debian-mirror-gitlab/doc/development/value_stream_analytics.md

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Value stream analytics development guide

For information on how to configure value stream analytics (VSA) in GitLab, see our analytics documentation.

How does Value Stream Analytics work?

Value Stream Analytics calculates the duration between two timestamp columns or timestamp expressions and runs various aggregations on the data.

For example:

• Duration between the Merge Request creation time and Merge Request merge time.
• Duration between the Issue creation time and Issue close time.

This duration is exposed in various ways:

• Aggregation: median, average
• Listing: list the duration for individual Merge Request and Issue records

Apart from the durations, we expose the record count within a stage.

Feature availability

• Group level (licensed): Requires Ultimate or Premium subscription. This version is the most feature-full.
• Project level (licensed): We are continually adding features to project level VSA to bring it in line with group level VSA.
• Project level (FOSS): Keep it as is.

Feature	Group level (licensed)	Project level (licensed)	Project level (FOSS)
Create custom value streams	Yes	No, only one value stream (default) is present with the default stages	no, only one value stream (default) is present with the default stages
Create custom stages	Yes	No	No
Filtering (author, label, milestone, etc.)	Yes	Yes	Yes
Stage time chart	Yes	No	No
Total time chart	Yes	No	No
Task by type chart	Yes	No	No
DORA Metrics	Yes	Yes	No
Cycle time and lead time summary (Key metrics)	Yes	Yes	No
New issues, commits and deploys (Key metrics)	Yes, excluding commits	Yes	Yes
Uses aggregated backend	Yes	No	No
Date filter behavior	Filters items finished within the date range	Filters items by creation date.	Filters items by creation date.
Authorization	At least reporter	At least reporter	Can be public.

VSA core domain objects

Stages

A stage represents an event pair (start and end events) with additional metadata, such as the name of the stage. Stages are configurable by the user within the pairing rules defined in the backend.

Example stage: Code Review

• Start event identifier: Merge request creation time.
• Start event column: uses the merge_requests.created_at timestamp column.
• End event identifier: Merge request merge time.
• End event column: uses the merge_request_metrics.merged_at timestamp column.
• Stage event hash ID: a calculated hash for the pair of start and end event identifiers.
  • If two stages have the same configuration of start and end events, then their stage event hash. IDs are identical.
  • The stage event hash ID is later used to store the aggregated data in partitioned database tables.

Historically, value stream analytics defined 7 stages which are always available to the end-users regardless of the subscription.

Value streams

Value streams are container objects for the stages. There can be multiple value streams per group focusing on different aspects of the Dev Ops lifecycle.

Events

Events are the smallest building blocks of the value stream analytics feature. A stage consists of two events:

• Start event
• End event

These events play a key role in the duration calculation.

Formula: duration = end_event_time - start_event_time

To make the duration calculation flexible, each Event is implemented as a separate class. They're responsible for defining a timestamp expression that is used in the calculation query.

Implementing an Event class

There are a few methods that are required to be implemented, the StageEvent base class describes them in great detail. The most important ones are:

• object_type
• timestamp_projection

The object_type method defines which domain object is queried for the calculation. Currently two models are allowed:

• Issue
• MergeRequest

For the duration calculation the timestamp_projection method is used.

def timestamp_projection
  # your timestamp expression comes here
end

# event will use the issue creation time in the duration calculation
def timestamp_projection
  Issue.arel_table[:created_at]
end

More complex expressions are also possible (for example, using COALESCE). Review the existing event classes for examples.

In some cases, defining the timestamp_projection method is not enough. The calculation query should know which table contains the timestamp expression. Each Event class is responsible for making modifications to the calculation query to make the timestamp_projection work. This usually means joining an additional table.

Example for joining the issue_metrics table and using the first_mentioned_in_commit_at column as the timestamp expression:

def object_type
  Issue
end

def timestamp_projection
  IssueMetrics.arel_table[:first_mentioned_in_commit_at]
end

def apply_query_customization(query)
  # in this case the query attribute will be based on the Issue model: `Issue.where(...)`
  query.joins(:metrics)
end

Validating start and end events

Some start/end event pairs are not "compatible" with each other. For example:

• "Issue created" to "Merge Request created": The event classes are defined on different domain models, the object_type method is different.
• "Issue closed" to "Issue created": Issue must be created first before it can be closed.
• "Issue closed" to "Issue closed": Duration is always 0.

The StageEvents module describes the allowed start_event and end_event pairings (PAIRING_RULES constant). If a new event is added, it needs to be registered in this module. To add a new event:

1. Add an entry in ENUM_MAPPING with a unique number, which is used in the Stage model as enum.
2. Define which events are compatible with the event in the PAIRING_RULES hash.

Supported start/end event pairings:

graph LR;
  IssueCreated --> IssueClosed;
  IssueCreated --> IssueFirstAddedToBoard;
  IssueCreated --> IssueFirstAssociatedWithMilestone;
  IssueCreated --> IssueFirstMentionedInCommit;
  IssueCreated --> IssueLastEdited;
  IssueCreated --> IssueLabelAdded;
  IssueCreated --> IssueLabelRemoved;
  MergeRequestCreated --> MergeRequestMerged;
  MergeRequestCreated --> MergeRequestClosed;
  MergeRequestCreated --> MergeRequestFirstDeployedToProduction;
  MergeRequestCreated --> MergeRequestLastBuildStarted;
  MergeRequestCreated --> MergeRequestLastBuildFinished;
  MergeRequestCreated --> MergeRequestLastEdited;
  MergeRequestCreated --> MergeRequestLabelAdded;
  MergeRequestCreated --> MergeRequestLabelRemoved;
  MergeRequestLastBuildStarted --> MergeRequestLastBuildFinished;
  MergeRequestLastBuildStarted --> MergeRequestClosed;
  MergeRequestLastBuildStarted --> MergeRequestFirstDeployedToProduction;
  MergeRequestLastBuildStarted --> MergeRequestLastEdited;
  MergeRequestLastBuildStarted --> MergeRequestMerged;
  MergeRequestLastBuildStarted --> MergeRequestLabelAdded;
  MergeRequestLastBuildStarted --> MergeRequestLabelRemoved;
  MergeRequestMerged --> MergeRequestFirstDeployedToProduction;
  MergeRequestMerged --> MergeRequestClosed;
  MergeRequestMerged --> MergeRequestFirstDeployedToProduction;
  MergeRequestMerged --> MergeRequestLastEdited;
  MergeRequestMerged --> MergeRequestLabelAdded;
  MergeRequestMerged --> MergeRequestLabelRemoved;
  IssueLabelAdded --> IssueLabelAdded;
  IssueLabelAdded --> IssueLabelRemoved;
  IssueLabelAdded --> IssueClosed;
  IssueLabelRemoved --> IssueClosed;
  IssueFirstAddedToBoard --> IssueClosed;
  IssueFirstAddedToBoard --> IssueFirstAssociatedWithMilestone;
  IssueFirstAddedToBoard --> IssueFirstMentionedInCommit;
  IssueFirstAddedToBoard --> IssueLastEdited;
  IssueFirstAddedToBoard --> IssueLabelAdded;
  IssueFirstAddedToBoard --> IssueLabelRemoved;
  IssueFirstAssociatedWithMilestone --> IssueClosed;
  IssueFirstAssociatedWithMilestone --> IssueFirstAddedToBoard;
  IssueFirstAssociatedWithMilestone --> IssueFirstMentionedInCommit;
  IssueFirstAssociatedWithMilestone --> IssueLastEdited;
  IssueFirstAssociatedWithMilestone --> IssueLabelAdded;
  IssueFirstAssociatedWithMilestone --> IssueLabelRemoved;
  IssueFirstMentionedInCommit --> IssueClosed;
  IssueFirstMentionedInCommit --> IssueFirstAssociatedWithMilestone;
  IssueFirstMentionedInCommit --> IssueFirstAddedToBoard;
  IssueFirstMentionedInCommit --> IssueLastEdited;
  IssueFirstMentionedInCommit --> IssueLabelAdded;
  IssueFirstMentionedInCommit --> IssueLabelRemoved;
  IssueClosed --> IssueLastEdited;
  IssueClosed --> IssueLabelAdded;
  IssueClosed --> IssueLabelRemoved;
  MergeRequestClosed --> MergeRequestFirstDeployedToProduction;
  MergeRequestClosed --> MergeRequestLastEdited;
  MergeRequestClosed --> MergeRequestLabelAdded;
  MergeRequestClosed --> MergeRequestLabelRemoved;
  MergeRequestFirstDeployedToProduction --> MergeRequestLastEdited;
  MergeRequestFirstDeployedToProduction --> MergeRequestLabelAdded;
  MergeRequestFirstDeployedToProduction --> MergeRequestLabelRemoved;
  MergeRequestLastBuildFinished --> MergeRequestClosed;
  MergeRequestLastBuildFinished --> MergeRequestFirstDeployedToProduction;
  MergeRequestLastBuildFinished --> MergeRequestLastEdited;
  MergeRequestLastBuildFinished --> MergeRequestMerged;
  MergeRequestLastBuildFinished --> MergeRequestLabelAdded;
  MergeRequestLastBuildFinished --> MergeRequestLabelRemoved;
  MergeRequestLabelAdded --> MergeRequestLabelAdded;
  MergeRequestLabelAdded --> MergeRequestLabelRemoved;
  MergeRequestLabelRemoved --> MergeRequestLabelAdded;
  MergeRequestLabelRemoved --> MergeRequestLabelRemoved;

Default stages

The original implementation of value stream analytics defined 7 stages. These stages are always available for each parent, however altering these stages is not possible.

To make things efficient and reduce the number of records created, the default stages are expressed as in-memory objects (not persisted). When the user creates a custom stage for the first time, all the stages are persisted. This behavior is implemented in the value stream analytics service objects.

The reason for this was that we'd like to add the abilities to hide and order stages later on.

Data Collector

DataCollector is the central point where the data is queried from the database. The class always operates on a single stage and consists of the following components:

• BaseQueryBuilder:
  • Responsible for composing the initial query.
  • Deals with Stage specific configuration: events and their query customizations.
  • Parameters coming from the UI: date ranges.
• Median: Calculates the median duration for a stage using the query from BaseQueryBuilder.
• RecordsFetcher: Loads relevant records for a stage using the query from BaseQueryBuilder and specific Finder classes to apply visibility rules.
• DataForDurationChart: Loads calculated durations with the finish time (end event timestamp) for the scatterplot chart.

For a new calculation or a query, implement it as a new method call in the DataCollector class.

To support the aggregated value stream analytics backend, these classes were reimplemented within Aggregated namespace.

Database query backend

VSA supports two backends: aggregated and "live". The live query backend can be considered legacy, which will be phased out at some point.

• "live": uses the standard IssuableFinders.
• aggregated: queries data from pre-aggregated database tables.

High-level overview

• Rails Controller (Analytics::CycleAnalytics module): Value stream analytics exposes its data via JSON endpoints, implemented within the analytics workspace. Configuring the stages are also implements JSON endpoints (CRUD).
• Services (Analytics::CycleAnalytics module): All Stage related actions are delegated to respective service objects.
• Models (Analytics::CycleAnalytics module): Models are used to persist the Stage objects ProjectStage and GroupStage.
• Feature classes (Gitlab::Analytics::CycleAnalytics module):
  • Responsible for composing queries and define feature specific business logic.
  • DataCollector, Event, StageEvents, etc.

Frontend

Project VSA is available for all users and:

• Includes a mixture of key and DORA metrics based on the tier.
• Uses the set of default stages.

Group VSA is only available for licensed users and extends project VSA to include:

• An overview stage.
• The ability to create custom value streams.

The group and project level VSA frontends are both built with Vue and Vuex and follow a similar pattern:

• The index.js file extracts any URL query parameters, creates the Vue app and Vuex store, and dispatches an initialize Vuex action.
• The base.vue file is used to render the main components for each page, metrics, filters, charts, and the stage table.

The group VSA Vuex store makes use of Vuex modules to separate some of the state and logic used for rendering the charts.

Shared components

Parts of the UI are shared between project VSA and group VSA such as the stage table and path. These shared components live in the project VSA directory app/assets/javascripts/cycle_analytics/components and are included at the group level VSA where needed.

All the frontend code for group-level features are located in ee/app/assets/javascripts/analytics/cycle_analytics/components.

Testing

Since we have a lots of events and possible pairings, testing each pairing is not possible. The rule is to have at least one test case using an Event class.

Writing a test case for a stage using a new Event can be challenging since data must be created for both events. To make this a bit simpler, each test case must be implemented in the data_collector_spec.rb where the stage is tested through the DataCollector. Each test case is turned into multiple tests, covering the following cases:

• Different parents: Group or Project
• Different calculations: Median, RecordsFetcher or DataForDurationChart

The VSA frontend is tested extensively on two different levels (integration, unit):

• End-to-end integration tests using a real backend via Capybara and RSpec.
• Jest frontend tests with pre-generated data fixtures.

Development setup and testing

Running Value Stream Analytics can be done via the GDK. By default, you'll be able to view the project-level (FOSS) version of the feature.

If your GDK is up and running, you can run the seed script to generate some data:

SEED_CYCLE_ANALYTICS=true SEED_VSA=true FILTER=cycle_analytics rake db:seed_fu

The data generator script creates a new group and a new project with issue and merge request data (see the output of the script). To view the group-level version of the feature, you need to request a license for your GDK instance.

After this step, you can access the group level value stream analytics page where you can create value streams and stages. The data aggregation might be delayed so you might not see the data right after the stage creation. To speed up this process, you can run the following command in your rails console (rails c):

Analytics::CycleAnalytics::ReaggregationWorker.new.perform