46 KiB
stage | group | info |
---|---|---|
Growth | Product Intelligence | 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 |
Implement Service Ping
Service Ping consists of two kinds of data:
- Counters: Track how often a certain event happened over time, such as how many CI/CD pipelines have run. They are monotonic and always trend up.
- Observations: Facts collected from one or more GitLab instances and can carry arbitrary data. There are no general guidelines for how to collect those, due to the individual nature of that data.
To implement a new metric in Service Ping, follow these steps:
- Implement the required counter
- Name and place the metric
- Test counters manually using your Rails console
- Generate the SQL query
- Optimize queries with
#database-lab
- Add the metric definition to the Metrics Dictionary
- Add the metric to the Versions Application
- Create a merge request
- Verify your metric
- Set up and test Service Ping locally
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'),
Types of counters
There are several types of counters in usage_data.rb
:
- Batch counters: Used for counts and sums.
- Redis counters: Used for in-memory counts.
- Alternative counters: Used for settings and configurations.
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.
Batch counters
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 |
Batch counting requires indexes on columns to calculate max, min, and range queries. In some cases, you must add a specialized index on the columns involved in a counter.
Ordinary batch counters
Simple count of a given ActiveRecord_Relation
, does a non-distinct batch count, smartly reduces batch_size
, and handles errors.
Handles the ActiveRecord::StatementInvalid
error.
Method:
count(relation, column = nil, batch: true, start: nil, finish: nil)
Arguments:
relation
the ActiveRecord_Relation to perform the countcolumn
the column to perform the count on, by default is the primary keybatch
: defaulttrue
to use batch countingstart
: custom start of the batch counting to avoid complex min calculationsend
: 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
Distinct count of a given ActiveRecord_Relation
on given column, a distinct batch count, smartly reduces batch_size
, and handles errors.
Handles the ActiveRecord::StatementInvalid
error.
Method:
distinct_count(relation, column = nil, batch: true, batch_size: nil, start: nil, finish: nil)
Arguments:
relation
: the ActiveRecord_Relation to perform the countcolumn
: the column to perform the distinct count, by default is the primary keybatch
: defaulttrue
to use batch countingbatch_size
: if none set it uses default value 10000 fromGitlab::Database::BatchCounter
start
: custom start of the batch counting to avoid complex min calculationsend
: 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
Sum the values of a given ActiveRecord_Relation on given column and handles errors.
Handles the ActiveRecord::StatementInvalid
error
Method:
sum(relation, column, batch_size: nil, start: nil, finish: nil)
Arguments:
relation
: the ActiveRecord_Relation to perform the operationcolumn
: the column to sum onbatch_size
: if none set it uses default value 1000 fromGitlab::Database::BatchCounter
start
: custom start of the batch counting to avoid complex min calculationsend
: 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
Sum the values given as parameters. Handles the StandardError
.
Returns -1
if any of the arguments are -1
.
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
Introduced 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 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
: FromGitlab::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:
-
The supplied
relation
must include the primary key defined as the numeric column. For example:id bigint NOT NULL
. -
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 ashas_many :boards
. -
Both
start
andfinish
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:
-
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) ofProject
relation:estimate_batch_distinct_count(::Project)
-
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
andfinish
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))
-
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 and -1 when a counter(Gitlab::UsageDataCounters)
is sent.
The different behavior is due to 2 different implementations of the Redis counter.
Method:
redis_usage_data(counter, &block)
Arguments:
counter
: a counter fromGitlab::UsageDataCounters
, that hasfallback_totals
method implemented- or a
block
: which is evaluated
Ordinary Redis counters
Examples of implementation:
- Using Redis methods
INCR
,GET
, andGitlab::UsageDataCounters::WikiPageCounter
- Using Redis methods
HINCRBY
,HGETALL
, andGitlab::UsageCounters::PodLogs
UsageData API tracking
-
Track event using
UsageData
APIIncrement event count using ordinary Redis counter, for given event name.
Tracking events using the
UsageData
API requires theusage_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 tracked400 Bad request
if event parameter is missing401 Unauthorized
if user is not authenticated403 Forbidden
for invalid CSRF token provided
-
Track events using JavaScript/Vue API helper which calls the API above
Note that
usage_data_api
andusage_data_#{event_name}
should be enabled to be able to track eventsimport 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%".
NOTE:
A user's consent for usage_stats (User.single_user&.requires_usage_stats_consent?
) is not checked during the data tracking stage due to performance reasons. Keys corresponding to those counters are present in Redis even if usage_stats_consent
is still required. However, no metric is collected from Redis and reported back to GitLab as long as usage_stats_consent
is required.
With Gitlab::UsageDataCounters::HLLRedisCounter
we have available data structures used to count unique values.
Implemented using Redis methods PFADD and PFCOUNT.
Add new events
-
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
withredis_slot: 'users'
builds Redis key{users}_creating_epics-2020-34
. Ifredis_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 adaily
basis does not pull more fine grained data. -
feature_flag
: optionaldefault_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.
-
-
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 thecustom_id
that we want to track. This overrides thevisitor_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 theusage_data_api
feature flag is set todefault_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 theusage_data_api
feature flag is set todefault_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'),
-
-
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 aredefault
,free
,bronze
,silver
,gold
,starter
,premium
,ultimate
.
-
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
- Events can be triggered using the
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 |
Name and place the metric
Add the metric in one of the top-level keys:
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.
How to get a metric name suggestion
The metric YAML generator can suggest a metric name for you.
To generate a metric name suggestion, first instrument the metric at the provided key_path
.
Then, generate the metric's YAML definition and
return to the instrumentation and update it.
- Add the metric instrumentation to
lib/gitlab/usage_data.rb
inside one of the top-level keys, using any name you choose. - Run the metrics YAML generator.
- Use the metric name suggestion to select a suitable metric name.
- Update the instrumentation you created in the first step and change the metric name to the suggested name.
- Update the metric's YAML definition with the correct
key_path
.
Test counters manually using your Rails console
# 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))
Generate the SQL query
Your Rails console returns the generated SQL queries. For example:
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
Optimize queries with #database-lab
#database-lab
is a Slack channel that uses a production-sized environment to test your queries.
Paste the SQL query into #database-lab
to see how the query performs at scale.
- GitLab.com's production database has a 15 second timeout.
- Any single query must stay below the 1 second execution time with cold caches.
- Add a specialized index on columns involved to reduce the execution time.
To understand the query's execution, we add the following information to a merge request description:
- For counters that have a
time_period
test, we add information for both:time_period = {}
for all time periods.time_period = { created_at: 28.days.ago..Time.current }
for the last 28 days.
- 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. For more details, see the database review guide.
Optimization recommendations and examples
- Use specialized indexes. For examples, see these merge requests:
- Use defined
start
andfinish
, and simple queries. These values can be memoized and reused, as in this example merge request. - Avoid joins and write the queries as simply as possible, as in this example merge request.
- Set a custom
batch_size
fordistinct_count
, as in this example merge request.
Add the metric definition
See the Metrics Dictionary guide for more information.
Add the metric to the Versions Application
Check if the new metric must be added to the Versions Application. See the usage_data
schema and Service Data parameters accepted. Any metrics added under the counts
key are saved in the stats
column.
Create a merge request
Create a merge request for the new Service Ping metric, and do the following:
- Add the
feature
label to the merge request. A metric is a user-facing change and is part of expanding the Service Ping feature. - Add a changelog entry that complies with the changelog entries guide.
- Ask for a Product Intelligence review. On GitLab.com, we have DangerBot set up to monitor Product Intelligence related files and recommend a Product Intelligence review.
Verify your metric
On GitLab.com, the Product Intelligence team regularly monitors Service Ping. They may alert you that your metrics need further optimization to run quicker and with greater success.
The Service Ping JSON payload for GitLab.com is shared in the #g_product_intelligence Slack channel every week.
You may also use the Service Ping QA dashboard to check how well your metric performs. The dashboard allows filtering by GitLab version, by "Self-managed" and "SaaS", and shows you how many failures have occurred for each metric. Whenever you notice a high failure rate, you can re-optimize your metric.
Set up and test Service Ping locally
To set up Service Ping locally, you must:
Set up local repositories
- Clone and start GitLab.
- Clone and start Versions Application.
Make sure you run
docker-compose up
to start a PostgreSQL and Redis instance. - Point GitLab to the Versions Application endpoint instead of the default endpoint:
- Open service_ping/submit_service.rb in your local and modified
PRODUCTION_URL
. - Set it to the local Versions Application URL:
http://localhost:3000/usage_data
.
- Open service_ping/submit_service.rb in your local and modified
Test local setup
-
Using the
gitlab
Rails console, manually trigger Service Ping:ServicePing::SubmitService.new.execute
-
Use the
versions
Rails console to check the Service Ping was successfully received, parsed, and stored in the Versions database:UsageData.last
Test Prometheus-based Service Ping
If the data submitted includes metrics queried from Prometheus 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. Service 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 Service Ping:
node_exporter
: Exports node metrics from the host machine.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 Service Ping.
To verify your change, build a new Omnibus image from your code branch using CI/CD, download the image, and run a local container instance:
- From your merge request, select the
qa
stage, then trigger thepackage-and-qa
job. This job triggers an Omnibus build in a downstream pipeline of theomnibus-gitlab-mirror
project. - In the downstream pipeline, wait for the
gitlab-docker
job to finish. - 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>
. - 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.
- Once signed in, download the new image by using
docker pull registry.gitlab.com/gitlab-org/build/omnibus-gitlab-mirror/gitlab-ee:<VERSION>
- For more information about working with and running Omnibus GitLab containers in Docker, refer to GitLab Docker images 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 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 is a good start.
The GCK has limited support for testing Prometheus based Service Ping. By default, it comes with a fully configured Prometheus service that is set up to scrape a number of components. However, it has the following limitations:
- It does not run a
gitlab-exporter
instance, so severalprocess_*
metrics from services such as Gitaly may be missing. - While it runs a
node_exporter
,docker-compose
services emulate hosts, meaning that it normally reports itself as not associated with any of the other running services. That is not how node metrics are reported in a production setup, wherenode_exporter
always runs as a process alongside other GitLab components on any given node. For Service Ping, 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, thenode_exporter
in GCK was arbitrarily "assigned" to theweb
service, meaning only for this servicenode_*
metrics appears in Service Ping.
Aggregated metrics
- Introduced in GitLab 13.6.
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:
config/metrics/aggregates/*.yaml
for metrics available in the Community Edition.ee/config/metrics/aggregates/*.yaml
for metrics available in the Enterprise Edition.
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 fordatabase
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 havedefault_enabled
attribute set tofalse
. Thefeature_flag
attribute is optional and can be omitted. Whenfeature_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
Introduced in GitLab 13.6.
To declare the aggregate of events collected with Redis HLL Counters, you must fulfill the following requirements:
- All events listed at
events
attribute must come fromknown_events/*.yml
files. - All events listed at
events
attribute must have the sameredis_slot
attribute. - All events listed at
events
attribute must have the sameaggregation
attribute. time_frame
does not includeall
value, which is unavailable for Redis sourced aggregated metrics.
Database sourced aggregated metrics
- Introduced in GitLab 13.9.
- It's deployed behind a feature flag, disabled by default.
- It's enabled on GitLab.com.
To declare an aggregate of metrics based on events collected from database, follow these steps:
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 methodrecorded_at
to fillrecorded_at_timestamp
argument, like this:recorded_at_timestamp: recorded_at
time_period
: The time period used to build therelation
argument passed intoestimate_batch_distinct_count
. To collect the metric with all available historical data, set anil
value as time period:time_period: nil
.data
: HyperLogLog buckets structure representing unique entries inrelation
. Theestimate_batch_distinct_count
method always passes the correct argument into the block, sodata
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 themetric_name
argument while persisting metrics in previous step. - Every metric listed in the
events:
attribute, has to be persisted for every selectedtime_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