Metrics instrumentation guide
This guide describes how to develop Service Ping metrics using metrics instrumentation.
For a video tutorial, see the Adding Service Ping metric via instrumentation class.
Nomenclature
-
Instrumentation class:
- Inherits one of the metric classes:
DatabaseMetric
,NumbersMetric
orGenericMetric
. - Implements the logic that calculates the value for a Service Ping metric.
- Inherits one of the metric classes:
-
Metric definition The Service Data metric YAML definition.
- Hardening: Hardening a method is the process that ensures the method fails safe, returning a fallback value like -1.
How it works
A metric definition has the instrumentation_class
field, which can be set to a class.
The defined instrumentation class should inherit one of the existing metric classes: DatabaseMetric
, NumbersMetric
or GenericMetric
.
The current convention is that a single instrumentation class corresponds to a single metric.
Using an instrumentation class ensures that metrics can fail safe individually, without breaking the entire process of Service Ping generation.
Database metrics
You can use database metrics to track data kept in the database, for example, a count of issues that exist on a given instance.
-
operation
: Operations for the givenrelation
, one ofcount
,distinct_count
,sum
, andaverage
. -
relation
: Assigns lambda that returns theActiveRecord::Relation
for the objects we want to perform theoperation
. The assigned lambda can accept up to one parameter. The parameter is hashed and stored under theoptions
key in the metric definition. -
start
: Specifies the start value of the batch counting, by default isrelation.minimum(:id)
. -
finish
: Specifies the end value of the batch counting, by default isrelation.maximum(:id)
. -
cache_start_and_finish_as
: Specifies the cache key forstart
andfinish
values and sets up caching them. Use this call whenstart
andfinish
are expensive queries that should be reused between different metric calculations. -
available?
: Specifies whether the metric should be reported. The default istrue
. -
timestamp_column
: Optionally specifies timestamp column for metric used to filter records for time constrained metrics. The default iscreated_at
.
Example of a merge request that adds a database metric.
Optimization recommendations and examples
Any single query for a Service Ping metric must stay below the 1 second execution time with cold caches.
- Use specialized indexes. For examples, see these merge requests:
- Use defined
start
andfinish
. These values can be memoized and reused, as in this example merge request. - Avoid joins and unnecessary complexity in your queries. See this example merge request as an example.
- Set a custom
batch_size
fordistinct_count
, as in this example merge request.
Database metric Examples
Count Example
module Gitlab
module Usage
module Metrics
module Instrumentations
class CountIssuesMetric < DatabaseMetric
operation :count
relation ->(options) { Issue.where(confidential: options[:confidential]) }
end
end
end
end
end
Batch counters Example
module Gitlab
module Usage
module Metrics
module Instrumentations
class CountIssuesMetric < DatabaseMetric
operation :count
start { Issue.minimum(:id) }
finish { Issue.maximum(:id) }
relation { Issue }
end
end
end
end
end
Distinct batch counters Example
# frozen_string_literal: true
module Gitlab
module Usage
module Metrics
module Instrumentations
class CountUsersAssociatingMilestonesToReleasesMetric < DatabaseMetric
operation :distinct_count, column: :author_id
relation { Release.with_milestones }
start { Release.minimum(:author_id) }
finish { Release.maximum(:author_id) }
end
end
end
end
end
Sum Example
# frozen_string_literal: true
module Gitlab
module Usage
module Metrics
module Instrumentations
class JiraImportsTotalImportedIssuesCountMetric < DatabaseMetric
operation :sum, column: :imported_issues_count
relation { JiraImportState.finished }
end
end
end
end
end
Average Example
# frozen_string_literal: true
module Gitlab
module Usage
module Metrics
module Instrumentations
class CountIssuesWeightAverageMetric < DatabaseMetric
operation :average, column: :weight
relation { Issue }
end
end
end
end
end
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
.
When correctly used, the estimate_batch_distinct_count
method enables efficient counting over
columns that contain non-unique values, which cannot 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))
Aggregated metrics
The aggregated metrics feature provides insight into the number of data attributes, for example pseudonymized_user_ids
, that occurred in a collection of events. For example, you can aggregate the number of users who perform multiple actions such as creating a new issue and opening
a new merge request.
You can use a YAML file to define your aggregated metrics. The following arguments are required:
-
options.events
: List of event names to aggregate into metric data. All events in this list must use the same data source. Additional data source requirements are described in Database sourced aggregated metrics and Event sourced aggregated metrics. -
options.aggregate.operator
: Operator that defines how the aggregated metric data is counted. Available operators are:-
OR
: Removes duplicates and counts all entries that triggered any of the listed events. -
AND
: Removes duplicates and counts all elements that were observed triggering all of the following events.
-
-
options.aggregate.attribute
: Information pointing to the attribute that is being aggregated across events. -
time_frame
: One or more valid time frames. Use these to limit the data included in aggregated metrics to events within a specific date-range. Valid time frames are:-
7d
: The last 7 days of data. -
28d
: The last 28 days of data. -
all
: All historical data, only available fordatabase
sourced aggregated metrics.
-
-
data_source
: Data source used to collect all events data included in the aggregated metrics. Valid data sources are:database
internal_events
-
redis_hll
: deprecated metrics using RedisHLL directly
Refer to merge request 98206 for an example of a merge request that adds an AggregatedMetric
metric.
Count unique user_ids
that occurred in at least one of the events: incident_management_alert_status_changed
,
incident_management_alert_assigned
, incident_management_alert_todo
, incident_management_alert_create_incident
.
time_frame: 28d
instrumentation_class: AggregatedMetric
data_source: internal_events
options:
aggregate:
operator: OR
attribute: user_id
events:
- `incident_management_alert_status_changed`
- `incident_management_alert_assigned`
- `incident_management_alert_todo`
- `incident_management_alert_create_incident`
Event sourced aggregated metrics
Introduced in GitLab 13.6.
To declare the aggregate of events collected with Internal Events, make sure time_frame
does not include the all
value, which is unavailable for Redis-sourced aggregated metrics.
While it is possible to aggregate EE-only events together with events that occur in all GitLab editions, it’s important to remember that doing so may produce high variance between data collected from EE and CE GitLab instances.
Database sourced aggregated metrics
Introduced in GitLab 13.9.
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.by_scan_types(scan_type).where(time_period)
pipelines_with_secure_jobs['dependency_scanning_pipeline'] = estimate_batch_distinct_count(relation, :pipeline_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. 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.
Availability-restrained Aggregated metrics
If the Aggregated metric should only be available in the report under specific conditions, then you must specify these conditions in a new class that is a child of the AggregatedMetric
class.
# frozen_string_literal: true
module Gitlab
module Usage
module Metrics
module Instrumentations
class MergeUsageCountAggregatedMetric < AggregatedMetric
available? { Feature.enabled?(:merge_usage_data_missing_key_paths) }
end
end
end
end
end
You must also use the class’s name in the YAML setup.
time_frame: 28d
instrumentation_class: MergeUsageCountAggregatedMetric
data_source: redis_hll
options:
aggregate:
operator: OR
attribute: user_id
events:
- `incident_management_alert_status_changed`
- `incident_management_alert_assigned`
- `incident_management_alert_todo`
- `incident_management_alert_create_incident`
Numbers metrics
-
operation
: Operations for the givendata
block. Currently we only supportadd
operation. -
data
: ablock
which contains an array of numbers. -
available?
: Specifies whether the metric should be reported. The default istrue
.
# frozen_string_literal: true
module Gitlab
module Usage
module Metrics
module Instrumentations
class IssuesBoardsCountMetric < NumbersMetric
operation :add
data do |time_frame|
[
CountIssuesMetric.new(time_frame: time_frame).value,
CountBoardsMetric.new(time_frame: time_frame).value
]
end
end
end
end
end
end
end
You must also include the instrumentation class name in the YAML setup.
time_frame: 28d
instrumentation_class: IssuesBoardsCountMetric
Generic metrics
You can use generic metrics for other metrics, for example, an instance’s database version.
-
value
: Specifies the value of the metric. -
available?
: Specifies whether the metric should be reported. The default istrue
.
Example of a merge request that adds a generic metric.
module Gitlab
module Usage
module Metrics
module Instrumentations
class UuidMetric < GenericMetric
value do
Gitlab::CurrentSettings.uuid
end
end
end
end
end
end
Prometheus metrics
This instrumentation class lets you handle Prometheus queries by passing a Prometheus client object as an argument to the value
block.
Any Prometheus error handling should be done in the block itself.
-
value
: Specifies the value of the metric. A Prometheus client object is passed as the first argument. -
available?
: Specifies whether the metric should be reported. The default istrue
.
Example of a merge request that adds a Prometheus metric.
module Gitlab
module Usage
module Metrics
module Instrumentations
class GitalyApdexMetric < PrometheusMetric
value do |client|
result = client.query('avg_over_time(gitlab_usage_ping:gitaly_apdex:ratio_avg_over_time_5m[1w])').first
break FALLBACK unless result
result['value'].last.to_f
end
end
end
end
end
end
Create a new metric instrumentation class
The generator takes the class name as an argument and the following options:
-
--type=TYPE
Required. Indicates the metric type. It must be one of:database
,generic
,redis
,numbers
. -
--operation
Required fordatabase
&numbers
type.- For
database
it must be one of:count
,distinct_count
,estimate_batch_distinct_count
,sum
,average
. - For
numbers
it must be:add
.
- For
-
--ee
Indicates if the metric is for EE.
rails generate gitlab:usage_metric CountIssues --type database --operation distinct_count
create lib/gitlab/usage/metrics/instrumentations/count_issues_metric.rb
create spec/lib/gitlab/usage/metrics/instrumentations/count_issues_metric_spec.rb
Migrate Service Ping metrics to instrumentation classes
This guide describes how to migrate a Service Ping metric from lib/gitlab/usage_data.rb
or ee/lib/ee/gitlab/usage_data.rb
to instrumentation classes.
- Choose the metric type:
-
Determine the location of instrumentation class: either under
ee
or outsideee
. -
Fill the instrumentation class body:
- Add code logic for the metric. This might be similar to the metric implementation in
usage_data.rb
. - Add tests for the individual metric
spec/lib/gitlab/usage/metrics/instrumentations/
. - Add tests for Service Ping.
- Add code logic for the metric. This might be similar to the metric implementation in
-
Remove the code from
lib/gitlab/usage_data.rb
oree/lib/ee/gitlab/usage_data.rb
. -
Remove the tests from
spec/lib/gitlab/usage_data.rb
oree/spec/lib/ee/gitlab/usage_data.rb
.
Troubleshoot metrics
Sometimes metrics fail for reasons that are not immediately clear. The failures can be related to performance issues or other problems. The following pairing session video gives you an example of an investigation in to a real-world failing metric.