"Kamal runs blue-green deploys — it starts a new container, health-checks it, then stops the old one. During the switchover, both containers are running. Both mount ultrathink_storage. Both have the SQLite files open."
WAL mode requires shared access to System V IPC mapped memory. This is unlikely to work across containers.
I don't know much about Kamal but I'd look into ways of "pausing" traffic during a deploy - the trick where a proxy pretends that a request is taking another second to finish when it's actually held in the proxy while the two containers switch over.
Pausing requests then running two sqlites momentarily probably won’t prevent corruption. It might make it less likely and harder to catch in testing.
The easiest approach is to kill sqlite, then start the new one. I’d use a unix lockfile as a last-resort mechanism (assuming the container environment doesn’t somehow break those).
I dug into this limitation a bit around a year ago on AWS, using a sqlite db stored on an EFS volume (I think it was EFS -- relying on memory here) and lambda clients.
Although my tests were slamming the db with reads and write I didn't induce a bad read or write using WAL.
But I wouldn't use experimental results to override what the sqlite people are saying. I (and you) probably just didn't happen to hit the right access pattern.
The containers would need to use a path on a shared FS to setup the SHM handle, and, even then, this sounds like the sort of thing you could probably break via arcane misconfiguration.
> The wal-index is implemented using an ordinary file that is mmapped for robustness. Early (pre-release) implementations of WAL mode stored the wal-index in volatile shared-memory, such as files created in /dev/shm on Linux or /tmp on other unix systems. The problem with that approach is that processes with a different root directory (changed via chroot) will see different files and hence use different shared memory areas, leading to database corruption. Other methods for creating nameless shared memory blocks are not portable across the various flavors of unix. And we could not find any method to create nameless shared memory blocks on windows. The only way we have found to guarantee that all processes accessing the same database file use the same shared memory is to create the shared memory by mmapping a file in the same directory as the database itself.
> WAL mode requires shared access to System V IPC mapped memory.
Incorrect. It requires access to mmap()
"The wal-index is implemented using an ordinary file that is mmapped for robustness. Early (pre-release) implementations of WAL mode stored the wal-index in volatile shared-memory, such as files created in /dev/shm on Linux or /tmp on other unix systems. The problem with that approach is that processes with a different root directory (changed via chroot) will see different files and hence use different shared memory areas, leading to database corruption."
> This is unlikely to work across containers.
I'd imagine sqlite code would fail if that was the case; in case of k8s at least mounting same storage to 2 containers in most configurations causes K8S to co-locate both pods on same node so it should be fine.
It is far more likely they just fucked up the code and lost data that way...
NeXTstep?
(Leaving aside fun spitballing about whether Tahoe is morally OPENSTEP 26, and whether it was NeXT that actually bought Apple for negative $400 million...)
"Not as a proof of concept. Not for a side project with three users. A real store" - suggestion for human writers, don't use "not X, not Y" - it carries that LLM smell whether or not you used an LLM.
I assumed that it was to ensure that the announced products were revealed in a controlled manner rather than because they aren't able to do updates to their product listings as a regular thing.
SQLite has a ".backup" command that you should always use to backup a SQLite DB. You're risking data loss/corruption using "cp" to backup your database as prescribed in the article.
Related, there is also sqlite3_rsync that lets you copy a live database to another (optionally) live database, where either can be on the network, accessed via ssh. A snapshot of the origin is used so writes can continue happening while the sqlite3_rsync is running. Only the differences are copied. The documentation is thorough:
Yeah, using cp to backup sqlite is a very bad idea. And yet, unless you know this, this is what Claude etc will implement for you. Every friggin' time.
Well, humans also default to 'cp' until they learn the better pattern or find out their backup is missing data.
Also, my n=1 is that I told Claude to create a `make backup` task and it used .backup.
I don't understand the double standard though. Why do we pretend us humans are immaculate in these AI convos? If you had the prescience to be the guy who looked up how to properly back up an sqlite db, you'd have the prescience to get Claude to read docs. It's the same corner cut.
There's this weird contradiction where we both expect and don't expect AI to do anything well. We expect it to yolo the correct solution without docs since that's what we tried to make it do. And if it makes the error a human would make without docs, of course it did, it's just AI. Or, it shouldn't have to read docs, it's AI.
> The sqlite_sequence table is the most underappreciated debugging tool in SQLite. It tracks the highest auto-increment value ever assigned for each table — even if that row was subsequently lost.
> WorkQueueTask.count returns ~300 (current rows). The sequence shows 3,700+ (every task ever created). If those numbers diverge unexpectedly, something deleted rows it shouldn't have.
Or it means that SQLite is exhibiting some of its "maybe I will, maybe I won't" behavior [0]:
> Note that "monotonically increasing" does not imply that the ROWID always increases by exactly one. One is the usual increment. However, if an insert fails due to (for example) a uniqueness constraint, the ROWID of the failed insertion attempt might not be reused on subsequent inserts, resulting in gaps in the ROWID sequence. AUTOINCREMENT guarantees that automatically chosen ROWIDs will be increasing but not that they will be sequential.
> No ILIKE. PostgreSQL developers reach for WHERE name ILIKE '%term%' instinctively. SQLite throws a syntax error. Use WHERE LOWER(name) LIKE '%term%' instead.
You should not be reaching for ILIKE, functions on predicates, or leading wildcards unless you're aware of the impacts those have on indexing.
> json_extract returns native types. json_extract(data, '$.id') returns an integer if the value was stored as a number. Comparing it to a string silently fails. Always CAST(json_extract(...) AS TEXT) when you need string comparison.
If you're using strings embedded in JSON as predicates, you're going to have a very bad time when you get more than a trivial number of rows in the table.
> Would We Choose SQLite Again? Yes. For a single-server deployment with moderate write volume, SQLite eliminates an entire category of infrastructure complexity. No connection pool tuning. No database server upgrades. No replication lag.
These are weird reasons. You can just install Postgres or MySQL locally too. Connection pool tuning certainly isn't anything you have to worry about for a moderate write volume. You don't ever need to upgrade the database if you don't want to, since you're not publicly exposing it. There's obviously no replication lag if you're not replicating, which you wouldn't be with a single server.
The reason you don't usually choose SQLite for the web is future-proofing. If you're totally sure you'll always stay single-server forever, then sure, go for it. But if there's even a tiny chance you'll ever need to expand to multiple web servers, then you'll wish you'd chosen a client-server database from the start. And again, you can run Postgres/MySQL locally, on even the tiniest cheapest VPS, basically just as easily as using SQLite.
Yeah a PG Docker container is basically magic. I too went down a rabbit-hole of trying to setup a write-heavy SQLite thing because my job is still using CentOS6 on their AWS cluster (don't ask). Once I finally got enough political capital to get my own EC2 box I could put a PG docker container on, so much nonsense I was doing just evaporated.
It's a spectrum. Installing Postgres locally is not 100% future-proofing since you'll still need to migrate your local Postgres to a central Postres. Using Sqlite is not 0% future-proofing since it's still using the SQL standard.
If the only argument for a piece of tech in comparison to another one is "future-proofing", that's pretty much acknowledging the other one is simpler to setup and maintain.
For web servers specifically, no, SQLite is not generally part of that spectrum. That makes as much sense as saying that in a kitchen, you want a spectrum of knives from Swiss Army Knives to chef's knives. No -- Swiss Army Knives are not part of the spectrum. For web servers, you do have a wide spectrum of database options from single servers to clusters to multi-region clusters, along with many other choices. But SQLite is not generally part of that spectrum, because it's not client-server.
> since you'll still need to migrate your local Postgres to a central Postres
No you don't. You leave your DB in-place and turn off the web server part. Or even if you do want to migrate to something beefier when needed, it's basically as easy as copying over a directory. It's nothing compared to migrating from SQLite to Postgres.
> since it's still using the SQL standard.
No, every variant of SQL is different. You'll generally need to review every single query to check what needs rewriting. Features in one database work differently from in another. Most of the basic concepts are the same, and the basic syntax is the same, but the intermediate and advanced concepts can have both different features and different syntax. Not to mention sometimes wildly different performance that needs to be re-analyzed.
> that's pretty much acknowledging the other one is simpler to setup and maintain.
No it's not. What logic led you there...? They're basically equally simple to set up and maintain, but one also scales while the other doesn't. That's the point.
The main advantage of SQLite has nothing to do with setup and maintenance, but rather the fact that it is file-based and can be integrated into the binary of other applications, which makes it amazing for locally embedded databases used by user-installed applications. But these aren't advantages when you're running a server. And it becomes a problem when you need to scale to multiple webservers.
Have run PG, MySQL, and SQLite locally for production sites. Backups are much more straightforward for SQLite. They are running Kamal, which means "just install Postgres" would also likely mean running PG in a container, which has its own idiosyncrasies.
> Backups are much more straightforward for SQLite.
Not sure how? All of them can be backed up with a single command. But if you want live backups (replication) as opposed to daily or hourly, SQLite is the only one that doesn't support that.
Yeah, it's weird "they" don't consider any middle ground between SQLite and replicated postgres cluster.
Locally running database servers are massively underrated as a working technology for smaller sites. You can even easily replicate it to another server for resiliency while keeping the local performance.
This. Spinning up Postgresql is easy once you know how. Just as SQLITE3 is easy once you know how. But I can find no benefit from not just learning postgres the first time around.
The fix appears to nicely asking the forgetful unreliable agent to please (very closely pretty please!) follow the deploy instructions (and also please never hallucinate or mess up, because statistics tells us an entity with no long term memory and no incentive to get everything right will do the job right 99.99999999% of the time, which is good enough to run an eshop) not deploy too often per hour.
With one simple instruction the system (99.9999% of the time) gains the handy property that “only” two processes end up with the database files open at once.
I have to work with agents as a part of my job and the very first thing I did when writing MCP tools for my workflow was to ensure they were read only or had a deterministic, hardcoded stopgap that evaluates the output.
I do not understand the level of carelessness and lack of thinking displayed in the OP.
Even just having the agent write scripts to disk and run those works wonders. It keeps the agent from having to rebuild a script for the same tasks, etc.
That too! Every time the agent does something I didn't intend, I end up making a tool or process guidance to prevent it from happening again. Not just add "don't do that" to the context.
I took three weeks off from tech, read books from last century, and travelled Europe. Coming back, reading LLM generated content and code feels like nails on a chalkboard. Taste, it does not have taste.
If the problem is excessive deployments via GitHub Actions, why not use concurrency control on GitHub Actions ( https://docs.github.com/en/actions/how-tos/write-workflows/c... ) instead of relying on agent randomness and the hope that it won't make the same mistake again? Am I missing something?
I don't know how Ultrathink works, and I have no "real world" experience with Kamal, but I find it intriguing to see someone consider 11 deployments in 2 hours to be "fast".
Instead of handicapping yourself, fix your deployment pipeline, 10 min deploys are not OK for an online store.
> json_extract returns native types. json_extract(data, '$.id') returns an integer if the value was stored as a number. Comparing it to a string silently fails. Always CAST(json_extract(...) AS TEXT) when you need string comparison.
SQLite is a rock solid piece of software that offers a great value prop: in-process database. For locally running apps (desktop or mobile), this makes perfect sense.
However, I genuinely don't see the appeal when you are in a client/server environment. Spinning up Postgres via a container is a one-liner and equally simple for tests (via testcontainers or pglite). The "simple" type system of SQLite feels like nothing but a limitation to me.
I see tons of articles like this, and I have no doubt sqlite proved to be a great piece of software in production environments, but what I rarely find discussed is that we lack tools that enable you to access and _maintain_ SQLite databases.
It's so convenient to just open Datagrip and have a look at all my PostgreSQL instances; that's not possible with sqlite AFAIK (not even SSH tunnelling?). If something goes wrong, you have to SSH into the machine and use raw SQL. I know there are some cool front-end interfaces to inspect the db but it requires more setup than you'd expect.
I think that most people give up on sqlite for this reason and not because of its performance.
I use SqLite for a small hobby project, fine for that. Wanted to read the article to see why I should not, but it attacked me with a "subscribe" popup, so I stopped there. The comments here seem to be based on daydreaming on scaling to a lot of users who need 24/7 uptime, which is not always the case.
> Yes. For a single-server deployment with moderate write volume, SQLite eliminates an entire category of infrastructure complexity. No connection pool tuning. No database server upgrades. No replication lag.
None of these is needed if you run sqlite sized workloads...
I like SQLite but right tools for right jobs... tho data loss is most likely code bug
I still haven't figured out a good way to due blue/green sqlite deploys on fly.io. Is this just a limitation of using sqlite or using Fly? I've been very happy with sqlite otherwise, rather unsure how to do a cutover to a new instance.
Anyone have some docs on how to cutover gracefully with sqlite on other providers?
I'm personally fine with a little bit of downtime for my particular small app. I'm just surprised there's not a more detailed story around deploying sqlite in a high availability prod environment given it's increased popularity and coverage over the last few years. Especially surprising with Rails' (my stack) going full "sqlite-first".
I've a busy app, i just deploy to canary. And use loadbalancer to move 5% traffic to it, i observe how it reacts and then rollout the canary changes to all.
Am I the only one finding this article highly suspect? It seems like the errors made are so basic, i.e. using the wrong SQL dialect for the db system in use, and there orders were apparently only at 17?
> Does cp actually work on live sqlite files? I wouldn’t expect it to, since cp does not create a crash-consistent snapshot.
cp "works" but it has a very strong possibility of creating a corrupt copy (the more active the db, the higher the chance of corruption). Anyone using "cp" for that purpose does not have a reliable backup.
sqlite3_rsync and SQLite's "vacuum into" exist to safely create backups of live databases.