---
stage: Growth
group: Product Intelligence
info: To determine the technical writer assigned to the Stage/Group associated with this page, see https://about.gitlab.com/handbook/engineering/ux/technical-writing/#assignments
---

# Usage Ping Guide

> Introduced in GitLab Ultimate 11.2, more statistics.

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

For more information about Product Intelligence, see:

- [Product Intelligence Guide](https://about.gitlab.com/handbook/product/product-intelligence-guide/)
- [Snowplow Guide](../snowplow/index.md)

More links:

- [Product Intelligence Direction](https://about.gitlab.com/direction/product-intelligence/)
- [Data Analysis Process](https://about.gitlab.com/handbook/business-ops/data-team/#data-analysis-process/)
- [Data for Product Managers](https://about.gitlab.com/handbook/business-ops/data-team/programs/data-for-product-managers/)
- [Data Infrastructure](https://about.gitlab.com/handbook/business-ops/data-team/platform/infrastructure/)

## What is Usage Ping?

- GitLab sends a weekly payload containing usage data to GitLab Inc. Usage Ping provides high-level data to help our product, support, and sales teams. It does not send any project names, usernames, or any other specific data. The information from the usage ping is not anonymous, it is linked to the hostname of the instance. Sending usage ping is optional, and any instance can disable analytics.
- The usage data is primarily composed of row counts for different tables in the instance's database. By comparing these counts month over month (or week over week), we can get a rough sense for how an instance is using the different features in the product. In addition to counts, other facts
    that help us classify and understand GitLab installations are collected.
- Usage ping is important to GitLab as we use it to calculate our Stage Monthly Active Users (SMAU) which helps us measure the success of our stages and features.
- While usage ping is enabled, GitLab gathers data from the other instances and can show usage statistics of your instance to your users.

### Why should we enable Usage Ping?

- The main purpose of Usage 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 the usage ping active, GitLab lets you analyze the users' activities over time of your GitLab installation.
- As a benefit of having the usage 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.
- Usage Ping is enabled by default. To disable it, see [Disable Usage Ping](#disable-usage-ping).

### Limitations

- Usage 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 Usage Ping to track aggregated backend events on self-managed.

## Usage Ping payload

You can view the exact JSON payload sent to GitLab Inc. in the administration panel. To view the payload:

1. Sign in as a user with [Administrator](../../user/permissions.md) permissions.
1. In the top navigation bar, click **(admin)** **Admin Area**.
1. In the left sidebar, go to **Settings > Metrics and profiling**.
1. Expand the **Usage statistics** section.
1. Click the **Preview payload** button.

For an example payload, see [Example Usage Ping payload](#example-usage-ping-payload).

## Disable Usage Ping

To disable Usage Ping in the GitLab UI:

1. Sign in as a user with [Administrator](../../user/permissions.md) permissions.
1. In the top navigation bar, click **(admin)** **Admin Area**.
1. In the left sidebar, go to **Settings > Metrics and profiling**.
1. Expand the **Usage statistics** section.
1. Clear the **Usage Ping** checkbox and click **Save changes**.

To disable Usage Ping and prevent it from being configured in the future through
the administration panel, Omnibus installs can set the following in
[`gitlab.rb`](https://docs.gitlab.com/omnibus/settings/configuration.html#configuration-options):

```ruby
gitlab_rails['usage_ping_enabled'] = false
```

Source installations can set the following in `gitlab.yml`:

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

## Usage 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:

```mermaid
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 Usage 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 Usage Ping works

1. The Usage Ping [cron job](https://gitlab.com/gitlab-org/gitlab/-/blob/master/app/workers/gitlab_usage_ping_worker.rb#L30) is set in Sidekiq to run weekly.
1. When the cron job runs, it calls [`Gitlab::UsageData.to_json`](https://gitlab.com/gitlab-org/gitlab/-/blob/master/app/services/submit_usage_ping_service.rb#L22).
1. `Gitlab::UsageData.to_json` [cascades down](https://gitlab.com/gitlab-org/gitlab/-/blob/master/lib/gitlab/usage_data.rb#L22) to ~400+ other counter method calls.
1. The response of all methods calls are [merged together](https://gitlab.com/gitlab-org/gitlab/-/blob/master/lib/gitlab/usage_data.rb#L14) into a single JSON payload in `Gitlab::UsageData.to_json`.
1. The JSON payload is then [posted to the Versions application]( https://gitlab.com/gitlab-org/gitlab/-/blob/master/app/services/submit_usage_ping_service.rb#L20)
   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/>.

## Usage Ping Metric Life cycle

### 1. New metrics addition

Please follow the [Implementing Usage Ping](#implementing-usage-ping) guide.

### 2. Existing metric change

Because we do not control when customers update their self-managed instances of GitLab,
we **STRONGLY DISCOURAGE** changes to the logic used to calculate any metric.
Any such changes lead to inconsistent reports from multiple GitLab instances.
If there is a problem with an existing metric, it's best to deprecate the existing metric,
and use it, side by side, with the desired new metric.

Example:
Consider following change. Before GitLab 12.6, the `example_metric` was implemented as:

```ruby
{
  ...
  example_metric: distinct_count(Project, :creator_id)
}
```

For GitLab 12.6, the metric was changed to filter out archived projects:

```ruby
{
  ...
  example_metric: distinct_count(Project.non_archived, :creator_id)
}
```

In this scenario all instances running up to GitLab 12.5 continue to report `example_metric`,
including all archived projects, while all instances running GitLab 12.6 and higher filters
out such projects. As Usage Ping data is collected from all reporting instances, the
resulting dataset includes mixed data, which distorts any following business analysis.

The correct approach is to add a new metric for GitLab 12.6 release with updated logic:

```ruby
{
  ...
  example_metric_without_archived: distinct_count(Project.non_archived, :creator_id)
}
```

and update existing business analysis artefacts to use `example_metric_without_archived` instead of `example_metric`

### 3. Metrics deprecation and removal

The process for deprecating and removing metrics is under development. For
more information, see the following [issue](https://gitlab.com/gitlab-org/gitlab/-/issues/284637).

## Implementing Usage Ping

Usage 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.

There are several types of counters which are all found 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 to isolate each counter to avoid breaking the entire Usage Ping.

### Why batch counting

For large tables, PostgreSQL can take a long time to count rows due to MVCC [(Multi-version Concurrency Control)](https://en.wikipedia.org/wiki/Multiversion_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 for your use:

- [Ordinary Batch Counters](#ordinary-batch-counters)
- [Distinct Batch Counters](#distinct-batch-counters)
- [Sum Batch Operation](#sum-batch-operation)
- [Add Operation](#add-operation)
- [Estimated Batch Counters](#estimated-batch-counters)

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:

```ruby
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. Take a look at the [iterating tables in batches](../iterating_tables_in_batches.md) guide for more details.

Examples:

```ruby
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:

```ruby
sum(JiraImportState.finished, :imported_issues_count)
```

### Grouping & 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:

```ruby
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

```ruby
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

> - [Introduced](https://gitlab.com/gitlab-org/gitlab/-/merge_requests/48233) in GitLab 13.7.

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](http://algo.inria.fr/flajolet/Publications/FlFuGaMe07.pdf) 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](https://gitlab.com/gitlab-org/gitlab/-/blob/master/lib/gitlab/utils/usage_data.rb#L63)
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`.
1. 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`.
1. Both `start` and `finish` arguments should always represent primary key relationship values,
   even if the estimated count refers to another column, for example:

   ```ruby
     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:

   ```ruby
     estimate_batch_distinct_count(::Project)
   ```

1. 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:

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

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

   ```ruby
     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:

```ruby
  "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:

- Using Redis methods [`INCR`](https://redis.io/commands/incr), [`GET`](https://redis.io/commands/get), and [`Gitlab::UsageDataCounters::WikiPageCounter`](https://gitlab.com/gitlab-org/gitlab/-/blob/master/lib/gitlab/usage_data_counters/wiki_page_counter.rb)
- Using Redis methods [`HINCRBY`](https://redis.io/commands/hincrby), [`HGETALL`](https://redis.io/commands/hgetall), and [`Gitlab::UsageCounters::PodLogs`](https://gitlab.com/gitlab-org/gitlab/-/blob/master/lib/gitlab/usage_counters/pod_logs.rb)

##### UsageData API Tracking

<!-- There's nearly identical content in `##### Adding new events`. If you fix errors here, you may need to fix the same errors in the other location. -->

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.

   ```plaintext
   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

1. 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

   ```javascript
   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](https://redis.io/commands/pfcount), 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](https://redis.io/commands/pfadd) and [PFCOUNT](https://redis.io/commands/pfcount).

##### Adding new events

1. Define events in [`known_events`](https://gitlab.com/gitlab-org/gitlab/-/blob/master/lib/gitlab/usage_data_counters/known_events/).

   Example event:

   ```yaml
   - name: i_compliance_credential_inventory
     category: compliance
     redis_slot: compliance
     expiry: 42  # 6 weeks
     aggregation: weekly
     feature_flag: usage_data_i_compliance_credential_inventory
   ```

   Keys:

   - `name`: unique event name.

     Name format `<prefix>_<redis_slot>_name`.

     Use one of the following prefixes for the event's name:

        - `g_` for group, as an event which is tracked for group.
        - `p_` for project, as an event which is tracked for project.
        - `i_` for instance, as an event which is tracked for instance.
        - `a_` for events encompassing all `g_`, `p_`, `i_`.
        - `o_` for other.

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

     Example names: `i_compliance_credential_inventory`, `g_analytics_contribution`.

   - `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. Used if needed to calculate totals
     for a group of metrics. Ensure keys are in the same slot. For example:
     `i_compliance_credential_inventory` with `redis_slot: 'compliance'` builds Redis key
     `i_{compliance}_credential_inventory-2020-34`. If `redis_slot` is not defined the Redis key will
     be `{i_compliance_credential_inventory}-2020-34`.
   - `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](../feature_flags/index.md) documentation. The feature flags are owned by the group adding the event tracking.

Use one of the following methods to track events:

1. Track event in controller using `RedisTracking` module with `track_redis_hll_event(*controller_actions, name:, if: nil, &block)`.

   Arguments:

   - `controller_actions`: controller actions we want to track.
   - `name`: event name.
   - `if`: optional custom conditions, using the same format as with Rails callbacks.
   - `&block`: optional block that computes and returns the `custom_id` that we want to track. This will override the `visitor_id`.

   Example usage:

   ```ruby
   # controller
   class ProjectsController < Projects::ApplicationController
     include RedisTracking

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

     def index
       render html: 'index'
     end

    def new
      render html: 'new'
    end

    def show
      render html: 'show'
    end
   end
   ```

1. Track event in API using `increment_unique_values(event_name, values)` helper method.

   Arguments:

   - `event_name`: event name.
   - `values`: values counted, one value or array of values.

   Example usage:

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

     increment_unique_values('i_list_repositories', current_user.id)

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

1. Track event using `track_usage_event(event_name, values)` in services and GraphQL

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

   Example:

   [Track usage event for incident created in service](https://gitlab.com/gitlab-org/gitlab/-/blob/v13.8.3-ee/app/services/issues/update_service.rb#L66)

   [Track usage event for incident created in GraphQL](https://gitlab.com/gitlab-org/gitlab/-/blob/v13.8.3-ee/app/graphql/mutations/alert_management/update_alert_status.rb#L16)

   ```ruby
     track_usage_event(:incident_management_incident_created, current_user.id)
   ```

<!-- There's nearly identical content in `##### UsageData API Tracking`. If you find / fix errors here, you may need to fix errors in that section too. -->

1. Track event using `UsageData` API

   Increment unique users count using Redis HLL, 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.

   ```plaintext
   POST /usage_data/increment_unique_users
   ```

   | Attribute | Type | Required | Description |
   | :-------- | :--- | :------- | :---------- |
   | `event` | string | yes | The event name it should be tracked |

   Response

   Return 200 if tracking failed for any reason.

   - `200` if event was tracked or any errors
   - `400 Bad request` if event parameter is missing
   - `401 Unauthorized` if user is not authenticated
   - `403 Forbidden` for invalid CSRF token provided

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

   Example usage for an existing event already defined in [known events](https://gitlab.com/gitlab-org/gitlab/-/blob/master/lib/gitlab/usage_data_counters/known_events/):

   Usage Data API is behind  `usage_data_api` feature flag which, as of GitLab 13.7, is
   now set to `default_enabled: true`.

   ```javascript
   import api from '~/api';

   api.trackRedisHllUserEvent('my_already_defined_event_name'),
   ```

1. 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`.

1. Testing tracking and getting unique events

Trigger events in rails console by using `track_event` method

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

Next, get the unique events for the current week.

   ```ruby
   # Get unique events for metric for current_week
   Gitlab::UsageDataCounters::HLLRedisCounter.unique_events(event_names: 'g_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](#adding-new-events):

- Event aggregation: weekly.
- Key expiry time:
  - Daily: 29 days.
  - Weekly: 42 days.
- When adding new metrics, use a [feature flag](../../operations/feature_flags.md) 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/Disable Redis HLL tracking

Events are tracked behind [feature flags](../feature_flags/index.md) due to concerns for Redis performance and scalability.

For a full list of events and corresponding feature flags see, [known_events](https://gitlab.com/gitlab-org/gitlab/-/blob/master/lib/gitlab/usage_data_counters/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](../feature_flags/index.md).

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

##### Known events are added automatically in usage data payload

All events added in [`known_events/common.yml`](https://gitlab.com/gitlab-org/gitlab/-/blob/master/lib/gitlab/usage_data_counters/known_events/common.yml) are automatically added to usage data generation under the `redis_hll_counters` key. This column is stored in [version-app as a JSON](https://gitlab.com/gitlab-services/version-gitlab-com/-/blob/master/db/schema.rb#L209).
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](#adding-new-events) events and data for the last complete week for weekly [aggregation](#adding-new-events) events.
- `#{event_name}_monthly`: Data for 28 days for daily [aggregation](#adding-new-events) events and data for the last 4 complete weeks for weekly [aggregation](#adding-new-events) 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:

```ruby
{:redis_hll_counters=>
  {"compliance"=>
    {"g_compliance_dashboard_weekly"=>0,
     "g_compliance_dashboard_monthly"=>0,
     "g_compliance_audit_events_weekly"=>0,
     "g_compliance_audit_events_monthly"=>0,
     "compliance_total_unique_counts_weekly"=>0,
     "compliance_total_unique_counts_monthly"=>0},
   "analytics"=>
    {"g_analytics_contribution_weekly"=>0,
     "g_analytics_contribution_monthly"=>0,
     "g_analytics_insights_weekly"=>0,
     "g_analytics_insights_monthly"=>0,
     "analytics_total_unique_counts_weekly"=>0,
     "analytics_total_unique_counts_monthly"=>0},
   "ide_edit"=>
    {"g_edit_by_web_ide_weekly"=>0,
     "g_edit_by_web_ide_monthly"=>0,
     "g_edit_by_sfe_weekly"=>0,
     "g_edit_by_sfe_monthly"=>0,
     "ide_edit_total_unique_counts_weekly"=>0,
     "ide_edit_total_unique_counts_monthly"=>0},
   "search"=>
    {"i_search_total_weekly"=>0, "i_search_total_monthly"=>0, "i_search_advanced_weekly"=>0, "i_search_advanced_monthly"=>0, "i_search_paid_weekly"=>0, "i_search_paid_monthly"=>0, "search_total_unique_counts_weekly"=>0, "search_total_unique_counts_monthly"=>0},
   "source_code"=>{"wiki_action_weekly"=>0, "wiki_action_monthly"=>0}
 }
```

Example usage:

```ruby
# 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('expand_vulnerabilities', values: visitor_id)

# Get unique events for metric
redis_usage_data { Gitlab::UsageDataCounters::HLLRedisCounter.unique_events(event_names: 'expand_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.

Usage:

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

### Adding counters to build new metrics

When adding the results of two counters, use the `add` usage data method that
handles fallback values and exceptions. It also generates a valid [SQL export](#exporting-usage-ping-sql-queries-and-definitions).

Example usage:

```ruby
add(User.active, User.bot)
```

### Prometheus Queries

In those cases where operational metrics should be part of Usage 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 usage data.

NOTE:
Prometheus as a data source for Usage Ping is currently only available for single-node Omnibus installations
that are running the [bundled Prometheus](../../administration/monitoring/prometheus/index.md) 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:

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

Please refer to [the `PrometheusClient` definition](https://gitlab.com/gitlab-org/gitlab/-/blob/master/lib/gitlab/prometheus_client.rb)
for how to use its API to query for data.

### Fallback values for UsagePing

We return fallback values in these cases:

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

## Developing and testing Usage Ping

### 1. Naming and placing the metrics

Add the metric in one of the top level keys

- `license`: for license related metrics.
- `settings`: for settings related metrics.
- `counts_weekly`: for counters that have data for the most recent 7 days.
- `counts_monthly`: for counters that have data for the most recent 28 days.
- `counts`: for counters that have data for all time.

### 2. Use your Rails console to manually test counters

```ruby
# count
Gitlab::UsageData.count(User.active)
Gitlab::UsageData.count(::Clusters::Cluster.aws_installed.enabled, :cluster_id)

# count distinct
Gitlab::UsageData.distinct_count(::Project, :creator_id)
Gitlab::UsageData.distinct_count(::Note.with_suggestions.where(time_period), :author_id, start: ::User.minimum(:id), finish: ::User.maximum(:id))
```

### 3. Generate the SQL query

Your Rails console returns the generated SQL queries.

Example:

```ruby
pry(main)> Gitlab::UsageData.count(User.active)
   (2.6ms)  SELECT "features"."key" FROM "features"
   (15.3ms)  SELECT MIN("users"."id") FROM "users" WHERE ("users"."state" IN ('active')) AND ("users"."user_type" IS NULL OR "users"."user_type" IN (6, 4))
   (2.4ms)  SELECT MAX("users"."id") FROM "users" WHERE ("users"."state" IN ('active')) AND ("users"."user_type" IS NULL OR "users"."user_type" IN (6, 4))
   (1.9ms)  SELECT COUNT("users"."id") FROM "users" WHERE ("users"."state" IN ('active')) AND ("users"."user_type" IS NULL OR "users"."user_type" IN (6, 4)) AND "users"."id" BETWEEN 1 AND 100000
```

### 4. Optimize queries with #database-lab

Paste the SQL query into `#database-lab` to see how the query performs at scale.

- `#database-lab` is a Slack channel which uses a production-sized environment to test your queries.
- GitLab.com's production database has a 15 second timeout.
- Any single query must stay below [1 second execution time](../query_performance.md#timing-guidelines-for-queries) with cold caches.
- Add a specialized index on columns involved to reduce the execution time.

To have an understanding of the query's execution we add in the MR description the following information:

- For counters that have a `time_period` test we add information for both cases:
  - `time_period = {}` for all time periods
  - `time_period = { created_at: 28.days.ago..Time.current }` for last 28 days period
- Execution plan and query time before and after optimization
- Query generated for the index and time
- Migration output for up and down execution

We also use `#database-lab` and [explain.depesz.com](https://explain.depesz.com/). For more details, see the [database review guide](../database_review.md#preparation-when-adding-or-modifying-queries).

#### Optimization recommendations and examples

- Use specialized indexes [example 1](https://gitlab.com/gitlab-org/gitlab/-/merge_requests/26871), [example 2](https://gitlab.com/gitlab-org/gitlab/-/merge_requests/26445).
- Use defined `start` and `finish`, and simple queries. These values can be memoized and reused, [example](https://gitlab.com/gitlab-org/gitlab/-/merge_requests/37155).
- Avoid joins and write the queries as simply as possible, [example](https://gitlab.com/gitlab-org/gitlab/-/merge_requests/36316).
- Set a custom `batch_size` for `distinct_count`, [example](https://gitlab.com/gitlab-org/gitlab/-/merge_requests/38000).

### 5. Add the metric definition

[Check Metrics Dictionary Guide](metrics_dictionary.md)

When adding, updating, or removing metrics, please update the [Metrics Dictionary](dictionary.md).

### 6. Add new metric to Versions Application

Check if new metrics need to be added to the Versions Application. See `usage_data` [schema](https://gitlab.com/gitlab-services/version-gitlab-com/-/blob/master/db/schema.rb#L147) and usage data [parameters accepted](https://gitlab.com/gitlab-services/version-gitlab-com/-/blob/master/app/services/usage_ping.rb). Any metrics added under the `counts` key are saved in the `stats` column.

### 7. Add the feature label

Add the `feature` label to the Merge Request for new Usage Ping metrics. These are user-facing changes and are part of expanding the Usage Ping feature.

### 8. Add a changelog file

Ensure you comply with the [Changelog entries guide](../changelog.md).

### 9. Ask for a Product Intelligence Review

On GitLab.com, we have DangerBot setup to monitor Product Intelligence related files and DangerBot recommends a [Product Intelligence review](product_intelligence_review.md). Mention `@gitlab-org/growth/product_intelligence/engineers` in your MR for a review.

### 10. Verify your metric

On GitLab.com, the Product Intelligence team regularly monitors Usage Ping. They may alert you that your metrics need further optimization to run quicker and with greater success. You may also use the [Usage Ping QA dashboard](https://app.periscopedata.com/app/gitlab/632033/Usage-Ping-QA) to check how well your metric performs. The dashboard allows filtering by GitLab version, by "Self-managed" & "SaaS" and shows you how many failures have occurred for each metric. Whenever you notice a high failure rate, you may re-optimize your metric.

### Usage Ping local setup

To set up Usage Ping locally, you must:

1. [Set up local repositories]#(set-up-local-repositories)
1. [Test local setup](#test-local-setup)
1. (Optional) [Test Prometheus-based usage ping](#test-prometheus-based-usage-ping)

#### Set up local repositories

1. Clone and start [GitLab](https://gitlab.com/gitlab-org/gitlab-development-kit).
1. Clone and start [Versions Application](https://gitlab.com/gitlab-services/version-gitlab-com).
   Make sure to run `docker-compose up` to start a PostgreSQL and Redis instance.
1. Point GitLab to the Versions Application endpoint instead of the default endpoint:
   1. Open [submit_usage_ping_service.rb](https://gitlab.com/gitlab-org/gitlab/-/blob/master/app/services/submit_usage_ping_service.rb#L4) in your local and modified `PRODUCTION_URL`.
   1. Set it to the local Versions Application URL `http://localhost:3000/usage_data`.

#### Test local setup

1. Using the `gitlab` Rails console, manually trigger a usage ping:

   ```ruby
   SubmitUsagePingService.new.execute
   ```

1. Use the `versions` Rails console to check the usage ping was successfully received,
   parsed, and stored in the Versions database:

   ```ruby
   UsageData.last
   ```

### Test Prometheus-based usage ping

If the data submitted includes metrics [queried from Prometheus](#prometheus-queries)
you want to inspect and verify, you must:

- Ensure that a Prometheus server is running locally.
- Ensure the respective GitLab components are exporting metrics to the Prometheus server.

If you do not need to test data coming from Prometheus, no further action
is necessary. Usage Ping should degrade gracefully in the absence of a running Prometheus server.

Three kinds of components may export data to Prometheus, and are included in Usage Ping:

- [`node_exporter`](https://github.com/prometheus/node_exporter): Exports node metrics
  from the host machine.
- [`gitlab-exporter`](https://gitlab.com/gitlab-org/gitlab-exporter): Exports process metrics
  from various GitLab components.
- Other various GitLab services, such as Sidekiq and the Rails server, which export their own metrics.

#### Test with an Omnibus container

This is the recommended approach to test Prometheus based Usage Ping.

The easiest way to verify your changes is to build a new Omnibus image from your code branch by using CI, then download the image
and run a local container instance:

1. From your merge request, click on the `qa` stage, then trigger the `package-and-qa` job. This job triggers an Omnibus
build in a [downstream pipeline of the `omnibus-gitlab-mirror` project](https://gitlab.com/gitlab-org/build/omnibus-gitlab-mirror/-/pipelines).
1. In the downstream pipeline, wait for the `gitlab-docker` job to finish.
1. Open the job logs and locate the full container name including the version. It takes the following form: `registry.gitlab.com/gitlab-org/build/omnibus-gitlab-mirror/gitlab-ee:<VERSION>`.
1. On your local machine, make sure you are signed in to the GitLab Docker registry. You can find the instructions for this in
[Authenticate to the GitLab Container Registry](../../user/packages/container_registry/index.md#authenticate-with-the-container-registry).
1. Once signed in, download the new image by using `docker pull registry.gitlab.com/gitlab-org/build/omnibus-gitlab-mirror/gitlab-ee:<VERSION>`
1. For more information about working with and running Omnibus GitLab containers in Docker, please refer to [GitLab Docker images](https://docs.gitlab.com/omnibus/docker/README.html) in the Omnibus documentation.

#### Test with GitLab development toolkits

This is the less recommended approach, because it comes with a number of difficulties when emulating a real GitLab deployment.

The [GDK](https://gitlab.com/gitlab-org/gitlab-development-kit) is not set up to run a Prometheus server or `node_exporter` alongside other GitLab components. If you would
like to do so, [Monitoring the GDK with Prometheus](https://gitlab.com/gitlab-org/gitlab-development-kit/-/blob/master/doc/howto/prometheus/index.md#monitoring-the-gdk-with-prometheus) is a good start.

The [GCK](https://gitlab.com/gitlab-org/gitlab-compose-kit) has limited support for testing Prometheus based Usage Ping.
By default, it already comes with a fully configured Prometheus service that is set up to scrape a number of components,
but with the following limitations:

- It does not run a `gitlab-exporter` instance, so several `process_*` metrics from services such as Gitaly may be missing.
- While it runs a `node_exporter`, `docker-compose` services emulate hosts, meaning that it would normally report itself to not be associated
with any of the other services that are running. That is not how node metrics are reported in a production setup, where `node_exporter`
always runs as a process alongside other GitLab components on any given node. From Usage Ping's perspective none of the node data would therefore
appear to be associated to any of the services running, because they all appear to be running on different hosts. To alleviate this problem, the `node_exporter` in GCK was arbitrarily "assigned" to the `web` service, meaning only for this service `node_*` metrics appears in Usage Ping.

## Aggregated metrics

> - [Introduced](https://gitlab.com/gitlab-org/gitlab/-/merge_requests/45979) in GitLab 13.6.

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

To add data for aggregated metrics into Usage Ping payload you should add corresponding definition at [`config/metrics/aggregates/*.yaml`](https://gitlab.com/gitlab-org/gitlab/-/blob/master/config/metrics/aggregates/) for metrics available at Community Edition and at [`ee/config/metrics/aggregates/*.yaml`](https://gitlab.com/gitlab-org/gitlab/-/blob/master/ee/config/metrics/aggregates/) for Enterprise Edition ones.

Each aggregate definition includes following parts:

- `name`: Unique name under which the aggregate metric is added to the Usage 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:
  - [`database`](#database-sourced-aggregated-metrics)
  - [`redis`](#redis-sourced-aggregated-metrics)
- `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](#database-sourced-aggregated-metrics) and
  [Redis sourced aggregated metrics](#redis-sourced-aggregated-metrics) sections.
- `feature_flag`: Name of [development feature flag](../feature_flags/index.md#development-type)
  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:

```yaml
- name: example_metrics_union
  operator: OR
  events:
    - 'i_search_total'
    - 'i_search_advanced'
    - 'i_search_paid'
  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 Usage Ping payload under the `aggregated_metrics` sub-key in the `counts_weekly` and `counts_monthly` top level keys.

```ruby
{
  :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`

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

### Redis sourced aggregated metrics

> - [Introduced](https://gitlab.com/gitlab-org/gitlab/-/merge_requests/45979) in GitLab 13.6.

To declare the aggregate of events collected with [Redis HLL Counters](#redis-hll-counters),
you must fulfill the following requirements:

1. All events listed at `events` attribute must come from
   [`known_events/*.yml`](#known-events-are-added-automatically-in-usage-data-payload) files.
1. All events listed at `events` attribute must have the same `redis_slot` attribute.
1. All events listed at `events` attribute must have the same `aggregation` attribute.
1. `time_frame` does not include `all` value, which is unavailable for Redis sourced aggregated metrics.

### Database sourced aggregated metrics

> - [Introduced](https://gitlab.com/gitlab-org/gitlab/-/merge_requests/52784) in GitLab 13.9.
> - It's [deployed behind a feature flag](../../user/feature_flags.md), disabled by default.
> - It's enabled on GitLab.com.

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

1. [Persist the metrics for aggregation](#persist-metrics-for-aggregation).
1. [Add new aggregated metric definition](#add-new-aggregated-metric-definition).

#### Persist metrics for aggregation

Only metrics calculated with [Estimated Batch Counters](#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](#estimate_batch_distinct_count-method) method.
This block should invoke the
`Gitlab::Usage::Metrics::Aggregates::Sources::PostgresHll.save_aggregated_metrics`
[method](https://gitlab.com/gitlab-org/gitlab/-/blob/master/lib/gitlab/usage/metrics/aggregates/sources/postgres_hll.rb#L21),
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 Usage Ping.
- `recorded_at_timestamp`: The timestamp representing the moment when a given
  Usage 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:

```ruby
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/`](https://gitlab.com/gitlab-org/gitlab/-/blob/master/config/metrics/aggregates/).

To declare the aggregate of metrics collected with [Estimated Batch Counters](#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:

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

## Example Usage Ping payload

The following is example content of the Usage Ping payload.

```json
{
  "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,
  "ingress_modsecurity_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](https://www.2ndquadrant.com/en/blog/support-for-postgresqls-system-identifier-in-barman/).

## Exporting Usage Ping SQL queries and definitions

Two Rake tasks exist to export Usage 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:

```shell
# 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 usage 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`
1. Create named screen: `$ screen -S <username>_usage_ping_<date>`
1. Connect to console host: `$ ssh $USER-rails@console-01-sv-gprd.c.gitlab-production.internal`
1. Run `SubmitUsagePingService.new.execute`
1. Detach from screen: `ctrl + a, ctrl + d`
1. Exit from bastion: `$ exit`

### Verification (After approx 30 hours)

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