Scaling out DB compute can only help with that to a (expensive) point; eventually, you end up wanting to either prevent the bad queries from being added to the system (DBA culture) or ensure that the bad query runs on database infrastructure that doesn't affect other queries. That's why partitioning DB compute (and storage: noisy-neighbor effects from a bad query at the storage layer don't require storage to be running e.g. a BookKeeper or whatever on a server; they can manifest as hot S3 keys or cloud object/block store rate limiting) is a necessary capability if your plan for dealing with a culture of "anyone can add any access pattern they want" is to scale the DB.
It’s much less of an issue. Also, 99%+ of Aws customers likely don’t have enough QPS to hit the per prefix limits anyway
I'm not saying there is no point of contention - depending upon your write patterns you have practical limits, but for reading data that is already stored you pretty much just pay more to do more and the rate of that cost is close to linear.
I'm sure there is a scale at which that pattern breaks, and I'm sure 99.9% of people reading this will never operate a system at that scale.
And get this. We pay them the exact same.
But I won't follow you as far as "expecting developers to have expertise in how and where their software runs is unreasonable".
Like, yeah, it sucks that added DevOps responsibilities etc. don't come with adjusted compensation/time allocation expectations. I'm with you there.
But it's simultaneously true that a ton of "just regular developer" people are significant liabilities because they don't understand anything about the environment where their software runs. That liability manifests operationally (if someone's just running integration tests on Windows for their Java business logic changes and don't have any familiarity with e.g. the Linux, container, or cloud environments where their code runs, they're going to be useless when their code breaks in production and operations staff needs context), and it also makes them less effective when writing code--this culture of "developers should just live in business logic and not have to context-switch or fill their brains with other levels of the stack" is what leads to full table scans, lack of awareness of memory use, N+1 query hell, looping microservice dependencies, misunderstanding of what HTTP fields are set on requests that are mutated by load balancers, mistaken assumptions about how many instances of code can run and what concurrency/thread/coroutine behaviors are present, and so on. Those are very common problems, and it's incumbent on developers in every specialty to gain familiarity with how and where their code runs in order to write and maintain that code effectively.
If your code runs on Linux in Kubernetes, all of your developers should know how to read Linux system logs, check database sessions/queries issued by parts of the application, ls/grep/cat/strace/ps their way around, interpret k8s/application dashboards, check application logs both in log storage and as they're emitted from a process, exec into a container, restart pods, check deployment liveness, etc. Even if they don't have permission to do those things in production.
That was true in 2005 when they deployed their code to IIS on Windows Server/MSSQL, too--just with different operational specifics.
That's a low bar that's often unmet, and all sorts of teams suffer from that failure. Those skills can be trained, kept up to date, and hired for; I don't think there's a great excuse for not expecting them.
In my professional experience, management tends to treat this as a dichotomy rather than a range. And its lead to situations where we're choosing to route every decision through a small number of 'experts' and expecting that to scale, or have people make very bad decisions about things like indices, data layout, etc.
I align with you on this. If you are using some sort of system, there is a sort-of minimum bar of expertise one should acquire. When using analytical platforms, having a basic understanding of how data is stored and the levers it gives you to optimize data processing should be standard.
Some examples:
MySQL - I would expect developers to be able to come up with a list of columns to index and why, understand why UUIDv4 is a bad choice for primary keys, know how to write performant SQL by hand and understand when their ORM is going to do the wrong thing, etc.
Redshift - Understand dist and sort keys and have some mental models on how data shuffles and is pruned based on each choice.
Iceberg + Spark/Trino - Understand that data tends to be stored/loaded as batch files. Understand how deletes work. Understand partitions and sort as options. Understand generally how bloom filters work and when you should decide to place a bloom filter. Understand why UUIDv7 is better than UUIDv4 when its part of a partition key.
What I'm calling out practically is a de-specialization in roles and the trade-offs it imposes.
For example, the "jack of all trades master of one" that was already propagating under the guise of "Full stack engineer" or "DevOps" is further exasperated when those consolidated roles are also delegating critical knowledge acquisition to LLMs.
Further, as we consolidate roles and delegate knowledge acquisition to llms, we're also paying people less, giving less job security, lowering the general quality of life not just for developers, but for operations staff and users, and generally promoting total consolidation within big tech.
Our DBAs are the first line of defense when a DB starts to struggle. They crack the database open, see what's going on, and troubleshoot and sometimes fix the problems. If it's just a simple stored proc change or table change, our DBAs are empowered to go in and fix them. If there's a code change needed, they work with the devs to fix the code.
But by and large the devs still write most of the queries and tables. We as devs get DB training, but we also have a very open door policy with the DBAs so the devs can lean on their expertise and ask advice before sending stuff out. Our devs get a much better sense of "This seems like it could be expensive, let's talk to the DBAs and see what they'd suggest".
It's worked great.
I think any company would benefit from this sort of subject matter expert employment.
I wanna re-emphasize that this is not a new problem. It's not because of DevOps culture or cloud complexity or scale or whatever. Very limited-specialty people were always operational liabilities and had limited positive impact on feature delivery once you accounted for the help they needed to do anything that spanned multiple levels of the stack. There are just more engineers working on more systems with tighter timeline expectations now, so it seems like the complexity incumbent on the engineering role went up in general. It didn't (it went up in some situations and down in some situations), we just started noticing operational pain more often.
I definitely do agree that there's widespread ignorance of the velocity and difficulty-of-work tradeoffs that arise from requiring a wider range of specialties from engineers, and a similarly widespread failure to adjust compensation and timeline expectations accordingly.
I've never seen a DevOps role in Big Tech (I've ping-ponged back and forth between the startup and FANG worlds a few times), and usually a full-stack engineer is someone very senior who designed a lot of the product when it was young and stuck with it for a decade. Instead there's usually a clear SRE/SWE split, and hyper-specialization, enough that someone might actually become the expert on one particular screen of a mobile app.
If you're frustrated with the de-specialization of roles, it's entirely possible you're working at companies at the wrong stage of their lifecycle. Move to a big company and the struggle is to avoid becoming pigeon-holed into one very specific task (and then laid off when the structure of the product changes so that task is no longer necessary). Such a career change can be challenging at first because your career can get specialized to "working for startups and early growth companies", and then big companies won't even look at your resume. But if you try enough places and say in the interview that you're looking for a place where you can really specialize and develop expertise, you can often find a manager desperate enough to take a chance.
LLMs are another issue. I'm worried that they're going to make the economy very unstable because the whole reason a firm exists is to capture institutional knowledge and the LLM captures that institutional knowledge in the model, where a.) it's not specific to a differentiated business and b.) it's subject to model-collapse issues. Could easily see the whole technological world collapse through lack of expertise, but if that happens it's a bigger problem that you can only fix by developing the expertise yourself.
How would you suggest to approach such a career change successfully?
Oh and you will do also business analysis with the business as claude is too complex for them (read any version of the nocode initiative).
Why are you complaining? You should be grateful of the learning opportunity not everyone gets to have. Sure, we pay you peanuts for it. But, it's ultimately for your own good. Don't mind my yacht and Ferrari, though.
Managers went happy coz now they don't need to have hire sysadmins, while in reality they hire sysadmins, call them devops, and have them know some programming on the side.
And the "savings" from not having onprem infrastructure are burned on expensive cloud and debugging cloud blackboxes
Maybe I'm old, and I am, but I just can't get past this point with such annoying writing. Like if you actually spoke like this people would hate you.
Disclaimer: I've never managed a ClickHouse cluster, let alone one of this size.