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> A Postgres `db.m6g.large` (the cheapest non-burstable instance) runs $114/month for a single AZ, and that's not counting storage or bandwidth. A `db.t4g.mediu

> A Postgres `db.m6g.large` (the cheapest non-burstable instance) runs $114/month for a single AZ, and that's not counting storage or bandwidth. A `db.t4g.medium` runs $47/month, again, not counting storage or bandwidth. This is why numbers do not stack up in the calculations – the premise that the DB has to be provisioned is not the correct one to start off with. The right way of cooking RDS in AWS is to go serverless from the start and configure the number of DCU's, e.g. 1 to N. That way it will be even cheaper than the originally quoted $200. Generally speaking, there is absolutely no need for anything to be provisioned at a fixed compute capacity in AWS unless there is a very specific or an edge case that, likewise, warrants a provisioned instance of something.

sgarland 2y ago
> The right way of cooking RDS in AWS is to go serverless from the start Nitpick, but there is no Serverless for RDS, only Aurora. The two are wildly different in their architecture and performance characteristics. Then there's RDS Multi-AZ Cluster, which is about as confusingly named as they could manage, but I digress. Let's take your stated Minimum ACU of 1 as an example. That gives you 2 GiB of RAM, with "CPU and networking similar to what is available in provisioned Aurora instances." Since I can't find anything more specific, I'll compare it to a `t4g.small`, which has 2 vCPU (since it's ARM, it's actual cores, not threads), and 0.128 / 5.0 Gbps [0] baseline/burst network bandwidth, which is 8 / 625 MBps. That burst is best-effort, and also only lasts for 5 – 60 minutes [1] "depending on instance size." Since this is tiny, I'm going to assume the low end of that scale. Also, since this is Aurora, we have to account for both [2] client <--> DB and DB-compute (each node, if more than one) <--> DB-storage bandwidth. Aurora Serverless v2 is $0.12/hour, or $87.60/month, plus storage, bandwidth, and I/O costs. So we have a Postgres-compatible DB with 2 CPUs, 2 GiB of RAM, and 64 Mbps of baseline network bandwidth that's shared between application queries and the cluster volume. Since Aurora doesn't use the OS page cache, its `shared_buffers` will be set to ~75% of RAM, or 1.5 GiB. Memory will also be consumed by the various processes, like the WAL writer, background writer, auto-vacuum daemon, and of course, each connection spawns a process. For the latter reason, unless you're operating at toy scale (single-digit connections at any given time), you need some kind of connection pooler with Postgres. Keeping in the spirit of letting AWS do everything, they have RDS Proxy, which despite the name, also works with Aurora. That's $0.015/ACU-hour, with a minimum 8 ACUs for Aurora Serverless, or $87.60/month. Now, you could of course just let Aurora scale up in response to network utilization, and skip RDS Proxy. You'll eventually bottleneck / it won't make any financial sense, but you could. I have no idea how to model that pricing, since it depends on so many factors. I went on about network bandwidth so much because it catches people by surprise, especially with Aurora, and doubly so with Postgres for many services. The reason is its WAL amplification from full page writes [3]. If you have a UUIDv4 (or anything else non-k-sortable) PK, the B+tree is getting thrashed constantly, leading to slower performance on reads and writes. Aurora doesn't suffer from the full page writes problem (that's still worth reading about and understanding), but it does still have the same problems with index thrashing, and it also has the same issues as Postgres with Heap-Only Tuple updates [4]. Unless you've carefully designed your schema around this, it's going to impact you, and you'll have more network traffic than you expected. Add to that dev's love of chucking everything into JSON[B] columns, and the tuples are going to be quite large. Anyway, I threw together an estimate [5] with just Aurora (1 ACU, no RDS Proxy, modest I/O), 2x ALBs with an absurdly low consumption, and 2x ECS tasks. It came out to $232.52/month. [0]: https://docs.aws.amazon.com/ec2/latest/instancetypes/gp.html#gp_network https://docs.aws.amazon.com/ec2/latest/instancetypes/gp.html... [1]: https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ec2-instance-network-bandwidth.html https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ec2-inst... [2]: https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/AuroraPostgreSQL_AnayzeResourceUsage.html#AuroraPostgreSQL_AnayzeResourceUsage.EvaluateInstanceUsage https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide... [3]: https://www.rockdata.net/tutorial/tune-full-page-writes/ https://www.rockdata.net/tutorial/tune-full-page-writes/ [4]: https://www.postgresql.org/docs/current/storage-hot.html https://www.postgresql.org/docs/current/storage-hot.html [5]: https://calculator.aws/#/estimate?id=8972061e6386602efdc28440f6881b80205e6d81 https://calculator.aws/#/estimate?id=8972061e6386602efdc2844...
- kbolino 2y ago
  I don't know much about Aurora, there was/is a little too much magic there for my taste, but I feel like once we start with "Postgres-compatible DB", we can't necessarily reason about how things perform under the hood in terms of ordinary Postgres servers. Is there a detailed breakdown of Aurora and its performance/architecture out there? My experience is that AWS is cagey about the details to maintain competitive advantage.
  - sgarland 2y ago
    You can find some re:Invent talks about it, but the level of depth may not be what you want. The tl;dr is they built a distributed storage system that is split across 3 AZs, each with 2 storage nodes. Storage is allocated in 10 GiB chunks, called protection groups (perhaps borrowing from Ceph’s placement group terminology), with each of these being replicated 6x across the nodes in AZs as mentioned. 4/6 are required for quorum. Since readers are all reading from the same volume, replica lag is typically minimal. Finally, there are fewer / no (not positive honestly; I have more experience with MySQL-compatible Aurora) checkpoints and full page writes. If you’ve used a networked file system with synchronous writes, you’ll know that it’s slow. This is of course exacerbated with a distributed system requiring 4/6 nodes to ack. To work around this, Aurora has “temporary local storage” on each node, which is a fixed size proportional to the instance size. This is used for sorts that spill to disk, and building secondary indices. This has the nasty side effect that if your table is too large for the local storage, you can’t build new indices, period. AWS will tell you “upsize the instance,” but IMO it’s extremely disingenuous to tout the ability for 128 TiB volumes without mentioning that if a single table gets too big, your schema becomes essentially fixed in place. Similarly, MySQL normally has something called a change buffer that it uses for updating secondary indices during writes. Can’t have that with Aurora’s architecture, so Aurora MySQL has to write through to the cluster volume, which is slow. AWS claims that Aurora is anywhere from 3-5x faster than the vanilla versions of the respective DBs, but I have never found this to be true. I’ve also had the goalposts shifted when arguing this point, with them saying “it’s faster under heavy write contention,” but again, I have not found this to be true in practice. You can’t get around data locality. EBS is already networked storage; requiring 4/6 quorum across 3 physically distant AZs makes it even worse. The 64 TiB limit of RDS is completely arbitrary AFAIK, and is purely to differentiate Aurora. Also, if you have a DB where you need that, and you don’t have a DB expert on staff, you’re gonna have a bad time.

sgarland 2y ago

> The right way of cooking RDS in AWS is to go serverless from the start Nitpick, but there is no Serverless for RDS, only Aurora. The two are wildly different in their architecture and performance characteristics. Then there's RDS Multi-AZ Cluster, which is about as confusingly named as they could manage, but I digress. Let's take your stated Minimum ACU of 1 as an example. That gives you 2 GiB of RAM, with "CPU and networking similar to what is available in provisioned Aurora instances." Since I can't find anything more specific, I'll compare it to a `t4g.small`, which has 2 vCPU (since it's ARM, it's actual cores, not threads), and 0.128 / 5.0 Gbps [0] baseline/burst network bandwidth, which is 8 / 625 MBps. That burst is best-effort, and also only lasts for 5 – 60 minutes [1] "depending on instance size." Since this is tiny, I'm going to assume the low end of that scale. Also, since this is Aurora, we have to account for both [2] client <--> DB and DB-compute (each node, if more than one) <--> DB-storage bandwidth. Aurora Serverless v2 is $0.12/hour, or $87.60/month, plus storage, bandwidth, and I/O costs. So we have a Postgres-compatible DB with 2 CPUs, 2 GiB of RAM, and 64 Mbps of baseline network bandwidth that's shared between application queries and the cluster volume. Since Aurora doesn't use the OS page cache, its `shared_buffers` will be set to ~75% of RAM, or 1.5 GiB. Memory will also be consumed by the various processes, like the WAL writer, background writer, auto-vacuum daemon, and of course, each connection spawns a process. For the latter reason, unless you're operating at toy scale (single-digit connections at any given time), you need some kind of connection pooler with Postgres. Keeping in the spirit of letting AWS do everything, they have RDS Proxy, which despite the name, also works with Aurora. That's $0.015/ACU-hour, with a minimum 8 ACUs for Aurora Serverless, or $87.60/month. Now, you could of course just let Aurora scale up in response to network utilization, and skip RDS Proxy. You'll eventually bottleneck / it won't make any financial sense, but you could. I have no idea how to model that pricing, since it depends on so many factors. I went on about network bandwidth so much because it catches people by surprise, especially with Aurora, and doubly so with Postgres for many services. The reason is its WAL amplification from full page writes [3]. If you have a UUIDv4 (or anything else non-k-sortable) PK, the B+tree is getting thrashed constantly, leading to slower performance on reads and writes. Aurora doesn't suffer from the full page writes problem (that's still worth reading about and understanding), but it does still have the same problems with index thrashing, and it also has the same issues as Postgres with Heap-Only Tuple updates [4]. Unless you've carefully designed your schema around this, it's going to impact you, and you'll have more network traffic than you expected. Add to that dev's love of chucking everything into JSON[B] columns, and the tuples are going to be quite large. Anyway, I threw together an estimate [5] with just Aurora (1 ACU, no RDS Proxy, modest I/O), 2x ALBs with an absurdly low consumption, and 2x ECS tasks. It came out to $232.52/month. [0]: https://docs.aws.amazon.com/ec2/latest/instancetypes/gp.html#gp_network https://docs.aws.amazon.com/ec2/latest/instancetypes/gp.html... [1]: https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ec2-instance-network-bandwidth.html https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ec2-inst... [2]: https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/AuroraPostgreSQL_AnayzeResourceUsage.html#AuroraPostgreSQL_AnayzeResourceUsage.EvaluateInstanceUsage https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide... [3]: https://www.rockdata.net/tutorial/tune-full-page-writes/ https://www.rockdata.net/tutorial/tune-full-page-writes/ [4]: https://www.postgresql.org/docs/current/storage-hot.html https://www.postgresql.org/docs/current/storage-hot.html [5]: https://calculator.aws/#/estimate?id=8972061e6386602efdc28440f6881b80205e6d81 https://calculator.aws/#/estimate?id=8972061e6386602efdc2844...

kbolino 2y ago
I don't know much about Aurora, there was/is a little too much magic there for my taste, but I feel like once we start with "Postgres-compatible DB", we can't necessarily reason about how things perform under the hood in terms of ordinary Postgres servers. Is there a detailed breakdown of Aurora and its performance/architecture out there? My experience is that AWS is cagey about the details to maintain competitive advantage.
- sgarland 2y ago
  You can find some re:Invent talks about it, but the level of depth may not be what you want. The tl;dr is they built a distributed storage system that is split across 3 AZs, each with 2 storage nodes. Storage is allocated in 10 GiB chunks, called protection groups (perhaps borrowing from Ceph’s placement group terminology), with each of these being replicated 6x across the nodes in AZs as mentioned. 4/6 are required for quorum. Since readers are all reading from the same volume, replica lag is typically minimal. Finally, there are fewer / no (not positive honestly; I have more experience with MySQL-compatible Aurora) checkpoints and full page writes. If you’ve used a networked file system with synchronous writes, you’ll know that it’s slow. This is of course exacerbated with a distributed system requiring 4/6 nodes to ack. To work around this, Aurora has “temporary local storage” on each node, which is a fixed size proportional to the instance size. This is used for sorts that spill to disk, and building secondary indices. This has the nasty side effect that if your table is too large for the local storage, you can’t build new indices, period. AWS will tell you “upsize the instance,” but IMO it’s extremely disingenuous to tout the ability for 128 TiB volumes without mentioning that if a single table gets too big, your schema becomes essentially fixed in place. Similarly, MySQL normally has something called a change buffer that it uses for updating secondary indices during writes. Can’t have that with Aurora’s architecture, so Aurora MySQL has to write through to the cluster volume, which is slow. AWS claims that Aurora is anywhere from 3-5x faster than the vanilla versions of the respective DBs, but I have never found this to be true. I’ve also had the goalposts shifted when arguing this point, with them saying “it’s faster under heavy write contention,” but again, I have not found this to be true in practice. You can’t get around data locality. EBS is already networked storage; requiring 4/6 quorum across 3 physically distant AZs makes it even worse. The 64 TiB limit of RDS is completely arbitrary AFAIK, and is purely to differentiate Aurora. Also, if you have a DB where you need that, and you don’t have a DB expert on staff, you’re gonna have a bad time.