I started setting up my workflows using Temporal. It deploys as relatively light weight local app. For an isolated local installation it uses SQLite. It makes the process of dealing with API retries and organizing workflows and tasks really simple. I recommend giving it a try. It is, philosophically, exactly what this article is suggesting, but it adds an incredibly rich and flexible interface for agents to work with. Additionally, the web UI makes it very easy to inspect workflows, review agent execution, etc. Temporal also encodes much higher reliability into your system, almost for free. Distributed and reliable systems are hard, don't reinvent the wheel IMO.
If you find yourself wanting things like an easy way to then introspect your SQLite database, figure out what is happening in the workflow, compose individual tasks, make workflows trivially callable, etc, give Temporal a look.
Alongside this, I have mostly moved away from files for agents. Markdown and JSON are great, but also feel like traps when building out smaller local apps. LLMs are great at SQLite and you can render anything you want out of it (Markdown, JSON, etc). It saves a lot of tokens when an agent can just query a specific row instead of having to fire up jq or grep through markdown. You get a nice portable self contained data management system that encourages agents to be more disciplined about how they structure their data than a bunch of files. It also continues to scale into MySQL/Postgres if your little local projects start to outgrow or become more formal, you already have schema and discipline around data.
It sounds like you’re running this mostly on a single machine?
Temporal gets much more complex with scale. Cassandra isn’t fun to manage. Ringpop and TChannel are hard to debug when things go wrong. The SQL backend support doesn’t support horizontally scaled replicas (just single instance) due to consistency requirements. Depending on how your code is written, modifying code baked into workflows becomes complex, as anything that modifies the history event ordering breaks determinism in already-deployed workers.
We use it heavily and everyone who started on it doing simple scripting/automation all love it, everyone who built real production systems on top of it all hate it. Possibly operator error, but my experience hasn’t matched the rosy picture painted in these comments.
Word on HN is that you're either paying more money than you expected for temporal's managed solution or taking on substantial ops burden ultimately running their very heavy system yourself.
I wouldn't know, I've not done either, but I'd like to learn more from your or other's experience.
I told an agent to set it up for me for some local stuff. It is written in Go. It has a painless path to run on a local SQLite DB. My agents use it to organize and coordinate workflows. It handles retries and long horizon tasks fine. As far as I can tell for the core workflows and tasks pieces it’s great. MIT license. Like anything it isn’t free to manage but it offers a lot in return. High reliability systems are hard. Temporal only solves some of it. It is far better than rolling it yourself.
I think a genuine problem right now is people are building agentic work flows and learning the hard way highly reliable agentic work flows are hard. Agents are unreliable. They are both not deterministic and not the backing APIs have pretty high error rates. Temporal has solved that pain for me and made it easy to diagnose problems.
I don’t have anything really large scale running. But big enough that it takes billions of tokens and high reliability to finish.
Could you expand on the "substantial ops burden"? Let's say you're using a managed Postgres instance as the underlying data store, how substantial is the ops burden in that case? I understand that temporal is actually a set of 4 or so microservices on top of a data store, but if you're already running a distributed system backed by k8s or something like that, it doesn't seem like it adds significant incremental ops on top of that. But I could be wrong.
My devops coworker just shrugs, pumps out some yaml and helm and away it goes.
It really depends on your experience and tolerance for a lot of things.
Usually maintenance burden doesent start to make itself known till you get off the happy path or something breaks. Sometimes it can be a long while before that happens, sometimes it happens right away.
Very heavy indeed, people will confuse the durability that Temporal provide with all the other properties a distributed system needs. They will then think that Temporal will solve all their problems.
My favorite lens on SQLite is that it is actually two things:
1. A robust durability implementation
2. A library of high performance data structure and algorithms
The fact this it's SQL is nice, but those two attributes are what make it great.
For example, I'm implement an in-process event log that I want to be durable. I started simple, but soon saw some edge cases and instead of playing whackamole I just swapped to using sqlite as an ordered kv store that gives me ACID.
Another example: ingesting multiple inter related datasets. Instead of a dozen hash maps in memory, I load them up into sqlite (no persistence) and then slice and dice as I need to.
> an example of a case where you'd use SQLite instead of jq or grep through Markdown?
Usually we end up writing a script to incrementally refresh a data-set I'm analyzing (or have someone send me a copy after they pull it).
I've been using sqlite for anything which needs an UPDATE - modifying a row deep inside the data-set with jsonl is a pain.
My github is full of java programs which update sqlite3 files with threadpools and a single big lock around the UPDATE (& then I write or have an agent write code to analyze it).
DuckDB is slowly replacing it in the context of python, simply because of the ease of pushing a UDF into the SQL.
Also because I really like expressing things as LEAD/LAG with a UDF on top.
SQLite is more efficient for large data sets. A single markdown or JSON file needs to be streamed to locate a piece of data O(n). Updating an existing entry in a sequential file is even worse because you have to rewrite the file. SQLite has the data structures to quickly find data in O(log n) time.
The moment my JSON has any sort of depth and I need to write a parser for it and potentially account for unspecified behavior. JSON's nice when it's nice, but it's terrible when it's terrible. It's 100x easier to write SQL than writing jq and... dear god if I have to use grep -A or -B, I'm doing something wrong. Constraints are actually a good thing!
The underlying database isn't the most important thing. Just use SQL. Its namespacing (eg, through CTEs) is good and you're more likely to have colleagues who know SQL compared to jq.
I'm someone else who has inherited a bunch of ad-hoc orchestration systems and also used Temporal quite heavily. The latter does certainly come with some overhead (not so bad in the age of LLMs), but it also guides you along a well-trodden path of good practices. The latter being very important - it means that when you want to take on more advanced capabilities, you probably haven't painted yourself into a corner too badly and can take that on fairly easily. Think: retries, multi-tenancy, multi-lang, observability, etc.
Well, just my experience. I installed it, had my agents configure it and it immediately solved problems I had with very little friction. Dealing with long running, long horizon agentic tasks that need very high reliability so I don’t have to babysit. I vibed the first version, realized I was reinventing reliable distributed systems. Stopped vibing and started surveying for something that fit :)
I can vouch for them too, being a super early adopter. One of the best early bets I've ever made. Awesome OSS product, glad the team decided to leave Uber to commercialize it.
I don't understand this obsession with SQLite for real, production apps. SQLite is an embedded database, completely unsuitable for managing concurrency. This is what database _servers_ are for, e.g., Postgres, MySQL, etc. Their entire job is to allow you to modify data from multiple processes, on different machines, at the same time.
This is a foundational principle of computer science. It seems to me that the "SQLite for everything" crowd is a little bit inexperienced.
You seem to have a rather limited understanding of what kinds of concurrency exist and how those needs are best met. Whether something is a server or not is not very relevant to this discussion.
SQLite is an excellent production db for many real world workloads, as has been widely documented. It is very different to Postgres, so requires learning a whole new skill set.
One way to think about it is that SQLite can work well for the parts of your system where there is naturally strong partitioning.
You can do big data in SQLite. Concurrent writes, sure, I'd recommend something else.
If you think the majority of systems require massively concurrent writes, I think you need to look a bit harder. SQLite is, after all, the most widely deployed database system, ever.
It’s widely deployed as a local DB for local apps like phones, desktops, and web browsers. But it’s not the most used for distributed, concurrent web apps, which db servers were designed for. Maybe people are talking past each other, but that’s the debate I see.
There are many cases where SQLite + concurrent front end (like a go net/http server) can handle all the load that a service might ever conceivably have to handle, especially if allowed to scale up hardware over time. You can trivially scale up SQLite to, what, hundreds of thousands of tps?
The only thing you really give up is HA/failover and DR. But there are solutions to deal with those. And single-server systems are generally surprisingly robust (since, in the absence of very complex control planes, uptime goes down with more systems).
I was thinking of using SQLite on top of k3s/Longhorn to replicate it. Anyone do something similar? Folks mention light steam and aws but Jeff Bezos’s biceps are too much for me to handle.
I've had pretty terrible experiences with SQLite and Longhorn/NFS.
It's just not the right database for pretty much ANY network based filesystem, where the locking primatives aren't as robust, and you might get two processes trying to hit it at the same time.
As someone who runs a fairly big personal cluster backed by a mix of giant NFS storage for media, and relatively large longhorn SSD drives for configs/temp data...
I avoid sqlite backing like the plague. It will get corrupted. Period. It's not the db for this use-case, and I'll take postgres/maria/mysql/mongo/ANYTHING else over it.
If you do it - back it up ALL THE TIME, because it's going to get corrupted.
Sqlite is good for lots of stuff, but you're probably focusing your days on high-scale webapps that want sharding with networked DBs. That's one domain, and an interesting one, but there are lots of others.
I'm a big fan of re-evaluating prior "best practices" in light of technology changes, especially in ways that improve simplicity. Running my family's social media site off a single sqlite DB on a VPS is great. ~15 users, almost zero maintenance. I run my FreshRSS instance off of sqlite, as well as my "now" page. At work, I used sqlite for all kinds of things over the past decades: as an ad hoc job queue, as a quick way to ingest and query lots of logs locally, and present/filter in realtime with simonw's excellent https://github.com/simonw/datasette.
I don't think it's every "sqlite for everything" as much as it is "sqlite in lots of places you probably didn't think to apply it."
I suspect being aware of all those little neat cases and wanting to leverage sqlite for them may be an indicator of experience, rather than the opposite.
That’s why there are billions of SQLite databases right?
SQLite is likely used more than all other database engines combined. Billions and billions of copies of SQLite exist in the wild. SQLite is found in:
Every Android device
Every iPhone and iOS device
Every Mac
Every Windows 10/11 installation
Every Firefox, Chrome, and Safari web browser
Every instance of Skype
Every instance of iTunes
Every Dropbox client
Every TurboTax and QuickBooks
PHP and Python
Most television sets and set-top cable boxes
Most automotive multimedia systems
Countless millions of other applications
Single-user, a single natural person, doesn't striclty mean single-accessor though. I don't think anyone here is suggesting that sqlite is a viable replacement a for any networked client/server postgresql system, but it is certainly capable of handling more than the most basic 1:1 tasks. Beyond that, the point is that you only need a file, so when you have natural data boundaries, a lot of problems decompose to that single user/single concern paradigm.
'production' doesn't equal 'multi-user concurrent access'. There are production uses where sqlite is a valid choice even if it may not be the best choice for multi-user production use cases.
GP calls out concurrency as a weakness of SQLite. Most of the examples here don't experience the same load even a moderately sized web service experience day to day.
And no, being a part of the python standard library doesn't means it is being used by the average python user. These days I'd say at least half of them are just there for machine learning.
SQLite requires writes run sequentially. Most SQLite write operations take single digit milliseconds or even microseconds. If your writes are inexpensive (inserting or updating single small rows) you'll probably never even notice the queue.
My read is that levkk is conflating concurrency with "real production apps" and this whole thread is starting to surface that "real production apps" and "high concurrency" are not measuring the same thing at all.
Sqlite is used in real production apps more than any other database.
Sqlite is also weak at any sort of write concurrency.
If your data is naturally sharded (users) with writes happening within a single shard, parallelism becomes easy. The request is routed to the shard hosting the user's data and reads/writes locally.
This makes scalability _much_ easier to reason about. It's cut-paste, cut-paste. Every N users needs another shard.
It does buy you a _different_ set of problems, like cross-shard querying (analytics) and how to do load leveling as users age out.
But it avoids the whole shared index scaling problems from inserts/updates with large user counts.
It becomes a hierarchical instead of a relational database.
I had very good results giving 1 SQL DB per go routine, so the accesses were serialized up front, on a very high volume (130K requests/second) service. Exact transactionality was not a product goal, and the SQLite was just to backup the in memory state. If we lost a little due to abend or something, that was ok (although for normal maintenance it caught SIGTERM and stopped the listen and then waited for in flight calls and then flushed the remaining changes to SQLite; then on startup it would read the SQLite into memory to populate before taking the listen; persistent storage across container runs, and never both reads and writes to the same file at the same time. (It also just closed the DB and opened a new one when it hit some limit of rows, so as not to fill the disk; the max size of the SQLite corresponded to the max size of the LRU map being served from in memory; then it just flipped A / B between "a full memory worth of data stored" and "the currently updating state." A lot easier than having to write out proto bufs to disk or whatever I would have done for transient (during restarts/maintenance) persistence.
Woof. That sounds very complicated. If you need that kind of write concurrency, use an unlogged table in postgres [0]. Then you don't have to invent a whole sharded thing yourself.
Thing is SQLite scales better than both those network databases [1] if you're prepared to stick with one big machine (+ a standby).
This is even more obvious when you start doing transactions processing an row locks across the network limit you to 1-3k TPS that you cannot scale out of (Pareto distribution is merciless).
Seeing as I can get about 200K TPS from a networked DB in my environment, I have to question your setup here.
In the real world we are looking at things like RPO (recovery point objective) and RTO (recovery time objective). You need to consider HA and DR. It’s in these areas where SQLite does not scale.
That’s why I struggle to see the fit for SQLite in any sort of multi-user server environment. If you need the data to be durable, then the bigger DB’s have the tools. If you don’t need the data to be durable, just keep it in memory. I’m sure there are niches I am missing.
there is a difference between concurrency in a distributed environment and concurrency on a single machine across processes. SQLite is incredibly useful for the latter.
Isn't concurrency also limited by your machines disk speed for writes, what difference does it make if you write sequentially vs concurrently? Why does concurrency even matter for databases?
> Isn't concurrency also limited by your machines disk speed for writes, what difference does it make if you write sequentially vs concurrently? Why does concurrency even matter for databases?
For a simplified example, having three processes reading blocks X, Y, Z in parallel is much faster than having a single process read block X, wait for the read to finish, read block Y, wait for the read to finish, read block Z and wait for the read to finish.
> Isn't concurrency also limited by your machines disk speed for writes
Yes, in theory: given a large enough database, and a disk that can only do one operation at a time, and a large enough operation that touches enough of the database. In practice, in a SQLite single tenant scenario? No, not at all.
> what difference does it make if you write sequentially vs concurrently. Why does concurrency even matter for databases?
As soon as your codebase involves reacting to events independently of a user taking action it becomes a practical concern. Generally, this is a broad question and has 1,000,000 answers.
EDIT: Originally I had "I think you understand generally, no?" appended but realized that's not helpful at all, if you did, you wouldn't be asking.
Something that may help is imagining what'd happen if a DB wasn't thread safe / didn't allow multiple writers. Ex. in SQLite's case, it allows multiple write operations to take place but there's a one-at-a-time queue. If we didn't have databases that were able to execute multiple writes simultaneously, you'd need a separate database for each concurrent writer you expect, and you'd effectively have a global lock. Orderly scaling would be ~impossible unless you did something crazy like have a single server per user
I guess I need to dive deeper into this as I do not understand the implications you gave me, but I appreciate the attempt. Generally I understand why concurrency is good in many cases, I just dont get why its important for database stuff too.
Edit: thanks for clarifying in the edit, makes a lot more sense.
Imagine if every tweet had to go through a one-at-a-time queue before being persisted. There's about 6000 tweets per second, so you would have to be able to save them at <0.17ms per tweet or else you would become backlogged. If you are getting backlogged, you have to buffer those incoming tweets somewhere until they can be writted, and eventually that buffer gets full and you start losing tweets.
Maybe that too is a native question, but there's a large scale between single user and 6000 tweets per second - most of our apps will never reach anything approaching even one save a second. So where to draw the line? I do far have gone the sqlite route for my hobby apps as it's so easy to handle and doesn't require setting up two docker containers for a single app. Am I drawing myself in a corner in case my apps ever do become relevant?
Excellent question, and I spent so many years asking myself it, this over and over. You asking it made me realize I just...don't anymore. So allow me to blather a bit / free associate because I won't be sure why myself until I've written it out.
TL;DR: whatever works for you is the right decision. (which isn't helpful, I heard this so many times and as the recipient, I thought "That's nice. Now how do I choose what works for me?")
I finally had to use Postgres a couple years ago after a career of only SQLite - startup founder & iOS app developer using SQLite, turned Googler on Android, turned doing-my-own-thing.
In retrospect, I have made only one bad decision:
I went way out of my way to make SQLite work at my 2009-iOS-startup. It was a restaurant point of sale system, and to allow a networked system, one of the iOS devices would act as a server. This was a really cool trick, even an advantage in marketing that was appreciated by users. It meant the restaurant could continue to operate if the internet went down. But it eventually became clear owners loved having internet-based access too, ex. to do reporting/financial analysis over the data. And I kept contorting, instead of moving past my fear of getting into things I didn’t know, I instead did some like rudimentary thing over port forwarding. The bad decision here was riding one horse for so long and letting it affect the product, having a real server database would have allowed for a lot more features, think, first party gift cards, and a 100 others.
After leaving Google I needed server-side storage and fought and fought to avoid it. Then it turned out Postgres is easy and, just like SQLite, 99.999% of the time I don’t even know I’m using it.
In retrospect, there’s ~0 switching cost to these, particularly in age of LLMs. If you do need something more one day, it’ll be easy to do, and if you have to do it in a rush because you’re successful, you’re in Good Problem territory.
Hope that helped, after writing it out, dunno how convincing it is. Feel free to follow up, I appreciate the curiosity/framing because I had the same thought for so long.
If we imagine 1 tweet = 1 transaction, that's only 6k tps. 6k tps is completely achievable, dare I say even pedestrian for an optimized database. And most systems are operating far below the scale of Twitter/X.
Round trip time is actually much faster than Postgres, since there’s no need to touch the network. You can get massive single threaded throughput. In order to achieve comparable throughput in Postgres you need a large amount of concurrent connections, since each conn spends most of its time passing messages, deserializing etc (with a much larger total amount of overhead). There are a surprising amount of bottlenecks and misconfiguration that can tank performance of networked systems, particularly DBs.
Like you suggest, the reason for not picking SQLite is not reliability, speed, etc. Networked DBs allow decoupling between app and db servers, which have operationally different characteristics. But most importantly, you can have multiple apps access the same DB at the same time. Eg analytics, one off queries, any 3p app that interacts with your data directly.
Or you can run postgres on the same machine as the application, which lets you much more easily migrate if the time comes when you need to scale to multiple application servers.
There's a world between "local file" and "network DB server", running a DB server locally has lots of benefits from being able to easily query from outside if needed to forcing you to consider concurrency without the latency overhead of a network hop.
This decision tree doesn't make much sense to me. Why you someone forego performance today in favor of adding a completely unnecessary network layer to every DB query in order to "satisfy" future imaginary "scaling concerns"?
I use postgres for very simple apps. I have a Dockerfile I use in my boilerplate repo. It takes a single make cmd for me to build, start and run migrations. Its as simple as using sqlite.
> But now you have another process to babysit. How do you keep it healthy?
I've been assured by many HN users that running apps/sites on a single VPS requires near-zero maintenance or monitoring to achieve acceptable uptime 24/7/365 for years on end, sooooo...just pretend it will never fail like your main server process?
Call me old-fashioned and quaint, but I don't like to build software that doesn't work all the time if I can help it, whether it's for 10 users or 10 million.
24/7/365 is needed (or achieved) just about never. our big tech is proving 90% will soon be utopia as well. being down has always been fine for 99.999975% of all projects on the planet.
It's almost as if Postgres isn't perfect, and one size shoe doesn't fit all.
Some people want some of the benefits you get from SQLite.
SQLite is obviously not perfect, but it's an incredible piece of software, and people regularly find good ways to make use of an excellent pieces of software.
I mean - I agree for the typical multi-user, SaaS webapp. But I don't think that's what these folks are proposing. If they are - yeesh, count me out.
If on the other hand they're talking about single-user, software in the small - hell yeah. In fact, I'd also promote DuckDB in this regard (mostly for analytics) - with the power of a single machine these days, you can do a surprising amount and never have to worry about distribution. Unless you know you'll have to, in which case you're probably just digging yourself a hole?
The reason the parent post is complaining that it doesn't make sense, is because people have indeed pushed the idea of using SQLite as an alternative for web apps like that.
The typical multi-user SaaS webapp doesn't have anywhere near enough users to overwhelm a single SQLite instance. Of the few that do succeed to the point where that's no longer true, a significant fraction can use techniques like sharding to stretch SQLite further.
So teach them. If you want to bring up computer science fundamentals, the question is where does SQLite sit with regards to the CAP theorem. Consistency, Availability, and Partition tolerance. SQLite isn't a distributed system, so there are no partitions to tolerate, so it's a CA system. Other databases make different tradeoffs. For systems that don't need concurrent writes, SQLite is pretty great! There are no users to manage, no permissions, no daemon to run, no server and port to mix up. Just open a file on disk using a library.
In the beginning apps and SQL were co-mingled. Oracle eventually came along and noticed that people wanted SQL on the network so that many different apps, running on different computers, could all access the same data. But then people realized that clients really want rich, 'tree'-like data, not simple rows and columns, so people started sticking networked databases in front of networked databases to serve as a transformation system. And now people are realizing that the second networked database layer is redundant and never used beyond what is required for the client-facing network database, so they are moving the storage back into the first network database layer, just like Oracle did all those years ago. What is old is new again.
What changed is SSDs. SSDs means that local access is faster than hitting the network. An expensive SAN stopped making sense because of this in specific cases. So for read heavy, or even read only database loads, you copy the SQLite file to the node that's processing the file, and just update that file whenever the data does get changed.
I absolutely 100% do not understand it either. At all. Every time I try to over the last year or two I come away with the conclusion its something that sounds cool (to me too!) but is guaranteed to cause more problems than more obvious solutions.
That being said I'd kill for someone who used it and benefited to explain it to me in a practical sense. (specifically where syncing is involved, and syncing a subset of the SQLite is necessary. If it's "just" a document store thats treated like a blob for syncing/backup, that's familiar. If it's all in one storage but only local, that's familiar.)
Re: TFA, I guess it would have helped if I knew what Obelisk was, which is on me, and a more in-depth explanation of how this ties into AI/agents, which is on the industry/writer.
It's very likely that you have multiple SQLite databases in your pocket right now. It's one of the most widely deployed pieces of software on the planet. If your conclusion is that it's guaranteed to cause more problems than other solutions, then that's on you.
Sure, SQLite doesn't solve every problem -- but in many cases it solves the need at hand with the reward of one less piece of infra required to support it.
I see obsessions with tooling/solutions constantly from experienced devs who fall in love with the original solution and think it's the only way to do things -- so the experience part cuts both ways.
I have dozens of products I sell, so I thought: why not build everything ourselves?
All of these run on the same server and use very little memory. I replaced all the SaaS tools we used with these.
I also moved to dedicated servers and dropped costs to about 1/10th of what we were paying for managed cloud solutions, while maintaining the same HA and even achieving lower latency (partly because noisy neighbors on VPSes were increasing tail latency).
We used to spend a ton on this stuff. These have now been in production for four months and have only needed minor updates.
Deployment is dirt simple. No Docker, no Kubernetes—just a systemd service and a binary built on the dev machine and deployed.
We also used to pay for services like MaxMind and IPData. I ended up hand-rolling my own IP geolocation service, which, in my tests, outperforms most existing solutions.
It all started with replacing Uptime Robot. Then I got more confident and replaced PagerDuty. After that, I replaced Intercom.
Finally, I had always heard people say, "Don't build your own billing stack." But I said YOLO, let me make that mistake myself. So I studied our existing billing solution, developed my own, and rolled it out. So far, we've had zero issues with it.
Caddy in front.
I found that we only use maybe 1–5% of the features most SaaS products offer, while the features we actually need keep getting buried deeper and deeper inside these "enterprise-grade" platforms, making our workflows more difficult.
I won't show my commercial products because our partners and clients probably wouldn't appreciate knowing how cheap I am—but I call it being resourceful.
I can show my free app, though, which has 20,000+ users and was launched recently: https://macrocodex.app/
It only uses the Zendesk clone. Email is handled through Cloudflare routing, so we pay almost nothing to run the app.
I just have uptime service hosted outside of our main infra. It connects to my service called Siren, which alerts me on my phone with an alarm on full volume with SWAT cat intro.
It's good enough for what we do, barely have any downtime. But it helped me figure out 6s downtime we would experience when our spot instances get knocked out, so it helped me increase health check frequency
6s downtime is a lot when you are getting hammered at 100 RPS.
There's a wide gap from files to multipartition databases.
Running databases in a container is not for me sorry whenever real production stuff is on the table.
Personally, lots of ETL can just be taken care of locally without involving enterprise databases. In such cases, DuckDB is 5x-10x better than SQLite and orders of magnitude simpler/faster than spinning up a dedicated Postgres database.
For general scripting, there's no match between a 20-lines awk script and a much cleaner, robust, maintainable equivalent SQL script based on DuckDB.
I just hope MotherDuck don't need to pump/dump for IPO - it would be sad losing that tool for the usual corporate greed.
Hello, DuckDB devrel here. First, thanks for the kind words :)
Second, it's funny you should mention the 20-line awk script. I was making a very similar argument yesterday at the Ubuntu Summit: at some point, using shell scripts with GNU coreutilus becomes impractical, while DuckDB SQL scripts scale better in terms of complexity and maintainability (and often also performance). My slides are here: https://blobs.duckdb.org/slides/duckdb-ubuntu-summit-2026.pd... (pages 32 to 36)
Third, MotherDuck develops a closed-source DBaaS on DuckDB. They build on DuckDB, and you connect to MotherDuck with DuckDB but they are a separate VC-funded company headquartered in Seattle. DuckDB is developed by DuckLabs, a bootstrapped (revenue-funded) company in Amsterdam. And the IP of the project is in a third organization: a Dutch non-profit called the DuckDB Foundation. For details, see https://duckdb.org/faq#how-are-duckdb-the-duckdb-foundation-...
I wrote a library[0] to let you concurrently update a sqlite db in s3 safely. It uses the little known sqlite sessions extension plus s3 compare-and-swap on a small metadata file to make this work reasonably efficiently and safely. I have been enjoying it for a bunch of small projects where I want a lambda function to have a db for state but I don't want to pay for a full database instance.
SQLite is surprisingly performant for single node applications even when comparing to Postgres. Postgres consumes a lot more memory and requires IO to hop through IPC whereas you can keep everything in process in SQLite with a shared connection pool.
I've been testing different storage engines for my agent harness and I can get up to 7.5k concurrent sessions on a single vCPU with SQLite whereas Postgres crashes or runs out connections.
When used properly, SQLite is effectively an in-process method invoke. If the only remaining things in the way are your runtime, kernel, file system and a local NVMe storage device, you may find it massively outperforms hosted alternatives.
Leaving the current thread is where you lose the game in terms of latency. SQLite can work on timescales measured in microseconds if you don't force interthread communication.
Pedantically it's an in process virtual machine for operating on structured data. Which is precisely where it shows it's weakness, in my experience, when you end up with complicated table structures and complex join mechanics you then need to start thinking ahead of the query planner and VM code a bit in order to maintain reasonable performance.
There are more than a few unusual things worth knowing:
Yes, agreed on SQLite/Postgres. But I'm going to benchmark RocksDB next and see what the performance characteristics are. I suspect the LSM tree storage engine of RocksDB might perform better since agents are so write heavy when running highly concurrent workloads. After all, you are streaming LLM tokens into disk and fanning them out to subscribed clients.
I started using SQLite for a home project after years of reading about it, I was shocked at the poor type system coming from Postgres. It is really inferior, not sure why it gets so much praise.
Working with date/time feels like using a 30years old database, nothing is enforced at insert. Really someone needs to explain why so many people like it.
create table events (
id integer primary key,
name text not null,
event_date text not null check (
-- YYYY-MM-DD
event_date glob '[0-9][0-9][0-9][0-9]-[0-9][0-9]-[0-9][0-9]'
and date(event_date) is not null
and date(event_date) = event_date
)
);
In Python that raises this error if the date is invalid:
This is the fault/price of backwards compatibility. Most users of SQLite should just fire off a few pragmas on each connection:
PRAGMA journal_mode = WAL
PRAGMA foreign_keys = ON
# Something non-null
PRAGMA busy_timeout = 1000
# This is fine for most applications, but see the manual
PRAGMA synchronous = NORMAL
# If you use it as a file format
PRAGMA trusted_schema = OFF
You might need additional options, depending on the binding. E.g. Python applications should not use the defaults of the sqlite3 module, which are simply wrong (with no alternative except out-of-stdlib bindings pre-3.12): https://docs.python.org/3/library/sqlite3.html#transaction-c...
While it has bad ergonomics, you can also use CHECK constraints. For example, using sqlite's built in date support, it's possible but awkward:
CHECK (
date(my_date_col) IS NOT NULL
AND my_date_col = date(my_date_col)
)
The IS NOT NULL is needed because date returns NULL for invalid dates; the other check because it also accepts Julian days (date('2026') is sometime during year 4707 BC).
It gets praise because of stuff other than the type system.
I agree it is disappointing, especially before strict tables.
You should check out DuckDB which is basically SQLite but with proper types. Although it is also OLAP (struct of arrays) rather than OLTP (array of structs) which may have worse performance for typical SQLite loads. In practice I doubt it matters if you have an application where you're considering either.
I went from using the various big player postgres clusters to SQLite, we have an MAU in 7 figures, all backed by SQLite durable objects. We have to think differently about the access patterns but the benefits have been worth it.
In all seriousness, I’d take a “s3 is all you need for durable workflows” and use it in data processing applications that move data from s3 -> s3 with no other dependencies.
Are logs all you need for durable workflows? I'm confused here. How'd persist and query nested or related data over logs? By logs I assume you mean something like elasticsearch or meilisearch?
I assume they meant a log like a WAL. A WAL should be (quite literally?) all you need for durable workflows.
A distributed WAL (to survive a machine death) would also probably be something I'd want, and … something I'm not sure you're getting directly from SQLite.
Pretty much every durable system has an intent log of some sort. The log provides durability, the database system just integrates that log into a more queryable format.
Litestream releases 5.9 and newer have a bug that causes instances to sync an insane amount of data. a DB with <10K of data in it and practically no writes/reads causes something like 10GB of daily replication traffic. For my toy project that got needlessly expensive.
Meta comment:
This is a domain under my countries TLD (Slovakia) and it is one of the handful of words that are a word with the TLD in my language (and coincidentally) also in English. Every now and then, I will check on the domains with a retrograde dictionary for domains that have this property and root of this particular domain had a roundcube email server on it (can be checked on archive.org). After further checking, the local company actually named themselves Obeli s.r.o. (s.r.o. is Ltd), presumably so that they could use a domain that is a real word when said together with the TLD. (EDIT:) Forgot to write the thing I wanted to mention in the first place: it appears the domain must have lapsed and/or the author bought it from the company that was using it.
Another fascinating fact: our countries TLD has been stolen Ocean's 11 style (I am not kidding). After Czechoslovakia split into Czech Republic and Slovak Republic, the newly created Slovak .sk TLD has been under the care of people from the local university. The university also had some offices that they were leasing out. Someone had leased this office space (EDIT: this is important as this means they had the same physical address), created a company that had the same name as the NGO that was taking care of the domain, so e.g. the NGO was named "My Company o.z." and the perpetrator created a "My Company s.r.o." (our countries version of the american Ltd). This person then wrote to ICANN to change the address to the "My Company s.r.o." presumably under the pretense that this was just an administrative error and from this point, they have functionally taken custody of the TLD. I was not able to find how they did it technically, but I presume they persuaded ICANN to then point to their servers instead of the real ones.
After this happened, it seems that no one noticed for some time. When they noticed, they tried taking it back, but they weren't able to. For some inexplicable reason, the government during that time (Šuster era, early 2000s) gave the new company a contract that was functionally uncancellable from the government side. Later governments made this even more uncancellable and in 2017, then Minister of IT (and as of this day president!) Pellegrini made the contract literally uncancellable.
As a result of this, we have one of the most expensive domains around (18e/year, rising each year for no good reason). (EDIT:) The company running our countries TLD is now a foreign entity that the whole thing has been sold to (multiple owners over time) and we as a country have no control over if I understand it correctly.
I might have gotten some details wrong as I am writing this from my memory of researching it a couple of years back, but you get the idea, crazy stuff. Here is an article in Czech [0] that tells the story a bit better, but you have to translate it.
// EDIT:
I have found that the article actually links the movement to return the TLD back [1]. It also has a story tab [2], so they have something much more precise than the paraphrasing I wrote.
if you have an application that needs to maintain state in a non-critical section or if you discover that using SQL is actually a good idea for some tasks (even in critical sections), SQLite is not only a good choice but it will save you a lot of time coming up with a brittle custom solution.
If you're just doing workflows from a single node, i guess it can be ok as long as theres a single writer. But scaling across multiple servers it clearly is not all you need.
> Postgres ... is the right choice when you need higher availability, broader shared scalability, or other deployment properties that are better served by a network database. It is also the better fit when asynchronous replication to object storage is not the durability model you want...
Many workflow systems do not need that on day one and should not start with more infrastructure than their state actually demands.
------
I see this kind of YAGNI thinking a lot, but in my view, it must be balanced against the effort you'd put into resolving any edge cases and adapting current architecture to your use case.
Imagine you deploy Sqlite, and thought it fine by itself, you keep running into some unforeseen challenges with the use to which you are putting. YOu'd need to sink valuable time and effort into addressing those. Then, when you have outgrown it, you'd beed to spend additional valuable times dping the same with Postgres.
This is why, when it comes to Architecture, I increasingly find my myself over-enigneering a bit. Assuming there is a good chance you might need to upgrade your architecture in the not too distant future, that approach is actually kind of very efficient. I find that I am able to uncover a lot of potential gotchas, which feeds back into the what the simplified current architecture should be, and helps me understand the roadmap I'm facing very well. I also avoid wasting too much time going too deep in directions that make sense now, but need a lot of plumbing to get right, when I can see that I'd likely have to throw it all out in a few years. Going from A -> B -C -> D, where each step is the optimal good-enough-for-now architecture but which requires a lot of work to stabilize and iron out the kinks of, is much less efficient than exploring D well enough to know whether you should build A, B, or C now.
Basically, some over-engineering, if done right, is not wasted. It cuts right to the heart of what you are dealing with, efficiently, and allows you to make (maybe) simpler but informed choices now as to how best to allocate your development resources now.
My version of this is the “N+1” principle. Build for one more foreseeable use case than you currently have. The domain model will click in when you need to generalize a solution, and you’ll gain the ability to see your particular solution as one of several to the problem, and thus evaluate fit and tradeoffs more clearly.
Don’t do N+2. The goal isn’t to predict the future, nobody can do that. The goal is a durable understanding of the domain and the best fit implementation you can get with that current understanding and resources.
That said, SQLite passes that bar for me in most use cases.
Haha, I just started doing this on my own. Found it helps the agents preserve state better. I typically ask them to design a DAG first based on a set of specifications and then execute it (each step stores something in a SQLite DB). Iteration is pretty simple then because I just ask for a tweak to one or two steps of the DAG, and then to re-run.
Funny how people are independently converging on similar patterns of "what works" here. Still feels like we're in the wild west with all these ad-hoc patterns of agent orchestration that people are coming up with.
The GitHub statistics for the project this website represents are insane. It has a sole author that has averaged approximately 20,000 lines of code every week in the past month. How do you even maintain that alone?
Instead of "just use Litestream," I'd like to see a review of different object stores one could use and which ones work well with Litestream. Is there a nice object store I could run in another Linux VM? As a hobbyist, which services providing an S3-like API make the most sense?
Probably going to get some winces for this but I do everything with flat files. Maybe my data aren't massive enough, but I mean I can do the relational thing by just having these metadata in some column, and returning rows that contain my desired information in these columns. Even if the file were too big to fit into memory one could just subset chunks of it and chew through. All this can be done with no dependencies, just base libraries of a lot of languages.
> The caveat is that Litestream replication is asynchronous. A restore can miss the newest local writes if the SQLite volume disappears before they are copied. That is fine for many AI and experimentation workflows
In short: SQLite is not all you need, unless you’re just experimenting don’t actually care about durability, in which case you also need litestream + object storage.
The suitability of Litestream for production disaster recovery is also an open question in my mind. I used 0.3.x for several years and when I tried to upgrade to the 0.5.x series there were runaway disk usage problems that would have caused downtime had they made it to prod. As far as I can tell these have not been entirely addressed, although recent bug reports suggest that they might be getting closer.
I want to love it, and I don't take open source projects like this for granted. But during my last production upgrade I chose to decommission Litestream in favor of a dumber, less granular solution using sqlite3_rsync and nightly backups because there is no point in using a backup system that is not rock solid.
Either you have durable storage or you do not. SQLite and Postgres can both ensure local durability of commits. If you want distributed durability, you need to ship that data elsewhere. That is another Postgres node, object store, whatever that’s still an external dependency.
I started setting up my workflows using Temporal. It deploys as relatively light weight local app. For an isolated local installation it uses SQLite. It makes the process of dealing with API retries and organizing workflows and tasks really simple. I recommend giving it a try. It is, philosophically, exactly what this article is suggesting, but it adds an incredibly rich and flexible interface for agents to work with. Additionally, the web UI makes it very easy to inspect workflows, review agent execution, etc. Temporal also encodes much higher reliability into your system, almost for free. Distributed and reliable systems are hard, don't reinvent the wheel IMO.
If you find yourself wanting things like an easy way to then introspect your SQLite database, figure out what is happening in the workflow, compose individual tasks, make workflows trivially callable, etc, give Temporal a look.
Alongside this, I have mostly moved away from files for agents. Markdown and JSON are great, but also feel like traps when building out smaller local apps. LLMs are great at SQLite and you can render anything you want out of it (Markdown, JSON, etc). It saves a lot of tokens when an agent can just query a specific row instead of having to fire up jq or grep through markdown. You get a nice portable self contained data management system that encourages agents to be more disciplined about how they structure their data than a bunch of files. It also continues to scale into MySQL/Postgres if your little local projects start to outgrow or become more formal, you already have schema and discipline around data.
It sounds like you’re running this mostly on a single machine? Temporal gets much more complex with scale. Cassandra isn’t fun to manage. Ringpop and TChannel are hard to debug when things go wrong. The SQL backend support doesn’t support horizontally scaled replicas (just single instance) due to consistency requirements. Depending on how your code is written, modifying code baked into workflows becomes complex, as anything that modifies the history event ordering breaks determinism in already-deployed workers.
We use it heavily and everyone who started on it doing simple scripting/automation all love it, everyone who built real production systems on top of it all hate it. Possibly operator error, but my experience hasn’t matched the rosy picture painted in these comments.
Word on HN is that you're either paying more money than you expected for temporal's managed solution or taking on substantial ops burden ultimately running their very heavy system yourself.
I wouldn't know, I've not done either, but I'd like to learn more from your or other's experience.
I told an agent to set it up for me for some local stuff. It is written in Go. It has a painless path to run on a local SQLite DB. My agents use it to organize and coordinate workflows. It handles retries and long horizon tasks fine. As far as I can tell for the core workflows and tasks pieces it’s great. MIT license. Like anything it isn’t free to manage but it offers a lot in return. High reliability systems are hard. Temporal only solves some of it. It is far better than rolling it yourself.
I think a genuine problem right now is people are building agentic work flows and learning the hard way highly reliable agentic work flows are hard. Agents are unreliable. They are both not deterministic and not the backing APIs have pretty high error rates. Temporal has solved that pain for me and made it easy to diagnose problems.
I don’t have anything really large scale running. But big enough that it takes billions of tokens and high reliability to finish.
whats an example of things that you have your agents do that use workflows and sqlite db
Could you expand on the "substantial ops burden"? Let's say you're using a managed Postgres instance as the underlying data store, how substantial is the ops burden in that case? I understand that temporal is actually a set of 4 or so microservices on top of a data store, but if you're already running a distributed system backed by k8s or something like that, it doesn't seem like it adds significant incremental ops on top of that. But I could be wrong.
As a dev I would tell you its an ops burden.
My devops coworker just shrugs, pumps out some yaml and helm and away it goes.
It really depends on your experience and tolerance for a lot of things.
Usually maintenance burden doesent start to make itself known till you get off the happy path or something breaks. Sometimes it can be a long while before that happens, sometimes it happens right away.
Very heavy indeed, people will confuse the durability that Temporal provide with all the other properties a distributed system needs. They will then think that Temporal will solve all their problems.
use oban and call it a day: https://oban.pro/
Could you give an example of a case where you'd use SQLite instead of jq or grep through Markdown?
My favorite lens on SQLite is that it is actually two things:
1. A robust durability implementation 2. A library of high performance data structure and algorithms
The fact this it's SQL is nice, but those two attributes are what make it great.
For example, I'm implement an in-process event log that I want to be durable. I started simple, but soon saw some edge cases and instead of playing whackamole I just swapped to using sqlite as an ordered kv store that gives me ACID.
Another example: ingesting multiple inter related datasets. Instead of a dozen hash maps in memory, I load them up into sqlite (no persistence) and then slice and dice as I need to.
It's a super useful tool.
mirrors my own experience creating a persistent event log. I started with JSON, then JSONL, etc until finally landing on SQLite.
> an example of a case where you'd use SQLite instead of jq or grep through Markdown?
Usually we end up writing a script to incrementally refresh a data-set I'm analyzing (or have someone send me a copy after they pull it).
I've been using sqlite for anything which needs an UPDATE - modifying a row deep inside the data-set with jsonl is a pain.
My github is full of java programs which update sqlite3 files with threadpools and a single big lock around the UPDATE (& then I write or have an agent write code to analyze it).
DuckDB is slowly replacing it in the context of python, simply because of the ease of pushing a UDF into the SQL.
Also because I really like expressing things as LEAD/LAG with a UDF on top.
UDF: User Defined Function
SQLite is more efficient for large data sets. A single markdown or JSON file needs to be streamed to locate a piece of data O(n). Updating an existing entry in a sequential file is even worse because you have to rewrite the file. SQLite has the data structures to quickly find data in O(log n) time.
The moment my JSON has any sort of depth and I need to write a parser for it and potentially account for unspecified behavior. JSON's nice when it's nice, but it's terrible when it's terrible. It's 100x easier to write SQL than writing jq and... dear god if I have to use grep -A or -B, I'm doing something wrong. Constraints are actually a good thing!
The underlying database isn't the most important thing. Just use SQL. Its namespacing (eg, through CTEs) is good and you're more likely to have colleagues who know SQL compared to jq.
Honest answer is: whenever your markdown or json files get to be big enough that grep/jq takes long enough that you get bored waiting for it.
> get to be big enough that grep/jq takes long enough
On a modern processor, that's about GBs of data typically, right?
Practically yes, but much earlier if agents are touching that data in my experience. Tens of GB even if you design well.
This reads like an advertisement for Temporal :)
I'm someone else who has inherited a bunch of ad-hoc orchestration systems and also used Temporal quite heavily. The latter does certainly come with some overhead (not so bad in the age of LLMs), but it also guides you along a well-trodden path of good practices. The latter being very important - it means that when you want to take on more advanced capabilities, you probably haven't painted yourself into a corner too badly and can take that on fairly easily. Think: retries, multi-tenancy, multi-lang, observability, etc.
Low key amazing tech, kinda like clickhouse - nobody is bragging it’s running their business
Well, just my experience. I installed it, had my agents configure it and it immediately solved problems I had with very little friction. Dealing with long running, long horizon agentic tasks that need very high reliability so I don’t have to babysit. I vibed the first version, realized I was reinventing reliable distributed systems. Stopped vibing and started surveying for something that fit :)
I can vouch for them too, being a super early adopter. One of the best early bets I've ever made. Awesome OSS product, glad the team decided to leave Uber to commercialize it.
Interesting about the files vs db approach. I have been going back and fourth. I landed on db as well.
I don't understand this obsession with SQLite for real, production apps. SQLite is an embedded database, completely unsuitable for managing concurrency. This is what database _servers_ are for, e.g., Postgres, MySQL, etc. Their entire job is to allow you to modify data from multiple processes, on different machines, at the same time.
This is a foundational principle of computer science. It seems to me that the "SQLite for everything" crowd is a little bit inexperienced.
You seem to have a rather limited understanding of what kinds of concurrency exist and how those needs are best met. Whether something is a server or not is not very relevant to this discussion.
SQLite is an excellent production db for many real world workloads, as has been widely documented. It is very different to Postgres, so requires learning a whole new skill set.
One way to think about it is that SQLite can work well for the parts of your system where there is naturally strong partitioning.
> SQLite can work well for the parts of your system where there is naturally strong partitioning.
Or the parts of your system that don't have big data and no need for massively concurrent writes. And that's the vast majority of systems!
You can do big data in SQLite. Concurrent writes, sure, I'd recommend something else.
If you think the majority of systems require massively concurrent writes, I think you need to look a bit harder. SQLite is, after all, the most widely deployed database system, ever.
It’s widely deployed as a local DB for local apps like phones, desktops, and web browsers. But it’s not the most used for distributed, concurrent web apps, which db servers were designed for. Maybe people are talking past each other, but that’s the debate I see.
Not sure why there’s a debate at all. The discussion is on using SQLite instead of jq and markdown files. People got lost on a tangent! :)
No it's not.. the context of other threads on this post (which mention jq) do not apply here. How poorly coded are you?
Like how poorly did Jesus code my DNA? Ask him. By him I mean ChatGPT Jesus mode.
There are many cases where SQLite + concurrent front end (like a go net/http server) can handle all the load that a service might ever conceivably have to handle, especially if allowed to scale up hardware over time. You can trivially scale up SQLite to, what, hundreds of thousands of tps?
The only thing you really give up is HA/failover and DR. But there are solutions to deal with those. And single-server systems are generally surprisingly robust (since, in the absence of very complex control planes, uptime goes down with more systems).
I was thinking of using SQLite on top of k3s/Longhorn to replicate it. Anyone do something similar? Folks mention light steam and aws but Jeff Bezos’s biceps are too much for me to handle.
A longhorn volume can only be attached to one node at at time. It can share it with other nodes over nfs. I don't think this is going to scale well.
Just use Postgres with ro replicas.
I'll echo the other response.
I've had pretty terrible experiences with SQLite and Longhorn/NFS.
It's just not the right database for pretty much ANY network based filesystem, where the locking primatives aren't as robust, and you might get two processes trying to hit it at the same time.
Frankly - they say this themselves: https://sqlite.org/howtocorrupt.html
As someone who runs a fairly big personal cluster backed by a mix of giant NFS storage for media, and relatively large longhorn SSD drives for configs/temp data...
I avoid sqlite backing like the plague. It will get corrupted. Period. It's not the db for this use-case, and I'll take postgres/maria/mysql/mongo/ANYTHING else over it.
If you do it - back it up ALL THE TIME, because it's going to get corrupted.
Sqlite is good for lots of stuff, but you're probably focusing your days on high-scale webapps that want sharding with networked DBs. That's one domain, and an interesting one, but there are lots of others.
I'm a big fan of re-evaluating prior "best practices" in light of technology changes, especially in ways that improve simplicity. Running my family's social media site off a single sqlite DB on a VPS is great. ~15 users, almost zero maintenance. I run my FreshRSS instance off of sqlite, as well as my "now" page. At work, I used sqlite for all kinds of things over the past decades: as an ad hoc job queue, as a quick way to ingest and query lots of logs locally, and present/filter in realtime with simonw's excellent https://github.com/simonw/datasette.
I don't think it's every "sqlite for everything" as much as it is "sqlite in lots of places you probably didn't think to apply it."
kentonv/Cloudflare's work on sqlite at the edge might have made this thinking a bit more popular, but it was always around. https://blog.cloudflare.com/sqlite-in-durable-objects/
I suspect being aware of all those little neat cases and wanting to leverage sqlite for them may be an indicator of experience, rather than the opposite.
> Running my family's social media site off a single sqlite DB on a VPS is great. ~15 users, almost zero maintenance.
Details, please!
That’s why there are billions of SQLite databases right?
SQLite is likely used more than all other database engines combined. Billions and billions of copies of SQLite exist in the wild. SQLite is found in:
Every Android device Every iPhone and iOS device Every Mac Every Windows 10/11 installation Every Firefox, Chrome, and Safari web browser Every instance of Skype Every instance of iTunes Every Dropbox client Every TurboTax and QuickBooks PHP and Python Most television sets and set-top cable boxes Most automotive multimedia systems Countless millions of other applications
https://sqlite.org/mostdeployed.html
That’s a comprehensive list of single user devices.
Single-user, a single natural person, doesn't striclty mean single-accessor though. I don't think anyone here is suggesting that sqlite is a viable replacement a for any networked client/server postgresql system, but it is certainly capable of handling more than the most basic 1:1 tasks. Beyond that, the point is that you only need a file, so when you have natural data boundaries, a lot of problems decompose to that single user/single concern paradigm.
'production' doesn't equal 'multi-user concurrent access'. There are production uses where sqlite is a valid choice even if it may not be the best choice for multi-user production use cases.
strawman? I have seen a dozens of these debates and never once have I seen someone questioned the validity of it for embedded usecases.
GP calls out concurrency as a weakness of SQLite. Most of the examples here don't experience the same load even a moderately sized web service experience day to day.
And no, being a part of the python standard library doesn't means it is being used by the average python user. These days I'd say at least half of them are just there for machine learning.
SQLite is good for read-concurrency, not great for write-concurrency.
SQLite requires writes run sequentially. Most SQLite write operations take single digit milliseconds or even microseconds. If your writes are inexpensive (inserting or updating single small rows) you'll probably never even notice the queue.
levkk is talking about concurrency. The list you gave doesn't explain high concurrency requirements for usage.
My read is that levkk is conflating concurrency with "real production apps" and this whole thread is starting to surface that "real production apps" and "high concurrency" are not measuring the same thing at all.
Sqlite is used in real production apps more than any other database.
Sqlite is also weak at any sort of write concurrency.
Both can be true.
sqlite is great for the contacts app on your phone, but that's it.
Hipp even said that it is not a replacement for a real multi-user, concurrent RDMS. Its primary competitor is "fsync".
If your data is naturally sharded (users) with writes happening within a single shard, parallelism becomes easy. The request is routed to the shard hosting the user's data and reads/writes locally.
This makes scalability _much_ easier to reason about. It's cut-paste, cut-paste. Every N users needs another shard.
It does buy you a _different_ set of problems, like cross-shard querying (analytics) and how to do load leveling as users age out.
But it avoids the whole shared index scaling problems from inserts/updates with large user counts.
It becomes a hierarchical instead of a relational database.
I had very good results giving 1 SQL DB per go routine, so the accesses were serialized up front, on a very high volume (130K requests/second) service. Exact transactionality was not a product goal, and the SQLite was just to backup the in memory state. If we lost a little due to abend or something, that was ok (although for normal maintenance it caught SIGTERM and stopped the listen and then waited for in flight calls and then flushed the remaining changes to SQLite; then on startup it would read the SQLite into memory to populate before taking the listen; persistent storage across container runs, and never both reads and writes to the same file at the same time. (It also just closed the DB and opened a new one when it hit some limit of rows, so as not to fill the disk; the max size of the SQLite corresponded to the max size of the LRU map being served from in memory; then it just flipped A / B between "a full memory worth of data stored" and "the currently updating state." A lot easier than having to write out proto bufs to disk or whatever I would have done for transient (during restarts/maintenance) persistence.
Woof. That sounds very complicated. If you need that kind of write concurrency, use an unlogged table in postgres [0]. Then you don't have to invent a whole sharded thing yourself.
[0] https://www.postgresql.org/docs/current/sql-createtable.html...
There are so many unfortunate footguns with unlogged tables, that I'd argue that the goroutine route is preferable.
What are the "footguns" with unlogged tables in Postgres?
Such as?
Thing is SQLite scales better than both those network databases [1] if you're prepared to stick with one big machine (+ a standby).
This is even more obvious when you start doing transactions processing an row locks across the network limit you to 1-3k TPS that you cannot scale out of (Pareto distribution is merciless).
[1] - https://andersmurphy.com/2025/12/02/100000-tps-over-a-billio...
Seeing as I can get about 200K TPS from a networked DB in my environment, I have to question your setup here.
In the real world we are looking at things like RPO (recovery point objective) and RTO (recovery time objective). You need to consider HA and DR. It’s in these areas where SQLite does not scale.
That’s why I struggle to see the fit for SQLite in any sort of multi-user server environment. If you need the data to be durable, then the bigger DB’s have the tools. If you don’t need the data to be durable, just keep it in memory. I’m sure there are niches I am missing.
there is a difference between concurrency in a distributed environment and concurrency on a single machine across processes. SQLite is incredibly useful for the latter.
you seem like the inexperienced one to me..
> This is a foundational principle of computer science
How exactly is this a foundational principle of computer science?
I think you'd be surprised to learn how many real production apps are actually running on top of SQLite (by way of Cloudflare D1).
Many DB servers are built upon embedded DB primitives (like RocksDB), that doesn’t mean the primitives are sufficient on their own.
Computer science no more get its hands dirty with concrete software than physics primarily being about building bridges.
It is not «a foundational principle of computer science».
Isn't concurrency also limited by your machines disk speed for writes, what difference does it make if you write sequentially vs concurrently? Why does concurrency even matter for databases?
> Isn't concurrency also limited by your machines disk speed for writes, what difference does it make if you write sequentially vs concurrently? Why does concurrency even matter for databases?
For a simplified example, having three processes reading blocks X, Y, Z in parallel is much faster than having a single process read block X, wait for the read to finish, read block Y, wait for the read to finish, read block Z and wait for the read to finish.
> Isn't concurrency also limited by your machines disk speed for writes
Yes, in theory: given a large enough database, and a disk that can only do one operation at a time, and a large enough operation that touches enough of the database. In practice, in a SQLite single tenant scenario? No, not at all.
> what difference does it make if you write sequentially vs concurrently. Why does concurrency even matter for databases?
As soon as your codebase involves reacting to events independently of a user taking action it becomes a practical concern. Generally, this is a broad question and has 1,000,000 answers.
EDIT: Originally I had "I think you understand generally, no?" appended but realized that's not helpful at all, if you did, you wouldn't be asking.
Something that may help is imagining what'd happen if a DB wasn't thread safe / didn't allow multiple writers. Ex. in SQLite's case, it allows multiple write operations to take place but there's a one-at-a-time queue. If we didn't have databases that were able to execute multiple writes simultaneously, you'd need a separate database for each concurrent writer you expect, and you'd effectively have a global lock. Orderly scaling would be ~impossible unless you did something crazy like have a single server per user
I guess I need to dive deeper into this as I do not understand the implications you gave me, but I appreciate the attempt. Generally I understand why concurrency is good in many cases, I just dont get why its important for database stuff too.
Edit: thanks for clarifying in the edit, makes a lot more sense.
Imagine if every tweet had to go through a one-at-a-time queue before being persisted. There's about 6000 tweets per second, so you would have to be able to save them at <0.17ms per tweet or else you would become backlogged. If you are getting backlogged, you have to buffer those incoming tweets somewhere until they can be writted, and eventually that buffer gets full and you start losing tweets.
Maybe that too is a native question, but there's a large scale between single user and 6000 tweets per second - most of our apps will never reach anything approaching even one save a second. So where to draw the line? I do far have gone the sqlite route for my hobby apps as it's so easy to handle and doesn't require setting up two docker containers for a single app. Am I drawing myself in a corner in case my apps ever do become relevant?
Excellent question, and I spent so many years asking myself it, this over and over. You asking it made me realize I just...don't anymore. So allow me to blather a bit / free associate because I won't be sure why myself until I've written it out.
TL;DR: whatever works for you is the right decision. (which isn't helpful, I heard this so many times and as the recipient, I thought "That's nice. Now how do I choose what works for me?")
I finally had to use Postgres a couple years ago after a career of only SQLite - startup founder & iOS app developer using SQLite, turned Googler on Android, turned doing-my-own-thing.
In retrospect, I have made only one bad decision:
I went way out of my way to make SQLite work at my 2009-iOS-startup. It was a restaurant point of sale system, and to allow a networked system, one of the iOS devices would act as a server. This was a really cool trick, even an advantage in marketing that was appreciated by users. It meant the restaurant could continue to operate if the internet went down. But it eventually became clear owners loved having internet-based access too, ex. to do reporting/financial analysis over the data. And I kept contorting, instead of moving past my fear of getting into things I didn’t know, I instead did some like rudimentary thing over port forwarding. The bad decision here was riding one horse for so long and letting it affect the product, having a real server database would have allowed for a lot more features, think, first party gift cards, and a 100 others.
After leaving Google I needed server-side storage and fought and fought to avoid it. Then it turned out Postgres is easy and, just like SQLite, 99.999% of the time I don’t even know I’m using it.
In retrospect, there’s ~0 switching cost to these, particularly in age of LLMs. If you do need something more one day, it’ll be easy to do, and if you have to do it in a rush because you’re successful, you’re in Good Problem territory.
Hope that helped, after writing it out, dunno how convincing it is. Feel free to follow up, I appreciate the curiosity/framing because I had the same thought for so long.
If we imagine 1 tweet = 1 transaction, that's only 6k tps. 6k tps is completely achievable, dare I say even pedestrian for an optimized database. And most systems are operating far below the scale of Twitter/X.
Sqlite can quite easily do 5000+ insert+commits per second on typical NVMe drives.
Speed is rarely the constraint that makes it unsuitable for an application.
Round trip time is actually much faster than Postgres, since there’s no need to touch the network. You can get massive single threaded throughput. In order to achieve comparable throughput in Postgres you need a large amount of concurrent connections, since each conn spends most of its time passing messages, deserializing etc (with a much larger total amount of overhead). There are a surprising amount of bottlenecks and misconfiguration that can tank performance of networked systems, particularly DBs.
Like you suggest, the reason for not picking SQLite is not reliability, speed, etc. Networked DBs allow decoupling between app and db servers, which have operationally different characteristics. But most importantly, you can have multiple apps access the same DB at the same time. Eg analytics, one off queries, any 3p app that interacts with your data directly.
While I understand your point and like the explanation, I gotta make the joke that some Tweets should be lost
I worked on an app that had sqlite databases per user... it was fine.
Well if you run a tiny single-threaded app then SQLite is a nice simplification over spinning up a separate machine for Postgres.
Or you can run postgres on the same machine as the application, which lets you much more easily migrate if the time comes when you need to scale to multiple application servers.
There's a world between "local file" and "network DB server", running a DB server locally has lots of benefits from being able to easily query from outside if needed to forcing you to consider concurrency without the latency overhead of a network hop.
This decision tree doesn't make much sense to me. Why you someone forego performance today in favor of adding a completely unnecessary network layer to every DB query in order to "satisfy" future imaginary "scaling concerns"?
Because you don't add a network layer by running a database locally.
That's still orders of magnitude more complexity for no real benefit. A migration from sqlite to postgres, if really required, is not that hard.
Yes, postgres should support a superset of SQLite functionality.
Now you've added a substantial dependency, and annoying setup requirements. Good luck doing this for a native app on mobile or desktop.
Obviously SQLite is the best choice for a mobile or desktop app, that's not what's being discussed here.
I use postgres for very simple apps. I have a Dockerfile I use in my boilerplate repo. It takes a single make cmd for me to build, start and run migrations. Its as simple as using sqlite.
Its 2x the infra. You have to manage an additional process, auth, backups, logging, etc.
But now you have another process to babysit. How do you keep it healthy? And you have to ensure the client-server communication won't break.
For me the main benefit of sqlite is that it's a library rather than an app.
> But now you have another process to babysit. How do you keep it healthy?
I've been assured by many HN users that running apps/sites on a single VPS requires near-zero maintenance or monitoring to achieve acceptable uptime 24/7/365 for years on end, sooooo...just pretend it will never fail like your main server process?
Ive been assured by many HN users that you must have 24/7/365 uptime for everything in case one of your 10 bi-monthly users decides to log on.
Call me old-fashioned and quaint, but I don't like to build software that doesn't work all the time if I can help it, whether it's for 10 users or 10 million.
24/7/365 is needed (or achieved) just about never. our big tech is proving 90% will soon be utopia as well. being down has always been fine for 99.999975% of all projects on the planet.
Ok, now tell me the stat by percentage of overall market revenue rather than project count
I have boilerplate for client-server communication that makes it pretty trivial to build on top of.
Im not saying that sqlite isn't useful, im mostly saying that using postgres doesnt have to be complicated.
It's almost as if Postgres isn't perfect, and one size shoe doesn't fit all.
Some people want some of the benefits you get from SQLite.
SQLite is obviously not perfect, but it's an incredible piece of software, and people regularly find good ways to make use of an excellent pieces of software.
Someone with experience would know that concurrency isn't a universal requirement.
Scale to zero is very useful.
I mean - I agree for the typical multi-user, SaaS webapp. But I don't think that's what these folks are proposing. If they are - yeesh, count me out.
If on the other hand they're talking about single-user, software in the small - hell yeah. In fact, I'd also promote DuckDB in this regard (mostly for analytics) - with the power of a single machine these days, you can do a surprising amount and never have to worry about distribution. Unless you know you'll have to, in which case you're probably just digging yourself a hole?
The reason the parent post is complaining that it doesn't make sense, is because people have indeed pushed the idea of using SQLite as an alternative for web apps like that.
The typical multi-user SaaS webapp doesn't have anywhere near enough users to overwhelm a single SQLite instance. Of the few that do succeed to the point where that's no longer true, a significant fraction can use techniques like sharding to stretch SQLite further.
And of course there are now several responses proving your point.
Are you one of my enterprise customers? What if your workload does not require write concurrency?
So teach them. If you want to bring up computer science fundamentals, the question is where does SQLite sit with regards to the CAP theorem. Consistency, Availability, and Partition tolerance. SQLite isn't a distributed system, so there are no partitions to tolerate, so it's a CA system. Other databases make different tradeoffs. For systems that don't need concurrent writes, SQLite is pretty great! There are no users to manage, no permissions, no daemon to run, no server and port to mix up. Just open a file on disk using a library.
Strawman, no? "run an Obelisk server with a SQLite database", now we're distributed.
SQLite is a nice local store. It's this server stuff that I don’t grok, well, yet. :)
In the beginning apps and SQL were co-mingled. Oracle eventually came along and noticed that people wanted SQL on the network so that many different apps, running on different computers, could all access the same data. But then people realized that clients really want rich, 'tree'-like data, not simple rows and columns, so people started sticking networked databases in front of networked databases to serve as a transformation system. And now people are realizing that the second networked database layer is redundant and never used beyond what is required for the client-facing network database, so they are moving the storage back into the first network database layer, just like Oracle did all those years ago. What is old is new again.
What changed is SSDs. SSDs means that local access is faster than hitting the network. An expensive SAN stopped making sense because of this in specific cases. So for read heavy, or even read only database loads, you copy the SQLite file to the node that's processing the file, and just update that file whenever the data does get changed.
I absolutely 100% do not understand it either. At all. Every time I try to over the last year or two I come away with the conclusion its something that sounds cool (to me too!) but is guaranteed to cause more problems than more obvious solutions.
That being said I'd kill for someone who used it and benefited to explain it to me in a practical sense. (specifically where syncing is involved, and syncing a subset of the SQLite is necessary. If it's "just" a document store thats treated like a blob for syncing/backup, that's familiar. If it's all in one storage but only local, that's familiar.)
Re: TFA, I guess it would have helped if I knew what Obelisk was, which is on me, and a more in-depth explanation of how this ties into AI/agents, which is on the industry/writer.
It's very likely that you have multiple SQLite databases in your pocket right now. It's one of the most widely deployed pieces of software on the planet. If your conclusion is that it's guaranteed to cause more problems than other solutions, then that's on you.
Correct! I'm not "worried" about it, I've been putting SQLites in your and my pocket for the last 17 years.
I don't want to be glib and leave it there, even though I'm slightly annoyed you missed several sigils in my post that I was well past that.
The point is, for the not in your pocket case, for the not a singular document store case, I'm curious what the use case is.
How many production apps do you think have enough users to justify these huge DB servers?
Huge?
Sure, SQLite doesn't solve every problem -- but in many cases it solves the need at hand with the reward of one less piece of infra required to support it.
I see obsessions with tooling/solutions constantly from experienced devs who fall in love with the original solution and think it's the only way to do things -- so the experience part cuts both ways.
sqlite is more like a file format than a database. it competes with .xlsx.
> "SQLite for everything" crowd is a little bit inexperienced.
every time i see it in a real application, it becomes a huge focus of issues (for example: jellyfin, hermes, openwebui, comfyui)
What kind of issues commonly arise?
anything that requires more than 1 user or not being down all the time
I've replaced all of these with Go + SQLite:
1. Intercom 2. Zendesk 3. Email marketing 4. Kanban 5. Todo 6. Our billing stack 7. Our issue tracker 8. Our forum 9. Uptime monitor 10. PagerDuty (clone)
I have dozens of products I sell, so I thought: why not build everything ourselves?
All of these run on the same server and use very little memory. I replaced all the SaaS tools we used with these.
I also moved to dedicated servers and dropped costs to about 1/10th of what we were paying for managed cloud solutions, while maintaining the same HA and even achieving lower latency (partly because noisy neighbors on VPSes were increasing tail latency).
We used to spend a ton on this stuff. These have now been in production for four months and have only needed minor updates.
Deployment is dirt simple. No Docker, no Kubernetes—just a systemd service and a binary built on the dev machine and deployed.
We also used to pay for services like MaxMind and IPData. I ended up hand-rolling my own IP geolocation service, which, in my tests, outperforms most existing solutions.
It all started with replacing Uptime Robot. Then I got more confident and replaced PagerDuty. After that, I replaced Intercom.
Finally, I had always heard people say, "Don't build your own billing stack." But I said YOLO, let me make that mistake myself. So I studied our existing billing solution, developed my own, and rolled it out. So far, we've had zero issues with it.
Caddy in front.
I found that we only use maybe 1–5% of the features most SaaS products offer, while the features we actually need keep getting buried deeper and deeper inside these "enterprise-grade" platforms, making our workflows more difficult.
I won't show my commercial products because our partners and clients probably wouldn't appreciate knowing how cheap I am—but I call it being resourceful.
I can show my free app, though, which has 20,000+ users and was launched recently: https://macrocodex.app/
It only uses the Zendesk clone. Email is handled through Cloudflare routing, so we pay almost nothing to run the app.
I rolled my own uptime but how do you vary region or do residential testing ?
I actually don't.
I just have uptime service hosted outside of our main infra. It connects to my service called Siren, which alerts me on my phone with an alarm on full volume with SWAT cat intro.
It's good enough for what we do, barely have any downtime. But it helped me figure out 6s downtime we would experience when our spot instances get knocked out, so it helped me increase health check frequency
6s downtime is a lot when you are getting hammered at 100 RPS.
There's a wide gap from files to multipartition databases. Running databases in a container is not for me sorry whenever real production stuff is on the table.
Personally, lots of ETL can just be taken care of locally without involving enterprise databases. In such cases, DuckDB is 5x-10x better than SQLite and orders of magnitude simpler/faster than spinning up a dedicated Postgres database.
For general scripting, there's no match between a 20-lines awk script and a much cleaner, robust, maintainable equivalent SQL script based on DuckDB.
I just hope MotherDuck don't need to pump/dump for IPO - it would be sad losing that tool for the usual corporate greed.
Hello, DuckDB devrel here. First, thanks for the kind words :)
Second, it's funny you should mention the 20-line awk script. I was making a very similar argument yesterday at the Ubuntu Summit: at some point, using shell scripts with GNU coreutilus becomes impractical, while DuckDB SQL scripts scale better in terms of complexity and maintainability (and often also performance). My slides are here: https://blobs.duckdb.org/slides/duckdb-ubuntu-summit-2026.pd... (pages 32 to 36)
Third, MotherDuck develops a closed-source DBaaS on DuckDB. They build on DuckDB, and you connect to MotherDuck with DuckDB but they are a separate VC-funded company headquartered in Seattle. DuckDB is developed by DuckLabs, a bootstrapped (revenue-funded) company in Amsterdam. And the IP of the project is in a third organization: a Dutch non-profit called the DuckDB Foundation. For details, see https://duckdb.org/faq#how-are-duckdb-the-duckdb-foundation-...
I wrote a library[0] to let you concurrently update a sqlite db in s3 safely. It uses the little known sqlite sessions extension plus s3 compare-and-swap on a small metadata file to make this work reasonably efficiently and safely. I have been enjoying it for a bunch of small projects where I want a lambda function to have a db for state but I don't want to pay for a full database instance.
[0]: https://github.com/psanford/s3db
SQLite is surprisingly performant for single node applications even when comparing to Postgres. Postgres consumes a lot more memory and requires IO to hop through IPC whereas you can keep everything in process in SQLite with a shared connection pool.
I've been testing different storage engines for my agent harness and I can get up to 7.5k concurrent sessions on a single vCPU with SQLite whereas Postgres crashes or runs out connections.
[0] https://github.com/impalasys/talon/pull/23#issuecomment-4577...
When used properly, SQLite is effectively an in-process method invoke. If the only remaining things in the way are your runtime, kernel, file system and a local NVMe storage device, you may find it massively outperforms hosted alternatives.
Leaving the current thread is where you lose the game in terms of latency. SQLite can work on timescales measured in microseconds if you don't force interthread communication.
> an in-process method invoke.
Pedantically it's an in process virtual machine for operating on structured data. Which is precisely where it shows it's weakness, in my experience, when you end up with complicated table structures and complex join mechanics you then need to start thinking ahead of the query planner and VM code a bit in order to maintain reasonable performance.
There are more than a few unusual things worth knowing:
https://sqlite.org/optoverview.html
> SQLite is surprisingly performant for single node applications even when comparing to Postgres.
In the context of SQLite being understood to be a quite excellent piece of software - shouldn't we expect it to be?
In the context of a single-node, Postgres is overkill. It should not be expected to be competitive with SQLite.
This is almost like benchmarking an in-memory HashMap to Redis and being surprised that it performs well in ideal conditions.
Yes, agreed on SQLite/Postgres. But I'm going to benchmark RocksDB next and see what the performance characteristics are. I suspect the LSM tree storage engine of RocksDB might perform better since agents are so write heavy when running highly concurrent workloads. After all, you are streaming LLM tokens into disk and fanning them out to subscribed clients.
You might want to start here: https://docs.cozodb.org/en/latest/releases/v0.3.html
That project has 0 commits for 2 years.
What does that have to do with their research on the exact topic OP was looking into?
Abandoned research of unknown quality is strong signal to downprioritize that direction
Sounds pretty stable
I started using SQLite for a home project after years of reading about it, I was shocked at the poor type system coming from Postgres. It is really inferior, not sure why it gets so much praise.
https://sqlite.org/datatype3.html
https://www.postgresql.org/docs/current/datatype.html
Working with date/time feels like using a 30years old database, nothing is enforced at insert. Really someone needs to explain why so many people like it.
You can use strict tables: https://sqlite.org/stricttables.html
This could enforce dates are strings. They wanted to enforce dates are dates I thought.
I see. The strict tables page did not mention the date and time functions.
Python would show the 1st line always? Or the failed part?
This is unreasonable for a very common type I think.
Storing dates as INTEGER (year * 10000 + month * 100 + day, e.g. 20260530) is not so bad. Proper date / timestamp types would be great though.
"feels like using a 30years old database"
Yes, this is basically my only issue with SQLite. SQLite with a strict type system would be great.
This is the fault/price of backwards compatibility. Most users of SQLite should just fire off a few pragmas on each connection:
You might need additional options, depending on the binding. E.g. Python applications should not use the defaults of the sqlite3 module, which are simply wrong (with no alternative except out-of-stdlib bindings pre-3.12): https://docs.python.org/3/library/sqlite3.html#transaction-c...Also use strict tables. https://www.sqlite.org/stricttables.html
While it has bad ergonomics, you can also use CHECK constraints. For example, using sqlite's built in date support, it's possible but awkward:
The IS NOT NULL is needed because date returns NULL for invalid dates; the other check because it also accepts Julian days (date('2026') is sometime during year 4707 BC).The price of compatibility could be a pragma.
it's a single file.
It gets praise because of stuff other than the type system.
I agree it is disappointing, especially before strict tables.
You should check out DuckDB which is basically SQLite but with proper types. Although it is also OLAP (struct of arrays) rather than OLTP (array of structs) which may have worse performance for typical SQLite loads. In practice I doubt it matters if you have an application where you're considering either.
Read their docs
I went from using the various big player postgres clusters to SQLite, we have an MAU in 7 figures, all backed by SQLite durable objects. We have to think differently about the access patterns but the benefits have been worth it.
Good framing. SQLite is often enough when the main problem is making workflow state durable, inspectable and easy to recover.
Can’t wait to see the next iteration of this idea with “Logs are all you need for durable workflows.”
Wait no further. It's already happening.
One reason why a "logs are all you need" solution may fail: untrusted-log-as-injection[1].
Check those SBOM, and don't forget to include their CICD pipelines[2].
[1] https://news.ycombinator.com/item?id=48315440
[2] https://github.com/jqwik-team/jqwik/issues/708#issuecomment-...
In all seriousness, I’d take a “s3 is all you need for durable workflows” and use it in data processing applications that move data from s3 -> s3 with no other dependencies.
Are logs all you need for durable workflows? I'm confused here. How'd persist and query nested or related data over logs? By logs I assume you mean something like elasticsearch or meilisearch?
I assume they meant a log like a WAL. A WAL should be (quite literally?) all you need for durable workflows.
A distributed WAL (to survive a machine death) would also probably be something I'd want, and … something I'm not sure you're getting directly from SQLite.
Is it common to use logs as a proxy for write-ahead logs?
Pretty much every durable system has an intent log of some sort. The log provides durability, the database system just integrates that log into a more queryable format.
I swear it didn't occur to me that that mean WAL, makes much more sense now LOL
Log as in the structure.
I read the parents comment as sarcasm and not a serious suggestion.
Shortly followed by:
"Sockets are all you need for durable workflows" and then finally "Kernel primitives are all you need for durable workflows."
But seriously, part of being a professional is using the right tool for the job.
All this SQLite hate from big vector db... leave SQLite alone!
Back in the day, I wrote a simple job queue with SQLite
https://github.com/guilt/squeue
It did the job, was fairly easy to use.
Litestream releases 5.9 and newer have a bug that causes instances to sync an insane amount of data. a DB with <10K of data in it and practically no writes/reads causes something like 10GB of daily replication traffic. For my toy project that got needlessly expensive.
Meta comment: This is a domain under my countries TLD (Slovakia) and it is one of the handful of words that are a word with the TLD in my language (and coincidentally) also in English. Every now and then, I will check on the domains with a retrograde dictionary for domains that have this property and root of this particular domain had a roundcube email server on it (can be checked on archive.org). After further checking, the local company actually named themselves Obeli s.r.o. (s.r.o. is Ltd), presumably so that they could use a domain that is a real word when said together with the TLD. (EDIT:) Forgot to write the thing I wanted to mention in the first place: it appears the domain must have lapsed and/or the author bought it from the company that was using it.
Another fascinating fact: our countries TLD has been stolen Ocean's 11 style (I am not kidding). After Czechoslovakia split into Czech Republic and Slovak Republic, the newly created Slovak .sk TLD has been under the care of people from the local university. The university also had some offices that they were leasing out. Someone had leased this office space (EDIT: this is important as this means they had the same physical address), created a company that had the same name as the NGO that was taking care of the domain, so e.g. the NGO was named "My Company o.z." and the perpetrator created a "My Company s.r.o." (our countries version of the american Ltd). This person then wrote to ICANN to change the address to the "My Company s.r.o." presumably under the pretense that this was just an administrative error and from this point, they have functionally taken custody of the TLD. I was not able to find how they did it technically, but I presume they persuaded ICANN to then point to their servers instead of the real ones. After this happened, it seems that no one noticed for some time. When they noticed, they tried taking it back, but they weren't able to. For some inexplicable reason, the government during that time (Šuster era, early 2000s) gave the new company a contract that was functionally uncancellable from the government side. Later governments made this even more uncancellable and in 2017, then Minister of IT (and as of this day president!) Pellegrini made the contract literally uncancellable. As a result of this, we have one of the most expensive domains around (18e/year, rising each year for no good reason). (EDIT:) The company running our countries TLD is now a foreign entity that the whole thing has been sold to (multiple owners over time) and we as a country have no control over if I understand it correctly.
I might have gotten some details wrong as I am writing this from my memory of researching it a couple of years back, but you get the idea, crazy stuff. Here is an article in Czech [0] that tells the story a bit better, but you have to translate it.
[0] https://www.root.cz/clanky/pribeh-domeny-sk-aneb-kradez-za-b...
// EDIT: I have found that the article actually links the movement to return the TLD back [1]. It also has a story tab [2], so they have something much more precise than the paraphrasing I wrote.
[1] https://www.nasadomena.sk/
[2] https://www.nasadomena.sk/historia/
That's a crazy story. National TLD is a weird business from the beginning.
if you have an application that needs to maintain state in a non-critical section or if you discover that using SQL is actually a good idea for some tasks (even in critical sections), SQLite is not only a good choice but it will save you a lot of time coming up with a brittle custom solution.
maintain an in-memory SQLite db and work it with SQL commands, and if you also want to preserve state across application restarts you can routinely save to disk or load from it: <https://www.sqlite.org/backup.html#example_1_loading_and_sav...>
this also happens to be the most convenient file-format (aka. application-format) I ever worked with.
If you're just doing workflows from a single node, i guess it can be ok as long as theres a single writer. But scaling across multiple servers it clearly is not all you need.
> Postgres ... is the right choice when you need higher availability, broader shared scalability, or other deployment properties that are better served by a network database. It is also the better fit when asynchronous replication to object storage is not the durability model you want... Many workflow systems do not need that on day one and should not start with more infrastructure than their state actually demands.
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I see this kind of YAGNI thinking a lot, but in my view, it must be balanced against the effort you'd put into resolving any edge cases and adapting current architecture to your use case.
Imagine you deploy Sqlite, and thought it fine by itself, you keep running into some unforeseen challenges with the use to which you are putting. YOu'd need to sink valuable time and effort into addressing those. Then, when you have outgrown it, you'd beed to spend additional valuable times dping the same with Postgres.
This is why, when it comes to Architecture, I increasingly find my myself over-enigneering a bit. Assuming there is a good chance you might need to upgrade your architecture in the not too distant future, that approach is actually kind of very efficient. I find that I am able to uncover a lot of potential gotchas, which feeds back into the what the simplified current architecture should be, and helps me understand the roadmap I'm facing very well. I also avoid wasting too much time going too deep in directions that make sense now, but need a lot of plumbing to get right, when I can see that I'd likely have to throw it all out in a few years. Going from A -> B -C -> D, where each step is the optimal good-enough-for-now architecture but which requires a lot of work to stabilize and iron out the kinks of, is much less efficient than exploring D well enough to know whether you should build A, B, or C now.
Basically, some over-engineering, if done right, is not wasted. It cuts right to the heart of what you are dealing with, efficiently, and allows you to make (maybe) simpler but informed choices now as to how best to allocate your development resources now.
My version of this is the “N+1” principle. Build for one more foreseeable use case than you currently have. The domain model will click in when you need to generalize a solution, and you’ll gain the ability to see your particular solution as one of several to the problem, and thus evaluate fit and tradeoffs more clearly.
Don’t do N+2. The goal isn’t to predict the future, nobody can do that. The goal is a durable understanding of the domain and the best fit implementation you can get with that current understanding and resources.
That said, SQLite passes that bar for me in most use cases.
Haha, I just started doing this on my own. Found it helps the agents preserve state better. I typically ask them to design a DAG first based on a set of specifications and then execute it (each step stores something in a SQLite DB). Iteration is pretty simple then because I just ask for a tweak to one or two steps of the DAG, and then to re-run.
Funny how people are independently converging on similar patterns of "what works" here. Still feels like we're in the wild west with all these ad-hoc patterns of agent orchestration that people are coming up with.
Same. The prompt was essentially, every checkbox in this PLAN.md should be task in SQLite.
The GitHub statistics for the project this website represents are insane. It has a sole author that has averaged approximately 20,000 lines of code every week in the past month. How do you even maintain that alone?
It's close enough that DBOS does support SQLite. [0] The default for prototyping is SQLite, but sure you can run it in production if you wanted.
Obligatory list of workflow engines and libraries because it's such a common need that a lot have rolled their own. [1]
[0] https://docs.dbos.dev/python/tutorials/database-connection
[1] https://github.com/meirwah/awesome-workflow-engines
Agreeing on the point, I needed NoSQL version on the similar uses, I've used TinyDB : https://mburaksayici.com/blog/2024/09/21/easy-to-use-nosql-p...
Cloudflare durable object is implemented with SQLite (or some variant of it)
Idk if this article was vibe written or the author just "got adjusted" but it's clearly is, and it's unreadable. Man this becomes anmoying
Instead of "just use Litestream," I'd like to see a review of different object stores one could use and which ones work well with Litestream. Is there a nice object store I could run in another Linux VM? As a hobbyist, which services providing an S3-like API make the most sense?
Litestream is just the replication layer, it works with any S3 compatible storage, and all of these in their guide as well https://litestream.io/guides/#replica-guides
No, I know that already. That's not a recommendation. Some storage layers have to be better than others on price, reliability, and so on?
Think Wirecutter, not install guide.
Related piece I wrote some time ago: https://www.morling.dev/blog/building-durable-execution-engi...
Excellent write up and inspired me for our next IA design run. After reading Fly’s Litestream work it makes me think this is a solid option.
Surprised no one has mentioned Turbopuffer yet [1] which natively supports dense vector similarity and BM25 keyword indexes out of the box
[1]. https://turbopuffer.com/
Big complex data model with ambiguous query patterns? Postgres
Small, well defined, data model with known query patterns? Bespoke model
There probably is a place for sqlite and my project space so far hasn't yet well-aligned with it.
Probably going to get some winces for this but I do everything with flat files. Maybe my data aren't massive enough, but I mean I can do the relational thing by just having these metadata in some column, and returning rows that contain my desired information in these columns. Even if the file were too big to fit into memory one could just subset chunks of it and chew through. All this can be done with no dependencies, just base libraries of a lot of languages.
No, mmap is all you need.
Until you scale past one machine…
Isn’t this very similar to cloudflare durable objects & workflows?
The biggest annoyance about SQLite for me is no ability to:
You have to create a new temporary table with correct schema, copy data into this new table, drop the old table, and then rename the temporary table.They've been improving that recently:
2026-04-09 (3.53.0) - "Enhance ALTER TABLE to permit adding and removing NOT NULL and CHECK constraints"
I use my own sqlite-utils CLI/Python library to work around these limitations: https://sqlite-utils.datasette.io/en/stable/python-api.html#...
Ahh that's very nice. Unfortunately the default version of sqlite3 provided by Debian Trixie is 3.46.1.
Is this just a AWS ads?
Related:
Building durable workflows on Postgres
https://news.ycombinator.com/item?id=48313530
Surfacing for the thread:
Armin Ronacher's "Absurd Workflows: Durable Execution With Just Postgres" https://lucumr.pocoo.org/2025/11/3/absurd-workflows/
And all you need is pen and paper to do calculations.
Files is all you need.
https://xkcd.com/378/
Post It Notes will do if you have a good system.
Those who don't understand Unix are condemned to reinvent it, poorly. - Henry Spencer .. via https://github.com/globalcitizen/taoup
Postgres doesn't cost any extra lol
it does cost network stack
> if you already trust your database, you do not need a separate orchestration tier
Wow. Really? I thought I needed to use an overpriced cloud SaaS for everything.
> The caveat is that Litestream replication is asynchronous. A restore can miss the newest local writes if the SQLite volume disappears before they are copied. That is fine for many AI and experimentation workflows
In short: SQLite is not all you need, unless you’re just experimenting don’t actually care about durability, in which case you also need litestream + object storage.
Right.
The suitability of Litestream for production disaster recovery is also an open question in my mind. I used 0.3.x for several years and when I tried to upgrade to the 0.5.x series there were runaway disk usage problems that would have caused downtime had they made it to prod. As far as I can tell these have not been entirely addressed, although recent bug reports suggest that they might be getting closer.
I want to love it, and I don't take open source projects like this for granted. But during my last production upgrade I chose to decommission Litestream in favor of a dumber, less granular solution using sqlite3_rsync and nightly backups because there is no point in using a backup system that is not rock solid.
try setting up replication/failover in postgres, it's much more work.
Postgres also does not synchronously replicate for free. You can setup both to get a confirmation write if you require that durability.
> postgresql also does not synchronously replicate
By default. Generally your primary database is in a completely different failure category than a kubernetes node running an ephemeral workflow pod.
Either you have durable storage or you do not. SQLite and Postgres can both ensure local durability of commits. If you want distributed durability, you need to ship that data elsewhere. That is another Postgres node, object store, whatever that’s still an external dependency.
Not for free, but without the needing additional software.
That’s about the local transaction, not replication. SQLite WAL also gives you strict durability.
S3 is strongly consistent, if you need it anyways you can just use s3 keys to deconflict and store the workflow state.
Yes, but directly using s3 as a key-value database is completely different from using SQLite + litestream.
"Durable workflows without the durability"
That's distributed workflows :)
i mean it's durable as long as nothing crashes or litestream has a data corruption bug which only happens every other release...