* Use uuidv7 not uuid in general (typically v4)
* in addition to minimizing locked records, make sure your locks are ordered deterministically across all queries (eg by id asc, always) or you’ll deadlock (but postgres has a really good deadlock detector so you’ll more likely just error out if you’re lucky)
* always use explain (generic_plan) to be able to a) copy-and-paste your queries with placeholders for parameters as-is, b) see how your query will actually be optimized when Postgres doesn’t have visibility into the specific parameter values
* use set seqscan = off when testing your query plans esp when tables are empty or nearly so so you can see if indexes will be used when seq scans become less cheap
* everyone defaults to btree indexes which are heavy and increase index bloat. Consider using a hash index instead if you just need to look up by column/id but not sort or get values greater/lesser than a param. You can’t create unique hash indexes but you can create exclude using hash constraints for the same effect (except no multicolumn unique index support)
* learn about GIN (and GIST) indexes. They can speed up common queries without needing new syntax, something people coming from MySQL might not expect to be possible; i.e. you can use them to speed up Plain Jane like ‘%foo%’ queries without switching to FTS.
> in addition to minimizing locked records, make sure your locks are ordered deterministically across all queries (eg by id asc, always) or you’ll deadlock (but postgres has a really good deadlock detector so you’ll more likely just error out if you’re lucky)
This is really good advice, I should put this somewhere in the guide. To add to this, not only can you deadlock by not having a consistent `ORDER BY` when you're locking sets of rows, but you should also be careful of locking rows on tables in different orders. For example, even if you lock each row in a table with an ORDER BY and FOR UPDATE, if one tx locks `table_a` and then `table_b`, and the other locks `table_b` and then `table_a`, you'll deadlock. This is obvious in theory but exponentially harder to debug in practice, because you need to be globally aware of every table that a write touches - something that's bitten us in particular with certain extensions.
> learn about GIN (and GIST) indexes
We're just testing GIN for fast key-value lookups for JSONB columns, and the performance improvements have been really massive. Interestingly there was a large performance skew between AND vs OR on these key-value queries.
More usefully, a uuidv7 can take the place of both the id and the created_at field (if precision is sufficient), and can (depending on your security comfort) also take the place of the uuidv4 public id field.
With uuidv7 the data is naturally sorted so you don’t run into the issues with btree worst case scenarios that you would with a uuidv4 id, and you can even take it a step further and use BRIN instead of btree indexes for a massive boost (also applicable to serial ids, though).
7/4 'converters' have been featured on HN a few times:
I find that most ID's in majority of tables are internal and only used for internal joins, so I use v7
The tables where the uuid will be sent to the client e.g. UserId etc, I also store an external_id of v4.
Much rather have a static column than continually convert.
How well does this work for you? I thought if you have _any_ index, Postgres will use it if you disabled sequential scans. Diabling sequential scans won't tell if you if you have the right index
> learn about GIN (and GIST) indexes
yes, but also learn about the trade-offs and in particular the "pending list". The flush of the pending list can be slow, causing timeout, causing the list to not be flushed, causing the next write to trigger flush again and failing in the same way, which means you're having downtime. The default pending list size is weirdly high IMO
What do you all use for your pg backups? Is Barman ( https://pgbarman.org/) still the way many do it? (I haven't deployed a new pg instance for a while, but thinking about it for a new project).
1. Don't use an ORM.
2. Use serial PKs, not meaningful fields (article mentions this).
3. Use jsonb if needed, but sparingly.
4. Make your source of truth append-only, meaning you only insert, never update or delete. You can have secondary denormalized tables that are mutated, but that's only for performance/convenience and shouldn't be your sot.
5. Use connection pools, but be mindful of how many connections you're using. You probably don't need PgBouncer unless you've messed something up.
6. In code, avoid explicit transactions unless there's a clear reason you need them. Usually only need those for denormalized parts. Just take a conn from the pool, do something, commit, return conn to pool. If you're going to keep an xact open, never do long-running stuff in the middle like RPCs. Too often I see people leave xacts open without much thought. Edit: Also don't use SERIALIZABLE xacts almost ever.
7. Something is probably wrong if you're using explicit locking like SELECT FOR UPDATE.
8. Don't reinvent a type system by having a single table where each row can mean many different things depending on a "type int" enum col. Seems oddly specific, but for some reason someone always tries this.
9. Related to above, don't reinvent a graph DB, typically with "node"/"edge" tables that FK into themselves or in a cycle. 99% of the time what you're trying to do is easily solvable with regular normalized tables.
For example, AWS will send you an email if you're approaching XID wraparound. In a startup that email is very likely to be missed, especially if it's sent on Boxing day. You want whatever AWS is watching to send you that email to be something connected to a pager.
> Use foreign keys with cascading deletes for low-volume tables, particularly where database consistency and correctness are important. Careful at higher volume.
This might be just me, but I hate cascades, for a very simple reason: at most places, the majority of developers "live" in the Python/Node/Go/whatever application that talks to the database, not the database itself. Cascading deletes (or updates) is basically magic and it can be very hard to understand "why did deleting a row from table A delete something from table B automatically". Especially if someone sets up the cascading wrong! IMO it's better for long-term maintainability to emit explicit delete clauses. Correct use of foreign keys will prevent any issues with database consistency.
> Tricks for large table migrations
The pitfalls and workarounds are all correct, but worth pointing out tooling already exists[0] for managing this for you. Making changes to large tables should be as simple as running a command (and then nervously monitoring for the next 24 hours as the data copies).
Other things to consider,
1. Get used to separating application and database deployments early. It is impossible to transactionally deploy both a schema change and an application change simultaneously, there will always be some delay where the versions of database and application are out of sync, and you will eventually run into a situation where the database change deploys fine but your application change does not. Once your app is in production, get in the habit of only doing backwards compatible schema changes: all new columns are nullable or have a default, no renaming of tables/columns, etc.
2. In the same vein, figure out a schema management strategy early. You really don't want your database deployment process to be "senior dev runs some DDL manually on production from his machine". I'm still partial to liquibase because it's the devil I know, but there's other tooling like Flyway which exists.
One small addendum here is we've had a lot of success performing joins in memory in a few very specific situations where the alternative is a single, often overcomplicated query. I've heard / seen advice many times in the past about performing fewer round trips to the database being something to optimize for (often good advice!). Sometimes this is taken too far, resulting in overly-complex queries requiring complicated JOIN or UNION logic, CASE logic, and so on.
We have a couple of places in our codebase where we perform two or more simpler queries independently instead, and then loop through their results and use maps to match the relevant rows. Conventional wisdom often suggests this path will hurt performance because of the extra database round trip in addition to the loops needed to perform the join, but it is actually beneficial in these cases because of more predictable query planning behavior. We use this trick sparingly, but it can be helpful in a pinch.
Note that some ORMs will also do this for you in the background, which we don't necessarily endorse, and we try to use this sparingly when writing a single query on its own is not realistic.
Few people know that there is a major bifurcation when it comes to connection pooling implementation.
1. Most application connection poolers follow a first-in-first-out (FIFO) algorithm, which is simple enough to implement and is enough to make sure the application always has a connection available to connect to the database. It optimizes low latency, and works great from the point of view from the application. The problem is that it has few mechanisms to remove redundant connections, since the application is constantly keeping them all "warm".
2. PgBouncer and very few external poolers follow the inverse idea – last-in-first-out (LIFO), and they optimize for reducing the number of connections that reach Postgres, thus improving its throughput. The idea might seem crazy at first – the last connection used is the first one to be picked up again – but this algorithm automatically removes excess connections, which will get cold and get closed.
When starting a new application, option (1) is enough, but as it scales up enough, at some time it is recommended to use (2), since having hundreds of open connections to Postgres is bad for performance if you can use PgBouncer or similar to cut it by 90%. Postgres' process-per-connection design works much better when there are fewer connections reaching it.
There needs to be more emphasis how important this is! I cant tell you how often I see it done "badly" (we let our ORM build the db for us). The best text I have ever found on this is "Database Design for Mere Mortals", over the years I have bought more that a few copies and I always end up giving them away to those in need (and there are always people around in pretty dire need).
The one thing I would say is missing from this article is to not be afraid of using postgres for "stupid" things. Cache, queue's, and so on, especially on the road to launch.
One should also not be afraid of having more than one Postgres instance, especially if you're using it as a work queue.
Lastly there is a stupid amount of power in Postgres roles (its "user" system). The manual here is somewhat OK, but really undersells richness that it makes available to you.
I end up with a mixture of serverless storage like DynamoDB, S3, DuckDB on S3, and SQLite.
Am I crazy? How can one have a decent Postgres and not pay at least $100/mo (yes, when I say frugal I mean really frugal ... think solo founder that likes to stay on free tiers haha) -- I am aware of Neon/Supabase, but last time I tried them they ended up becoming a tightly coupled annoying dependency after scale that defeated the cost savings as they grew in costs and we ended up migrating to Aurora / RDS lol
EDIT: I'm aware of the self-hosted path but I find configuring the above things faster/cheaper in terms of my admin hours than the self hosted postgres db. Maybe I just suck at being a DBA or need better education on it, that said, I have AI now so I should give it a chance again as it's been a minute since I created a fresh thing
SKIP LOCKED is useful for implementing job queues with interactive transactions – you lock the row while working on it in the application and keeping the transaction open. For high-performance applications it is best to avoid interactive transactions at all, and just update the rows to "pending" immediately. There is no need for SKIP LOCKED in this case.
As a rule of thumb, as you scale up the application, you want to have less state in the database memory, and interactive transactions are just that. Idempotence beats atomicity at scale.
There is no line that says X is important for a startup and Y isn't, it is mostly a matter of degree.
Instead, what the guide has done although could be a little clearer is e.g. explain that there are types of indexes that are a better trade-off of space and performance for particular scenarios, here is one example and click here to learn about the other indexes. That is enough for a startup to understand.
A second example might be, "Optimizing your postgres resource limits is important for x, y, and z reasons. You should try and balance giving your server as much RAM as it can but without using up RAM that is needed by other things. A starting point might be X percent of total RAM for shared_buffers but for more details see the main docs here".
I love postgres but it is not a toy and it takes investment of time. I think what most people want is a map with a few examples so they can pick out what concerns them the most.
tx1: update a
tx2: update b
tx1: update b
tx2: update a
It's also important to notice the query planner optimizes for the average case, but often it would be better for the app developer if it was optimized for the worst case. But optimizing for the former is a much more tractable problem, so no wonder that's what is implemented.
I had to fight against the query planner when it would optimize a query for the average user, with few rows in a given table, and it would pick one index that made sense for that situation and return a result in less than 10ms. However, when a heavy user issued the same query, depending on the exact parameters the worst case could take over 1 second. So I had to write a much more complex query to force it to take another path with a different index, which would be slower in the average case, but in the worst case would take still less than 100ms. Avoiding timeouts was much more important for my company than taking 10ms more in the average case.
* If your disks are ssd and scsi in different volumes, adapt the random cost of io in the config to let the planner now
* if you have RAID controller and a Battery-Backed Write Cache (BBWC), you can disable Linux filesystem write barriers. removing excessive fsync from the mouth of the psql demigod in SoCaL ~linux 2015, Bruce Momjian IIRC
* monitor disk usage, in backups pipe to gz, never to disk
* counter-intuitevely modern hardware may have an io bottleneck and plenty of cpu, so try Filesystems like ZFS using Zstandard (zstd), for boost in your Transactions per second
* if possible, schema multitenancy instead of database multitenancy, instagram talked about this decades ago
* indexes index functions results too, precompute those fields and partial indexes help a lot
* BM25 and FTS in pg are so good you probably don't need Elasticsearch and you will save a lot in de-sync between both of them
* you may be hacked in this brave new world post Mythos, thus, learn PITR to an external only write, no override medium like S3
- Don't use long-running transactions. They are a risk for db health. Only use transaction when you have a strong justification
- Set idle_in_transaction_session_timeout to prevent a long-running transaction from holding on to locks or tuples
- Set lock_timeout for migrations to prevent a single DDL statement to bringing down your system
- Set statement_timeout to prevent an expensive query from bringing down your system
Things like connections stats, deadlock monitoring, slow queries. All come standard in AWS/GCP.
As far as storing a datetime with an associated timezone, I agree that usually this can be problematic. However, for things like weekly repeats, you may want to store broken out components so it handles cleanly across time switch boundaries - e.g. when going in and out of DST. So you'd have `timezone`, `time` (no TZ, no date), repeat schedule (likely using interval, internally stored in months/days/microseconds), and use these to set up your next exact timestamptz value.
Schema blot issues are real when you are vibe coding. Our engineers vibe coded and bloated our schema from 230 tables to 600+ tables. Many of them have repetitions of columns across the tables and often too much indexing. If your Postgres is on Aurora, the bloat easily multiplies your bills.
I’ve seen this pointed out in other comments but I’d also strongly recommend expanding with a section on monitoring and alerting. One could write a blog post almost of this length just on monitoring :)
One bit not mentioned, and particularly useful in more modern RDBMS with JSON binary expressions in the database are to leverage JSON columns and avoid joins altogether for a lot of use cases. There are a lot of times where you have variance of sub-information, or other data where table normalization and joins work against you. Even with indexes, joins are costly, especially under load at scale with millions of simultaneous users. You can avoid a lot of this by simply having that sub-table information inside a JSON field with the row in question.
For example, logs and notes related to a specific field. Variable transaction data (paypal vs amazon vs google payments), where the logs/details from the API aren't something that really needs to be in a separate table but related to the transaction.
Another would be something like a classifieds site where many fields are repeated, but sub-fields can vary dramatically by the type of item or category.
Knowing how/when to leverage denormalization and JSON can be one of the most impactful things you can do in terms of performance in practice, short of falling back to a search database (Elastic, Quickwit, etc), which can also be practical depending on your needs, but adds complexity.
Similarly, knowing how your datagase uses certain types of data/serialization... for example UUIDv7 if you don't mind storing creation time (utc) of a record, or COMB if using say MS-SQL in particular... the serialization of said field in practice helps in terms of understanding how indexes update and impact performance.
I do wish the guide was expanded a bit with lots of specific examples and details... a lot of it is hand-wavy blurbs.
The infrastructure decisions in early-stage startups are brutal.
Most founders I know underestimate Postgres connection pooling
until it bites them at 10x scale. PgBouncer saved us twice.In most (all?) cases the pooler manages one pool per database user, so even if there was something leaking, it would not be anything that the database user couldn't access anyway.
But if you are paranoid, you can configure the pooler to run "RESET ALL", "RESET ROLE", "RESET SESSION AUTHORIZATION" and "ROLLBACK" before handing out a connection.
If you’re a startup, the performance cost of storing everything in JSONB is going to outstrip any gains you might get from denormalization. JOINs are simply not that hard if you design your schema intelligently. Additionally, allowing freeform text columns for things like statuses will eventually bite you with fun problems like `closed != CLOSED != Closed`.
> Use foreign keys with cascading deletes for low-volume tables, particularly where database consistency and correctness are important. Careful at higher volume.
Absolutely. Just be careful with 1:M, or M:N, for large values of M and N. You don’t want to trigger a surprise deletion of hundreds of thousands of rows.
> Indexes by default use a btree implementation. It’s most helpful to think of indexes as just another table in Postgres, with data stored in a specific format which is optimized for lookups (more on this later).
For a single row lookup (which is what this section was referring to), yes. For range scans, if the indexed column isn’t k-sortable, a sequential scan can start beating the performance of the multiple lookups pretty quickly.
> There are cases where you think an index should be used, but the query planner is still seq scanning anyway, despite table statistics being up to date and the index being valid.
This is usually caused by one of two things: forgetting that indices are (generally) B+trees and having data laid out in a manner that is inefficient for the query, or having data that isn’t uniformly distributed - for example, for some / many companies, the geographical distribution of users is going to be heavily clustered around more populous cities. Histograms are one way to deal with this.
Another topic not discussed in TFA is other index types - BRIN in particular can be incredibly performant while adding almost zero overhead, if the shape of your data makes sense for them (time-series is the obvious one, but anything with useful clustering should be considered).
All in all, this is one of the better tl;dr articles on Postgres I’ve read. Well done, Hatchet.
I would compare it to the number of lawyers over population
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Unimpressive. Not even the most cursory of discussion of stored functions ?
Given that many startup's Postgres instances will no doubt be backing some web-ui or app that takes untrusted input, surely they could have at least had a brief discussion about how stored functions can help against SQL injection attacks ?
Not only that but it means you have to think, it prevents devs just writing their own random queries.
Also zero mention of `text`, which is highly encouraged in Postgres instead of the silly old `varchar(255)`
Yes, being ordered uuid7 leads to less splitting and index bloat than the fully random uuid4. So while the lookup is pretty similar the index is in much better shape.
And if lookup is correlated with recency (which is probably the case for most record types) recent entries will be grouped together on the same pages in the uuid7 index leading to better cache presence, whereas in a uuid4 index they’ll be randomly spread over the entire index set.
So while I partly agree with you, a lot of companies don't really need HA, read replicas, or even PITR (though I would argue the last one is so trivial and cheap to enable that why not), but they click the expensive check box, and I would argue that companies who do need these features should consider hiring at least a couple of DBAs and get more flexibility instead of the current status quo of everyone being scared of the database and everyone just hoping cloud support will come to their rescue if ever needed
Every place I’ve ever worked at that had DBAs had the complete opposite of more flexibility. You have to do things the DBA’s way, and if their way doesn’t work for your service, you need to fight for their time and priority.
Meanwhile every place I worked at where every team completely owned their databases + did periodic data recovery drills had much more flexibility and no data loss.
My personal experience has been the opposite of yours: lots of data inconsistency issues, data loss only resolved by the database team spinning up backups, etc. And somehow, even after yet another incident, there’s never been appetite to properly fix things.
Obviously "let RDS manage your database" doesn't require egregious read replicas. The decision to use read replicas or not is completely orthogonal to whether you use RDS to manage them.
Of course not, but an easy checkbox, a best practice AWS or terraform guide and someone doing AWS certified X associate makes it easier to happen without anyone ever really discussing it.
> The decision to use read replicas or not is completely orthogonal to whether you use RDS to manage them.
Assuming you're talking about letting RDS manage anything, then sure - apart from it being more likely to slip through the net if nobody has to configure them. Database is just an expensive cost nobody necessarily drills into.
However if you mean the decision to let $cloud manage the replicas (and keep the primary managed), that totally depends on the cloud and the options. For example have you ever tried having a primary in GCP Cloud SQL but the replica not in cloud SQL?
If you're a SaaS, 1h of downtime costs a lot of money. In reputation, in lost business, in support time, in on-call response, in making a public postmortem and sharing it to customers and dealing with responses... For saving a few thousand bucks, it's often indeed a very bad deal.
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What does the equivalent RDS setup cost you?
Also, if you are nickel and diming over expenses in the 50€ range, you’re probably at the wrong startup.
Offers point-in-time recovery which is an improvement over a custom solution we used to have which gave us nightly backups.
We have it backing up to Backblaze B2 (S3 like). Was relatively easy to setup and no problems really.
I didn't understand that, and after 5 months of usage caught my backblaze to be using 40TB, and nightly restores taking forever for other reasons. So: not ideal, and be careful to check!
Obviously past a certain point carting around full backups becomes time/dollar prohibitive, but this can take you very far.
Having done both, I'd recommend just starting with pgbackrest.
We used EBS snapshots on AWS for multi-TB MongoDB to get incremental backups that are fast to create and fast to restore (with some performance degradation after restore).
It doesn't support point-in-time recovery, but since it's fast you can create frequent snapshots (e.g. hourly). I'd consider adding this as a secondary backup strategy, even if you use a higher-level postgres-specific backup tool.
Here's a complete walkthrough on how I backup and restore: https://nickjanetakis.com/blog/how-to-back-up-postgresql-in-...
It covers using Plakar too (optionally) if you want deduplication and encryption.
This guide is only satisfactory if the database is managed, otherwise there are a whole bunch of things going on.
Highly debatable. When your highest cost is developers salaries.
Don't reinvent a type system by having a single table where each row can mean many different things depending on a "type int" enum col.
Easy to say, harder to not do when you have business requirements on table, customer pressure and budget already gone on discussing with DBA who maybe is right but you are burning money right here and right now. The same with point no. 9
1. Most of these advices are unfortunately impractical and incomplete for startups. A good Data Model is highly dependent on understanding the business requirements, data flows. Means unless you are repeating yourself in the same domain its really hard to come up with a good schema in first iteration.
2. Startups are in the mode of discovering the schema for most part
3. Deleting columns is harder than adding additional columns, no one takes that risk so everyone ends up with schema bloat
4. Once you go a little bigger you will realize that the integer based UUID are not that great of a choice. Those are separate tables which maintain those index counters and not part of your DDLs. They have their own set of issues with data merging, backups and recovery
For the OP, I looked at the repo (https://github.com/hatchet-dev/hatchet/blob/main/sql/schema/...) ,
1. seems like the schema has database functions - Thats a potential scaling issue, plus you are asking vertical only scalable component to do something which could have taken care by horizontally scalable component
2. TEXT datatype for pretty much every attribute - this is a footgun, you cant use them for indexes properly, in the absence of length checks they can be abused from client side
The UUID backup/recovery thing isn't an issue if you're doing append-only. Joins on UUIDs are far slower, enough that even at small scale it can cause issues when you have many joins. Anyway I won't argue too hard against UUIDs cause they work too, just anything is better than using meaningful fields as the PKs.
Getting rid of SQL queries is not the job of ORM.
ORM saves you time on object mapping boilerplate THAT IS THE JOB of an ORM it is right there in the name „object relational mapper”.
The biggest consequence of forcing the RDBMS through a lazily evaluated object graph is that we've become decoupled from the mechanical realities of what the database provider can do. I had a client quickly drop the "you must use EF" commandment for the project I was assisting on once they saw how many rows it was loading per unit time without EF. "I didn't know that was possible". Many such cases.
The application->database layer is pretty impactful and it pays to pay attention to it, because poor access patterns will cause a lot of trouble in the future, and its made worse by not having a very accurate understanding of whats going on in that layer.
I think a lot of developers don't have a good sense on where their time sinks actually are. Boilerplate is not pleasant to write but also not the timeline-destroyer people tend to think it is. And its often not enough of a time-sink to warrant introducing "magic" that will have very large negative future impacts.
I think everyone always is needlessly partisan when ORMs get mentioned.
Personally, I view it like so:
* using something separate from your ORM for migrations *can* be good (e.g. you don't couple your DB to your app's ORM), like dbmate; needs to be said
* making plenty of views for MOST of your complex queries is a good idea, if approaches like BFF are popular for APIs, then why not the same in regards to interacting with your DB? also makes testing a breeze, instead of having to pull some unreadable generated SQL bullshit from trace logs
* you can make read only entity mappings against those views and keep the querying complexity in the DB but let the ORM handle the app side stuff
* with that in mind, using the ORM for most of the simple queries and regular CRUD stuff is also a breeze
* sprinkle in some DB procedures/functions if needed, but also don't go all gung-ho in storing 90% of your business logic in the DB and using the app as a glorified view/controller, while that might appeal to a subset of people, in the *present* time that inevitably sucks
* similarly to CTE's, your views can reference other views, as building blocks; also sometimes having two similar views that just compose others is nicer than dynamically generated SQL (since once you start trying to generate it dynamically, people get too eager about it and before you know it you can't figure out WTF is going on)
Reading some other comments, I'd also add: know SQL, use SQL for the problem it's good at, same for ORMs; don't let either overstep the other. In my opinion a really good litmus test for this is whether you are capable of generating all of your ORM mappings when you point a tool at your schema and its tables/views. If not, and you need complex workarounds, something is wrong.
There are reasonable arguments for leaving most to the db; effectively exposing some VIEWs and FUNCTIONs as the application interface for any and all bindings. Like legacy java and greenfield php for example.
I'd say it's different work rather than strictly more work.
I'd rather spend the time building out my product than prematurely optimizing and overthinking my DB schemas at the earliest phases of a project.
I'd like to add: stay away from using arrays and user defined types. They feel like a good idea at first, but will become a problem when using fetched data in your code.
And another miss, if you're managing your servers: pgtune. The default postgres configuration is very conservative regarding resources so you can get good performance gains just by adjusting them to your server specifications.
And what is people's opinion of the reverse: source-of-truth in traditionally-modelled mutable relational tables, with a change log written out with triggers?
It does have its legitimate uses, but there are footguns that general SWEs not super familiar with DBs won't know about, like how it still doesn't prevent all types of race conditions unless you're in SERIALIZABLE mode.
While it's not my first choice, or habit, in many use cases, using an ORM is still OK looking back in the start especially when the schema is being fleshed out prior to understanding what needs optimizing. It's trivial to start optimizing a query after taking it away from ORM. The new angle I think is the ability for LLMs to use ORMs given the documentation, etc.
The only other thing I'd say is the benefits of running something that works with Postgres, such as Supabase, Hasura, etc. It really can be the best of multiple worlds, especially in the beginning in terms of having flexibility in one place.
I'm generally an immutable first, type developer but ive not had to do much schema design for a while and looking back to previous attempts we never hit scale to stress test my approaches.
There are disadvantages to this, but it's a safe default. The alternative is possibly losing important data, finding out later you want historical records of things that are stored in kludgy separate tables, getting into more advanced locking situations, and having more complex DB migrations. Which I've had to pull teams out of many times.
Yes, in practice you may find that one or two of these don't apply to your own special start-up. But probably they all actually do.
If any of the data is PII and you will be subject to GDPR (you probably want to be at some point) then you will need a way to hard delete it.
A large portion of append-only datasets I’ve worked with have run into some edge case that required an update.
Also if you’re doing an append only log plus mutable view then please use triggers or materlialized views. I ran into a few cases where the approach was to just update both tables and that lead to divergences between the two.
If you are doing some kind of full cross product where the join creates a much larger set of rows, it could optimize the DB load and network traffic to fetch the source sets and then generate the permuted set locally.
But, many inner join patterns are selective. They produce a much smaller output than the source records. The traffic to pull all the records and then intersect and filter locally is much worse than having the DB do it.
And that's before you even consider indexed joins, where the query plann is able to make good use of indexes to avoid doing brute-force table scans, sorting, and filtering.
Also, to re-emphasize: we do this rarely, but it's been helpful the times we've done it
* Materialised views (especially if the computation/joins are particularly nasty).
* Left outer joins are a good alternative to 'joins using case' and more likely to use the index.
You can run this on $10x2 = $20 per month setup for 2 replicas and 1 monitor node for maybe $2-3.
For most other projects i just use sqlite, backup periodically to s3.
some report (coincidentally i was checking health of my small cluster for an app)
Common application queries average under 4 ms:frequent analytics queries: ~0.9–1.4 ms average common inserts: ~0.4–3.4 ms average the slower recurring reporting query: 62 ms average across 53 calls, 308 ms worst case
Query volume is approximately 2.30 million SQL statements/day (~26.6 statements/sec), based on pg_stat_statements over the last 97.3 days. That includes every SQL statement, not just user-facing requests: BEGIN/COMMIT alone account for ~1.05M/day, analytics inserts for ~522K/day, and HA/monitoring checks for ~118K/day.
You mentioned not wanting to spend admin hours fine-tuning a self-hosted instance though. Are you really hitting the DB hard enough for that to matter, but SQLite works fine? Cause I haven't tuned local Postgres in years.
I think most of the work I've done just fits in the other storage models and they scale really far really fast at $0 cost
I haven't paid for then ever, similarly, I have a few super tiny apps on Supabase and Neon too that cost me $0
On another project that got traction though, Postgres became better DX than DynamoDB and such, but that traction came with the overhead cost
The "fine-tuning" was setting up things like pgbouncer, restart, vaccuum, query optimizer, etc. Like, the db would always create some amount of work every fortnight. Coming from managed services, especially these serverless ones, they just work and you do nothing so it makes you spoiled.
Different mental model maybe?
On the "hitting the DB hard enough for that to matter but SQLite works fine" -- Absolutely! I have a <10gb SQLite inside a Lambda for some complex queries, and that gets refreshed whenever that data changes (which is not that often).
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I, at least, don't know of a perfect fix here. Re: the original comment - Postgres will also error on deadlocks after it detects them without setting your isolation level to Serializable, but I agree with you that often retrying doesn't help, and could even cause cascading / snowballing failures if you have a backlog of retries piling up because of deadlocks.
I don't know if there's a good solution, really. We've fixed deadlocks incrementally over time as we've found them, which has worked pretty well, but of course that means also needing to deal with the "finding" part, which has generally come in the form of lots of `deadlock detected` log lines and errors (and retries accompanying those).
One thing that might be worth auditing is why there are two different bits of application code that are updating the same rows in two different tables in different orders. I know it's a contrived example, but it seems like it could be a code smell to me. Maybe this is the kind of thing that arises when two different subteams are working on the same database and are largely siloed.
Alexander will likely have more thoughts here as well, just my two cents!
In degenerate table/index situations, this could lead to index scans that are as slow as table scans, or not using an index at all! Especially common in SaaS schemas where you’re dealing with a tenancy key in many of the indicies: that tenancy key should almost always suffix the composite key not prefix it!!
I appreciate the feedback; I'm usually someone who tends to go into way too much detail, so this was difficult to write - I tried to focus on the "mental model" of understanding Postgres rather than very nuanced specifics. I tried to link out to my favorite articles on a number of subjects, and the Postgres manual is quite good.
Some external links from the article:
- https://www.digitalocean.com/community/tutorials/database-no...
- https://www.cybertec-postgresql.com/en/benefits-of-a-descend...
- https://martinfowler.com/bliki/ParallelChange.html
- https://www.cybertec-postgresql.com/en/tuning-autovacuum-pos...
Some internal links on where I've gone into our own use-cases in more detail:
- https://hatchet.run/blog/multi-tenant-queues (PG-backed queues)
- https://hatchet.run/blog/postgres-partitioning (PG partitioning)
(edit: formatting)
They’re really not that bad. Even on large-ish tables (hundreds of millions of rows), the typical query time I see for a query with 1-2 inner joins is 1-2 msec. That can of course vary with result set size, but in general it’s going to be dwarfed by network RTT.
If you’ve tested your schema with normalized and denormalized versions and found a significant difference that justifies it, by all means, but IME query speed for any non-trivial query is generally dominated by query shape, index design, and schema design (specifically for clustering indices, not taking advantage of it to have physical and logical tuple correlation).
I mean, sure start with a unified schema file until you have a production release... deploy, populate with placeholder data, etc... but once released, having a file for each set of changes isn't a bad thing.
Also, the management tools you can have single files for each view/sproc, etc... it's just schema migrations you need to take care of.
But thanks for info about level of issue.
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We're heavy users of stored functions because we're (perhaps overly) reliant on Postgres triggers, which can improve performance by reducing network round-trips but are fairly risky because they're difficult to monitor and observe.
Stored procedures are useful in cases such as annoying data type conversions (for example, before the newer ltree versions, its path couldn't accept hyphens and so if you were using UUIDs you needed a way to convert the UUID to a ltree compatible representation) or when you want to write a function that is used by a constraint, but it's not something I would generally reach for and certainly not for SQL injection reasons.
which in turn makes every single change in schema or logic dependent on a DBA making the change in Postgres balanced against their lunch schedule. Good for DBA job security but terrible for productivity and sanity.
I have all my database functions version controlled and deployed by liquibase on every release (along with any other migrations that need to go out).
They are treated like code like any other piece of code in my codebase, get changed along with the rest of the application as necessary, and are deployed automatically with the rest of my application.
DB functions / stored procedures are the right tool for certain jobs. When they are the right fit, they can save your ass performance wise.
I've spent a lot of time writing my own random queries. I don't know that I've ever written a stored function.
And I've spent a lot of my working life cleaning up after people who write random queries who then start blaming the database for being "slow" and insisting they need some sort of over-engineered Redis caching layer or whatever.
100% of the time the database is perfectly fine, but the query is slop.
Not saying you are one of them, but you would very much be in the tiny minority if you are not. ;)
That's something that OP didn't discuss is shared databases vs services owning their own. When you are startup, it's really tempting to have services reach into database and skip API call.
In most situations I'd try to avoid using stored procedures. Unless you're all in on them, the effect will be that it hides some logic from the developers since it is not in the main part of the codebase.
I do not buy this argument.
Its called a documented function.
The developers know the function's inputs and outputs and what it does.
That's all they should need to know.
Its no different to functions in the libraries of whatever programming language you are using.
Devs just do their coding based off the function signature and docs. They know what goes in, what comes out and what the function does.
How many developers do you know who've gone back and read the source code of the function ? Assuming its open-source anyway and not a OS API.
And of course devs read the content of functions they call. Unless it's a well written library used by many different people, odds are the function isn't documented well enough and has quirks that force you to understand in more detail how it works. This is not external library code, it's still part of your application.
Even worse !
Don't get me started on people who treat databases like a black-box dumping ground and insist they must have "portable schemas".
And if they "do have", they're not spending enough time with their service-market match
If they have time to write SQL queries, they have time to write stored functions.
Its really not that difficult and it certainly does not take a substantial amount of time.
They have the time to write SQL queries in their code
They don't have time to (or better, shouldn't) materialize them as a stored function in the DB
"Oh but your CI/CD should automatically..." Let me stop right there
The time they spend with this can be better used to ship and to improve their SW to customers, not with yak shaving
Which is why they end up spending time on mea-culpa "we take your data security seriously, but clearly not seriously enough" emails when they inevitably get pwned by a completely predictable and avoidable SQL injection attack.
The sort of startups you describe are jokes that barley take security seriously, let alone know what a pen-test or code audit is, let alone actually do them on a regular basis.
Managed databases don’t lose customer data. They would be sued and lose, and if word got out, they would lose their customers. They are optimized for the machines they run on. The proprietary tooling from AWS for managing HA clusters, doing blue-green upgrades and monitoring for slow queries is all miles ahead of the open source tooling available for self-managed databases.
There’s nothing left for the DBA to do.
So the same thing I said we do, a periodic validation of recovery, but with an acceptably short period.
SWEs can do that too. They can even write software to do most of it automatically.
They went through systems design interviews to ensure they know how to scale without picking technology choices that require a dedicated human caretaker.
It’s the opposite. Needing a DBA to operate a completely managed database is a sign of not enough maturity.
And yet here we still are
I'm defending your employer or their mindset, I'm just disagreeing with your claim that this follows from the parent's cost analysis claims. It _seems_ like your organization's problems are precisely because they _weren't_ doing the kind of cost analysis that the parent advocated. In other words, nothing in the parent's comment advocated for blindly following some Terraform guide. It feels unfair to the parent to suggest that their mindset caused your organization problems when it seems like your organization's problems were caused by _not having_ the parent's mindset.
Like I have no doubt that they’re all greedy bastards, but they aren’t doing much to lock people in. The people who complain about lock in are usually talking about “cloud providers making their services so much easier to use than bespoke platforms on bare Linux hosts such that no one will want to go back to the latter”.
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Of course this isn't true for some companies and the cost of downtime far exceeds the cost of HA. Most companies think they are that company, most aren't. And even if they are, "AWS had an outage" does a lot of lifting.
Every time I get a new client they need constant point in time backups, failover, HA everything. Usually when the costs are explained they change their mind and an hour old snapshot restore is actually fine
We’re not talking about maintaining your own k8s cluster here mind you, but a basic Postgres setup. If you can’t handle that comfortably in an afternoon, you probably shouldn’t be entrusted with customer data.
And RDS is doing a lot more for you than what you can set up in an afternoon w.r.t observability and features that are just a click away. I actually implemented some of those features when I worked there :)
I just don't understand why people seem so drawn to the bad solution just because it ships with the database.
If you are on reasonable storage, I think it is arguable that the main reason you would have to recover is due to a software failure leading to corruption of the PostgreSQL backing state files. Otherwise, you would simply be restarting PostgreSQL on your durable and available storage. So, if the content has been corrupted by bugs, can you trust the recent WAL log in this scenario? Or can a plain dump be a more reliable "known-good" database recovery point?
And what is the business cost to rolling back to a less frequent dump such as nightly? Not every DB is some kind of multi-party OLTP ledger. Sometimes, a day of lost "new data" may be just a day of labor to repeat some data entry or other repeatable work...
A really robust contingency plan probably has both of these kinds of backup, and scenarios where one recovery versus the other is attempted. But if you are starting at a very small scale, hosted on some reasonable cloud storage like EBS, a cron-based dump that goes into a normal hierarchical file backup system may be totally sufficient for the extreme disaster scenario where you cannot simply restart your DB server on top of the surviving and available storage volume?
I'd much rather just do it right though.
Direct attached storage (or whatever storage is fastest / lowest latency for the environment you have available).
Setup pgbackreset or barman, use WAL archiving and setup block incremental backups with a new full backup every so often.
Now you have point in time recovery with very low RPO/RTO, and your database infrastructure can be treated a bit more like cattle instead of a pet, as you should be testing restores often, and this will become part of your yearly major postgres upgrade strategy.
You also get the ability to have a replica for almost free, as replicas can be created from the backup repo very cheaply, and get caught up to the primary without requiring the primary keep around the WAL for a long time. It just pulls it from the repo until caught up.
Any case where you think EBS was the right choice is better handled by having one (or more) replicas and a failover strategy.
Just do the right thing from the start and you save a lot of headaches. People have run production systems already, and have hit the pain points. Why keep hitting the same ones?
They think they can bolt on dashboards or make interface having basically OLAP in the same project and make it perfectly performant.
Here in this thread you have bunch of devs claiming ORM doesn’t work - it works perfectly fine for OLTP. Those devs who claim to be so great knowing SQL also don’t seem to understand that business people are throwing them under bus requiring OLAP stuff to be bolted on OLTP application.
To add on that business people expect OLAP views or dashboards to be „real time” — only after they have dashboard developed to look at it once a month or once a quarter.
What also pains me when I deal with devs is that they pick up synchronization jobs, well batch jobs are fine but then they are scolded by business that batch jobs are slow. Where we have all kinds of tech to make event driven updates to OLAP environments.
That’s a silly take. I somehow learned over the long run: SQL different flavors, couple of ORMs, multiple programming languages and multiple frameworks. Not counting different troubleshooting different operating systems and different applications I had to work with.
When it comes to advanced queries learning the ORM equivalent to the SQL it should write… ORM’s can be outright terrible, and I completely agree with you.
Every ORM has their own names and way of doing things, so this knowledge is hard to port to other ORMs. It’s requires you knowing the right SQL first, then knowing how to write that in the ORM dialect.
Reaching for the more advanced ORM trickery means grasping hard to grasp subjects twice with the risk of misunderstanding twice, and as a bonus worse ORM documentation than plain active record features. Oh, and others need to understand what you’ve written as well.
I also never seen anyone claiming that you doesn’t have to know SQL and ORM is enough from the opposite side.
If you really run into spot where your ORM breaks you can always drop to SQL. If you build project „SQL first” you robbed yourself from ORM upsides and you are bound to badly implement your own one in the long run.
You can use well known library that more people will comfortably using
or
given enough time you will build crooked, half baked ORM of your own.
Also you can make SQL much more tolerable if you use migrations. That’s really where ORMs shine, but you don’t need the rest. Also query builders, although those also have a risk of abuse similar to ORMs. 99% of SQL should be static. That sounds like an absurdly high percent but it’s true if you use all the SQL features.
Your Python application maintains a connection to Pgbouncer during the lifecycle of the request, but the physical Postgres connection is allocated only during the DB transaction. You will need open/close transactions in your code though.
I also get it, xact should be 1:1 with connection in a lot of these backend applications. Sometimes I have a few little helpers for that, like pool.sql() will take conn, open xact, execute, close xact, return conn. If the DB driver doesn't already have that.
* read from the database
* make a request to an API (or really any kind of long running non-database thing)
* write to the database
You're going to end up with a transaction that is open way longer than it needs to be, particularly if you're upstream API is misbehaving, which will potentially end up causing a lot more grief.
Some people in this thread don’t seem to think it’s that hard or overcomplicated.
When reading Designing Data-Intensive Applications my main takeaway was that event sourcing can make it easier to solve a lot of issues like performance, scaling, consistency, auditability, etc.
It would be interesting to look into what a low overhead way of implementing CRUD with event sourcing in Postgres would look like, then decide if it’s too complex.
This only works if you don't care about being able to auto roll back DB changes without making a new commit, cause Postgres doesn't have a declarative DDL.
I meant this type of bug, of accessing values in memory not explicitly meant for access, can exist at every level of the stack. It would be a very very very serious memory flaw/bug/exploit if a Postgres cursor could access data unrelated to that cursor, old or new, since it would mean serving bad data. Also, most people use connection pooling, after all, and you're not the first to consider this. A reasonable test for these concerns, if you don't want to believe the documentation/source, is looking for previous CVE related to it.
And, zeroing memory is more of a bandaid against a specific type of memory access bug, since accessing memory that isn't yours means they found a way to access beyond the bytes meant for the value, which often means you're going to be accessing allocated memory adjacent to what was zeroed.
So I guess your question is maybe, how robust is the code against unknown memory access bugs of a very specific type.
But again ... why do they need to ?
If you are a developer and you want to convert a string to upper case, you just use `strings.ToUpper($foo)` or whatever based on the signature.
Again, its no different with stored procedures.
Dev wants to add a new user ? They look through the stored procedure headers and see a `add_user($foo,$bar)` signature and code a call against that.
Do they need to know that in the background `add_user($foo,$bar)` inserts into table `users`, `groups` etc. ? No.
Infact it makes the dev's life simpler because instead of sending multiple calls themselves (or perhaps forgetting one or two), they just called the stored procedure.
That is my problems with devs who think they are some sort of geniuses that need absolute access to the database because they think nobody can write SQL queries as well as they can ... too many times they come running to me complaining the database is "slow" when in fact it is their sloppy SQL queries that are slow.
A few years ago we were trying to do a live migration from on-prem into GCP (mostly to have GCP as a failover option but move everything there if on-prem went down). We configured an async GCP slave from the on-prem cluster and once running it was easy. Multi-TB migration straight into CloudSQL with seconds of downtime.
Before we signed this off, we also tested the reverse - getting back from CloudSQL into on-prem. Turns out you couldn't (at the time, I'm not sure about currently) use CloudSQL as master for an external slave, only a slave to an external master. You could migrate the data in with no downtime, to get it back out was a multi-TB transfer. The door only opens one way.
So no, I am talking about lock in.
Intelligence is being able to set up your own postgres database with backups, PITR, retention policies, HA/replicas, and everything else needed to prevent catastrophic data loss. Wisdom is realizing that's a solved problem completely unrelated to your business, so you don't have to.
1. Don't use a third-party auth provider, if you can't build out an OAuth provider in an afternoon you shouldn't be entrusted with login credentials
2. Don't use Github, if you can't build out an internal Gitlab instance in an afternoon you shouldn't be entrusted with securing source code
3. Don't use Github Actions or CircleCI, if you can't self-host a Jenkins server in an afternoon you shouldn't be entrusted with secure deployments
4. Don't use a lawyer or outsourced HR, if you can't draft legally binding employment agreement in an afternoon you shouldn't be entrusted with hiring people
I'm all for treating DB as cattle, but your cattle needs to be able to survive long enough.
EBS is expensive but it works for PG. AWS uses EBS for RDS databases. Calling that a 'mistake' is a tall order.
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I still have to get a connection from the pool, I just do it inside the function body now, right?
So this
@app.get("/users")
def get_users(conn = Depends[get_db_conn]):
users = conn.execute("SELECT * FROM users")
return users
@app.get("/users")
def get_users(pool = Depends[get_db_pool]):
with pool.get_conn() as conn:
users = conn.execute("SELECT * FROM users")
return users
[send Query 1] -> [send Query 2] -> [send Query 3] -> [receive Result 1] -> [receive Result 2] -> [receive Result 3]
But in Postgres and MySQL, pipelined queries are not executed in parallel. They're just queued up for a single thread (per connection) to execute sequentially. Thus, if Query 1 is a transaction that takes too long, then it ends up blocking the execution of Query 2 and Query 3.
Other than that, yes! Setting up a Keycloak instance as opposed to handing over login credentials to Octa should be a consideration. Keeping your source code in GitLab over tying your entire development workflow to Microsoft vibe coders with an awful track record is a sensible stance.
> Don't use a lawyer or outsourced HR, if you can't draft legally binding employment agreement in an afternoon you shouldn't be entrusted with hiring people
That's a little ridiculous; there's an overlap between someone setting up a database for a startup and someone programmatically using a database at a startup. The overlap between that person and someone with a legal background is infinitesimally smaller.