I recently started digging into databases for the first time since college, and from a novice's perspective, postgres is absolutely magical. You can throw in 10M+ rows across twenty columns, spread over five tables, add some indices, and get sub-100ms queries for virtually anything you want. If something doesn't work, you just ask it for an analysis and immediately know what index to add or how to fix your query. It blows my mind. Modern databases are miracles.
I do agree, I don’t know why more people don’t just use Postgres. If I’m doing data exploration with lots of data (e.g., GIS, nD vectors), I’ll just spin up a Postgres.app on my macOS laptop, install what little I need, and it just works and is plenty fast for my needs. It’s a really great choice for a lot of domains.
That being said, while I think Postgres is “the right tool for the job” in many cases, sometimes you just want (relative) simplicity, both in terms of complexity and deployment, and should use something like SQLite. I think it’s unwise to understate simplicity, and I use it to run a few medium-traffic servers (at least, medium traffic for the hardware I run it on).
> in many cases, sometimes you just want (relative) simplicity, both in terms of complexity and deployment, and should use something like SQLite.
So many times when trying to just go for simplicity with SQLite it takes me like one working day until I run up against enough annoyances to where resolving those is more work than setting up the "set up and forget" postgres instance.
Granted, this is for personal stuff... but "Postgres packaged for low maintenance" is present in a lot of OS package managers! Even for smaller data analysis work SQLite perf leads _loads_ to be desired (once had QGIS struggling with a sqlite DB... pg made everything mostly instant. Indices etc... but stuff I _couldn't easily get with sqlite_)
If SQLite works for you that's great, I do think it's worth it for people to _try_ to do simple pg setups to understand just how painful it is to use pg (for me: not that high)
Oh, yes, that's one of my points! I think Postgres is a great way to deal with tons of data, and it's really the only thing I use to do any sort of analysis or informatics (that and Parquet + Dask).
I am also a fan of SQLite. One of the best parts during development is how easy it is to spin up and spin down databases for full integration tests without containers or anything. It also simplifies backups, and is probably good enough.
These days I would recommend PGlite for testing purposes when you use Postgres in production. That way you don't need any specific SGQLite vs Postgres behavior switches.
Where I have used SQLite most successfully is really two use cases. First, I use it for data processing. Say I need to retrieve lots of data and transform it to a different setup. I could do that in something like Python but SQL is just more expressive for that and I can also create a new database, populate it with data I have, fetch new data, combine it together, export the update to a permanent data store (usually Postgres).
Second, when I need a local save file. Sometimes small local apps are better served by a save file and they save file might as well have an extensible format that I can update as I go. This is more rare but still can be useful.
The first use case is very powerful. A temporary SQL database that can be blown away with zero trace of it is great. And the ability to run complex queries on it can really help.
But 99% of the time I just use Postgres. It works, it has sane defaults, it is crazy extensible, and it has never not met my needs, unlike Oracle or MySQL.
A bit off topic but the one thing I've never been able to figure out with Postgres easily & reliably is what magic incantations allow a user account full access to a specific database but not to others, particularly in cases of managed postgres offered by cloud providers. `GRANT ALL PRIVILEGES` never seems to work.
Having to look up and spend time fixing permissions every time itself makes using Postgres for simple uses difficult for me but if you're using it ad hoc, any tips?
I wish PostgreSQL had a native vector implementation instead of using extensions. They're kind of a pain in the ass to maintain, especially with migrations.
Standardizing on one single tiny little project is always a bad idea. Why? Some examples (which are admittedly not related to postgres, because I don't know their structure):
1) A single person, doing a ton of heavy lifting, leaves, or worse, turns it over, or silently sells out to a nefarious person.
2) A severe security vulnerability is found. If everyone is using postgres, everyone is vulnerable. Bonus points if the vulnerability is either not publicly disclosed or it is hard to fix.
3) Commercial/Government interests heavily influence and push the project into places that could make it vulnerable in any given way. This is absolutely a thing.
4) AI. No clarification here. Just use your imagination, with recent news regarding FFMPEG and other projects in mind.
i think the topic of "what data backend" gets super conflated into many different variations of what the hell people need it for. discussions here go so many different directions. some people are building simple webapps, some are building complex webapps that need to scale for a gazillion users, some are building local apps, some are just tinkering, some are thinking broadly about how their backend needs to sync with a datalake->some data warehouse at an org, yadda yadda ya.
i personally like postgres myself for just about all use cases that must be shared with others (app with more than one client that might be providing CRUD updates or anything really that demands a central data store). ive used sqlite a couple times with WAL to try and make a small app shared between 2-3 people who all would contribute updates thru it but it wasnt ideal. for postgres so many features/extensions its concurrent writes are fast as hell and if you just want to one-shot a solution then you cant go wrong, but it's ofc not the same as sqlite setup.
i think a lot of the pain with postgres is just learning to effectively be a knowledgeable db admin of sorts. its somewhere between being a competent devops guy and a dbadmin expert all in one. if you're actually doing some kind of production deployment it is kinda scary hoping you've got everything set up right. even supabase which makes this whole process trivial to get going requires an understanding of not-always-understood security premises that just make things spooky.
lot of words to say i dont get much out of these discussions tbh. theres just too many use cases and variables in everyones working/hobby lives im not sure that there is a proverbial bottom to any of it. some will use sqlite and some will use postgres and some will use some weird thing no ones heard of because they're afraid to rawdog sql and just want immediate graphql capability to be the main mode of data retrieval. some will show up here and talk about why you need redis in the middle.
its too much noise so i just keep using postgres because its free and boring and fast. end of the day i just want to make stuff people can use. it's a hard endeavor to do well alone, if you dont have a team of other experts who can help you put all the puzzle pieces together on how to deploy things the right way and also add pieces like redis or whatever... it's just a lot. it's hard to find where to get started. sqlite is the only solution that really by nature of what it is seems to champion the lonely developer, but the tradeoffs are big if you're trying to make something that should get used by many people.
I’m a huge Postgres fan. That said, I don’t agree with the blanket advice of “just use Postgres.” That stance often comes from folks who haven’t been exposed enough to (newer) purpose-built technologies and the tremendous value they can create
The argument, as in this blog, is that a single Postgres stack is simpler and reduces complexity. What’s often overlooked is the CAPEX and OPEX required to make Postgres work well for workloads it wasn’t designed for, at even reasonable scale. At Citus Data, we saw many customers with solid-sized teams of Postgres experts whose primary job was constant tuning, operating, and essentially babysitting the system to keep it performing at scale.
Side note, we’re seeing purpose-built technologies show up much earlier in a company’s lifecycle, likely accelerated by AI-driven use cases. At ClickHouse, many customers using Postgres replication are seed-stage companies that have grown extremely quickly. We pulled together some data on these trends here:
https://clickhouse.com/blog/postgres-cdc-year-in-review-2025...
A better approach would be to embrace the integration of purpose-built technologies with Postgres, making it easier for users to get the best of both worlds, rather than making overgeneralized claims like “Postgres for everything” or “Just use Postgres.”
I personally see a difference between “just use Postgres” and “make Postgres your default choice.” The latter leaves room to evaluate alternatives when the workload calls for it, while the former does not. When that nuance gets lost, it can become misleading for teams that are hitting or even close to hitting—the limits of Postgres, who may continue tuning Postgres spending not only time but also significant $$. IMO a better world is one where developers can have a mindset of using best-in-class where needed. This is where embracing integrations with Postgres will be helpful!
I think that the key point being made by this crowd, of which I'm one, is somewhere in the middle. The way I mean it is "Make Postgres your default choice. Also *you* probably aren't doing anything special enough to warrant using something different".
In other words, there are people and situations where it makes sense to use something else. But most people believing they're in that category are wrong.
The point is really that you can only evaluate which of alternatives is better once you have working product with data big enough - else it's just basically following trends and hoping your barely informed decision won't be wrong.
Agree to disagree here. I see a world where developers need to think about (reasonable) scale from day one, or at least very early. We’ve been seeing this play out at ClickHouse - the need for purpose-built OLAP is reducing from years to months. Also integration with ClickHouse is few weeks of effort for potentially significantly faster performance for analytics.
Here's my opinion: just use postgres. If you're experienced enough to not when I say that, go for it, the advice isn't for you. If you aren't, I'm probably saving you from yourself. "Reasonable scale" to these people could mean dozens of inserts per second, which is why people talking vagueries around scale is madenning to me. If you aren't going to actually say what that means, you will lead people who don't know better down the wrong path.
> I don’t agree with the blanket advice of “just use Postgres.”
I take it as meaning use Postgres until there's a reason not to. ie build for the scale / growth rate you have not "how will this handle the 100 million users I dream of." A simpler tech stack will be simpler to iterate on.
Yes. That's a good framing. PostgreSQL is a good default for online LOB-y things. There are all sorts of reasons to use something other than PostgreSQL, but raw performance at scale becomes such a reason later than you think.
Cloud providers will rent you enormous beasts of machines that, while expensive, will remain cheaper than rewriting for a migration for a long time.
I hate how developers are often very skeptical but all the skepticism goes out the window if the tech is sufficiently hyped up.
And TBH, developers are pretty dumb not to realize that the tech tools monoculture is a way for business folks to make us easily replaceable across different organizations... If all companies use the same tech, it turns us into exchangeable commodities which can easily be replaced and sourced across different organizations.
Look at the typical React dev. They have zero leverage and can be replaced by vibe coding kiddies straight out of school or sourced from literally any company on earth. And there are some real negatives to using silver bullet tools. They're not even the best tools for a lot of cases! The React dev is a commodity and they let it happen to them. Outsmarted by dumb business folks who dropped out of college. Be smarter people.
> At Citus Data, we saw many customers with solid-sized teams of Postgres experts whose primary job was constant tuning, operating, and essentially babysitting the system to keep it performing at scale.
Oh no, not a company hiring a team of specialist in a core technology you need! What next, paying them a good wage? C'mon, it's so much better to get a bunch of random, excuse me, "specialized" SaaS tools that will _surely_ not lead to requiring five teams of specialists in random technologies that will eventually be discontinued once Google acquires the company running them.
OK but seriously, yeah sometimes "specialized" is good, though much less rarely than people pretend it to be. Having specialists ain't bad, and I'd say is better than telling a random developer to become a specialist in some cloud tech and pretending you didn't just end up turning a - hopefully decent - developer into a poor DBA. Not to mention that a small team of Postgres specialists can maintain a truly stupendous amount of Postgres.
This kind of thing gets posted every couple of months. Databases like Pinecone and Redis are more cost-effective and capable for their special use case, often dramatically so. In some circumstances the situation favours solving the problem in Postgres rather than adding a database. But that should be evaluated on a case-by-case basis. For example, if you run something at scale and have an ops team the penalty of adding a second database is much smaller.
(I run a medium-sized Postgres deployment and like it, but I don't feel like it's a cost-effective solution to every database problem.)
Once you have an app, a lot of data, and actual problems, it's far easier to pick the right alternative.
PostgreSQL is good enough to get to medium sized with nearly every use case. Once you are there, you have the use case and the test data to test any alternative for it well, rather than trying to guess beforehand what you actually need.
The advice is basically "PostgreSQL is probably good enough for whatever you're building now, and you should only look for other solution once you are big enough that it stops being that"
I have two fundamental problems with Postgres - an excellent piece of technology, no questions about that.
First, to use Postgres for all those cases you have to learn various aspects of Postgres. Postgres isn't a unified tool which can do everything - instead it's a set of tools under the same umbrella. As a result, you don't save much from similarly learning all those different systems and using Postgres only as a RDBMS. And if something isn't implemented in Postgres better than in a 3rd party system, it could be easier to replace that 3rd party system - just one part of the system - rather than switching from Postgres-only to Postgres-and-then-some. In other words, Postgres has little benefits when many technologies are needed comparing with the collection of separate tools. The article notwithstanding.
Second, Postgres is written for HDDs - hard disk drives, with their patterns of data access and times. Today we usually work with SSDs, and we'd benefit from having SSD-native RDBMSes. They exist, and Postgres may lose to them - both in simplicity and performance - significantly enough.
MySQL is definitely easier to use if you don’t want to ever have to think about DB maintenance; on the other hand, you’re giving up a TON of features that could make your queries enormously performant if your schema is designed around them - like BRIN indices, partial indices, way better partition management, etc.
OTOH, if and only if you design your schema to exploit MySQL’s clustering index (like for 1:M, make the PK of the child table something like (FK, some_id)), your range scans will become incredibly fast. But practically no one does that.
As someone who learned to think in MySQL, this is really true, at the time Postgres was a viable alternative too, only the tooling to get started reached me a little easier and quicker.
The major thing I advocate for is don't pick a NOSQL database to avoid relational dbs, only to try and do a bunch of relational work in NOSQL that would have been trivial in an RBDMS. Postgres can even power graph query results which is great.
There's a fascinating gap between PostgreSQL theory and practice here. Elsewhere in this thread, I complained that PostgreSQL extensions can't do everything yet. One thing they can do, however, or ought to be able to do, is provide alternative storage engines. That's the central thing they're supposed to be especially good at providing.
Now we only need easy self-hosted Postgres clustering for HA. Postgres seems to need additional tooling. There is Patroni, which doesn't provide container images. There is Spilo, which provides Postgres images with Patroni, but they are not really maintained. There is a timescaledb-ha image with Patroni, but no documentation how to use it. It seems the only easy way for hosting a Postgres cluster is to use CloudNativePG, but that requires k8s.
It would be awesome to have easy clustering directly built-in. Similar to MongoDB, where you tell the primary instance to use a replica set, then simply connect two secondaries to primary, done.
Postgres is not a CP database, and even with synchronous replication, it can lose writes during network partitions. It would not pass the Jepsen test suite.
This is very hard to fix and requires significant architectural changes (like Yugabyte or Neon have done).
Caching is mentioned in the article: What do you guys feel about using PostgreSQL for caching instead of Redis?
Redis is many times faster, so much that it doesn't seem comparable to me.
A lot of data you can get away with just caching in-mem on each node, but when you have many nodes there are valid cases where you really want that distributed cache.
Just use memcache for query cache if you have to. And only if you have to, because invalidation is hard. It's cheap, reliable, mature, fast, scalable, requires little understanding, has decent quality clients in most languages, is not stateful and available off the shelf in most cloud providers and works in-clusetr in kubernetes if you want to do it that way.
I can't find a use case for Redis that postgres or postgres+memcache isn't a simpler and/or superior solution.
Just to give you an idea how good memcache is, I think we had 9 billion requests across half a dozen nodes over a few years without a single process restart.
memcached is multithreaded, so it scales up better per node.
memcached clients also frequently uses ketama consistent hashing, so it is much easier to do load/clustering, being much simpler than redis clustering (sentinel, etc).
Mcrouter[1] is also great for scaling memcached.
dragonfly, garnet, and pogocache are other alternatives too.
Run benchmarks that show that, for your application under your expected best-case loads, using Redis for caching instead of PostgreSQL provides a meaningful improvement.
If it doesn't provide a meaningful improvement, stick with PostgreSQL.
This is the proper approach when deciding whether to use any type of tool or technology. Is the increased amount of cognitive overhead for someone with minimal exposure to your system (who will have to maintain it when you’ve moved on) worth the increased performance on a dollars-per-hour basis? If so, it may be a good option. If not, it doesn’t matter how much better the relative performance is.
If you want to compare Redis and PostgreSQL as a cache, be sure to measure an unlogged table, as suggested in the article. Much of the slowness of PostgreSQL is to ensure durability and consistency after a crash. If that isn't a concern, disable it. Unlogged tables are automatically truncated after a crash.
Depends on your app cache needs. If it's moderate, I'd start with postgres...ie. not have operate another piece of infra and the extra code. If you are doing the shared-nothing app server approach (rails, django) where the app server remembers nothing after each request Redis can be a handy choice. I often go with having a fat long lived server process (jvm) where it also acts for my live caching needs. #tradeoffs
I say do it, if it simplifies the architecture. For example if you are using firestore with a redis cache layer, that's 2 dbs. If you can replace 2 dbs with 1 db (postgres), I think it's worth it. But if you are suggesting using a postgres cache layer in front of firestore instead of redis... to me that's not as clear cut.
Depends how much you have to cache and how much speed you really need from it.
I like Redis a lot, but for things in the start I'm not sure the juice is always worth the squeeze to get it setup and manage another item in teh stack.
Luckily, search is something that has been thought about and worked on for a while and there's lots of ways to slice it initially.
I'm probably a bit biased though from past experiences from seeing so many different search engines shimmed beside or into a database that there's often an easier way in the start than adding more to the stack.
Skeptical about replacing Redis with a table serialized to disk. The point of Redis is that it is in memory and you can smash it with hot path queries while taking a lot of load off the backing DB. Also that design requires a cron which means the table could fill disk between key purges.
I the article is wrong. UNLOGGED means it isn't written to WAL which means recovery and rollback guarantees won't work since the transaction can finish before the page can be synchronized on disk. The table loses integrity as a trade off for a faster write.
I am looking for a db that runs using existing json/yaml/csv files, saves data back to those files in a directory, which I can sync using Dropbox or whatever shared storage. Now I can run this db wherever I am & run the application. Postgres feels a bit more for my needs
Why? Why would separate json/yaml/csv files be better than just... syncing using postgres itself? You point `psql` to the host you need, because clearly you have internet access even on the go, and done: you don't need to sync anything, you already have remote access to the database?
I've found that Postgres consumes (by default) more disk than, for example, MySQL. And the difference is quite significant. That means more money that I have to pay every month. But, sure Postgres seems like I system that integrates a lot of subsystems, that adds a lot of complexity too. I'm just marking the bad points because you mention the good points in the post. You're also trying to sell you service, which is good too.
The problem is that Postgres uses something like 24B overhead per row. That is not a issue with small Tables, but when your having a few billion around, each byte starts to add up fast. Then you a need link tables that explode that number even more, etc ... It really eats a ton of data.
At some point you end up with binary columns and custom encoded values, to save space by reducing row count. Kind of doing away with the benefits of a DB.
On flipside, restore from plain postgresql dump is much, much faster than plain mysql backup. There are alternative strategies for mysql but that's extra work
I am curious if you know anyone using Btrfs for this too. I like ZFS, but it Btrfs can do this it would be easier to use with some distros, etc. as it's supported in kernel.
The big problem for me from running DB on Btrfs is that when I delete large dirs or files (100GB+), it locks disk system, and Db basically stop responding on any queries.
I am very surprised that FS which is considered prod grade having this issue..
I haven't used XFS in almost two decades, does it have compression support in the same way? Also, does it do JBOD stuff? I know it's a bit of a different thing, but I really enjoy the pool many disks together part of Btrfs, although it has its limitations.
XFS doesn't have inline compression, nor does it have volume management functionality. It's a nice filesystem (and it's very fast) but it's just a filesystem.
I'll take it one step further and say you should always ask yourself if the application or project even needs a beefy database like Postgres or if you can get by with using SQLite. For example, I've found a few self-hosted services that just overcomplicated their setup and deployment because they picked Postgres or MariaDB over SQLite, despite it being a much better self-contained solution.
I find that if I want to use JSON storage I'm somewhat stuck choosing my DB stack. If I want to use JSON, and change my database from SQLite to Postgres I have to substantially change my interface to the DB. If I use only SQLite, or only Postgres it's not so bad, but the transition cost to "efficient" JSON use in Postgres from a small demo in SQLite is kind of high compared to just starting with an extra docker run (for a Postgres server) / docker compose / k8s yaml / ... that has my code + a Postgres database.
I really like having some JSON storage because I don't know my schema up front all the time, and just shoving every possible piece of potentially useful metadata in there has (generally) not bit me, but not having that critical piece of metadata has been annoying (that field that should be NOT NULL is NULL because I can't populated it after the fact).
Postgres can definitely handle a lot of use cases; background job scheduling always had me tempted to reach for something like rabbitmq but so far happy enough with riverqueue[0] for Go projects.
Pinecone allows hybrid search, merging dense and sparse vector embeddings that Postgres can't do AFAIK. That results in ~10% worse retrieval scores which might be the difference between making it in the business or not.
The lesson for me isn't "don't use Pinecone", but more like "did you already max out Postgres?"
In many cases, it is going to save you time by having less infra and one less risk while you're getting started. And if you find yourself outgrowing the capabilities of Pg then you look for an alternative.
You're absolutely right! Let's delve into why blog posts like this highlight the conflict between the speed and convenience of AI and authentic human expression. Because it's not just about fears of hallucination—it's about ensuring the author's voice gets heard. Clearly. Completely. Unmistakably.
But it's perfect HN bait, really. The title is spicy enough that folks will comment without reading the article (more so than usual), and so it survives a bit longer before being flagged as slop.
Is HN guidelines to flag AI content? I am unsure of how flagging for this is supposed to work on HN and have only ever used the flag feature for obvious spam or scams.
It might be wrong, but I have started flagging this shit daily. Garbage articles that waste my time as a person who comes on here to find good articles.
I understand that reading the title and probably skimming the article makes it a good jumping off point for a comment thread. I do like the HN comments but I don't want it to be just some forum of curious tech folks, I want it to be a place I find interesting content too.
I agree. It seems this is kind of a shelling point right now on HN and there isn't a clear guideline yet. I think your usage of flagging makes sense. Thanks
Granted it's often easy to tell on your own, but when I'm uncertain I use GPTZero's Chrome extension for this. Eventually I'll stop doing that and assume most of what I read outside of select trusted forums is genAI.
Love the sentiment! And I'm a user - but what about aggregations? Elasticsearch offers a ton of aggregates out of the box for "free" completely configurable by query string.
Tiger Data offers continuous aggs via hypertable but they need to be configured quite granularly and they're not super flexible. How are you all thinking about that when it comes to postgres and aggregations?
I love postgres and it really is a supertool. But to get exactly what you need can require digging deep and really having control over the lowest levels. My experience after using timescale/tigerdata for the last couple years is that I really just wish RDS supported the timescale extension; TigerData's layers on top of that have caused as many problems as they've solved.
Gad, they sure like to say "BM25" over and over again. That's a near worthless approach to result ranking. Doing any halfway ok job requires much more tuned and/or more powerful approaches.
This post is discussing more specialized databases, but why would people choose Oracle/Microsoft DB instead of Postgres? Your own experience is welcome.
I'd pick MSSQL if I was being compensated based upon the accuracy, performance, durability and availability of the system. Also in any cases where the customer can pay and won't complain even once about how much this costs.
I'd never advocate for a new oracle install. But, I'd likely defend an existing one. I've seen how effective pl/sql can be in complex environments. Rewriting all that sql just because "oracle man bad" (or whatever) is a huge fucking ask of any rational business owner.
Easy answer here - nearly every LOB app we have uses MSSQL.
I've had engineers want to talk about syncing it to MySQL using some custom plumbing so that they can build a reporting infra around their MySQL stack, but it's just another layer of complexity over having code just use Microsoft's reporting services.
I'll add, having finance people with Excel really like being able to pull data directly from MSSQL, they do not like hearing about a technican's python app.
I made the switch from MySQL to postgres a few years ago I didn't really understand what everyone was excited about before I made the switch. I haven't used MySQL since and I think postgres provides everything I need the only thing that I ever snarl at is how many dials and knobs and options there are that's not a bad thing!
> the only thing that I ever snarl at is how many dials and knobs and options there are that's not a bad thing!
yea this is me. postgres is actually insane for how much is offered at checks notes free.99.
_however_ we are probably due for like. I don't know a happy configurator type tool that has reasonable presets and a nice user friendly config tool that helps people get going without sidequesting for a year on devops/dbadmin expertise. that isn't even a favored outcome imo, you just get pretty lukewarm postgres-deployers who are probably missing a bunch of important settings/flags. my team mates would probably shit themselves in the face of postgres configs currently, they are absolute rats in the code but good and proper deployment of postgres is just a whole other career-arc they haven't journeyed and a _lot_ of organizations don't always have a devops/dbadmin type guy readily available any time you want to scrap together an app who's just going to wait for your signal to deploy for you. or said devops/dbadmin guy is just.. one guy and he's supporting 500 other things. not saying the absence/failing to scale teams with such personnel is right, it's just the reality and being up against workplace politics and making the case to convince orgs to hire a bigger team of devops/dbadmin guys involves a lot of shitty meetings and political prowess that is typically beyond an engineers set of capabilities, at least below the senior level. any engineer can figure out how to deploy postgres to something, but are they doing it in a way that makes an orgs security/infra guys happy? probably not. are they prepared to handle weird storage scenarios (log or temp space filling grinding server to a halt) and understand the weird and robust ways to manage a deployment? probably not.
Elixir + Postgres is the microservices killer...last time I saw VP try to convince a company with this stack to go microservices he was out in less than 6mo
Can anyone comment on whether postgres can replace full columnar DB? I see "full text search" but it feels like this is falling a little short of the full power of elastic -- but would be happy to be wrong (one less tech to remember).
I agree that managing lots of databases can be a pain in the ass, but trying to make Postgres do everything seems like a problem as well. A lot of these things are different things and trying to make Postgres do all of them seems like it will lead to similar if not worse outcomes than having separate dedicated services.
I understand that people were too overeager to jump on the MongoDB web scale nosql crap, but at this point I think there might have been an overcorrection. The problem with the nosql hype wasn't that they weren't using SQL, it's that they were shoehorning it everywhere, even in places where it wasn't a good fit for the job. Now this blog post is telling us to shoehorn Postgres everywhere, even if it isn't a good fit for the job...
Ok, I'm a little skeptical of that claim but let's grant it. I still don't think Postgres is going to do everything Kafka and Redis can do as well as Kafka or Redis.
I don't disagree, but I think big enterprises expect support, roadmaps, and the ability to ask for deliverables depending on the sale or context of the service.
Only if DuckDB is an acceptable value of PostgreSQL. I agree that PostgreSQL has eaten many DB use-cases, but the breathless hype is becoming exhausting.
Look. In a PostgreSQL extension, I can't:
1. extend the SQL language with ergonomic syntax for my use-case,
2. teach the query planner to understand execution strategies that can't be made to look PostgreSQL's tuple-and-index execution model, or
3. extend the type system to plumb new kinds of metadata through the whole query and storage system via some extensible IR.
(Plus, embedded PostgreSQL still isn't a first class thing.)
Until PostgreSQL's extension mechanism is powerful enough for me to literally implement DuckDB as an extension, PostgreSQL is not a panacea. It's a good system, but nowhere near universal.
Now, once I can do DuckDB (including its language extensions) in PostgreSQL, and once I can use the thing as a library, let's talk.
(pg_duckdb doesn't count. It's a switch, not a unified engine.)
Something TFA doesn’t mention, but which I think is actually the most important distinction of all to be making here:
If you follow this advice naively, you might try to implement two or more of these other-kind-of-DB simulacra data models within the same Postgres instance.
And it’ll work, at first. Might even stay working if only one of the workloads ends up growing to a nontrivial size.
But at scale, these different-model workloads will likely contend with one-another, starving one-another of memory or disk-cache pages; or you’ll see an “always some little thing happening” workload causing a sibling “big once-in-a-while” workload to never be able to acquire table/index locks to do its job (or vice versa — the big workloads stalling the hot workloads); etc.
And even worse, you’ll be stuck when it comes to fixing this with instance-level tuning. You can only truly tune a given Postgres instance to behave well for one type-of-[scaled-]workload at a time. One workload-type might use fewer DB connections and depend for efficiency on them having a higher `work_mem` and `max_parallel_workers` each; while another workload-type might use many thousands of short-lived connections and depend on them having small `work_mem` so they’ll all fit.
But! The conclusion you should draw from being in this situation shouldn’t be “oh, so Postgres can’t handle these types of workloads.”
No; Postgres can handle each of these workloads just fine. It’s rather that your single monolithic do-everything Postgres instance, maybe won’t be able to handle this heterogeneous mix of workloads with very different resource and tuning requirements.
But that just means that you need more Postgres.
I.e., rather than adding a different type-of-component to your stack, you can just add another Postgres instance, tuned specifically to do that type of work.
Why do that, rather than adding a component explicitly for caching/key-values/documents/search/graphs/vectors/whatever?
Well, for all the reasons TFA outlines. This “Postgres tuned for X” instance will still be Postgres, and so you’ll still get all the advantages of being able to rely on a single query language, a single set of client libraries and tooling, a single coherent backup strategy, etc.
Where TFA’s “just use Postgres” in the sense of reusing your Postgres instance only scales if your DB is doing a bare minimum of that type of work, interpreting “just use Postgres” in the sense of adding a purpose-defined Postgres instance to your stack will scale nigh-on indefinitely. (To the point that, if you ever do end up needing what a purpose-built-for-that-workload datastore can give you, you’ll likely be swapping it out for an entire purpose-defined PG cluster by that point. And the effort will mostly serve the purpose of OpEx savings, rather than getting you anything cool.)
And, as a (really big) bonus of this approach, you only need to split PG this way where it matters, i.e. in production, at scale, at the point that the new workload-type is starting to cause problems/conflicts. Which means that, if you make your codebase(s) blind to where exactly these workloads live (e.g. by making them into separate DB connection pools configured by separate env-vars), then:
- in dev (and in CI, staging, etc), everything can default to happening on the one local PG instance. Which means bootstrapping a dev-env is just `brew install postgres`.
- and in prod, you don’t need to pre-build with new components just to serve your new need. No new Redis instance VM just to serve your so-far-tiny KV-storage needs. You start with your new workload-type sharing your “miscellaneous business layer” PG instance; and then, if and when it becomes a problem, you migrate it out.
The point of Redis is data structures and algorithmic complexity of operations. If you use Redis well, you can't replace it with PostgreSQL. But I bet you can't replace memcached either for serious use cases.
As someone who is a huge fan of both Redis and Postgres, I whole heartedly agree with the "if you are using Redis well, you can't replace it with PostgreSQL" statement.
What I like about the "just use PostgreSQL" idea is that, unfortunately, most people don't use Redis well. They are just using it as a cache, which IMHO, isn't even equivalent to scratching the surface of all the amazing things Redis can do.
As we all know, it's all about tradeoffs. If you are only using Redis as a cache, then does the performance improvement you get by using it out weight the complexity of another system dependency? Maybe? Depends...
Side note: If you are using Redis for caching and queue management, those are two separate considerations. Your cache and queues should never live on the same Redis instance because the should have different max-memory policies! </Side note>
The newest versions of Rails have really got me thinking about the simplicity of a PostgreSQL only deployment, then migrating to other data stores as needed down the line. I'd put the need to migrate squarely into the "good problems" to have because it indicates that your service is growing and expanding past the first few stages of growth.
All that being said, man I think Redis is sooooo cool. It's the hammer I am always for a nail to use on.
well, redis is a bit of a junk bin of random barely related tools. It's just very likely that any project of non-trivial complexity will need at least some of them and I wouldn't necessarily advocate for trying jerry-rigging most of them in postgresql like the author of article, for example why would anyone want wasting their SQL DB server performance on KV lookups?
“well” is doing a lot of heavy lifting in your comment. Across a number of companies using Redis, I’ve never seen it used correctly. Adding it to the tech stack is always justified with hand waving about scalability.
They may be its point, but I frankly didn't see much use in the wild. You might argue that then those systems didn't need Redis in the first place and I'd agree, but then note that that is the point tigerdata makes.
edit: it's not about serious uses, it's about typical uses, which are sad (and same with Kafka, Elastic, etc, etc)
All the time here in HN, I'm proud of it -- happy to have opinions not necessarily aligned with what users want to listen to. Also: never trust the establishment without thinking! ;D
I was one of the downvoters, and at the time I downvoted it, it was a very different comment. this is the original (copied from another tab that I hadn't refreshed yet):
> Tell me you don't understand Redis point is data structures without telling me you don't understand Redis point is data structures.
regardless of the author, I think slop of that sort belongs on reddit, not HN.
HA is not about exceeding the limits of a server. Its about still serving traffic when that best server I bought goes offline (or has failed memory chip, or a disk or... ).
Postgres replication, even in synchronous mode, does not maintain its consistency guarantees during network partitions. It's not a CP system - I don't think it would actually pass a Jepsen test suite in a multi-node setup[1]. No amount of tooling can fix this without a consensus mechanism for transactions.
Same with MySQL and many other "traditional" databases. It tends to work out because these failures are rare and you can get pretty close with external leader election and fencing, but Postgres is NOT easy (likely impossible) to operate as a CP system according to the CAP theorem.
There are various attempts at fixing this (Yugabyte, Neon, Cockroach, TiDB, ...) which all come with various downsides.
This is the future of all devtools in the AI era. There's no reason for tool innovation because we'll just use whatever AIs know best which will always be the most common thing in their training data. It's a self-reinforcing loop. The most common languages, tools, libraries of today are what we will be stuck with for the foreseeable future.
Lots of familiar things here except for this UNLOGGED table as a cache thing. That's totally new to me. Has someone benched this approach against memcached and redis ? I'm extremely skeptical PGs query / protocol overheads are going to be competitive with memcached, but I'm making this up and have nothing to back it up.
They don't compare exactly. Author is mistaken in thinking that UNLOGGED means "in memory". It means "no WAL", so there's considerable speed up there, but traded in with also more volatility. To be a viable alternative to Redis or Memcached though, the savings you get from the latter two must really be superfluous to your use case. Which could be true for many (most?).
Its not only about performance, Redis data structures offer an even more advanced caching and data processing. I even use Redis as a cache for ClickHouse.
Nice! How do you "preinstall the extensions" so that you can have eg timescaledb and others available to install in your Postgres? Do you need to install some binaries first?
Good stuff, I turned my gist into an info site and searchable directory (and referenced this article as well, which seems to pay homage to my gist, which in turn inspired the site)
I recently started digging into databases for the first time since college, and from a novice's perspective, postgres is absolutely magical. You can throw in 10M+ rows across twenty columns, spread over five tables, add some indices, and get sub-100ms queries for virtually anything you want. If something doesn't work, you just ask it for an analysis and immediately know what index to add or how to fix your query. It blows my mind. Modern databases are miracles.
I do agree, I don’t know why more people don’t just use Postgres. If I’m doing data exploration with lots of data (e.g., GIS, nD vectors), I’ll just spin up a Postgres.app on my macOS laptop, install what little I need, and it just works and is plenty fast for my needs. It’s a really great choice for a lot of domains.
That being said, while I think Postgres is “the right tool for the job” in many cases, sometimes you just want (relative) simplicity, both in terms of complexity and deployment, and should use something like SQLite. I think it’s unwise to understate simplicity, and I use it to run a few medium-traffic servers (at least, medium traffic for the hardware I run it on).
> in many cases, sometimes you just want (relative) simplicity, both in terms of complexity and deployment, and should use something like SQLite.
So many times when trying to just go for simplicity with SQLite it takes me like one working day until I run up against enough annoyances to where resolving those is more work than setting up the "set up and forget" postgres instance.
Granted, this is for personal stuff... but "Postgres packaged for low maintenance" is present in a lot of OS package managers! Even for smaller data analysis work SQLite perf leads _loads_ to be desired (once had QGIS struggling with a sqlite DB... pg made everything mostly instant. Indices etc... but stuff I _couldn't easily get with sqlite_)
If SQLite works for you that's great, I do think it's worth it for people to _try_ to do simple pg setups to understand just how painful it is to use pg (for me: not that high)
Oh, yes, that's one of my points! I think Postgres is a great way to deal with tons of data, and it's really the only thing I use to do any sort of analysis or informatics (that and Parquet + Dask).
I am also a fan of SQLite. One of the best parts during development is how easy it is to spin up and spin down databases for full integration tests without containers or anything. It also simplifies backups, and is probably good enough.
These days I would recommend PGlite for testing purposes when you use Postgres in production. That way you don't need any specific SGQLite vs Postgres behavior switches.
Where I have used SQLite most successfully is really two use cases. First, I use it for data processing. Say I need to retrieve lots of data and transform it to a different setup. I could do that in something like Python but SQL is just more expressive for that and I can also create a new database, populate it with data I have, fetch new data, combine it together, export the update to a permanent data store (usually Postgres).
Second, when I need a local save file. Sometimes small local apps are better served by a save file and they save file might as well have an extensible format that I can update as I go. This is more rare but still can be useful.
The first use case is very powerful. A temporary SQL database that can be blown away with zero trace of it is great. And the ability to run complex queries on it can really help.
But 99% of the time I just use Postgres. It works, it has sane defaults, it is crazy extensible, and it has never not met my needs, unlike Oracle or MySQL.
DuckDB via Python is my go-to for that first use case. It’s easier than ever to use Python and SQL together with Marimo notebooks.
``` uv run --with marimo marimo run --sandbox ```
and you’re ready to go.
A bit off topic but the one thing I've never been able to figure out with Postgres easily & reliably is what magic incantations allow a user account full access to a specific database but not to others, particularly in cases of managed postgres offered by cloud providers. `GRANT ALL PRIVILEGES` never seems to work.
Having to look up and spend time fixing permissions every time itself makes using Postgres for simple uses difficult for me but if you're using it ad hoc, any tips?
Just let Claude fuck it up for you and learn from its mistakes.
I wish PostgreSQL had a native vector implementation instead of using extensions. They're kind of a pain in the ass to maintain, especially with migrations.
Standardizing on one single tiny little project is always a bad idea. Why? Some examples (which are admittedly not related to postgres, because I don't know their structure):
1) A single person, doing a ton of heavy lifting, leaves, or worse, turns it over, or silently sells out to a nefarious person.
2) A severe security vulnerability is found. If everyone is using postgres, everyone is vulnerable. Bonus points if the vulnerability is either not publicly disclosed or it is hard to fix.
3) Commercial/Government interests heavily influence and push the project into places that could make it vulnerable in any given way. This is absolutely a thing.
4) AI. No clarification here. Just use your imagination, with recent news regarding FFMPEG and other projects in mind.
I'm not sure I would call either PostgreSQL or SQLite "tiny."
i think the topic of "what data backend" gets super conflated into many different variations of what the hell people need it for. discussions here go so many different directions. some people are building simple webapps, some are building complex webapps that need to scale for a gazillion users, some are building local apps, some are just tinkering, some are thinking broadly about how their backend needs to sync with a datalake->some data warehouse at an org, yadda yadda ya.
i personally like postgres myself for just about all use cases that must be shared with others (app with more than one client that might be providing CRUD updates or anything really that demands a central data store). ive used sqlite a couple times with WAL to try and make a small app shared between 2-3 people who all would contribute updates thru it but it wasnt ideal. for postgres so many features/extensions its concurrent writes are fast as hell and if you just want to one-shot a solution then you cant go wrong, but it's ofc not the same as sqlite setup.
i think a lot of the pain with postgres is just learning to effectively be a knowledgeable db admin of sorts. its somewhere between being a competent devops guy and a dbadmin expert all in one. if you're actually doing some kind of production deployment it is kinda scary hoping you've got everything set up right. even supabase which makes this whole process trivial to get going requires an understanding of not-always-understood security premises that just make things spooky.
lot of words to say i dont get much out of these discussions tbh. theres just too many use cases and variables in everyones working/hobby lives im not sure that there is a proverbial bottom to any of it. some will use sqlite and some will use postgres and some will use some weird thing no ones heard of because they're afraid to rawdog sql and just want immediate graphql capability to be the main mode of data retrieval. some will show up here and talk about why you need redis in the middle.
its too much noise so i just keep using postgres because its free and boring and fast. end of the day i just want to make stuff people can use. it's a hard endeavor to do well alone, if you dont have a team of other experts who can help you put all the puzzle pieces together on how to deploy things the right way and also add pieces like redis or whatever... it's just a lot. it's hard to find where to get started. sqlite is the only solution that really by nature of what it is seems to champion the lonely developer, but the tradeoffs are big if you're trying to make something that should get used by many people.
See also, PGLite: https://pglite.dev/
And Turso: https://turso.tech/
Because it's not web scale, mongoDB is web scale
You turn it on, and it scales right up.
Who cares what we store so long as we do it quickly?
I think it's disgenious by the author to publish this article heavily edited by AI and not disclose it.
I’m a huge Postgres fan. That said, I don’t agree with the blanket advice of “just use Postgres.” That stance often comes from folks who haven’t been exposed enough to (newer) purpose-built technologies and the tremendous value they can create
The argument, as in this blog, is that a single Postgres stack is simpler and reduces complexity. What’s often overlooked is the CAPEX and OPEX required to make Postgres work well for workloads it wasn’t designed for, at even reasonable scale. At Citus Data, we saw many customers with solid-sized teams of Postgres experts whose primary job was constant tuning, operating, and essentially babysitting the system to keep it performing at scale.
Side note, we’re seeing purpose-built technologies show up much earlier in a company’s lifecycle, likely accelerated by AI-driven use cases. At ClickHouse, many customers using Postgres replication are seed-stage companies that have grown extremely quickly. We pulled together some data on these trends here: https://clickhouse.com/blog/postgres-cdc-year-in-review-2025...
A better approach would be to embrace the integration of purpose-built technologies with Postgres, making it easier for users to get the best of both worlds, rather than making overgeneralized claims like “Postgres for everything” or “Just use Postgres.”
I took it to mean “make Postgres your default choice”, not “always use Postgres no matter what”
I personally see a difference between “just use Postgres” and “make Postgres your default choice.” The latter leaves room to evaluate alternatives when the workload calls for it, while the former does not. When that nuance gets lost, it can become misleading for teams that are hitting or even close to hitting—the limits of Postgres, who may continue tuning Postgres spending not only time but also significant $$. IMO a better world is one where developers can have a mindset of using best-in-class where needed. This is where embracing integrations with Postgres will be helpful!
I think that the key point being made by this crowd, of which I'm one, is somewhere in the middle. The way I mean it is "Make Postgres your default choice. Also *you* probably aren't doing anything special enough to warrant using something different".
In other words, there are people and situations where it makes sense to use something else. But most people believing they're in that category are wrong.
The point is really that you can only evaluate which of alternatives is better once you have working product with data big enough - else it's just basically following trends and hoping your barely informed decision won't be wrong.
Agree to disagree here. I see a world where developers need to think about (reasonable) scale from day one, or at least very early. We’ve been seeing this play out at ClickHouse - the need for purpose-built OLAP is reducing from years to months. Also integration with ClickHouse is few weeks of effort for potentially significantly faster performance for analytics.
Reasonable scale means... what exactly?
Here's my opinion: just use postgres. If you're experienced enough to not when I say that, go for it, the advice isn't for you. If you aren't, I'm probably saving you from yourself. "Reasonable scale" to these people could mean dozens of inserts per second, which is why people talking vagueries around scale is madenning to me. If you aren't going to actually say what that means, you will lead people who don't know better down the wrong path.
I think you should mention ClickHouse a few more times, otherwise it's not clear to us what tremendous value it provides
That wasn’t my intention though to mention my workplace multiple times for the sake of PR. Try to avoid being that, atleast on hn. :)
What I really want to know is, is it mighty mighty? And does it let it all hang out?
> I don’t agree with the blanket advice of “just use Postgres.”
I take it as meaning use Postgres until there's a reason not to. ie build for the scale / growth rate you have not "how will this handle the 100 million users I dream of." A simpler tech stack will be simpler to iterate on.
Yes. That's a good framing. PostgreSQL is a good default for online LOB-y things. There are all sorts of reasons to use something other than PostgreSQL, but raw performance at scale becomes such a reason later than you think.
Cloud providers will rent you enormous beasts of machines that, while expensive, will remain cheaper than rewriting for a migration for a long time.
In my experience the functionality of “purpose built systems” is found in Postgres but you have to read the manual.
I personally think reading manuals and tuning is a comparably low risk form of software development.
Exactly. Use cases differ. https://www.geeksforgeeks.org/mysql/difference-between-mysql...
I hate how developers are often very skeptical but all the skepticism goes out the window if the tech is sufficiently hyped up.
And TBH, developers are pretty dumb not to realize that the tech tools monoculture is a way for business folks to make us easily replaceable across different organizations... If all companies use the same tech, it turns us into exchangeable commodities which can easily be replaced and sourced across different organizations.
Look at the typical React dev. They have zero leverage and can be replaced by vibe coding kiddies straight out of school or sourced from literally any company on earth. And there are some real negatives to using silver bullet tools. They're not even the best tools for a lot of cases! The React dev is a commodity and they let it happen to them. Outsmarted by dumb business folks who dropped out of college. Be smarter people.
> At Citus Data, we saw many customers with solid-sized teams of Postgres experts whose primary job was constant tuning, operating, and essentially babysitting the system to keep it performing at scale.
Oh no, not a company hiring a team of specialist in a core technology you need! What next, paying them a good wage? C'mon, it's so much better to get a bunch of random, excuse me, "specialized" SaaS tools that will _surely_ not lead to requiring five teams of specialists in random technologies that will eventually be discontinued once Google acquires the company running them.
OK but seriously, yeah sometimes "specialized" is good, though much less rarely than people pretend it to be. Having specialists ain't bad, and I'd say is better than telling a random developer to become a specialist in some cloud tech and pretending you didn't just end up turning a - hopefully decent - developer into a poor DBA. Not to mention that a small team of Postgres specialists can maintain a truly stupendous amount of Postgres.
This kind of thing gets posted every couple of months. Databases like Pinecone and Redis are more cost-effective and capable for their special use case, often dramatically so. In some circumstances the situation favours solving the problem in Postgres rather than adding a database. But that should be evaluated on a case-by-case basis. For example, if you run something at scale and have an ops team the penalty of adding a second database is much smaller.
(I run a medium-sized Postgres deployment and like it, but I don't feel like it's a cost-effective solution to every database problem.)
Once you have an app, a lot of data, and actual problems, it's far easier to pick the right alternative.
PostgreSQL is good enough to get to medium sized with nearly every use case. Once you are there, you have the use case and the test data to test any alternative for it well, rather than trying to guess beforehand what you actually need.
The advice is basically "PostgreSQL is probably good enough for whatever you're building now, and you should only look for other solution once you are big enough that it stops being that"
It's 2026, just use Planetscale Postgres
I have two fundamental problems with Postgres - an excellent piece of technology, no questions about that.
First, to use Postgres for all those cases you have to learn various aspects of Postgres. Postgres isn't a unified tool which can do everything - instead it's a set of tools under the same umbrella. As a result, you don't save much from similarly learning all those different systems and using Postgres only as a RDBMS. And if something isn't implemented in Postgres better than in a 3rd party system, it could be easier to replace that 3rd party system - just one part of the system - rather than switching from Postgres-only to Postgres-and-then-some. In other words, Postgres has little benefits when many technologies are needed comparing with the collection of separate tools. The article notwithstanding.
Second, Postgres is written for HDDs - hard disk drives, with their patterns of data access and times. Today we usually work with SSDs, and we'd benefit from having SSD-native RDBMSes. They exist, and Postgres may lose to them - both in simplicity and performance - significantly enough.
Still, Postgres is pretty good, yes.
I've actually started moving away from Postgres to MySQL and SQLite. I don't want to have to deal with the vacuums/maintenance/footguns.
MySQL is definitely easier to use if you don’t want to ever have to think about DB maintenance; on the other hand, you’re giving up a TON of features that could make your queries enormously performant if your schema is designed around them - like BRIN indices, partial indices, way better partition management, etc.
OTOH, if and only if you design your schema to exploit MySQL’s clustering index (like for 1:M, make the PK of the child table something like (FK, some_id)), your range scans will become incredibly fast. But practically no one does that.
As someone who learned to think in MySQL, this is really true, at the time Postgres was a viable alternative too, only the tooling to get started reached me a little easier and quicker.
The major thing I advocate for is don't pick a NOSQL database to avoid relational dbs, only to try and do a bunch of relational work in NOSQL that would have been trivial in an RBDMS. Postgres can even power graph query results which is great.
I’ve thought of doing this myself. Upgrades also seem easier in MySQL. That said, it does seem to be stagnating relative to Postgres.
SQLite is awesome, no services to run, ports to open or anything like that, just a nice little database to use as you need.
I'm in the same boat, the idiosyncrasies of postgres are real; mysql / sqlite are far more predictable.
If you think Postgres has idiosyncrasies…
https://sqlite.org/quirks.html
Which one is an issue?
There's a fascinating gap between PostgreSQL theory and practice here. Elsewhere in this thread, I complained that PostgreSQL extensions can't do everything yet. One thing they can do, however, or ought to be able to do, is provide alternative storage engines. That's the central thing they're supposed to be especially good at providing.
So what did the VACUUM-free, undo-based MVCC storage engine project stall? https://wiki.postgresql.org/wiki/Zheap
Why is there no InnoDB for PostgreSQL?
(Maybe OrioleDB will avoid a similar fate.)
Now we only need easy self-hosted Postgres clustering for HA. Postgres seems to need additional tooling. There is Patroni, which doesn't provide container images. There is Spilo, which provides Postgres images with Patroni, but they are not really maintained. There is a timescaledb-ha image with Patroni, but no documentation how to use it. It seems the only easy way for hosting a Postgres cluster is to use CloudNativePG, but that requires k8s.
It would be awesome to have easy clustering directly built-in. Similar to MongoDB, where you tell the primary instance to use a replica set, then simply connect two secondaries to primary, done.
Postgres is not a CP database, and even with synchronous replication, it can lose writes during network partitions. It would not pass the Jepsen test suite.
This is very hard to fix and requires significant architectural changes (like Yugabyte or Neon have done).
Caching is mentioned in the article: What do you guys feel about using PostgreSQL for caching instead of Redis?
Redis is many times faster, so much that it doesn't seem comparable to me.
A lot of data you can get away with just caching in-mem on each node, but when you have many nodes there are valid cases where you really want that distributed cache.
Neither.
Just use memcache for query cache if you have to. And only if you have to, because invalidation is hard. It's cheap, reliable, mature, fast, scalable, requires little understanding, has decent quality clients in most languages, is not stateful and available off the shelf in most cloud providers and works in-clusetr in kubernetes if you want to do it that way.
I can't find a use case for Redis that postgres or postgres+memcache isn't a simpler and/or superior solution.
Just to give you an idea how good memcache is, I think we had 9 billion requests across half a dozen nodes over a few years without a single process restart.
is there anything memcache gives you that a redis instance configured with an eviction policy of allkeys-lru doesn't give you
memcached is multithreaded, so it scales up better per node.
memcached clients also frequently uses ketama consistent hashing, so it is much easier to do load/clustering, being much simpler than redis clustering (sentinel, etc).
Mcrouter[1] is also great for scaling memcached.
dragonfly, garnet, and pogocache are other alternatives too.
[1]: https://github.com/facebook/mcrouter
I imagine the answer here is: less complexity.
Prove that you need the extra speed.
Run benchmarks that show that, for your application under your expected best-case loads, using Redis for caching instead of PostgreSQL provides a meaningful improvement.
If it doesn't provide a meaningful improvement, stick with PostgreSQL.
This is the proper approach when deciding whether to use any type of tool or technology. Is the increased amount of cognitive overhead for someone with minimal exposure to your system (who will have to maintain it when you’ve moved on) worth the increased performance on a dollars-per-hour basis? If so, it may be a good option. If not, it doesn’t matter how much better the relative performance is.
Yep. And often, if you do find a need for a cache, you can get away with an in-app cache before moving to something like Redis.
If you want to compare Redis and PostgreSQL as a cache, be sure to measure an unlogged table, as suggested in the article. Much of the slowness of PostgreSQL is to ensure durability and consistency after a crash. If that isn't a concern, disable it. Unlogged tables are automatically truncated after a crash.
Materialized views work pretty well in Postgres. But yes at some level of load it’s just helpful to have the traffic shared elsewhere.
But As soon as you go outside Postgres you cannot guarantee consistent reads within a transaction.
That’s usually ok, but it’s a good enough reason to keep it in until you absolutely need to.
Depends on your app cache needs. If it's moderate, I'd start with postgres...ie. not have operate another piece of infra and the extra code. If you are doing the shared-nothing app server approach (rails, django) where the app server remembers nothing after each request Redis can be a handy choice. I often go with having a fat long lived server process (jvm) where it also acts for my live caching needs. #tradeoffs
I say do it, if it simplifies the architecture. For example if you are using firestore with a redis cache layer, that's 2 dbs. If you can replace 2 dbs with 1 db (postgres), I think it's worth it. But if you are suggesting using a postgres cache layer in front of firestore instead of redis... to me that's not as clear cut.
Depends how much you have to cache and how much speed you really need from it.
I like Redis a lot, but for things in the start I'm not sure the juice is always worth the squeeze to get it setup and manage another item in teh stack.
Luckily, search is something that has been thought about and worked on for a while and there's lots of ways to slice it initially.
I'm probably a bit biased though from past experiences from seeing so many different search engines shimmed beside or into a database that there's often an easier way in the start than adding more to the stack.
Skeptical about replacing Redis with a table serialized to disk. The point of Redis is that it is in memory and you can smash it with hot path queries while taking a lot of load off the backing DB. Also that design requires a cron which means the table could fill disk between key purges.
From the article, using UNLOGGED tables puts them in memory, not on disk
I the article is wrong. UNLOGGED means it isn't written to WAL which means recovery and rollback guarantees won't work since the transaction can finish before the page can be synchronized on disk. The table loses integrity as a trade off for a faster write.
https://www.postgresql.org/docs/current/sql-createtable.html...
I am looking for a db that runs using existing json/yaml/csv files, saves data back to those files in a directory, which I can sync using Dropbox or whatever shared storage. Now I can run this db wherever I am & run the application. Postgres feels a bit more for my needs
Why? Why would separate json/yaml/csv files be better than just... syncing using postgres itself? You point `psql` to the host you need, because clearly you have internet access even on the go, and done: you don't need to sync anything, you already have remote access to the database?
I've found that Postgres consumes (by default) more disk than, for example, MySQL. And the difference is quite significant. That means more money that I have to pay every month. But, sure Postgres seems like I system that integrates a lot of subsystems, that adds a lot of complexity too. I'm just marking the bad points because you mention the good points in the post. You're also trying to sell you service, which is good too.
The problem is that Postgres uses something like 24B overhead per row. That is not a issue with small Tables, but when your having a few billion around, each byte starts to add up fast. Then you a need link tables that explode that number even more, etc ... It really eats a ton of data.
At some point you end up with binary columns and custom encoded values, to save space by reducing row count. Kind of doing away with the benefits of a DB.
On flipside, restore from plain postgresql dump is much, much faster than plain mysql backup. There are alternative strategies for mysql but that's extra work
Some people do Postgres on compressed ZFS volumes to great success.
On average I get around 4x compression on PostgreSQL data with zstd-1
I am curious if you know anyone using Btrfs for this too. I like ZFS, but it Btrfs can do this it would be easier to use with some distros, etc. as it's supported in kernel.
I do it.
The big problem for me from running DB on Btrfs is that when I delete large dirs or files (100GB+), it locks disk system, and Db basically stop responding on any queries.
I am very surprised that FS which is considered prod grade having this issue..
Try XFS if you havn’t yet.
Very solid and no such issues.
I haven't used XFS in almost two decades, does it have compression support in the same way? Also, does it do JBOD stuff? I know it's a bit of a different thing, but I really enjoy the pool many disks together part of Btrfs, although it has its limitations.
XFS doesn't have inline compression, nor does it have volume management functionality. It's a nice filesystem (and it's very fast) but it's just a filesystem.
No compression.
I'll take it one step further and say you should always ask yourself if the application or project even needs a beefy database like Postgres or if you can get by with using SQLite. For example, I've found a few self-hosted services that just overcomplicated their setup and deployment because they picked Postgres or MariaDB over SQLite, despite it being a much better self-contained solution.
I find that if I want to use JSON storage I'm somewhat stuck choosing my DB stack. If I want to use JSON, and change my database from SQLite to Postgres I have to substantially change my interface to the DB. If I use only SQLite, or only Postgres it's not so bad, but the transition cost to "efficient" JSON use in Postgres from a small demo in SQLite is kind of high compared to just starting with an extra docker run (for a Postgres server) / docker compose / k8s yaml / ... that has my code + a Postgres database.
I really like having some JSON storage because I don't know my schema up front all the time, and just shoving every possible piece of potentially useful metadata in there has (generally) not bit me, but not having that critical piece of metadata has been annoying (that field that should be NOT NULL is NULL because I can't populated it after the fact).
It's 5th of feb 2026, and we already get our monthly "just use postgres" thread
btw, big fan of postgres :D
Postgres can definitely handle a lot of use cases; background job scheduling always had me tempted to reach for something like rabbitmq but so far happy enough with riverqueue[0] for Go projects.
[0]: https://riverqueue.com/
Pinecone allows hybrid search, merging dense and sparse vector embeddings that Postgres can't do AFAIK. That results in ~10% worse retrieval scores which might be the difference between making it in the business or not.
The lesson for me isn't "don't use Pinecone", but more like "did you already max out Postgres?"
In many cases, it is going to save you time by having less infra and one less risk while you're getting started. And if you find yourself outgrowing the capabilities of Pg then you look for an alternative.
Blog posts, like academic papers, should have to divulge how AI has been used to write them.
People used to write Medium/Linkedin slop by hand and they didn't have to disclose it. Slopping is its own punishment.
You're absolutely right! Let's delve into why blog posts like this highlight the conflict between the speed and convenience of AI and authentic human expression. Because it's not just about fears of hallucination—it's about ensuring the author's voice gets heard. Clearly. Completely. Unmistakably.
If nothing else, I sure got amusement from this.
Yes this is clearly verbatim output from an LLM.
But it's perfect HN bait, really. The title is spicy enough that folks will comment without reading the article (more so than usual), and so it survives a bit longer before being flagged as slop.
Is HN guidelines to flag AI content? I am unsure of how flagging for this is supposed to work on HN and have only ever used the flag feature for obvious spam or scams.
It might be wrong, but I have started flagging this shit daily. Garbage articles that waste my time as a person who comes on here to find good articles.
I understand that reading the title and probably skimming the article makes it a good jumping off point for a comment thread. I do like the HN comments but I don't want it to be just some forum of curious tech folks, I want it to be a place I find interesting content too.
I agree. It seems this is kind of a shelling point right now on HN and there isn't a clear guideline yet. I think your usage of flagging makes sense. Thanks
Granted it's often easy to tell on your own, but when I'm uncertain I use GPTZero's Chrome extension for this. Eventually I'll stop doing that and assume most of what I read outside of select trusted forums is genAI.
Love the sentiment! And I'm a user - but what about aggregations? Elasticsearch offers a ton of aggregates out of the box for "free" completely configurable by query string.
Tiger Data offers continuous aggs via hypertable but they need to be configured quite granularly and they're not super flexible. How are you all thinking about that when it comes to postgres and aggregations?
I love postgres and it really is a supertool. But to get exactly what you need can require digging deep and really having control over the lowest levels. My experience after using timescale/tigerdata for the last couple years is that I really just wish RDS supported the timescale extension; TigerData's layers on top of that have caused as many problems as they've solved.
Oh wow, the "Postgres for Developers, Devices, and Agents" company wants us to use Postgres?
Postgres has its merits without the company who's also enjoying using it.
Gad, they sure like to say "BM25" over and over again. That's a near worthless approach to result ranking. Doing any halfway ok job requires much more tuned and/or more powerful approaches.
Can you please elaborate why?
It's common to do a hybrid of BM25 with other fuzzy search or pgvector.
BM25 is quite bad and needs to be retrained for each corpus anew. SPLADEv2 is much better and there are even better sparse embeddings these days.
See my Comment on why hybrid db's like tigerDB(data) are good
- https://news.ycombinator.com/item?id=46876037
This post is discussing more specialized databases, but why would people choose Oracle/Microsoft DB instead of Postgres? Your own experience is welcome.
I'd pick MSSQL if I was being compensated based upon the accuracy, performance, durability and availability of the system. Also in any cases where the customer can pay and won't complain even once about how much this costs.
I'd never advocate for a new oracle install. But, I'd likely defend an existing one. I've seen how effective pl/sql can be in complex environments. Rewriting all that sql just because "oracle man bad" (or whatever) is a huge fucking ask of any rational business owner.
Easy answer here - nearly every LOB app we have uses MSSQL.
I've had engineers want to talk about syncing it to MySQL using some custom plumbing so that they can build a reporting infra around their MySQL stack, but it's just another layer of complexity over having code just use Microsoft's reporting services.
I'll add, having finance people with Excel really like being able to pull data directly from MSSQL, they do not like hearing about a technican's python app.
I made the switch from MySQL to postgres a few years ago I didn't really understand what everyone was excited about before I made the switch. I haven't used MySQL since and I think postgres provides everything I need the only thing that I ever snarl at is how many dials and knobs and options there are that's not a bad thing!
> the only thing that I ever snarl at is how many dials and knobs and options there are that's not a bad thing!
yea this is me. postgres is actually insane for how much is offered at checks notes free.99.
_however_ we are probably due for like. I don't know a happy configurator type tool that has reasonable presets and a nice user friendly config tool that helps people get going without sidequesting for a year on devops/dbadmin expertise. that isn't even a favored outcome imo, you just get pretty lukewarm postgres-deployers who are probably missing a bunch of important settings/flags. my team mates would probably shit themselves in the face of postgres configs currently, they are absolute rats in the code but good and proper deployment of postgres is just a whole other career-arc they haven't journeyed and a _lot_ of organizations don't always have a devops/dbadmin type guy readily available any time you want to scrap together an app who's just going to wait for your signal to deploy for you. or said devops/dbadmin guy is just.. one guy and he's supporting 500 other things. not saying the absence/failing to scale teams with such personnel is right, it's just the reality and being up against workplace politics and making the case to convince orgs to hire a bigger team of devops/dbadmin guys involves a lot of shitty meetings and political prowess that is typically beyond an engineers set of capabilities, at least below the senior level. any engineer can figure out how to deploy postgres to something, but are they doing it in a way that makes an orgs security/infra guys happy? probably not. are they prepared to handle weird storage scenarios (log or temp space filling grinding server to a halt) and understand the weird and robust ways to manage a deployment? probably not.
Just use sqlite until you can’t.
Then use Postgres until you can’t.
Elixir + Postgres is the microservices killer...last time I saw VP try to convince a company with this stack to go microservices he was out in less than 6mo
This is the killer combo. Working on something now that uses pgmq + Elixir for DAG workflows: https://github.com/agoodway/pgflow
Can anyone comment on whether postgres can replace full columnar DB? I see "full text search" but it feels like this is falling a little short of the full power of elastic -- but would be happy to be wrong (one less tech to remember).
There are several different plugins for postgres that do columnar tables, including the company TigerData which wrote this blog
According to the LLM google search result, yes.
Have you looked into it?
probably not many Firebase users here but I love Firebase's Firestore
It's very bad to search stuff, or have structured database, also it costs a lot
its dirt cheap and you can do basic search
Do tell about all your greenfield yet large scale persistence needs where this discussion even applies
Unless you're doing OLTP. Then, TigerBeetle ;)
postgres is great, but its not great at sharding tables.
Postgres is king of its own, other solutions can be incorporated in it eventually by someone or some organization, that's it
Meh.
I agree that managing lots of databases can be a pain in the ass, but trying to make Postgres do everything seems like a problem as well. A lot of these things are different things and trying to make Postgres do all of them seems like it will lead to similar if not worse outcomes than having separate dedicated services.
I understand that people were too overeager to jump on the MongoDB web scale nosql crap, but at this point I think there might have been an overcorrection. The problem with the nosql hype wasn't that they weren't using SQL, it's that they were shoehorning it everywhere, even in places where it wasn't a good fit for the job. Now this blog post is telling us to shoehorn Postgres everywhere, even if it isn't a good fit for the job...
to be fair, Postgres cana basically do everything Mongo can and just as well.
Ok, I'm a little skeptical of that claim but let's grant it. I still don't think Postgres is going to do everything Kafka and Redis can do as well as Kafka or Redis.
pgmq gets very close for a lot of Kafka use cases
I really wonder how "It's year X" could establish itself as an argument this popular.
I don't disagree, but I think big enterprises expect support, roadmaps, and the ability to ask for deliverables depending on the sale or context of the service.
Only if DuckDB is an acceptable value of PostgreSQL. I agree that PostgreSQL has eaten many DB use-cases, but the breathless hype is becoming exhausting.
Look. In a PostgreSQL extension, I can't:
1. extend the SQL language with ergonomic syntax for my use-case,
2. teach the query planner to understand execution strategies that can't be made to look PostgreSQL's tuple-and-index execution model, or
3. extend the type system to plumb new kinds of metadata through the whole query and storage system via some extensible IR.
(Plus, embedded PostgreSQL still isn't a first class thing.)
Until PostgreSQL's extension mechanism is powerful enough for me to literally implement DuckDB as an extension, PostgreSQL is not a panacea. It's a good system, but nowhere near universal.
Now, once I can do DuckDB (including its language extensions) in PostgreSQL, and once I can use the thing as a library, let's talk.
(pg_duckdb doesn't count. It's a switch, not a unified engine.)
fair points made but I use sqlite for many things because sometimes you just need a tent
Something TFA doesn’t mention, but which I think is actually the most important distinction of all to be making here:
If you follow this advice naively, you might try to implement two or more of these other-kind-of-DB simulacra data models within the same Postgres instance.
And it’ll work, at first. Might even stay working if only one of the workloads ends up growing to a nontrivial size.
But at scale, these different-model workloads will likely contend with one-another, starving one-another of memory or disk-cache pages; or you’ll see an “always some little thing happening” workload causing a sibling “big once-in-a-while” workload to never be able to acquire table/index locks to do its job (or vice versa — the big workloads stalling the hot workloads); etc.
And even worse, you’ll be stuck when it comes to fixing this with instance-level tuning. You can only truly tune a given Postgres instance to behave well for one type-of-[scaled-]workload at a time. One workload-type might use fewer DB connections and depend for efficiency on them having a higher `work_mem` and `max_parallel_workers` each; while another workload-type might use many thousands of short-lived connections and depend on them having small `work_mem` so they’ll all fit.
But! The conclusion you should draw from being in this situation shouldn’t be “oh, so Postgres can’t handle these types of workloads.”
No; Postgres can handle each of these workloads just fine. It’s rather that your single monolithic do-everything Postgres instance, maybe won’t be able to handle this heterogeneous mix of workloads with very different resource and tuning requirements.
But that just means that you need more Postgres.
I.e., rather than adding a different type-of-component to your stack, you can just add another Postgres instance, tuned specifically to do that type of work.
Why do that, rather than adding a component explicitly for caching/key-values/documents/search/graphs/vectors/whatever?
Well, for all the reasons TFA outlines. This “Postgres tuned for X” instance will still be Postgres, and so you’ll still get all the advantages of being able to rely on a single query language, a single set of client libraries and tooling, a single coherent backup strategy, etc.
Where TFA’s “just use Postgres” in the sense of reusing your Postgres instance only scales if your DB is doing a bare minimum of that type of work, interpreting “just use Postgres” in the sense of adding a purpose-defined Postgres instance to your stack will scale nigh-on indefinitely. (To the point that, if you ever do end up needing what a purpose-built-for-that-workload datastore can give you, you’ll likely be swapping it out for an entire purpose-defined PG cluster by that point. And the effort will mostly serve the purpose of OpEx savings, rather than getting you anything cool.)
And, as a (really big) bonus of this approach, you only need to split PG this way where it matters, i.e. in production, at scale, at the point that the new workload-type is starting to cause problems/conflicts. Which means that, if you make your codebase(s) blind to where exactly these workloads live (e.g. by making them into separate DB connection pools configured by separate env-vars), then:
- in dev (and in CI, staging, etc), everything can default to happening on the one local PG instance. Which means bootstrapping a dev-env is just `brew install postgres`.
- and in prod, you don’t need to pre-build with new components just to serve your new need. No new Redis instance VM just to serve your so-far-tiny KV-storage needs. You start with your new workload-type sharing your “miscellaneous business layer” PG instance; and then, if and when it becomes a problem, you migrate it out.
The point of Redis is data structures and algorithmic complexity of operations. If you use Redis well, you can't replace it with PostgreSQL. But I bet you can't replace memcached either for serious use cases.
As someone who is a huge fan of both Redis and Postgres, I whole heartedly agree with the "if you are using Redis well, you can't replace it with PostgreSQL" statement.
What I like about the "just use PostgreSQL" idea is that, unfortunately, most people don't use Redis well. They are just using it as a cache, which IMHO, isn't even equivalent to scratching the surface of all the amazing things Redis can do.
As we all know, it's all about tradeoffs. If you are only using Redis as a cache, then does the performance improvement you get by using it out weight the complexity of another system dependency? Maybe? Depends...
Side note: If you are using Redis for caching and queue management, those are two separate considerations. Your cache and queues should never live on the same Redis instance because the should have different max-memory policies! </Side note>
The newest versions of Rails have really got me thinking about the simplicity of a PostgreSQL only deployment, then migrating to other data stores as needed down the line. I'd put the need to migrate squarely into the "good problems" to have because it indicates that your service is growing and expanding past the first few stages of growth.
All that being said, man I think Redis is sooooo cool. It's the hammer I am always for a nail to use on.
well, redis is a bit of a junk bin of random barely related tools. It's just very likely that any project of non-trivial complexity will need at least some of them and I wouldn't necessarily advocate for trying jerry-rigging most of them in postgresql like the author of article, for example why would anyone want wasting their SQL DB server performance on KV lookups?
“well” is doing a lot of heavy lifting in your comment. Across a number of companies using Redis, I’ve never seen it used correctly. Adding it to the tech stack is always justified with hand waving about scalability.
There are data structures in Redis?
They may be its point, but I frankly didn't see much use in the wild. You might argue that then those systems didn't need Redis in the first place and I'd agree, but then note that that is the point tigerdata makes.
edit: it's not about serious uses, it's about typical uses, which are sad (and same with Kafka, Elastic, etc, etc)
Did someone really downvote the creator of Redis?
All the time here in HN, I'm proud of it -- happy to have opinions not necessarily aligned with what users want to listen to. Also: never trust the establishment without thinking! ;D
IIRC there was a pre-edit version with snark.
Yes, but the downvotes came later too, I edited it with the same exact content but without the asshole that is in me. Still downvotes received.
I was one of the downvoters, and at the time I downvoted it, it was a very different comment. this is the original (copied from another tab that I hadn't refreshed yet):
> Tell me you don't understand Redis point is data structures without telling me you don't understand Redis point is data structures.
regardless of the author, I think slop of that sort belongs on reddit, not HN.
I mean, it's a pain at times to keep elastic in sync with the main db, but saying elastic is just an algorithm for text search feels odd.
I find myself needing to start something quickly with a bit of login and data/user management.
Postgres won as the starting point again thanks to Supabase.
No thanks. In 2026 I want HA and replication out of the box without the insanity.
Came to say the same thing. Personally I'd only touch Postgres in a couple cases.
1. Downtime doesn't matter. 2. Paying someone else (eg. AWS) to manage redundancy and fail-over.
It just feels crazy to me that Postgres still doesn't have a native HA story since I last battled with this well over a decade ago.
You exceeded the step of maxing out the best server you can buy?
HA is not about exceeding the limits of a server. Its about still serving traffic when that best server I bought goes offline (or has failed memory chip, or a disk or... ).
Replication?
Postgres replication, even in synchronous mode, does not maintain its consistency guarantees during network partitions. It's not a CP system - I don't think it would actually pass a Jepsen test suite in a multi-node setup[1]. No amount of tooling can fix this without a consensus mechanism for transactions.
Same with MySQL and many other "traditional" databases. It tends to work out because these failures are rare and you can get pretty close with external leader election and fencing, but Postgres is NOT easy (likely impossible) to operate as a CP system according to the CAP theorem.
There are various attempts at fixing this (Yugabyte, Neon, Cockroach, TiDB, ...) which all come with various downsides.
[1]: Someone tried it with Patroni and failed miserably, https://www.binwang.me/2024-12-02-PostgreSQL-High-Availabili...
Exactly my thoughts immediately after reading the word “just”. Also, PITR.
This is the future of all devtools in the AI era. There's no reason for tool innovation because we'll just use whatever AIs know best which will always be the most common thing in their training data. It's a self-reinforcing loop. The most common languages, tools, libraries of today are what we will be stuck with for the foreseeable future.
Is that any different from "we'll just use whatever devs know best, which will always be the most common thing?"
eg Python, react... very little OCaml, Haskell, etc.
I feel like this is selling redis short on its features.
Im also curious about benchmark results.
Lots of familiar things here except for this UNLOGGED table as a cache thing. That's totally new to me. Has someone benched this approach against memcached and redis ? I'm extremely skeptical PGs query / protocol overheads are going to be competitive with memcached, but I'm making this up and have nothing to back it up.
They don't compare exactly. Author is mistaken in thinking that UNLOGGED means "in memory". It means "no WAL", so there's considerable speed up there, but traded in with also more volatility. To be a viable alternative to Redis or Memcached though, the savings you get from the latter two must really be superfluous to your use case. Which could be true for many (most?).
Its not only about performance, Redis data structures offer an even more advanced caching and data processing. I even use Redis as a cache for ClickHouse.
Get AWS to actually support pgvectorscale and timescaledb for RDS or Aurora and then maybe... sigh....
Nice! How do you "preinstall the extensions" so that you can have eg timescaledb and others available to install in your Postgres? Do you need to install some binaries first?
Good stuff, I turned my gist into an info site and searchable directory (and referenced this article as well, which seems to pay homage to my gist, which in turn inspired the site)
https://PostgresIsEnough.dev
This is a good summary, though I'd love to see a "High Availability and Automated Failover" entry in that table.