You are browsing a read-only backup copy of Wikitech. The primary site can be found at wikitech.wikimedia.org
This private data lives in same server cluster that runs Wikimedia's production websites. This means you will need production shell access to get it.
However, since this access gets you closer to both those production websites and this confidential data, it is not freely given out. First, you have to demonstrate a need for these resources. Second, you need to have a non-disclosure agreement with the Wikimedia Foundation. If you're a Foundation employee, this was included as part of your employment agreement. If you're a researcher, it's possible to be sponsored through a formal collaboration with the Wikimedia Foundation's Research team.
You must remember this access is extremely sensitive. You have a duty to protect the privacy of our users. As Uncle Ben says, "with great power comes great responsibility." Always follow the rules outlined in the Acknowledgement of Server Access Responsibilities, even if you don't have requested ssh access to stat100x clients since it contains good guidelines about how to handle sensitive data.
In addition, keep in mind the following important principles:
- Read data access guidelines, this is important.
- Be paranoid about personally identifiable information (PII). Familiarize yourself with the data you are working on, and determine if it contains any PII. It's better to double and triple check than to assume anything, but if you have any doubt ask the Analytics team (via IRC or email or Phabricator). Please see the data retention guidelines.
- Don't copy sensitive data (for example, data accessible only by the users in the analytics-privatedata-users) from its origin location to elsewhere (in HDFS or on any other host/support) unless strictly necessary. And most importantly, do it only if you know what you are doing. If you are in doubt, please reach out to the Analytics team first.
- Restrict access. If you do need to copy sensitive data somewhere, please make sure that you are the only one able to access the data. For example, if you copy Webrequest data from its location on HDFS to your /user/$your-username directory, make sure that the permissions are set to avoid everybody with access to HDFS to read the data. This is essential to avoid accidental leaks of PII/sensitive data or retention over our guidelines (https://meta.wikimedia.org/wiki/Data_retention_guidelines).
- Clean up copies of data. Please make sure that any data that you copied is deleted as soon as your work has been done.
If you ever have any questions or doubts, err on the side of caution and contact the Analytics team. We are very friendly and happy to help!
To get access, you submit a request on phabricator and tag
SRE-Access-Requests for shell access: Production access#Access Request Process. You will need to specify what access group you need.
'analytics-*' groups have access to the Analytics Cluster (which mostly means Hadoop) and to stat* servers for local (non distributed) compute resources. These groups overlap in what servers they grant ssh access to, but further posix permissions restrict access to things like MySQL, Hadoop, and files.
Here's a summary of groups you might need (as of 2020-12-01):
Groups to avoid (deprecated):
Team specific (they do not grant access to PII data on Hadoop, for that see analytics-privatedata-users):
- For Wikimedia Deutschland employees, mostly used for crons running automation jobs as the
analytics-wmdesystem user. Grants access to all stat100x hosts, to the MariaDB replicas via
/etc/mysql/conf.d/research-wmde-client.cnfand to the
analytics-wmdesystem user. It is not required that every WMDE user is placed into this group, only those who needs to take care of the the aforementioned automation will require access (so they'll ask it explicitly).
- For members of the Wikimedia Foundation Search Platform team , used for various Analytics-Search jobs). Grants access to all stat100x hosts, an-airflow1001 and to the
- For members of the Product Analytics team, used for various Analytics jobs. Grants access to all stat100x hosts, and to the
Generic data access (can go together with the Team specific ones):
analytics-privatedata-users (no kerberos, no ssh)
The Analytics team offers various UIs to fetch data from Hadoop, like Turnilo and Superset. They are both guarded by CAS authentication (requiring the user to be in either the wmf or the nda LDAP groups), fetching data from Druid (currently not authenticated). Superset is also able to fetch data from Hadoop/Hive on behalf of the logged in user via a (read-only) tool called Presto. There are two use cases:
- Sql-lab panel: the user is able to make sql-like queries on Hadoop datasets (pageviews/event/etc..) without the need to log in on a stat100x host.
- Dashboards: data visualized in dashboards fetched from Hadoop.
In both cases, Superset works on behalf of the user, so eventually the username will need to hold read permissions for Hadoop data to correctly visualize what requested. This is guaranteed by being into
analytics-privatedata-users, that gets deployed on the Hadoop master nodes (without ssh access) to outline user permissions on HDFS. This is why some users might want to be in the group without either kerberos or ssh.
analytics-privatedata-users (no kerberos)
analytics-privatedata-users (with kerberos)
- Grants access to all the analytics clients, the analytics cluster (Hadoop/Hive) and the private data hosted there, and to MariaDB replicas, using the credentials at
- Users in this group also need a Kerberos authentication principal. If you're already a group member and don't have one, follow the instructions in the the Kerberos user guide. If you're requesting membership in this group, the SRE team will create this for you when they add you to the group.
Host access granted
There used to be a lot of differences in what hosts an Analytics POSIX group could have had access to, but now there is none anymore.
Data access granted
|Access Groups||Hadoop access
(No private data)
|Mariadb credentials||System user||Other|
Data access expiration
Data access is given to collaborators and contractors with a time limit. Normally the end date is set to be the contract or collaboration end date. For staff data access terminates upon employment termination unless there is a collaboration in place.
Once a user is terminated their home directory is deleted, if the team wishes to preserve some of the user work (work, not data as data as strict guidelines for deletion) it can be done via archiving that work to hadoop. Please file a phab ticket to have this done. Archival to hadoop would happen in the following directory:
Note that a developer account comes with two different usernames; some services need one and some services need the other. You can find both by logging into this wiki and visiting the "user profile" section of Special:Preferences. Your Wikitech username is listed under "Username", while your developer shell username is listed under "Instance shell account name". Thankfully, there's only one password!
Then, create a Phabricator task: Read and follow the instructions for LDAP-access-requests to request getting added to the appropriate group. Make sure you include both your usernames.
Note that this access has similar requirements to shell access: you will need to either be a Wikimedia Foundation employee or have a signed volunteer NDA.
The analytics clients are servers in the production cluster where you can run your code and queries. In fact, you should use them to run all your analysis, so that sensitive data never leaves the production cluster.
They have a number of useful capabilities, from large amounts of memory to Jupyter notebooks.
The Analytics MariaDB cluster contains copies of the production MediaWiki databases (both actively-used mainstream projects and small internal-facing wikis, like various projects' Arbitration Committees).
In addition to SSH, we use Kerberos to manage access to data in Hadoop.
If you're writing some analysis code, you will probably need to access data first. There are a couple of software packages that have been developed to make this easy. Note that both of them are designed to work on the analytics clients only.
For Python, there is wmfdata. It can access data through MariaDB, Hive, and Spark and has a number of other useful functions, like creating custom Spark sessions.
For R, there is wmf. It can access data from MariaDB and Hive and has many other useful functions, particularly for graphing and statistics.
Data sets and data streams can be found in Category:Data_stream
Data Dashboards. Superset and Turnilo
You need a wikitech login that is in the "wmf" or "nda" LDAP groups. If you don't have it, please create a Phabricator task by following instructions on phab:tag/ldap-access-requests/.
Before requesting access, please make sure you:
- have a functioning Wikitech login. Get one: https://toolsadmin.wikimedia.org/register/
- are an employee or contractor with wmf OR have signed an NDA
Depending on the above, you can request to be added to the wmf group or the nda group. Please indicate the motivation on the task about why you need access and ping the analytics team if you don't hear any feedback soon from the Opsen on duty.
MediaWiki application data
You can do a lot of work with the data stored by MediaWiki in the normal course of running itself. This includes data about:
- Users' edit counts (consult the
- Edits to a particular page (consult the
revisiontable, joined with the
pagetable if necessary)
- Account creations (consult the
You can access this data using the replica MariaDB databases. These are accessible from the stat100* machines, as detailed below.
For an overview of how the data is laid out in those databases, consult the database layout manual.
There are a few things that aren't available from the databases replicas. The main example of this is the actual content of pages and revisions. Instead, you can access them through the API or in the XML dumps, which are both described below.
A subset of this application data, which doesn't present privacy concerns, is also publicly accessible through the API (except for private wikis, which you shouldn't really need to perform research on anyway!). A good way to understand it, and to test queries, is Special:ApiSandbox, which provides a way of easily constructing API calls and testing them. The output includes "Request URL" - a direct URL for making that query in the future, that should work on any and all Wikimedia production wikis.
If you're interested in common API tasks, and don't feel like reinventing the wheel, there are a number of Python-based API wrappers and MediaWiki utilities. Our very own Aaron Halfaker maintains MediaWiki Utilities, which includes a module dedicated to API interactions. There's no equivalent for R yet.
Every month, XML snapshots of the databases are generated. Since they're generated monthly, they're always slightly outdated, but make up for it by being incredibly cohesive (and incredibly large). They contain both the text of each revision of each page, and snapshots of the database tables. As such, they're a really good way of getting large amounts of diffs or information on revisions without running into the query limits on the API.
Aaron's MediaWiki-utilities package contains a set of functions for handling and parsing through the XML dumps, which should drastically simplify dealing with them. They're also stored internally, as well as through dumps.wikimedia.org, and can be found in
/mnt/data/xmldatadumps/public on stat1006, stat1007, notebook1003, and notebook1004.
One analytics-specific source of data is EventLogging. This allows us to track things we're interested in as researchers that MediaWiki doesn't normally log. Examples include:
- A log of changes to user preferences;
- A/B testing data;
- Clicktracking data.
These datasets are stored in the
event_sanitized Hive databases, subject to HDFS access control.
An important piece of community-facing data is information on our pageviews; what articles are being read, and how much? This is currently stored in our Hadoop cluster, which contains aggregated pageview data as well as the mostly-raw database of web requests. See the detailed documentation here.
When you have IP addresses - be they from the RequestLogs, EventLogging or MediaWiki itself - you can do geolocation. This can be a very useful way of understanding user behaviour and evaluating how our ecosystem works. We currently use the MaxMind geolocation services, which are accessible on both stat1006 and stat1007: a full guide to geolocation and some examples of how to do it can be found on the 'geolocation' page.
- Other groups including
statistics-web-usersare for people doing system maintenance and administration, so you don't need them just to access data.