Use DuckDB-WASM to query TB of data in browser(lil.law.harvard.edu)
lil.law.harvard.edu
Use DuckDB-WASM to query TB of data in browser
https://lil.law.harvard.edu/blog/2025/10/24/rethinking-data-discovery-for-libraries-and-digital-humanities/
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DuckDB-WASM supports parquet file decompression though, so if you have a backend process generating them it's a non issue.
How large was your WASM build? I'm using the standard duckdb-wasm, along with JS functions to form the SQL queries, and not seeing onerous load times.
How large was your WASM build? I'm using the standard duckdb-wasm, along with JS functions to form the SQL queries, and not seeing onerous load times.
> WASM build doesn't have feature-parity with the "normal" variant
It's a good point, but the wasm docs state that feature-parity isn't there - yet. It could certainly be more detailed, but it seems strange that your company would do all this work without first checking the feature-coverage / specs.
> WebAssembly is basically an additional platform, and there might be platform-specific limitations that make some extensions not able to match their native capabilities or to perform them in a different way.
https://duckdb.org/docs/stable/clients/wasm/extensions
It's a good point, but the wasm docs state that feature-parity isn't there - yet. It could certainly be more detailed, but it seems strange that your company would do all this work without first checking the feature-coverage / specs.
> WebAssembly is basically an additional platform, and there might be platform-specific limitations that make some extensions not able to match their native capabilities or to perform them in a different way.
https://duckdb.org/docs/stable/clients/wasm/extensions
Note that your docs specifically mentions parquet was supported, but we found out the hard way some specific features turned out not to be supported with WASM + parquet. I did a quick glance at your docs and could not find references to that, so I'm not surprised it was missed.
It was a project that exploited a new opportunity so time-to-market was the most important thing, I'm not suprised these things were missed, and replacing the data loading mechanism was maybe 1 week of work for 1 person, so it wasn't that impactful a change later.
It was a project that exploited a new opportunity so time-to-market was the most important thing, I'm not suprised these things were missed, and replacing the data loading mechanism was maybe 1 week of work for 1 person, so it wasn't that impactful a change later.
Fair point, thx for sharing your experiences ! You might want to edit the duck-wasm docs in that regard to alert others/the team of this constraint.
OK, this is really neat:
- S3 is really cheap static storage for files.
- DuckDB is a database that uses S3 for its storage.
- WASM lets you run binary (non-JS) code in your browser.
- DuckDB-Wasm allows you to run a database in your browser.
Put all of that together, and you get a website that queries S3 with no backend at all. Amazing.
Put all of that together, and you get a website that queries S3 with no backend at all. Amazing.
S3 might be relatively cheap for storing files, but with bandwidth you could easily be paying $230/mo. If you make it public facing & want to try to use their cloud reporting, metrics, etc. to prevent people for running up your bandwidth, your "really cheap" static hosting could easily cost you more than $500/mo.
R2 is S3 compatible with no egress fees.
Cloudflare actually has built in iceberg support for R2 buckets. It's quite nice.
Combine that with their pipelines it's a simple http request to ingest, then just point duckdb to the iceberg enabled R2 bucket to analyze.
Cloudflare actually has built in iceberg support for R2 buckets. It's quite nice.
Combine that with their pipelines it's a simple http request to ingest, then just point duckdb to the iceberg enabled R2 bucket to analyze.
> R2 is S3 compatible with no egress fees.
There's no egress data transfer fees, but you still pay for the GET request operations. Lots of little range requests can add up quick.
There's no egress data transfer fees, but you still pay for the GET request operations. Lots of little range requests can add up quick.
For a demo of this (although not sure with duckdb wasm that it works with iceberg) https://andrewpwheeler.com/2025/06/29/using-duckdb-wasm-clou...
Was about to jump in to say the same thing. R2 is a much cheaper alternative to S3 that just works and I have used it with DuckDB, works smoothly
I think this approach makes sense for services with a small number of users relative to the data they are searching. That just isn't a good fit for a lot of hosted services. Think how much that TB's of data would cost on Algolia or similar services.
You have to store the data somehow anyway, and you have to retrieve some of it to service a query. If egress costs too much you could always change later to put the browser code on a server. Also it would presumably be possible to quantify the trade-off between processing the data client side and on the server.
You have to store the data somehow anyway, and you have to retrieve some of it to service a query. If egress costs too much you could always change later to put the browser code on a server. Also it would presumably be possible to quantify the trade-off between processing the data client side and on the server.
S3 is doing quite a lot of sophisticated lifting to qualify as no backend at all.
But yeah - this is pretty neat. Easily seems like the future of static datasets should wind up in something like this. Just data, with some well chosen indices.
But yeah - this is pretty neat. Easily seems like the future of static datasets should wind up in something like this. Just data, with some well chosen indices.
I believe all S3 has to do here is respond to HTTP Range queries, which are supported by almost every static server out there - Apache, Nginx etc should all support the same trick.
100%. I’m with y’all - this is what I would also call a “no-backend” solution and I’m all in on this type of approach for static data sets - this is the future, and could be served with a very simple web server.
I’m just bemused that we all refer to one of the larger, more sophisticated storage systems on the plant, composed of dozens of subsystems and thousands of servers as “no backend at all.” Kind of a “draw the rest of the owl”.
I’m just bemused that we all refer to one of the larger, more sophisticated storage systems on the plant, composed of dozens of subsystems and thousands of servers as “no backend at all.” Kind of a “draw the rest of the owl”.
Still qualifies imo. Everything is static and on a CDN.
Lack of server/dynamic code qualifies as no backend.
Lack of server/dynamic code qualifies as no backend.
Can you replace S3 with a directory and nginx and save lot of money?
Yes. Especially if you use Storage Combinators.
They let you easily abstract over storage.
https://2019.splashcon.org/details/splash-2019-Onward-papers...
They let you easily abstract over storage.
https://2019.splashcon.org/details/splash-2019-Onward-papers...
Yes, i.i.r.c. it's not S3 specific just URLs
Or use R2 instead. It’s even easier.
Yesterday there was a somewhat similar DuckDB post, "Frozen DuckLakes for Multi-User, Serverless Data Access". https://news.ycombinator.com/item?id=45702831
I set up something similar at work. But it was before the DuckLake format was available, so it just uses manually generated Parquet files saved to a bucket and a light DuckDB catalog that uses views to expose the parquet files. This lets us update the Parquet files using our ETL process and just refresh the catalog when there is a schema change.
We didn't find the frozen DuckLake setup useful for our use case. Mostly because the frozen catalog kind of doesn't make sense with the DuckLake philosophy and the cost-benefit wasn't there over a regular duckdb catalog. It also made making updates cumbersome because you need to pull the DuckLake catalog, commit the changes, and re-upload the catalog (instead of just directly updating the Parquet files). I get that we are missing the time travel part of the DuckLake, but that's not critical for us and if it becomes important, we would just roll out a PostgreSQL database to manage the catalog.
We didn't find the frozen DuckLake setup useful for our use case. Mostly because the frozen catalog kind of doesn't make sense with the DuckLake philosophy and the cost-benefit wasn't there over a regular duckdb catalog. It also made making updates cumbersome because you need to pull the DuckLake catalog, commit the changes, and re-upload the catalog (instead of just directly updating the Parquet files). I get that we are missing the time travel part of the DuckLake, but that's not critical for us and if it becomes important, we would just roll out a PostgreSQL database to manage the catalog.
This also reminded me of an approach using SQLite: https://news.ycombinator.com/item?id=45748186
My initial thought is why query 1TB of data in a browser, maybe I'm the wrong target audience for this but it seems that it's pushing that everything has to be in a browser rather than using appropriate tools
Browsers are now the write-once works everywhere target. Where java failed, many hope browsers succeed. WASM is definitely a key to that, particularly because it can be output by tools like rust, so they can also be the appropriate tools.
I doubt they are querying 1 TB of data in the browser. DuckDB-WASM issues http range requests on behalf of client to request only the bytes required, especially handy with parquet files (columnar format) that will exclude columns you don't even need.
But the article is a little light on technical details. In some cases it might make sense to bring the entire file client-side.
But the article is a little light on technical details. In some cases it might make sense to bring the entire file client-side.
For small databases, SQLite is handy, as there are multiple ways to parse the format for clients.
The one word answer is cost.
But, if you'd like to instead read the article, you'll see that they qualify the reasoning in the first section of the article, titled, "Rethinking the Old Trade-Off: Cost, Complexity, and Access".
But, if you'd like to instead read the article, you'll see that they qualify the reasoning in the first section of the article, titled, "Rethinking the Old Trade-Off: Cost, Complexity, and Access".
Why pay for RAM for servers when you can let your users deal with it? ;)
(Does not seem like a realistic scenario to me for many uses, for RAM among other resource reasons.)
(Does not seem like a realistic scenario to me for many uses, for RAM among other resource reasons.)
What appropriate tool would you use for this instead?
It's one of the best tricks in the book.
We have been doing it for quite some time in our product to bring real time system observability with eBPF to the browser and have even found other techniques to really max-it-out beyond what you get off the shelf.
https://yeet.cx
We have been doing it for quite some time in our product to bring real time system observability with eBPF to the browser and have even found other techniques to really max-it-out beyond what you get off the shelf.
https://yeet.cx
That's pretty cool. Any technical blog posts?
we got a couple blog posts
https://yeet.cx/blog
https://yeet.cx/blog
I built something on top of DuckDB last year but it never got deployed. They wanted to trust Postgres.
I didn't use the in browser WASM but I did expose an api endpoint that passed data exploration queries directly to the backend like a knock off of what new relic does. I also use that same endpoint for all the graphs and metrics in the UI.
DuckDB is phenomenal tech and I love to use it with data ponds instead of data lakes although it is very capable of large sets as well.
I didn't use the in browser WASM but I did expose an api endpoint that passed data exploration queries directly to the backend like a knock off of what new relic does. I also use that same endpoint for all the graphs and metrics in the UI.
DuckDB is phenomenal tech and I love to use it with data ponds instead of data lakes although it is very capable of large sets as well.
Cool thing about DuckDB is it can be embedded. We have a data pipeline that produces a duckdb file and puts it on S3. The app periodically checks that assets etag and pulls it down when it changes. Most of our DB interactions use PSQL, but we have one module that leverages DuckDB and this file for reads. So it's definitely not all-or-nothing.
Are you using pg_duckdb to embedded it inside postgres and access it via psql or other pg clients?
> data ponds instead of data lakes
What are data ponds? Never heard the term before
What are data ponds? Never heard the term before
Haha, my term. Somewhere between a data lake and warehouse - still unstructured but not _everything_ in one place. For instance, if I have a multi-tenant app I might choose to have a duckdb setup for each customer with pre-filtered data living alongside some global unstructured data.
Maybe there's already a term that covers this but I like the imagery of the metaphor... "smaller, multiple data but same idea as the big one".
Maybe there's already a term that covers this but I like the imagery of the metaphor... "smaller, multiple data but same idea as the big one".
Got it, Thanks for the explanation.
I tried DuckDB - liked it a lot - was ready to go further.
But found it to be a real hassle to help it understand the right number of threads and the amount of memory to use.
This led to lots of crashes. If you look at the projects github issues you will see many OOM out of memory errors.
And then there was some indexed bug that crashed seemingly unrelated to memory.
Life is too short for crashy database software so I reluctantly dropped it. I was disappointed because it was exactly what I was looking for.
But found it to be a real hassle to help it understand the right number of threads and the amount of memory to use.
This led to lots of crashes. If you look at the projects github issues you will see many OOM out of memory errors.
And then there was some indexed bug that crashed seemingly unrelated to memory.
Life is too short for crashy database software so I reluctantly dropped it. I was disappointed because it was exactly what I was looking for.
I can recommend earlyoom (https://github.com/rfjakob/earlyoom). Instead of freezing or crashing your system this tool kills the memory eating process just in time (in this case duckdb). This allows you repeat with smaller chunks of the dataset, until it fits into your mem.
When I there is a specific program I want to run with a limit on how much memory it is allowed to allocate, I have found systemd-run to work well.
It uses cgroups to enforce resource limits.
For example, there’s a program I wrote myself which I run on one of my Raspberry Pi. I had a problem where my program would on rare occasions use up too much memory and I wouldn’t even be able to ssh into the Raspberry Pi.
I run it like this:
In order to figure out if I had the right name for it, I tested different names for it with a super small limit that I knew was less than the program needs even in normal conditions. And when I found the right name, the program would as expected be killed right off the bat and so then I could set the limit to 5G (five gigabytes) and be confident that if it exceeds that then it will be killed instead of making my Raspberry Pi impossible to ssh into again.
It uses cgroups to enforce resource limits.
For example, there’s a program I wrote myself which I run on one of my Raspberry Pi. I had a problem where my program would on rare occasions use up too much memory and I wouldn’t even be able to ssh into the Raspberry Pi.
I run it like this:
systemd-run --scope -p MemoryMax=5G --user env FOOBAR=baz ./target/release/myprog
In order to figure out if I had the right name for it, I tested different names for it with a super small limit that I knew was less than the program needs even in normal conditions. And when I found the right name, the program would as expected be killed right off the bat and so then I could set the limit to 5G (five gigabytes) and be confident that if it exceeds that then it will be killed instead of making my Raspberry Pi impossible to ssh into again.
Yeah memory and thread management is the job of the application, not me.
This looks amazing!
Have you used this in conjunction with DuckDB?
Have you used this in conjunction with DuckDB?
Yes, it works just fine.
+1 this was my experience trying it out as well. I find that for getting started and for simple usecases it works amazing. But I have quite a lot of concerns about how it scales to more complex and esoteric workloads.
Non-deterministic OOMs especially are some of the worst things in the sort of tools I'd want to use DuckDB in and as you say, I found it to be more common than I would like.
Non-deterministic OOMs especially are some of the worst things in the sort of tools I'd want to use DuckDB in and as you say, I found it to be more common than I would like.
How long ago was this, or can you share more context about data and mem size you experienced this with?
DuckDB has introduced spilling to disk and some other tweaks since a good year now: https://duckdb.org/2024/07/09/memory-management
DuckDB has introduced spilling to disk and some other tweaks since a good year now: https://duckdb.org/2024/07/09/memory-management
3 days ago.
The final straw was an index which generated fine on MacOS and failed on Linux - exact same code.
Machine had plenty of RAM.
The thing is, it is really the responsibility of the application to regulate its behavior based on available memory. Crashing out just should not be an option but that's the way DuckDB is built.
The final straw was an index which generated fine on MacOS and failed on Linux - exact same code.
Machine had plenty of RAM.
The thing is, it is really the responsibility of the application to regulate its behavior based on available memory. Crashing out just should not be an option but that's the way DuckDB is built.
I had the same experience - everything runs great on an AWS Linux EC2 with 32GB of memory, same workload in a docker on ECS with 32GB allocated gets an OOM.
But for smaller workloads, DuckDB is fantastic... however, there's a certain point when Spark or Snowflake start to make more sense.
what did you use instead? if you hit OOM with the dataset in duckdb, I'd think you'd hit the OOM with most other things on the same machine.
The software should manage its own memory not require the developer to set specific memory thresholds. Sure, a good thing to be able to say "use no more than X RAM".
Also similar procedure used on joblist.today https://github.com/joblisttoday to fetch hiring companies and their jobs and store them into sqlite and duckdb, and retrieved on the client side with their wasm modules. The database are generated with a daily github workflow and hosted as artifact on a github page.
Where do I learn how to set up this sort of stuff? Trial and error? I kinda never need it for personal projects (so far), which always leads me to forget this stuff in between jobs kinda quickly. Is there a decent book?
If you want to learn it the best way is probably to come up with a personal project idea that requires it specifically? Idk how much you'd get out of a book but you could always do a side project with the specific goal of doing it just to learn a particular stack or whatever
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Neat. Can you use duckdb backed on another store like rocksdb or something? Also, I wonder how one stops ddos. Put the whole thing behind Cloudflare?
This is brilliant guys, omg this is brilliant. If you think about it, freely available data always suffer with this burden... "But but we don't make money, all this stuff is public data by law, and government doesn't give us a budget". This solves that, the "can't afford it" spirit of public agencies.
On paper it seemed like a great fit, but it turned out the WASM build doesn't have feature-parity with the "normal" variant, so things that caused us to pick it like support for parquet compression and lazy loading were not supported. So it ended up not having great performance while introducing a lot of complexity, and also was terrible for first page load time due to needing the large WASM blob. Build pipeline complexity was also inherently higher due to the dependency and data packaging needed.
Just something to be aware of if you're thinking of using it. Our conclusion was that it wasn't worth it for most use cases, which is a shame because it seems like such a cool tech.