Community metrics

''This page is about wikimedia.biterg.io, the Wikimedia Tech community metrics dashboard. For a list of links to metrics and statistics, refer to Development statistics. For metrics not related to Wikimedia's technical community (e.g. page views), refer to the Analytics mailing list.''

The data sources of the Wikimedia Tech community metrics dashboard include Git and Gerrit repositories, Phabricator's Maniphest (though only basic support), mediawiki.org, some mailing lists, and some IRC channels. The data sources are defined in a configuration file. Its data is refreshed regularly. For other data sources (on-wiki code, Github repositories) currently not covered by Wikimedia Tech community metrics dashboard, see the section below.

Bug reports are welcome in the wikimedia.biterg.io project in Phabricator. Feedback and questions are welcome on the discussion page.

wikimedia.biterg.io offers:
 * Drill down: clicking an element and a filtered view will be applied
 * Time frame selection
 * Exporting data
 * API access via the Elasticsearch API
 * Wikimedia administrators to create widget and panels themselves
 * an advanced filter search box

User interface


The top bar lists Dashboards (also called Panels). By default the  is chosen. Each dashboard offers numerous widgets, and a result list at the bottom of the page (commits in Git, emails in mailing lists, etc.).

The interactive Widgets at the bottom display the actual data. Some panels support clicking displayed items to get more specific information about those items and some panels also allow downloading and exporting the displayed data as CSV or JSON.

Applying filters
In the right corner of the top bar, the Time filter allows adjusting the time span of all the data being displayed in the widgets.

Some widgets allow creating Filters: When the mouse pointer hovers over an item in a list, two small magnifier icons will be displayed. They allow showing only data for that very item on the page, or filtering out that very item from the displayed results on the page.

When creating a filter in Kibana, the filter is displayed below the Advanced filter text field and is applied to all widgets. In the screenshot above, only changesets with 'status: Merged' and by independent authors are shown in the panels. When hovering over a filter, you can enable/disable, pin/unpin (the filter will still be applied when you open that page again), invert (e.g. to get all companies listed except for one), remove or edit (e.g. to change the organization name) the filter. The "Actions" menu on the very right of the filters offers the same actions to apply them to all filters at once. For more information, see Discover Filters.

The Advanced filter text field allows searching for text in any items (commit messages, user names, repository names, etc.). It allows querying a subset of results provided by the time filter and filters already applied. By default, any free text items in any database columns are included (entering this also resets a search). All available fields (database columns) are listed when clicking "Add a filter" next to the filters.

The query syntax in the Advanced filter text field is based on the Lucene query syntax: You enter a field name followed by a colon followed by the value. Query examples:
 * Gerrit:
 * Git:

The available fields across databases can also be looked up via the Discover functionality in the main panel. (use the "git" dropdown below the text field to change to another database). See Kibana Queries and Filters for more information.

Some more notes on advanced filters:
 * The type of field (string, number, date, etc.) influences the query syntax
 * Queries are case sensitive
 * You can only create queries which use fields within the respective index (simplified, "indices" in ElasticSearch are kind of databases) that is used in a panel, otherwise the search will return "No results found".
 * Fields not available in an index by default use  for numbers and   for strings

Behavior that might surprise you

 * The data for Git repositories shows some individuals and companies that have never contributed to Wikimedia. That is because Wikimedia uses many upstream software projects and imports their Git repositories (including their change history) into Wikimedia Git. "There are cases where a repository's history consists primarily of upstream commits, but with some substantial packaging work by Wikimedia engineers, for example." You may want to look at Wikimedia Gerrit data instead.
 * The Gerrit statistics by default include bots (like L10n-bot) so the numbers look higher. Set  as an Advanced filter to exclude non-human activity.
 * Some numbers which you might expect to be identical might differ because they are based on different data fields. For example, on the "Gerrit Overview" dashboard, the number of "Changeset Submitters" in the top widget can differ from the number of entries in the "Submitters" list widget at the bottom. The two widgets use different data fields in the database. See T184741 for more information.
 * You might not always easily find yourself's (your ) in the system, due to how the data is stored and collected: Every identity can have several profiles (for example one profile for the Gerrit account, another profile for a Phabricator account, another profile for a mediawiki.org account, and all those profile name might differ). The name of your one identity depends on which of these profile names was indexed first or it could also be an identity name that was manually edited.
 * Your data might be incomplete. That can happen when your profiles in different systems (Gerrit, Phabricator, mediawiki.org, IRC, etc) have not all been merged yet into one identity. Also note that for Phabricator, only created tasks are indexed currently but not activity such as commenting.
 * Wikimedia mirrors many code repositories between Wikimedia Gerrit and Github. If the commit hash is the same in both Github and Gerrit then the commit is correctly only counted once even if you had both the Gerrit repository and the Github repository for the same project displayed.

Architecture and source code
Everything is based on Kibana dashboards and Elasticsearch. The database provides indexes whose fields are used in panels, widgets and for searches.

Details on the underlying software architecture can be found on grimoirelab.github.io. A comprehensive GrimoireLab Tutorial and some webinar videos are available.

Source code of the Grimoirelab components is available. Most code is written in Python. The existing repositories are:
 * : Data retrieval platform which creates JSON files.  contains the available backends. Data is stored in Elasticsearch. (Source code)
 * : Commander tool to run perceval and set up the panels. (Source code)
 * : Visualization on top of ElasticSearch. A fork of Kibana which contains changes until they get merged in the upstream code base. (Source code)
 * : Numerous JSON files. Contains all of the panels currently available for the current architecture. (Source code)
 * : An incubator for new ideas. (Source code)
 * : Command line interface to manage the data in our database. For admins, a complete database dump is available as a JSON file which allows manual account merging, updating affiliations, adding country information or marking an account as a bot. (Source code)
 * : Web-based interface to manage the data in our database. (Source code; Access for Wikimedia administrators)
 * : Orchestrates the execution of tools to produce a dashboard.
 * : Web-based interface to manage the Sirmordred projects.json configuration files which specify the URLs of data sources and structure of projects. (Source code)
 * The configuration of our data sources is defined in a json configuration file. See the documentation for what is supported.

The steps performed are basically: Sources → Data gathering (mining via Perceval) → Data enrichment (e.g. producing indexes in ElasticSearch via GrimoireELK) → Visualization (ElasticSearch and Kibana).

For administrators
Once logged in via the "Login" item at the bottom of the main panel, functionality such as taking a look at parameters of visualizations or saving dashboards and saving widgets will be possible and not display error messages like for a non-logged in user. You can analyze specific data, create and edit widgets, visualizations and dashboards (also custom elements). To exit from edit mode, click the "Logout" item again at the bottom of the main panel.

Discover allows you to analyze specific data.


 * Choose a database from the dropdown in the left panel. Then expand the time span.
 * Results are displayed as a list of dropdown data items. Opening a dropdown displays all fields and their values as JSON or a table. A Kibana/ES visualization based on the JSON data is displayed on top.
 * Specific fields can be added as columns to the displayed results by adding/removing those fields in the left panel. It is basically a huge matrix, and if we wanted more data, more fields could be added in the future (e.g. "Gender").

Visualize allows creating a new visualization/widget (available types are e.g. data table, line chart, pie chart) or opening an existing saved visualization. Admins could rearrange and save. If you alter a saved visualization and want to keep the previous one, save the new one under a new name and then insert it into the dashboard.


 * When opening an existing saved visualization, the right panel shows the visualization view. The left panel shows the definitions: There are y-axis metrics (for each group; what am I going to solve) and x-axis buckets (grouping things).
 * Metrics have an Aggregation (e.g. medium, sum, unique count, percentiles) on a certain Field and a CustomLabel to display.
 * Buckets have the same parameters and an Interval (e.g. to display yearly instead of weekly bars).


 * To write a new visualization from scratch, click the + button and select for example "Pie". Choose From a New Search, Select Index and select for example the "git" index. An empty pie chart will be shown as nothing is defined yet (no buckets, hence it is the total number of everything).
 * Under buckets, choose Select buckets type and choose for example Split Slices. Set Aggregation to for example Terms (means: look for a specific field in every commit). Set Field to a value, for example "author_org_name" (means: by organization names). Set Order for example to Descending and Size to 10 to display the ten biggest companies in the pie chart. To display these changes, click the green Apply changes bottom at the top on the left.


 * Advanced: You can also Add sub-buckets at the bottom. For example, if you visualize bars and want to split each displayed bar to display several companies, go for Split bars. The order of buckets can be important when having sub-buckets, for example if you split bars before the x-axis in the previous example, the legend field in the visualization will be ordered by displaying the most active company in the first place of the legend list.


 * Advanced: When creating a new visualization you can also choose Or, From a Saved search to create new visualizations on top of searches instead of indices to avoid using a full index. Beforehand, under Discover you have to define a search as a specific view of a search.

If you are interested in certain visualizations, contact the.

Dashboard allows creating and editing dashboards.


 * When an administrator loads an existing dashboard (via choosing it from the displayed list of dashboards), modifies it (e.g. dragging around widgets), and saves the changes under the same name, the view of that dashboard is modified for all users. When using a different name for a dashboard, an administrator would still have to add the link to that new dashboard to make it available for all users.

Timelion is supposed to allow you create time series using DSL queries. Example query for the  visualization in our instance:

Dev Tools: The Console allows building custom Elasticsearch queries.

Management offers access to internal stuff.


 * The Index Patterns tab allows to configure an index pattern. It lists information about all indices and all index series (a collection of indices). You can see all the fields by name or type. Via the controls column on the right, you could for example convert the type of a field from "string" to "date". This is also the place to make Kibana know about new stuff in ElasticSearch by adding the name of the index in ElasticSearch.
 * The Saved Objects tab lists all saved objects such as Dashboards, Searches and Visualizations and allows editing them directly, e.g. to change the number of buckets from 5 to 10 in a visualization. This is currently not possible via the UI and it is also prone to break the raw configuration.
 * You can choose any object from the lists and export it as a JSON file.

In general, names of custom objects (such as dashboards) created manually by an admin should have the prefix  so they do not get overwritten by the next upstream software update.

Hatstall offers a web user interface to update (affiliations etc.) and merge user account data.

Team
Andre Klapper from the Developer Advocacy team coordinates the Metrics Dashboard project, which is being implemented by Bitergia as contractors.

The Bitergia team working in the MediaWiki dashboard is formed by Daniel Izquierdo, Luis Cañas and Jesus Gonzalez Barahona and Alvaro del Castillo as project manager.

https://github.com/chaoss/ is used to track any general upstream issues. The Wikimedia Foundation can also file support requests to Bitergia in a non-public GitLab instance.

Further links

 * GrimoireLab training tutorial
 * Extensive user documentation by the Xen Project
 * Kibana User Guide (upstream documentation)
 * Kibana User Guide: Dashboards and Panels (upstream documentation)
 * Building visualizations, GrimoireCon 2017, Brussels
 * Dashboards of other organizations: Document Foundation, Eclipse, Opnfv, CoreOS , Mozilla's Rust , Linux , Xen, Onap.
 * Analytics/Metric definitions (not related to software development)
 * meta:Research:Standard_metrics (not related to software development)

If you would like to see specific customizations, please file a request in Wikimedia Phabricator including a user story.

Limitations
Wikimedia code development happens in many places, and some places are not indexed by the Wikimedia Tech community metrics dashboard:
 * Canonical Wikimedia repositories on GitHub: The software can index Github repositories and this is planned in T186736 but currently (December 2018) blocked on T109939.
 * Code hosted in wiki pages (gadgets, user scripts, modules, potentially templates): The software currently only indexes activity on mediawiki.org and not on other Wikimedia sites, and querying activity on (code related) wiki pages only in specific namespaces would need to be implemented. Regarding external tools, Quarry allows to run SQL queries but currently (December 2018) does not support queries across sites (see T95582, and WMF Product Analytics uses Hive though it is unclear how it could be potentially useful in this context.
 * Maintainers of tools hosted on Toolforge are free to host the code repositories of their tools wherever they would like to, which makes it hard to identify and index these places.