_value: 100

When Monte Carlo runs your monitor and the return results are greater than 100, we will fire an alert to any routes configured to be notified about breaches to this monitor.

• type: threshold, dynamic_threshold or change. If threshold, threshold_value below is an absolute value. If dynamic_threshold no threshold is needed (it will be determined automatically). If change, threshold_value as change from the historical baseline
• operator: One of EQ, NEQ, GT, GTE, LT, LTE. Operator of comparison, =, ≠, >, ≥, <, ≤ respectively.
• threshold_value: Threshold value
• baseline_agg_function: If type = change, the aggregation function used to aggregate data points to calculate historical baseline. One of AVG, MAX, MIN.
• baseline_interval_minutes: If type = change, the time interval in minutes (backwards from current time) to aggregate over to calculate historical baseline
• is_threshold_relative: If type = change, whether or not threshold_value is a relative vs absolute threshold. is_threshold_relative: true would be a percentage measurement, is_threshold_relative: false would be a numerical measurement. Relative means the threshold_value will be treated as a percentage value, Absolute means the threshold_value will be treated as an actual count of rows.

SQL Rule variables

📘

When defining custom sql sentences, you can use variables to execute the same sentence for different combinations of values. Variables are defined as {{variable_name}}. Then, you can define one or more values for each variable, and all combinations will be tested.

Here is an example defining the same sentence for several tables and conditions (4 sentences will be executed):

YAML

custom_sql:
    - sql: |
         select foo from {{table}} where {{cond}}
      variables:
        table: 
            - project:dataset.table1 
            - project:dataset.table2
        cond: 
            - col1 > 1
            - col2 > 2 

Field Quality Rule

• name: Optional name used to identify the rule (to determine whether to create a new one or update an existing one for the namespace). By default, it will be autogenerated using field-quality.
• table: MC global table ID (format <database>:<schema>.<table name>)
• tables: Instead of table, you can also use tables to define a list of tables (check example with Getting Started multiple tables below](https://docs.getmontecarlo.com/docs/monitors-as-code#example-with-multiple-tables)).
• field: Field name
• fields: Instead of field, you can also use fields to define a list of fields. All the fields must be present in the selected tables and have the same type if multiple tables are provided.
• metric_type: What metric will be collected. Certain metrics are only available on specific data types:
  • Numeric: NULL_RATE, APPROX_DISTINCTNESS, NEGATIVE_RATE, ZERO_RATE, NUMERIC_MEAN, NUMERIC_MAX, NUMERIC_MIN
  • Text: NULL_RATE, APPROX_DISTINCTNESS, MEAN_LENGTH, MAX_LENGTH, MIN_LENGTH
  • Time: NULL_RATE
  • Date: NULL_RATE, APPROX_DISTINCTNESS
  • Boolean: NULL_RATE
• comparisons: See comparisons below
• filters: See filters below
• description: Friendly description of rule
• notes: Additional context for the rule
• schedule
  • type: Can be fixed or manual. Manual would be for rules used during processes like Circuit Breakers.
  • interval_minutes: How frequently to run the monitor (in minutes).
  • interval_crontab: How frequently to run the monitor (using a list of CRON expressions, check example below).
  • start_time: When to start the schedule. Required for fixed
  • timezone: Optional - select regional timezone for daylight savings ex. America/Los_Angeles
  • event_rollup_count: Optional - a Reduce Noise option to only send notifications every X incidents
  • event_rollup_until_changed: Optional - a Reduce Noise option to send subsequent notifications if breach changes
• labels: Optional list of labels associated with the rule
• severity: Optional, pre-set the severity of incidents generated by this monitor. Only use SEV-0 through SEV-4 as custom values will be rejected.

Field Quality Rule comparisons

• type: threshold
• operator: One of EQ, NEQ, GT, GTE, LT, LTE. Operator of comparison, =, ≠, >, ≥, <, ≤ respectively.
• threshold_value: Threshold value

Field Quality Rule filters

• field: The field name
• operator: One of EQ, NEQ, GT, GTE, LT, LTE. Operator of comparison, =, ≠, >, ≥, <, ≤ respectively.
• value: The value to filter on

Freshness Rule

• table: MC global table ID (format <database>:<schema>.<table name>)
• tables: Instead of table, can also use tables to define a list of tables (check example with Getting Started multiple tables below](https://docs.getmontecarlo.com/docs/monitors-as-code#example-with-multiple-tables)).
• name: Optional name used to identify the rule (to determine whether to create a new one or update an existing one for the namespace). By default, it will be autogenerated using table
• freshness_threshold: Freshness breach threshold in minutes
• description: Friendly description of rule
• schedule
  • type: Must be fixed
  • interval_minutes: How frequently to run the monitor (in minutes).
  • interval_crontab: How frequently to run the monitor (using a list of CRON expressions, check example below).
  • start_time: When to start the schedule
  • timezone: Optional - select regional timezone for daylight savings ex. America/Los_Angeles
  • event_rollup_count: Optional - a Reduce Noise option to only send notifications every X incidents
  • event_rollup_until_changed: Optional - a Reduce Noise option to send subsequent notifications if breach changes
• labels: Optional list of labels associated with the rule.
• severity: Optional, pre-set the severity of incidents generated by this monitor. Only use SEV-0 through SEV-4 as custom values will be rejected.

Volume Rule

• table: MC global table ID (format <database>:<schema>.<table name>
• tables: Instead of table, can also use tables to define a list of tables (check example with multiple tables below).
• name: Optional name used to identify the rule (to determine whether to create a new one or update an existing one for the namespace). By default, it will be autogenerated using table.
• volume_metric: Must be total_row_count or total_byte_count — defines which volume metric to monitor
• comparisons: See comparisons below
• description: Friendly description of rule
• schedule
  • type: Must be "fixed"
  • interval_minutes: How frequently to run the monitor (in minutes).
  • interval_crontab: How frequently to run the monitor (using a list of CRON expressions, check example below).
  • start_time: When to start the schedule
  • timezone: Optional - select regional timezone for daylight savings ex. America/Los_Angeles
  • event_rollup_count: Optional - a Reduce Noise option to only send notifications every X incidents
  • event_rollup_until_changed: Optional - a Reduce Noise option to send subsequent notifications if breach changes
• labels: Optional list of labels associated with the rule.
• severity: Optional, pre-set the severity of incidents generated by this monitor. Only use SEV-0 through SEV-4 as custom values will be rejected.

Volume Rule comparisons

• type: absolute_volume or growth_volume.

If absolute_volume:

• operator: One of EQ, GTE, LTE. Operator of comparison, =, ≥, ≤ respectively.
• threshold_lookback_minutes: if operator is EQ, the time to look back to compare with the current value.
• threshold_value: If operator is GTE or LTE, the threshold value

If growth_volume:

• operator: One of EQ, GT, GTE, LT, LTE. Operator of comparison, =, >, ≥, <, ≤ respectively.
• baseline_agg_function: the aggregation function used to aggregate data points to calculate historical baseline. One of AVG, MAX, MIN.
• number_of_agg_periods: the number of periods to use in the aggregate comparison.
• baseline_interval_minutes: the aggregation period length.
• min_buffer_value / max_buffer_value: the lower / upper bound buffer to modify the alert threshold.
• min_buffer_modifier_type / max_buffer_modifier_type: the modifier type of min / max buffer, can be METRIC (absolute value) or PERCENTAGE.

Example

montecarlo:
  field_health:
    - table: project:dataset.table_name
      timestamp_field: created
      schedule:
        type: dynamic
      labels:
        - label_name1
    - table: project:dataset.table_name
      timestamp_field: created
      fields:
        - field_name
      segmented_expressions:
        - segmented_expression
      schedule:
        type: dynamic
  dimension_tracking:
    - table: project:dataset.table_name
      timestamp_field: created
      field: order_status
      labels:
        - label_name2
  custom_sql:
    - description: Test rule
      sql: |
         select foo from project.dataset.my_table
      comparisons:
        - type: threshold
          operator: GT
          threshold_value: 0
      schedule:
        type: fixed
        interval_minutes: 60
        start_time: "2021-07-27T19:00:00"
      severity: SEV-1
  field_quality:
    - description: Test rule
      table: project.dataset.my_table
      field: my_field
      metric_type: NULL_RATE
      comparisons:
        - type: threshold
          operator: GT
          threshold_value: 0
      schedule:
        type: fixed
        interval_minutes: 60
        start_time: "2021-07-27T19:00:00"
      severity: SEV-1
  freshness:
    - table: project:dataset.table_name
      freshness_threshold: 30
      schedule:
        type: fixed
        interval_minutes: 30
        start_time: "2021-07-27T19:00:00"

Example with multiple tables

montecarlo:
  freshness:
    - tables: 
          - project:dataset.table_name1
          - project:dataset.table_name2
      freshness_threshold: 30
      schedule:
        type: fixed
        interval_minutes: 30
        start_time: "2021-07-27T19:00:00"

Example with CRON expressions

montecarlo:
  custom_sql:
    - description: Test rule
      sql: |
         select foo from project.dataset.my_table
      comparisons:
        - type: threshold
          operator: GT
          threshold_value: 0
      schedule:
        type: fixed
        interval_crontab:
          - "0 10,16 * * MON-FRI"
          - "0 12 * * SAT-SUN"   
        start_time: "2021-07-27T19:00:00"