debian-mirror-gitlab/doc/development/service_ping/metrics_instrumentation.md

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Metrics instrumentation guide

This guide describes how to develop Service Ping metrics using metrics instrumentation.

For a video tutorial, see the Adding Service Ping metric via instrumentation class.

Nomenclature

• Instrumentation class:

  • Inherits one of the metric classes: DatabaseMetric, RedisMetric, RedisHLLMetric, NumbersMetric or GenericMetric.
  • Implements the logic that calculates the value for a Service Ping metric.

• Metric definition The Service Data metric YAML definition.

• Hardening: Hardening a method is the process that ensures the method fails safe, returning a fallback value like -1.

How it works

A metric definition has the instrumentation_class field, which can be set to a class.

The defined instrumentation class should inherit one of the existing metric classes: DatabaseMetric, RedisMetric, RedisHLLMetric, NumbersMetric or GenericMetric.

The current convention is that a single instrumentation class corresponds to a single metric. On rare occasions, there are exceptions to that convention like Redis metrics. To use a single instrumentation class for more than one metric, please reach out to one of the @gitlab-org/analytics-section/product-intelligence/engineers members to consult about your case.

Using the instrumentation classes ensures that metrics can fail safe individually, without breaking the entire process of Service Ping generation.

We have built a domain-specific language (DSL) to define the metrics instrumentation.

Database metrics

You can use database metrics to track data kept in the database, for example, a count of issues that exist on a given instance.

• operation: Operations for the given relation, one of count, distinct_count, sum, and average.
• relation: Assigns lambda that returns the ActiveRecord::Relation for the objects we want to perform the operation. The assigned lambda can accept up to one parameter. The parameter is hashed and stored under the options key in the metric definition.
• start: Specifies the start value of the batch counting, by default is relation.minimum(:id).
• finish: Specifies the end value of the batch counting, by default is relation.maximum(:id).
• cache_start_and_finish_as: Specifies the cache key for start and finish values and sets up caching them. Use this call when start and finish are expensive queries that should be reused between different metric calculations.
• available?: Specifies whether the metric should be reported. The default is true.
• timestamp_column: Optionally specifies timestamp column for metric used to filter records for time constrained metrics. The default is created_at.

Example of a merge request that adds a database metric.

module Gitlab
  module Usage
    module Metrics
      module Instrumentations
        class CountIssuesMetric < DatabaseMetric
          operation :count

          relation ->(options) { Issue.where(confidential: options[:confidential]) }
        end
      end
    end
  end
end

Ordinary batch counters Example

module Gitlab
  module Usage
    module Metrics
      module Instrumentations
        class CountIssuesMetric < DatabaseMetric
          operation :count

          start { Issue.minimum(:id) }
          finish { Issue.maximum(:id) }

          relation { Issue }
        end
      end
    end
  end
end

Distinct batch counters Example

# frozen_string_literal: true

module Gitlab
  module Usage
    module Metrics
      module Instrumentations
        class CountUsersAssociatingMilestonesToReleasesMetric < DatabaseMetric
          operation :distinct_count, column: :author_id

          relation { Release.with_milestones }

          start { Release.minimum(:author_id) }
          finish { Release.maximum(:author_id) }
        end
      end
    end
  end
end

Sum Example

# frozen_string_literal: true

module Gitlab
  module Usage
    module Metrics
      module Instrumentations
        class JiraImportsTotalImportedIssuesCountMetric < DatabaseMetric
          operation :sum, column: :imported_issues_count

          relation { JiraImportState.finished }
        end
      end
    end
  end
end

Average Example

# frozen_string_literal: true

module Gitlab
  module Usage
    module Metrics
      module Instrumentations
        class CountIssuesWeightAverageMetric < DatabaseMetric
          operation :average, column: :weight

          relation { Issue }
        end
      end
    end
  end
end

Redis metrics

You can use Redis metrics to track events not kept in the database, for example, a count of how many times the search bar has been used.

Example of a merge request that adds a Redis metric.

The RedisMetric class can only be used as the instrumentation_class for Redis metrics with simple counters classes (classes that only inherit BaseCounter and set PREFIX and KNOWN_EVENTS constants). In case the counter class has additional logic included in it, a new instrumentation_class, inheriting from RedisMetric, needs to be created. This new class needs to include the additional logic from the counter class.

Count unique values for source_code_pushes event.

Required options:

• event: the event name.
• prefix: the value of the PREFIX constant used in the counter classes from the Gitlab::UsageDataCounters namespace.

time_frame: all
data_source: redis
instrumentation_class: RedisMetric
options:
  event: pushes
  prefix: source_code

Availability-restrained Redis metrics

If the Redis metric should only be available in the report under some conditions, then you must specify these conditions in a new class that is a child of the RedisMetric class.

# frozen_string_literal: true

module Gitlab
  module Usage
    module Metrics
      module Instrumentations
        class MergeUsageCountRedisMetric < RedisMetric
          available? { Feature.enabled?(:merge_usage_data_missing_key_paths) }
        end
      end
    end
  end
end

You must also use the class's name in the YAML setup.

time_frame: all
data_source: redis
instrumentation_class: MergeUsageCountRedisMetric
options:
  event: pushes
  prefix: source_code

Redis HyperLogLog metrics

You can use Redis HyperLogLog metrics to track events not kept in the database and incremented for unique values such as unique users, for example, a count of how many different users used the search bar.

Example of a merge request that adds a RedisHLL metric.

Count unique values for i_quickactions_approve event.

time_frame: 28d
data_source: redis_hll
instrumentation_class: RedisHLLMetric
options:
  events:
    - i_quickactions_approve

Availability-restrained Redis HyperLogLog metrics

If the Redis HyperLogLog metric should only be available in the report under some conditions, then you must specify these conditions in a new class that is a child of the RedisHLLMetric class.

# frozen_string_literal: true

module Gitlab
  module Usage
    module Metrics
      module Instrumentations
        class MergeUsageCountRedisHLLMetric < RedisHLLMetric
          available? { Feature.enabled?(:merge_usage_data_missing_key_paths) }
        end
      end
    end
  end
end

You must also use the class's name in the YAML setup.

time_frame: 28d
data_source: redis_hll
instrumentation_class: MergeUsageCountRedisHLLMetric
options:
  events:
    - i_quickactions_approve

Aggregated metrics

See the video from: Product Intelligence Office Hours Oct 6th for an aggregated metrics walk-through.

The aggregated metrics feature provides insight into the number of data attributes, for example pseudonymized_user_ids, that occurred in a collection of events. For example, you can aggregate the number of users who perform multiple actions such as creating a new issue and opening a new merge request.

You can use a YAML file to define your aggregated metrics. The following arguments are required:

• options.events: List of event names to aggregate into metric data. All events in this list must use the same data source. Additional data source requirements are described in Database sourced aggregated metrics and Redis sourced aggregated metrics.
• options.aggregate.operator: Operator that defines how the aggregated metric data is counted. Available operators are:
  • OR: Removes duplicates and counts all entries that triggered any of the listed events.
  • AND: Removes duplicates and counts all elements that were observed triggering all of the following events.
• options.aggregate.attribute: Information pointing to the attribute that is being aggregated across events.
• time_frame: One or more valid time frames. Use these to limit the data included in aggregated metrics to events within a specific date-range. Valid time frames are:
  • 7d: The last 7 days of data.
  • 28d: The last 28 days of data.
  • all: All historical data, only available for database sourced aggregated metrics.
• data_source: Data source used to collect all events data included in the aggregated metrics. Valid data sources are:
  • database
  • redis_hll

Refer to merge request 98206 for an example of a merge request that adds an AggregatedMetric metric.

Count unique user_ids that occurred in at least one of the events: incident_management_alert_status_changed, incident_management_alert_assigned, incident_management_alert_todo, incident_management_alert_create_incident.

time_frame: 28d
instrumentation_class: AggregatedMetric
data_source: redis_hll
options:
    aggregate:
        operator: OR
        attribute: user_id
    events:
        - `incident_management_alert_status_changed`
        - `incident_management_alert_assigned`
        - `incident_management_alert_todo`
        - `incident_management_alert_create_incident`

Availability-restrained Aggregated metrics

If the Aggregated metric should only be available in the report under specific conditions, then you must specify these conditions in a new class that is a child of the AggregatedMetric class.

# frozen_string_literal: true

module Gitlab
  module Usage
    module Metrics
      module Instrumentations
        class MergeUsageCountAggregatedMetric < AggregatedMetric
          available? { Feature.enabled?(:merge_usage_data_missing_key_paths) }
        end
      end
    end
  end
end

You must also use the class's name in the YAML setup.

time_frame: 28d
instrumentation_class: MergeUsageCountAggregatedMetric
data_source: redis_hll
options:
    aggregate:
        operator: OR
        attribute: user_id
    events:
        - `incident_management_alert_status_changed`
        - `incident_management_alert_assigned`
        - `incident_management_alert_todo`
        - `incident_management_alert_create_incident`

Numbers metrics

• operation: Operations for the given data block. Currently we only support add operation.
• data: a block which contains an array of numbers.
• available?: Specifies whether the metric should be reported. The default is true.

# frozen_string_literal: true

module Gitlab
  module Usage
    module Metrics
      module Instrumentations
          class IssuesBoardsCountMetric < NumbersMetric
            operation :add

            data do |time_frame|
              [
                 CountIssuesMetric.new(time_frame: time_frame).value,
                 CountBoardsMetric.new(time_frame: time_frame).value
              ]
            end
          end
        end
      end
    end
  end
end

You must also include the instrumentation class name in the YAML setup.

time_frame: 28d
instrumentation_class: IssuesBoardsCountMetric

Generic metrics

You can use generic metrics for other metrics, for example, an instance's database version. Observations type of data will always have a Generic metric counter type.

• value: Specifies the value of the metric.
• available?: Specifies whether the metric should be reported. The default is true.

Example of a merge request that adds a generic metric.

module Gitlab
  module Usage
    module Metrics
      module Instrumentations
        class UuidMetric < GenericMetric
          value do
            Gitlab::CurrentSettings.uuid
          end
        end
      end
    end
  end
end

Support for instrumentation classes

There is support for:

• count, distinct_count, estimate_batch_distinct_count, sum, and average for database metrics.
• Redis metrics.
• Redis HLL metrics.
• add for numbers metrics.
• Generic metrics, which are metrics based on settings or configurations.

There is no support for:

• add, histogram for database metrics.

You can track the progress to support these.

Create a new metric instrumentation class

To create a stub instrumentation for a Service Ping metric, you can use a dedicated generator:

The generator takes the class name as an argument and the following options:

• --type=TYPE Required. Indicates the metric type. It must be one of: database, generic, redis, numbers.
• --operation Required for database & numbers type.
  • For database it must be one of: count, distinct_count, estimate_batch_distinct_count, sum, average.
  • For numbers it must be: add.
• --ee Indicates if the metric is for EE.

rails generate gitlab:usage_metric CountIssues --type database --operation distinct_count
        create lib/gitlab/usage/metrics/instrumentations/count_issues_metric.rb
        create spec/lib/gitlab/usage/metrics/instrumentations/count_issues_metric_spec.rb

Migrate Service Ping metrics to instrumentation classes

This guide describes how to migrate a Service Ping metric from lib/gitlab/usage_data.rb or ee/lib/ee/gitlab/usage_data.rb to instrumentation classes.

1. Choose the metric type:

• Database metric
• Redis HyperLogLog metrics
• Redis metric
• Numbers metric
• Generic metric

1. Determine the location of instrumentation class: either under ee or outside ee.

2. Generate the instrumentation class file.

3. Fill the instrumentation class body:

   • Add code logic for the metric. This might be similar to the metric implementation in usage_data.rb.
   • Add tests for the individual metric spec/lib/gitlab/usage/metrics/instrumentations/.
   • Add tests for Service Ping.

4. Generate the metric definition file.

5. Remove the code from lib/gitlab/usage_data.rb or ee/lib/ee/gitlab/usage_data.rb.

6. Remove the tests from spec/lib/gitlab/usage_data.rb or ee/spec/lib/ee/gitlab/usage_data.rb.

Troubleshoot metrics

Sometimes metrics fail for reasons that are not immediately clear. The failures can be related to performance issues or other problems. The following pairing session video gives you an example of an investigation in to a real-world failing metric.

See the video from: Product Intelligence Office Hours Oct 27th to learn more about the metrics troubleshooting process.