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Service Ping Guide (FREE SELF)

Introduced in GitLab Ultimate 11.2, more statistics.

This guide describes Service Ping's purpose and how it's implemented.

For more information about Product Intelligence, see:

More links:

What is Service Ping?

Service Ping is a process in GitLab that collects and sends a weekly payload to GitLab Inc. The payload provides important high-level data that helps our product, support, and sales teams understand how GitLab is used. For example, the data helps to:

  • Compare counts month over month (or week over week) to get a rough sense for how an instance uses different product features.
  • Collect other facts that help us classify and understand GitLab installations.
  • Calculate our Stage Monthly Active Users (SMAU), which helps to measure the success of our stages and features.

Service Ping information is not anonymous. It's linked to the instance's hostname. However, it does not contain project names, usernames, or any other specific data.

Sending a Service Ping payload is optional and can be disabled on any self-managed instance. When Service Ping is enabled, GitLab gathers data from the other instances and can show your instance's usage statistics to your users.

Terminology

We use the following terminology to describe the Service Ping components:

  • Service Ping: the process that collects and generates a JSON payload.
  • Service Data: the contents of the Service Ping JSON payload. This includes metrics.
  • Metrics: primarily made up of row counts for different tables in an instance's database. Each metric has a corresponding metric definition in a YAML file.

Why should we enable Service Ping?

  • The main purpose of Service Ping is to build a better GitLab. Data about how GitLab is used is collected to better understand feature/stage adoption and usage, which helps us understand how GitLab is adding value and helps our team better understand the reasons why people use GitLab and with this knowledge we're able to make better product decisions.
  • As a benefit of having Service Ping active, GitLab lets you analyze the users' activities over time of your GitLab installation.
  • As a benefit of having Service Ping active, GitLab provides you with The DevOps Report,which gives you an overview of your entire instance's adoption of Concurrent DevOps from planning to monitoring.
  • You get better, more proactive support. (assuming that our TAMs and support organization used the data to deliver more value)
  • You get insight and advice into how to get the most value out of your investment in GitLab. Wouldn't you want to know that a number of features or values are not being adopted in your organization?
  • You get a report that illustrates how you compare against other similar organizations (anonymized), with specific advice and recommendations on how to improve your DevOps processes.
  • Service Ping is enabled by default. To disable it, see Disable Service Ping.
  • When Service Ping is enabled, you have the option to participate in our Registration Features Program and receive free paid features.

Registration Features Program

Introduced in GitLab 14.1.

Starting with GitLab version 14.1, free self-managed users running GitLab EE can receive paid features by registering with GitLab and sending us activity data via Service Ping.

The paid feature available in this offering is Email from GitLab. Administrators can use this Premium feature to streamline their workflow by emailing all or some instance users directly from the Admin Area.

NOTE: Registration is not yet required for participation, but will be added in a future milestone.

Limitations

  • Service Ping does not track frontend events things like page views, link clicks, or user sessions, and only focuses on aggregated backend events.
  • Because of these limitations we recommend instrumenting your products with Snowplow for more detailed analytics on GitLab.com and use Service Ping to track aggregated backend events on self-managed.

View the Service Ping payload (FREE SELF)

You can view the exact JSON payload sent to GitLab Inc. in the Admin Area. To view the payload:

  1. Sign in as a user with the Administrator role.
  2. On the top bar, select Menu > {admin} Admin.
  3. On the left sidebar, select Settings > Metrics and profiling.
  4. Expand the Usage statistics section.
  5. Select Preview payload.

For an example payload, see Example Service Ping payload.

Disable Service Ping (FREE SELF)

NOTE: The method to disable Service Ping in the GitLab configuration file does not work in GitLab versions 9.3 to 13.12.3. See the troubleshooting section on how to disable it.

You can disable Service Ping either using the GitLab UI, or editing the GitLab configuration file.

Disable Service Ping using the UI

To disable Service Ping in the GitLab UI:

  1. Sign in as a user with the Administrator role.
  2. On the top bar, select Menu > {admin} Admin.
  3. On the left sidebar, select Settings > Metrics and profiling.
  4. Expand the Usage statistics section.
  5. Clear the Enable service ping checkbox.
  6. Select Save changes.

Disable Service Ping using the configuration file

To disable Service Ping and prevent it from being configured in the future through the Admin Area:

For installations using the Linux package:

  1. Edit /etc/gitlab/gitlab.rb:

    gitlab_rails['usage_ping_enabled'] = false

  2. Reconfigure GitLab:

    sudo gitlab-ctl reconfigure

For installations from source:

  1. Edit /home/git/gitlab/config/gitlab.yml:

    production: &base
  # ...
  gitlab:
    # ...
    usage_ping_enabled: false

  2. Restart GitLab:

    sudo service gitlab restart

Service Ping request flow

The following example shows a basic request/response flow between a GitLab instance, the Versions Application, the License Application, Salesforce, the GitLab S3 Bucket, the GitLab Snowflake Data Warehouse, and Sisense:

sequenceDiagram
    participant GitLab Instance
    participant Versions Application
    participant Licenses Application
    participant Salesforce
    participant S3 Bucket
    participant Snowflake DW
    participant Sisense Dashboards
    GitLab Instance->>Versions Application: Send Service Ping
    loop Process usage data
        Versions Application->>Versions Application: Parse usage data
        Versions Application->>Versions Application: Write to database
        Versions Application->>Versions Application: Update license ping time
    end
    loop Process data for Salesforce
        Versions Application-xLicenses Application: Request Zuora subscription id
        Licenses Application-xVersions Application: Zuora subscription id
        Versions Application-xSalesforce: Request Zuora account id  by Zuora subscription id
        Salesforce-xVersions Application: Zuora account id
        Versions Application-xSalesforce: Usage data for the Zuora account
    end
    Versions Application->>S3 Bucket: Export Versions database
    S3 Bucket->>Snowflake DW: Import data
    Snowflake DW->>Snowflake DW: Transform data using dbt
    Snowflake DW->>Sisense Dashboards: Data available for querying
    Versions Application->>GitLab Instance: DevOps Report (Conversational Development Index)

How Service Ping works

  1. The Service Ping cron job is set in Sidekiq to run weekly.
  2. When the cron job runs, it calls Gitlab::UsageData.to_json.
  3. Gitlab::UsageData.to_json cascades down to ~400+ other counter method calls.
  4. The response of all methods calls are merged together into a single JSON payload in Gitlab::UsageData.to_json.
  5. The JSON payload is then posted to the Versions application If a firewall exception is needed, the required URL depends on several things. If the hostname is version.gitlab.com, the protocol is TCP, and the port number is 443, the required URL is https://version.gitlab.com/.

On a Geo secondary site

We also collect metrics specific to Geo secondary sites to send with Service Ping.

  1. The Geo secondary service ping cron job is set in Sidekiq to run weekly.

  2. When the cron job runs, it calls SecondaryUsageData.update_metrics!. This collects the relevant metrics from Prometheus and stores the data in the Geo secondary tracking database for transmission to the primary site during a Geo node status update.

  3. Geo node status data is sent with the JSON payload in the process described above. The following is an example of the payload where each object in the array represents a Geo node:

    [
  {
    "repository_verification_enabled"=>true,
    "repositories_replication_enabled"=>true,
    "repositories_synced_count"=>24,
    "repositories_failed_count"=>0,
    "attachments_replication_enabled"=>true,
    "attachments_count"=>1,
    "attachments_synced_count"=>1,
    "attachments_failed_count"=>0,
    "git_fetch_event_count_weekly"=>nil,
    "git_push_event_count_weekly"=>nil,
    ... other geo node status fields
  }
]

Implementing Service Ping

Service Ping consists of two kinds of data, counters and observations. Counters track how often a certain event happened over time, such as how many CI pipelines have run. They are monotonic and always trend up. Observations are facts collected from one or more GitLab instances and can carry arbitrary data. There are no general guidelines around how to collect those, due to the individual nature of that data.

Types of counters

There are several types of counters in usage_data.rb:

  • Ordinary Batch Counters: Simple count of a given ActiveRecord_Relation
  • Distinct Batch Counters: Distinct count of a given ActiveRecord_Relation in a given column
  • Sum Batch Counters: Sum the values of a given ActiveRecord_Relation in a given column
  • Alternative Counters: Used for settings and configurations
  • Redis Counters: Used for in-memory counts.

NOTE: Only use the provided counter methods. Each counter method contains a built-in fail-safe mechanism that isolates each counter to avoid breaking the entire Service Ping process.

Instrumentation classes

We recommend you use instrumentation classes in usage_data.rb where possible.

For example, we have the following instrumentation class: lib/gitlab/usage/metrics/instrumentations/count_boards_metric.rb.

You should add it to usage_data.rb as follows:

boards: add_metric('CountBoardsMetric', time_frame: 'all'),

Batch counting

For large tables, PostgreSQL can take a long time to count rows due to MVCC (Multi-version Concurrency Control). Batch counting is a counting method where a single large query is broken into multiple smaller queries. For example, instead of a single query querying 1,000,000 records, with batch counting, you can execute 100 queries of 10,000 records each. Batch counting is useful for avoiding database timeouts as each batch query is significantly shorter than one single long running query.

For GitLab.com, there are extremely large tables with 15 second query timeouts, so we use batch counting to avoid encountering timeouts. Here are the sizes of some GitLab.com tables:

Table Row counts in millions
merge_request_diff_commits 2280
ci_build_trace_sections 1764
merge_request_diff_files 1082
events 514

The following operation methods are available:

Batch counting requires indexes on columns to calculate max, min, and range queries. In some cases, you may need to add a specialized index on the columns involved in a counter.

Ordinary batch counters

Handles ActiveRecord::StatementInvalid error

Simple count of a given ActiveRecord_Relation, does a non-distinct batch count, smartly reduces batch_size, and handles errors.

Method: count(relation, column = nil, batch: true, start: nil, finish: nil)

Arguments:

  • relation the ActiveRecord_Relation to perform the count
  • column the column to perform the count on, by default is the primary key
  • batch: default true to use batch counting
  • start: custom start of the batch counting to avoid complex min calculations
  • end: custom end of the batch counting to avoid complex min calculations

Examples:

count(User.active)
count(::Clusters::Cluster.aws_installed.enabled, :cluster_id)
count(::Clusters::Cluster.aws_installed.enabled, :cluster_id, start: ::Clusters::Cluster.minimum(:id), finish: ::Clusters::Cluster.maximum(:id))

Distinct batch counters

Handles ActiveRecord::StatementInvalid error

Distinct count of a given ActiveRecord_Relation on given column, a distinct batch count, smartly reduces batch_size, and handles errors.

Method: distinct_count(relation, column = nil, batch: true, batch_size: nil, start: nil, finish: nil)

Arguments:

  • relation the ActiveRecord_Relation to perform the count
  • column the column to perform the distinct count, by default is the primary key
  • batch: default true to use batch counting
  • batch_size: if none set it uses default value 10000 from Gitlab::Database::BatchCounter
  • start: custom start of the batch counting to avoid complex min calculations
  • end: custom end of the batch counting to avoid complex min calculations

WARNING: Counting over non-unique columns can lead to performance issues. For more information, see the iterating tables in batches guide.

Examples:

distinct_count(::Project, :creator_id)
distinct_count(::Note.with_suggestions.where(time_period), :author_id, start: ::User.minimum(:id), finish: ::User.maximum(:id))
distinct_count(::Clusters::Applications::CertManager.where(time_period).available.joins(:cluster), 'clusters.user_id')

Sum batch operation

Handles ActiveRecord::StatementInvalid error

Sum the values of a given ActiveRecord_Relation on given column and handles errors.

Method: sum(relation, column, batch_size: nil, start: nil, finish: nil)

Arguments:

  • relation the ActiveRecord_Relation to perform the operation
  • column the column to sum on
  • batch_size: if none set it uses default value 1000 from Gitlab::Database::BatchCounter
  • start: custom start of the batch counting to avoid complex min calculations
  • end: custom end of the batch counting to avoid complex min calculations

Examples:

sum(JiraImportState.finished, :imported_issues_count)

Grouping and batch operations

The count, distinct_count, and sum batch counters can accept an ActiveRecord::Relation object, which groups by a specified column. With a grouped relation, the methods do batch counting, handle errors, and returns a hash table of key-value pairs.

Examples:

count(Namespace.group(:type))
# returns => {nil=>179, "Group"=>54}

distinct_count(Project.group(:visibility_level), :creator_id)
# returns => {0=>1, 10=>1, 20=>11}

sum(Issue.group(:state_id), :weight))
# returns => {1=>3542, 2=>6820}

Add operation

Handles StandardError.

Returns -1 if any of the arguments are -1.

Sum the values given as parameters.

Method: add(*args)

Examples:

project_imports = distinct_count(::Project.where.not(import_type: nil), :creator_id)
bulk_imports = distinct_count(::BulkImport, :user_id)

 add(project_imports, bulk_imports)

Estimated batch counters

Estimated batch counter functionality handles ActiveRecord::StatementInvalid errors when used through the provided estimate_batch_distinct_count method. Errors return a value of -1.

WARNING: This functionality estimates a distinct count of a specific ActiveRecord_Relation in a given column, which uses the HyperLogLog algorithm. As the HyperLogLog algorithm is probabilistic, the results always include error. The highest encountered error rate is 4.9%.

When correctly used, the estimate_batch_distinct_count method enables efficient counting over columns that contain non-unique values, which can not be assured by other counters.

estimate_batch_distinct_count method

Method: estimate_batch_distinct_count(relation, column = nil, batch_size: nil, start: nil, finish: nil)

The method includes the following arguments:

  • relation: The ActiveRecord_Relation to perform the count.
  • column: The column to perform the distinct count. The default is the primary key.
  • batch_size: From Gitlab::Database::PostgresHll::BatchDistinctCounter::DEFAULT_BATCH_SIZE. Default value: 10,000.
  • start: The custom start of the batch count, to avoid complex minimum calculations.
  • finish: The custom end of the batch count to avoid complex maximum calculations.

The method includes the following prerequisites:

  1. The supplied relation must include the primary key defined as the numeric column. For example: id bigint NOT NULL.

  2. The estimate_batch_distinct_count can handle a joined relation. To use its ability to count non-unique columns, the joined relation must not have a one-to-many relationship, such as has_many :boards.

  3. Both start and finish arguments should always represent primary key relationship values, even if the estimated count refers to another column, for example:

      estimate_batch_distinct_count(::Note, :author_id, start: ::Note.minimum(:id), finish: ::Note.maximum(:id))

Examples:

  1. Simple execution of estimated batch counter, with only relation provided, returned value represents estimated number of unique values in id column (which is the primary key) of Project relation:

      estimate_batch_distinct_count(::Project)

  2. Execution of estimated batch counter, where provided relation has applied additional filter (.where(time_period)), number of unique values estimated in custom column (:author_id), and parameters: start and finish together apply boundaries that defines range of provided relation to analyze:

      estimate_batch_distinct_count(::Note.with_suggestions.where(time_period), :author_id, start: ::Note.minimum(:id), finish: ::Note.maximum(:id))

  3. Execution of estimated batch counter with joined relation (joins(:cluster)), for a custom column ('clusters.user_id'):

      estimate_batch_distinct_count(::Clusters::Applications::CertManager.where(time_period).available.joins(:cluster), 'clusters.user_id')

When instrumenting metric with usage of estimated batch counter please add _estimated suffix to its name, for example:

  "counts": {
    "ci_builds_estimated": estimate_batch_distinct_count(Ci::Build),
    ...

Redis counters

Handles ::Redis::CommandError and Gitlab::UsageDataCounters::BaseCounter::UnknownEvent returns -1 when a block is sent or hash with all values -1 when a counter(Gitlab::UsageDataCounters) is sent different behavior due to 2 different implementations of Redis counter

Method: redis_usage_data(counter, &block)

Arguments:

  • counter: a counter from Gitlab::UsageDataCounters, that has fallback_totals method implemented
  • or a block: which is evaluated

Ordinary Redis counters

Examples of implementation:

UsageData API tracking

  1. Track event using UsageData API

    Increment event count using ordinary Redis counter, for given event name.

    Tracking events using the UsageData API requires the usage_data_api feature flag to be enabled, which is enabled by default.

    API requests are protected by checking for a valid CSRF token.

    To be able to increment the values, the related feature usage_data_<event_name> should be enabled.

    POST /usage_data/increment_counter
    Attribute Type Required Description
    event string yes The event name it should be tracked

    Response:

    • 200 if event was tracked
    • 400 Bad request if event parameter is missing
    • 401 Unauthorized if user is not authenticated
    • 403 Forbidden for invalid CSRF token provided

  2. Track events using JavaScript/Vue API helper which calls the API above

    Note that usage_data_api and usage_data_#{event_name} should be enabled to be able to track events

    import api from '~/api';

api.trackRedisCounterEvent('my_already_defined_event_name'),

Redis HLL counters

WARNING: HyperLogLog (HLL) is a probabilistic algorithm and its results always includes some small error. According to Redis documentation, data from used HLL implementation is "approximated with a standard error of 0.81%".

With Gitlab::UsageDataCounters::HLLRedisCounter we have available data structures used to count unique values.

Implemented using Redis methods PFADD and PFCOUNT.

Add new events

  1. Define events in known_events.

    Example event:

    - name: users_creating_epics
  category: epics_usage
  redis_slot: users
  aggregation: weekly
  feature_flag: track_epics_activity

    Keys:

    • name: unique event name.

      Name format for Redis HLL events <name>_<redis_slot>.

      See Metric name for a complete guide on metric naming suggestion.

      Consider including in the event's name the Redis slot to be able to count totals for a specific category.

      Example names: users_creating_epics, users_triggering_security_scans.

    • category: event category. Used for getting total counts for events in a category, for easier access to a group of events.

    • redis_slot: optional Redis slot. Default value: event name. Only event data that is stored in the same slot can be aggregated. Ensure keys are in the same slot. For example: users_creating_epics with redis_slot: 'users' builds Redis key {users}_creating_epics-2020-34. If redis_slot is not defined the Redis key will be {users_creating_epics}-2020-34. Recommended slots to use are: users, projects. This is the value we count.

    • expiry: expiry time in days. Default: 29 days for daily aggregation and 6 weeks for weekly aggregation.

    • aggregation: may be set to a :daily or :weekly key. Defines how counting data is stored in Redis. Aggregation on a daily basis does not pull more fine grained data.

    • feature_flag: optional default_enabled: :yaml. If no feature flag is set then the tracking is enabled. One feature flag can be used for multiple events. For details, see our GitLab internal Feature flags documentation. The feature flags are owned by the group adding the event tracking.

  2. Use one of the following methods to track the event:

    • In the controller using the RedisTracking module and the following format:

      track_redis_hll_event(*controller_actions, name:, if: nil, &block)

      Arguments:

      • controller_actions: the controller actions to track.
      • name: the event name.
      • if: optional custom conditions. Uses the same format as Rails callbacks.
      • &block: optional block that computes and returns the custom_id that we want to track. This overrides the visitor_id.

      Example:

      # controller
class ProjectsController < Projects::ApplicationController
  include RedisTracking

  skip_before_action :authenticate_user!, only: :show
  track_redis_hll_event :index, :show, name: 'users_visiting_projects'

  def index
    render html: 'index'
  end

 def new
   render html: 'new'
 end

 def show
   render html: 'show'
 end
end

    • In the API using the increment_unique_values(event_name, values) helper method.

      Arguments:

      • event_name: the event name.
      • values: the values counted. Can be one value or an array of values.

      Example:

      get ':id/registry/repositories' do
  repositories = ContainerRepositoriesFinder.new(
    user: current_user, subject: user_group
  ).execute

  increment_unique_values('users_listing_repositories', current_user.id)

  present paginate(repositories), with: Entities::ContainerRegistry::Repository, tags: params[:tags], tags_count: params[:tags_count]
end

    • Using track_usage_event(event_name, values) in services and GraphQL.

      Increment unique values count using Redis HLL, for a given event name.

      Examples:

        track_usage_event(:incident_management_incident_created, current_user.id)

    • Using the UsageData API.

      Increment unique users count using Redis HLL, for a given event name.

      To track events using the UsageData API, ensure the usage_data_api feature flag is set to default_enabled: true. Enabled by default in GitLab 13.7 and later.

      API requests are protected by checking for a valid CSRF token.

      POST /usage_data/increment_unique_users
      Attribute Type Required Description
      event string yes The event name to track

      Response:

      • 200 if the event was tracked, or if tracking failed for any reason.
      • 400 Bad request if an event parameter is missing.
      • 401 Unauthorized if the user is not authenticated.
      • 403 Forbidden if an invalid CSRF token is provided.

    • Using the JavaScript/Vue API helper, which calls the UsageData API.

      To track events using the UsageData API, ensure the usage_data_api feature flag is set to default_enabled: true. Enabled by default in GitLab 13.7 and later.

      Example for an existing event already defined in known events:

      import api from '~/api';

api.trackRedisHllUserEvent('my_already_defined_event_name'),

  3. Get event data using Gitlab::UsageDataCounters::HLLRedisCounter.unique_events(event_names:, start_date:, end_date:, context: '').

    Arguments:

    • event_names: the list of event names.
    • start_date: start date of the period for which we want to get event data.
    • end_date: end date of the period for which we want to get event data.
    • context: context of the event. Allowed values are default, free, bronze, silver, gold, starter, premium, ultimate.

  4. Testing tracking and getting unique events

Trigger events in rails console by using track_event method

Gitlab::UsageDataCounters::HLLRedisCounter.track_event('users_viewing_compliance_audit_events', values: 1)
Gitlab::UsageDataCounters::HLLRedisCounter.track_event('users_viewing_compliance_audit_events', values: [2, 3])

Next, get the unique events for the current week.

# Get unique events for metric for current_week
Gitlab::UsageDataCounters::HLLRedisCounter.unique_events(event_names: 'users_viewing_compliance_audit_events',
start_date: Date.current.beginning_of_week, end_date: Date.current.next_week)
Recommendations

We have the following recommendations for adding new events:

  • Event aggregation: weekly.
  • Key expiry time:
    • Daily: 29 days.
    • Weekly: 42 days.
  • When adding new metrics, use a feature flag to control the impact.
  • For feature flags triggered by another service, set default_enabled: false,
    • Events can be triggered using the UsageData API, which helps when there are > 10 events per change
Enable or disable Redis HLL tracking

Events are tracked behind optional feature flags due to concerns for Redis performance and scalability.

For a full list of events and corresponding feature flags see, known_events files.

To enable or disable tracking for specific event in https://gitlab.com or https://about.staging.gitlab.com, run commands such as the following to enable or disable the corresponding feature.

/chatops run feature set <feature_name> true
/chatops run feature set <feature_name> false

We can also disable tracking completely by using the global flag:

/chatops run feature set redis_hll_tracking true
/chatops run feature set redis_hll_tracking false
Known events are added automatically in Service Data payload

All events added in known_events/common.yml are automatically added to Service Data generation under the redis_hll_counters key. This column is stored in version-app as a JSON. For each event we add metrics for the weekly and monthly time frames, and totals for each where applicable:

  • #{event_name}_weekly: Data for 7 days for daily aggregation events and data for the last complete week for weekly aggregation events.
  • #{event_name}_monthly: Data for 28 days for daily aggregation events and data for the last 4 complete weeks for weekly aggregation events.

Redis HLL implementation calculates automatic total metrics, if there are more than one metric for the same category, aggregation, and Redis slot.

  • #{category}_total_unique_counts_weekly: Total unique counts for events in the same category for the last 7 days or the last complete week, if events are in the same Redis slot and we have more than one metric.
  • #{category}_total_unique_counts_monthly: Total unique counts for events in same category for the last 28 days or the last 4 complete weeks, if events are in the same Redis slot and we have more than one metric.

Example of redis_hll_counters data:

{:redis_hll_counters=>
  {"compliance"=>
    {"users_viewing_compliance_dashboard_weekly"=>0,
     "users_viewing_compliance_dashboard_monthly"=>0,
     "users_viewing_compliance_audit_events_weekly"=>0,
     "users_viewing_audit_events_monthly"=>0,
     "compliance_total_unique_counts_weekly"=>0,
     "compliance_total_unique_counts_monthly"=>0},
 "analytics"=>
    {"users_viewing_analytics_group_devops_adoption_weekly"=>0,
     "users_viewing_analytics_group_devops_adoption_monthly"=>0,
     "analytics_total_unique_counts_weekly"=>0,
     "analytics_total_unique_counts_monthly"=>0},
   "ide_edit"=>
    {"users_editing_by_web_ide_weekly"=>0,
     "users_editing_by_web_ide_monthly"=>0,
     "users_editing_by_sfe_weekly"=>0,
     "users_editing_by_sfe_monthly"=>0,
     "ide_edit_total_unique_counts_weekly"=>0,
     "ide_edit_total_unique_counts_monthly"=>0}
 }

Example:

# Redis Counters
redis_usage_data(Gitlab::UsageDataCounters::WikiPageCounter)
redis_usage_data { ::Gitlab::UsageCounters::PodLogs.usage_totals[:total] }

# Define events in common.yml https://gitlab.com/gitlab-org/gitlab/-/blob/master/lib/gitlab/usage_data_counters/known_events/common.yml

# Tracking events
Gitlab::UsageDataCounters::HLLRedisCounter.track_event('users_expanding_vulnerabilities', values: visitor_id)

# Get unique events for metric
redis_usage_data { Gitlab::UsageDataCounters::HLLRedisCounter.unique_events(event_names: 'users_expanding_vulnerabilities', start_date: 28.days.ago, end_date: Date.current) }

Alternative counters

Handles StandardError and fallbacks into -1 this way not all measures fail if we encounter one exception. Mainly used for settings and configurations.

Method: alt_usage_data(value = nil, fallback: -1, &block)

Arguments:

  • value: a simple static value in which case the value is simply returned.
  • or a block: which is evaluated
  • fallback: -1: the common value used for any metrics that are failing.

Example:

alt_usage_data { Gitlab::VERSION }
alt_usage_data { Gitlab::CurrentSettings.uuid }
alt_usage_data(999)

Add counters to build new metrics

When adding the results of two counters, use the add Service Data method that handles fallback values and exceptions. It also generates a valid SQL export.

Example:

add(User.active, User.bot)

Prometheus queries

In those cases where operational metrics should be part of Service Ping, a database or Redis query is unlikely to provide useful data. Instead, Prometheus might be more appropriate, because most GitLab architectural components publish metrics to it that can be queried back, aggregated, and included as Service Data.

NOTE: Prometheus as a data source for Service Ping is only available for single-node Omnibus installations that are running the bundled Prometheus instance.

To query Prometheus for metrics, a helper method is available to yield a fully configured PrometheusClient, given it is available as per the note above:

with_prometheus_client do |client|
  response = client.query('<your query>')
  ...
end

Refer to the PrometheusClient definition for how to use its API to query for data.

Fallback values for Service Ping

We return fallback values in these cases:

Case Value
Deprecated Metric -1000
Timeouts, general failures -1
Standard errors in counters -2

Aggregated metrics

WARNING: This feature is intended solely for internal GitLab use.

To add data for aggregated metrics to the Service Ping payload, add a corresponding definition to:

Each aggregate definition includes following parts:

  • name: Unique name under which the aggregate metric is added to the Service Ping payload.
  • 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 listed events.
    • AND: Removes duplicates and counts all elements that were observed triggering all of following events.
  • time_frame: One or more valid time frames. Use these to limit the data included in aggregated metric to events within a specific date-range. Valid time frames are:
    • 7d: Last seven days of data.
    • 28d: Last twenty eight days of data.
    • all: All historical data, only available for database sourced aggregated metrics.
  • source: Data source used to collect all events data included in aggregated metric. Valid data sources are:
  • events: list of events names to aggregate into metric. All events in this list must relay on the same data source. Additional data source requirements are described in the Database sourced aggregated metrics and Redis sourced aggregated metrics sections.
  • feature_flag: Name of development feature flag that is checked before metrics aggregation is performed. Corresponding feature flag should have default_enabled attribute set to false. The feature_flag attribute is optional and can be omitted. When feature_flag is missing, no feature flag is checked.

Example aggregated metric entries:

- name: example_metrics_union
  operator: OR
  events:
    - 'users_expanding_secure_security_report'
    - 'users_expanding_testing_code_quality_report'
    - 'users_expanding_testing_accessibility_report'
  source: redis
  time_frame:
    - 7d
    - 28d
- name: example_metrics_intersection
  operator: AND
  source: database
  time_frame:
    - 28d
    - all
  events:
    - 'dependency_scanning_pipeline_all_time'
    - 'container_scanning_pipeline_all_time'
  feature_flag: example_aggregated_metric

Aggregated metrics collected in 7d and 28d time frames are added into Service Ping payload under the aggregated_metrics sub-key in the counts_weekly and counts_monthly top level keys.

{
  :counts_monthly => {
    :deployments => 1003,
    :successful_deployments => 78,
    :failed_deployments => 275,
    :packages => 155,
    :personal_snippets => 2106,
    :project_snippets => 407,
    :promoted_issues => 719,
    :aggregated_metrics => {
      :example_metrics_union => 7,
      :example_metrics_intersection => 2
    },
    :snippets => 2513
  }
}

Aggregated metrics for all time frame are present in the count top level key, with the aggregate_ prefix added to their name.

For example:

example_metrics_intersection

Becomes:

counts.aggregate_example_metrics_intersection

{
  :counts => {
    :deployments => 11003,
    :successful_deployments => 178,
    :failed_deployments => 1275,
    :aggregate_example_metrics_intersection => 12
  }
}

Redis sourced aggregated metrics

To declare the aggregate of events collected with Redis HLL Counters, you must fulfill the following requirements:

  1. All events listed at events attribute must come from known_events/*.yml files.
  2. All events listed at events attribute must have the same redis_slot attribute.
  3. All events listed at events attribute must have the same aggregation attribute.
  4. time_frame does not include all value, which is unavailable for Redis sourced aggregated metrics.

Database sourced aggregated metrics

To declare an aggregate of metrics based on events collected from database, follow these steps:

  1. Persist the metrics for aggregation.
  2. Add new aggregated metric definition.

Persist metrics for aggregation

Only metrics calculated with Estimated Batch Counters can be persisted for database sourced aggregated metrics. To persist a metric, inject a Ruby block into the estimate_batch_distinct_count method. This block should invoke the Gitlab::Usage::Metrics::Aggregates::Sources::PostgresHll.save_aggregated_metrics method, which stores estimate_batch_distinct_count results for future use in aggregated metrics.

The Gitlab::Usage::Metrics::Aggregates::Sources::PostgresHll.save_aggregated_metrics method accepts the following arguments:

  • metric_name: The name of metric to use for aggregations. Should be the same as the key under which the metric is added into Service Ping.
  • recorded_at_timestamp: The timestamp representing the moment when a given Service Ping payload was collected. You should use the convenience method recorded_at to fill recorded_at_timestamp argument, like this: recorded_at_timestamp: recorded_at
  • time_period: The time period used to build the relation argument passed into estimate_batch_distinct_count. To collect the metric with all available historical data, set a nil value as time period: time_period: nil.
  • data: HyperLogLog buckets structure representing unique entries in relation. The estimate_batch_distinct_count method always passes the correct argument into the block, so data argument must always have a value equal to block argument, like this: data: result

Example metrics persistence:

class UsageData
  def count_secure_pipelines(time_period)
    ...
    relation = ::Security::Scan.latest_successful_by_build.by_scan_types(scan_type).where(security_scans: time_period)

    pipelines_with_secure_jobs['dependency_scanning_pipeline'] = estimate_batch_distinct_count(relation, :commit_id, batch_size: 1000, start: start_id, finish: finish_id) do |result|
      ::Gitlab::Usage::Metrics::Aggregates::Sources::PostgresHll
        .save_aggregated_metrics(metric_name: 'dependency_scanning_pipeline', recorded_at_timestamp: recorded_at, time_period: time_period, data: result)
    end
  end
end

Add new aggregated metric definition

After all metrics are persisted, you can add an aggregated metric definition at aggregated_metrics/.

To declare the aggregate of metrics collected with Estimated Batch Counters, you must fulfill the following requirements:

  • Metrics names listed in the events: attribute, have to use the same names you passed in the metric_name argument while persisting metrics in previous step.
  • Every metric listed in the events: attribute, has to be persisted for every selected time_frame: value.

Example definition:

- name: example_metrics_intersection_database_sourced
  operator: AND
  source: database
  events:
    - 'dependency_scanning_pipeline'
    - 'container_scanning_pipeline'
  time_frame:
    - 28d
    - all

Example Service Ping payload

The following is example content of the Service Ping payload.

{
  "uuid": "0000000-0000-0000-0000-000000000000",
  "hostname": "example.com",
  "version": "12.10.0-pre",
  "installation_type": "omnibus-gitlab",
  "active_user_count": 999,
  "recorded_at": "2020-04-17T07:43:54.162+00:00",
  "edition": "EEU",
  "license_md5": "00000000000000000000000000000000",
  "license_id": null,
  "historical_max_users": 999,
  "licensee": {
    "Name": "ABC, Inc.",
    "Email": "email@example.com",
    "Company": "ABC, Inc."
  },
  "license_user_count": 999,
  "license_starts_at": "2020-01-01",
  "license_expires_at": "2021-01-01",
  "license_plan": "ultimate",
  "license_add_ons": {
  },
  "license_trial": false,
  "counts": {
    "assignee_lists": 999,
    "boards": 999,
    "ci_builds": 999,
    ...
  },
  "container_registry_enabled": true,
  "dependency_proxy_enabled": false,
  "gitlab_shared_runners_enabled": true,
  "gravatar_enabled": true,
  "influxdb_metrics_enabled": true,
  "ldap_enabled": false,
  "mattermost_enabled": false,
  "omniauth_enabled": true,
  "prometheus_enabled": false,
  "prometheus_metrics_enabled": false,
  "reply_by_email_enabled": "incoming+%{key}@incoming.gitlab.com",
  "signup_enabled": true,
  "web_ide_clientside_preview_enabled": true,
  "projects_with_expiration_policy_disabled": 999,
  "projects_with_expiration_policy_enabled": 999,
  ...
  "elasticsearch_enabled": true,
  "license_trial_ends_on": null,
  "geo_enabled": false,
  "git": {
    "version": {
      "major": 2,
      "minor": 26,
      "patch": 1
    }
  },
  "gitaly": {
    "version": "12.10.0-rc1-93-g40980d40",
    "servers": 56,
    "clusters": 14,
    "filesystems": [
      "EXT_2_3_4"
    ]
  },
  "gitlab_pages": {
    "enabled": true,
    "version": "1.17.0"
  },
  "container_registry_server": {
    "vendor": "gitlab",
    "version": "2.9.1-gitlab"
  },
  "database": {
    "adapter": "postgresql",
    "version": "9.6.15",
    "pg_system_id": 6842684531675334351
  },
  "analytics_unique_visits": {
    "g_analytics_contribution": 999,
    ...
  },
  "usage_activity_by_stage": {
    "configure": {
      "project_clusters_enabled": 999,
      ...
    },
    "create": {
      "merge_requests": 999,
      ...
    },
    "manage": {
      "events": 999,
      ...
    },
    "monitor": {
      "clusters": 999,
      ...
    },
    "package": {
      "projects_with_packages": 999
    },
    "plan": {
      "issues": 999,
      ...
    },
    "release": {
      "deployments": 999,
      ...
    },
    "secure": {
      "user_container_scanning_jobs": 999,
      ...
    },
    "verify": {
      "ci_builds": 999,
      ...
    }
  },
  "usage_activity_by_stage_monthly": {
    "configure": {
      "project_clusters_enabled": 999,
      ...
    },
    "create": {
      "merge_requests": 999,
      ...
    },
    "manage": {
      "events": 999,
      ...
    },
    "monitor": {
      "clusters": 999,
      ...
    },
    "package": {
      "projects_with_packages": 999
    },
    "plan": {
      "issues": 999,
      ...
    },
    "release": {
      "deployments": 999,
      ...
    },
    "secure": {
      "user_container_scanning_jobs": 999,
      ...
    },
    "verify": {
      "ci_builds": 999,
      ...
    }
  },
  "topology": {
    "duration_s": 0.013836685999194742,
    "application_requests_per_hour": 4224,
    "query_apdex_weekly_average": 0.996,
    "failures": [],
    "nodes": [
      {
        "node_memory_total_bytes": 33269903360,
        "node_memory_utilization": 0.35,
        "node_cpus": 16,
        "node_cpu_utilization": 0.2,
        "node_uname_info": {
          "machine": "x86_64",
          "sysname": "Linux",
          "release": "4.19.76-linuxkit"
        },
        "node_services": [
          {
            "name": "web",
            "process_count": 16,
            "process_memory_pss": 233349888,
            "process_memory_rss": 788220927,
            "process_memory_uss": 195295487,
            "server": "puma"
          },
          {
            "name": "sidekiq",
            "process_count": 1,
            "process_memory_pss": 734080000,
            "process_memory_rss": 750051328,
            "process_memory_uss": 731533312
          },
          ...
        ],
        ...
      },
      ...
    ]
  }
}

Notable changes

In GitLab 13.5, pg_system_id was added to send the PostgreSQL system identifier.

Export Service Ping SQL queries and definitions

Two Rake tasks exist to export Service Ping definitions.

  • The Rake tasks export the raw SQL queries for count, distinct_count, sum.
  • The Rake tasks export the Redis counter class or the line of the Redis block for redis_usage_data.
  • The Rake tasks calculate the alt_usage_data metrics.

In the home directory of your local GitLab installation run the following Rake tasks for the YAML and JSON versions respectively:

# for YAML export
bin/rake gitlab:usage_data:dump_sql_in_yaml

# for JSON export
bin/rake gitlab:usage_data:dump_sql_in_json

# You may pipe the output into a file
bin/rake gitlab:usage_data:dump_sql_in_yaml > ~/Desktop/usage-metrics-2020-09-02.yaml

Generating and troubleshooting Service Ping

This activity is to be done via a detached screen session on a remote server.

Before you begin these steps, make sure the key is added to the SSH agent locally with the ssh-add command.

Triggering

  1. Connect to bastion with agent forwarding: $ ssh -A lb-bastion.gprd.gitlab.com
  2. Create named screen: $ screen -S <username>_usage_ping_<date>
  3. Connect to console host: $ ssh $USER-rails@console-01-sv-gprd.c.gitlab-production.internal
  4. Run SubmitUsagePingService.new.execute
  5. Detach from screen: ctrl + a, ctrl + d
  6. Exit from bastion: $ exit

Verification (After approx 30 hours)

  1. Reconnect to bastion: $ ssh -A lb-bastion.gprd.gitlab.com
  2. Find your screen session: $ screen -ls
  3. Attach to your screen session: $ screen -x 14226.mwawrzyniak_usage_ping_2021_01_22
  4. Check the last payload in raw_usage_data table: RawUsageData.last.payload
  5. Check the when the payload was sent: RawUsageData.last.sent_at

Troubleshooting

Cannot disable Service Ping using the configuration file

The method to disable Service Ping using the GitLab configuration file does not work in GitLab versions 9.3.0 to 13.12.3. To disable it, you need to use the Admin Area in the GitLab UI instead. For more information, see this issue.

GitLab functionality and application settings cannot override or circumvent restrictions at the network layer. If Service Ping is blocked by your firewall, you are not impacted by this bug.

Check if you are affected

You can check if you were affected by this bug by using the Admin Area or by checking the configuration file of your GitLab instance:

  • Using the Admin Area:

    1. On the top bar, select Menu > {admin} Admin.

    2. On the left sidebar, select Settings > Metrics and profiling.

    3. Expand Usage Statistics.

    4. Are you able to check or uncheck the checkbox to disable Service Ping?

      • If yes, your GitLab instance is not affected by this bug.
      • If you can't check or uncheck the checkbox, you are affected by this bug. See the steps on how to fix this.

  • Checking your GitLab instance configuration file:

    To check whether you're impacted by this bug, check your instance configuration settings. The configuration file in which Service Ping can be disabled depends on your installation and deployment method, but is typically one of the following:

    • /etc/gitlab/gitlab.rb for Omnibus GitLab Linux Package and Docker.
    • charts.yaml for GitLab Helm and cloud-native Kubernetes deployments.
    • gitlab.yml for GitLab installations from source.

    To check the relevant configuration file for strings that indicate whether Service Ping is disabled, you can use grep:

    # Linux package
grep "usage_ping_enabled'\] = false" /etc/gitlab/gitlab.rb

# Kubernetes charts
grep "enableUsagePing: false" values.yaml

# From source
grep "usage_ping_enabled'\] = false" gitlab/config.yml

    If you see any output after running the relevant command, your GitLab instance may be affected by the bug. Otherwise, your instance is not affected.

How to fix the "Cannot disable Service Ping" bug

To work around this bug, you have two options:

  • Update to GitLab 13.12.4 or newer to fix this bug.

  • If you can't update to GitLab 13.12.4 or newer, enable Service Ping in the configuration file, then disable Service Ping in the UI. For example, if you're using the Linux package:

    1. Edit /etc/gitlab/gitlab.rb:

      gitlab_rails['usage_ping_enabled'] = true

    2. Reconfigure GitLab:

      sudo gitlab-ctl reconfigure

    3. In GitLab, on the top bar, select Menu > {admin} Admin.

    4. On the left sidebar, select Settings > Metrics and profiling.

    5. Expand Usage Statistics.

    6. Clear the Enable service ping checkbox.

    7. Select Save Changes.