Summary

2022 took forever. At the same time, it kind of flew by. 2023 is already moving along, so this post is a month late. Here's the retrospective of Socorro engineering in 2022.

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What is it?

Bleach is a Python library for sanitizing and linkifying text from untrusted sources for safe usage in HTML.

Bleach v6.0.0 released!

Bleach 6.0.0 cleans up some issues in linkify and with the way it uses html5lib so it's easier to reason about. It also adds support for Python 3.11 and cleans up the project infrastructure.

There are several backwards-incompatible changes, hence the 6.0.0 version.

https://bleach.readthedocs.io/en/latest/changes.html#version-6-0-0-january-23rd-2023

I did some rough testing with a corpus of Standup messages data and it looks like bleach.clean is slightly faster with 6.0.0 than 5.0.0.

Using Python 3.10.9:

• 5.0.0: bleach.clean on 58,630 items 10x: minimum 2.793s

• 6.0.0: bleach.clean on 58,630 items 10x: minimum 2.304s

The other big change 6.0.0 brings with it is that it's now deprecated.

Bleach is deprecated

Bleach sits on top of html5lib which is not actively maintained. It is increasingly difficult to maintain Bleach in that context and I think it's nuts to build a security library on top of a library that's not in active development.

Over the years, we've talked about other options:

1. find another library to switch to

2. take over html5lib development

3. fork html5lib and vendor and maintain our fork

4. write a new HTML parser

5. etc

With the exception of option 1, they greatly increase the scope of the work for Bleach. They all feel exhausting to me.

Given that, I think Bleach has run its course and this journey is over.

What happens now?

Possibilities:

1. Pass it to someone else?

   No, I won't be passing Bleach to someone else to maintain. Bleach is a security-related library, so making a mistake when passing it to someone else would be a mess. I'm not going to do that.

2. Switch to an alternative?

   I'm not aware of any alternatives to Bleach. I don't plan to work on coordinating the migration for everyone from Bleach to something else.

3. Oh my goodness--you're leaving us with nothing?

   Sort of.

I'm going to continue doing minimal maintenance:

1. security updates

2. support for new Python versions

3. fixes for egregious bugs (begrudgingly)

I'll do that for at least a year. At some point, I'll stop doing that, too.

I think that gives the world enough time for either something to take Bleach's place, or for the sanitizing web api to kick in, or for everyone to come to the consensus that they never really needed Bleach in the first place.

Thanks!

Many thanks to Greg who I worked with on Bleach for a long while and maintained Bleach for several years. Working with Greg was always easy and his reviews were thoughtful and spot-on.

Many thanks to Jonathan who, over the years, provided a lot of insight into how best to solve some of Bleach's more squirrely problems.

Many thanks to Sam who was an indispensible resource on HTML parsing and sanitizing text in the context of HTML.

Where to go for more

For more specifics on this release, see here: https://bleach.readthedocs.io/en/latest/changes.html#version-6-0-0-january-23rd-2023

Documentation and quickstart here: https://bleach.readthedocs.io/en/latest/

Source code and issue tracker here: https://github.com/mozilla/bleach

Project

time:

2+ years

impact:

• radically reduced risk of data leaks due to misconfigured permissions

• centralized and simplified configuration and management of fields

• normalization and validation performed during processing

• documentation of data reviews, data caveats, etc

• reduced risk of bugs when adding new fields--testing is done in CI

• new crash reporting data dictionary with Markdown-formatted descriptions, real examples, relevant links

Summary

I've been working on Socorro (crash ingestion pipeline at Mozilla) since the beginning of 2016. During that time, I've focused on streamlining maintainence of the project, paying down technical debt, reducing risk, and improving crash analysis tooling.

One of the things I identified early on is how the crash ingestion pipeline was chaotic, difficult to reason about, and difficult to document. What did the incoming data look like? What did the processed data look like? Was it valid? Which fields were protected? Which fields were public? How do we add support for a new crash annotation? This was problematic for our ops staff, engineering staff, and all the people who used Socorro. It was something in the back of my mind for a while, but I didn't have any good thoughts.

In 2020, Socorro moved into the Data Org which has multiple data pipelines. After spending some time looking at how their pipelines work, I wanted to rework crash ingestion.

The end result of this project is that:

1. the project is easier to maintain:

   • adding support for new crash annotations is done in a couple of schema files and possibly a processor rule

2. risk of security issues and data breaches is lower:

   • typos, bugs, and mistakes when adding support for a new crash annotation are caught in CI

   • permissions are specified in a central location, changing permission for fields is trivial and takes effect in the next deploy, setting permissions supports complex data structures in easy-to-reason-about ways, and mistakes are caught in CI

3. the data is easier to use and reason about:

   • normalization and validation of crash annotation data happens during processing and downstream uses of the data can expect it to be valid; further we get a signal when the data isn't valid which can indicate product bugs

   • schemas describing incoming and processed data

   • crash reporting data dictionary documenting incoming data fields, processed data fields, descriptions, sources, data gotchas, examples, and permissions

What is Socorro?

Socorro is the crash ingestion pipeline for Mozilla products like Firefox, Fenix, Thunderbird, and MozillaVPN.

When Firefox crashes, the crash reporter asks the user if the user would like to send a crash report. If the user answers "yes!", then the crash reporter collects data related to the crash, generates a crash report, and submits that crash report as an HTTP POST to Socorro. Socorro saves the submitted crash report, processes it, and has tools for viewing and analyzing crash data.

State of crash ingestion at the beginning

The crash ingestion system was working and it was usable, but it was in a bad state.

• Poor data management

  Normalization and validation of data was all over the codebase and not consistent:

  • processor rule code

  • AWS S3 crash storage code

  • Elasticsearch indexing code

  • Telemetry crash storage code

  • Super Search querying and result rendering code

  • report view and template code

  • signature report code and template code

  • crontabber job code

  • any scripts that used the data

  • tests -- many of which had bad test data so who knows what they were really testing

  Naive handling of minidump stackwalker output which meant that any changes in the stackwalker output were predominantly unnoticed and there was no indication as to whether changed output created issues in the system.

  Further, since it was all over the place, there were no guarantees for data validity when downloading it using the RawCrash, ProcessedCrash, and SuperSearch APIs. Anyone writing downstream systems would also have to normalize and validate the data.

• Poor permissions management

  Permissions were defined in multiple places:

  • Elasticsearch json redactor

  • Super Search fields

  • RawCrash API allow list

  • ProcessedCrash API allow list

  • report view and template code

  • Telemetry crash storage code

  • and other places

  We couldn't effectively manage permissions of fields in the stackwalker output because we had no idea what was there.

• Poor documentation

  No documentation of crash annotation fields other than CrashAnnotations.yaml which didn't enforce anything in crash ingestion (process, valid type, data correctness, etc) and was missing important information like data gotchas, data review urls, and examples.

  No documentation of processed crash fields at all.

• Making changes was high risk

  Changing fields from public to protected was high risk because you had to find all the places it might show up which was intractable. Adding support for new fields often took multiple passes over several weeks because we'd miss things. Server errors happend with some regularity due to weirdness with crash annotation values affecting the Crash Stats site.

• Tangled concerns across the codebase

  Lots of tangled concerns where things defined in one place affected other places that shouldn't be related. For example, the Super Search fields definition was acting as a "schema" for other parts of the system that had nothing to do with Elasticsearch or Super Search.

• Difficult to maintain

  It was difficult to support new products.

  It was difficult to debug issues in crash ingestion and crash reporting.

  The Crash Stats webapp contained lots of if/then/else bits to handle weirdness in the crash annotation values. Nulls, incorrect types, different structures, etc.

  Socorro contained lots of vestigial code from half-done field removal, deprecated fields, fields that were removed from crash reports, etc. These vestigial bits were all over the code base. Discovering and removing these bits was time consuming and error prone.

  The code for exporting data to Telemetry built the export data using a list of fields to exclude rather than a list of fields to include. This is backwards and impossible to maintain--we never should have been doing this. Further, it pulled data from the raw crash which we had no validation guarantees for which would cause issues downstream in the Telemetry import code.

  There was no way to validate the data used in the unit tests which meant that a lot of it was invalid. We had no way to validate the test data which meant that CI would pass, but we'd see errors in our stage and production environments.

• Different from other similar systems

  In 2020, Socorro was moved to the Data Org in Mozilla which had a set of standards and conventions for collecting, storing, analyzing, and providing access to data. Socorro didn't follow any of it which made it difficult to work on, to connect with, and to staff. Things Data Org has that Socorro didn't:

  • a schema covering specifying fields, types, and documentation

  • data flow documentation

  • data review policy, process, and artifacts for data being collected and how to add new data

  • data dictionary for fields for users including documentation, data review urls, data gotchas

In summary, we had a system that took a lot of effort to maintain, wasn't serving our users' needs, and was high risk of security/data breach.

Project plan

Many of these issues can be alleviated and reduced by moving to a schema-driven system where we:

1. define a schema for annotations and a schema for the processed crash

2. change crash ingestion and the Crash Stats site to use those schemas

When designing this schema-driven system, we should be thinking about:

1. how easy is it to maintain the system?

2. how easy is it to explain?

3. how flexible is it for solving other kinds of problems in the future?

4. what kinds of errors will likely happen when maintaining the system and how can we avert them in CI?

5. what kinds of errors can happen and how much risk do they pose for data leaks? what of those can we avert in CI?

6. how flexible is the system which needs to support multiple products potentially with different needs?

I worked out a minimal version of that vision that we could migrate to and then work with going forward.

The crash annotations schema should define:

1. what annotations are in the crash report?

2. which permissions are required to view a field

3. field documentation (provenance, description, data review, related bugs, gotchas, analysis tips, etc)

The processed crash schema should define:

1. what's in the processed crash?

2. which permissions are required to view a field

3. field documentation (provenance, description, related bugs, gotchas, analysis tips, etc)

Then we make the following changes to the system:

1. write a processor rule to copy, nomralize, and validate data from the raw crash based on the processed crash schema

2. switch the Telemetry export code to using the processed crash for data to export

3. switch the Telemetry export code to using the processed crash schema for permissions

4. switch Super Search to using the processed crash for data to index

5. switch Super Search to using the processed crash schema for documentation and permissions

6. switch Crash Stats site to using the processed crash for data to render

7. switch Crash Stats site to using the processed crash schema for documentation and permissions

8. switch the RawCrash, ProcessedCrash, and SuperSearch APIs to using the crash annotations and processed crash schemas for documentation and permissions

After doing that, we have:

1. field documentation is managed in the schemas

2. permissions are managed in the schemas

3. data is normalized and validated once in the processor and everything uses the processed crash data for indexing, searching, and rendering

4. adding support for new fields and changing existing fields is easier and problems are caught in CI

Implementation decisions

Use JSON Schema.

Data Org at Mozilla uses JSON Schema for schema specification. The schema is written using YAML.

https://mozilla.github.io/glean_parser/metrics-yaml.html

The metrics schema is used to define metrics.yaml files which specify the metrics being emitted and collected.

For example:

https://searchfox.org/mozilla-central/source/toolkit/mozapps/update/metrics.yaml

One long long long term goal for Socorro is to unify standards and practices with the Data Ingestion system. Towards that goal, it's prudent to build out a crash annotation and processed crash schemas using whatever we can take from the equivalent metrics schemas.

We'll additionally need to build out tooling for verifying, validating, and testing schema modifications to make ongoing maintenance easier.

Use schemas to define and drive everything.

We've got permissions, structures, normalization, validation, definition, documentation, and several other things related to the data and how it's used throughout crash ingestion spread out across the codebase.

Instead of that, let's pull it all together into a single schema and change the system to be driven from this schema.

The schema will include:

1. structure specification

2. documentation including data gotchas, examples, and implementation details

3. permissions

4. processing instructions

We'll have a schema for supported annotations and a schema for the processed crash.

We'll rewrite existing parts of crash ingestion to use the schema:

1. processing 1. use processing instructions to validate and normalize annotation data

2. super search 1. field documentation 2. permissions 3. remove all the normalization and validation code from indexing

3. crash stats 1. field documentation 2. permissions 3. remove all the normalization and validation code from page rendering

Only use processed crash data for indexing and analysis.

The indexing system has its own normalization and validation code since it pulls data to be indexed from the raw crash.

The crash stats code has its own normalization and validation code since it renders data from the raw crash in various parts of the site.

We're going to change this so that all normalization and validation happens during processing, the results are stored in the processed crash, and indexing, searching, and crash analysis only work on processed crash data.

By default, all data is protected.

By default, all data is protected unless it is explicitly marked as public. This has some consequences for the code:

1. any data not specified in a schema is treated as protected

2. all schema fields need to specify permissions for that field

3. any data in a schema is either: * marked public, OR * lists the permissions required to view that data

4. for nested structures, any child field that is public has public ancesters

We can catch some of these issues in CI and need to write tests to verify them.

This is slightly awkward when maintaining the schema because it would be more reasonable to have "no permissions required" mean that the field is public. However, it's possible to accidentally not specify the permissions and we don't want to be in that situation. Thus, we decided to go with explicitly marking public fields as public.

Work done

Random thoughts

This was a very very long-term project with many small steps and some really big ones. Getting large projects done is futile and the only way to do it successfully is to break it into a million small steps each of which stand on their own and don't create urgency for getting the next step done.

Any time I changed field names or types, I'd have to do a data migration. Data migrations take 6 months to do because I have to wait for existing data to expire from storage. On the one hand, it's a blessing I could do migrations at all--you can't do this with larger data sets or with data sets where the data doesn't expire without each migration becoming a huge project. On the other hand, it's hard to juggle being in the middle of multiple migrations and sometimes the contortions one has to perform are grueling.

If you're working on a big project that's going to require changing data structures, figure out how to do migrations early with as little work as possible and use that process as often as you can.

Conclusion and where we could go from here

This was such a huge project that spanned years. It's so hard to finish projects like this because the landscape for the project is constantly changing. Meanwhile, being mid-project has its own set of complexities and hardships.

I'm glad I tackled it and I'm glad it's mostly done. There are some minor things to do, still, but this new schema-driven system has a lot going for it. Adding support for new crash annotations is much easier, less risky, and takes less time.

It took me about a month to pull this post together.

That's it!

That's the story of the schema-based overhaul of crash ingestion. There's probably some bits missing and/or wrong, but the gist of it is here.

If you have any questions or bump into bugs, I hang out on #crashreporting on chat.mozilla.org. You can also write up a bug for Socorro.

Hopefully this helps. If not, let us know!

Installing Windows (2022)

I work at Mozilla. We get a laptop refresh periodically. I got a new laptop that I was going to replace my older laptop with. I'm a software engineer and I work on services that are built using Docker and tooling that runs on Linux.

This post covers my attempt at setting up a Windows laptop for software development for the projects I work on after having spent the last 20 years predominantly using Linux and Linux-like environments.

Spoiler: This is a failed attempt and I gave up and stuck with Linux.

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Summary

Back in June, I saw a note about Volunteer Responsibility Amnesty Day in Sumana's Changeset Consulting newsletter. The idea of it really struck a chord with me. I wondered whether running an event like this at work would help. With that, I coordinated an event, ran it, and this is the blog post summarizing how it went.

The context

As people leave Mozilla, the libraries, processes, services, and other responsibilities (hidden and visible) all suddenly become unowned. In some cases, these things get passed to teams and individuals and there's a clear handoff. In a lot of cases, stuff just gets dropped on the floor.

Some of these things should remain on the floor--we shouldn't maintain all the things forever. Sometimes things get maintained because of inertia rather than actual need. Letting these drop and decay over time is fine.

Some of these things turn out to be critical cogs in the machinations of complex systems. Letting these drop and decay over time can sometimes lead to a huge emergency involving a lot of unscheduled scrambling to fix. That's bad. No one likes that.

In the last year, I had picked up a bunch of stuff from people who had left and it was increasingly hard to juggle it all. Thus taking a day to audit all the things on my plate and figuring out which ones I don't want to do anymore seemed really helpful.

Further, even without people leaving, new projects show up, pipelines are added, new services are stood up--there's more stuff running and more stuff to do to keep it all running.

Thus I wondered, what if other people in Data Org at Mozilla had similar issues? What if there were tasks and responsibilities that we had accumulated over the years that, if we stepped back and looked at them, didn't really need to be done anymore? What if there were people who had too many things on their plate and people who had a lot of space? Maybe an audit would surface this and let us collectively shuffle some things around.

Setting it up

In that context, I decided to coordinate a Volunteer Responsibility Amnesty Day for Data Org.

I decided to structure it a little differently because I wanted to run something that people could participate in regardless of what time zone they were in. I wanted it to produce an output that individuals could talk with their managers about--something they could use to take stock of where things were at, surface work individuals were doing that managers may not know about, and provide a punch list of actions to fix any problems that came up.

I threw together a Google doc that summarized the goals, provided a template for the audit, and included a next steps which were pretty much tell us on Slack and bring it up with your manager in your next 1:1. Here's the doc:

https://docs.google.com/document/d/19NF69uavGXii_DEkRpQsJuklHxWoPTWwxBp_ucRela4/edit#

I talked to my manager about it. I mentioned it in meetings and in various channels on Slack.

On the actual day, I posted a few reminders in Slack.

How'd it go?

I figured it was worth doing once. Maybe it would be helpful? Maybe not? Maybe it helps us reduce the amount of stuff we're doing solely for inertia purposes?

I didn't get a lot of signal about how it went, though.

I know chutten participated and the audit was helpful for him. He has a ton of stuff on his plate.

I know Jan-Erik participated. I don't know if it was helpful for him.

I heard that Alessio decided to do this with his team every 6 months or so.

While I did organize the event, I actually didn't participate. I forget what happened, but something came up and I was bogged down with that.

That's about all I know. I think there are specific people who have a lot of stuff on their plate and this was helpful, but generally either people didn't participate (Maybe they were bogged down like me? Maybe they don't have much they're juggling?) or I never found out they participated.

Epilog

I think it was useful to do. It was a very low-effort experiment to see if something like this would be helpful. If it was the case that people had a lot on their plates, seems like this would have surfaced a bunch of things allowing us to improve peoples' work lives. I think for specific people who have a lot on their plate, it was a helpful exercise.

I didn't get enough signal to make me want to spend the time to run it again in December.

Given that:

1. Ift think it's good to run individually. If you're feeling overwhelmed with stuff, an audit is a great place to start figuring out how to fix that.

2. It might be good to run in a small team as an excercise in taking stock of what's going on and rebalance things.

3. It's probably not helpful to run in an org where maybe it ends up being more bookkeeping work than it's worth.

What is it?

Dennis is a Python command line utility (and library) for working with localization. It includes:

• a linter for finding problems in strings in .po files like invalid Python variable syntax which leads to exceptions

• a template linter for finding problems in strings in .pot files that make translator's lives difficult

• a statuser for seeing the high-level translation/error status of your .po files

• a translator for strings in your .po files to make development easier

v1.0.0 released!

It's been 5 years since I released Dennis v0.9. That's a long time.

This brings several minor things and clean up. Also, I transferred the repository from "willkg" to "mozilla" in GitHub.

• b38a678 Drop Python 3.5/3.6; add Python 3.9/3.10 (#122, #123, #124, #125)

• b6d34d7 Redo tarrminal printin' and colorr (#71)

  There's an additional backwards-incompatible change here in which we drop the --color and --no-color arguments from dennis-cmd lint.

• 658f951 Document dubstep (#74)

• adb4ae1 Rework CI so it uses a matrix

• transfer project from willkg to mozilla for ongoing maintenance and support

Retrospective

I worked on Dennis for 9 years.

It was incredibly helpful! It eliminated an entire class of bugs we were plagued with for critical Mozilla sites like AMO, MDN, SUMO, Input [1], and others. It did it in a way that supported and was respectful of our localization community.

It was pretty fun! The translation transforms are incredibly helpful for fixing layout issues. Some of them also produce hilarious results:

There were a variety of dennis recipes including using it in a commit hook to translate commit messages. https://github.com/mozilla/dennis/commits/main

I enjoyed writing silly things at the bottom of all the release blog posts.

I learned a lot about gettext, localization, and languages! Learning about the nuances of plurals was fascinating.

The code isn't great. I wish I had redone the tokenization pipeline. I wish I had gotten around to adding support for other gettext variable formats.

Regardless, this project had a significant impact on Mozilla sites which I covered briefly in my Dennis Retrospective (2013).

Handing it off

It's been 6 years since I worked on sites that have localization, so I haven't really used Dennis in a long time and I'm no longer a stakeholder for it.

I need to reduce my maintenance load, so I looked into whether to end this project altogether. Several Mozilla projects still use it for linting PO files for deploys, so I decided not to end the project, but instead hand it off.

Welcome @diox and @akatsoulas who are picking it up!

Where to go for more

For more specifics on this release, see here: https://dennis.readthedocs.io/en/latest/changelog.html#version-1-0-0-june-10th-2022

Documentation and quickstart here: https://dennis.readthedocs.io/en/latest/

Source code and issue tracker here: https://github.com/mozilla/dennis

39 of 7,952,991,938 people were aware that Dennis existed but tens--nay, hundreds!--of millions were affected by it.