🔥 The DB Grill 🔥

Ah, yes. A delightful little dispatch from the digital coal mines. One simply must applaud the sheer ingenuity on display here. To think, for decades, we in the Ivory Tower have insisted that students understand the fundamentals of query construction and relational algebra. How foolish we've been! It seems the solution was never to write a proper, non-correlated subquery—perhaps using a JOIN or a CTE, as a first-year student might—but to simply find the right magical incantation in the "advanced configuration" menu.

It's a rather quaint notion, this idea of fixing a poorly constructed question by adjusting the room's acoustics instead of rephrasing the question itself. It has a certain... artisanal charm. The authors celebrate that one can achieve performance "without requiring you to modify a single line of SQL code." And why would you want to? Writing clear, efficient, and logical SQL is such a dreadful chore. It's far better to treat the database as an inscrutable black box and beseech the cloud provider's proprietary daemons to kindly make your N+1 query problem disappear.

This is, of course, a bold new paradigm. Codd gave us twelve rules for relational purity, a framework for a system whose behavior is predictable and grounded in first-order logic. This... this is more like architectural alchemy. Don't understand the load-bearing principles? No problem! Just sprinkle some "advanced optimization" dust on it and hope the entire structure doesn't collapse. Clearly they've never read Stonebraker's seminal work on query optimizers; the goal was to create a system smart enough to handle well-formed declarative instructions, not to build a patient so sick it needs a room full of life-support machines just to breathe.

One is reminded of the industry's general philosophy, which seems to be a willful ignorance of anything published before last Tuesday's keynote. They chant "ACID properties" like a mantra, yet seem horrified by the notion that the Consistency of a system's performance might actually be related to the consistency of the queries you feed it. They talk about the CAP theorem as if it's a menu from which you can pick two, but they fail to grasp that the real trade-offs are often made between "rigorous understanding" and "shipping it by Friday."

This approach is a masterclass in treating the symptom. Why bother with:

• Understanding execution plans?
• The logical and physical independence of data?
• Writing set-based operations instead of the procedural loop a correlated subquery effectively is?

...when you can just flip a switch? It's a testament to modern engineering.

...transform these performance challenges into efficient operations without requiring you to modify a single line of SQL code.

Truly magnificent. An intellectual absolution for the sin of writing a bad query in the first place. Who needs to learn when you can configure? I suppose I shouldn't be surprised. This is the generation that thinks "database theory" is a series of Medium articles on why MongoDB is "web scale." It’s all so tiresome.

I must thank the authors for this brief, if terrifying, glimpse into the modern state of database "expertise." It has been profoundly illuminating. Rest assured, it is with the most sincere and cheerful disposition that I promise to never read this blog again. Now, if you'll excuse me, I have a first-edition copy of A Relational Model of Data for Large Shared Data Banks that needs dusting. At least that makes sense.

Oh, this is fantastic. Truly. Reading that first sentence just gave me a familiar jolt, like the phantom buzz of a PagerDuty alert on a Saturday morning. I can almost taste the stale coffee and feel the cold sweat of watching the latency graphs form a beautiful, terrifying hockey stick. Thank you for this trip down memory lane.

It's so refreshing to see someone tackle the persistent problem of blissful benchmark buffoonery. I’m in awe of the solution you’re presenting. It seems so... thorough. I'm sure this elegantly engineered elixir will put an end to all our perilous production problems. It’s certainly a far cry from my last "simple" migration, which was pitched to leadership as a “quick weekend upgrade” and devolved into a 72-hour odyssey of on-call insanity.

That was the migration where we discovered:

• The "idempotent" data backfill script was, in fact, spectacularly potent, duplicating about a third of our user records in a uniquely un-de-dupe-able way.
• The rollback plan was written on a whiteboard that got erased by the cleaning crew on Friday night.
• Our primary node decided it had philosophical objections to the concept of leader election and chose to pursue a life of quiet contemplation, responding to zero queries.

So you can imagine my sheer delight at seeing a new tool that promises to simulate real user traffic. I am absolutely certain this will be the silver bullet. Previous attempts at this have always been flawless, never missing the one obscure, once-a-year cron job that runs a monstrous aggregation query and brings the entire cluster to its knees.

For years, database administrators and developers have relied on a standard suite of tools...

And I love that you’re calling out the old ways! It’s about time. All those old tools ever gave us was a false sense of security, a pretty graph for the VP of Engineering, and a spectacular, cascading, catastrophic cluster-calamity three weeks after launch. I'm sure this time is different. This new system, with its hyper-realistic load generation and dynamic traffic shaping, will definitely not just create a new, more exotic species of failure mode. I'm not at all picturing a future where the traffic-generation tool itself has a memory leak and brings down our staging environment, giving us a completely different, but equally misleading, set of performance metrics.

No, this is the one. The final solution to a problem that is definitely technological and not at all related to hubris, unrealistic deadlines, and the collective delusion that a sufficiently complex tool can save us from ourselves. It’s a beautiful dream.

Anyway, this was a great read! Really. A delightful reminder of my manic migration miseries. I’ve gone ahead and set a filter to block this domain, just to preserve the lovely memory. All the best on your next paradigm shift

Oh, wonderful. A new blog post meticulously detailing how the next-generation database we've been promised will "simplify our stack" is, in fact, slower. I'm so glad someone ran benchmarks in a sterile lab to confirm what my battle-scarred intuition has been screaming for weeks. "I need to change that claim," he says. You think? I have a closet full of free startup t-shirts from companies that had to "change that claim" right after we bet our entire infrastructure on them.

Let me guess, the performance regression is only for "write-heavy tests." You mean, the part of the database that actually does the work? The part that processes user signups, transactions, and every other critical path that keeps the lights on? Shocking. A mere 15% less throughput on the 24-core server. That’s fantastic. My manager will be thrilled to hear that our upgrade to MySQL 9.5 comes with a free, automatic 15% reduction in efficiency. It's not a bug, it's a synergistic de-optimization.

And the cause is just beautiful. The new default settings, gtid_mode and enforce_gtid_consistency, are now ON. A silent, little change in the defaults that just happens to tank performance. This gives me a warm, fuzzy feeling, a nostalgic flashback to that time a "simple" Postgres extension update changed a query planner default and turned our main dashboard's p99 latency from 200 milliseconds to 45 seconds. The on-call alert just said "slowness." The PTSD is real. I can still taste the cold 3 AM pizza.

The regressions are larger when gtid_mode and enforce_gtid_consistency are set to ON

You don't say. So the feature designed for more robust replication and consistency—you know, the entire reason we'd even consider this painful migration—is what makes it slower. It's like buying a sports car and finding out the engine only works if you keep it in first gear. This is perfect. We can have the shiny new version number, as long as we turn off the shiny new features.

I love the clinical breakdown here. The lists of hardware, the eight different versions tested for every permutation of -gtid and -nogtid. It’s like a horror movie where the scientist calmly documents every new way the monster has learned to kill.

• 8.0.44-nogtid: The good old days, when things just worked.
• 9.5.0-gtid: The future, where CPU usage is higher, context switches are through the roof, and we write more to disk for the privilege of doing less work.

Look at these metrics, they're poetry. "Context switches per operation (cs/o) are 1.26X larger." That's not just a number. That's the sound of my PagerDuty alarm. That's the ghost of a future incident report I'll have to write, explaining why our "upgraded" database is spending all its time thrashing instead of serving queries.

My absolute favorite part is this little gem:

This result is odd. I might try to reproduce it in the future.

Oh, you might? That’s fantastic. Don't worry, I'm sure we'll reproduce it for you. At peak traffic. On a holiday. When the one person who understands that subsystem is on a flight with no Wi-Fi. That "odd result" is the gremlin that will live in our system for the next six months, only showing up under a full moon and causing cascading failures that nobody can explain. And then some VP will ask why our cloud bill is 20% higher. Because we're paying for more "CPU per operation," my friend. We're paying for the innovation.

So, thank you for this incredibly detailed roadmap of my next year of sleepless nights. It's great to see all the new and exciting ways this "simple" version bump is going to create a whole new class of problems for me to solve, while management celebrates the successful migration.

Anyway, I'm going to go ahead and bookmark this. Just kidding. I'm closing this tab and will do my absolute best to forget I ever saw it.

Alright, let's take a look at this... masterpiece of engineering. I've read marketing fluff with a more robust security posture. You’ve essentially written a detailed instruction manual on how to build a faster car by removing the brakes and seatbelts.

Let’s break down this spectacular monument to misplaced optimism.

• First, you’re cheering about moving to Graviton4. That's adorable. A brand-new architecture that security researchers are just itching to poke holes in. Remember Spectre? Meltdown? You’ve just signed up to be the beta tester for the next generation of side-channel attacks. But hey, at least the attacker exfiltrating your entire customer database will benefit from those performance gains. Lower latency on data theft is a feature now, I guess.

• You proudly mention running on Aurora PostgreSQL 17.5. A dot-five release. Are you serious? You might as well have said you're running your production database on a release candidate you downloaded from a public FTP server. Every new extension, every "optimized" function, is a fresh attack surface for SQL injection vectors we haven't even named yet. I can already hear the SOC 2 auditor laughing as they stamp "Material Weakness" all over your report.

• And the pièce de résistance: the "Optimized Reads-enabled tiered cache." This is my favorite part. You’ve created a complex, multi-layered caching system. Or, as I call it, a hierarchical data-leakage engine. What’s your strategy for preventing cache poisoning? How do you guarantee that a user in one security context can't infer data from another user's cache timing?

  ...using an Optimized Reads-enabled tiered cache. You didn't just add a feature; you added a beautiful, intricate new way to violate data segregation and leak PII between tenants. It’s not a cache; it's a compliance time bomb.

• Let’s not forget about those shiny R8gd instances. That 'd' stands for local NVMe storage, which means you've just introduced a whole new set of physical data remanence problems into your "ephemeral" cloud environment. What's your certified data destruction process when an underlying drive is decommissioned? A strongly worded email to the hypervisor? You’re one hardware failure away from your sensitive data ending up on eBay.

• Finally, the whole stack—Graviton4, a beta-level database, a custom I/O path, and a tiered cache—is a support nightmare that screams "unproven." When—not if—a vulnerability is found, it won't be in just one component; it'll be a cascading failure across this Rube Goldberg machine of dependencies. The CVE for this won't be a number; it'll be a novel.

Anyway, fantastic work on this. It's a bold strategy to prioritize benchmarks over basic operational security. I'll be sure to never read this blog again.

Alright, settle down, kids. Let me put down my coffee—the kind that's brewed strong enough to dissolve a spoon, not your pumpkin spice soy latte—and read this... this manifesto.

Heh. "Database interoperability is a common requirement." You don't say. It's only been a common requirement since the second database was invented, probably on a clay tablet next to the first one. We were trying to get a VSAM flat file to talk to a DB2 relational table while you were still trying to figure out how to share your Legos. But please, tell me more about this revolutionary concept.

So you've got this... oracle_fdw. A "foreign data wrapper." How precious. It's a cute name for a gateway. Back in my day, we called it "writing a goddamn COBOL program with a cursor." You wrote a batch job, you fed it a stack of punch cards that would choke a donkey, and it ran overnight. By morning, you either had your data or a sixteen-pound printout of hexadecimal error codes that you'd spend the rest of the day deciphering. We didn't call it an "excellent and efficient solution," we called it "doing your job."

But this is where it gets good.

How do we perform the reverse? How can an Oracle SQL query execute a SELECT statement […]

You're telling me you built a beautiful, one-way street with all your fancy extensions and wrappers, and now you're standing at the other end, scratching your heads, wondering why you can't drive back? Congratulations. You've engineered a cul-de-sac and are trying to sell it as a freeway interchange.

This is what happens when you don't think more than one step ahead. Back in '89, we had to sync the production mainframe with the new AS/400 the marketing department bought without telling anyone. We didn't have a "wrapper." We had:

• A pot of coffee that was 90% tar.
• A 3270 terminal with a screen burn-in so bad you could still see the payroll menu from three jobs ago.
• A ream of JCL that looked like an ancient curse.
• And about three hundred lines of CICS middleware that we wrote ourselves, because that's what you did. You built the bridge. You didn't download it from a package manager and pray it worked.

You kids and your "efficiency." You think efficiency is a low-latency query. Let me tell you about efficiency. Efficiency is swapping out the right tape reel from a library of thousands for the nightly backup, in a server room that's 55 degrees, while the graveyard shift operator is telling you about his conspiracy theories. Efficiency is making sure that when that tape gets shipped to the Iron Mountain salt mine, it's the right damn tape, because if it's not, you're not restoring anything. You're just updating your resume.

Mark my words, this little Oracle-to-Postgres pipe dream will work beautifully... until it doesn't. The first time a network packet gets sneezed on sideways or Oracle pushes a minor patch that changes one tiny authentication protocol, this whole fragile contraption will fall apart. You'll get errors so cryptic they'll look like they were written in Elvish. And everyone will stand around pointing fingers because the "wrapper" was supposed to be a magic black box that just worked.

Nothing just works. We learned that the hard way when a cleaning lady unplugged the mainframe to plug in her vacuum. You'll learn it when your "seamless integration" costs you a terabyte of unrecoverable customer data.

Now if you'll excuse me, I've got to go check on a batch job that's been running flawlessly since 1992. Let me know when you figure out how to build a two-way street. I'll be here. Probably.

Alright, team. Gather 'round the lukewarm coffee pot. I just read the whitepaper for our next game-changing database migration, and I’m already feeling that familiar twitch in my left eye. You know, the one I got during the "five-minute" Cassandra schema update that took 72 hours and cost me my nephew's birthday party. So, here’s my pre-mortem on Oracle's MongoDB API, because I prefer to have my existential crises on a predictable schedule.

• First, let's celebrate this brilliant new architecture. We get a MongoDB API, but it's secretly just good ol' Oracle SQL underneath. It's the best of both worlds! You get the easy, flexible query language of a document store, and the simple, transparent performance debugging of a multi-thousand-page Oracle tuning manual. What could possibly go wrong with adding another abstraction layer? It’s like putting a sportscar body kit on a cargo ship and then wondering why it still corners like a cargo ship.

• My favorite feature is the "intuitive" troubleshooting process. A query that takes 1 millisecond in actual MongoDB takes over a second here. And how do we find out why? Oh, it’s easy! You just stop writing your application code, switch hats to become a seasoned Oracle DBA, and run a dbms_sqltune.report_sql_monitor. The output is a beautiful, concise wall of text that looks like my terminal trying to render Beowulf in the original Old English. It's giving me flashbacks to deciphering EXPLAIN plans that had more forks than a royal wedding.

• And the solution to the abysmal performance? Hints! Of course it's hints. Instead of the database optimizer, you know, optimizing, we get to whisper sweet nothings to it like {"$native":"NO_USE_HASH_AGGREGATION MONITOR"}. This isn't a query; it's a plea. It’s the database equivalent of putting a sticky note on the server that says "Please, for the love of God, use the index this time. Kisses, Sarah." We're not engineering; we're practicing enterprise-grade voodoo.

• But the real masterstroke, the chef's kiss of this whole affair, is this little admission at the end:

  ...when you use MongoDB emulation on Oracle, you cannot apply the ESR (Equality, Sort, Range) Guideline. ...indexes are limited to equality and range predicates. They cannot be used to optimize pagination queries.

  Let me translate: The most fundamental indexing strategy we use to make our application not-terrible at scale... just doesn't work. It’s like being sold a "fully-featured" car and then finding out the steering wheel is purely decorative. But hey, at least we have "the full range of Oracle SQL instrumentation" to tell us exactly how broken it is. What a feature.

So, to summarize, we get the familiar API of MongoDB, the performance of a SQL query that has to parse JSON on every row, and the debugging tools of a 1990s sysadmin, all while losing the core indexing strategies that made Mongo useful in the first place.

Can't wait for the 3 AM PagerDuty alert that just says SELECT dbms_xplan.display_cursor().

Well, isn't this just a delightful find in my morning reading. "Build a ChatGPT app that connects to your Supabase database." How wonderful. I was just thinking that what our company truly needs is another project for the engineering department to call "mission-critical R&D" while my capital expenditure budget quietly bleeds out in a back alley. Let's translate this from 'aspirational tech blog' into 'line item on my Q3 budget forecast,' shall we?

"Connects to your Supabase database." Ah, Supabase. The "open-source Firebase alternative." That’s vendor-speak for, "The first hit is free, kid." It’s the free puppy of databases. Looks adorable, wags its tail, and seems like a fantastic, low-cost addition to the family. Then you get the bill for the food, the vet visits, the emergency surgery after it eats a sock, and the professional trainer you have to hire because it chewed through the drywall. They lure you in with a generous "free tier" that’s perfect for a weekend project, and the second you start serving actual traffic, the pricing tiers start looking like the altitude markers on a Mount Everest expedition.

And what are we using to build this marvel? "mcp-use" and "Edge Functions." My goodness, the jargon alone makes my wallet clench. "Edge Functions" is just a charming little euphemism for "death by a thousand financial cuts." You only pay for what you use! they chirp. Yes, and every single one of our ten thousand users clicking a button a few times a day will trigger one of these "functions," and suddenly I'm looking at a bill that has more zeroes than our CEO's bonus check. It's a pricing model designed by someone who clearly gets a commission.

But let's not get ahead of ourselves. Let's calculate the True Cost of Ownership™ on this little adventure, because I can guarantee you it's not on their pricing page.

Let’s do some simple, back-of-the-napkin math, shall we?

• Initial "Build" Phase: Let's be generous and say two of our senior engineers, who I'm definitely not paying a combined $350k/year to build internal widgets, spend a "mere" three weeks on this. That's about $20,000 in salary walking out the door just to get a prototype of something we don't need.
• The "Supabase Surprise": The free tier will last for exactly one week of testing. Then we hit the "Pro" plan for $25/month. Cute. But wait, we need daily backups, more compute, and God forbid, SOC2 compliance. Suddenly we're in the "Enterprise" tier, which has a price tag of "Let's have a conversation." I've had those conversations. They start with a friendly chat and end with me signing away the naming rights to my firstborn child. Let's pencil in a conservative $30,000/year.
• The Inevitable Migration & Lock-in: What happens in two years when Supabase gets acquired by some behemoth that triples the price, or our "mcp-use" library is abandoned? We're stuck. Migrating data is never a copy-paste job. It’s a six-month, multi-team death march. The cost to get out of this "easy" solution will be, let’s see... carry the one... ah, yes. A soul-crushing $150,000 in engineering time and lost productivity.
• The Consultants: When the original engineers who built this bespoke contraption inevitably leave for a startup with a better kombucha tap, who will maintain it? Not our new hires. No, we'll have to call in the "Synergistic Cloud-Native AI Database Artisans" who charge $400 an hour to read documentation. That’s another $50,000/year contingency, easy.

So, for the low, low price of an article scroll, our "free" ChatGPT-powered widget has a first-year TCO of roughly $100,000 and a three-year cost spiraling towards a quarter of a million dollars.

And the promised ROI? The grand prize at the bottom of this financial Cracker Jack box?

Create interactive widgets for schema exploration, data viewing, and SQL queries.

We are spending a quarter of a million dollars to build a custom, bug-ridden, and unmaintainable version of DBeaver. We are setting a mountain of cash on fire to provide "interactive widgets" that save an engineer, maybe, five minutes a day. That's an ROI so deeply negative it's approaching absolute zero. This isn’t a technology stack; it’s a financial black hole dressed up in a hoodie.

So, thank you, author, for this insightful post. You’ve given me a valuable lesson and the perfect slide for my next "Why We Can't Have Nice Things" presentation to the board. I will now go back to my spreadsheets, where the numbers are honest, even if they are horrifying.

And I can cheerfully promise I will never be reading this blog again. I'm having IT block the domain. It's cheaper.

Well, I’ve just finished reading this... truly inspiring piece on building a "production-ready real-time analytics API." I must thank the author. It’s not often you find a single webpage that so elegantly outlines a plan to vaporize an entire quarter’s budget. It’s a masterpiece of financial destruction disguised as a technical tutorial.

The ambition here is just wonderful. "Real-time" is my favorite buzzword. It has a magical quality that makes engineers' eyes light up and my quarterly projections spontaneously combust. And pairing it with Kafka? Genius. That’s not just a technology choice; it’s a long-term commitment. You don’t just use Kafka; you hire a Kafka team, you pay for a Kafka managed service that charges by the byte, and you inevitably hire a Kafka consultant who tells you the first team did it all wrong. It's the gift that keeps on giving... invoices.

I was particularly charmed by the casual mention of data enrichment with PostgreSQL and materialized views. It’s presented with such breezy confidence, as if we aren't talking about gluing a speedboat (the stream) to a majestic, but slow-moving, cargo ship (the database). The solution, of course, is to upgrade the cargo ship. Then the docks. Then the entire shipping lane. It's a beautifully simple and predictably expensive cascade.

But let's not get lost in the technical weeds. I’m a numbers person. So, I did some quick, back-of-the-napkin math on the "true cost" of this little adventure, since that part seemed to be missing.

Let's call it the Total Cost of Innovation™:

• Initial Vendor Licenses & "Setup": We’ll lowball this. Let’s say the managed services for Kafka, Postgres, and the "API Gateway" come out to a charming $150,000 annually. A rounding error, really.
• The Inevitable Consultants: You need experts to build this "production-ready" system. They bill at $400/hour and are very good at finding "unexpected complexities." Let's pencil in a 6-month engagement for two of them. That's a mere $384,000. They’ll probably need an extension.
• Employee Training & Hiring: My current team doesn't know this stack. We can either spend 4 months training them (at the cost of all their other work) or hire two new "Senior Real-Time Data Alchemists" at $220k a pop. Let’s be optimistic and say we only need two. That’s $440,000 in new payroll.
• Infrastructure "Surprises": Running this at scale will require more compute, more storage for those "materialized views," and a premium, high-availability network. I’ll just add a modest 30% overhead to our current cloud bill. Let's call that another $250,000 per year.
• The Migration & Lock-in Fee: Getting our data out of this system in two years when the next "real-time" thing comes along will require another project of this exact same scope. This isn’t a cost, it’s an annuity payment to the vendor. Let’s amortize that future pain and add another $500,000 to the initial build.

So, the grand total for this "API" isn't the cost of a few engineers' afternoons. It’s a cool $1,724,000 for the first year.

And the ROI? The article implies we’ll gain incredible insights. Let’s say these insights increase customer conversions by a whopping 0.05%. On our $20 million in annual revenue, that’s a staggering $10,000. At this rate, we'll break even sometime in the year 2198. I find that timeline... aggressive.

I truly have to applaud the vendor ecosystem that produced this article. The strategy is brilliant: create a system so complex and so intertwined that the cost of leaving is even greater than the cost of staying. It's a Roach Motel for data. You check in, but you don't check out.

Thank you for the clarity. You’ve made my decision-making process incredibly simple.

It’s been a real education. I look forward to never reading this blog again.

Alright team, gather 'round the warm glow of the terminal. I just read the latest pamphlet from the "move fast and break things" school of data architecture, and it’s a real masterpiece of optimistic marketing. Let's review the playbook for our next on-call rotation, shall we?

• First up, we have "data exports to Kafka." Let me see if I have this right. We're going to pull data out of our Kafka-powered analytics API... just to push it right back into another Kafka topic? That's a bold strategy for doubling our infrastructure costs, creating an elegant feedback loop of potential failure, and ensuring that when something goes wrong, we have absolutely no idea which side of the mirror to debug. It’s the data engineering equivalent of printing out an email to scan it and send it back to yourself.

• Then there's the promise of "BI tool integration." I love this one. It's my favorite fantasy genre. This always translates to a senior engineer spending a solid week wrestling with an obscure, half-documented JDBC driver that only works on Tuesdays if you're running a specific minor version of Java from 2017. The 'integration' will work perfectly until someone in marketing tries to run a report with a GROUP BY clause, at which point the entire connector will fall over and take a broker with it for good measure.

• And my personal favorite, "comprehensive monitoring." In my experience, 'comprehensive' means you get a lovely, pre-canned Grafana dashboard that shows a big green "OK" right up until the moment the entire system is a smoking crater. It’ll track broker CPU, sure, but will it tell me why consumer lag just jumped from five seconds to five days? Will it alert me before the disk is full because some new 'optimization pattern' decided to write uncompressed logs to /tmp? Of course not. That's what my pager is for.

• This brings me to the unspoken promise behind all these "extensions" and "advanced optimization patterns." I can see it now. Someone will flip a switch on one of these "patterns" on a Thursday afternoon. It’ll look great. Throughput will spike. Then, at precisely 3:17 AM on Saturday of Memorial Day weekend, a subtle shift in the upstream data format will trigger a cascading failure that corrupts the topic indexes.

  The 'zero-downtime' migration will require, of course, a four-hour maintenance window where we have to re-ingest everything from cold storage. Assuming the backups even work.

• You know, all of this sounds… familiar. It has the same ambitious, world-changing energy as the last half-dozen "next-gen data platforms" whose vendor stickers are now slowly peeling off my old server rack in the data center. Anyone remember InfluxDB before it pivoted for the third time? Or that one distributed SQL engine that promised the moon and then got acquired for its logo? This new feature set will look great right next to them.

But hey, I'm sure this time is different. Go ahead, get excited. What’s one more dashboard to ignore?

Alright, settle down, whippersnappers. I was scrolling through the ol' ARPANET on my monochrome monitor and stumbled across this little announcement. Another "revolutionary" database version, another round of applause for features we had before most of you were born. It seems Postgres 18 is here to change the world. Again. Let's pour a cup of lukewarm coffee and see what all the fuss is about, shall we?

• Oh, a new asynchronous I/O system! How completely and utterly groundbreaking. It's adorable, really. It reminds me of the time we implemented something similar on a System/370 mainframe to keep the batch jobs from taking all weekend. We didn't call it some fancy marketing term; we called it "writing competent JCL." It's not a feature, kids, it's just not writing your database engine to do one thing at a time like it's waiting in line at the DMV.

• Then we have the breathless reveal of built-in UUIDv7. A time-ordered, sortable unique identifier! My goodness. Back in my day, we had sequence generators. If we needed it to be unique across two whole machines, we'd tack on a server ID. It took five minutes and a change to a COBOL copybook. We didn't need a committee and a version bump to figure out how to generate a number that gets bigger over time. I once generated globally unique IDs using a punch card machine and a very determined intern named Gary. It worked just fine.

• I see they're mighty proud that "more queries can make use of multi-column indexes" thanks to a new Skip Scan optimization. Let me translate that for you from marketing-speak into English: "Our query planner is slightly less dumb than it was before." Getting your index to work as intended isn't an achievement you put in a press release. That's like a car company bragging that in their new model, the steering wheel is actually connected to the wheels. We expected that from DB2 in 1985.

• But this... this is the real gem. This is the punchline that writes itself. For all this talk of cloud-native, serverless, automated synergy, here's how you get to this magical new version:

  We don't currently offer an automated, in-place upgrade from Postgres 17 to 18. To upgrade, create a new Postgres 18 database and perform an online migration...

  You have to manually migrate your own data. I'm having flashbacks to swapping 9-track tape reels for a weekend-long data center move in '92, only now you get to do it with more YAML and less job security. We had to do that because the new machine was physically across the state. What's their excuse? Did the new version get deployed to a different cloud? At least when my tape backups failed, I could blame a faulty read head or cosmic rays. This is just... progress.

So there you have it. Some old ideas polished up, a bug fix disguised as a feature, and a migration path that would've gotten a sysprog laughed out of the server room. The more things change, the more they stay the same. Now if you'll excuse me, I think I have a set of ISAM files that need reorganizing. At least that's honest work.