DuckLake is pretty cool, and we obviously love everything the DuckDB is doing. It's what made pg_lake possible, and what motivated part of our team to step away from Microsoft/Citus.
DuckLake can do things that pg_lake cannot do with Iceberg, and DuckDB can do things Postgres absolutely can't (e.g. query data frames). On the other hand, Postgres can do a lot of things that DuckDB cannot do. For instance, it can handle >100k single row inserts/sec.
Transactions don't come for free. Embedding the engine in the catalog rather than the catalog in the engine enables transactions across analytical and operational tables. That way you can do a very high rate of writes in a heap table, and transactionally move data into an Iceberg table.
Postgres also has a more natural persistence & continuous processing story, so you can set up pg_cron jobs and use PL/pgSQL (with heap tables for bookkeeping) to do orchestration.
There's also the interoperability aspect of Iceberg being supported by other query engines.
How does this compare to https://www.mooncake.dev/pgmooncake? It seems there are several projects like this now, with each taking a slightly different approach optimized for different use cases?
> For instance, it can handle >100k single row inserts/sec.
DuckLake already has data-inlining for the DuckDB catalog, seems this will be possible once it's supported in the pg catalog.
> Postgres also has a more natural persistence & continuous processing story, so you can set up pg_cron jobs and use PL/pgSQL (with heap tables for bookkeeping) to do orchestration.
This is true, but it's not clear where I'd use this in practice. e.g. if I need to run a complex ETL job, I probably wouldn't do it in pg_cron.
Not to mention one of my favorite tools for adding a postgres db to your backend service: PostgREST. Insanely powerful DB introspection and automatic REST endpoint. Pretty good performance too!
What does data frames mean in this context? I'm used to them in spark or pandas but does this relate to something in how duckDB operates or is it something else?
i have so desperately wanted to love and use ducklake, but have come across some issues with it in practice (pg catalog). they seem to have to do with maintenance activities and ducklake suddenly throwing http/400 errors on files it created. i’m not sure if it’s due to my write patterns (gather data from sources into a polars dataframe and insert into the ducklake table from the df) into the partitioned tables or something else.
it’s ok in dev/test and for me as the person in the team who’s enamored with duckdb, but it’s made the team experience challenging and so i’ve just kinda reverted to hive partitioned parquet files with a duckdb file that has views created on top of the parquet. attach that file as read only and query away.
i may work up a full example to submit as an issue but up until now too may other things are dominating my time.
Honestly. Just pay snowflake for the amazing DB and ecosystem it is. And then go build cool stuff unless your value add to customers is infra let them handle all that.
I’m not a data engineer but work in an adjacent role. Is there anyone here who could dumb the use case down? Maybe an example of a problem this solves. I am struggling to understand the value proposition here.
Some service writes a lot of data in parquet files stored on S3 (e.g. logs), and now you want that data to be queryable from your application as if it was in postgres (e.g. near real-time analytics dashboard). pg_lake allows you to load these parquet files into postgres and query the data. You can also join that data with existing tables in postgres.
Been experimenting with OpenTelemetry->Parquet conversion lately for logs, metrics, and traces. Lots of related projects popping up in this area. It's powerful and cheap.
I guess my confusion is that there already are ways to query this data with DuckDB or something like that. So is the magic here that it’s Postgres? What makes being able to query something in Postgres special? And when we say it’s now queryable by Postgres, does this mean that it takes that data and stores it in your PG db? Or it remains in S3 and this is a translation layer for querying with PG?
When Snowflake bought Crunchy Data I was hoping they were going to offer a managed version of this
It's great that I can run this locally in a Docker container, I'd love to be able to run a managed instance on AWS billed through our existing Snowflake account
How do you use your data lake? For me it is much more than just storing data, it is just as much for crunching numbers in unpredictable ways.
And this is where postgres does not cut it.
You need some more CPU and RAM than what you pay for in your postgres instance. I.e. a distributed engine where you don't have to worry about how big your database instance is today.
The point about a datalake is to separate computer and storage. Postgres isn’t a compute layer it’s an access layer.
Your compute asks Postgres “what is the current data for these keys?” Or “what was the current data as of two weeks ago for these keys?” And your compute will then download and aggregate your analytics query directly from the parquet files.
I like data lakes and their SQL-like query languages. They feel like an advanced version of the "Everything is a file" philosophy.
Under "Everything is a file", you can read or manipulate a wide variety of information via simple, open/read/write() APIs.
Linux provides APIs to modify system settings via filesystem. Get the screen brightness setting? `cat /sys/class/backlight/device0/brightness`. Update? `echo 500 > /sys/class/backlight/device0/brightness`.
No need for special APIs, just generic file operations and the kernel handles everything.
FUSE (Filesystem in Userspace) provides even more flexibility by allowing user space programs to build their own drivers that handle any data operation via the filesystem.
You can mount remote systems (via SSH) and google drive, and copying files is as easy as `cp /mnt/remote/data/origin /mnt/googledrive/data/`.
Or using unique FUSE like pgfuse and redisfs, updating redis value by postgres DB data is just `cat /mnt/postgres/users/100/full_name > /mnt/redis/user_100_full_name`.
But filesystems are only good for hierarchical data while a lot of real world data is relational. Many FUSE software tries hard to represent inherently non-hierarchical data in a filesystem.
Data lake allows to use SQL, the elegant abstraction for relational data, across different underlying data sources. They can be physically distant and have different structures.
A lot of real world applications are just CRUD on relational data. You can accomplish much more much easier if those data are just a big single database.
(1) Are there any plans to make this compatible with the ducklake specification? Meaning: Instead of using Iceberg in the background, you would use ducklake with its SQL tables? My knowledge is very limited but to me, besides leveraging duckdb, another big point of ducklake is that it's using SQL for the catalog stuff instead of a confusing mixture of files, thereby offering a bunch of advantages like not having to care about number of snapshots and better concurrent writes.
(2) Might it be possible that pg_duckdb will achieve the same thing in some time or do things not work like that?
(1) We've thought about it, no current plans. We'd ideally reimplement DuckLake in Postgres directly such that we can preserve Postgres transaction boundaries, rather than reuse the Ducklake implementation that would run in a separate process. The double-edged sword is that there's a bunch of complexity around things like inlined data and passing the inlined data into DuckDB at query time, though if we can do that then you can get pretty high transaction performance.
(2) In principle, it's a bit easier for pg_duckdb to reuse the existing Ducklake implementation because DuckDB sits in every Postgres process and they can call into each other, but we feel that architecture is less appropriate in terms resource management and stability.
One thing I admire about Snowflake is a real commitment to self-cannibalization. They were super out front with Iceberg even though it could disrupt them, because that's what customers were asking for and they're willing to bet they'll figure out how to make money in that new world
Have you interacted with Snowflake teams much? We are using external iceberg tables with snowflake. Every interaction pretty much boils down to you really should not be using iceberg you should be using snowflake for storage. It's also pretty obvious some things are strategically not implemented to push you very strongly in that direction.
Supporting Iceberg is eventually having people leaving you because they have better elsewhere, but this is birectionnal, it means you can welcome people from Databricks because you have feature parity.
It's not going to scale as well as Snowflake, but it gets you into an Iceberg ecosystem which Snowflake can ingest and process at scale. Analytical data systems are typically trending to heterogenous compute with a shared storage backend -- you have large, autoscaling systems to process the raw data down to something that is usable by a smaller, cheaper query engine supporting UIs/services.
When we first developed pg_lake at Crunchy Data and defined GTM we considered whether it could be a Snowflake competitor, but we quickly realised that did not make sense.
Data platforms like Snowflake are built as a central place to collect your organisation's data, do governance, large scale analytics, AI model training and inference, share data within and across orgs, build and deploy data products, etc. These are not jobs for a Postgres server.
Pg_lake foremost targets Postgres users who currently need complex ETL pipelines to get data in and out of Postgres, and accidental Postgres data warehouses where you ended up overloading your server with slow analytical queries, but you still want to keep using Postgres.
For testing, we at least have a Dockerfile to automate the setup of the pgduck_server and a minio instance so it Just Works™ with the extensions installed in your local Postgres cluster (after installing the extensions).
The configuration mainly involves just defining the default iceberg location for new tables, pointing it to the pgduck_server, and providing the appropriate auth/secrets for your bucket access.
For anyone looking to easily ingest data into a Postgres Wire compatible database, check out https://github.com/slingdata-io/sling-cli. Use CLI, YAML or Python to run etl jobs.
This is so cool! We have files in Iceberg that we then move data to/from to a PG db using a custom utility. It always felt more like a workaround that didn’t fully use the capabilities of both the technologies. Can’t wait to try this out.
A usecase I see for this personally I have encountered a lot of “hot cache for some time then offload for historical queries” usecases which I have built by hand multiple times. This should be a great fit. E.g. write to Postgres then periodically offload to lakehouse and even query together (if needed). Very cool!
I was going to ask if you could then put DuckDB over Postgres for the OLAP query engine -- looks like that's already what it does! very interesting development in the data lake space alongside DuckLake and things
You create foreign tables in postgres using either the pg_lake_table wrapper or pg_lake_iceberg.
Once those tables exist, queries against them are able to either push down entirely to the remote tables and uses a Custom Scan to execute and pull results back into postgres, or we transform/extract the pieces that can be executed remotely using a FDW and then treat it as a tuple source.
In both cases, the user does not need to know any of the details and just runs queries inside postgres as they always have.
I think I don't understand postgres enough, so forgive this naive question, but what does pushing down to the remote tables mean? Does it allow parallelism? If I query a very large iceberg table, will this system fan the work out to multiple duckdb executors and gather the results back in?
Very cool. One question that comes up for me is whether pg_lake expects to control the Iceberg metadata, or whether it can be used purely as a read layer. If I make schema updates and partition changes to iceberg directly, without going through pg_lake, will pg_lake's catalog correctly reflect things right away?
More integrations are great. Anyway, the "this is awesome" moment (for me) will be when you could mix row- and column-oriented tables in Postgres, a bit like Timescale but native Postgres and well done. Hopefully one day.
I want MPP HTAP where SQL inserts/COPYs store data in three(!) formats:
- row-based (low latency insert, fast row-based indexed query for single-row OLTP)
- columnar-based (slow inserts/updates, fast aggregates/projections)
- iceberg-columnar-based (better OLAP price/performance and less lockin than native columnar)
And for SELECTs the query engine picks which storage engine satisfies the query using some SQL extension like DB2 "WAITFORDATA" or TiDB @@tidb_read_staleness or MemSQL columnstore_latency and/or similar signalling for performance-vs-cost preference.
And a common permissioning/datasharing layer so I can share data to external and internal parties who can in turn bring their own compute to make their own latency choices.
This is awesome, I will be trying this out in the coming months. Its just made it to the top of my R&D shortlist for things that could massively simplify our data stack for a b2b saas.
This is really nice though looking at the code - a lot of the postgres types are missing as well a lot of the newer parquet logical types - but this is a great start and a nice use of FDW.
Postgres has like 300+ types but mostly stuff like decimals should work the same way it does with Postgres (with the edge cases like NaN existing in Postgres but not parquets accordingly)
I love this. There are definitely shops where the data is a bit too much for postgres but something like Snowflake would be overkill. Wish this was around a couple years ago lol
One usecase we have (we built it ourselves) is to periodically offload data from Postgres to lake house partitioned data on GCS. The way I see it this can now be done with a single query. Another one is the other way around to use posters as a query engine or to merge offloaded data with your live data.
DuckDB provided a lot of infrastructure for reading/writing parquet files and other common formats here. It also was inherently multi-threaded and supported being embedded in a larger program (similar to sqllite), so made it a good basis for something that could work outside of the traditional process model of Postgres.
Additionally, the postgres extension system supports most of the current project, so wouldn't say it was forced in this case, it was a design decision. :)
With DuckLake, the query frontend and query engine are DuckDB, and Postgres is used as a catalog in the background.
With pg_lake, the query frontend and catalog are Postgres, and DuckDB is used as a query engine in the background.
Of course, they also use different table formats (though similar in data layer) with different pros and cons, and the query frontends differ in significant ways.
An interesting thing about pg_lake is that it is effectively standalone, no external catalog required. You can point Spark et al. directly to Postgres with pg_lake by using the Iceberg JDBC driver.
> This separation also avoids the threading and memory-safety limitations that would arise from embedding DuckDB directly inside the Postgres process, which is designed around process isolation rather than multi-threaded execution. Moreover, it lets us interact with the query engine directly by connecting to it using standard Postgres clients.
- Separation of concerns, since with a single external process we can share object store caches without complicated locking dances between multiple processes.
- Memory limits are easier to reason about with a single external process.
- Postgres backends end up being more robust, as you can restart the pgduck_server process separately.
> This separation also avoids the threading and memory-safety limitations that would arise from embedding DuckDB directly inside the Postgres process, which is designed around process isolation rather than multi-threaded execution. Moreover, it lets us interact with the query engine directly by connecting to it using standard Postgres clients.
Hi, one of the developers here. You define credentials that can access the S3 buckets and use those as DuckDB secrets, usually in an init script for pgduck_server. (You can see some examples of this in the testing framework.)
I'll see if we can improve the docs or highlight that part better, if it is already documented—we did move some things around prior to release.
Interesting. I am working on a project to integrate access management to iceberg/parquet files for sagemaker. Controlling what users logged into sagemaker studio have access to in s3. It’s fine using static policies for mvp, but eventually it needs to be dynamic and integrated into enterprise iam tools. Those tools generally have great support for managing sql grants. Not so much for s3 bucket policies.
There are Postgres roles for read/write access to the S3 object that DuckDB has access to. Those roles can create tables from specific files or at specific locations, and can then assign more fine-grained privileges to other Postgres roles (e.g. read access on a specific view or table).
Oh datalakes. The most ridiculous idea in data processing, right after data frames in python.
We've had this discussion like a week ago about how stupid is to use filesystem for this kind of data storage and here we go again. Actually i had to implement this "idea" in practice. What a nonsense.
Why not just use Ducklake?[1] That reduces complexity[2] since only DuckDB and PostgreSQL with pg_duckdb are required.
[1] https://ducklake.select/
[2] DuckLake - The SQL-Powered Lakehouse Format for the Rest of Us by Prof. Hannes Mühleisen: https://www.youtube.com/watch?v=YQEUkFWa69o
DuckLake is pretty cool, and we obviously love everything the DuckDB is doing. It's what made pg_lake possible, and what motivated part of our team to step away from Microsoft/Citus.
DuckLake can do things that pg_lake cannot do with Iceberg, and DuckDB can do things Postgres absolutely can't (e.g. query data frames). On the other hand, Postgres can do a lot of things that DuckDB cannot do. For instance, it can handle >100k single row inserts/sec.
Transactions don't come for free. Embedding the engine in the catalog rather than the catalog in the engine enables transactions across analytical and operational tables. That way you can do a very high rate of writes in a heap table, and transactionally move data into an Iceberg table.
Postgres also has a more natural persistence & continuous processing story, so you can set up pg_cron jobs and use PL/pgSQL (with heap tables for bookkeeping) to do orchestration.
There's also the interoperability aspect of Iceberg being supported by other query engines.
How does this compare to https://www.mooncake.dev/pgmooncake? It seems there are several projects like this now, with each taking a slightly different approach optimized for different use cases?
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> For instance, it can handle >100k single row inserts/sec.
DuckLake already has data-inlining for the DuckDB catalog, seems this will be possible once it's supported in the pg catalog.
> Postgres also has a more natural persistence & continuous processing story, so you can set up pg_cron jobs and use PL/pgSQL (with heap tables for bookkeeping) to do orchestration.
This is true, but it's not clear where I'd use this in practice. e.g. if I need to run a complex ETL job, I probably wouldn't do it in pg_cron.
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Not to mention one of my favorite tools for adding a postgres db to your backend service: PostgREST. Insanely powerful DB introspection and automatic REST endpoint. Pretty good performance too!
What does data frames mean in this context? I'm used to them in spark or pandas but does this relate to something in how duckDB operates or is it something else?
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i have so desperately wanted to love and use ducklake, but have come across some issues with it in practice (pg catalog). they seem to have to do with maintenance activities and ducklake suddenly throwing http/400 errors on files it created. i’m not sure if it’s due to my write patterns (gather data from sources into a polars dataframe and insert into the ducklake table from the df) into the partitioned tables or something else.
it’s ok in dev/test and for me as the person in the team who’s enamored with duckdb, but it’s made the team experience challenging and so i’ve just kinda reverted to hive partitioned parquet files with a duckdb file that has views created on top of the parquet. attach that file as read only and query away.
i may work up a full example to submit as an issue but up until now too may other things are dominating my time.
Boils down to design decisions; see: https://news.ycombinator.com/item?id=45813631
This is huge!
When people ask me what’s missing in the Postgres market, I used to tell them “open source Snowflake.”
Crunchy’s Postgres extension is by far the most ahead solution in the market.
Huge congrats to Snowflake and the Crunchy team on open sourcing this.
Honestly. Just pay snowflake for the amazing DB and ecosystem it is. And then go build cool stuff unless your value add to customers is infra let them handle all that.
Sounds great until you're locked into Snowflake - so glad iceberg is becoming the standard, anything is great.
The trap you end up in is you have to pay snowflake to access your data, iceberg and other technology help with the walled garden.
Not just snowflake, any pay on use provider.
(Context - have spent 5+ years working with Snowflake, it's great, have built drivers for various languages, etc).
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I’m not a data engineer but work in an adjacent role. Is there anyone here who could dumb the use case down? Maybe an example of a problem this solves. I am struggling to understand the value proposition here.
> Maybe an example of a problem this solves.
Some service writes a lot of data in parquet files stored on S3 (e.g. logs), and now you want that data to be queryable from your application as if it was in postgres (e.g. near real-time analytics dashboard). pg_lake allows you to load these parquet files into postgres and query the data. You can also join that data with existing tables in postgres.
Been experimenting with OpenTelemetry->Parquet conversion lately for logs, metrics, and traces. Lots of related projects popping up in this area. It's powerful and cheap.
- https://github.com/smithclay/otlp2parquet (shameless plug, based on Clickhouse's Otel schema) - https://github.com/Mooncake-Labs/moonlink (also has OTLP support) - https://github.com/open-telemetry/otel-arrow (official community project under early dev)
I guess my confusion is that there already are ways to query this data with DuckDB or something like that. So is the magic here that it’s Postgres? What makes being able to query something in Postgres special? And when we say it’s now queryable by Postgres, does this mean that it takes that data and stores it in your PG db? Or it remains in S3 and this is a translation layer for querying with PG?
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When Snowflake bought Crunchy Data I was hoping they were going to offer a managed version of this
It's great that I can run this locally in a Docker container, I'd love to be able to run a managed instance on AWS billed through our existing Snowflake account
Man, we are living in the golden era of PostgreSQL.
How do you use your data lake? For me it is much more than just storing data, it is just as much for crunching numbers in unpredictable ways.
And this is where postgres does not cut it.
You need some more CPU and RAM than what you pay for in your postgres instance. I.e. a distributed engine where you don't have to worry about how big your database instance is today.
The point about a datalake is to separate computer and storage. Postgres isn’t a compute layer it’s an access layer.
Your compute asks Postgres “what is the current data for these keys?” Or “what was the current data as of two weeks ago for these keys?” And your compute will then download and aggregate your analytics query directly from the parquet files.
With S3 Table Buckets, Cloudflare R2 Data Catalog and now this, Iceberg seems to be winning.
I like data lakes and their SQL-like query languages. They feel like an advanced version of the "Everything is a file" philosophy.
Under "Everything is a file", you can read or manipulate a wide variety of information via simple, open/read/write() APIs. Linux provides APIs to modify system settings via filesystem. Get the screen brightness setting? `cat /sys/class/backlight/device0/brightness`. Update? `echo 500 > /sys/class/backlight/device0/brightness`. No need for special APIs, just generic file operations and the kernel handles everything.
FUSE (Filesystem in Userspace) provides even more flexibility by allowing user space programs to build their own drivers that handle any data operation via the filesystem. You can mount remote systems (via SSH) and google drive, and copying files is as easy as `cp /mnt/remote/data/origin /mnt/googledrive/data/`. Or using unique FUSE like pgfuse and redisfs, updating redis value by postgres DB data is just `cat /mnt/postgres/users/100/full_name > /mnt/redis/user_100_full_name`.
But filesystems are only good for hierarchical data while a lot of real world data is relational. Many FUSE software tries hard to represent inherently non-hierarchical data in a filesystem. Data lake allows to use SQL, the elegant abstraction for relational data, across different underlying data sources. They can be physically distant and have different structures. A lot of real world applications are just CRUD on relational data. You can accomplish much more much easier if those data are just a big single database.
Two questions:
(1) Are there any plans to make this compatible with the ducklake specification? Meaning: Instead of using Iceberg in the background, you would use ducklake with its SQL tables? My knowledge is very limited but to me, besides leveraging duckdb, another big point of ducklake is that it's using SQL for the catalog stuff instead of a confusing mixture of files, thereby offering a bunch of advantages like not having to care about number of snapshots and better concurrent writes.
(2) Might it be possible that pg_duckdb will achieve the same thing in some time or do things not work like that?
(1) We've thought about it, no current plans. We'd ideally reimplement DuckLake in Postgres directly such that we can preserve Postgres transaction boundaries, rather than reuse the Ducklake implementation that would run in a separate process. The double-edged sword is that there's a bunch of complexity around things like inlined data and passing the inlined data into DuckDB at query time, though if we can do that then you can get pretty high transaction performance.
(2) In principle, it's a bit easier for pg_duckdb to reuse the existing Ducklake implementation because DuckDB sits in every Postgres process and they can call into each other, but we feel that architecture is less appropriate in terms resource management and stability.
Why would Snowflake develop and release this? Doesn't this cannibalize their main product?
One thing I admire about Snowflake is a real commitment to self-cannibalization. They were super out front with Iceberg even though it could disrupt them, because that's what customers were asking for and they're willing to bet they'll figure out how to make money in that new world
Video of their SVP of Product talking about it here: https://youtu.be/PERZMGLhnF8?si=DjS_OgbNeDpvLA04&t=1195
Have you interacted with Snowflake teams much? We are using external iceberg tables with snowflake. Every interaction pretty much boils down to you really should not be using iceberg you should be using snowflake for storage. It's also pretty obvious some things are strategically not implemented to push you very strongly in that direction.
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Supporting Iceberg is eventually having people leaving you because they have better elsewhere, but this is birectionnal, it means you can welcome people from Databricks because you have feature parity.
It's not going to scale as well as Snowflake, but it gets you into an Iceberg ecosystem which Snowflake can ingest and process at scale. Analytical data systems are typically trending to heterogenous compute with a shared storage backend -- you have large, autoscaling systems to process the raw data down to something that is usable by a smaller, cheaper query engine supporting UIs/services.
But if you are used to this type of compute per dollar what on earth would make you want to move to Snowflake?
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When we first developed pg_lake at Crunchy Data and defined GTM we considered whether it could be a Snowflake competitor, but we quickly realised that did not make sense.
Data platforms like Snowflake are built as a central place to collect your organisation's data, do governance, large scale analytics, AI model training and inference, share data within and across orgs, build and deploy data products, etc. These are not jobs for a Postgres server.
Pg_lake foremost targets Postgres users who currently need complex ETL pipelines to get data in and out of Postgres, and accidental Postgres data warehouses where you ended up overloading your server with slow analytical queries, but you still want to keep using Postgres.
It'll probably be really difficult to set up.
If it's anything like super base, your question the existence of God when trying to get it to work properly.
You pay them to make it work right.
For testing, we at least have a Dockerfile to automate the setup of the pgduck_server and a minio instance so it Just Works™ with the extensions installed in your local Postgres cluster (after installing the extensions).
The configuration mainly involves just defining the default iceberg location for new tables, pointing it to the pgduck_server, and providing the appropriate auth/secrets for your bucket access.
For anyone looking to easily ingest data into a Postgres Wire compatible database, check out https://github.com/slingdata-io/sling-cli. Use CLI, YAML or Python to run etl jobs.
This is so cool! We have files in Iceberg that we then move data to/from to a PG db using a custom utility. It always felt more like a workaround that didn’t fully use the capabilities of both the technologies. Can’t wait to try this out.
A usecase I see for this personally I have encountered a lot of “hot cache for some time then offload for historical queries” usecases which I have built by hand multiple times. This should be a great fit. E.g. write to Postgres then periodically offload to lakehouse and even query together (if needed). Very cool!
I was going to ask if you could then put DuckDB over Postgres for the OLAP query engine -- looks like that's already what it does! very interesting development in the data lake space alongside DuckLake and things
You create foreign tables in postgres using either the pg_lake_table wrapper or pg_lake_iceberg.
Once those tables exist, queries against them are able to either push down entirely to the remote tables and uses a Custom Scan to execute and pull results back into postgres, or we transform/extract the pieces that can be executed remotely using a FDW and then treat it as a tuple source.
In both cases, the user does not need to know any of the details and just runs queries inside postgres as they always have.
I think I don't understand postgres enough, so forgive this naive question, but what does pushing down to the remote tables mean? Does it allow parallelism? If I query a very large iceberg table, will this system fan the work out to multiple duckdb executors and gather the results back in?
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This is cool to see! Looks like a compete against pg_mooncake which Databricks acquired. But how is this different from pg_duckdb?
Very cool. One question that comes up for me is whether pg_lake expects to control the Iceberg metadata, or whether it can be used purely as a read layer. If I make schema updates and partition changes to iceberg directly, without going through pg_lake, will pg_lake's catalog correctly reflect things right away?
We have some level of external iceberg table read-only support, but it is limited at the moment. See this example/caveat: https://github.com/Snowflake-Labs/pg_lake/blob/main/docs/fil...
You can use it as a read layer for for specific metadata JSON URL or a table in a REST catalog. The latter got merged quite recently, not yet in docs.
More integrations are great. Anyway, the "this is awesome" moment (for me) will be when you could mix row- and column-oriented tables in Postgres, a bit like Timescale but native Postgres and well done. Hopefully one day.
I want MPP HTAP where SQL inserts/COPYs store data in three(!) formats: - row-based (low latency insert, fast row-based indexed query for single-row OLTP) - columnar-based (slow inserts/updates, fast aggregates/projections) - iceberg-columnar-based (better OLAP price/performance and less lockin than native columnar) And for SELECTs the query engine picks which storage engine satisfies the query using some SQL extension like DB2 "WAITFORDATA" or TiDB @@tidb_read_staleness or MemSQL columnstore_latency and/or similar signalling for performance-vs-cost preference.
And a common permissioning/datasharing layer so I can share data to external and internal parties who can in turn bring their own compute to make their own latency choices.
Hypertables definitely had the arrays columns auto-expanding with the custom node type. Not sure what else it would look like for what you describe.
That said, don't sleep on the "this is awesome" parts in this project... my personal favorite is the automatic schema detection:
``` CREATE TABLE my_iceberg_table () USING iceberg WITH (definition_from = 's3://bucket/source_data.parquet'); ```
This is awesome, I will be trying this out in the coming months. Its just made it to the top of my R&D shortlist for things that could massively simplify our data stack for a b2b saas.
Nice does this also allow me to write to parquet from my Postgres table?
Yes, just COPY table TO 's3://mybucket/data.parquet'
Or COPY table TO STDOUT WITH (format 'parquet') if you need it on the client side.
Ong yes it works this would have made my past job so much easier.
I’m not super into the Data sphere but my company relies heavily on Snowflake which is becoming an issue.
This announcement seems huge to me, no?!
Is this really an open source Snowflake covering most use cases?
there's also plenty of other options for warehouse/compute processing of iceberg data storage.
I think this is a pretty big deal, though.
Snowflake does a lot more, though, especially around sharing data across company boundaries.
This is really nice though looking at the code - a lot of the postgres types are missing as well a lot of the newer parquet logical types - but this is a great start and a nice use of FDW.
Hi, what types are you expecting to see that aren't supported? I believe we had support for most/all builtin postgres types.
Postgres has like 300+ types but mostly stuff like decimals should work the same way it does with Postgres (with the edge cases like NaN existing in Postgres but not parquets accordingly)
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Also, any planned support for more catalogs?
I think we have recently merged (or are getting ready to merge) REST catalog support, so that will open some things up in this department.
I love this. There are definitely shops where the data is a bit too much for postgres but something like Snowflake would be overkill. Wish this was around a couple years ago lol
Can someone dumb this down a bit for a non data-engineer? Hard to fully wrap my head around who this is/isn’t best suited for.
One usecase we have (we built it ourselves) is to periodically offload data from Postgres to lake house partitioned data on GCS. The way I see it this can now be done with a single query. Another one is the other way around to use posters as a query engine or to merge offloaded data with your live data.
What services do people primarily use to accomplish these tasks today? All custom work?
Very cool! Was there any inherent limitation with postgresql or its extension system that forced pg_lake to use duckdb as query engine?
I gave a talk on that at Data Council, then still discussing the pg_lake extensions as part of Crunchy Data Warehouse.
https://youtu.be/HZArjlMB6W4?si=BWEfGjMaeVytW8M1
Also, nicer recording from POSETTE: https://youtu.be/tpq4nfEoioE?si=Qkmj8o990vkeRkUa
It comes down to the trade-offs made by operational and analytical query engines being fundamentally different at every level.
DuckDB provided a lot of infrastructure for reading/writing parquet files and other common formats here. It also was inherently multi-threaded and supported being embedded in a larger program (similar to sqllite), so made it a good basis for something that could work outside of the traditional process model of Postgres.
Additionally, the postgres extension system supports most of the current project, so wouldn't say it was forced in this case, it was a design decision. :)
Interesting! How does it compare with ducklake?
You could say
With DuckLake, the query frontend and query engine are DuckDB, and Postgres is used as a catalog in the background.
With pg_lake, the query frontend and catalog are Postgres, and DuckDB is used as a query engine in the background.
Of course, they also use different table formats (though similar in data layer) with different pros and cons, and the query frontends differ in significant ways.
An interesting thing about pg_lake is that it is effectively standalone, no external catalog required. You can point Spark et al. directly to Postgres with pg_lake by using the Iceberg JDBC driver.
It’s amazing to see Postgres growing into something this powerful
I love postgres and have created my own "data lake" sorta systems -- what would this add to my workflows?
If anyone from Supabase is reading, it would be awesome to have this extension!
Crunchydata did it first :) but nice to get more options
It's the same team and same project :). Crunchy Data was acquired by Snowflake.
Holy shit thats amazing!! Congrats to the team
Curious why pgduck_server is a totally separate process?
The README explains it:
> This separation also avoids the threading and memory-safety limitations that would arise from embedding DuckDB directly inside the Postgres process, which is designed around process isolation rather than multi-threaded execution. Moreover, it lets us interact with the query engine directly by connecting to it using standard Postgres clients.
What has been pointed out from the README; also:
- Separation of concerns, since with a single external process we can share object store caches without complicated locking dances between multiple processes. - Memory limits are easier to reason about with a single external process. - Postgres backends end up being more robust, as you can restart the pgduck_server process separately.
from the README:
> This separation also avoids the threading and memory-safety limitations that would arise from embedding DuckDB directly inside the Postgres process, which is designed around process isolation rather than multi-threaded execution. Moreover, it lets us interact with the query engine directly by connecting to it using standard Postgres clients.
Thanks! Didn’t scroll down far enough
RDS really needs to make it easy to install your own PG modules.
110% this!
Does anyone know how access control works to the underlying s3 objects? I didn’t see anything regarding grants in the docs.
Hi, one of the developers here. You define credentials that can access the S3 buckets and use those as DuckDB secrets, usually in an init script for pgduck_server. (You can see some examples of this in the testing framework.)
I'll see if we can improve the docs or highlight that part better, if it is already documented—we did move some things around prior to release.
Maybe this could help: https://github.com/Snowflake-Labs/pg_lake?tab=readme-ov-file...
Interesting. I am working on a project to integrate access management to iceberg/parquet files for sagemaker. Controlling what users logged into sagemaker studio have access to in s3. It’s fine using static policies for mvp, but eventually it needs to be dynamic and integrated into enterprise iam tools. Those tools generally have great support for managing sql grants. Not so much for s3 bucket policies.
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There are Postgres roles for read/write access to the S3 object that DuckDB has access to. Those roles can create tables from specific files or at specific locations, and can then assign more fine-grained privileges to other Postgres roles (e.g. read access on a specific view or table).
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Oh datalakes. The most ridiculous idea in data processing, right after data frames in python.
We've had this discussion like a week ago about how stupid is to use filesystem for this kind of data storage and here we go again. Actually i had to implement this "idea" in practice. What a nonsense.