Voice of the DBA

Tag: software development

  • Every Database Has Problems

    Every database platform has some strengths and weaknesses. Some more than others. I caught this site (NSFW) from Erik Darling, and it made my day. I was having a tough one when this site got me to smile and chuckle out loud a few times. I especially like the MySQL and SQL Lite links (again NSFW).

    Every platform that you might choose to use to back an application can work in many situations. Certainly scale and load are factors to consider, but for the major relational database platforms, most will work fine for many applications. Some might work better than others, but there are always tradeoffs. There are pros and cons. This is also true for the major NoSQL platforms, though most of my experience is with relational ones, so I tend to lean in that direction.

    At the same time, any platform can fail horribly.

    What’s the difference? Quality database design and software engineering. If you have a knowledgeable staff that works with the platform, they can likely make it work well. If they don’t consider the database impact when they code, or aren’t skilled with that platform, they can easily make it seem like the database doesn’t work well at all. Lots of hardware can help, but it often can’t outrun poor data models, poor query structures, or a lack of indexing.

    Quality of code matters, as many data professionals know. We often aren’t given enough time to do the job right, but we know that’s the case. It doesn’t do any good to complain or bemoan the fact that there is never enough time to fix things or improve them.

    We need to write better code to start with, which means learning to write better code. Understand what impacts performance, where you can change your patterns and habits. Watch Erik’s posts, learn from Jeff how to build test data sets to stress your queries if you don’t have good test data. Learn to do a better job in the same amount of time.

    Changing platforms won’t magically fix things, no matter what your CTO/director/manager thinks. Especially if your team doesn’t already have experience on the new platform.

    Steve Jones

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  • Working Better Under Pressure

    One of my colleagues wrote a great post about DBAs and developers, about how a DBA’s pushback on bad code isn’t to be difficult, it’s because they can see the future. I never thought of myself as a modern-day Nostradamus, predicting the future of system performance. Apparently I had another title besides DBA.

    Working under pressure and with short deadlines often leads to short cuts. I’ve made them. I’ve implemented quick hot fixes. I’ve forgotten to port changes back to development databases. I’ve increased our tech debt load, just to solve a more immediate problem.

    The challenge is cleaning things up later, when we have more deadlines and things to fix. It seems that we never have enough time to do the job the way we would like, and there’s certainly no time to go back later and fix things. Most management won’t make this a priority until things get so bad that we have to rewrite a lot of code (which we should never do).

    DevOps, pipelines, automations, and yes, AI, helping can reduce some of the tech debt we create if we use those tools appropriately. Which is a big IF. Often we have more immediate pressures that prevent us from finding time to invest in our systems or in ourselves.

    Getting a handle on bad code, checking it early, and doing so every time with automation can help prevent some of these issues, even when we are in a hurry. That’s why it pays to adapt our work and learn from others. Listen to that DBA that keeps your systems alive. Listen to the DevOps engineers that want you to automate things. Certainly, make them prove their suggestions work, but adopt those patterns. Learn to work with them, rather than against them.

    They can see the future.

    Steve Jones

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  • Who is Irresponsible?

    There was a post on X recently from a founder in the EU about an engineer using Claude and ChatGPT to build a feature. I am not sure how true these posts are or if they are designed to just create engagement, but it’s still an interesting topic. The part that makes me think is that (supposedly) the engineer was fired because their “data” (code) was sent to American servers. The code was then deleted and the feature will be built without AI.

    First, read some of the responses before you form an opinion. There are some funny ones in there. There are a few I think are overblown and silly, and I skim past them. Someone is always more upset than I am, and more than I think they rationally should be, so I tend to let their outrage flow by me.

    There are two interesting things here. First, the debate about sending data to America. There certainly is some cause for concern here if data is being sent to a place outside of the EU where GDPR rules might apply. There possibly could be some legal issue here, though I doubt some of the responses about all code being compromised are an issue here. I don’t think code is PII, though if it were re-used or appears in AI output, perhaps investors could sue this company.

    The second thing here is whether someone should be fired for doing this. There might be a policy and some training about not doing this, and in that case, perhaps the person should be fired. However, I find this kind of thing happening too often, and it’s the type of thing that has happened before AI where people used outside sources (SQL Server Central, Stack Overflow, etc.) to post code in a question and get an answer. And then often use that code without changing or testing it.

    Is this rational? Some people might say yes, some no, many unsure. In the past, before AI, what would you think? To me, sometimes there have been solutions engineers have found but couldn’t use code written by someone else. There are real IP/copyright concerns here. You could rebuild the solution, rewriting the code, which in some sense is what Google did with Java APIs and successfully defended that effort. If another human or an AI gives you code, can you rewrite that code, keeping the same idea for the solution?

    I think that in most cases this is acceptable. I use AI for a lot of things and I throw away a lot of AI output, but it often gets me started down a path, whether in writing, coding, or something else.

    Who was more irresponsible here, the founder or the engineer? I think the former.

    Steve Jones

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  • Barely Reviewed Code

    Years ago I was giving a talk on software development and asked the audience how long it takes to review a PR that has 10 lines changed. Answers were in the minutes to tens of minutes range. I then asked how long it takes to review a PR that has 1,000 lines changed. Some people said hours, but a few people said seconds.

    I’ve often taken the latter, pessimistic view. Not because I don’t think engineers want to do a good job, but because I know human behavior. Most humans will get bored, lose focus, and end up skimming through a large amount of code. Many (most?) people don’t want to spend all that time, after all they have they their own code to write. They’ll just approve the PR and assume testing will catch any major issues.

    Even if a reviewer wants to do a great job, they likely will still miss things. It’s very hard to focus across that much code.

    This is a funny visual (from X) about code reviews. It’s titled “me reviewing code written by Claude before pushing it to production.” Plenty of people are probably laughing, or thinking this is a good reason to not use an AI to write code.

    However, I don’t think the problem is an AI writing code. If you trust the AI without reviewing things, that’s on you. You deserve blame if things fall apart.

    The bigger problem is that an AI can write code so quickly and can make so many changes that PRs will tend to be large. These changes will tend to not get human-reviewed with any level of focus or quality control. The problem is volume, not who wrote the code (or the quality). Certainly quality matters, but it’s easy to catch changes if you have a small volume of code. Harder if you have a lot.

    The more I use AI for spot work, to handle tedious things, to do something like subtly adjust spacing in a UI or focus on adjusting a few things in a data model, the easier it is to judge the focus and quality of the code. Is the change doing the job I need done, and is it doing it well?

    Code quality is a problem we’ve had ever since we started writing code. AI can make the problem worse, not because of poor coding, but because it will write so much code in a PR that you can’t review it appropriately.

    Steve Jones

    Listen to the podcast at Libsyn, Spotify, or iTunes.

    Note, podcasts are only available for a limited time online.