DuckDB Internals Part 1

https://www.greybeam.ai/blog/duckdb-internals-part-1

by marklit • 1 month ago

480 153 1 month ago

DuckDB has received widespread adoption because it's just so damn easy to use.

This was a major factor in my initial adoption. Since then it has stuck because it’s also absurdly capable, versatile, and fast.

If it wasn’t so easy to use I suspect I wouldn’t have adopted it when I did. The ergonomics are crazy. It still impresses me regularly.

jkubicek• 1 month ago

What do you use it for? I’m perpetually interested in using DuckDB, but it doesn’t seem to do anything I need.

edweis• 1 month ago

I personally find it useful to search logs with AI

medvezhenok5• 1 month ago

Basically like a locally hosted Snowflake - it only shines if you have enough data to analyze (100 MB - 100 GB is probably the sweet-spot range - less than that and the benefits are small, more than that and you risk flying too close to the sun with memory usage).

It has connectors for Postgres & other stores, so I find it faster to connect to a Postgres instance, pull all of the data from a table (even if the table is like 50GB - if you have 30 cores on the machine it will pull from Postgres using 30 cores in parallel, so it will only take a minute or two) - and then any analytical queries on the data are 10+ times faster in DuckDB over native Postgres (GROUP BY, regexp_replace, count(distinct...) etc).

orthoxerox2• 1 month ago

All kinds of data processing. For example, you download a million rows of metrics and load them in Excel to build pivot tables. It works, but now it's a billion rows. If you know SQL, it's a snap to point DuckDB at the source CSV or JSON and get the results in a second.

steve_adams_864• 1 month ago

The most interesting use case lately has been using it as the transformation and validation engine for a CLI that handles scientific data. Some datasets are small and could have been handled at the application layer, but some are quite massive (especially genomic data). DuckDB bundles with the CLI and travels around any platform, is super lightweight, allows for easily running in CI, on a user’s machine, against datasets of all sizes, and so on.

There are other embeddable options out there but I found DuckDb fit better for the potentially massive datasets, and also because of how naturally it ingests the types of data we work with, some of its unique features, and how trivial it was to learn and integrate with the project.

Otherwise I use it almost daily for doing guardrailed data exploration with LLMs. I prefer SQL over random DSLs in AWS or Sentry or what have you. I’ll ingest the data I need and just run SQL against it. I mentioned in another comment that I’ll tend to store more useful data (especially data I export routinely, like infra cost reports) on S3 and use a Rill instance to do basic exploration in a GUI (it will query remote parquet files).

raihansaputra• 1 month ago

throwing in my 2 cents: It just replaced pandas for me. It's just so much easier to write sql against csv/json/whatever format data in jupyter/marimo notebooks through duckdb rather than reasoning through pandas. SQL is far more natural for me, and agents also work through it easily.

hilariously3• 1 month ago

Honestly as someone whose super SQL focused and spends less time focusing on python I just can write generic SQL to transform things in memory to do whatever I want, its very helpful for that.

wiredfool• 1 month ago

Few different use cases, other than just a general swiss army knife for vaguely tabular data.

* fastapi + duckdb + parquet for the backend for a relatively high profile website

* wasm duckdb + react for a few visualization websites

* yaml driven ETL from lots of sources, principally ugly spreadsheets, into usable data. More T than E or L really

skeeter20208• 1 month ago

the taste that hooked me: the next time you have a bunch of json data, csvs or other data - local or remote - and someone wants some charts (for me it was "productivity" metrics from Jira combined with a bunch of other stuff). First it is very easy/fast to load this data; DuckDB has a very liberal parsing engine and good connectors. Second, I used to worry a lot about my table definitions and cleaning data before structuring it. Not anymore! With DuckDB I find myself iteratively transforming data and creating new tables, combining sources, converting columns, slicing/dicing/rotating. It's very easy to "remix" data and there are functions or extensions for everything you might want to do. There's so little friction to get started that I've found it just naturally becomes the multitool in my toolbox.

THis will give you some experience and you'll start to see applicable problem spaces for DuckDB in product areas, especially anything with BI or DW.

thefourthchime• 1 month ago

I’m a huge fan, I’ve been wanting to know into the internals. Look forward to digging in.

anitil• 1 month ago

DuckDb makes so much of my life easier, though I've never used it for large problems. The ability to run `select * from 'data.json'` is just lovely. The fact that it's also a powerhouse is so impressive, I'd usually expect a project to be good at small problems (like mine) xor large problems, but not both

medvezhenok5• 1 month ago

Yup. And an extra benefit that you can treat any file like a table, so you can also do something like

  UPDATE my_table
  SET x = file1.x,
      y = file2.y
  FROM 'first_file.csv' file1
  LEFT JOIN 's3://my_bucket/second_file.parquet' file2
    ON file1.id = file2.id
  WHERE mytable.id = file1.id;

jdw643• 1 month ago

The data scientists I work with use this. Why do they use it? I don't really know much about it, but I've noticed they use it quite often. I mainly use MySQL or PostgreSQL. What are the advantages of DuckDB? It seems like they usually use it as an alternative to Pandas.

bdcravens• 1 month ago

Primarily the ability to work directly with data in its native format (CSV for example) without needing ETL.

jdw643• 1 month ago

Then it definitely makes sense. Scientists usually handle a lot of CSV files. Thank you

throwaway7783• 1 month ago

How does this work in a production setup? Can this be set up like a server, or is it mostly for individual users to play around with data?

tannenfreund872• 1 month ago

DuckDB is a column-oriented database. This is why it's so fast compared to SQLite for a lot of analytical queries. CSV is a row-based format. DuckDB has to load it completley to memory before it can run any queries. Depending on the amount of RAM and Swap you have avialable, it's better to use parquet or similar column-based formats for data storage. Even SQLite as format is faster than CSV, since SQLite is already optimized, integers and floats take less space in SQLite format than as CSV.

Demiurge• 1 month ago

Here is the thing, it’s a write only single file format. If you need to run analytical queries it’s optimized for reading, you just open a file and query for the parts you want. If you have multiple clients that read and write data to the database, you should use postgresql.

It’s not really a database in the traditional sense, there is no ACID complexity, it’s a library that lets use write SQL to query a tabular data file.

medvezhenok5• 1 month ago

DuckDB has been probably my most used tool in 2026 - if you're comfortable with SQL it's incredible at quickly prototyping and slicing / dicing data.

I do a lot of experiments with regexes, and if you get used to the RE2 syntax that DuckDB uses, you can see up to 10-100x uplift in terms of speed compared to Postgres on things like regexp_matches(), regexp_extract(), etc (depending on query/table/machine specifics). It has quite powerful scripting with custom Macros, fixes a lot of annoyances of SQL for me compared to Postgres.

I think if you have access to a machine with a lot of RAM / cores and a beefy data set, then it's basically like a RAMdisk version of Snowflake running locally on your machine.

(and of course the fact that it makes it convenient to read CSV/parquet, read/write from S3, etc) - it's a very ergonomic tool.

jdw643• 1 month ago

Thank you for your kind reply. I should look into it too. In my case, knowing various libraries is directly related to my livelihood. Have a good day.

JoelJacobson• 1 month ago

I was curious what the claim

"10-100x uplift in terms of speed compared to Postgres on things like regexp_matches()"

was about, so I checked, and DuckDB's regexp_matches() is not the same as PostgreSQL's regexp_matches(). DuckDB's version "Returns true if string contains the regexp pattern, false otherwise." [1] while PostgreSQL's "returns a set of text arrays of matching substring(s)" [2].

I think the closest think in PostgreSQL to DuckDB's regexp_matches() is `string ~ pattern` or `regexp_like(string, pattern)`.

[1] https://duckdb.org/docs/lts/sql/functions/regular_expression... [2] https://www.postgresql.org/docs/current/functions-matching.h...

smithclay• 1 month ago

If you're reading this and curious: consider writing a duckdb community extension* or contributing to an existing one*

duckdb is becoming a kind of data superglue between a lot of data ecosystems (GIS, observability, analytics, lakehouses, object storage, etc) that don't talk to each other typically, and it's worth checking out in 2026.

* https://github.com/duckdb/extension-template * https://duckdb.org/community_extensions/

pknerd7• 1 month ago

Just curious whether one can earn money making these exts?

faangguyindia• 1 month ago

You can definately offer consultation or custom integration.

vovavili• 1 month ago

Open source in general is notoriously difficult to easily monetize.

aleda145• 1 month ago

I just started doing this last week!

I'm not very good at C++, but coupled with the extension template and codex I got a basic version of my extension working within an hour. Go for it!

melih1im2• 24 days ago

DuckDB is amazing, especially extensions.

codingbear• 1 month ago

duckdb is so nice coupled with claude code. It extensive file support and some very interesting decisions on local caching data (like from S3 or snowflake) makes it easy to slice and dice almost any kind of tabular data.

blackoil2• 1 month ago

duckdb is so nice coupled with claude code

Can you expand upon it? You mean claude code use it to store its memory/state or it can do business queries using DuckDB.

medvezhenok5• 1 month ago

Claude code can write exploratory queries for you to give you a quick rundown on the shape of the data set, frequencies, missing values, etc etc (without having to load it into a more persistent data store or writing custom python scripts). I also find SQL snippets inherently more re-usable than custom python code.

You can also write a skill that CC can re-use if you're analyzing a lot of similar data sets with minor variance.

0xferruccio2• 1 month ago

DuckDB is amazing for any sort of fast data analysis when the data is small enough that it can fit on your laptop

Recently at work I've been using it to analyse the Claude code sessions of every engineer at our company (that we upload to S3) and it's been extremely helpful to help us find gaps in devex and have clear metrics to back up the impact of fixing them

Another thing it's been really useful for has been getting metrics on Claude skills usage and then dive into use-cases by looking at the transcripts

Other engineers that had never touched DuckDB were so impressed with how easy it is for AI agents to write queries on our dataset

zurfer• 1 month ago

Like sqlite, duckdb is underappreciated as a production database. You can totally run it on servers or even "serverless" and do some heavy data transformations or with the right server size work with large scale datasets (up to a TB compressed seems fine).

tomnipotent• 1 month ago

Not to mention it can query across heterogeneous sources, so the same query can use a duckdb table, sqlite, csv, and parquet (including predicate pushdown).

ndr2• 1 month ago

This. I've recently used both duckdb and sqlite to power a dashboard for a small restaurant of a family member. It converts all their sales to a very tiny parquet files, daily.

The file fits in memory and can do all sort of computation in the browser itself. The backend is extremely simple, it just loads the JS and serves the parquet files.

It was also trivial to let the owner do their own queries, just give the schema to an LLM and let it use the charting library, no data hallucinations. If they need it in the dashboard they can either use that one or ask me to review that query.

To be honest, given how simple some things became, it's been really fun to work on.

cyanide911• 1 month ago

Recently at work I've been using it to analyse the Claude code sessions of every engineer at our company (that we upload to S3) and it's been extremely helpful to help us find gaps in devex and have clear metrics to back up the impact of fixing them

Nice! How do you set things up so that your engineers's claude code sessions upload to S3? Thanks for the help in advance

pimeys• 1 month ago

If you use OpenCode, the sessions are all in a local sqlite database. After lunch I'm pushing one of my agents to crunch some data from that using duckdb...

_boffin_• 1 month ago

Probably on a business / Enterprise plan, which has managed settings and also telemetry export. Give it a collector endpoint to export to and then have collector send to s3.

0xferruccio2• 1 month ago

We have a hook that runs on session start and session end that sends data to a lambda with a hard coded JWT token that we ship in the code

We added that to the managed settings for our Claude instance as a “base” plugin and provision it to all machines using JAMF

A non-enterprises version of that would be to add this hook in your main repository’s .claude folder

ashu1461• 1 month ago

Can you please expand more on the claude analysis part. What exactly you analysed and what outcome it helped with ?

ryanchants• 1 month ago

Not who you responded to, but I've been working on cctx. It's an open source tool for analyzing claude code sessions to see where things went wrong(tool failure loops, bloated context, and the like).

https://github.com/jacquardlabs/cctx

tosh• 1 month ago

Agree, in addition to that DuckDB also works quite well for data that is too big to fit in memory or on the machine DuckDB is on (predicate push down, out of core processing, …).

skeeter20208• 1 month ago

> DuckDB is amazing for any sort of fast data analysis when the data is small enough that it can fit on your laptop

I agree, and the dirty (not so) secret big data providers like Snowflake try to hide: the majority of your work is not big data and WILL fit on your local machine. My last company was spending $2M/yr on contract with Snowflake, and another million between Fivetran and Matillion. Of the 1200 clients using analytics maybe 2 had enough data to warrant "infinite scalability" and a dozen wanted Snowflake because they already had corporate warehouses in Snowflake (they probably didn't need it either). Turns out the Extract and Load could be handled by bog-standard C# code and a bunch of SQL, while almost everyone was better off with a DuckDB database running locally, often in the browser. You've probably heard YAGNI before (You Ain't Gonna Need It) but it's even more likely with "Big Data". #SmallDataConvert

tomjakubowski3• 1 month ago

Folks have been beating this drum for as long as I've worked in software, dating to the Hadoop era, and it remains true today. So much of "big data" only appears big because it's poorly stored, or is represented wastefully (in persistent storage or in memory).

nxm• 1 month ago

A good portion of users querying Snowflake at large companies are not technical so you can’t expect them to run DuckDB, not to mention data access controls

fastasucan• 1 month ago

DuckDB is amazing for any sort of fast data analysis when the data is small enough that it can fit on your laptop

It also works great for data that doesn't fit on my laptop.

holografix• 1 month ago

Why is DuckDB so popular when one can use Python + Pandas?

Better perf + SQL is that mostly it?

RobinL• 1 month ago

I wrote a blog post a while back to address this question here: https://www.robinlinacre.com/recommend_duckdb/

paytonjjones• 1 month ago

Pandas has lots and lots of problems.

Performance is definitely one of them, but it also has inconsistent and duplicated methods, inconsistent defaults (e.g. some methods are inplace by default), copy by reference issues, I could go on.

It was an early winner in an extremely popular language. That's really the main thing going for it, but alternatives have been a long time coming.

estetlinus• 1 month ago

Why would you prefer Python and Pandas over good old SQL? Pandas is so verbose and hard to debug, most of the times struggle to be performant on small datasets.

SQL has been around since the dawn of databases. I am happy to see a trend away from pandas.

refactor_master2• 1 month ago

The better question is, why is DuckDB so popular when one can use Polars which has a sane, lintable, typesafe API compared to the mess that is SQL:

  WITH lagged AS (
      SELECT 
          *, 
          LAG(event_time) OVER (PARTITION BY user_id ORDER BY event_time) AS prev_time
      FROM events
  ),
  sessions AS (
      SELECT 
          *, 
          SUM(COALESCE((date_diff('minute', prev_time, event_time) > 30)::INT, 1)) 
              OVER (PARTITION BY user_id ORDER BY event_time) AS session_id
      FROM lagged
  )
  SELECT
      user_id,
      session_id,
      MIN(event_time) AS session_start,
      MAX(event_time) AS session_end,
      COUNT(*) AS event_count
  FROM sessions
  GROUP BY ALL
  ORDER BY user_id, session_start;

  result = (
      df.sort(["user_id", "event_time"])
      .with_columns(
          session_id=(
              pl.when(pl.col("event_time").diff().is_null())
              .then(1)
              .when(pl.col("event_time").diff().dt.total_minutes() > 30)
              .then(1)
              .otherwise(0)
              .cum_sum()
              .over("user_id")
          )
      )
      .group_by(["user_id", "session_id"])
      .agg(
          session_start=pl.col("event_time").min(),
          session_end=pl.col("event_time").max(),
          event_count=pl.col("event_time").count(),
      )
      .sort(["user_id", "session_start"])
  )

porridgeraisin• 1 month ago

I understand the linting aspect but not gonna lie I understood the first one immediately way more than the 2nd one due to knowing SQL well.

homebessguy• 1 month ago

"Languages come and go, but SQL is forever"

brikym• 1 month ago

Polars typesafe? It doesn't show you any errors until runtime right? Kusto query language is the best I've seen at type safety and I wish open source DBs would steal some ideas from it.

IshKebab• 1 month ago

That does look nicer if you have a Parquet file and want to analyze it. But DuckDB is also a database - if you want a persistent, reliable and mutable data store I don't think Polars would be suitable would it? (Genuine question - you sound like an expert and I'm not.)

coldtea• 1 month ago

Precisely to avoid the custom NIH Polars API, and use SQL which works everywhere (yes, inconsistencies aside).

sceadu2• 1 month ago

I really hate SQL as a language for doing data-related tasks, but the core tech for it is often much better in terms of reliability (and is close to having performance parity IME)... specifically for working with larger-than-memory datasets. I think the team at Polars is working on improving larger-than-memory operations though.

willj• 1 month ago

How can you tell if someone is a polars fan? Don’t worry, they’ll tell you. :)

SmirkingRevenge• 1 month ago

Polars and duckdb interoperate so it's not really one or the other.

Other dataframe libraries work directly on duckdb also, like Ibis.

FWIW, I think SQLAlchemy does also.

In any case though, I find both the SQL and the code there equally readable.

tannenfreund872• 1 month ago

Because I can just write SQL to access all my data. The data comes from CSVs, SQLite, MySQL, Postgres, ...

I can write SQL everywhere. I write SQL from within my R scripts, I write SQL from within my Python scripts. SQL is THE language for querying databases, others came and went, SQL will stay with us for the next decades. Even the dbplyr people say (parapfhrased): If you can't express what you want with the tidyverse, just write your SQL and load the result as a dataframe.

pknerd7• 1 month ago

FTA:

..In-process means there's no server. You don't connect to DuckDB; you load it as a library inside your program, the same way you'd load NumPy or Polars

Does it mean it can perform all statistical computations as well if I want to use for algo trading?

Panzerschrek• 1 month ago

If DuckDB is so fast and has no data transfer overheads, does it need all this typical SQL machinery with filtering and joining via SELECT queries? Wouldn't it be simpler and faster to return all data to the caller code (all table rows, but only requested columns) and let it perform all other necessary data processing logic?

jauco• 1 month ago

You’d end up implementing your own home grown version of hash join and query pushdown (skipping parquet row groups entirely) etc and your own home grown heuristics in selecting the right one (planning)

Which can outperform a generic solution like this of course, but it’s not less work to make faster for most cases.

Also duckdb can give you access to an in memory representation (e.g. `fetch_arrow_table()`) so you have less “language data structure wrapping” overhead. And you can do filtering yourself on that. In most cases the “where” statements will win though.

efromvt• 1 month ago

The SELECT machinery is the product with databases! SQL often the shortest description of the processing logic, and the database has an efficient local execution engine that can prune/reduce data read based on the plan. Very hard to match in app, especially when joins get involved.

pknerd7• 1 month ago

umm can we say it can replace SQLite?

3eb7988a1663• 1 month ago

OLAP vs OLTP. Sure you could use one for the other, but they have ideal use cases. Updating a single record in SQLite is going to be more efficient than doing the same in DuckDB.

steve_adams_864• 1 month ago

They seem similar at a glance but they’re quite different. You can think of SQLite as a transactional database while DuckDB is better used as an analytical database.

I can see applications having valid reasons to use both. You can use SQLite as the catalog in duck lake systems, for example. SQLite is your metadata record, DuckDB is your ingestion/scanning/aggregating/joining engine.

pierregillesl• 1 month ago

I wouldn’t see it as a replacement.

SQLite and DuckDB solve different problems and actually complement each other quite well. SQLite is excellent for transactional workloads (OLTP), while DuckDB shines for analytical workloads (OLAP), especially time-series data and aggregations.

We’ve been using both side-by-side in an open-source project for about two years: SQLite for configuration and transactional data, DuckDB for historical sensor data and analytics. So far, it’s been a very good combination.

tomjakubowski3• 1 month ago

You can even use DuckDB to query SQLite :^)

skeeter20208• 1 month ago

DuckDB kind of created this false comparison by their own early positioning, but I've tried to charitably interpret it as modeling the spirit and motivations of SQLite, not literally being "the SQLite for Analytics". Aside from both being in-process databases they are very different.

snissn• 1 month ago

I'm just curious - is duckdb too slow for people? This benchmark from clickhouse shows it being fairly slow compared to some options: https://jsonbench.com/

conradkay• 1 month ago

That's for their `JSON` data types. In DuckDB it's just a string meaning lots of queries will have to do JSON parsing on every row, but the inserts are very fast. Definitely a bit of a footgun and when you actually just need STRUCT or MAP.

There's a talk about ClickHouse's approach from its creator: https://www.youtube.com/watch?v=xHj9mysh0GI , but the gist is that it maintains (sub)columns to store different paths in the JSON

In other ways DuckDB has very good JSON support, like you can do `CREATE TABLE name AS `SELECT * FROM 'data.json';` and it'll infer the schema when possible.

hilariously3• 1 month ago

Not just that, you can SELECT * FROM read_json('folder/*') and read an entire schematically consistent folder of json files, or parquet files, or basically anything it supports with its various functions. Duckdb is insanely useful.

thijsbruin• 1 month ago

Hi, DuckDB engineer here, to tackle this problem of efficiently storing and querying semi-structured data (aka JSON), we've put in a lot of work into the VARIANT type and still continue to pour a lot more work into it regularly, so expect to see some developments on this area in the upcoming major release!

f311a• 1 month ago

I wish this article was not LLM written

bunsenhoneydew• 1 month ago

DuckDB is a fantastic piece of tech. One of the best, if not the best, I’ve found in several years.

willtemperley2• 1 month ago

The one huge caveat for anyone that cannot use dynamic linking e.g. in an AppStore context, DuckDB isn’t a great choice. It’s very hard to statically link extensions.

This is where Arrow wins I think. Arrow CPP for example has very portable builds and the C interface is very usable for building bindings.

DuckDB is excellent, but it’s more a black box than a library.

Edit: after a conversation with a robot, it would seem that the DuckDB and ArrowCPP C APIs are complimentary, so it's very possible to have Arrow CPP and DuckDB to coexist in an app, each with its own strength. Arrow CPP doen't have a simple SQL story for example.

tobilg• 1 month ago

I can't confirm this, I have several instances which have statically linked extensions...

willtemperley2• 1 month ago

I didn't say it was impossible but there are specific situations in which it's very difficult. What I was unable to resolve was getting DuckDB to statically link with httpfs and I'm not the only one [1].

So being more specific, I don't know how I could get a static build of DuckDB to work with Parquet and httpfs (i.e. query S3) working in an app store environment. It was a day's work to get Arrow CPP to call back into Swift for the transport layer.

However I do now see that DuckDB recently provided an extension point for providing your own transport layer, so my point might well be moot for that reason [2].

[1] https://github.com/duckdb/duckdb/issues/16190 [2] https://github.com/duckdb/duckdb/pull/17464

tobyhinloopen2• 1 month ago

The only reason I know and use DuckDB is because my (internal, private-use-only, experimental) vibe coded projects use it a ton. I didn't pick it - LLMs did. Until this article, I wasn't aware of what it actually is capable of.

Most of these projects use JSON(L) files for storage, and duckdb to process them.

ai_fry_ur_brain• 1 month ago

What an incredible way to build software

mootothemax• 1 month ago

If you haven’t investigated storing in parquet format - and it doesn’t break other consumers that need your jsonl formatted files - it could be worth trialling for your use case. You’ll see vastly smaller file sizes (even more so if you use zstd compression), and querying time will shoot up.

Usual caveats apply, but as a general rule it’s held up well for me. Only downside is that inspecting the results moves from vi on the output file to duckdb and a select * from.

tobyhinloopen2• 1 month ago

I'll 100% try DuckDB in more serious projects where I would normally use Sqlite.

medvezhenok5• 1 month ago

Yup, especially data backups (although I wouldn't store critical backups like this, only nice-to-have ones). One minor note is that parquet file sizes / compressed sizes can be sensitive to ordering, so you can try different sort orders to get optimal compression.

I found with using various tricks I can get the zstd parquet to be up to 10x (or more) smaller than an equivalent Postgres table - but obviously the exact compression ratios will depend on the kind of data you have and how well your Postgres table is normalized.

mcv• 1 month ago

Is everything becoming columnar? Parquet stores data per column instead of per row because it improves compression. I get that. Arrow apparently is columnar, and now DuckDB also gets its efficiency by treating data as columns instead of rows?

I still need to wrap my head around how that works, but it's a fascinating development.

levanten• 1 month ago

It depends on your task. In analytics where you need to scan lots of data points within few columns, then columnar storage is very much the best. But for transactional workloads where you have to deal with specific entities, row based would be more advantageous. There are hybrid systems that try to be both at the same time but in my experience they end not doing either very well.

hilariously3• 1 month ago

Often used to be referred to as HTAP, and yeah in most data engineering its moving things from OLTP to OLAP forms, and OLAP pretty much always benefit from columnar compression for aggregations and rollups.

brandmeyer• 1 month ago

Some day we'll get CREATE TABLE ... ( ... STORAGE ORDER COLUMN MAJOR) to have our transactional cake on the tables that need it and eat our analytics cake on the tables that need that.

But until then, separate tools for separate purposes isn't a bad place to be when those tools are both fantastic.

squirrellous• 1 month ago

Those three things you mentioned kind of live in the same niche - offline data storage and querying. In that world yes everything has become columnar since it’s just better. Row-oriented is still the solution for online streaming use cases.

skeeter20208• 1 month ago

compression is a side effect but not really the goal. To simplify, analytical queries often filter on a specific column value, and if these are laid out contiguously it makes disk-level reads much faster than rows that would involve read-skip-read-etc. In transactional systems data is typically written as rows though, so that's likely slower in a columnar system. As a general rule, heavy read workflows with known access patterns is going to benefit from a columnar layout.

charlieflowers• 1 month ago

BTW, columnar is very similar to struct of arrays (SOA) and some of the reasons it works well overlap with SOA.

axegon_• 1 month ago

I use duckdb HEAVILY at work and it's been a game changer. I'm sifting through terabytes of data multiple times a day, mixing, matching, updating, filtering, DuckDB is second to none. For anyone that hasn't used it: you are missing out.

xtracto• 1 month ago

This may be useful for somebody: We are also using DuckDB heavily at my workplace (we do Tax analytics of very large companies with huge amounts of data). We have certain DuckDB processes that happened in AWS infrastructure, where the data is saved in GP3 disks.

We didn't know that for GP3 disks, you can increase not only IOPS but also Read/Write Throughput [1] which by default is 125 MB/s. So by default we were not seeing the performance we expected.

Once we increased the throughput of the EBS, it was amazing. So if you are not seeing the performance you read about online when using DuckDB, it may be something like that.

[1] https://docs.aws.amazon.com/emr/latest/ManagementGuide/emr-p...

pants2• 1 month ago

This seems crazy low to me. AWS has default 3K IOPS and 125 MB/s throughput, meanwhile my Macbook Pro has 700K IOPS and 14.5GB/s throughput.

Is Amazon running on super outdated legacy networking?

aamargulies• 1 month ago

I asked Claude which tools it would most want to add to a machine we were working on. #1 was DuckDB.

Jgrubb• 1 month ago

I asked Claude some ways I could make my teeth whiter, it suggested DuckDB!

moribvndvs• 1 month ago

I recently got a task to sift through a daily drop of parquet files that need to be processed and given a UI for arbitrary queries by a non-technical team. Naively I started by looking at redshift, Athena/glue, etc. I spun my wheels for a week and barely made progress, resulting in a miserably fragile, slow, overly complicated, and/or expensive set of underwhelming PoCs. So I decided to give DuckDB a shot and I got the whole project done in under a day. It was so intuitive and runs so fast I was sure I was missing something. I wasn’t. What an absolute joy to use, and the supporting documentation is outstanding. My problem now is it’s my favorite hammer and I want everything to be a nail.

ilia-a• 1 month ago

DuckDB is really neat, recently added PDO interface for it for PHP https://github.com/iliaal/pdo_duckdb

Still a bit raw, but getting there

sigbottle• 1 month ago

What the fuck.

I've never been that strong of an engineer. TIL that at one of my internships I was building DuckDB but for the company's private use cases. Well, trying to anyways. I didn't really get the whole picture, the pieces did not fit into place.

Didn't get the return offer obviously, probably because I didn't make the connection (or really a coherent narrative of what I was building). RIP. You live and you learn, I guess.

romaniv• 1 month ago

It's an interesting project, but the discussion on HN looks weird. It gets brought up every few weeks[1] and everyone just spams comments with messages about how "fast" it is.

DuckDB is fast for some specific workloads. If you use it for most other things, it is at least an order of magnitude slower than SQLite. It also has some limitations in terms of what SQL it will currently run (e.g. I immediately ran into an issue with recursive queries). That will probably get better with time.

[1] If you search HN for "sqlite" and "duckdb" you get 4,310 hits and 2,398 hits respectively. That's a very heavy skew, considering SQLite is everywhere and had been around for a quarter century, while DuckDB effectively appeared on the scene two years ago.

skeeter20208• 1 month ago

I'm going to sound like a broken record but... different use cases. They're analogous in the comparison "sqlite for analytics" but completely different architectures and implementations. Part of this is the fault of the developers, but I feel they were trying to highlight the similar focus on in-process, zero dependencies, simplicity and test coverage - not a direct "vs" comparison. IME recursive queries in analytical workflows are not very common; they typically work against the fundamental data layout on disk.

SQLite is awesome and I would love to see more posts about it, but the reality is one of the major reasons it's awesome is the no-drama/stability/it just works. DuckDB is seeing a lot of development on many fronts so there's a lot more to learn and talk about right now.

skotobaza• 1 month ago

DuckDB is fast for some specific workloads

Yes, it's specifically promoted as DBMS for OLAP workload. And it's usually compared to ClickHouse, another analytical DBMS. So people who use it know why it's good.

xearl• 1 month ago

while DuckDB effectively appeared on the scene two years ago.

duckdb is ~7 years old by now. it was quite popular long before it became 1.0. heck, even motherduck has been founded 4 years ago.

rubenvanwyk• 1 month ago

I’m sure the use of duckdb may seem weird for normal developers, but for data people it really is game-changing, especially for data scientists or business analysts.

rplnt• 1 month ago

The article has an explanation for what kind of database it is. After reading that one sentence you wouldn't write the second paragraph.

it's optimized for the kind of queries that scan millions of rows to filter, aggregate, and join — not the kind that look up a single record by primary key

BretonForearm• 1 month ago

DuckDB effectively appeared on the scene two years ago

I don't think so.

pedromlsreis• 1 month ago

DuckDB is a great example of how far you can get by removing unnecessary layers... Columnar layout and vectorized execution is a powerful combination for OLAP workloads.

skeeter20208• 1 month ago

the CSV parser is really good too. Anyone who's struggled with consuming CSV/TSV knows this is not trivial. DuckDB uses hueristics to be very liberal in what it accepts without crapping out like many parsers.

bunbun69• 1 month ago

Holy LLM slop article…

explodes• 1 month ago

You open a connection, send SQL over TCP (a protocol to send data over a network),

Thanks for the clarification, LLM!!

greenavocado• 1 month ago

To be fair, most managers don't know what TCP is

tdhz77• 1 month ago

Is duckdb multi region active active?

M4R5H4LL• 1 month ago

I’ve enjoyed reading that article a lot. I have used DuckDB as a tool in a code assistant and I am very happy with what I see so far.

nikolay• 1 month ago

DuckDB is a major RDBMS, yet there's almost no support for it from DB migration frameworks, etc.

roywiggins• 1 month ago

ai; dr

globalise83• 1 month ago

I have a database on my local machine with 200 million records in one table and 2 other related tables. Even the most complex queries take about 1 minute to vibe code and paste into the DuckDB browser playground UI and always less than 5 seconds to return the result. For a humble product manager it feels like a superpower.

solarized• 1 month ago

Its also powerfull for data migration. you can do this:

create pgsql.table as (select * from mysql.table)

all data types automatically inferred. you dont have to define it manually.

bonus: latest version already support pager. proTips: use pspg.

My_Name• 1 month ago

One of my projects was using a RRD database to store 10-second samples from 20 data streams with a RRD time of 1 year, which works out to around 82 million entries. I display this interactively on a raspberry pi and I used to use RRDTool in python, but switching to my own code leveraging mySQL (and using some judicious rollup tables) means that instead of a larger time span taking ~5 seconds to update, it is now fast enough to give me 20fps no matter the viewed range.

Not sure DuckDB will provide any benefit to my specific case, but I'll check it out.

melih1im2• 24 days ago

I hope it will not go to the same path like MySQL or other Open source software bought and to the graveyard!

DuckDB Internals Part 1

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