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

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

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}

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.

📘
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 -

You have multiple people (or teams) working on managing monitors and don’t want to conflict or override configurations.

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).

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>

Description of options:

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

Monitors are defined in YAML files within directories nested within the 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.

Standalone monitor YAML files can be contained in any .yml file in any directory nested within the project. Example:

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: project:dataset.table_name
            timestamp_field: created
            field: order_status

📘
Tip: Using monitors as code with DBT
You may find that embedding monitor configurations within DBT schema.yml may make maintenance easier, as all configuration/metadata concerning a given table are maintained in the same location.

Monitor configuration reference

montecarlo:
  field_health:
    - table: <string>  # required
      name: <string> # optional -- by default it will be autogenerated
      fields:
        - <string>
      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
      labels:
        - <string>
  dimension_tracking:
    - table: <string>  # required
      name: <string> # optional -- by default it will be autogenerated
      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
      labels:
        - <string>
  json_schema:
    - table: <string>  # required
      name: <string> # optional -- by default it will be autogenerated
      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
      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: fixed  # must be fixed
        start_time: <date as isoformatted string>
        interval_minutes: <integer> 
      labels:
        - <string>
  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>
      labels:
        - <string>
  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>
      labels:
        - <string>

🚧
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

Configures a 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.
fields: List of fields in table to monitor. Optional — by default all fields are monitored
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
labels: Optional list of labels associated with the monitor.

dimension_tracking

Configures a 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.
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
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
labels: Optional list of labels associated with the monitor.

json_schema

Configures a 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.
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
labels: Optional list of labels associated with the monitor.

custom_sql

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). Only supported for value-based thresholds (query_result_type is SINGLE_NUMERIC, see above).
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. Required for fixed.
- start_time: When to start the schedule. Required for fixed.
labels: Optional list of labels associated with the 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.

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

freshness

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.
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
- start_time: When to start the schedule
labels: Optional list of labels associated with the rule.

volume

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
- start_time: When to start the schedule
labels: Optional list of labels associated with the 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"
  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"

Developing and testing locally

To apply monitor configuration to MC:

montecarlo monitors apply --namespace <namespace>

Monitors configured using the CLI are organized under namespaces. All apply operations are scoped to a namespace. Namespaces make it easier to organize and manage monitors as code.

The apply command behaves as follows:

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.
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

👍
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.

The apply command also supports a --dry-run argument which will dry run the configuration update and report each operation.

To delete (destroy) a namespace:

montecarlo monitors delete --namespace <namespace>

This will delete all monitors for a given namespace.

Monitors as Code

Overview

Prerequisites

Integrating into your CI/CD pipeline

📘
Namespaces

Using code to define monitors

Defining individual monitors

📘
Tip: Using monitors as code with DBT

Monitor configuration reference

🚧
Lookback Limits

field_health

dimension_tracking

json_schema

custom_sql

comparisons

📘

variables

📘

freshness

volume

comparisons

Example

Example with multiple tables

Developing and testing locally

👍
Dry Runs

Overview

Prerequisites

Integrating into your CI/CD pipeline

📘Namespaces

Using code to define monitors

Defining individual monitors

📘Tip: Using monitors as code with DBT

Monitor configuration reference

🚧Lookback Limits

field_health

dimension_tracking

json_schema

custom_sql

comparisons

📘

variables

📘

freshness

volume

comparisons

Example

Example with multiple tables

Developing and testing locally

👍Dry Runs

📘
Namespaces

📘
Tip: Using monitors as code with DBT

🚧
Lookback Limits

👍
Dry Runs