My notes on Gitlab's Postgres schema design (2022)

For example, Github had 128 million public repositories in 2020. Even with 20 issues per repository it will cross the serial range. Also changing the type of the table is expensive.

I expect the majority of those public repositories are forks of other repositories, and those forks only exist so someone could create pull requests against the main repository. As such, they won't ever have any issues, unless someone makes a mistake.

Beyond that, there are probably a lot of small, toy projects that have no issues at all, or at most a few. Quickly-abandoned projects will suffer the same fate.

I suspect that even though there are certainly some projects with hundreds and thousands of issues, the average across all 128M of those repos is likely pretty small, probably keeping things well under the 2B limit.

Having said that, I agree that using a 4-byte type (well, 31-bit, really) for that table is a ticking time bomb for some orgs, github.com included.

> Having said that, I agree that using a 4-byte type (well, 31-bit, really) for that table is a ticking time bomb for some orgs

A bomb defused in a migration that takes eleven seconds

The migration has to rewrite the whole table, bigint needs 8 bytes so you have to make room for that.

I have done several such primary key migrations on tables with 500M+ records, they took anywhere from 30 to 120 minutes depending on the amount of columns and indexes. If you have foreign keys it can be even longer.

Edit: But there is another option which is logical replication. Change the type on your logical replica, then switch over. This way the downtime can be reduced to minutes.

This largely depends on the disk. I wouldn't expect that to take 30mins on a modern NVME drive, but of course it depends on table size.

Large tables take hours, if not days. I attempted a test case on AWS using souped up io2 disks (the fastest most expensive disks they have) and a beast of a DB server (r5.12xl I think) and it became abundantly clear that at certain scale you won't be doing any kind of in-place table updates like that on the system. Especially if your allowed downtime is one hour maintenance window per week...

I did it on a r6.24xlarge RDS instance and the CPU wasn't doing much during the operation. IO peaked at 40k IOPS on EBS with provisioned IOPS, I'm not sure if a local disk would be any faster but I already know that rewriting the table and creating the indexes are all single threaded so there isn't much you could gain.

Once I got the logical replication setup to work I changed 20 tables on the replica and made the switch with 15 minutes of downtime. That saved me a lot of long nights.

You can get an idea of how long this could take by running pg_repack, it's basically doing the same thing: Copying the data and recreating all the indexes.

Disk wasn't the limit in my case, index creation is single threaded.

Which is still very IO bound... Wonder what kinda IOPS you were observing? Also they make pretty fast CPUs these days :)

In practice the only option that I’ve seen work for very large teams and very large relational databases is online schema change tools like https://github.com/shayonj/pg-osc and https://github.com/github/gh-ost (the latter developed for GitHub’s monolith). It’s just too difficult to model what migrations will cause problems under load. Using a binlog/shadowtable approach for all migrations mostly obviates the problem.

Rails migrations really fail to be viable at scale. And its not really because of the migrations in Rails, its because changes in PostgreSQL get very very expensive. Things have gotten better in PG 15, but its still not quite there yet.

I've ran into an integer PK being almost exhausted in values twice now. In one of these cases I used pg-osc to rewrite a 8TB table on disk without downtime over a period of a week to fix the issue. In the other case it was about 6TB within 2 days. Its doable, but the amount of planning and time it takes is non-trivial.

I so far have no found any other viable solutions either. I keep hearing about logical replication but I haven't seen a great real-world solution at scale so far.

You can also migrate it using logical replication.

In JavaScript land, postgres bigints deserialize as strings. Is your application resilient to this? Are your downstream customers ready to handle that sort of schema change?

Running the db migration is the easy part.

Depends on the lib. Max safe int size is like 9 quadrillion. You can safely deserialize serial bigints to this without ever worrying about hitting that limit in many domains.

Max safe int size is like 9 quadrillion.

2^53, to be precise. If your application involves assigning a unique identifier to every ant on the planet Earth (approx. 10^15 ≈ 2^50), you might need to think about this. Otherwise, I wouldn't worry about it.

11 seconds won't fix all your foreign keys. And all the code written against it that assumes an int type will accommodate the value.

It is still under the limit today with 362,107,148 repositories and 818,516,506 unique issues and pull requests:

https://play.clickhouse.com/play?user=play#U0VMRUNUIHVuaXEoc...

Elapsed: 12.618 sec, read 7.13 billion rows, 42.77 GB

This is too long, seems the ORDER BY is not set up correctly for the table.

Also,

`repo_name` LowCardinality(String),

This is not a low cardinality:

7133122498 = 7.1B

Don't use low cardinality for such columns!

I'm guessing this won't be including issues & PRs from private repos, which could be substantial

That query took a long time

Do we know for sure if gitlab cloud uses a multi-tenanted database, or a db per user/customer/org? In my experience products that offer both a self hosted and cloud product tend to prefer a database per customer, as this greatly simplifies the shared parts of the codebase, which can use the same queries regardless of the hosting type.

If they use a db per customer then no one will ever approach those usage limits and if they do they would be better suited to a self hosted solution.

Unless something has substantially changed since I last checked, gitlab.com is essentially self-hosted gitlab ultimate with a few feature flags to enable some marginally different behaviour. That is, it uses one multitennant DB for the whole platform.

Not according to [1] where the author said

This effectively results in two code paths in many parts of your platform: one for the SaaS version, and one for the self-hosted version. Even if the code is physically the same (i.e. you provide some sort of easy to use wrapper for self-hosted installations), you still need to think about the differences.

1. https://yorickpeterse.com/articles/what-it-was-like-working-...

I've toyed with various SaaS designs and multi tenanted databses always come to th forefront of my mind. It seems to simplify the architecture a lot.

I'm convinced that GitHub's decision to move away from Rails was partly influenced by a significant flaw in ActiveRecord: its lack of support for composite primary keys. The need for something as basic as PRIMARY KEY(repo_id, issue_id) becomes unnecessarily complex within ActiveRecord, forcing developers to use workarounds that involve a unique key alongside a singular primary key column to meet ActiveRecord's requirements—a less than ideal solution.

Moreover, the use of UUIDs as primary keys, while seemingly a workaround, introduces its own set of problems. Despite adopting UUIDs, the necessity for a unique constraint on the (repo_id, issue_id) pair persists to ensure data integrity, but this significantly increases the database size, leading to substantial overhead. This is a major trade-off with potential repercussions on your application's performance and scalability.

This brings us to a broader architectural concern with Ruby on Rails. Despite its appeal for rapid development cycles, Rails' application-level enforcement of the Model-View-Controller (MVC) pattern, where there is a singular model layer, a singular controller layer, and a singular view layer, is fundamentally flawed. This monolithic approach to MVC will inevitably lead to scalability and maintainability issues as the application grows. The MVC pattern would be more effectively applied within modular or component-based architectures, allowing for better separation of concerns and flexibility. The inherent limitations of Rails, especially in terms of its rigid MVC architecture and database management constraints, are significant barriers for any project beyond the simplest MVPs, and these are critical factors to consider before choosing Rails for more complex applications.

I can't really comment on GitHub, but Rails supports composite primary keys as of Rails 7.1, the latest released version [1].

About modularity, there are projects like Mongoid which can completely replace ActiveRecord. And there are plugins for the view layer, like "jbuilder" and "haml", and we can bypass the view layer completely by generating/sending data inside controller actions. But fair, I don't know if we can completely replace the view and controller layers.

I know I'm missing your larger point about architecture! I don't have so much to say, but I agree I've definitely worked on some hard-to-maintain systems. I wonder if that's an inevitability of Rails or an inevitability of software systems—though I'm sure there are exceptional codebases out there somewhere!

[1] https://guides.rubyonrails.org/7_1_release_notes.html#compos...

Do you have any sources on GitHub moving away from Rails? This is the first that I've heard and my googlefu has returned zero results around this. Just last year they had a blog post around Building GitHub with Ruby and Rails[0] so your remark caught my off guard.

[0]: https://github.blog/2023-04-06-building-github-with-ruby-and...

Perhaps too late, but Rails 7.1[1] introduced composite primary key support, and there's been a third-party gem[2] offering the functionality for earlier versions of ActiveRecord.

[1] https://guides.rubyonrails.org/7_1_release_notes.html#compos...

[2] https://github.com/composite-primary-keys/composite_primary_...

github.com included

Typo?

Migrating primary keys from int to bigint is feasible. Requires some preparation and custom code, but zero downtime.

I'm managing a big migration following mostly this recipe, with a few tweaks: http://zemanta.github.io/2021/08/25/column-migration-from-in...

FKs, indexes and constraints in general make the process more difficult, but possible. The data migration took some hours in my case, but no need to be fast.

AFAIK GitLab has tooling to run tasks after upgrade to make it work anywhere in a version upgrade.

Being two orders of magnitude away from running out of ids is too close for comfort anyway.

Is it just me that thinks in general schema design and development is stuck in the stone ages?

I mainly know dotnet stuff, which does have migrations in EF (I note the point about gitlab not using this kind of thing because of database compatibility). It can point out common data loss while doing them.

However, it still is always quite scary doing migrations, especially bigger ones refactoring something. Throw into this jsonb columns and I feel it is really easy to screw things up and suffer bad data loss.

For example, renaming a column (at least in EF) will result in a column drop and column create on the autogenerated migrations. Why can't I give the compiler/migration tool more context on this easily?

Also the point about external IDs and internal IDs - why can't the database/ORM do this more automatically?

I feel there really hasn't been much progress on this since migration tooling came around 10+ years ago. I know ORMs are leaky abstractions, but I feel everyone reinvents this stuff themselves and every project does these common things a different way.

Are there any tools people use for this?

Not a silver bullet for every project but the Django ORM largely solves this with its migrations. You define your table classes and it just generates the migrations.

Throw in a type checker and you're in pretty good shape.

Rust also has sqlx which will type check your code against the DB.

I'm assuming this is why you say it's not a silver bullet, but to make it more explicit: the Django ORM will happily generate migrations that will lock crucial tables for long amounts of time and bring down your production application in the process.

You still need to know what SQL the migration will run (take a look at `manage.py sqlmigrate`) and most importantly how your database will apply it.

Dealing with a bunch of automigrate headaches in the Prisma ORM convinced me to just drop the layer entirely and write plain old SQL everywhere. It’s forced me to learn a bunch of new stuff, but the app runs faster now that I can optimize every query and migrations are much simpler with a single idempotent SQL setup script I can run to provision whatever deployment of the DB I need. I’m sure some problem spaces might benefit from all the additional complexity and abstraction, but the average app certainly can make do without for a long time.

It's a tradeoff! I think using the ORM to start and then move off it later is valid, depending on how much time you have to get an MVP out.

I also switched to plain SQL migrations and queries because I find it much simpler. I hear this a lot, that ORMs are easier/quicker to use, but I've found that writing plain SQL has almost no friction for me. I mean, learning at least basic SQL is part of the first year in most CS degrees.

There are some things that are really annoying in raw SQL and that are much easier with ORMs. Dynamic queries are one of those, if you don't want to concatenate SQL strings yourself you need at least a query builder at that point. The other part is updating/creating entities with many relations, that gets very tedious quickly and is somethings ORMs can handle for you.

It depends a lot on the specific application, for me those things are so common that I prefer to use an ORM even though I could write the SQL myself. ORMs are easier to use, but you still need to learn what they do under the hood and understand them or you will run into issues along the way.

I’m not suggesting anyone implement the raw binary protocol themselves! The postgres connection library you’re using should be able to handle dynamic queries perfectly fine.

An ORM is NEVER the solution, ever ever ever. Repeat after me: ORMs are not the solution to this problem. They work in your little toy apps with 4 customers but they are nothing but pain on real enterprise grade software.

also, every company I've worked at used ORMs in some capacity. Sometimes, not always.

Also, I don't really work on any apps with only four customers. either they have almost a million or zero :P Try again. :)

I don't think insults are really necessary here.

Also ORMs can be very useful, just don't do dumb stuff, like with any technology.

I use them when appropriate.

One thing I like about hand designing schema is it makes you sit down and make very clear choices about what your data is, how it interrelates, and how you’ll use it. You understand your own goals more clearly.

So many people I encounter seem to think it’s the code that’s important when building the back end of an application. You see this when people discussing database schemas start comparing, say, rails to hibernate. But ORMs emphasise code instead of data, which in my experience is a big mistake.

In my experience, getting the data structures right is 99% of the battle. If you get that right, the code that follows is simple and obvious.

For database applications, this means getting the schema right. To this end, I always start with the underlying table structures, and only start coding once I understand how the various tables are going to interact.

Sadly, too many people think of the database as the annoying hoops we jump through in order to store the results of our code. In my world, the code I write is the minimum required to safely manipulate the database; it’s the data that counts.

Some people seem to think I’m weird for starting with the database (and for using plpgsql), but I think it’s actually a superpower.

It wasn’t always code first - you mention Hibernate but 15-20 years ago it was entirely feasible to inherit a database schema or design one up front, and then create performant metadata mappings to a usable object model. That sort of practise was tainted by the Bad Enterprise brushes of verbosity and XML in general, and so everyone moved to some flavour of active record. This allowed programmers to write less code and fit it into neater boxes, at a time when there was enormous demand for basic CRUD web apps, but a lot of power and expressiveness was lost.

Somewhat ironically, many modern enterprises have peeled all the way back to SQL for a huge amount of logic anyway, so I don’t think we’re done caring about database schemas quite yet.

You are absolutely correct; my previous business ended up ejecting almost all database logic from Java ORMs, and moved almost everything to straight SQL manipulated by stored procedures. Doing so resulted in a significant performance and productivity increase, relative to all the layers of nonsense we used to jump through.

One of the difficulties of doing this was that the tooling isn’t great. We had to write our own tools to make it work, but the benefits of going back to SQL were more than worth it.

(Many years later I made an open source version of that tooling, https://github.com/pgpkg/pgpkg)

A simple function call as a wrapper for a well optimized sql query just can’t be beat for performance. I have never understood why anybody would use an ORM, it’s usually as much work to learn and maintain them as SQL.

Yup - and you can’t code your way to real scale either. At real scale the game is all about data structures. Code just gets them from A to B.

Or as they say at Google, the job of SWE is “moving protos”.

This is true for in memory data as well. Object oriented programming is great for some problems, but it's also limiting the way we think about data by putting it close to the code operating on it. ORMs do the same to databases by pretending that rows are objects when that's only one way of modeling your problem.

It’s part of the dirty little secret of why document databases and other NoSQL systems became popular.

Required even less up front thinking about how to model your data. Throw some blobs of JSON into Mongo or whatever, and worry about the rest later.

Exactly that. Sitting down and thinking about your data structures and APIs before you start writing code seems to be a fading skill.

It absolutely shows in the final product, too.

I wish more companies evaluated the suitability of software based on reviewing the back-end data storage schema. A lot of sins can be hidden in the application layer but many become glaringly obvious when you look at how the data is represented and stored.

Theres no right abstraction for it because everyones data is different. From my experience what most developers dont realize is that data is more complex than code. Code is merely the stuff that sits on top of the data, shuffling it around... but designing and handling the data in an efficient way is the real engineering problem.

Any abstraction you could come up with wouldnt fit 90% of the other cases

I think that stuff works about as well as it possibly could. If you think that's painful think about something like DynamoDB where if you didn't really think through the access patterns up front you're in for a world of pain.

If you use MySQL, Planetscale’s branching is really amazing. Not using them, but wish I could for that. Gives you a complete diff of what you’re doing, and can also pre-plan migrations and only apply them when you need with their gating.

Also the point about external IDs and internal IDs - why can't the database/ORM do this more automatically?

It has pretty big implications for how your application code interacts with the database. Queries that involve id's will need to perform joins in order to check the external id. Inserts or updates that need to set a foreign key need to perform an extra lookup to map the external id to the correct FK value (whether it's literally a separate query or a CTE/subquery). Those are things that are way outside the realm of what EF can handle automatically, at least as it exists today.

EF core doesn’t to drop/create for columns in db providers that support renaming columns. It only does it for ones that don’t like MySQL or SQLite

It is generally a good practice to not expose your primary keys to the external world. This is especially important when you use sequential auto-incrementing identifiers with type integer or bigint since they are guessable.

What value would there be in preventing guessing? How would that even be possible if requests have to be authenticated in the first place?

I see this "best practice" advocated often, but to me it reeks of security theater. If an attacker is able to do anything useful with a guessed ID without being authenticated and authorized to do so, then something else has gone horribly, horribly, horribly wrong and that should be the focus of one's energy instead of adding needless complexity to the schema.

The only case I know of where this might be valuable is from a business intelligence standpoint, i.e. you don't want competitors to know how many customers you have. My sympathy for such concerns is quite honestly pretty low, and I highly doubt GitLab cares much about that.

In GitLab's case, I'm reasonably sure the decision to use id + iid is less driven by "we don't want people guessing internal IDs" and more driven by query performance needs.

I see this "best practice" advocated often, but to me it reeks of security theater. If an attacker is able to do anything useful with a guessed ID without being authenticated and authorized to do so, then something else has gone horribly, horribly, horribly wrong and that should be the focus of one's energy instead of adding needless complexity to the schema.

Yes, but the ability to guess IDs can make this security issue horrible, or much much worse.

If you had such a vulnerability and you are exposing the users to UUIDs, now people have to guess UUIDs. Even a determined attacker will have a hard time doing that or they would need secondary sources to get the IDs. You have a data breach, but you most likely have time to address it and then you can assess the amount of data lost.

If you can just <seq 0 10000 | xargs -I ID curl service/ticket/ID> the security issue is instantly elevated onto a whole new level. Suddenly all data is leaked without further effort and we're looking at mandatory report to data protection agencies with a massive loss of data.

To me, this is one of these defense in depth things that should be useless. And it has no effect in many, many cases.

But there is truely horrid software out there that has been popped in exactly the described way.

Case in point, a recent security issue Gitlab experienced (CVE-2023-7028; arbitrary password reset by knowing one of the accounts associated mail addresses) was made worse by a feature of gitlab that few people know about; that the "userID" is associated with a meta/internal mail address.

This meant that people could send password resets for any user if they knew their userID. The mail format was like user-1@no-reply.gitlab.com or something.

Since it's a safe bet that "user ID 1" is an admin user, someone weaponised this.

I've already resolved to never use Gitlab entirely on the basis of that CVE but that makes it worse.

Password resets should just never go to an email that hasn't been deliberately attached to an account by the account's owner, full stop. There should not be a code path where it is possible to send any such thing to arbitrary emails. And redirect emails should never be treated as account emails in any way.

In general it's a defense-in-depth thing. You definitely shouldn't be relying on it, but as an attacker it just makes your life a bit harder if it's not straightforward to work out object IDs.

For example, imagine you're poking around a system that uses incrementing ints as public identifiers. Immediately, you can make a good guess that there's probably going to be some high privileged users with user_id=1..100 so you can start probing around those accounts. If you used UUIDs or similar then you're not leaking that info.

In gitlabs case this is much less relevant, and it's more fo a cosmetic thing.

In gitlabs case this is much less relevant (...)

Why, though? GitLab is often self hosted, so being able to iterate through objects, like users, can be useful for an attacker.

You're right, fair point.

I follow this best practice, there’s a few reasons why I do this. It doesn’t have to do with using a guessed primary ID for some sort of privilege escalation, though. It has more to do with not leaking any company information.

When I worked for an e-commerce company, one of our biggest competitors used an auto-incrementing integer as primary key on their “orders” table. Yeah… You can figure out how this was used. Not very smart by them, extremely useful for my employer. Neither of these will allow security holes or leak customer info/payment info, but you’d still rather not leak this.

extremely useful for my employer.

I've been in these shoes before, and finding this information doesn't help you as an executive or leader make any better decisions than you could have before you had the data. No important decision is going to be swayed by something like this, and any decision that is probably wasn't important.

Knowing how many orders is placed isn't so useful without average order value or items per cart, and the same is true for many other kinds of data gleamed from this method.

That’s not correct. Not every market is the same in it’s dynamics.

Yes, most of the time that information was purely insightful and was simply monitored. However, at some moments it definitely drove important decisions.

The only case where this might be valuable is business intelligence

Nitpick: I would not call this "business intelligence" (which usually refers to internal use of the company's own data) but "competitive intelligence". https://en.wikipedia.org/wiki/Competitive_intelligence

See also "German Tank Problem" https://en.m.wikipedia.org/wiki/German_tank_problem

What value would there be in preventing guessing?

It prevents enumeration, which may or may not be a problem depending on the data. If you want to build a database of user profiles it's much easier with incremental IDs than UUID.

It is at least a data leak but can be a security issue. Imagine a server doing wrong password correctly returning "invalid username OR password" to prevent enumeration. If you can still crawl all IDs and figure out if someone has an account that way it helps filter out what username and password combinations to try from previous leaks.

Hackers are creative and security is never about any single protection.

It’s mentioned in the article. It’s more to do with business intelligence than security. A simple auto-incrementing ID will reveal how many total records you have in a table and/or their growth rate.

If you expose the issues table primary key id then when you create an issue in your project it will not start with 1 and you can easily guess how many issues exist in the GitLab.

It can be really handy for scraping/archiving websites if they're kind enough to use a guessable id

Bugs happen also in access control. Unguessable IDs make it much harder to exploit some of those bugs. Of course the focus should be on ensuring correct access control in the first place, but unguessable IDs can make the difference between a horrible disaster and a close call.

It's also possible to use auto-incrementing database IDs and encrypt them, if using UUIDs doesn't work for you. With appropriate software layers in place, encrypted IDs work more or less automatically.

It really depends but useful knowledege can be derived from this. If user accounts use sequential ids the id 1 is most likely the admin account that is created as first user.

> This is especially important when you use sequential auto-incrementing identifiers with type integer or bigint since they are guessable.

I thought we had long since moved past that to GUIDs or UUIDs for primary keys. Then if you still need some kind of sequential numbering that has meaning in relation to the other fields, make a separate column for that.

The point about the storage size of UUID columns is unconvincing. 128 bits vs. 64 bits doesn't matter much when the table has five other columns.

A much more salient concern for me is performance. UUIDv4 is widely supported but is completely random, which is not ideal for index performance. UUIDv7[0] is closer to Snowflake[1] and has some temporal locality but is less widely implemented.

There's an orthogonal approach which is using bigserial and encrypting the keys: https://github.com/abevoelker/gfc64

But this means 1) you can't rotate the secret and 2) if it's ever leaked everyone can now Fermi-estimate your table sizes.

Having separate public and internal IDs seems both tedious and sacrifices performance (if the public-facing ID is a UUIDv4).

I think UUIDv7 is the solution that checks the most boxes.

[0]: https://uuid7.com/

[1]: https://en.wikipedia.org/wiki/Snowflake_ID

Would it ever make sense to have a uuidv7 as primary key but then anther slug field for a public-id, e.g. one that is shorter and better in a url or even allowing user to customize it?

Yes sure but now you have to handle two ids and guaranteeing uniqueness across machines or clusters becomes hard.

That and a uuid is going to be unique across all tables and objects, whereas a slug will only be unique within a certain subset e.g. users within an organization. I’ve seen a production issue IRL where someone (definitely not me) wrote a query fetching objects by slug and forgot to include the ‘AND parent_slug = xxx’

The point about the storage size of UUID columns is unconvincing. 128 bits vs. 64 bits doesn't matter much when the table has five other columns.

But it's not just the size of that one column, it's also the size of all the places that id is used as a FK and the indexes that may be needed on those FK columns. Think about something like a user id that might be referenced by dozens or even hundreds of FKs throughout your database.

...and this has not just a size impact but also a substantial performance impact.

There's an orthogonal approach which is using bigserial and encrypting the keys...

Another variant of this approach: https://pgxn.org/dist/permuteseq/

It is also feasible to encrypt the value on display (when placing it in URLs, emails, &c):

https://wiki.postgresql.org/wiki/Pseudo_encrypt

This maintains many of the benefits of sequential indexes and does allow you to change the key. However, if the key is changed, it would break any bookmarks, invalidate anything sent in older emails -- it would have the same effect as renaming everything.

The v7 isn’t a silver bullet. In many cases you don’t want to leak the creation time of a resource. E.g. you want to upload a video a month before making it public to your audience without them knowing.

It very much does when you have a ton of FKs (enforced or not) using such a column, and thus indexed and used in many joins. Making it twice as hard for the hot part of an index to fit to RAM is never good for performance, nor for the cloud bill.

If you have a column that is used in many joins, there are performance reasons to make it as compact as possible (but not smaller).

If I’ve learned anything in my 7 years of software development it’s that this kind of expertise is just “blah blah blah” that will get you fired. Just make the system work. This amount of trying to anticipate problems will just screw you up. I seriously can’t imagine a situation where knowing this would actually improve the performance noticeably.

think of the primary keys in a database like typedef void* ie it's your fundamental pointer and the size of it will impact every aspect of performance throughout - memory/disk footprint and corresponding throughput bottlenecks, cpu time comparing keys which is what every operation reduces to in the deepest inner-most loops of joins and lookups etc.

when x86-64 cpus were new the performance impact from switching to 64-bit pointers was so bad we had to create x32/ilp32 and the reason .NET still has "prefer 32-bit" as a default even today.

using 128-bit uuids as PKs in a database is an awful mistake

Has anyone written about or noticed the performance differences between Gitlab and GitHub?

They're both Rails-based applications but I find page load times on Gitlab in general to be horrific compared to GitHub.

I mean GitHub in general has been pretty reliable minus the two outages they had last year and is usually pretty performant or I wouldn’t use their keyboard shortcuts.

There are some complaints here from a former dev about gitlab that might provide insight into its culture and lack of regard for performance: https://news.ycombinator.com/item?id=39303323

Ps: I do not use gitlab enough to notice performance issues but thought you might appreciate the article

I mean GitHub in general has been pretty reliable minus the two outages they had last year

Huh? GitHub has had major outages practically every other week for a few years now. There are pages of HN threads[1].

There's a reason why githubstatus.com doesn't show historical metrics and uptime percentages: it would make them look incompetent. Many outages aren't even officially reported there.

I do agree that when it's up, performance is typically better than Gitlab's. But describing GH as reliable is delusional.

[1]: https://hn.algolia.com/?dateRange=all&page=0&prefix=false&qu...

Delusional? Anecdotal maybe…I was describing my experience so thanks for elaborating.

I only use it as a code repository. Was it specific services within GitHub that failed a lot?

My apologies, that came off a bit harsh.

Was it specific services within GitHub that failed a lot?

Well, everything from its frontends, to the CI service, to the Git service itself. People weren't able to push, pull, deploy or review code for hours at a time. Just major disruptions all around, which happened regularly.

I do think this has improved slightly over the past few months, but you were lucky if you weren't affected much.

No worries you’re fine. I guess downtime was much more widespread than I could even imagine.

I do remember one time all of us at work joking that we should get the day off when GitHub was done. :D

More comments on this submission: https://news.ycombinator.com/item?id=39333220

I used Gitlab a few years ago, but then it had severe client-side performance problems on large pull requests. Github isn't ideal with them too, but it manages to be decent.

Yeah, good reason to spit up pull requests ;) I do think it improved a lot over the last two years, though

As I discussed in an earlier post[3] when you use Postgres native UUID v4 type instead of bigserial table size grows by 25% and insert rate drops to 25% of bigserial. This is a big difference.

Does anyone know why UUIDv4 is so much worse than bigserial? UUIDs are just 128 bit numbers. Are they super expensive to generate or something? Whats going on here?

UUIDv4s are fully random, and btree indices expect "right-leaning" values with a sensible ordering. This makes indexing operations on UUIDv4 columns slow, and was the motivation for the development of UUIDv6 and UUIDv7.

I'm curious to learn more about this heuristic and how the database leverages it for indexing. What does right-leaning mean formally and what does analysis of the data structure look like in that context? Do variants like B+ or B* have the same charactersistics?

I think its because of btrees. Btrees and the pages work better if only the last page is getting lots of writes. Iuids cause lots of un ordered writes leading to page bloat.

The 25% increase in size is true but it's 8 bytes, a small and predictable linear increase per row. Compared to the rest of the data in the row, it's not much to worry about.

The bigger issue is insert rate. Your insert rate is limited by the amount of available RAM in the case of UUIDs. That's not the case for auto-incrementing integers! Integers are correlated with time while UUID4s are random - so they have fundamentally different performance characteristics at scale.

The author cites 25% but I'd caution every reader to take this with a giant grain of salt. At the beginning, for small tables < a few million rows, the insert penalty is almost negligible. If you did benchmarks here, you might conclude there's no practical difference.

As your table grows, specifically as the size of the btree index starts reaching the limits of available memory, postgres can no longer handle the UUID btree entirely in memory and has to resort to swapping pages to disk. An auto-integer type won't have this problem since rows close in time will use the same index page thus doesn't need to hit disk at all under the same load.

Once you reach this scale, The difference in speed is orders of magnitude. It's NOT a steady 25% performance penalty, it's a 25x performance cliff. And the only solution (aside from a schema migration) is to buy more RAM.

Random distribution in the sort order mean the cache locality of a btree is poor - instead of inserts going to the last page, they go all over the place. Locality of batch inserts is also then bad at retrieval time, where related records are looked up randomly later.

So you pay taxes at both insert time and later during selection.

1 quintillion is equal to 1000000000 billions

it is pretty wild that we generally choose between int32 and int64. we really ought to have a 5 byte integer type which would support cardinalities of ~1T

Yeah it doesn't make sense to pick something that's not a power of 2 unless you are packing it.

I guess that depends on how the index works.

we usually work with some page size anyways, 64 5-byte ints fit nicely into 5 512-bit registers, and ~1T is a pretty flexible cardinality limit after ~2B

Slight nit-pick, but I would pick up the author on the text vs varchar section.

The author effectively wastes many words trying to prove a non-existent performance difference and then concludes "there is not much performance difference between the two types".

This horse bolted a long time ago. Its not "not much", its "none".

The Postgres Wiki[1] explicitly tells you to use text unless you have a very good reason not to. And indeed the docs themselves[2] tell us that "For many purposes, character varying acts as though it were a domain over text" and further down in the docs in the green Tip box, "There is no performance difference among these three types".

Therefore Gitlab's use of (mostly) text would indicate that they have RTFM and that they have designed their schema for their choice of database (Postgres) instead of attempting to implement some stupid "portable" schema.

[1] https://wiki.postgresql.org/wiki/Don%27t_Do_This#Don.27t_use... [2] https://www.postgresql.org/docs/current/datatype-character.h...

The author effectively wastes many words trying to prove a non-existent performance difference and then concludes "there is not much performance difference between the two types".

They then also show that there is in fact a significant performance difference when you need to migrate your schema to accodomate a change in length of strings being stored. Altering a table to a change a column from varchar(300) to varchar(200) needs to rewrite every single row, where as updating the constraint on a text column is essentially free, just a full table scan to ensure that the existing values satisfy your new constraints.

FTA:

So, as you can see, the text type with CHECK constraint allows you to evolve the schema easily compared to character varying or varchar(n) when you have length checks.

They then also show that there is in fact a significant performance difference when you need to migrate your schema to accodomate a change in length of strings being stored.

Which is a pointless demonstration if you RTFM and design your schema correctly, using text, just like the manual and the wiki tells you to.

the text type with CHECK constraint allows you to evolve the schema easily compared to character varying or varchar(n) when you have length checks.

Which is exactly what the manual tells you ....

"For many purposes, character varying acts as though it were a domain over text"

I found this post very useful. I'm wondering where I could find others like it?

Here's another - https://zerodha.tech/blog/working-with-postgresql/

I recommend Postgres FM podcast, e.g. available as video on Postgres TV yt channel. Good content on its own, and many resources of this kind are linked in the episode notes. I believe one of the authors even helped Gitlab specifically with Postgres performance issues not that long ago.

It's reasonable to not have auto increment id's, but it's not clear to me if there is benefits to have 2 IDs, one internal and one external. This increases the number of columns / indexes, makes you always do a lookup first, and I can't see a security scenario where I would change the internal key without changing the external key. Am I missing something?

You always have the information at hand anyway when doing anything per project. It’s also more user friendly to have every project’s issues start with 1 instead of starting with two trillion, seven hundred billion, three hundred and five million, sevenhundred and seventeen thousand three hundred twentyfive.

I always wondered what the purpose of that extra “I” was in the CI variables `CI_PIPELINE_IID` and `CI_MERGE_REQUEST_IID` were for. Always assumed it was a database related choice, but this article confirms it.

Great read! And even better comments here.

Foreign keys are expensive is an oft repeated rarely benched claim. There are tons of ways to do it incorrectly. But in your stack you are always enforcing integrity _somewhere_ anyway. Leveraging the database instead of reimplementing it requires knowledge and experimentation, and it more often than not it will save your bacon.