[pavlov]

There are some web apps still in production that I wrote almost a decade ago in Node+Express in the simplest, dumbest style imaginable. The only dependencies are Express and some third-party API connectors. The database is an append-only file of JSON objects separated by newlines. When the app restarts, it reads the file and rebuilds its memory image. All data is in RAM.

I figured these toys would be replaced pretty quickly, but turns out they do the job for these small businesses and need very little maintenance. Moving the app to a new server instance is dead simple because there's basically just the script and the data file to copy over, so you can do OS updates and RAM increases that way. Nobody cares about a few minutes of downtime once a year when that happens.

There are good reasons why we have containers and orchestration and stuff, but it's interesting to see how well this dumb single-process style works for apps that are genuinely simple.

[danenania]

Built-in first-class concurrency (ala node, golang, rust, etc.) is a huge win for simple architectures, since it lets you avoid adding a background queue, or at least delay it for a very long time.

I think people are also too quick to add secondary data stores and caches. If you can do everything with a transactional SQL database + app process memory instead, that is generally going to save you tons of trouble on ops, consistency, and versioning issues, and it can perform about as well with the right table design and indexes.

For example: instead of memcache/redis, set aside ~100 MB of memory in your app process for an LRU cache. When an object is requested, hit the DB with an indexed query for just the 'updatedAt' timestamp (should be a sub-10ms query). If it hasn't been modified, return the cached object from memory, otherwise fetch the full object from the DB and update the local cache. For bonus points, send an internal invalidation request to any other app instances you have running when an object gets updated. Now you have a fast, scalable, consistent, distributed cache with minimal ops complexity. It's also quite economical, since the RAM it uses is likely already over-provisioned.

This is exactly the approach that EnvKey v2[1] is using, and it's a huge breath of fresh air compared to our previous architecture. Just MySQL, Node/TypeScript, and eventually consistent replication to S3 for failover. We also moved to Fargate from EKS (AWS kubernetes product), and that's been a lot simpler to manage as well.

1 - https://v2.envkey.com

[gregmac]

> For example: instead of memcache/redis, set aside a ~100 MB of memory in your app process for an LRU cache. When an object is requested, hit the DB with an indexed query for just the 'updatedAt' timestamp (should be a sub-10ms query). If it hasn't been modified, return the cached object from memory, otherwise fetch the full object from the DB and update the local cache.

I've never built something with this type of mechanism for a DB query, but it's interesting. I don't think I've ever timed a query like this, but I feel like it's going to be an "it depends" situation based on what fields you're pulling back, if you're using a covering index, just how expensive the index seek operation is, and how frequently data changes. I've mainly always treated it as "avoid round trips to the database" -- zero queries is better than one, and one is better than five.

I also guess it depends on how frequently it's updated: if 100% of the time the timestamp is changed, you might as well just fetch (no caching). Based on all the other variables above, the inflection point where it makes sense to do this is going to change.

Interesting idea though, thanks.

> For bonus points, send an internal invalidation request to any other app instances you have running when an object gets updated. Now you have a fast, scalable, consistent, distributed cache with minimal ops complexity.

Now you have to track what other app servers exist, handle failures/timeouts/etc in the invalidation call, as well as have your app's logic able to work properly if this invalidation doesn't happen for any reason (classic cache invalidation problem). My inclination is at this point you're on the path of replicating a proper cache service anyways, and using Redis/Memcache/whatever would ultimately be simpler.

[danenania]

It definitely does depend on various factors, but if your query is indexed, both the SQL DB request and the Redis/Memcache lookup of the full object are likely to be dominated by internal network latency. If your object is large, the DB single-field lookup could easily be faster since you're sending less back over the wire.

In other words, a single-field indexed DB lookup can be treated more like a cache request. Though for heavier/un-indexed queries, your "avoid round trips to the database" advice certainly applies.

With this architecture, the internal invalidation request is just an optimization. It isn't necessary and it doesn't matter if it fails, since you always check the timestamp with a strongly consistent DB read before returning a cached object.

[conradfr]

> Built-in first-class concurrency (ala node, golang, rust, etc.) is a huge win for simple architectures, since it lets you avoid adding a background queue, or at least delay it for a very long time.

>For example: instead of memcache/redis, set aside ~100 MB of memory in your app process for an LRU cache.

Erlang/Elixir for the win with (almost transparent multi-core) concurrency and ETS ;)

[danenania]

Oh yeah, Erlang/Elixir certainly belong in that list (probably at the front of it).

[dmw_ng]

> database is an append-only file of JSON objects separated by newlines. When the app restarts, it reads the file and rebuilds its memory image. All data is in RAM

Apps like this tend to perform like an absolute whippet too (or if they dont, getting them to perform well is often a 5 line change). It's really freeing to be able to write scans and filters with simple loops that still return results faster than a network roundtrip to a database.

The problem is always growth, either GC jank from a massive heap, running out of RAM, or those loops eventually catching up with you. Fixing any one of these eventually involves either serialization or IO, at which point the balance is destroyed and a real database wins again.

[Beltalowda]

Another issue with "just a JSON file" as a database is that you need to be a bit careful to avoid race conditions and the like, e.g. if two web pages try to write the same database at the same time. It's not an issue for all applications, and not that hard to get right, but does require some effort. This is a huge reason I prefer SQLite for simple file storage needs.

[brundolf]

A normal Express app (assuming it's one process per JSON file) shouldn't have that problem, because JavaScript is single-threaded

[spiffytech]

It can definitely be a problem in Node.js. Assuming the workflow is read from disk -> modify -> write to disk, and that you're using the async fs functions, two async code paths running at the same time will have last-write-wins semantics and will lose data.

That's the naive scenario. If all code paths write out a global data structure, then it'd be fine. Or if the file is written append-only instead of as a single, atomic data structure, then it could be fine.

[midrus]

You are confusing parallelism with concurrency. It definitely can be a problem.

[djbusby]

Is it possible a write is interrupted on it's turn in the event-loop, and crossed with another?

[brundolf]

Hmm. I wouldn't think so, but I don't actually know

Still, given the strategy at hand, the in-memory JS object (exclusively single-threaded) is the source of truth, and just gets mirrored in the file system (and doesn't get read again until the next startup). So you should have an eventual-consistency situation in the worst case (any racing issue between file-writes would just put the file in a stale state, and the next file-write would bring it back up to consistency)

[endorphine]

Doesn't the fact that its opened in append only mode (Linux) mitigate data races with regards to writes?

[Beltalowda]

Your write will be fine; that is, it's not as if data from one write will be interspersed with the data from another write. It's just that the order might be wrong, or opening the file multiple times (possibly from multiple processes) could be fun too. The program or computer crashing mid-write can also cause problems. Things like that.

Again, may not be an issue at all for loads of applications. But I used a lot of "flat file databases" in the past, and found it's not an issue right up to the point that it is. Overall, I found SQLite simple, fast, and ubiquitous enough to serve as a good fopen() replacement. In some cases it can even be faster!

[lelanthran]

> Your write will be fine; that is, it's not as if data from one write will be interspersed with the data from another write.

Are you sure? I thought it could be if the first write had more data than the size of the kernel/fs-driver buffer, not all of it would be written, and then it could be interrupted when another thread calls write() with a small buffer that gets written in one go.

[Beltalowda]

No, I'm not sure haha; but in my experience it usually works like that, but no doubt there could be edge cases there, too. Another good reason to use SQLite.

[dmoy]

Here is my list of numbers: 1,Here is my list of letters: a,b,2,3,d

[stevenhuang]

Although not a POSIX requirement, in practice for unix-like systems, file writes are atomic across concurrent writers.

You maybe thinking of stdio buffering, where calls to printf etc get split into multiple write calls. Then in those cases, it's possible to get errant interleaved writes.

[kragen]

It eliminates them if they're smaller than PIPE_BUF (IIRC, Beltalowda, dmoy, and stevenhuang are wrong about this), but the thing that prevents data races with regard to writes is running the application in Node, which is completely single-threaded.

[bob1029]

> The problem is always growth, either GC jank from a massive heap, running out of RAM, or those loops eventually catching up with you

Absolutely. The challenge is having enough faith that it will take long enough to catch up to you.

Statistically speaking, it won't catch up to you and if it does, it will take so long you should have seen it coming from miles away and had time to prepare.

In my systems that use an in-memory/append-only technique, I try to keep only the pointers and basic indexes in memory. With modern PCIe flash storage, there is no good justification for keeping big fat blobs around in memory anymore.

[aprdm]

Could you expand what you mean by keeping pointers and basic index in memory?

[bob1029]

Pointers are tuples of (Id, LogOffset) and are used to map logical identities to positions of those objects in the append-only log.

Indexes are usually a tuple of (Some64BitKey, Id) and are used to map physical business keys to logical object identities. These entries are only candidates in the case where the key material needs to be hashed and inspected for actual equivalence.

One big advantage with this approach is that you can stream big blobs directly out of the log to a caller-supplied buffer or stream. No intermediate allocations required aside from some small buffers.

[aprdm]

Awesome, thank you for sharing!

[pavlov]

Yes, you need to be sure that you understand the growth pattern if you want to YOLO in RAM. If your product aims to be the next Instagram, this is clearly not the architecture.

But a lot of small businesses are genuinely small. They may not sign up new customers that often. When they do, the impact to the service is often very predictable ("Amy at customer X uses this every other day, she's very happy, it generates 100 requests / week"). If growth picks up, there would be signs well in advance of the toy service becoming an actual problem.

[jkaptur]

> If your product aims to be the next Instagram, this is clearly not the architecture.

But maybe! https://instagram-engineering.com/dismissing-python-garbage-...

[uuyi]

I would love to see more stuff like that.

An application I have written recently for personal use is a double entry accounting system after GNUcash hosed itself and gave me a headache. This is based on Go and SQLite. The entire thing is one file (go embed rocks) and serves a simple http interface with a few JS functions like it is 2002 again. The back end is a proper relational model that is stored in one .db file. It is fully transactional with integrity checks. To run it you just start program and open a browser. To backup you just copy the .db file. You can run reports straight out of SQLite in a terminal if you want.

This whole concept could scale to tens of users fine for LOB applications and consume little memory or resources.

[CraigJPerry]

>> This whole concept could scale to tens of users

I strongly suspect this approach scales to tens of thousands of users. Maybe 30-40k users would be my guess on a garden variety intel i5 desktop from the past 3 years or so.

I say this because that hardware (assuming NVMe storage) will do north of 100k connect + select per second (connect is super cheap in sqlite, you're just opening a local file), assuming 2-3 selects per page serve gets me to the 30-40k number. The http server side won't be the bottleneck unless there's some seriously intensive logic being run.

[uuyi]

Interesting point. I may have to write a performance test suite for it now and test this.

[weaksauce]

sqlite really does very well with reads but not as much for locking writes. not saying it couldn't scale to many users but I think the other person is a bit optimistic on a double entry accounting app being only reads. I would imagine it could certainly easily serve a few hundred though if not a few thousand.

[kirab]

SQLite does really well for locking writes as well as soon as you activate the WAL.

[powersurge360]

Check out alpinejs or stimulusjs and combine it with htmx to get to a SPA like experience with very little additional complexity! Htmx let’s you serve partials over the wire instead of a page load so you can update the page incrementally and alpine and stimulus are both tools to add JS sprinkles like you’ve described in a way that is unobtrusive.

[uuyi]

I appreciate the notion but my objective was to do the exact opposite of this and keep away from external dependencies and scripts where possible, apart from the solitary go-sqlite3.

The result is about 30K of source (including Go, CSS, HTML templates) which is less than minified alpinejs!

[mbrodersen]

Adding dependencies is the opposite of simple.

[smm11]

This. Not that I'm all about janky, but my road is littered with stuff I didn't think would make it through summer, and everything I check is still ticking 5, 7, 10 years later.

LONG ago I was amused by a Sun box in a closet that nobody knew anything about. I heard about the serial label printer that stopped working eight months ago, which was eight months after I shut off the Sun. I brought it back up again late one Friday, and the old/broken label printer magically worked again.

Now my stuff is that.

[uxamanda]

Ha, now you can use the label printer to label the machine!

[derefr]

How do you feel about SQLite? Because when I read this architecture description, it mostly vibes with me, until I think about what happens to the data in the event of a power cut.

Is there logic in your app to potentially throw the last line away (incl. truncating it from the file) if it's invalid due to being the result of a non-atomic write? If so, seems a bit Not-Invented-Here compared to just using a (runtime-embedded) library that does that for you :)

[weaksauce]

> SQLite responds gracefully to memory allocation failures and disk I/O errors. Transactions are ACID even if interrupted by system crashes or power failures. All of this is verified by the automated tests using special test harnesses which simulate system failures.

from the sqlite about page. it's one of the most bullet proof and hardened pieces of software out there I think. it was made basically for exactly the ops use case of a file on disk. but who knows what the use case is for them. maybe writes to the db are few and far between so it's a fairly moot point.

[pavlov]

The AOF reader will discard and emit a warning about lines that can’t be parsed, but that’s the extent of it.

These apps are on Digital Ocean, and I don’t remember ever having unplanned downtime with them. They do sometimes migrate instances with advance notice, but that’s a clean shutdown.

I’m sure SQLite is a better choice for almost any app. My reason for not using it was to try avoiding dependencies out of curiosity, and also that I honestly really don’t like writing SQL — it just feels boring and error-prone. (Like eating celery, I know objectively it’s good for me.)

[derefr]

> My reason for not using it was to try avoiding dependencies out of curiosity, and also that I honestly really don’t like writing SQL — it just feels boring and error-prone.

Well, sure, but you can just read and write JSON blobs to a single-column table in SQLite. See also, "SQLite makes a better BLOB store than the filesystem does" (https://www.sqlite.org/fasterthanfs.html)

[eternityforest]

SQLite doesn't do "lines" like a text file, or truncate anything(Or, it probably does internally, but that's not how users think of it).

It's a real SQL DB with real records and transactions, and it is one of the most trusted and reliable pieces of software ever made. Like, check out the change log to get a sense of how they do stuff.

[derefr]

I think you misread what I said, or maybe didn't read the post I was replying to. I was pointing out that flat files have a problem with truncation; and that SQLite, despite being a very different thing than an append-only text file, can be effectively used as "an append only text file, but ACID." My question on how the GP poster feels about SQLite, is down to SQLite being the obvious solution to a problem I wasn't sure they realized they had — but also potentially still being "too many dependencies" for them.

[justinpombrio]

If you're not writing data very often, this isn't a concern. For example, if you assume 5 microseconds to write a line, and 1 line written per hour on average, then the chance that the power goes out while you're writing a line is 10^-9, i.e. will never happen.

[ammanley]

Reminds me a lot of this (first paragraph): https://litestream.io/blog/why-i-built-litestream/

Well done on building an easy-to-maintain single node app with few dependencies. You would be the SWE I would send prayers of thanks too after onboarding (and for not making me crawl through a massive Helm chart/CloudFormation template hell).

[spurgu]

I have literal dozens of these kinds (Node+Express under PM2) of small apps running around everywhere in production (almost 8 years = almost a decade ;). Using SQLite (when you need an actual DB) makes things a lot easier in this regard as well.

I've tried doing some thing in Python (my initial programming love) over the years but I keep going insane because every time I'm forced to read up on the state of ecosystem (choosing versions and package managers and whatnot) and it drives me insane. I just install Node+Express and can get to work immediately (and finish quickly).

[mbrodersen]

I suspect that 95% of business applications could be implemented just fine with that architecture. However I would use SQLite instead of a plain file. Just for added commit safety.

[kragen]

For simple write patterns like this, I've had better robustness experiences with dumb filesystem access than with SQLite.

[deterministic]

That makes sense.

[epolanski]

I often rewrite in my free time what I do at work without dependencies and I'm often amazed at how far and faster you can move.

[sam0x17]

I did a very similar setup to this as a boy using a PHP file that re-wrote itself as a sort of key-based data store. This was fantastic on free shared web hosting sites since no free db options were available. This was circa 2005.

[mftb]

I do almost this exact thing for all my personal stuff. I have 5 or 6 going in a vm for simple things like my bookmarks, etc... works great. I could definitely see it solving many small business use-cases.

[anandh07]

Totally agree with simple architecture for simple and small use cases. Just curious to know, are you running any small businesses this way?