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In this section


  • Stay in alignment across the whole organization.
  • Build teams, not groups of people.
  • Provide substantial time for engineers to work on "focused work."


  • Support the Maker Schedule by keeping meetings to a minimum.
  • Each individual must have a weekly or biweekly sync 1:1 meeting with their manager. This is key to making sure each individual has a voice within the organization.
  • Favor async communication when possible. This is very important to make sure every stakeholder on a project can have a clear understanding of what’s happening or what was decided, without needing to attend every meeting (i.e., if a person is sick or on vacation or just life happened.)
  • If an async conversation is not proving to be effective, never hesitate to hop on or schedule a call. Always document the decisions made in a ticket, document, or whatever makes sense for the conversation.

The following is the subset of proposed engineering meetings. Each group is free to treat these as a subset of the expected meetings and add any other meetings as they see fit.


  • Fleet engineering teams practice scrum as an agile methodology.
  • Sprints are 3 weeks long to match our release cadence.
  • There are 5 “Scrum Ceremonies” performed during each sprint:
    • Sprint planning - At the first Monday of the sprint the team and stakeholders meet and pick items from the backlog. The team commit to finish those items.
    • Daily sync standup - The team meets daily for updates. Each team member presents what they did since the last sync and what they intend to do until next sync. Any road blocks are raised.
    • Weekly estimation sessions - once a week (3 times a sprint) the team will estimate items in the backlog. Goals:
      • Have Stakeholders know in advance the time it will take to achieve those items.
      • Make it easier in the next planning meeting to know what the team can take for the next sprint.
    • Sprint Demo - At the last Friday of the sprint all engineering teams and stakeholders meet to present their work. Engineers will get 3-10 minutes to present what was done / not done.
    • Sprint Retrospective - At the last Friday of the sprint each team will meet with stakeholders and hold a discussion answering the 3 questions:
      • What went well?
      • What could we have done better?
      • What did we learn?
  • Scrum items:
    • Objectives / Epics: TBD - Will probably not be used (further discussion needed)
    • User Story - A description of missing functionality typically visible by our customers. The description answers three questions: Who is the user that wants it? What is the task? What is the purpose of it (what problem it solves or what value it adds)? Typically written in this format: As the <user> I would like to <have something done or changed> so that I can <benefit from it in this specific way>. A Story will include all the tasks required in order to achieve it or will have technical-sub-task(s) bound to it.
    • Technical Sub Task - Typically a task that is part of a bigger Story. e.g. design, code, create a test/document. Will typically be blocking the Story they are part of.

Eng Together

This meeting is to disseminate engineering-wide announcements, to promote cohesion across groups within the engineering team, and to connect with engineers (and the "engineering-curious") in other departments. Held weekly for one hour.


Everyone at the company is welcome to attend. Subject matter is focused on engineering.

Sample agenda

  • Announcements
  • “Show and tell”
    • Each engineer gets two minutes to explain (showing, if desired) what they are working on and why it’s important to the business and/or engineering team.
  • Deeper dive
    • One or a few engineers go deeper on a topic relevant to all of engineering.
  • Social
    • Structured and/or unstructured social activities

Release Retro

Gather feedback from all participants in each release. Used to improve communication and processes. Held each release for 30 minutes.

This meeting will likely need to be split in the future as the number of attendees increases.


This would include members of each group (+ quality).

Sample agenda

For each attendee:

  • What went well this release cycle?
  • What could have gone better this release cycle?
  • What should we remember next time?

Group Weeklies

A chance for deeper, synchronous discussion on topics relevant to that group. Held weekly for 30 minutes - one hour.

e.g., “Frontend Weekly” - “Backend Weekly” - “Agent Weekly”

In some groups, this may be split into smaller discussions related to the different focuses of members within the group.


This would include anyone who wishes to participate.

Sample Agenda (MDM)

  • Anything at risk for the release?
  • Confirm response payload matches spec
  • Discuss common patterns and conventions in the codebase

Engineering Leadership Weekly (Weekly ~ one hour)

Engineering leaders discuss topics of importance that week.


This would include the CTO + Engineering managers.

Sample agenda

  • Fullstack engineer hiring
  • Engineering process discussion
  • Review Q2 OKRs

Release process

This section outlines the release process at Fleet.

The current release cadence is once every three weeks and is concentrated around Wednesdays.

Release freeze period

In order to make sure quality releases, Fleet has a freeze period for testing prior to each release. Effective at the start of the freeze period, new feature work will not be merged.

Release blocking bugs are exempt from the freeze period and are defined by the same rules as patch releases, which include:

  1. Regressions
  2. Security concerns
  3. Issues with features targeted for current release

Non-release blocking bugs may include known issues that were not targeted for the current release, or newly documented behaviors that reproduce in older stable versions. These may be addressed during a release period by mutual agreement between the Product and Engineering teams.

Release day

Documentation on completing the release process can be found here.

Deploying to dogfood

After each Fleet release, the new release is deployed to Fleet's dogfood (internal) instance.

How to deploy a new release to dogfood:

  1. Head to the Tags page on the fleetdm/fleet Docker Hub:
  2. In the Filter tags search bar, type in the latest release (ex. v4.19.0).
  3. Locate the tag for the new release and copy the image name. An example image name is "fleetdm/fleet:v4.19.0".
  4. Head to the "Deploy Dogfood Environment" action on GitHub:
  5. Select Run workflow and paste the image name in the The image tag wished to be deployed. field.
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Note that this action will not handle down migrations. Always deploy a newer version than is currently deployed.

Note that "fleetdm/fleet:main" is not a image name, instead use the commit hash in place of "main".

Oncall rotation

In this section

The rotation

See the internal Google Doc for the engineers in the rotation.

Fleet team members can can also subscribe to the shared calendar for calendar events.


1. Second-line response

The oncall engineer is a second-line responder to questions raised by customers and community members.

The community contact (Kathy) is responsible for the first response to GitHub issues, pull requests, and Slack messages in the #fleet channel of osquery Slack, and other public Slacks. Kathy and Zay are responsible for the first response to messages in private customer Slack channels.

We respond within 1-hour (during business hours) for interactions and ask the oncall engineer to address any questions sent their way promptly. When Kathy is unavailable, the oncall engineer may sometimes be asked to take over the first response duties. Note that we do not need to have answers within 1 hour -- we need to at least acknowledge and collect any additional necessary information, while researching/escalating to find answers internally. See Escalations for more on this.

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Response SLAs help us measure and guarantee the responsiveness that a customer can expect from Fleet. But SLAs aside, when a Fleet customer has an emergency or other time-sensitive situation ongoing, it is Fleet's priority to help them find them a solution quickly.

2. PR reviews

Pull requests may be routed through the oncall engineer for review.

For PRs from the community, the community contact (Kathy) will request review from the oncall. (Kathy)will review all PRs to the Fleet documentation, and she will either merge (for typo fixes, when only documentation files are changed) or request a review from the current oncall (for changes to code samples, or to the meaning of the text).

In either case, if the oncall engineer has the knowledge and confidence to review, they should go ahead and do so. Otherwise, they should request a review from an engineer with the appropriate domain knowledge. It is the oncall engineer's responsibility to confirm that these PRs are moved forward (either by review with feedback or merge).

3. Customer success meetings

The oncall engineer is encouraged to attend some of the customer success meetings during the week. Post a message to the #g-customer-experience Slack channel requesting invitations to upcoming meetings.

This has a dual purpose of providing more context for how our customers use Fleet. The engineer should actively participate and provide input where appropriate (if not sure, please ask your manager or organizer of the call).

4. Improve documentation

The oncall engineer is asked to read, understand, test, correct, and improve at least one doc page per week. Our goal is to 1, ensure accuracy and verify that our deployment guides and tutorials are up to date and work as expected. And 2, improve the readability, consistency, and simplicity of our documentation – with empathy towards first-time users. See Writing documentation for writing guidelines, and don't hesitate to reach out to #g-digital-experience on Slack for writing support. A backlog of documentation improvement needs is kept here.

Clearing the plate

Engineering managers are asked to be aware of the oncall rotation and schedule a light workload for engineers while they are oncall. While it varies week to week considerably, the oncall responsibilities can sometimes take up a substantial portion of the engineer's time.

The remaining time after fulfilling the responsibilities of oncall is free for the engineer to choose their own path. Please choose something relevant to your work or Fleet's goals to focus on. If unsure, feel free to speak with your manager.

Some ideas:

  • Do training/learning relevant to your work.
  • Improve the Fleet developer experience.
  • Hack on a product idea. Note: Experiments are encouraged, but not all experiments will ship! Check in with the product team before shipping user-visible changes.
  • Create a blog post (or other content) for
  • Try out an experimental refactor.

At the end of your oncall shift, you will be asked to share about how you spent your time.

How to reach the oncall engineer

Oncall engineers do not need to actively monitor Slack channels, except when called in by the Community or Customer teams. Members of those teams are instructed to @oncall in #help-engineering to get the attention of the oncall engineer to continue discussing any issues that come up. In some cases, the Community or Customer representative will continue to communicate with the requestor. In others, the oncall engineer will communicate directly (team members should use their judgment and discuss on a case-by-case basis how to best communicate with community members and customers).


When the oncall engineer is unsure of the answer, they should follow this process for escalation.

To achieve quick "first-response" times, you are encouraged to say something like "I don't know the answer and I'm taking it back to the team," or "I think X, but I'm confirming that with the team (or by looking in the code)."

How to escalate:

  1. Spend 30 minutes digging into the relevant code (osquery, Fleet) and/or documentation (osquery, Fleet). Even if you don't know the codebase (or even the programming language), you can sometimes find good answers this way. At the least, you'll become more familiar with each project. Try searching the code for relevant keywords, or filenames.

  2. Create a new thread in the #help-engineering channel, tagging @zwass and provide the information turned up in your research. Please include possibly relevant links (even if you didn't find what you were looking for there). Zach will work with you to craft an appropriate answer or find another team member who can help.


The oncall engineer changes each week on Wednesday.

A Slack reminder should notify the oncall of the handoff. Please do the following:

  1. The new oncall engineer should change the @oncall alias in Slack to point to them. In the search box, type "people" and select "People & user groups." Switch to the "User groups" tab. Click @oncall. In the right sidebar, click "Edit Members." Remove the former oncall, and add yourself.

  2. Hand off newer conversations (Slack threads, issues, PRs, etc.). For more recent threads, the former oncall can unsubscribe from the thread, and the new oncall should subscribe. The former oncall should explicitly share each of these threads and the new oncall can select "Get notified about new replies" in the "..." menu. The former oncall can select "Turn off notifications for replies" in that same menu. It can be helpful for the former oncall to remain available for any conversations they were deeply involved in, so use your judgment on which threads to hand off. Anything not clearly handed off remains the responsibility of the former oncall engineer.

At the weekly "Eng Together" meeting, the oncall is asked to make a report of how they spent their time. Please answer the following:

  1. What were the most common support requests over the week? This can potentially give the new oncall an idea of which documentation to focus their efforts on.

  2. Which documentation page did you focus on? What changes were necessary?

  3. How did you spend the rest of your oncall week? This is a chance to demo or share what you learned.

Incident postmortems

At Fleet, we take customer incidents very seriously. After working with customers to resolve issues, we will conduct an internal postmortem to determine any documentation or coding changes to prevent similar incidents from happening in the future. Why? We strive to make Fleet the best osquery management platform globally, and we sincerely believe that starts with sharing lessons learned with the community to become stronger together.

At Fleet, we do postmortem meetings for every production incident, whether it's a customer's environment or on

Postmortem document

Before running the postmortem meeting, copy this Postmortem Template document and populate with some initial data to enable a productive conversation.

Postmortem meeting

Invite all stake holders, typically the team involved and QA representatives.

Follow the document topic by topic. Keep the goal in mind which is to take action items for addressing the root cause and making sure a similar incident will not happen again.

Distinguish between the root-cause of the bug, which by that time was solved and released, and the root-cause of why this issue reached our customers. These could be different issues. (e.g. the root-cause of the bug was a coding issue, but the root causes (plural) of the event was the test plan did not cover a specific scenario, a lack of unit tests and a lack of metrics to identify the issue quickly).

Example Finished Document

Postmortem action items

Each action item will have an owner that will be responsible for creating a Github issue promptly after the meeting. This Github issue should be prioritized with the relevant PM and/or engineering manager.


At Fleet, we consider an outage to be a situation where new features or previously stable features are broken or unusable.

  • Occurences of outages are tracked in the Outages spreadsheet.
  • Fleet encourages embracing the inevitability of mistakes and discourages blame games.
  • Fleet stresses the critical importance of avoiding outages because they make customers' lives worse instead of better.

Do we estimate released bugs and outages?

Estimating bugs and outages can be helpful in getting on the same page about how much time we want to spend on a given piece of unplanned work, and how much the ∑ of unplanned work is affecting a team's throughput.

Project boards

🚀 Release - The current release (daily go-to board) for engineers.

⚗️ Roadmap - Planning for the next release (shared with the Product team).

Scaling gotchas


Nowadays, Fleet, as a Go server, scales horizontally very well. It’s not very CPU or memory intensive. In terms of load in infrastructure, from highest to lowest are: MySQL, Redis, and Fleet.

In general, we should burn a bit of CPU or memory on the Fleet side if it allows us to reduce the load on MySQL or Redis.

In many, caching helps, but given that we are not doing load balancing based on host id (i.e., make sure that the same host ends up in the same Fleet server). This goes only so far. Caching host-specific data is not done because round-robin LB means all Fleet instances end up circling the total list of hosts.

How to prevent most of this

The best way we’ve got so far to prevent any scaling issues is to load test things. Every new feature must have its corresponding osquery-perf implementation as part of the PR, and it should be tested at a reasonable scale for the feature.

Besides that, you should consider the answer(s) to the following question: how can I know that the feature I’m working on is working and performing well enough? Add any logs, metrics, or anything that will help us debug and understand what’s happening when things unavoidably go wrong or take longer than anticipated.

HOWEVER (and forgive this Captain Obvious comment): do NOT optimize before you KNOW you have to. Don’t hesitate to take an extra day on your feature/bug work to load test things properly.

What have we learned so far?

This is a document that evolves and will likely always be incomplete. If you feel like something is missing, either add it or bring it up in any way you consider.

Connecting to Dogfood MySQL & Redis


  1. Setup VPN
  2. Configure SSO



Get the database host:

DB_HOST=$(aws rds describe-db-clusters --filter Name=db-cluster-id,Values=fleetdm-mysql-iam --query "DBClusters[0].Endpoint" --output=text)

Get the database user:

DB_USER=$(aws rds describe-db-clusters --filter Name=db-cluster-id,Values=fleetdm-mysql-iam --query "DBClusters[0].MasterUsername" --output=text)

Get the database password:

DB_PASSWORD=$(aws secretsmanager get-secret-value --secret-id /fleet/database/password/master --query "SecretString" --output=text)


mysql -h"${DB_HOST}" -u"${DB_USER}" -p"${DB_PASSWORD}"


Get the Redis Host:

REDIS_HOST=$(aws elasticache describe-replication-groups --replication-group-id fleetdm-redis --query "ReplicationGroups[0].NodeGroups[0].PrimaryEndpoint.Address" --output=text)


redis-cli -h "${REDIS_HOST}"

Foreign keys and locking

Among the first things you learn in database data modeling is: that if one table references a row in another, that reference should be a foreign key. This provides a lot of assurances and makes coding basic things much simpler.

However, this database feature doesn’t come without a cost. The one to focus on here is locking, and here’s a great summary of the issue:

The TLDR is: understand very well how a table will be used. If we do bulk inserts/updates, InnoDB might lock more than you anticipate and cause issues. This is not an argument to not do bulk inserts/updates, but to be very careful when you add a foreign key.

In particular, host_id is a foreign key we’ve been skipping in all the new additional host data tables, which is not something that comes for free, as with that, we have to keep the data consistent by hand with cleanups.

In this section

Insert on duplicate update

It’s very important to understand how a table will be used. If rows are inserted once and then updated many times, an easy reflex is to do an INSERT … ON DUPLICATE KEY UPDATE. While technically correct, it will be more performant to try to do an update, and if it fails because there are no rows, then do an insert for the row. This means that it’ll fail once, and then it’ll update without issues, while on the INSERT … ON DUPLICATE KEY UPDATE, it will try to insert, and 99% of the time, it will go into the ON DUPLICATE KEY UPDATE.

This approach has a caveat. It introduces a race condition between the UPDATE and the INSERT where another INSERT might happen in between the two, making the second INSERT fail. With the right constraints (and depending on the details of the problem), this is not a big problem. Alternatively, the INSERT could be one with an ON DUPLICATE KEY UPDATE at the end to recover from this scenario.

This is subtle, but an insert will update indexes, check constraints, etc. At the same time, an update might sometimes not do any of that, depending on what is being updated.

While not a performance GOTCHA, if you do use INSERT … ON DUPLICATE KEY UPDATE, beware that LastInsertId will return non-zero only if the INSERT portion happens. If an update happens, the LastInsertId will be 0.

Host extra data and JOINs

Indexes are great. But like most good things, the trick is in the dosage. Too many indexes can be a performance killer on inserts/updates. Not enough, and it kills the performance of selects.

Data calculated on the fly cannot be indexed unless it’s precalculated (see counts section below for more information).

Host data is among the data that changes and grows the most in terms of what we store. It used to be that we used to add more columns in the host table for the extra data in some cases.

Nowadays, we don’t update the host table structure unless we really, really, REALLY need to. Instead, we create adjacent tables that reference a host by id (without an FK). These tables can then be JOINed with the host table whenever needed.

This approach works well for most cases, and for now, it should be the default when gathering more data from hosts in Fleet. However, it’s not a perfect approach as it has its limits.

JOINing too many tables, sorting based on the JOINed table, etc., can have a big performance impact on selects.

Sometimes one strategy that works is selecting and filtering the adjacent table with the right indexes; then, JOIN the host table to that. This works when only filtering/sorting by one adjacent table and pagination can be tricky.

Solutions can become a curse too. Be mindful of when we might cross that threshold between good and bad performance.

What DB tables matter more when thinking about performance?

While we need to be careful about handling everything in the database, not every table is the same. The host and host_* tables are the main cases where we have to be careful when using them in any way.

However, beware of tables that go through async aggregation processes (such as scheduled_query and scheduled_query_stats) or those that are read often as part of the osquery distributed/read and config endpoints.

Expose more host data in the host listing

Particularly with extra host data (think MDM, Munki, Chrome profiles, etc.), another GOTCHA is that some users have built scripts that go through all hosts by using our list host endpoint. This means that any extra data we add might cause this process to be too slow or timeout (this has happened in the past).

Beware of this, and consider gating the extra data behind a query parameter to allow for a performant backward compatible API that can expose all the data needed in other use cases.

Calculated data is also tricky in the host listing API at scale, as those calculations have to happen for each host row. This can be extra problematic if the sort happens on the calculated data, as all data has to be calculated across all hosts before being able to sort and limit the results (more on this below).

Understand main use-cases for queries

Be aware of the use cases for an API. For example, take the software listing endpoint. This endpoint lists software alongside the number of hosts with that item installed. It was designed to be performant in a limited use case: list the first eight software items, then count hosts for those software ids.

The problem came later when we learned that we missed an important detail: the UI wanted to sort by amount of host count so that the most popular software appeared on top of this.

This resulted in basically a full host_software table scan per each software row to calculate the count per software. Then, sort and limit. The API worked in the simple case, but it timed out for most customers in the real world.

On constantly changing data

It can be difficult to show real-time presence data. For Fleet, that is the host seen_time -- the time a host last connected to the server -- which is used to determine whether a host is online.

Host seen_time is updated with basically every check-in from any kind of host. Hosts check in every 10 seconds by default. Given that it’s a timestamp reflecting the last time a host contacted Fleet for anything, it’s always different.

While we are doing a few things to make this better, this is still a big performance pain point we have. In particular, we are updating it in bulk. It used to be a column of the hosts' table, which caused a lot of locking. Now it’s an adjacent table without FK.

Luckily, we don’t have anything else (at least up to the moment of this writing) that changes as often as seen_time. However, other features such as software last used can cause similar issues.

Counts and aggregated data

UX is key for any software. APIs that take longer than 500ms to respond can cause UX issues. Counting things in the database is a tricky thing to do.

In the ideal case, the count query will be covered by an index and be extremely fast. In the worst case, the query will be counting filtering on calculated data, which results in a full (multi) table scan on big tables.

An approach we've taken to addressing this is pre-calculating aggregations and counts that take a long time to generate. By generating these results beforehand and storing them, we can return results by reading a single row from a table when the information is needed.

This approach has a handful of issues:

  • The accuracy of the data is worse. We will never get truly accurate counts (the “real-time” count the API returns could change 1ms after we get the value).
  • Depending on how many ways we want to count things, we will have to calculate and store them.
  • Communicating to the user the interval at which things update can sometimes be tricky.

All of this said, Fleet and osquery work in an “update at an interval” fashion, so we have exactly one pattern to communicate to the user, and then they can understand how most things work in the system.

Caching data such as app config

Caching is a usual strategy to solve some performance issues in the case of Fleet level data, such as app config (of which we will only have one), is easy, and we cache at the Fleet server instance level, refreshing the value every one second. App config gets queried with virtually every request, and with this, we reduce drastically how many times the database is hit with that query. The side effect is that a configuration would take one second to be updated in each Fleet instance, which is a price we are willing to pay.

Caching host-level data is a different matter, though. Given that Fleet is usually deployed in infrastructure where the load balancer distributes the load in a round-robin-like fashion (or maybe other algorithms, but nothing aware of anything within Fleet itself). Then virtually all hosts end up being seen by all Fleet instances, so caching host-level data (in the worst case) results in having a copy of all the hosts in each Fleet instance and refreshing that at an interval.

Caching at the Fleet instance level is a great strategy if it can reasonably handle big deployments, as Fleet utilizes minimal RAM.

Another place to cache things would be Redis. The improvement here is that all instances will see the same cache. However, Redis can also be a performance bottleneck depending on how it’s used.

Redis SCAN

Redis has solved many scaling problems in general, but it’s not devoid of scaling problems of its own. In particular, we learned that the SCAN command scans the whole key space before it does the filtering. This can be very slow, depending on the state of the system. If Redis is slow, a lot suffers from it.

Fleet docs

Fleet documentation explains how to use, deploy, and contribute to Fleet as simply as possible. This goes a long way in driving adoption while building our community of fans.

Community support

As an open-core company, Fleet endeavors to build a community of engaged users, customers, and contributors.

Version support

To provide the most accurate and efficient support, Fleet will only target fixes based on the latest released version. In the current version fixes, Fleet will not backport to older releases.

Community version supported for bug fixes: Latest version only

Community support for support/troubleshooting: Current major version

Premium version supported for bug fixes: Latest version only

Premium support for support/troubleshooting: All versions

Merge Community PRs

When merging a pull request from a community contributor:

  • Ensure that the checklist for the submitter is complete.
  • Verify that all necessary reviews have been approved.
  • Merge the PR.
  • Thank and congratulate the contributor.
  • Share the merged PR with the team in the #help-promote channel of Fleet Slack to be publicized on social media. Those who contribute to Fleet and are recognized for their contributions often become great champions for the project.

Reviewing PRs from the community

If you're assigned a community pull request for review, it is important to keep things moving for the contributor. The goal is to not go more than one business day without following up with the contributor.

A PR should be merged if:

  • It's a change that is needed and useful.
  • The CI is passing.
  • Tests are in place.
  • Documentation is updated.
  • Changes file is created.

For PRs that aren't ready to merge:

  • Thank the contributor for their hard work and explain why we can't merge the changes yet.
  • Encourage the contributor to reach out in the #fleet channel of osquery Slack to get help from the rest of the community.
  • Offer code review and coaching to help get the PR ready to go (see note below).
  • Keep an eye out for any updates or responses.
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Sometimes (typically for Fleet customers), a Fleet team member may add tests and make any necessary changes to merge the PR.

If everything is good to go, approve the review.

For PRs that will not be merged:

  • Thank the contributor for their effort and explain why the changes won't be merged.
  • Close the PR.

Updating docs and FAQ

When someone asks a question in a public channel, it's pretty safe to assume that they aren't the only person looking for an answer to the same question. To make our docs as helpful as possible, the Community team gathers these questions and uses them to make a weekly documentation update.

Our goal is to answer every question with a link to the docs and/or result in a documentation update.

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Remember, when submitting any pull request that changes Markdown files in the docs, request an editor review from Kathy Satterlee, who will escalate to the on-call engineer as needed.


When responding to a question or issue in the #fleet channel of the osquery Slack workspace, push the thread to Zapier using the TODO: Update docs Zap. This will add information about the thread to the Slack Questions Spreadsheet. In the Notes field, you can include any information that you think will be helpful when making weekly doc updates. That may be something like

  • proposed change to the documentation.
  • documentation link that was sent as a response.
  • link to associated thread in #help-oncall.

In this section

How to request a review for Markdown changes to the docs

When creating a pull request for Markdown changes in the docs, request a review from Kathy Satterlee, who will do an editor pass, and then hand over the review to the oncall engineer if necessary.


Fleet's documentation and handbook are written in Markdown. Using Markdown lets us keep our documentation consistently formatted and viewable directly from the Fleet GitHub repo. The Markdown files in the /docs and /handbook folders are converted to HTML for the Fleet website.

When making changes to the Fleet docs or handbook, there are a few differences in how the Markdown will render on GitHub and the Fleet website.

Linebreaks and newlines

Any time you need to add a line break in Markdown, you should add a new line. It is vital to make sure paragraphs are separated by new lines. Otherwise, they will render as the same HTML element.

For example, if you were adding this section to the docs:

line one
line two

The Markdown would render on the Fleet website as

line one line two

To make sure formatting is consistent across GitHub and the Fleet website, you need to add a new line anywhere you want a line break. For example, if we separate the lines with a new line:

line one

line two

The Markdown will render correctly as

line one

line two

Ordered lists

Content nested within an ordered list needs to be indented. If the list is not formatted correctly, the number will reset on each list item.

For example, this list:

1. Item one

Paragraph about item one

2. Item two

On the Fleet website, this ordered list would be rendered as

  1. Item one

Paragraph about item one

  1. Item two

To make sure that ordered lists increment on the Fleet website, you can indent the content nested within the list. For example, the same ordered list with indentation:

1. Item one

   Paragraph about item one

2. Item two

This ordered list will render correctly as

  1. Item one

    Paragraph about item one

  2. Item two

You can link documentation pages to each other using relative paths. For example, in docs/Using-Fleet/, you can link to docs/Using-Fleet/ by writing [permissions](./ This will automatically be transformed into the appropriate URL for

However, the compilation process does not account for relative links to directories outside of /docs. This is why it’s essential to follow the file path exactly when adding a link to Fleet docs. When directly linking to a specific section, always format the spaces within a section name to use a hyphen instead of an underscore. For example, when linking to the osquery_result_log_plugin section of the configuration reference docs, use a relative link like the following: ./

Linking to a location on GitHub

When adding a link to a location on GitHub outside of /docs, be sure to use the canonical form of the URL.

Navigate to the file's location on GitHub, and press "y" to transform the URL into its canonical form.

For instances when a broken link is discovered on, always check if the link is a relative link to a directory outside of /docs.

An example of a link that lives outside of /docs is:


If the link lives outside /docs, head to the file's location on GitHub (in this case,, and press "y" to transform the URL into its canonical form (a version of the link that will always point to the same version of the file) ( Replace the relative link with this link in the Markdown file.

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Note that the instructions above also apply to adding links in the Fleet handbook.

Page order

The order we display documentation pages on is determined by pageOrderInSection meta tags. These pages are sorted in their respective sections in ascending order by the pageOrderInSection value. Every Markdown file (except readme and faq pages) in the docs/ folder must have a meta tag with a positive 'pageOrderInSection' value.

We leave large gaps between values to make future changes easier. For example, the first page in the "Using Fleet" section of the docs has a pageOrderInSection value of 100, and the next page has a value of 200. The significant difference between values allows us to add, remove and reorder pages without changing the value of multiple pages at a time.

When adding or reordering a page, try to leave as much room between values as possible. If you were adding a new page that would go between the two pages from the example above, you would add <meta name="pageOrderInSection" value="150"> to the page.

Adding an image

Try to keep images in the docs at a minimum. Images can be a quick way to help users understand a concept or direct them towards a specific user interface(UI) element. Still, too many can make the documentation feel cluttered and more difficult to maintain.

When adding images to the Fleet documentation, follow these guidelines:

  • UI screenshots should be a 4:3 aspect ratio (1280x960). This is an optimal size for the container width of the docs and ensures that content in screenshots is as clear as possible to view in the docs (and especially on mobile devices).
  • You can set up a custom preset in the Google Chrome device toolbar (in Developer Tools) to quickly adjust your browser to the correct size for taking a screenshot.
  • Keep the images as simple as possible to maintain. Screenshots can get out of date quickly as UIs change.
  • Exclude unnecessary images. Images should be used to help emphasize information in the docs, not replace it.
  • Minimize images per doc page. For doc maintainers and users, more than one or two per page can get overwhelming.
  • The goal is for the docs to look good on every form factor, from 320px window width all the way up to infinity. Full window screenshots and images with too much padding on the sides will be less than the width of the user's screen. When adding a large image, make sure it is easily readable at all widths.

Images can be added to the docs using the Markdown image link format, e.g., ![Schedule Query Sidebar]( The images used in the docs live in docs/images/. Note that you must provide the URL of the image in the Fleet GitHub repo for it to display properly on both GitHub and the Fleet website.

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Note that the instructions above also apply to adding images in the Fleet handbook.

Adding a mermaid diagram

The Fleet Docs support diagrams that are written in mermaid.js syntax. Take a look at the Mermaid docs to learn about the syntax language and what types of diagrams you can display.

To add a mermaid diagram to the docs, you need to add a code block and specify that it is written in the mermaid language by adding mermaid to the opening backticks (i.e., ```mermaid).

For example, the following code block is a mermaid diagram that has not been specified as a mermaid code block:

graph TD;

Once we specify the mermaid as the language in the code block, it will render as a mermaid diagram on and GitHub.

graph TD; A-->D B-->D C-->D D-->E

If the mermaid syntax is incorrect, the diagram will be replaced with an image displaying an error, as shown in the following example where the code block was written with intentional syntax errors:

graph TD; A--D


Human-oriented QA

Fleet uses a human-oriented quality assurance (QA) process to make sure the product meets the standards of users and organizations.

Automated tests are important, but they can't catch everything. Many issues are hard to notice until a human looks empathetically at the user experience, whether in the user interface, the REST API, or the command line.

The goal of quality assurance is to identify corrections and improvements before release:

  • Bugs
  • Edge cases
  • Error message UX
  • Developer experience using the API/CLI
  • Operator experience looking at logs
  • API response time latency
  • UI comprehensibility
  • Simplicity
  • Data accuracy
  • Perceived data freshness

Finding bugs

To try Fleet locally for QA purposes, run fleetctl preview, which defaults to running the latest stable release.

To target a different version of Fleet, use the --tag flag to target any tag in Docker Hub, including any git commit hash or branch name. For example, to QA the latest code on the main branch of fleetdm/fleet, you can run: fleetctl preview --tag=main.

To start a preview without starting the simulated hosts, use the --no-hosts flag (e.g., fleetctl preview --no-hosts).

For each bug found, please use the bug report template to create a new bug report issue.

For unreleased bugs in an active sprint, no bug report issue is created. Instead, QA moves the associated story (ignoring any technical sub-task issues) back to the "In progress" column in the appropriate project board and assigns the engineering manager (EM). QA adds comments about the back or lack of expected functionality that was found. Fixing the bug becomes part of the story.

Bug process

Bug States

The lifecycle stages of a bug at Fleet are:

  1. Inbox
  2. Reproduced
  3. In engineering process
  4. Awaiting QA

The above are all the possible states for a bug as envisioned in this process. These states each correspond to a set of GitHub labels, assignees, and board memberships.

See Bug states and filters at the end of this document for descriptions of these states and links to each GitHub filter.


When a new bug is created using the bug report form, it is in the "inbox" state.

At this state, the bug review DRI (QA) is responsible for going through the inbox and documenting reproduction steps, asking for more reproduction details from the reporter, or asking the product team for more guidance. QA has one week to move the bug to the next step (reproduced).

For community-reported bugs, this may require QA to gather more information from the reporter. QA should reach out to the reporter if more information is needed to reproduce the issue. Reporters have six weeks to provide follow-up information for each report. We'll ping them again as a reminder at three weeks. After six weeks, we'll close the bug to remove it from our visibility, but reporters are welcome to re-open and provide context.

Once reproduced, QA documents the reproduction steps in the description and moves it to the reproduced state. If QA or the engineering manager feels the bug report may be expected behavior, or if clarity is required on the intended behavior, it is assigned to the group's product manager.

Weekly bug review

QA has weekly check-in with product to go over the inbox items. QA is responsible for proposing “not a bug”, closing due to lack of response (with a nice message), or raising other relevant questions. All requires product agreement

QA may also propose that a reported bug is not actually a bug. A bug is defined as “behavior that is not according to spec or implied by spec.” If agreed that it is not a bug, then it's assigned to the relevant product manager to determine its priority.


When reproduced, the bug is assigned to the appropriate EM and added to the product backlog. The EM is responsible for investigating the root cause of the bug and proposing solutions to their product counterpart if it requires discussion. Otherwise, the EM includes it in this release (if there's space) or the next release.

After reproduced

After it's in a release formally, the bug should be treated like any other piece of work per the standard engineering process.

Fast track for Fleeties

Fleeties do not have to wait for QA to reproduce the bug. If you're confident it's reproducible, it's a bug, and the reproduction steps are well-documented, it can be moved directly to the reproduced state.

Release testing

When a release is in testing, QA should use the the Slack channel #help-qa to keep everyone aware of issues found. All bugs found should be reported in the channel after creating the bug first.

In the #help-qa channel, product may decide whether the bug is a release blocker. When a release-blocking bug is found, product is responsible for communicating the delay to company stakeholders.

Release blockers include:

  1. Critical bugs (defined below)
  2. New functionality that impacts previous stable functionality
  3. Incomplete features as defined or implied in the specs

Release blockers must be fixed before a release can be cut. Non-release-blocking bugs may be addressed during a subsequent release per the standard bug process (defined above).

Critical bugs

A critical bug is defined as behavior that:

  • Blocks the normal use a workflow
  • Prevents upgrades to Fleet
  • Causes irreversible damage, such as data loss
  • Introduces a security vulnerability

We need to inform customers and the community about critical bugs immediately so they don’t trigger it themselves. When a bug meeting the definition of critical is found, the bug finder is responsible for raising an alarm. Raising an alarm means pinging @here in the #help-product channel with the filed bug.

If the bug finder is not a Fleetie (e.g., a member of the community), then whoever sees the critical bug should raise the alarm. (We would expect this to be customer experience in the community Slack or QA in the bug inbox, though it could be anyone.) Note that the bug finder here is NOT necessarily the first person who sees the bug. If you come across a bug you think is critical, but it has not been escalated, raise the alarm!

Once raised, product confirms whether or not it's critical and defines expected behavior. When outside of working hours for the product team or if no one from product responds within 1 hour, then fall back to the #help-p1.

Once the critical bug is confirmed, customer experience needs to ping both customers and the community to warn them. If CX is not available, the oncall engineer is responsible for doing this. If a quick fix workaround exists, that should be communicated as well for those who are already upgraded.

When a critical bug is identified, we will then follow the patch release process in our documentation.


We will track the success of this process by observing the throughput of issues through the system and identifying where buildups (and therefore bottlenecks) are occurring. The metrics are:

  • Number of bugs opened this week
  • Total # bugs open
  • Bugs in each state (inbox, acknowledged, reproduced)
  • Number of bugs closed this week

Each week these are tracked and shared in the weekly update by Charlie Chance.


Occasionally, bugs may get lost if, for example, a label is misapplied. Miscategorized issues may slip through the filters and languish in a grey zone. The “orphan” and “reproduced orphan” states exist to catch these issues. Every week, the head of product is responsible for reviewing these two states to identify any that are not properly categorized in the process.

Bug states and filters


The bug has just come in.

If using the standard bug report, the bug is labeled “bug” and “reproduce." It is not assigned to anyone. See on GitHub.


QA has reproduced the issue successfully. It should now be transferred to engineering.

Remove the “reproduce” label, add the label of the relevant team (e.g. #cx, #mdm, #compliance), add the "product" label, and assign it to the relevant engineering manager. (Make your best guess as to which team. The EM will re-assign if they think it belongs to another team.) See on GitHub.


These are bugs that do not have the reproduce label but do not have the "release" or "product" label on them. As such, they will not appear in the boards and thus are likely to be forgotten by our process. This filter serves as a sanity check. There should be no bugs in this state. See on GitHub.

All bugs

See on GitHub.

Bugs opened this week

This filter returns all "bug" issues opened after the specified date. Simply replace the date with a YYYY-MM-DD equal to one week ago. See on GitHub.

Bugs closed this week

This filter returns all "bug" issues closed after the specified date. Simply replace the date with a YYYY-MM-DD equal to one week ago. See on Github.


In the above process, any reference to "product" refers to: Mo Zhu, Head of Product. In the above process, any reference to "QA" refers to: Reed Haynes, QA Engineer.


The following rituals are engaged in by the directly responsible individual (DRI) and at the frequency specified for the ritual.

Ritual Frequency Description DRI
Pull request review Daily Engineers go through pull requests for which their review has been requested. Luke Heath
Engineering group discussions Weekly See "Group Weeklies". Zach Wasserman
Oncall handoff Weekly Hand off the oncall engineering responsibilities to the next oncall engineer. Luke Heath
Vulnerability alerts ( Weekly Review and remediate or dismiss vulnerability alerts for the codebase on GitHub. Eric Shaw
Vulnerability alerts (frontend) Weekly Review and remediate or dismiss vulnerability alerts for the Fleet frontend codebase (and related JS) on GitHub. Zach Wasserman
Vulnerability alerts (backend) Weekly Review and remediate or dismiss vulnerability alerts for the Fleet backend codebase (and all Go code) on GitHub. Zach Wasserman
Release ritual Every three weeks Go through the process of releasing the next iteration of Fleet. Luke Heath
Create patch release branch Every patch release Go through the process of creating a patch release branch, cherry picking commits, and pushing the branch to Luke Heath
Bug review Weekly Review bugs that are in QA's inbox. Reed Haynes
Release testing/QA Every three weeks Every release cycle, by end of day Wednesday of release week, all issues move to "Ready for release" on the 🚀Release board. Reed Haynes
Release testing/QA report Every three weeks Every release cycle, on the Monday of release week, the DRI for the release ritual is updated on status of testing. Reed Haynes

24/7 on-call

The 24/7 on-call (aka infrastructure on-call) team is responsible for alarms related to, Fleet sandbox, Fleet managed cloud, as well as delivering 24/7 support for Fleet Ultimate customers. The infrastructure (24/7) on-call responsibility happen in shifts of 1 week. The people involved in them will be:

First responders:

  • Zachary Winnerman
  • Robert Fairburn

Escalations (in order):

  • Zay Hanlon
  • Zach Wasserman (Fleet app)
  • Eric Shaw (
  • Mike McNeil

The first responder oncall will take ownership of the @infrastructure-oncall alias in Slack first thing Monday morning. The previous weeks oncall will provide a summary in the #g-customer-experience Slack channel with a an update on alarms that came up the week before, open issues with or without direct end user impact, and other things to keep an eye out for.

Expected response times: during business hours, 1 hour. Outside of business hours <4 hours.

For and sandbox alarms, if the issue is not user facing (e.g. provisioner/deprovisioner/temporary errors in osquery/etc), the oncall engineer will proceed to address the issue. If the issue is user facing (e.g. the user noticed this error first hand through the Fleet UI), then the oncall engineer will proceed to identify the user and contact them letting them know that we are aware of the issue and working on a resolution. They may also request more information from the user if it is needed. They will cc the VP of Customer Success on any user correspondence.

For Fleet managed cloud alarms that are user facing, the first responder should collect the email address for the customer and all available information on the error. If the error occurs during business hours, the first responder should make a best effort to understand where in the app the error might have occurred. Assistance can be requested in #help-engineering by including the data they know regarding the issue, and when available, a frontend or backend engineer can help identify what might be causing the problem. If the error occurs outside of business hours, then the oncall engineer will contact the user letting them know that we are aware of the issue and working on a resolution. It’s more helpful to say something like “we saw that you saw an error while trying to create a query” than to say “your POST /api/blah failed”.

Escalation of issues will be done manually by the first responder according to the escalation contacts mentioned above. An outage issue (template available) should be created in the Fleet confidential repo addressing: who was affected?, for how long?, what expected behavior occurred?, how do you know?, what near-term resolution can be taken to recover the affected user?, what is the underlying reason or suspected reason for the outage?, and what are the next steps Fleet will take to address the root cause?.

All infrastructure alarms (, Fleet managed cloud, and sandbox) will go to #help-p1.

The information needed to evaluate and potentially fix any issues is documented in the runbook.

When an infrastructure oncall engineer is out of office, Mike McNeil will serve as a backup to oncall in #help-p1. All absences must be communicated in advance with Zay Hanlon and Mike McNeil.

Slack channels

The following Slack channels are maintained by this group:

Slack channel DRI
#help-engineering Zach Wasserman
#g-mdm Luke Heath
#g-customer-experience Sharon Katz
#_pov-environments Ben Edwards
#help-qa Reed Haynes