[karlmdavis]

From personal experience, it scales very well vertically. Have a system in production with tens of billions of rows and north of 12 TB of storage total. That system is read-heavy with large batched inserts, not many deletes or updates.

Biggest limiter is memory, where the need for it grows linearly with table index size. Postgres really really wants to keep the index pages hot in the OS cache. Gets very sad and weird if it can’t: will unpredictably resort to table scans sometimes.

We are running on AWS Aurora, on a db.r6i.12xlarge. Nowhere even close to maxed out on potential vertical scaling.

[brightball]

Isn’t Aurora horizontal by default?

EDIT: Here's what I was thinking about. It's chunked in 10gb increments that are replicated across AZs.

> Fault-tolerant and self-healing storage

Aurora's database storage volume is segmented in 10 GiB chunks and replicated across three Availability Zones, with each Availability Zone persisting 2 copies of each write. Aurora storage is fault-tolerant, transparently handling the loss of up to two copies of data without affecting database write availability and up to three copies without affecting read availability. Aurora storage is also self-healing; data blocks and disks are continuously scanned for errors and replaced automatically.

https://aws.amazon.com/rds/aurora/features/

[dalyons]

No? it’s a standard single node primary with replicas setup. With a fancy log based storage layer.

[SOLAR_FIELDS]

I recently did some db maintenance on a write heavy workload and I found that eventually it will bloat over time with a table with 500 million records. Switching it to use a proper partitioning scheme helped a lot. So people should not read this and assume you can just dump massive workloads into pg and they will be screamingly performant without some tuning and thoughtful design (I don’t think this is what you are implying, just a PSA)

[riku_iki]

is there a chance you run some older version of PG? They reduced bloating significantly in last few releases.

[SOLAR_FIELDS]

Yes! PG12 in this case. Thanks, this definitely motivates my inflight project to get these db’s up to PG16. Very happy to hear that

[wlll]

If you need more motivation check out the feature matrix, there have been a fair number of pretty big advances since 12.

https://www.postgresql.org/about/featurematrix/

[mrbonner]

My cluster is clocked in at 230TB in Aurora. It is hitting a hard limit of 250TB AWS can support.

[mrbonner]

No, we do not store log or IoT. The data are all business related metrics. I didn't choose aurora but inherited from another team. We have 4 replication reads to scale out the read access. The internal team owns the ingestion (insert) to the write node. All other external accesses are read.

I think the reason behind aurora pick is to support arbitrary aggregation, filtering and low latency read (p90 < 3000ms). We could not pick distributed DB based on Presto, Athena or Redshift mainly for latency requirements.

The other contender I consider is Elastic search. But, I do think using it in this case is akin to fitting a square peg in round hole saying.

[LunaSea]

Being curious I was wondering what type of applications could generate this quantity of data.

Is it IoT / remote sensing related?

[manquer]

You are thinking of normalized ( bcnf if not 3nf) well architectures application storing structured data , unless the app is 100 million+ users or grew super fast 250TB size would be hard to get to .

Timeseries (like IoT you mentioned ) or binary blobs or logs or any other data in SQL storage that shouldn’t be really there can hit any size wouldn’t be all that interesting.

Can’t speak for OP, however managing data for few million user apps, what I have observed is most SQL stores hit single TB range and then start getting broken down into smaller dbs either coz now teams have grown want their own Micro-service or DB or infra wants easier to handle in variety of ways including Backup /recovery larger DBs are extremely difficult to get reasonable RTO/RPO numbers for.

[SJC_Hacker]

If you want to store video data as BLOBs in a DB, you can get there easily.

Maybe not the best idea, I guess a file system would be better for that, and just use the DB for metadata.

But OTOH all the data is one place, so you just migrate the DB. Less to worry about.

I just looked up, all of English Wikipedia (including images) is barely even 100 GB ... crazy world we live in.

[manquer]

You wouldn’t say less to worry about when you have to do full backup or show recovery from backup within a set recovery time .

This one data store is easier is a myth , it just offloads complexity from developer to infra teams who are now provisioning premium NVMe storage instead of cold object stores for binary data .

Binary data is not indexed or aggregated in a SQL store there is no value in doing this is one place expect dev experience at the cost of infra team experience.

[wavemode]

Super easy to generate arbitrary amounts of data if you start using postgres as a log, of any sort.

I worked for a company that had only a few thousand active customers yet had dozens of terabytes of data, for this reason.