I look forward to trying this out. Any benchmarks or demos on how long it actually takes to restore? I ended up cooking my own boring S3 backup because previously litestream took 20 minutes to restore something like 1000 rows. It felt extremely unoptimized. How long does restoration take today?
Very excited to see Fly restart development on Litestream after a 2ish year freeze!
I love Litestream and use it in every app I build now.
They advertise it as costing "pennies per day," but it's even less expensive than that. It obviously varies depending on how much storage you need, but I had a real app in production, and Litestream replication to S3 only cost me 2-3 cents ($0.02-$0.03) per month.[0]
We have an in house application installed on a remote fleet with spotty internet access. Because of the spotty internet we have a lot of trouble setting up a reliable system for getting data home.
Could how does litestream handle backing up through a spotty connection and can we consolidate the backups into a central db an query against it?
> But the market has spoken! Users prefer Litestream. And honestly, we get it: Litestream is easier to run and to reason about. So we’ve shifted our focus back to it.
That makes sense to me. LiteFS used FUSE, which meant figuring out how to run and mount a custom filesystem. Litestream is a single compiled Go binary that you point at the SQLite database file (and accompanying WAL file).
i am not a big fly.io fan per se, but their blog posts, especially in this sort of sphere, is delightful, and i absolutely commend them for this. fly has a fascinating combination of “dev-forward” and “we made this complicated because it shits us” that continues to confound me, but i must confess i am a big fan of this project, and a number of other projects (e.g. svelte) that they seem to genuinely treat in an arms-length, “let a thousand flowers bloom” sort of way.
I once was responsible for migrating a legacy business app to Azure, and the app had a local MSSQL server co-running with the app (the same pattern that Litestream is using).
As have been mentioned below, the app had been developed assuming the local access (and thus <1ms latency), so it had a ton of N+1 everywhere.
This made it almost impossible to migrate/transition to another configuration.
So, if this style of app hosting doesn't take off and you're at all worried about this being a dead end storage once you reach a certain scale, I'd recommend not doing this, otherwise your options will be very limited.
Then again - I bet you could get very very far on a single box, so maybe it'd be a non factor! :)
Single instance is underappreciated in general. There's a used server reseller near me, and sometimes I check their online catalogue out of curiosity. For only $1000ish I could have some few generations old box with dual socket 32-core chips and 1TB of RAM. I don't have any purpose for which I'd need that, but it's surprisingly cheap if I did. And things can scale up from there. AWS will charge you the same per month that it costs to get one of your own forever - not counting electricity or hard drives.
I run my entire business on a single OVH box that costs roughly $45/month. It has plenty of headroom for growth. The hardest part is getting comfortable with k8s (still worth it for a single node!) but I’ve never had more uptime and resiliency than I do now. I was spending upwards of $800/mo on AWS a few years ago with way less stability and speed. I could set up two nodes for availability, but it wouldn’t really gain me much. Downtime in my industry is expected, and my downtime is rarely related to my web services (externalities). In a worst case scenario, I could have the whole platform back up in under 6 hours on a new box. Maybe even faster.
any notes or pointers on how to get comfortable with k8? For a simple nodejs app I was looking down the pm2 route but I wonder of learning k8 is just more future proof.
Use K3s in cluster mode, start doing. Cluster mode uses etcd instead of kine, kine is not good.
Configure the init flags to disable all controllers and other doodads, deploy them yourself with Helm. Helm sucks to work with but someone has already gone through the pain for you.
AI is GREAT at K8s since K8s has GREAT docs which has been trained on.
A good mental model is good: It's an API with a bunch of control loops
I used to work on a product where the app server and database were in the same rack - so similar low latency. But the product was successful, so our N+1 would generate thousands of queries and 1ms would become >500ms or more easily. Every other month we would look at New Relic and find some slow spot.
It was a Rails app, therefore easy to get into the N+1 but also somewhat easy to fix.
For our rails app we actually added tests asserting no N+1s in our controller tests. Think a test setup with 1 post vs 10 posts (via factorybot) and you could do an assertion that the DB query count was not different between the two. A useful technique for any Railsheads reading this!
I mean, that's not much of a trade off given that it seems that what you're saying is that using such a service might just show you how shit your code actually is.
There's a common access pattern with object-relational mapping frameworks where an initial query will be used to get a list of ids, then an individual queries are emitted for each item to get the details of the items. For example, if you have a database table full of stories, and you want to see only the stories written by a certain author, it is common for a framework to have a function like
stories = get_stories(query)
which results in a SQL query like
SELECT id FROM stories WHERE author = ?
with the '?' being bound to some concrete value like "Jim".
Then, the framework will be used to do something like this
for id in stories {
story = get_story_by_id(id)
// do something with story
}
which results in N SQL queries with
SELECT title, author, date, content FROM stories WHERE id = ?
Oh yeah, the ORM thing (common side-effect with DB query abstractions) - I must not have been fully awake. Cheers and thank you for humoring me, @cbm-vic-20!
The thing where your app displays 20 stories in the homepage, but for each story it runs an extra query to fetch the author, and another to fetch the tags.
It's usually a big problem for database performance because each query carries additional overhead for the network round trip to the database server.
SQLite queries are effectively a C function call accessing data on local disk so this is much less of an issue - there's an article about that in the SQLite docs here: https://www.sqlite.org/np1queryprob.html
Object Relational Mapping (ORM) tools, which focus on mapping between code based objects and SQL tables, often suffer from what is called the N+1 problem.
A naive ORM setup will often end up doing a 1 query to get a list of object it needs, and then perform N queries, one per object, usually fetching each object individually by ID or key.
So for example, if you wanted to see “all TVs by Samsung” on a consumer site, it would do 1 query to figure out the set of items that match, and then if say 200 items matched, it would do 200 queries to get those individual items.
ORMs are better at avoiding it these days, depending on the ORM or language, but it still can happen.
I dislike ORMs as much as the next ORM disliker, but people who are more comfortable in whatever the GP programming language is than SQL will write N+1 queries with or without an ORM.
Very true. But ORMs did make it particularly easy to trigger N+1 selects.
It used to be a very common pitfall - and often not at all obvious. You’d grab a collection of objects from the ORM, process them in a loop, and everything looked fine because the objects were already rehydrated in memory.
Then later, someone would access a property on a child object inside that loop. What looked like a simple property access would silently trigger a database query. The kicker was that this could be far removed from any obvious database access, so the person causing the issue often had no idea they were generating dozens (or hundreds) of extra queries.
I defense of the application developer, it is very difficult to adopt set theory thinking which helps with SQL when you've never had any real education in this area, and it's tough to switch between it and the loop-oriented processing you're likely using in your application code for almost everyone. ORMs bridge this divide which is why they fall in the trap consistently. Often it's an acceptable trade-off for the value you get from the abstraction, but then you pay the price when you need to address the leak!
This problem is associated with ORMs but the moment there's a get_user(id) function which does a select and you need to display a list of users someone will run it in a loop to generate the list and it will look like it's working until the user list gets long.
I really wish there was a way to compose SQL so you can actually write the dumb/obvious thing and it will run a single query. I talked with a dev once who seemed to have the beginnings of a system that could do this. It leveraged async and put composable queryish objects into a queue and kept track of what what callers needed what results, merged and executed the single query, and then returned the results. Obviously far from generalizable for arbitrary queries but it did seem
to work.
I think many ORMs can solve (some of) this these days.
e.g. for ActiveRecord there's ar_lazy_preloader[0] or goldiloader[1] which fix many N+1s by keeping track of a context: you load a set of User in one go, and when you do user.posts it will do a single query for all, and when you then access post.likes it will load all likes for those and so on. Or, if you get the records some other way, you add them to a shared context and then it works.
The N+1 problem basically means instead of making one efficient query, you end up making N separate queries inside a loop. For example, fetching a list of tables, then for each table fetching its columns individually — that’s N+1 queries. It works, but it’s slow.
We ran into this while building, funnily enough, a database management app called DB Pro (https://dbpro.app) At first we were doing exactly that: query for all schemas, then for each schema query its tables, and then for each table query its columns. On a database with hundreds of tables it took ~3.8s.
We fixed it by flipping the approach: query all the schemas, then all the tables, then all the columns in one go, and join them in memory. That dropped the load time to ~180ms.
N+1 is one of those things you only really “get” when you hit it in practice.
Really love litestream. Easy to use and never crashes on me. I still recommend using it as a systemd unit service.
I'm not only using it as a backup tool but also to mirror databases. Looking forward to their read-replica feature.
What I'd like to see is a system where a single-writer SQLite database is replicated to object storage so that you can spin up really cheap read replicas. Is anyone working on something like that?
Such a system would also require a side channel propagating WAL updates (over Kafka or similar) to replicas, so that the running replicas can update themselves incrementally and stay fresh without loading anything from object storage.
Litestream is basically that, though the OP article has "The next major feature we’re building out is a Litestream VFS for read replicas" as a What's Next todo at the bottom.
I don't Litestream does that yet? It appears to be for backing up to S3, and you manually "restore" the image to a file. You can't point an SQLite client at the S3 bucket, and there's no provision for getting low latency updates. But it sounds like they're working on this.
Turso looks cool and is a project I will keep an eye on, but it's replica mode seems to be designed for apps rather than mechanism to rapidly scale out read replicas in a server cluster. Also, the web site warns that it's not ready for production use.
A VFS that allows you to directly open a Litestream replication target (e.g. S3) as a read-only database and run queries against it without ever having to download the entire database (e.g. to an ephemeral instance that doesn't even have the disk space for it).
I… honestly don't know. I saw the announcement some time ago (the revamped post) and started following their repo. They did mention caching to hide latency.
When I saw the v0.5.0 tag, I dived into just porting it. It's just over a couple hundred lines, and I have more experience with SQLite VFSes than most, so why not.
I'm curious about the same, but also am wondering if there can be an automatic election of a new primary through the use of conditional writes (or as Fly.io say, CASAAS: Compare-and-Swap as a Service).
Maybe I misunderstand what this is, but why would I use this and not MySQL, Postgres, or any other proper database? Seems like a hack to get SQLite to do what those do by design.
One of the big advantages people enjoy is the elimination of the network latency between the application server and the DB. With SQLite your DB is right there often directly attached over NVME. This improves all access latencies and even enables patterns like N+1 queries which would typically be considered anti-patterns in other DBs.
> One of the big advantages people enjoy is the elimination of the network latency between the application server and the DB. With SQLite your DB is right there often directly attached over NVME.
You can install MySQL/PostgreSQL on the application server, connect over a unix socket and get the same benefits as if you'd used SQLite on the application server (no network latency, fast queries). Plus the other benefits that come from using these database servers (Postgres extensions, remote connections, standard tooling etc). I'm guessing more RAM is required on the application server than if you used SQLite but I haven't benchmarked it.
Unix sockets don't actually give you the same benefit. You're still doing IPC which can incur substantial memory subsystem utilization. SQLite is on the same thread/core as whatever is using it.
Real talk, how do you actually avoid N+1? I realize you can do complicated JOINs, but isn't that almost as bad from a performance perspective? What are you really supposed to do if you need to, e.g. fetch a list of posts along with the number of comments on each post?
Often you can use joins to get the data in a single complex SQL query. Number of comments for a post is relatively straight-forward, but you can also do increasingly complex associated data fetches with modern databases.
In particular, JSON aggregations mean you can have a single query that does things like fetch a blog entry and the earliest 10 comments in a single go. I wrote up some patterns for doing that in SQLite and PostgreSQL here: https://github.com/simonw/til/blob/main/sqlite/related-rows-...
select
blog_entry.id,
title,
slug,
created,
coalesce(json_agg(json_build_object(blog_tag.id, blog_tag.tag)) filter (
where
blog_tag.tag is not null
), json_build_array()) as tags
from
blog_entry
left join blog_entry_tags on blog_entry.id = blog_entry_tags.entry_id
left join blog_tag on blog_entry_tags.tag_id = blog_tag.id
group by
blog_entry.id
order by
blog_entry.id desc
The alternative, more common path is the pattern that Django calls "prefetch_related". Effectively looks like this:
select id, title, created from posts order by created desc limit 20
-- Now extract the id values from that and run:
select
blog_entry.id,
blog_tag.tag
from
blog_entry
join blog_entry_tags on blog_entry.id = blog_entry_tags.entry_id
join blog_tag on blog_entry_tags.tag_id = blog_tag.id
where
blog_entry.id in (?, ?, ?, ...)
-- Now you can re-assemble the list of tags for
-- each entry in your application logic
Once you have a list of e.g. 20 IDs you can run a bunch of cheap additional queries to fetch extra data about all 20 of those items.
it gets more complicated when you need to also display something like "last comment: <author> <3 days ago>" for each post, or if the comment counts need to be filtered by various flags/states/etc.
of course, it's all possible with custom SQL but it gets complicated quick.
That's like saying it gets more complicated when you have to use loops with break statements in programming. It's just what programming is.
The filtering you describe is trivial with COUNT(flag IN (...) AND state=...) etc.
If you want to retrieve data on the last comment, as opposed to an aggregate function of all comments, you can do that with window functions (or with JOIN LATERAL for something idiomatic specifically to Postgres).
Learning how to do JOIN's in SQL is like learning pointers in C -- in that it's a basic building block of the language. Learning window functions is like learning loops. These are just programming basics.
Avoiding N+1 doesn't have to mean limiting yourself to 1 query. You can still fetch the posts in one query and the comments of _all_ posts in a separate query, just don't issue a query for _each_ post.
More formally, the number of queries should be constant and not linearly scaling with the number of rows you're processing.
A proper join is the right answer. But, it's not always possible to make those run well. [1] A "client side join" in the right situation can be much better, but then you probably want to do a 1+1 rathet than N+1. Do the first query to get the ids for the second query, and then construct the second query with IN or UNION depending on what works best for you database. UNION likely bloats your query string, but I've seen plenty of situations where UNION is gobs faster than IN.
Alternately, if you can separate query issuance from result parsing, you can make N+1 palletable. Ex, do your query to get the ids, wait for the results, loop and issue the N queries, then loop and wait for results in order. That will be two-ish round trips rather than N+1 round trips. But you have to search to find database apis that allow that kind of separation.
[1] You can almost always express the query you want in SQL, but that doesn't mean it will have a reasonable runtime. Sometimes server side join and client side join have about the same runtime... if it's significant and you have the usual case where clients are easier to scale than database servers, it might be worthwhile to have the join run on the client to reduce server load.
AFAIK the problem of N+1 isn't necessarily one more DB query, but one more network roundtrip. So if for each page of your app you have an API endpoint that provides exactly all of the data required for that page, it doesn't matter how many DB queries your API server makes to fulfill that request (provided that the API server and the DB are on the same machine).
This is essentially what GraphQL does instead of crafting each of these super tailored API endpoints for each of your screens, you use their query language to ask for the data you want, it queries the DB for you and get you the data back in a single network roundtrip from the user perspective.
(Not an expert, so I trust comments to correct what I got wrong)
You still have to write the resolver for graphql. I've seen. N+1 with graphql if you don't actually use data loader+batch pattern OR if you use it incorrectly.
A JOIN is fast, fetching the whole list in one extra query with "WHERE id IN (...)" is also pretty fast and results in less complex queries if you have serval of these. Doing all queries separate is slow because of the network round-trip for each query.
Either joins for a fat query, or aggregate the subqueries.
For the latter, it's along the lines of `select * from posts where ...` and `select * from authors where id in {posts.map(author_id)}`. And then once it's in memory you manually work out the associations (or rely on your ORM to do it).
You do indeed use JOINS. The goal is to retrieve exactly the data you require in a single query. Then you get the DB to `EXPLAIN VERBOSE` or similar and ensure that full table scans aren't happening and that you have indexed the columns the query is being filtered on.
The actual thing that we're getting N+1 of is network round-trips. An additional network round-trip is way, way slower than an extra JOIN clause. That's why N+1 query patterns aren't a problem when you're using a local database: There's no round-trip.
A well-written JOIN against a well-designed database (regardless if we're talking postgres, SQLite, MySQL/MariaDB, or MS SQL) should not be slow. If it's slow, you're using it wrong.
The performance problem in N+1 is (mostly) not in fetching the N rows from disk, but rather from multiplying the network latency by a factor of N. Joins solve this; so do stored procedures.
In general, you want to ask the remote server once for all the data you need, then read all the results. It applies to databases as well as APIs.
Pipelined requests also solve the problem and can be more flexible.
Also, joins can be optimised in different ways. Sometimes the optimal way to do a join isn't to query each row one-by-one, but to do something like (when the rows you want are a large fraction of the rows that exist) making a bloom filter of the rows you want and then sequentially reading all the rows in the table.
To avoid operating a database by yourself and dealing with incidents, backups, replicas, failovers, etc... You can use cheap commoditised S3-like storage and run your application statelessly.
If you have access to a database that is well managed on your behalf I would definitely still go with that for many usecases.
It's significantly faster and incurs less ops overhead. That's it.
But most apps should just use a classic n-tier database architecture like Postgres. We mostly do too (though Litestream does back some stuff here like our token system).
I think you're focusing on the wrong parts of the comment.
People care about things like long-term support. Postgres 13, from 2020, is still officially supported. Litestream 0.1.0 was the first release, also from 2020, but I can't tell if it is supported still. Worrying about the maturity, stability, and support of an application database is very reasonable in risk adverse projects.
Litestream is just a backup solution. Should probably be compared to a backup solution for postgres that does automated backups over the network etc. That isnt part of postgres.
Besides the question wasnt litestream vs postgres backup apps. It was sqlite vs postgres.
this is essentially the "no one ever got fired for buying IBM" statement. One counter is why buy & manage a rack-mounted server when all you need is a raspberry Pi?
More than once I've started a project with sqlite and then had to migrate to postgres. In my experience it's because of the way sqlite has to lock the whole database file while writing to it, blocking concurrent reads - this isn't a problem in postgres. (There's WAL mode, but it still serialises all writes, and requires periodic explicit checkpointing IME)
You may also find you really want a feature postgres has, for example more advanced data types (arrays, JSON), more advanced indices (GIN inverted index on array members), replication...
Both are mature. There are way more sqlite databases running than postgres. The code base is smaller and has less new features added to it every year. What is unstable with sqlite?
Let's say I'm building a small app that I'm hosting on some shared vps, if I think about the effort involved in setting up sqlite with litestream and just getting a $5 (or free) postgres provider I don't think sqlite makes my life easier.
Now if I'm building a local app then absolutely sqlite makes the most sense but I don't see it otherwise.
Litestream is dead simple to setup. You make an S3 bucket (or any compatible storage bucket), paste the access keys and the path to your db file in /etc/litestream, and then run
Effort of setting up litestream and sqlite is less time than you spend signing up for supabase. And you can have 100 apps with their own databases for almost free (just a few cents of storage) vs 5*100 for postgres.
I love postgres but in no way is it as simple to run as sqlite (pretty sure even postgres core team would agree that postgres is more complex than sqlite).
I'd argue that anything larger than a desktop app should not use SQLite. If you need Litestream for replication and backup it is probably better to just use Postgres. There are a ton of one-click deployment offerings for proper databases, Fly.io actually offers managed Postgres.
That's not entirely true. SQLite is designed to support many processes reading the same file on disk at once. It only allows one process to write at a time, using locks - but since most writes finish in less than a ms in most cases having a process wait until another process finishes their write isn't actually a problem.
If you have lots of concurrent writes SQLite isn't the right solution. For concurrent reads it's fine.
SQLite also isn't a network database out-of-the-box. If you want to be able to access it over the network you need to solve that separately.
the reality is very few workloads have access patterns that SQLite can't support. I would much rather start with a strategy like 1. use sqlite for my beta / single client, 2. duplicate the entire environment for the next n clients, 3. solve the "my application is wildly successful" and SQLite is no longer appropriate problem at a future date. Spoiler: you're never going to get to step #3.
> 2. duplicate the entire environment for the next n clients
That becomes an instant problem if users ever write to your database. You can't duplicate the environment unless it's read-only.
And even if the database is read-only for users, the fact that every time you update it you need to redeploy the database to every client, is pretty annoying.
That's why it's usually better to start with Postgres or MySQL. A single source of truth for data makes everything vastly easier.
For a cloud service, I think it comes down to whether you’ll ever want more than one app server.
If you’re building something as a hobby project and you know it will always fit on one server, sqlite is perfect.
If it’s meant to be a startup and grow quickly, you don’t want to have to change your database to horizontally scale.
Deploying without downtime is also much easier with multiple servers. So again, it depends whether you’re doing something serious enough that you can’t tolerate dropping any requests during deploys.
Serving users is one thing. Then you want to run some interactive analytics or cronjobs for cleanup etc on the db. Even if the load can manage it, how would the admin jobs connect to the database. I’ve never seen a db with only one client. There is always some auxiliary thing, even when you don’t consider yourself a microservice shop.
agree - with SQLite and DuckDB I've really switched my mindset from one perfect, pristine DB to multiple copies and a medallion architecture that looks more like participation ribbons for everyone! The resources required are so cheap & lightweight when you avoid the big "BI focused" tech stacks.
you can also scale out across unlimited tiny servers, because the entire stack is so lightweight and cheap. This will also force you to focus on devops, which otherwise can become a grind with this approach. The only challenge is when you have cross-DB concerns, either data or clients.
Right, but if your goal is to have a lot of users (and minimal downtime), there's no point in putting a big avoidable obstacle in your path when the alternative is just as easy.
If your goal is to serve billions of users you should probably use cassandra etc. Why limit yourself to postgres if your goal is to have a billion users online at the same time?
This is the idea behind LiteFS --- to transparently scale out SQLite (in some very common configurations and workloads) to support multiple app servers. It's still there and it works! It's just a little ahead of its time. :)
That makes sense, and it seems really cool from a tech perspective. I guess I'm just inherently skeptical about using something shiny and new vs. battle hardened databases that were designed from the beginning to be client-server.
It's definitely really nice though that if you do choose sqlite initially to keep things as small and simple as possible, you don't immediately need to switch databases if you want to scale.
I think that's very fair. But the use case for Litestream is much simpler and you can get your head around it immediately. It also doesn't ask you to do anything that would commit you to SQLite rather than switching to Postgres later. It's just a way of very easily getting a prod caliber backend up for an app without needing a database server.
The common answer (especially from Fly.io) is "at-the-edge" computing/querying. There is network latency involved in sending a query to MySQL or Postgres and getting the data returned, whereas with Litestream you could put a read replica of the entire SQLite DB at every edge. Queries become fast and efficient only to the local read replica. There's still network latency associated with updating that read replica over time, but it is amortized based on the number of overall writes rather than the number of queries, is more fault tolerant in "eventually consistent" workflows (you can answer queries from the read replica at the edge in the state that you have it while you wait for the network to reconnect and replay the writes you missed during the fault), and with SQLite backing it still has much of the same full relational DB query power of SQL you would expect from a larger (or "proper") database like MySQL or Postgres.
I'm not sure, I've never done it, but I think the idea is to have many tiny customer-specific databases and move them to be powered by sqlite very close to the customer.
But I'd love to hear more from someone more well-versed in the use cases for reliable sql-lite
In every case where I had a SQLite vertical that required resilience, the customer simply configured the block storage device for periodic snapshots. Litestream is approximately the same idea, except you get block device snapshots implicitly as part of being in the cloud. There is no extra machinery to worry about and you won't forget about a path/file/etc.
Also, streaming replication to S3 is not that valuable an idea to me when we consider the recovery story. All other solutions support hot & ready replicas within seconds.
It's a good question, and I don't think answered sufficiently in the recent sqlite hype.
In my opinion if you have an easy way to run postgres,MySQL,... - just run that.
There's usually a lot of quirks in the details of DB usage (even when it doesn't immediately seem like it - got bitten by it a few times). Features not supported, different semantics, ...
IMO every project has an "experimental stuff" budget and if you go over it it's too broken to recover, and for most projects there's just not that much to win by spending them on a new database thing
The rails creator dhh has been hyping it up a lot in the first 6 month of this year, and quite a few followed of the "Dev influencers" scene. Fly's litestream came out around that time, and there's been more sqlite in the cloud companies/discussions, in particular with the AI agent use-case.
Not super sure who followed who but there was all of a sudden a lot of excitement
Maybe it's a local bump, but it sure seems like SQLite has become a fair more popular topic in the Rails world. I wouldn't expect to find it in a HN search tool. SQLite has gone from the little database you might use to boostrap or simplify local development to something products are shipping with in production. Functionality like solid_cable, solid_cache, and solid_queue allow SQLite to be used in more areas of Rails applications and is pitched as a way to simplify the stack.
While I don't have stats about every conference talk for the last decade, my experience has been that SQLite has been featured more in Rails conference talks. There's a new book titled "SQLite on Rails: The Workbook" that I don't think would have had an audience five years ago. And I've noticed more blog posts and more discussion in Rails-related discussion platforms. Moreover, I expect we'll see SQLite gain even more in popularity as it simplifies multi-agent development with multiple git worktrees.
this is infra for a single-user app. SQLite is THE replacement for file databases like MSAccess, but the box goes down and your database dies with all your data.
So this fills that gap by giving you a database as a service level of QOL without needing to provision a database as a service backend. Otherwise you're dicking about maintaining a service with all that comes with that (provisioning, updating, etc) when really all you need is a file that is automagically backed up or placed somewhere on the web to avoid the drawbacks of the local file system.
Whatever database you end up using, you'll need some sort of backup solution. Litestream is a streamed backup solution which effectively doubles as replication for durability purposes.
MySQL, Postgres, etc. have a much greater overhead for setup, unless you want to pay for a managed database, which is not going to be worth the price for small quantities of data.
I have a branch office in boondocks with limited internet connection. The branch office cannot manage a RDBMS or access cloud services. They can use sqlite app on LAN and we could do reconciliation at end of the business day.
Interesting information that they chose modernc.org/sqlite over mattn/go-sqlite3 as a Quality-of-Life improvement. Going forward I guess I'll do the same for new projects.
The NATS Jetstream use case is something I'm curious about.
All the public benchmarks (and my own applications) indicate that there is only a small (often unnoticeable) performance penalty associated with modernc.org/sqlite and this is far outweighed by the ability to eliminate CGO. I'd use it on future projects without hesitation.
litestream makes very few consistency guarantees compared to other datastores, and so I would expect most any issues found would be "working as intended".
at the end of the day with litestream, when you respond back to a client with a successful write you are only guaranteeing a replication factor of 1.
By "replication factor of 1" you mean your data is stored on local disk only, right? That matches my understanding: Litestream replication is asynchronous, so there's usually a gap of a seconds or two between your write being accepted and the resulting updated page being pushed off to S3 or similar.
Yes. the acknowledgement you're getting in your application code is that the data was persisted in sqlite on that host. There's no mechanism to delay acknowledgement until the write has been asynchronously persisted elsewhere.
I look forward to trying this out. Any benchmarks or demos on how long it actually takes to restore? I ended up cooking my own boring S3 backup because previously litestream took 20 minutes to restore something like 1000 rows. It felt extremely unoptimized. How long does restoration take today?
Very excited to see Fly restart development on Litestream after a 2ish year freeze!
I love Litestream and use it in every app I build now.
They advertise it as costing "pennies per day," but it's even less expensive than that. It obviously varies depending on how much storage you need, but I had a real app in production, and Litestream replication to S3 only cost me 2-3 cents ($0.02-$0.03) per month.[0]
[0] https://mtlynch.io/litestream/#using-logpaste-in-production
We have an in house application installed on a remote fleet with spotty internet access. Because of the spotty internet we have a lot of trouble setting up a reliable system for getting data home.
Could how does litestream handle backing up through a spotty connection and can we consolidate the backups into a central db an query against it?
Interesting tidbit regarding LiteFS/Litestream:
> But the market has spoken! Users prefer Litestream. And honestly, we get it: Litestream is easier to run and to reason about. So we’ve shifted our focus back to it.
That makes sense to me. LiteFS used FUSE, which meant figuring out how to run and mount a custom filesystem. Litestream is a single compiled Go binary that you point at the SQLite database file (and accompanying WAL file).
relevant: Litestream: Revamped (99 comments)
https://fly.io/blog/litestream-revamped/ https://news.ycombinator.com/item?id=44045292
i am not a big fly.io fan per se, but their blog posts, especially in this sort of sphere, is delightful, and i absolutely commend them for this. fly has a fascinating combination of “dev-forward” and “we made this complicated because it shits us” that continues to confound me, but i must confess i am a big fan of this project, and a number of other projects (e.g. svelte) that they seem to genuinely treat in an arms-length, “let a thousand flowers bloom” sort of way.
A small warning for folks.
I once was responsible for migrating a legacy business app to Azure, and the app had a local MSSQL server co-running with the app (the same pattern that Litestream is using).
As have been mentioned below, the app had been developed assuming the local access (and thus <1ms latency), so it had a ton of N+1 everywhere.
This made it almost impossible to migrate/transition to another configuration.
So, if this style of app hosting doesn't take off and you're at all worried about this being a dead end storage once you reach a certain scale, I'd recommend not doing this, otherwise your options will be very limited.
Then again - I bet you could get very very far on a single box, so maybe it'd be a non factor! :)
> I bet you could get very very far on a single box,
With single instances topping out at 20+ TBs of RAM and hundreds of cores, I think this is likely very under-explored as an option
Even more if you combine this with cell-based architecture, splitting on users / tenants instead of splitting the service itself.
Single instance is underappreciated in general. There's a used server reseller near me, and sometimes I check their online catalogue out of curiosity. For only $1000ish I could have some few generations old box with dual socket 32-core chips and 1TB of RAM. I don't have any purpose for which I'd need that, but it's surprisingly cheap if I did. And things can scale up from there. AWS will charge you the same per month that it costs to get one of your own forever - not counting electricity or hard drives.
I run my entire business on a single OVH box that costs roughly $45/month. It has plenty of headroom for growth. The hardest part is getting comfortable with k8s (still worth it for a single node!) but I’ve never had more uptime and resiliency than I do now. I was spending upwards of $800/mo on AWS a few years ago with way less stability and speed. I could set up two nodes for availability, but it wouldn’t really gain me much. Downtime in my industry is expected, and my downtime is rarely related to my web services (externalities). In a worst case scenario, I could have the whole platform back up in under 6 hours on a new box. Maybe even faster.
What's the benefit of using K3 on a single node?
any notes or pointers on how to get comfortable with k8? For a simple nodejs app I was looking down the pm2 route but I wonder of learning k8 is just more future proof.
Use K3s in cluster mode, start doing. Cluster mode uses etcd instead of kine, kine is not good.
Configure the init flags to disable all controllers and other doodads, deploy them yourself with Helm. Helm sucks to work with but someone has already gone through the pain for you.
AI is GREAT at K8s since K8s has GREAT docs which has been trained on.
A good mental model is good: It's an API with a bunch of control loops
I used to work on a product where the app server and database were in the same rack - so similar low latency. But the product was successful, so our N+1 would generate thousands of queries and 1ms would become >500ms or more easily. Every other month we would look at New Relic and find some slow spot.
It was a Rails app, therefore easy to get into the N+1 but also somewhat easy to fix.
For our rails app we actually added tests asserting no N+1s in our controller tests. Think a test setup with 1 post vs 10 posts (via factorybot) and you could do an assertion that the DB query count was not different between the two. A useful technique for any Railsheads reading this!
That's a good trick. In Django world I like pytest-django's django_assert_max_num_queries fixture: https://pytest-django.readthedocs.io/en/latest/helpers.html#...
Or django_assert_num_queries to assert an exact number.Bad query practices are always going to bite you eventually. I would not call that a shortcoming of this approach
I mean, that's not much of a trade off given that it seems that what you're saying is that using such a service might just show you how shit your code actually is.
Its not its fault. :)
What is N+1?
There's a common access pattern with object-relational mapping frameworks where an initial query will be used to get a list of ids, then an individual queries are emitted for each item to get the details of the items. For example, if you have a database table full of stories, and you want to see only the stories written by a certain author, it is common for a framework to have a function like
which results in a SQL query like with the '?' being bound to some concrete value like "Jim".Then, the framework will be used to do something like this
which results in N SQL queries with and there's your N+1This plagues (plagued?) pretty much everything to do with WordPress, from core to every theme and plugin developed.
Oh yeah, the ORM thing (common side-effect with DB query abstractions) - I must not have been fully awake. Cheers and thank you for humoring me, @cbm-vic-20!
With orms, it can be easy, but also often fixed with eager fetching too.
The thing where your app displays 20 stories in the homepage, but for each story it runs an extra query to fetch the author, and another to fetch the tags.
It's usually a big problem for database performance because each query carries additional overhead for the network round trip to the database server.
SQLite queries are effectively a C function call accessing data on local disk so this is much less of an issue - there's an article about that in the SQLite docs here: https://www.sqlite.org/np1queryprob.html
Object Relational Mapping (ORM) tools, which focus on mapping between code based objects and SQL tables, often suffer from what is called the N+1 problem.
A naive ORM setup will often end up doing a 1 query to get a list of object it needs, and then perform N queries, one per object, usually fetching each object individually by ID or key.
So for example, if you wanted to see “all TVs by Samsung” on a consumer site, it would do 1 query to figure out the set of items that match, and then if say 200 items matched, it would do 200 queries to get those individual items.
ORMs are better at avoiding it these days, depending on the ORM or language, but it still can happen.
I dislike ORMs as much as the next ORM disliker, but people who are more comfortable in whatever the GP programming language is than SQL will write N+1 queries with or without an ORM.
Very true. But ORMs did make it particularly easy to trigger N+1 selects.
It used to be a very common pitfall - and often not at all obvious. You’d grab a collection of objects from the ORM, process them in a loop, and everything looked fine because the objects were already rehydrated in memory.
Then later, someone would access a property on a child object inside that loop. What looked like a simple property access would silently trigger a database query. The kicker was that this could be far removed from any obvious database access, so the person causing the issue often had no idea they were generating dozens (or hundreds) of extra queries.
I defense of the application developer, it is very difficult to adopt set theory thinking which helps with SQL when you've never had any real education in this area, and it's tough to switch between it and the loop-oriented processing you're likely using in your application code for almost everyone. ORMs bridge this divide which is why they fall in the trap consistently. Often it's an acceptable trade-off for the value you get from the abstraction, but then you pay the price when you need to address the leak!
This problem is associated with ORMs but the moment there's a get_user(id) function which does a select and you need to display a list of users someone will run it in a loop to generate the list and it will look like it's working until the user list gets long.
I really wish there was a way to compose SQL so you can actually write the dumb/obvious thing and it will run a single query. I talked with a dev once who seemed to have the beginnings of a system that could do this. It leveraged async and put composable queryish objects into a queue and kept track of what what callers needed what results, merged and executed the single query, and then returned the results. Obviously far from generalizable for arbitrary queries but it did seem to work.
I think many ORMs can solve (some of) this these days.
e.g. for ActiveRecord there's ar_lazy_preloader[0] or goldiloader[1] which fix many N+1s by keeping track of a context: you load a set of User in one go, and when you do user.posts it will do a single query for all, and when you then access post.likes it will load all likes for those and so on. Or, if you get the records some other way, you add them to a shared context and then it works.
Doesn't solve everything, but helps quite a bit.
[0] https://github.com/DmitryTsepelev/ar_lazy_preload
[1] https://github.com/salsify/goldiloader
Yep, people who think OOP is all you need will just "abstract away the database".
The N+1 problem basically means instead of making one efficient query, you end up making N separate queries inside a loop. For example, fetching a list of tables, then for each table fetching its columns individually — that’s N+1 queries. It works, but it’s slow.
We ran into this while building, funnily enough, a database management app called DB Pro (https://dbpro.app) At first we were doing exactly that: query for all schemas, then for each schema query its tables, and then for each table query its columns. On a database with hundreds of tables it took ~3.8s.
We fixed it by flipping the approach: query all the schemas, then all the tables, then all the columns in one go, and join them in memory. That dropped the load time to ~180ms.
N+1 is one of those things you only really “get” when you hit it in practice.
https://stackoverflow.com/questions/97197
Really love litestream. Easy to use and never crashes on me. I still recommend using it as a systemd unit service. I'm not only using it as a backup tool but also to mirror databases. Looking forward to their read-replica feature.
What I'd like to see is a system where a single-writer SQLite database is replicated to object storage so that you can spin up really cheap read replicas. Is anyone working on something like that?
Such a system would also require a side channel propagating WAL updates (over Kafka or similar) to replicas, so that the running replicas can update themselves incrementally and stay fresh without loading anything from object storage.
LiteFS can do that, but you need to run a custom FUSE filesystem for it - hence why Litestream remained more popular. https://fly.io/docs/litefs/how-it-works/#capturing-sqlite-tr...
Litestream is working on that now - the code is already in https://github.com/benbjohnson/litestream/blob/v0.5.0/vfs.go but it's not yet a working, documented feature.
Litestream is basically that, though the OP article has "The next major feature we’re building out is a Litestream VFS for read replicas" as a What's Next todo at the bottom.
https://litestream.io/guides/s3/
I think this is also roughly what Turso is, although it's becoming a SQLite-compatible db rather than vanilla
https://docs.turso.tech/features/embedded-replicas/introduct...
https://docs.turso.tech/cloud/durability
I don't Litestream does that yet? It appears to be for backing up to S3, and you manually "restore" the image to a file. You can't point an SQLite client at the S3 bucket, and there's no provision for getting low latency updates. But it sounds like they're working on this.
Turso looks cool and is a project I will keep an eye on, but it's replica mode seems to be designed for apps rather than mechanism to rapidly scale out read replicas in a server cluster. Also, the web site warns that it's not ready for production use.
I mean, that's literally their "What’s next?" from the OP: https://fly.io/blog/litestream-v050-is-here/#whats-next
They already have a prototype, and... it's pretty rough on the edges.
I'm porting it to my Go SQLite driver and already ran into a bunch of issues. But it seems at least feasible to get it into a working shape.
https://github.com/benbjohnson/litestream/issues/772
https://github.com/ncruces/go-sqlite3/compare/main...litestr...
Interesting! I really like your wasm compiled SQLite more than the pure Go one, so it is what I use most of the time.
What exactly are you trying to port?
A VFS that allows you to directly open a Litestream replication target (e.g. S3) as a read-only database and run queries against it without ever having to download the entire database (e.g. to an ephemeral instance that doesn't even have the disk space for it).
Interesting. Any plans for a cache? Or would that just be a litestream thing
I… honestly don't know. I saw the announcement some time ago (the revamped post) and started following their repo. They did mention caching to hide latency.
When I saw the v0.5.0 tag, I dived into just porting it. It's just over a couple hundred lines, and I have more experience with SQLite VFSes than most, so why not.
But it's still pretty shaky.
I'm curious about the same, but also am wondering if there can be an automatic election of a new primary through the use of conditional writes (or as Fly.io say, CASAAS: Compare-and-Swap as a Service).
Maybe I misunderstand what this is, but why would I use this and not MySQL, Postgres, or any other proper database? Seems like a hack to get SQLite to do what those do by design.
One of the big advantages people enjoy is the elimination of the network latency between the application server and the DB. With SQLite your DB is right there often directly attached over NVME. This improves all access latencies and even enables patterns like N+1 queries which would typically be considered anti-patterns in other DBs.
> One of the big advantages people enjoy is the elimination of the network latency between the application server and the DB. With SQLite your DB is right there often directly attached over NVME.
You can install MySQL/PostgreSQL on the application server, connect over a unix socket and get the same benefits as if you'd used SQLite on the application server (no network latency, fast queries). Plus the other benefits that come from using these database servers (Postgres extensions, remote connections, standard tooling etc). I'm guessing more RAM is required on the application server than if you used SQLite but I haven't benchmarked it.
Unix sockets don't actually give you the same benefit. You're still doing IPC which can incur substantial memory subsystem utilization. SQLite is on the same thread/core as whatever is using it.
Real talk, how do you actually avoid N+1? I realize you can do complicated JOINs, but isn't that almost as bad from a performance perspective? What are you really supposed to do if you need to, e.g. fetch a list of posts along with the number of comments on each post?
Often you can use joins to get the data in a single complex SQL query. Number of comments for a post is relatively straight-forward, but you can also do increasingly complex associated data fetches with modern databases.
In particular, JSON aggregations mean you can have a single query that does things like fetch a blog entry and the earliest 10 comments in a single go. I wrote up some patterns for doing that in SQLite and PostgreSQL here: https://github.com/simonw/til/blob/main/sqlite/related-rows-...
Here's an example PostgreSQL query that does this to fetch tags for posts: https://simonwillison.net/dashboard/json-agg-example
The alternative, more common path is the pattern that Django calls "prefetch_related". Effectively looks like this: Once you have a list of e.g. 20 IDs you can run a bunch of cheap additional queries to fetch extra data about all 20 of those items.> I realize you can do complicated JOINs, but isn't that almost as bad from a performance perspective?
No, JOINs should be orders of magnitude faster.
> What are you really supposed to do if you need to, e.g. fetch a list of posts along with the number of comments on each post?
You're really supposed to do a JOIN, together with a GROUP BY and a COUNT(). This is elementary SQL.
it gets more complicated when you need to also display something like "last comment: <author> <3 days ago>" for each post, or if the comment counts need to be filtered by various flags/states/etc.
of course, it's all possible with custom SQL but it gets complicated quick.
That's like saying it gets more complicated when you have to use loops with break statements in programming. It's just what programming is.
The filtering you describe is trivial with COUNT(flag IN (...) AND state=...) etc.
If you want to retrieve data on the last comment, as opposed to an aggregate function of all comments, you can do that with window functions (or with JOIN LATERAL for something idiomatic specifically to Postgres).
Learning how to do JOIN's in SQL is like learning pointers in C -- in that it's a basic building block of the language. Learning window functions is like learning loops. These are just programming basics.
Avoiding N+1 doesn't have to mean limiting yourself to 1 query. You can still fetch the posts in one query and the comments of _all_ posts in a separate query, just don't issue a query for _each_ post.
More formally, the number of queries should be constant and not linearly scaling with the number of rows you're processing.
A proper join is the right answer. But, it's not always possible to make those run well. [1] A "client side join" in the right situation can be much better, but then you probably want to do a 1+1 rathet than N+1. Do the first query to get the ids for the second query, and then construct the second query with IN or UNION depending on what works best for you database. UNION likely bloats your query string, but I've seen plenty of situations where UNION is gobs faster than IN.
Alternately, if you can separate query issuance from result parsing, you can make N+1 palletable. Ex, do your query to get the ids, wait for the results, loop and issue the N queries, then loop and wait for results in order. That will be two-ish round trips rather than N+1 round trips. But you have to search to find database apis that allow that kind of separation.
[1] You can almost always express the query you want in SQL, but that doesn't mean it will have a reasonable runtime. Sometimes server side join and client side join have about the same runtime... if it's significant and you have the usual case where clients are easier to scale than database servers, it might be worthwhile to have the join run on the client to reduce server load.
AFAIK the problem of N+1 isn't necessarily one more DB query, but one more network roundtrip. So if for each page of your app you have an API endpoint that provides exactly all of the data required for that page, it doesn't matter how many DB queries your API server makes to fulfill that request (provided that the API server and the DB are on the same machine).
This is essentially what GraphQL does instead of crafting each of these super tailored API endpoints for each of your screens, you use their query language to ask for the data you want, it queries the DB for you and get you the data back in a single network roundtrip from the user perspective.
(Not an expert, so I trust comments to correct what I got wrong)
You still have to write the resolver for graphql. I've seen. N+1 with graphql if you don't actually use data loader+batch pattern OR if you use it incorrectly.
A JOIN is fast, fetching the whole list in one extra query with "WHERE id IN (...)" is also pretty fast and results in less complex queries if you have serval of these. Doing all queries separate is slow because of the network round-trip for each query.
No, JOINs are pretty much always faster than performing N+1 queries.
Either joins for a fat query, or aggregate the subqueries.
For the latter, it's along the lines of `select * from posts where ...` and `select * from authors where id in {posts.map(author_id)}`. And then once it's in memory you manually work out the associations (or rely on your ORM to do it).
You do indeed use JOINS. The goal is to retrieve exactly the data you require in a single query. Then you get the DB to `EXPLAIN VERBOSE` or similar and ensure that full table scans aren't happening and that you have indexed the columns the query is being filtered on.
The actual thing that we're getting N+1 of is network round-trips. An additional network round-trip is way, way slower than an extra JOIN clause. That's why N+1 query patterns aren't a problem when you're using a local database: There's no round-trip.
A well-written JOIN against a well-designed database (regardless if we're talking postgres, SQLite, MySQL/MariaDB, or MS SQL) should not be slow. If it's slow, you're using it wrong.
The performance problem in N+1 is (mostly) not in fetching the N rows from disk, but rather from multiplying the network latency by a factor of N. Joins solve this; so do stored procedures.
In general, you want to ask the remote server once for all the data you need, then read all the results. It applies to databases as well as APIs.
Pipelined requests also solve the problem and can be more flexible.
Also, joins can be optimised in different ways. Sometimes the optimal way to do a join isn't to query each row one-by-one, but to do something like (when the rows you want are a large fraction of the rows that exist) making a bloom filter of the rows you want and then sequentially reading all the rows in the table.
To avoid operating a database by yourself and dealing with incidents, backups, replicas, failovers, etc... You can use cheap commoditised S3-like storage and run your application statelessly.
If you have access to a database that is well managed on your behalf I would definitely still go with that for many usecases.
It's significantly faster and incurs less ops overhead. That's it.
But most apps should just use a classic n-tier database architecture like Postgres. We mostly do too (though Litestream does back some stuff here like our token system).
Why use Postgres if all you need is sqlite? Postgres is way overkill for a simple app with few users and no advanced database functionality.
Because Postgres is mature, works, and has a version number above v1.0?
If v1.0 is your North Star, you should re-evaluate a whole lot of software in your stack: https://0ver.org/#notable-zerover-projects
I think you're focusing on the wrong parts of the comment.
People care about things like long-term support. Postgres 13, from 2020, is still officially supported. Litestream 0.1.0 was the first release, also from 2020, but I can't tell if it is supported still. Worrying about the maturity, stability, and support of an application database is very reasonable in risk adverse projects.
Litestream is just a backup solution. Should probably be compared to a backup solution for postgres that does automated backups over the network etc. That isnt part of postgres.
Besides the question wasnt litestream vs postgres backup apps. It was sqlite vs postgres.
The original response at least concerned litestream because the not-1.0 comment only applies to that.
this is essentially the "no one ever got fired for buying IBM" statement. One counter is why buy & manage a rack-mounted server when all you need is a raspberry Pi?
Postgres can also run on the Pi.
More than once I've started a project with sqlite and then had to migrate to postgres. In my experience it's because of the way sqlite has to lock the whole database file while writing to it, blocking concurrent reads - this isn't a problem in postgres. (There's WAL mode, but it still serialises all writes, and requires periodic explicit checkpointing IME)
You may also find you really want a feature postgres has, for example more advanced data types (arrays, JSON), more advanced indices (GIN inverted index on array members), replication...
I'm guessing this is a joke?
Why would saying that Postgres is a mature database - more mature than SQLite - be a joke?
Both are mature. There are way more sqlite databases running than postgres. The code base is smaller and has less new features added to it every year. What is unstable with sqlite?
SQLite is probably more mature than Postgres in terms of service hours.
I understood the comparison to be with Litestream, not SQLite in general.
Why would we compare Litestream (a backup solution) with Postgres (a database)? Would be like comparing Linux with NGINX.
Postgres has a similar replication solution built-in. It's like comparing Samba with Windows Server if your purpose is to run a SMB file server.
Version numbers dont mean anything as the whole Elixir ecosystem shows:D
Let's say I'm building a small app that I'm hosting on some shared vps, if I think about the effort involved in setting up sqlite with litestream and just getting a $5 (or free) postgres provider I don't think sqlite makes my life easier.
Now if I'm building a local app then absolutely sqlite makes the most sense but I don't see it otherwise.
Litestream is dead simple to setup. You make an S3 bucket (or any compatible storage bucket), paste the access keys and the path to your db file in /etc/litestream, and then run
The fact it's so simple is my favourite thing about it.Are there any use cases/documentation about how litestream can be used within a docker based deployment? (Eg where systemctl wouldn't be used)
Here's their docs on running in a Docker container: https://litestream.io/guides/docker/
You'd probably want to put the sqlite db in a volume & run litestream in a separate container that restarts automatically on failure.
Systemctl's only in there to restart it if it crashes; litestream itself is (iirc) a single cli binary.
This is documented on the Litestream website.
Effort of setting up litestream and sqlite is less time than you spend signing up for supabase. And you can have 100 apps with their own databases for almost free (just a few cents of storage) vs 5*100 for postgres.
I love postgres but in no way is it as simple to run as sqlite (pretty sure even postgres core team would agree that postgres is more complex than sqlite).
I'd argue that anything larger than a desktop app should not use SQLite. If you need Litestream for replication and backup it is probably better to just use Postgres. There are a ton of one-click deployment offerings for proper databases, Fly.io actually offers managed Postgres.
Why would you argue that? Do you have some benchmarks backing it up or is it more a personal preference?
It's literally what they're designed for.
SQLite is designed for one local client at a time. Client-server relational databases are designed for many clients at a time.
That's not entirely true. SQLite is designed to support many processes reading the same file on disk at once. It only allows one process to write at a time, using locks - but since most writes finish in less than a ms in most cases having a process wait until another process finishes their write isn't actually a problem.
If you have lots of concurrent writes SQLite isn't the right solution. For concurrent reads it's fine.
SQLite also isn't a network database out-of-the-box. If you want to be able to access it over the network you need to solve that separately.
(Don't try and use NFS. https://sqlite.org/howtocorrupt.html#_filesystems_with_broke... )
the reality is very few workloads have access patterns that SQLite can't support. I would much rather start with a strategy like 1. use sqlite for my beta / single client, 2. duplicate the entire environment for the next n clients, 3. solve the "my application is wildly successful" and SQLite is no longer appropriate problem at a future date. Spoiler: you're never going to get to step #3.
> 2. duplicate the entire environment for the next n clients
That becomes an instant problem if users ever write to your database. You can't duplicate the environment unless it's read-only.
And even if the database is read-only for users, the fact that every time you update it you need to redeploy the database to every client, is pretty annoying.
That's why it's usually better to start with Postgres or MySQL. A single source of truth for data makes everything vastly easier.
Not true. Can you back up your claim that the developers of Sqlite says they dont recommend it for webservers? (hint they recommend it).
If you have a read-heavy app (99% of saas) that runs on one server and dont have millions of users then sqlite is a great option.
I didn't say that. I said one local client at a time. If you're running on one server then your webserver is the one local client.
Usually you want to be able to run multiple webservers against a single database though, since that's the first thing you'll usually need to scale.
For a cloud service, I think it comes down to whether you’ll ever want more than one app server.
If you’re building something as a hobby project and you know it will always fit on one server, sqlite is perfect.
If it’s meant to be a startup and grow quickly, you don’t want to have to change your database to horizontally scale.
Deploying without downtime is also much easier with multiple servers. So again, it depends whether you’re doing something serious enough that you can’t tolerate dropping any requests during deploys.
99,99% of apps dont need more than one app server. You can serve a lot of traffic on the larges instances.
For sure downtime is easier with kubernete etc but again overkill for 99,99% of apps.
Serving users is one thing. Then you want to run some interactive analytics or cronjobs for cleanup etc on the db. Even if the load can manage it, how would the admin jobs connect to the database. I’ve never seen a db with only one client. There is always some auxiliary thing, even when you don’t consider yourself a microservice shop.
For analytics the solution is very simple. Just copy the database and run the queries on the copy.
agree - with SQLite and DuckDB I've really switched my mindset from one perfect, pristine DB to multiple copies and a medallion architecture that looks more like participation ribbons for everyone! The resources required are so cheap & lightweight when you avoid the big "BI focused" tech stacks.
you can also scale out across unlimited tiny servers, because the entire stack is so lightweight and cheap. This will also force you to focus on devops, which otherwise can become a grind with this approach. The only challenge is when you have cross-DB concerns, either data or clients.
Right, but if your goal is to have a lot of users (and minimal downtime), there's no point in putting a big avoidable obstacle in your path when the alternative is just as easy.
If your goal is to serve billions of users you should probably use cassandra etc. Why limit yourself to postgres if your goal is to have a billion users online at the same time?
your goal should be to solve this problem when you have a million or 10M concurrent users. YAGNI
Because cassandra isn't easy to set up and has all kinds of tradeoffs on consistency, transactions, et al compared to an SQL db.
On the other side, why not just store everything in memory and flush to a local json file if you won't have any users? sqlite is overkill!
This is the idea behind LiteFS --- to transparently scale out SQLite (in some very common configurations and workloads) to support multiple app servers. It's still there and it works! It's just a little ahead of its time. :)
That makes sense, and it seems really cool from a tech perspective. I guess I'm just inherently skeptical about using something shiny and new vs. battle hardened databases that were designed from the beginning to be client-server.
It's definitely really nice though that if you do choose sqlite initially to keep things as small and simple as possible, you don't immediately need to switch databases if you want to scale.
I think that's very fair. But the use case for Litestream is much simpler and you can get your head around it immediately. It also doesn't ask you to do anything that would commit you to SQLite rather than switching to Postgres later. It's just a way of very easily getting a prod caliber backend up for an app without needing a database server.
The common answer (especially from Fly.io) is "at-the-edge" computing/querying. There is network latency involved in sending a query to MySQL or Postgres and getting the data returned, whereas with Litestream you could put a read replica of the entire SQLite DB at every edge. Queries become fast and efficient only to the local read replica. There's still network latency associated with updating that read replica over time, but it is amortized based on the number of overall writes rather than the number of queries, is more fault tolerant in "eventually consistent" workflows (you can answer queries from the read replica at the edge in the state that you have it while you wait for the network to reconnect and replay the writes you missed during the fault), and with SQLite backing it still has much of the same full relational DB query power of SQL you would expect from a larger (or "proper") database like MySQL or Postgres.
Law Theorem[1] fits perfectly for this scenario
1- https://law-theorem.com/
I'm not sure, I've never done it, but I think the idea is to have many tiny customer-specific databases and move them to be powered by sqlite very close to the customer.
But I'd love to hear more from someone more well-versed in the use cases for reliable sql-lite
I find myself mostly in this camp now.
In every case where I had a SQLite vertical that required resilience, the customer simply configured the block storage device for periodic snapshots. Litestream is approximately the same idea, except you get block device snapshots implicitly as part of being in the cloud. There is no extra machinery to worry about and you won't forget about a path/file/etc.
Also, streaming replication to S3 is not that valuable an idea to me when we consider the recovery story. All other solutions support hot & ready replicas within seconds.
It's a good question, and I don't think answered sufficiently in the recent sqlite hype.
In my opinion if you have an easy way to run postgres,MySQL,... - just run that.
There's usually a lot of quirks in the details of DB usage (even when it doesn't immediately seem like it - got bitten by it a few times). Features not supported, different semantics, ...
IMO every project has an "experimental stuff" budget and if you go over it it's too broken to recover, and for most projects there's just not that much to win by spending them on a new database thing
>> the recent sqlite hype.
This is an interesting take; why do you see recent hype around the most boring and stone-age of technologies, SQLite?
The rails creator dhh has been hyping it up a lot in the first 6 month of this year, and quite a few followed of the "Dev influencers" scene. Fly's litestream came out around that time, and there's been more sqlite in the cloud companies/discussions, in particular with the AI agent use-case.
Not super sure who followed who but there was all of a sudden a lot of excitement
Litestream's first release was February 2021: https://news.ycombinator.com/item?id=26103776
SQLite's "buzz" isn't new, type "sqlite" into my https://tools.simonwillison.net/hacker-news-histogram tool and you'll see interest (on HN at least) has been pretty stable since 2021.
Maybe it's a local bump, but it sure seems like SQLite has become a fair more popular topic in the Rails world. I wouldn't expect to find it in a HN search tool. SQLite has gone from the little database you might use to boostrap or simplify local development to something products are shipping with in production. Functionality like solid_cable, solid_cache, and solid_queue allow SQLite to be used in more areas of Rails applications and is pitched as a way to simplify the stack.
While I don't have stats about every conference talk for the last decade, my experience has been that SQLite has been featured more in Rails conference talks. There's a new book titled "SQLite on Rails: The Workbook" that I don't think would have had an audience five years ago. And I've noticed more blog posts and more discussion in Rails-related discussion platforms. Moreover, I expect we'll see SQLite gain even more in popularity as it simplifies multi-agent development with multiple git worktrees.
this is infra for a single-user app. SQLite is THE replacement for file databases like MSAccess, but the box goes down and your database dies with all your data.
So this fills that gap by giving you a database as a service level of QOL without needing to provision a database as a service backend. Otherwise you're dicking about maintaining a service with all that comes with that (provisioning, updating, etc) when really all you need is a file that is automagically backed up or placed somewhere on the web to avoid the drawbacks of the local file system.
Whatever database you end up using, you'll need some sort of backup solution. Litestream is a streamed backup solution which effectively doubles as replication for durability purposes.
MySQL, Postgres, etc. have a much greater overhead for setup, unless you want to pay for a managed database, which is not going to be worth the price for small quantities of data.
I have a branch office in boondocks with limited internet connection. The branch office cannot manage a RDBMS or access cloud services. They can use sqlite app on LAN and we could do reconciliation at end of the business day.
they can also run the entire application in these scenarios on the resources of a 10-yr-old phone.
To enable local-first or offline-first design. I prefer having data stored on-device and only optionally backed up to cloud
Interesting information that they chose modernc.org/sqlite over mattn/go-sqlite3 as a Quality-of-Life improvement. Going forward I guess I'll do the same for new projects.
The NATS Jetstream use case is something I'm curious about.
Cheers and keep up the great work on Litestream.
All the public benchmarks (and my own applications) indicate that there is only a small (often unnoticeable) performance penalty associated with modernc.org/sqlite and this is far outweighed by the ability to eliminate CGO. I'd use it on future projects without hesitation.
I don't even care about the topic (much) (right now), but this is a really well written blog post / announcement.
Thanks, what'd you like about it?
Can’t wait for Kyle to set Jepsen loose on this.
Jepsen is primarily for testing distributed systems and consensus systems. SQLite has neither of those.
Litestream is pretty boring for the testing Kyle does. It's not a distributed consensus system, but something more like a storage engine.
litestream makes very few consistency guarantees compared to other datastores, and so I would expect most any issues found would be "working as intended".
at the end of the day with litestream, when you respond back to a client with a successful write you are only guaranteeing a replication factor of 1.
By "replication factor of 1" you mean your data is stored on local disk only, right? That matches my understanding: Litestream replication is asynchronous, so there's usually a gap of a seconds or two between your write being accepted and the resulting updated page being pushed off to S3 or similar.
Yes. the acknowledgement you're getting in your application code is that the data was persisted in sqlite on that host. There's no mechanism to delay acknowledgement until the write has been asynchronously persisted elsewhere.
An interesting comparison here is Cloudflare's Durable Objects, which provide a blocking SQLite write that doesn't return until the data has been written to multiple network replicas: https://blog.cloudflare.com/sqlite-in-durable-objects/#trick...
Is this a transcript?