Incidents/2022-10-04 jupyterhub-conda outage

document status: draft

Summary

Incident metadata (see Incident Scorecard)
Incident ID	2022-10-04 jupyterhub-conda outage	Start	YYYY-MM-DD hh:mm:ss
Task		End	YYYY-MM-DD hh:mm:ss
People paged		Responder count
Coordinators		Affected metrics/SLOs
Impact	Who was affected and how? For user-facing outages: Estimate how many queries were lost, which regions were affected, or which types of clients (editors? readers? bots?), etc. Do not assume the reader knows what your service is or who uses it.

…

Summary of what happened, in one or two paragraphs. Avoid assuming deep knowledge of the systems here, and try to differentiate between proximate causes and root causes.

Timeline

Write a step by step outline of what happened to cause the incident, and how it was remedied. Include the lead-up to the incident, and any epilogue.

Consider including a graphs of the error rate or other surrogate.

Link to a specific offset in SAL using the SAL tool at https://sal.toolforge.org/ (example)

All times in UTC.

00:00 (TODO) OUTAGE BEGINS
00:04 (Something something)
00:06 (Voila) OUTAGE ENDS
00:15 (post-outage cleanup finished)

TODO: Clearly indicate when the user-visible outage began and ended.

Detection

Write how the issue was first detected. Was automated monitoring first to detect it? Or a human reporting an error?

Copy the relevant alerts that fired in this section.

Did the appropriate alert(s) fire? Was the alert volume manageable? Did they point to the problem with as much accuracy as possible?

TODO: If human only, an actionable should probably be to "add alerting".

Conclusions

OPTIONAL: General conclusions (bullet points or narrative)

What went well?

…

OPTIONAL: (Use bullet points) for example: automated monitoring detected the incident, outage was root-caused quickly, etc

What went poorly?

…

OPTIONAL: (Use bullet points) for example: documentation on the affected service was unhelpful, communication difficulties, etc

Where did we get lucky?

…

OPTIONAL: (Use bullet points) for example: user's error report was exceptionally detailed, incident occurred when the most people were online to assist, etc

Links to relevant documentation

…

Add links to information that someone responding to this alert should have (runbook, plus supporting docs). If that documentation does not exist, add an action item to create it.

Actionables

…

Create a list of action items that will help prevent this from happening again as much as possible. Link to or create a Phabricator task for every step.

Add the #Sustainability (Incident Followup) and the #SRE-OnFIRE (Pending Review & Scorecard) Phabricator tag to these tasks.

Scorecard

Incident Engagement ScoreCard
	Question	Answer (yes/no)	Notes
People	Were the people responding to this incident sufficiently different than the previous five incidents?
	Were the people who responded prepared enough to respond effectively
	Were fewer than five people paged?
	Were pages routed to the correct sub-team(s)?
	Were pages routed to online (business hours) engineers? Answer “no” if engineers were paged after business hours.
Process	Was the "Incident status" section atop the Google Doc kept up-to-date during the incident?
	Was a public wikimediastatus.net entry created?
	Is there a phabricator task for the incident?
	Are the documented action items assigned?
	Is this incident sufficiently different from earlier incidents so as not to be a repeat occurrence?
Tooling	To the best of your knowledge was the open task queue free of any tasks that would have prevented this incident? Answer “no” if there are open tasks that would prevent this incident or make mitigation easier if implemented.
	Were the people responding able to communicate effectively during the incident with the existing tooling?
	Did existing monitoring notify the initial responders?
	Were the engineering tools that were to be used during the incident, available and in service?
	Were the steps taken to mitigate guided by an existing runbook?
Total score (count of all “yes” answers above)