>Without those periodic full page images in the log, the storage layer would have to replay an infinitely long chain of small deltas to reconstruct a page for a read request. What was once a bounded O(checkpoint frequency) replay becomes an unbounded chain, leading to a spike in read latency and resource consumption.
I don't follow: read requests are not served from the WAL. They read the current state of the page from the buffer cache, where the page is updated after the change (FPI or not) is written to the WAL.
This applies to our storage implementation. In Lakebase architecture storage serves pages and it doesn't always have the most recent version of the page and therefore it reconstructs it on demand.
In the past we relied on Postgres compute to periodically send a full page so reconstructive a page was always a bounded process. Once we turned it off (and got all those perf gains) we got another problem: unbounded page reconstruction which we had to solve separately.
So, the general architecture described here is solid, and I support it, but I take issue with the "Lakebase" naming thing.
Disaggregated storage and disaggregated compute have been an open trend in DBMS development for the last half-decade. This is an obvious move with modern computing paradigms, and the academic literature has a standard name for it.
This feels like "JAMStack" from Netlify happening all over again.
I tweeted about this in 2022, as a general trend, and also from the RocksDB meetup emphasizing disaggregated storage:
I don't think it should be surprising that vendors are not going to lead with "disaggregated storage". I don't see that taking off either. This isn't a paper in a journal. Aurora doesn't call it that either. But yes, it is not a new idea.
Lakebase is referring to the fact that in addition to disaggregated storage s3 is authoritative storage for older data.
Since data is on s3 (or lake) you can perform direct to s3 type operations like data loading, reading this data by engines that are not Postgres and more
> in addition to disaggregated storage s3 is authoritative storage for older data
Suppose a person retrives cold data from another Object Storage protocol rather than S3. This is no longer a "Lakebase", so we have to come up with a different name to avoid confusion.
But if you say "Disaggregated Storage on S3" then you have the flexibility to change that to "Disaggregated Storage on FOOBAR" to avoid confusion.
> Suppose a person retrives cold data from another Object Storage protocol rather than S3. This is no longer a "Lakebase", so we have to come up with a different name to avoid confusion.
I've never seen "lake" or adjacent terminology refer to S3 specifically like that vs other object storage. A data lake on Ceph would still be a data lake.
(My quibble would be that "lake" often refers to inconsistent or unstructured, and itself has always been a bit handwavy compared to "warehouse," whereas this is very structured data on object storage.)
Maybe I’m wrong, but AFAICT this is block (page) storage backed by S3, tuned for Postgres with some paxos-linked storage/caching servers sitting in front? Sounds good, but I’m not sure “lake” or “warehouse” is a word I’d choose… much closer to Litestream-with-reads, or the somewhat-famous “I ran out of RAM so I downloaded some more” blog article.
In the blog article[1] that linked to, it says "Unified transactional and analytical workloads: Lakebase integrates seamlessly with the Lakehouse, sharing the same storage layer across OLTP and OLAP. This makes it possible to run real-time analytics, machine learning, and AI-driven optimization directly on transactional data without moving or duplicating it."
Is the "without moving or duplicating" part actually a true statement? If the actual table state is only reconstructed by the pageserver, its not like Spark can just read it from S3.
Read replicas can be "shallow". You don't need to replicate all the data to create a replica. This allows to create them very very quickly (sub second).
All the extension still work. We don't support Citus today, but mostly because customers are not asking for it rather due to technical limitations. We support lots of extensions: https://docs.databricks.com/aws/en/oltp/projects/extensions
Thanks for offering. In the graph labeled "Prod customer throughput: (higher is better)" eyeballing it within a week you are seeing ~2k qps peak increase over the previous week.
Operationally, how do you handle landing that large of a perf improvement? If my data store changed that much in a week it could break something.
Generally the more throughput the system supports the better. In this case we were hitting limits (btw each operation is many queries of different sizes) and the customer observed higher latencies which is typical if the system can't sustain the throughput required.
After this change latencies are back to normal and throughput increased.
Everyone thinks they need a data lake when most people just need a data pond or data puddle. This is made worse by the industry disappearance of the DBA role and compounded by the fact that PG is not especially easy to tune.
All of this to say that a ton of people are on some sort of managed cloud postgres where the compute is almost always separated from the storage even for the small instances.
Neon et al. will tell you they scale, and I am sure they can but the number of enterprises that actually exceed when can be put on a few large servers in pretty low. You gotta lock them in early so their orgs never develop the expertise to move off on the off chance they get big.
I don't follow: read requests are not served from the WAL. They read the current state of the page from the buffer cache, where the page is updated after the change (FPI or not) is written to the WAL.
In the past we relied on Postgres compute to periodically send a full page so reconstructive a page was always a bounded process. Once we turned it off (and got all those perf gains) we got another problem: unbounded page reconstruction which we had to solve separately.
Disaggregated storage and disaggregated compute have been an open trend in DBMS development for the last half-decade. This is an obvious move with modern computing paradigms, and the academic literature has a standard name for it.
This feels like "JAMStack" from Netlify happening all over again.
I tweeted about this in 2022, as a general trend, and also from the RocksDB meetup emphasizing disaggregated storage:
- https://x.com/GavinRayDev/status/1607769112234823680
- https://x.com/GavinRayDev/status/1600666127025156096
"Basic literacy" -> "Prompt Engineering"
"P2P networking" -> "Web3"
"Service-Oriented Architecture" -> "Microservices"
Maybe I'm old-man-yelling-at-cloud.
Since data is on s3 (or lake) you can perform direct to s3 type operations like data loading, reading this data by engines that are not Postgres and more
But if you say "Disaggregated Storage on S3" then you have the flexibility to change that to "Disaggregated Storage on FOOBAR" to avoid confusion.
I've never seen "lake" or adjacent terminology refer to S3 specifically like that vs other object storage. A data lake on Ceph would still be a data lake.
(My quibble would be that "lake" often refers to inconsistent or unstructured, and itself has always been a bit handwavy compared to "warehouse," whereas this is very structured data on object storage.)
Maybe I’m wrong, but AFAICT this is block (page) storage backed by S3, tuned for Postgres with some paxos-linked storage/caching servers sitting in front? Sounds good, but I’m not sure “lake” or “warehouse” is a word I’d choose… much closer to Litestream-with-reads, or the somewhat-famous “I ran out of RAM so I downloaded some more” blog article.
[0] https://www.orioledb.com/
[0] https://ducklake.select/
Is the "without moving or duplicating" part actually a true statement? If the actual table state is only reconstructed by the pageserver, its not like Spark can just read it from S3.
[1] https://www.databricks.com/blog/what-is-a-lakebase
However generally disaggregating storage makes HA simpler and allows for things like zero downtime patching: https://www.databricks.com/blog/zero-downtime-patching-lakeb...
Read replicas can be "shallow". You don't need to replicate all the data to create a replica. This allows to create them very very quickly (sub second).
All the extension still work. We don't support Citus today, but mostly because customers are not asking for it rather due to technical limitations. We support lots of extensions: https://docs.databricks.com/aws/en/oltp/projects/extensions
Operationally, how do you handle landing that large of a perf improvement? If my data store changed that much in a week it could break something.
After this change latencies are back to normal and throughput increased.
Great write up, cheers to the people involved.
This appears to only have any effect with datalake style installs, where storage is separate from compute.
Not going to have any effect on those small postgres installs for that generic one off app.
All of this to say that a ton of people are on some sort of managed cloud postgres where the compute is almost always separated from the storage even for the small instances.
Neon et al. will tell you they scale, and I am sure they can but the number of enterprises that actually exceed when can be put on a few large servers in pretty low. You gotta lock them in early so their orgs never develop the expertise to move off on the off chance they get big.
Small and large instances benefit from this performance optimization.