S3: "Block Public Access is now enabled by default on new buckets."
On the one hand, this is obviously the right decision. The number of giant data breeches caused by incorrectly configured S3 buckets is enormous.
But... every year or so I find myself wanting to create an S3 bucket with public read access to I can serve files out of it. And every time I need to do that I find something has changed and my old recipe doesn't work any more and I have to figure it out again from scratch!
The thing to keep in mind with the "Block Public Access" setting is that is a redundancy built in to save people from making really big mistakes.
Even if you have a terrible and permissive bucket policy or ACLs (legacy but still around) configured for the S3 bucket, if you have Block Public Access turned on - it won't matter. It still won't allow public access to the objects within.
If you turn it off but you have a well scoped and ironclad bucket policy - you're still good! The bucket policy will dictate who, if anyone, has access. Of course, you have to make sure nobody inadvertantly modifies that bucket policy over time, or adds an IAM role with access, or modifies the trust policy for an existing IAM role that has access, and so on.
I just stick CloudFront in front of those buckets. You don't need to expose the bucket at all then and can point it at a canonical hostname in your DNS.
I honestly don't mind that you have to jump through hurdles to make your bucket publically available and that it's annoying. That to me seems like a feature, not a bug
>In EC2, you can now change security groups and IAM roles without shutting the instance down to do it.
Hasn't it been this way for many years?
>Spot instances used to be much more of a bidding war / marketplace.
Yeah because there's no bidding any more at all, which is great because you don't get those super high spikes as availability drops and only the ones who bid super high to ensure they wouldn't be priced out are able to get them.
>You don’t have to randomize the first part of your object keys to ensure they get spread around and avoid hotspots.
This one was a nightmare and it took ages to convince some of my more pig headed coworkers in the past that they didn't need to do it any more. The funniest part is that they were storing their data as millions and millions of 10-100kb files, so the S3 backend scaling wasn't the thing bottlenecking performance anyway!
>Originally Lambda had a 5 minute timeout and didn’t support container images. Now you can run them for up to 15 minutes, use Docker images, use shared storage with EFS, give them up to 10GB of RAM (for which CPU scales accordingly and invisibly), and give /tmp up to 10GB of storage instead of just half a gig.
This was/is killer. It used to be such a pain to have to manage pyarrow's package size if I wanted a Python Lambda function that used it. One thing I'll add that took me an embarrassingly long time to realize is that your Python global scope is actually persisted, not just the /tmp directory.
> You don’t have to randomize the first part of your object keys to ensure they get spread around and avoid hotspots.
Sorry, this is absolutely still the case if you want to scale throughput beyond the few thousand IOPS a single shard can serve. S3 will automatically reshard your key space, but if your keys are sequential (eg leading timestamp) all your writes will still hit the same shard.
Re: SG, yeah I wasnt doing any cloud stuff when that was the case. Never had to restart anything for an SG change and this must be at least 5-6 years..
> Glacier restores are also no longer painfully slow.
I had a theory (based on no evidence I'm aware of except knowing how Amazon operates) that the original Glacier service operated out of an Amazon fulfillment center somewhere. When you put it a request for your data, a picker would go to a shelf, pick up some removable media, take it back, and slot it into a drive in a rack.
This, BTW, is how tape backups on timesharing machines used to work once upon a time. You'd put in a request for a tape and the operator in the machine room would have to go get it from a shelf and mount it on the tape drive.
Which is basically exactly what you described but the picker is a robot.
Data requests go into a queue; when your request comes up, the robot looks up the data you requested, finds the tape and the offset, fetches the tape and inserts it into the drive, fast-forwards it to the offset, reads the file to temporary storage, rewinds the tape, ejects it, and puts it back. The latency of offline storage is in fetching/replacing the casette and in forwarding/rewinding the tape, plus waiting for an available drive.
Realistically, the systems probably fetch the next request from the queue, look up the tape it's on, and then process every request from that tape so they're not swapping the same tape in and out twenty times for twenty requests.
I can't talk about it, but I've yet to see an accurate guess at how Glacier was originally designed. I think I'm in safe territory to say Glacier operated out of the same data centers as every other AWS service.
It's been a long time, and features launched since I left make clear some changes have happened, but I'll still tread a little carefully (though no one probably cares there anymore):
One of the most crucial things to do in all walks of engineering and product management is to learn how to manage the customer expectations. If you say customers can only upload 10 images, and then allow them to upload 12, they will come to expect that you will always let them upload 12. Sometimes it's really valuable to manage expectations so that you give yourself space for future changes that you may want to make. It's a lot easier to go from supporting 10 images to 20, than the reverse.
I think folks have missed what I think would have been clever about the implentation I (apparently) dreamt up. It's not that "it's just a tape library", it's that it would have used the existing FC and picker infrastructure that Amazon had already built, with some racks containing drives for removable media. I was thinking that it would not have been some special facility purely for Glacier, but rather one or more regular FCs would just have had some shelves with Glacier media (not necessarily tapes).
Then the existing pickers would get special instructions on their handhelds: Go get item number NNNN from Row/shelf/bin X/Y/Z and take it to [machine-M] and slot it in, etc.
They would definitely be using rubies robots given how uniform hard drives are. The only reason warehouses still have humans is that heterogeneity (different sizes, different textures, different squishiness, etc).
I'll add: When doing instance to instance communication (in the same AZ) always use private ips. If you use public ip routing (even the same AZ) this is charged as regional data transfer.
Even worse, if you run self hosted NAT instance(s) don't use a EIP attached to them. Just use a auto-assigned public IP (no EIP).
NAT instance with EIP
- AWS routes it through the public AWS network infrastructure (hairpinning).
- You get charged $0.01/GB regional data transfer, even if in the same AZ.
NAT instance with auto-assigned public IP (no EIP)
- Traffic routes through the NAT instance’s private IP, not its public IP.
- No regional data transfer fee — because all traffic stays within the private VPC network.
- auto-assigned public IP may change if the instance is shutdown or re-created so have automations to handle that. Though you should be using the network interface ID reference in your VPC routing tables.
I think there is more of us who kind of degenerated from doing it the AWS way - API Gateway, serverless lambdas mess around with IAM roles until it works, ... - to - Give me EC2 / LightSail VPS instance maybe an S3 bucket let's set domain through Route53 and go away with the rest of your orchestrion AWS.
At what point is AWS worth using over other compute competitors when you’re using them as a storage bucket + VPS. They’re wholly more expensive at that point. Why not go with a more traditional but rock solid VPS provider?
I have the opposite philosophy for what it’s worth: if we are going to pay for AWS I want to use it correctly, but maximally. So for instance if I can offload N thing to Amazon and it’s appropriate to do so, it’s preferable. Step Functions, lambda, DynamoDB etc, over time, have come to supplant their alternatives and its overall more efficient and cost effective.
That said, I strongly believe developers don’t do enough consideration as to how to maximize vendor usage in an optimal way
There are entire industries that have largely de-volved their clouds primarily for footprint flexibility (not all AWS services are in all regions) and billing consistency.
Honestly just having to manage IAM is such a time-suck that the way I've explained it to people is that we've traded the time we used to spend administering systems for time spent just managing permissions, and IAM is so obtuse that it comes out as a net loss.
There's a sweet spot somewhere in between raw VPSes and insanely detailed least-privilege serverless setups that I'm trying to revert to. Fargate isn't unmanageable as a candidate, not sure it's The One yet but I'm going to try moving more workloads to it to find out.
You know what's still stupid? That if you have an S3 bucket in the same region as your VPC that you will get billed on your NAT Gateway to send data out to the public internet and right back in to the same datacenter. There is simply no reason to not default that behavior to opt out vs opt in (via a VPC endpoint) beyond AWS profiting off of people's lack of knowledge in this realm. The amount of people who would want the current opt-in behavior is... if not zero, infinitesimally small.
It's a design that is secure by default. If you have no NAT gateway and no VPC Gateway Endpoint for S3 (and no other means of Internet egress) then workloads cannot access S3. Networking should be closed by default, and it is. If the user sets up things they don't understand (like NAT gateways), that's on them. Managed NAT gateways are not the only option for Internet egress and users are responsible for the networks they build on top of AWS's primitives (and yes, it is indeed important to remember that they are primitives, this is an IaaS, not a PaaS).
Having experienced the joy of setting up VPC, subnets and PrivateLink endpoints the whole thing just seems absurd.
They spent the effort of branding private VPC endpoints "PrivateLink". Maybe it took some engineering effort on their part, but it should be the default out of the box, and an entirely unremarkable feature.
In fact, I think if you have private subnets, the only way to use S3 etc is Private Link (correct me if I'm wrong).
VPC endpoints in general should be free and enabled by default. That you need to pay extra to reach AWS' own API endpoints from your VPC feels egregious.
If you had an ALB inside the VPC that routed the requests to something that lives inside the VPC, which called the AWS PutObject api on the bucket, would that still be the case?
Some good stuff here. I wish AWS would just focus on these boring, but ultimately important, things that they’re good at instead of all the current distractions trying to play catch up on “AI.” AWS leadership missed the boat there big time, but that’s OK.
Ultimately AWS doesn’t have the right leadership or talent to be good at GenAI, but they do (or at least used to) have decent core engineers. I’d like to see them get back to basics and focus there. Right now leadership seems panicked about GenAI and is just throwing random stuff at the wall desperately trying to get something to stick. Thats really annoying to customers.
They continue to have large teams working on core stuff. It’s just that they’re working at such a low level (like high perf virtualized networking on their custom network cards) that most people don’t hear about it or care that much.
>VPC peering used to be annoying; now there are better options like Transit Gateway, VPC sharing between accounts, resource sharing between accounts, and Cloud WAN.
They did this to stop people from overloading us-east-1a.
It was fine, until there started to be ways of wiring up networks between accounts (eg PrivateLink endpoint services) and you had to figure out which AZ was which so you could be sure you were mapping to the the same AZs in each account.
I built a whole methodology for mapping this out across dozens of AWS accounts, and built lookup tables for our internal infrastructure… and then AWS added the zone ID to AZ metadata so that we could just look it up directly instead.
It was for spreading load out. If someone was managing resources in a bunch of accounts and always defaulted to, say, 1b, AWS randomized what AZs corresponded to what datacenter segments to avoid hot spots.
The canonical AZ naming was provided because, I bet, they realized that the users who needed canonical AZ identifiers were rarely the same users that were causing hot spots via always picking the same AZ.
> You don’t have to randomize the first part of your object keys to ensure they get spread around and avoid hotspots.
From my understanding, I don't think this is completely accurate. But, to be fair, AWS doesn't really document this very well.
From my (informal) conversations with AWS engineers a few months ago, it works approximately like this (modulo some details I'm sure the engineers didn't really want to share):
S3 requests scale based on something called a 'partition'. Partitions form automatically based on the smallest common prefixes among objects in your bucket, and how many requests objects with that prefix receive. And the bucket starts out with a single partition.
So as an example, if you have a bucket with objects "2025-08-20/foo.txt" and "2025-08-19/foo.txt", the smallest common prefix is "2" (or maybe it considers the root as the generator partition, I don't actually know). (As a reminder, a / in an object key has no special significance in S3 -- it's just another character. There are no "sub-directories"). Therefore a partition forms based on that prefix. You start with a single partition.
Now if the object "2025-08-20/foo.txt" suddenly receives a ton of requests, what you'll see happen is S3 throttle those requests for approximately 30-60 minutes. That's the amount of time it takes for a new partition to form. In this case, the smallest common prefix for "2025-08-20/foo.txt" is "2025-08-2". So a 2nd partition forms for that prefix. (Again, the details here may not be fully accurate, but this is the example conveyed to me). Once the partition forms, you're good to go.
But the key issue here with the above situation is you have to wait for that warm up time. So if you have some workload generating or reading a ton of small objects, that workload may get throttled for a non-trivial amount of time until partitions can form. If the workload is sensitive to multi-minute latency, then that's basically an outage condition.
The way around this is that you can submit an AWS support ticket and have them pre-generate partitions for you before your workload actually goes live. Or you could simulate load to generate the partitions. But obviously, neither of these is ideal. Ideally, you should just really not try and store billions of tiny objects and expect unlimited scalability and no latency. For example, you could use some kind of caching layer in front of S3.
Yep, this is still a thing. In the past year I’ve been throttled due to hot partitions. They’ve improved the partitioning so you hit it less, but if you scale too fast you will get limited.
Hit it when building an iceberg Lakehouse using pre existing data. Using object prefixes fixed the issue.
This is my understanding too, and this is particularly problematic for workloads that are read/write heavy on very recent data. When partitioning by a date or by an auto-incrementing id, you still run into the same issue.
Ex: your prefix is /id=12345. S3, under the hood, generates partitions named `/id=` and `/id=1`. Now, your id rolls over to `/id=20000`. All read/write activity on `/id=2xxxx` falls back to the original partition. Now, on rollover, you end up with read contention.
For any high-throughput workloads with unevenly distributed reads, you are best off using some element of randomness, or some evenly distributed partition key, at the root of your path.
I think there is some nuance needed here. If you ask support to partition your bucket then they will be a bit annoying if you ask for specific partition points and the first part of the prefix is not randomised. They tried to push me to refactor the bucket first to randomise the beginning of the prefix, but eventually they did it.
The auto partitioning is different. It can isolate hot prefixes on its own and can intelligently pick the partition points. Problem is the process is slow and you can be throttled for more than a day before it kicks in.
It would've been nice if each of those claims in the article also linked to either the relevant announcement or to the documentation. If I'm interested in any of these headline items, I'd like to learn more.
I don't believe AWS offers permalinks, so it would only help until they rolled over the next documentation release :-(
They actually used to have the upstream docs in GitHub, and that was super nice for giving permalinks but also building the docs locally in a non-pdf-single-file setup. Pour one out, I guess
Also S3 related: the bucket owner can now be configured as the object owner no matter where the object originated. In the past this was exceedingly painful if you wanted to allow one account contribute objects to a bucket in another account. You could do the initial contribution, but the contributor always owned the object, and you couldn't delegate access to a third account.
This article was a relief. I’m always a tiny bit worried Amazon will change some thing drastically and I’ll have to migrate. I’ve had an ec2 instance running since 2013. It requires effectively zero maintenance. So I am glad there were no surprises in this article. Thanks OP.
Every AWS update can potentially affect your SOC 2 or HIPAA compliance posture. I've seen companies fail audits because they assumed their security configurations were still current.
The cloud moves fast. Compliance processes need to keep up. Manual annual reviews aren't enough when your infrastructure is changing constantly.
This is also why we built automated compliance monitoring - because what worked last quarter might not work today.
A "Catch me up" on AWS (and for that matter other large platforms) would be very useful for many folks.
Ideally it should be a stream of important updates that can be interactively filtered by time-range. For example, if I have not been actively consuming AWS updates firehose for last 18 months, I should be able to "summarize" that length of updates.
Why this is not already a feature of "What's New" section of AWS and other platforms -- I dont know. Waiting to be built -- either by OEM or by the Community.
I played a lot of DOTA2 in the past and I've often thought that big tech could learn something from Valve's patch notes. Especially in the context of process changes, stuff you should know, etc. Expecting folk to read a series of lengthy emails/blog posts to stay up to date is unrealistic.
I haven’t used AWS in the last 5 years. Is IPv6 still somewhat of an issue? I remember some services not supporting it at all and making it impossible to manage as a IPv6-only network.
I have a preempt-able workload for which I could use Spot instances or Savings Plans.
Does anyone have experience running Spot in 2025? If you were to start over, would you keep using Spot?
- I observe with pricing that Spot is cheaper
- I am running on three different architectures, which should limit Spot unavailability
- I've been running about 50 Spot EC2 instances for a month without issue. I'm debating turning it on for many more instances
In terms of cost, from cheapest to most expensive:
1. Spot with autoscaling to adjust to demand and a savings plan that covers the ~75th percentile scale
2. On-demand with RIs (RIs will definitely die some day)
3. On-demand with savings-plans (More flexible but more expensive than RIs)
3. Spot
4. On-demand
I definitely recommend spot instances. If you're greenfielding a new service and you're not tied to AWS, some other providers have hilariously cheap spot markets - see http://spot.rackspace.com/. If you're using AWS, definitely auto-scaling spot with savings plans are the way to go. If you're using Kubernetes, the AWS Karpenter project (https://karpenter.sh/) has mechanisms for determining the cheapest spot price among a set of requirements.
Overall tho, in my experience, ec2 is always pretty far down the list of AWS costs. S3, RDS, Redshift, etc wind up being a bigger bill in almost all past-early-stage startups.
I just saw Weird Al in concert, and one of my favorite songs of his is "Everything You Know is Wrong." This is the AWS version of that song! Nice work Corey!
> Once upon a time Glacier was its own service that had nothing to do with S3. If you look closely (hi, billing data!) you can see vestiges of how this used to be, before the S3 team absorbed it as a series of storage classes.
Your assumption holds if they still use tape. But this paragraph hints at it not being tape anymore. The eternal battle between tape versus drive backup takes another turn.
Generally speaking this isn't something Amazon S3 customers need to worry about - as others have said, S3 will automatically scale index performance over time based on load. The challenge primarily comes when customers need large bursts of requests within a namespace that hasn't had a chance to scale - that's when balancing your workload over randomized prefixes is helpful.
FWIW The optimal way we were told was to partition our data was to do this:
010111/some/file.jpg.
Where `010111/` is a random binary string which will please both the automatic partitioning (503s => partition) and manual partitioning you could ask AWS. Please as in the cardinality of partitions grows slower at each characters vs prefixes like `az9trm/`.
We were told that the later version makes manual partitioning a challenge because as soon as you reach two characters you've already created 36x36 partitions (1,296).
The issue with that: your keys are no more meaningful if you're relying on S3 to have "folders" by tenants for example (customer1/..).
It was always there but it required much more activity to get it done (document your use case & traffic levels and then work with your TAM to get the limit changed).
I just started working with a vendor who has a service behind API Gateway. It is a bit slow(!) and times out at 30 seconds. I've since modified my requests to chunk subsets of the whole dataset, to keep things under the timeout.
Has this changed? Is 30 secs the new or the old timeout?
For our VM solution, we get around this by hot staging VM's. As soon as one customer stops theirs, we reset everything and start it up again. To the end user, our compute seems to be instantly available. Unless of course, we run out.
> DynamoDB now supports empty values for non-key String and Binary attributes in DynamoDB tables. Empty value support gives you greater flexibility to use attributes for a broader set of use cases without having to transform such attributes before sending them to DynamoDB. List, Map, and Set data types also support empty String and Binary values.
Lots of this seems to boil down to: AWS shipped something that was barely usable, but then iterated.
That's a reasonable approach, but the fact this post exists shows that this practice is a reputational risk. By all means do this if you think it's the right thing to do, but be aware that first impressions matter and will stick for a long time.
simonw|6 months ago
On the one hand, this is obviously the right decision. The number of giant data breeches caused by incorrectly configured S3 buckets is enormous.
But... every year or so I find myself wanting to create an S3 bucket with public read access to I can serve files out of it. And every time I need to do that I find something has changed and my old recipe doesn't work any more and I have to figure it out again from scratch!
sylens|6 months ago
Even if you have a terrible and permissive bucket policy or ACLs (legacy but still around) configured for the S3 bucket, if you have Block Public Access turned on - it won't matter. It still won't allow public access to the objects within.
If you turn it off but you have a well scoped and ironclad bucket policy - you're still good! The bucket policy will dictate who, if anyone, has access. Of course, you have to make sure nobody inadvertantly modifies that bucket policy over time, or adds an IAM role with access, or modifies the trust policy for an existing IAM role that has access, and so on.
andrewmcwatters|6 months ago
Yeah, what month?
crinkly|6 months ago
cedws|6 months ago
awongh|6 months ago
Once I have that I can also ask it for the custom tweaks I need.
reactordev|6 months ago
SOLAR_FIELDS|6 months ago
viccis|6 months ago
Hasn't it been this way for many years?
>Spot instances used to be much more of a bidding war / marketplace.
Yeah because there's no bidding any more at all, which is great because you don't get those super high spikes as availability drops and only the ones who bid super high to ensure they wouldn't be priced out are able to get them.
>You don’t have to randomize the first part of your object keys to ensure they get spread around and avoid hotspots.
This one was a nightmare and it took ages to convince some of my more pig headed coworkers in the past that they didn't need to do it any more. The funniest part is that they were storing their data as millions and millions of 10-100kb files, so the S3 backend scaling wasn't the thing bottlenecking performance anyway!
>Originally Lambda had a 5 minute timeout and didn’t support container images. Now you can run them for up to 15 minutes, use Docker images, use shared storage with EFS, give them up to 10GB of RAM (for which CPU scales accordingly and invisibly), and give /tmp up to 10GB of storage instead of just half a gig.
This was/is killer. It used to be such a pain to have to manage pyarrow's package size if I wanted a Python Lambda function that used it. One thing I'll add that took me an embarrassingly long time to realize is that your Python global scope is actually persisted, not just the /tmp directory.
Inufu|6 months ago
Sorry, this is absolutely still the case if you want to scale throughput beyond the few thousand IOPS a single shard can serve. S3 will automatically reshard your key space, but if your keys are sequential (eg leading timestamp) all your writes will still hit the same shard.
Source: direct conversations with AWS teams.
indigodaddy|6 months ago
jp57|6 months ago
I had a theory (based on no evidence I'm aware of except knowing how Amazon operates) that the original Glacier service operated out of an Amazon fulfillment center somewhere. When you put it a request for your data, a picker would go to a shelf, pick up some removable media, take it back, and slot it into a drive in a rack.
This, BTW, is how tape backups on timesharing machines used to work once upon a time. You'd put in a request for a tape and the operator in the machine room would have to go get it from a shelf and mount it on the tape drive.
danudey|6 months ago
https://www.reddit.com/r/DataHoarder/comments/12um0ga/the_ro...
Which is basically exactly what you described but the picker is a robot.
Data requests go into a queue; when your request comes up, the robot looks up the data you requested, finds the tape and the offset, fetches the tape and inserts it into the drive, fast-forwards it to the offset, reads the file to temporary storage, rewinds the tape, ejects it, and puts it back. The latency of offline storage is in fetching/replacing the casette and in forwarding/rewinding the tape, plus waiting for an available drive.
Realistically, the systems probably fetch the next request from the queue, look up the tape it's on, and then process every request from that tape so they're not swapping the same tape in and out twenty times for twenty requests.
Twirrim|6 months ago
It's been a long time, and features launched since I left make clear some changes have happened, but I'll still tread a little carefully (though no one probably cares there anymore):
One of the most crucial things to do in all walks of engineering and product management is to learn how to manage the customer expectations. If you say customers can only upload 10 images, and then allow them to upload 12, they will come to expect that you will always let them upload 12. Sometimes it's really valuable to manage expectations so that you give yourself space for future changes that you may want to make. It's a lot easier to go from supporting 10 images to 20, than the reverse.
jp57|6 months ago
Then the existing pickers would get special instructions on their handhelds: Go get item number NNNN from Row/shelf/bin X/Y/Z and take it to [machine-M] and slot it in, etc.
browningstreet|6 months ago
christina97|6 months ago
nodesocket|6 months ago
Even worse, if you run self hosted NAT instance(s) don't use a EIP attached to them. Just use a auto-assigned public IP (no EIP).
themafia|6 months ago
My understanding is that transfer gets charged on both sides as well. So if you own both sides you'll pay $0.02/GB.
general1726|6 months ago
no_wizard|6 months ago
I have the opposite philosophy for what it’s worth: if we are going to pay for AWS I want to use it correctly, but maximally. So for instance if I can offload N thing to Amazon and it’s appropriate to do so, it’s preferable. Step Functions, lambda, DynamoDB etc, over time, have come to supplant their alternatives and its overall more efficient and cost effective.
That said, I strongly believe developers don’t do enough consideration as to how to maximize vendor usage in an optimal way
calmbonsai|6 months ago
regularfry|6 months ago
There's a sweet spot somewhere in between raw VPSes and insanely detailed least-privilege serverless setups that I'm trying to revert to. Fargate isn't unmanageable as a candidate, not sure it's The One yet but I'm going to try moving more workloads to it to find out.
PaulDavisThe1st|6 months ago
Want to set up MFA ... login required to request device.
Yes, I know, they warned us far ahead of time. But not being able to request one of their MFA devices without a login is ... sucky.
austinshea|6 months ago
berlesi|6 months ago
SOLAR_FIELDS|6 months ago
solatic|6 months ago
otterley|6 months ago
https://docs.aws.amazon.com/vpc/latest/privatelink/vpc-endpo...
(Disclaimer: I work for AWS, opinions are my own.)
afandian|6 months ago
They spent the effort of branding private VPC endpoints "PrivateLink". Maybe it took some engineering effort on their part, but it should be the default out of the box, and an entirely unremarkable feature.
In fact, I think if you have private subnets, the only way to use S3 etc is Private Link (correct me if I'm wrong).
It's just baffling.
dmart|6 months ago
tux3|6 months ago
People who are probably shouldn't be on aws - but they usually have to for unrelated reasons, and they will work to reduce their bill.
kbolino|6 months ago
They should be, of course, at least when the destination is an AWS service in the same region.
[edit: I'm speaking about interface endpoints, but S3 and DynamoDB can use gateway endpoints, which are free to the same region]
torginus|6 months ago
immibis|6 months ago
unknown|6 months ago
[deleted]
JCM9|6 months ago
Ultimately AWS doesn’t have the right leadership or talent to be good at GenAI, but they do (or at least used to) have decent core engineers. I’d like to see them get back to basics and focus there. Right now leadership seems panicked about GenAI and is just throwing random stuff at the wall desperately trying to get something to stick. Thats really annoying to customers.
anon7000|6 months ago
PartiallyTyped|6 months ago
aaronblohowiak|6 months ago
TGW is... twice as expensive as vpc peering?
klysm|6 months ago
alFReD-NSH|6 months ago
Hikikomori|6 months ago
But unlike peering TGW traffic flows through an additional compute layer so it has additional cost.
raffraffraff|6 months ago
WTH?
skywhopper|6 months ago
It was fine, until there started to be ways of wiring up networks between accounts (eg PrivateLink endpoint services) and you had to figure out which AZ was which so you could be sure you were mapping to the the same AZs in each account.
I built a whole methodology for mapping this out across dozens of AWS accounts, and built lookup tables for our internal infrastructure… and then AWS added the zone ID to AZ metadata so that we could just look it up directly instead.
zbentley|6 months ago
The canonical AZ naming was provided because, I bet, they realized that the users who needed canonical AZ identifiers were rarely the same users that were causing hot spots via always picking the same AZ.
mpyne|6 months ago
slashdev|6 months ago
biimugan|6 months ago
From my understanding, I don't think this is completely accurate. But, to be fair, AWS doesn't really document this very well.
From my (informal) conversations with AWS engineers a few months ago, it works approximately like this (modulo some details I'm sure the engineers didn't really want to share):
S3 requests scale based on something called a 'partition'. Partitions form automatically based on the smallest common prefixes among objects in your bucket, and how many requests objects with that prefix receive. And the bucket starts out with a single partition.
So as an example, if you have a bucket with objects "2025-08-20/foo.txt" and "2025-08-19/foo.txt", the smallest common prefix is "2" (or maybe it considers the root as the generator partition, I don't actually know). (As a reminder, a / in an object key has no special significance in S3 -- it's just another character. There are no "sub-directories"). Therefore a partition forms based on that prefix. You start with a single partition.
Now if the object "2025-08-20/foo.txt" suddenly receives a ton of requests, what you'll see happen is S3 throttle those requests for approximately 30-60 minutes. That's the amount of time it takes for a new partition to form. In this case, the smallest common prefix for "2025-08-20/foo.txt" is "2025-08-2". So a 2nd partition forms for that prefix. (Again, the details here may not be fully accurate, but this is the example conveyed to me). Once the partition forms, you're good to go.
But the key issue here with the above situation is you have to wait for that warm up time. So if you have some workload generating or reading a ton of small objects, that workload may get throttled for a non-trivial amount of time until partitions can form. If the workload is sensitive to multi-minute latency, then that's basically an outage condition.
The way around this is that you can submit an AWS support ticket and have them pre-generate partitions for you before your workload actually goes live. Or you could simulate load to generate the partitions. But obviously, neither of these is ideal. Ideally, you should just really not try and store billions of tiny objects and expect unlimited scalability and no latency. For example, you could use some kind of caching layer in front of S3.
kentm|6 months ago
Hit it when building an iceberg Lakehouse using pre existing data. Using object prefixes fixed the issue.
cmcarthur|6 months ago
Ex: your prefix is /id=12345. S3, under the hood, generates partitions named `/id=` and `/id=1`. Now, your id rolls over to `/id=20000`. All read/write activity on `/id=2xxxx` falls back to the original partition. Now, on rollover, you end up with read contention.
For any high-throughput workloads with unevenly distributed reads, you are best off using some element of randomness, or some evenly distributed partition key, at the root of your path.
chisleu|6 months ago
As of when? According to internal support, this is still required as of 1.5 years ago.
arpinum|6 months ago
The auto partitioning is different. It can isolate hot prefixes on its own and can intelligently pick the partition points. Problem is the process is slow and you can be throttled for more than a day before it kicks in.
laurent_du|6 months ago
gurjeet|6 months ago
mdaniel|6 months ago
They actually used to have the upstream docs in GitHub, and that was super nice for giving permalinks but also building the docs locally in a non-pdf-single-file setup. Pour one out, I guess
csours|6 months ago
Everything you know is wrong.
Weird Al. https://www.youtube.com/watch?v=W8tRDv9fZ_c
Firesign Theatre. https://www.youtube.com/watch?v=dAcHfymgh4Y
lysace|6 months ago
My recent interactions with them would probably have been better if they were an LLM.
oblio|6 months ago
beaviskhan|6 months ago
abullinan|6 months ago
ElijahLynn|6 months ago
scubbo|6 months ago
digianarchist|6 months ago
aranelsurion|6 months ago
selinkocalar|6 months ago
The cloud moves fast. Compliance processes need to keep up. Manual annual reviews aren't enough when your infrastructure is changing constantly.
This is also why we built automated compliance monitoring - because what worked last quarter might not work today.
albert_e|6 months ago
Ideally it should be a stream of important updates that can be interactively filtered by time-range. For example, if I have not been actively consuming AWS updates firehose for last 18 months, I should be able to "summarize" that length of updates.
Why this is not already a feature of "What's New" section of AWS and other platforms -- I dont know. Waiting to be built -- either by OEM or by the Community.
ash_091|6 months ago
kassner|6 months ago
skywhopper|6 months ago
1oooqooq|6 months ago
mdaniel|6 months ago
causal|6 months ago
topher200|6 months ago
Does anyone have experience running Spot in 2025? If you were to start over, would you keep using Spot?
erulabs|6 months ago
1. Spot with autoscaling to adjust to demand and a savings plan that covers the ~75th percentile scale
2. On-demand with RIs (RIs will definitely die some day)
3. On-demand with savings-plans (More flexible but more expensive than RIs)
3. Spot
4. On-demand
I definitely recommend spot instances. If you're greenfielding a new service and you're not tied to AWS, some other providers have hilariously cheap spot markets - see http://spot.rackspace.com/. If you're using AWS, definitely auto-scaling spot with savings plans are the way to go. If you're using Kubernetes, the AWS Karpenter project (https://karpenter.sh/) has mechanisms for determining the cheapest spot price among a set of requirements.
Overall tho, in my experience, ec2 is always pretty far down the list of AWS costs. S3, RDS, Redshift, etc wind up being a bigger bill in almost all past-early-stage startups.
stevejb|6 months ago
jeffbarr|6 months ago
Oh yeah, we were in the same row!
havefunbesafe|6 months ago
bob1029|6 months ago
Wouldn't this always depend on the length of the queue to access the robotic tape library? Once your tape is loaded it should move really quickly:
https://www.ibm.com/docs/en/ts4500-tape-library?topic=perfor...
hinkley|6 months ago
Your assumption holds if they still use tape. But this paragraph hints at it not being tape anymore. The eternal battle between tape versus drive backup takes another turn.
cldcntrl|6 months ago
Not strictly true.
QuinnyPig|6 months ago
ed_g|6 months ago
Please see the documentation: https://docs.aws.amazon.com/AmazonS3/latest/userguide/optimi...
This 2024 re:Invent session "Optimizing storage performance with Amazon S3 (STG328)" which goes very deep on the subject: https://www.youtube.com/watch?v=2DSVjJTRsz8
And this blog that discusses Iceberg's new base-2 hash file layout which helps optimize request scaling performance of large-scale Iceberg workloads running on S3: https://aws.amazon.com/blogs/storage/how-amazon-ads-uses-ice...
vvoyer|6 months ago
"If you want to partition your data even better, you can introduce some randomness in your key names": https://youtu.be/2DSVjJTRsz8?t=2206
FWIW The optimal way we were told was to partition our data was to do this: 010111/some/file.jpg.
Where `010111/` is a random binary string which will please both the automatic partitioning (503s => partition) and manual partitioning you could ask AWS. Please as in the cardinality of partitions grows slower at each characters vs prefixes like `az9trm/`.
We were told that the later version makes manual partitioning a challenge because as soon as you reach two characters you've already created 36x36 partitions (1,296).
The issue with that: your keys are no more meaningful if you're relying on S3 to have "folders" by tenants for example (customer1/..).
rthnbgrredf|6 months ago
TheP1000|6 months ago
coredog64|6 months ago
quesera|6 months ago
I just started working with a vendor who has a service behind API Gateway. It is a bit slow(!) and times out at 30 seconds. I've since modified my requests to chunk subsets of the whole dataset, to keep things under the timeout.
Has this changed? Is 30 secs the new or the old timeout?
the8472|6 months ago
Not true for GPU instances, they're stuck 5 minutes in a stopping state because they run some GPU health checks.
latchkey|6 months ago
Ayesh|6 months ago
romperstomper|6 months ago
When this was changed? I think this is still an issue, I've had some such errors quite recently.
bluecassette|6 months ago
> DynamoDB now supports empty values for non-key String and Binary attributes in DynamoDB tables. Empty value support gives you greater flexibility to use attributes for a broader set of use cases without having to transform such attributes before sending them to DynamoDB. List, Map, and Set data types also support empty String and Binary values.
https://docs.aws.amazon.com/amazondynamodb/latest/developerg...
danpalmer|6 months ago
That's a reasonable approach, but the fact this post exists shows that this practice is a reputational risk. By all means do this if you think it's the right thing to do, but be aware that first impressions matter and will stick for a long time.
bambax|6 months ago
Then you can self-cloud. Several startips are in this space. It gets you the best of both worlds: scaling, freedom, cost-control.
And no marketing jargon that you need to learn, and then unlearn!
Annatar|6 months ago
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