🔥 The DB Grill 🔥

Oh, fantastic. Just what my weekend needed: another blog post about a revolutionary new tech stack that promises to abstract away all the hard problems. "AgentKit," "Tinybird MCP Server," "OpenAI's Agent Builder." It all sounds so clean, so effortless. I can almost forget the smell of stale coffee and the feeling of my soul slowly leaking out of my ears during the last "painless" data platform migration.

Let's break down this glorious new future, shall we? From someone who still has flashbacks when they hear the words data consistency.

• They say it’s a suite of tools for effortless building and deployment. I love that word, effortless. It has the same hollow ring as simple, turnkey, and just a quick script. I remember the last "effortless" integration. It effortlessly took down our primary user database for six hours because of an undocumented API rate limit. This isn't a suite of tools; it's a beautifully wrapped box of new, exciting, and completely opaque failure modes.

• Building "data-driven, analytical workflows" sounds amazing on a slide deck. In reality, it means that when our new AI agent starts hallucinating and telling our biggest customer that their billing plan is "a figment of their corporate imagination," I won't be debugging our code. No, I'll be trying to figure out what magical combination of tea leaves and API calls went wrong inside a black box I have zero visibility into. My current nightmare is a NullPointerException; my future nightmare is a VagueExistentialDreadException from a model I can't even inspect.

• And the Tinybird MCP Server! My god, it sounds so... delicate. I'm sure its performance is rock-solid, right up until the moment it isn't. Remember our last "infinitely scalable" cloud warehouse? The one that scaled its monthly bill into the stratosphere but fell over every Black Friday?

  This just shifts the on-call burden. Instead of our database catching fire, we now get to file a Sev-1 support ticket and pray that someone at Tinybird is having a better 3 AM than we are. It’s not a solution; it’s just delegating the disaster.

• My favorite part of any new platform is the inevitable vendor lock-in. We're going to build our most critical, "data-driven" workflows on "OpenAI's Agent Builder." What happens in 18 months when they decide to 10x the price? Or better yet, deprecate the entire V1 of the Agent Builder API with a six-month notice? I've already lived through this. I have the emotional scars and the hastily written Python migration scripts to prove it. We're not building a workflow; we're meticulously constructing our own future hostage situation.

• Ultimately, this whole thing just creates another layer. Another abstraction. And every time we add a layer, we're just trading a known, solvable problem for an unknown, "someone-else's-problem" problem that we still get paged for. I'm not solving scaling issues anymore; I'm debugging the weird, unpredictable interaction between three different vendors' services. It’s like a murder mystery where the killer is a rounding error in a billing API and the only witness is a Large Language Model that only speaks in riddles.

Call me when you've built an agent that can migrate itself off your own platform in two years. I'll be waiting.

Ah, another dispatch from the front lines of "progress." I must confess, my morning tea nearly went cold as I absorbed this... truly breathtaking announcement. One must marvel at the sheer audacity. They're bringing on a new talent to expand "third-party integrations" and "offline-first capabilities." How wonderful. It's always a joy to see the next generation so enthusiastically speed-running the seven stages of data corruption.

It's particularly heartening to see such a bold commitment to "integrations." For decades, we toiled under the oppressive yoke of relational algebra and schema normalization. We were foolishly concerned with quaint notions like "data integrity" and a "single source of truth." How refreshing it is to see a company bravely cast off those shackles and embrace the unbridled chaos of simply plugging... things... into other things. I'm sure the resulting data model will be a testament to simplicity and clarity. Edgar Codd's rules? Oh, those were more like gentle suggestions, weren't they? A charming historical footnote.

I suppose his First Rule, the Information Rule, that all information in the database must be represented in one and only one way—namely as values in tables—was simply too restrictive for today's dynamic, agile, synergistic data landscape.

But the true masterstroke, the pièce de résistance, is the focus on "offline-first." Magnificent! They've looked upon the sacred ACID guarantees—Atomicity, Consistency, Isolation, Durability—and decided that the 'C' for Consistency was, perhaps, a bit much. A trifle inconvenient. It gets in the way of a snappy user experience, after all.

One can only applaud this courageous interpretation of the CAP theorem. It's as if they read the Wikipedia summary and decided it was a menu from which one could order two, and then try to invent a third in the kitchen with duct tape and wishful thinking. They've chosen Availability and Partition Tolerance, and now they will "innovate" their way back to a state of... well, what shall we call it? "Eventual Correctness-ish?" Clearly they've never read Stonebraker's seminal work on distributed systems, or they'd understand that you don't simply "solve" for consistency after the fact. It's not a bug you patch; it is a fundamental, mathematical constraint of the universe.

I can just picture the design meetings.

• "But what about conflicting writes when a user comes back online?"
• "Don't be a blocker! We'll call it a 'Merge Strategy,' put it behind a toggle, and write a think-piece about 'user-empowered data resolution.'"

It truly is a brave new world. A world where every application is its own bespoke, ad-hoc, and deeply flawed implementation of a distributed database, written by people who believe academic papers are things you skim for keywords before a job interview.

I shall watch this venture with great interest from my ivory tower. I predict a glorious future for them, filled with frantic support tickets, blog posts titled "Our Journey Through Data Reconciliation," and eventually, a quiet, enterprise-wide migration to a system that, bless its heart, actually enforces constraints. One eagerly awaits the inevitable "Great Reconciliation" of 2026, when terabytes of "synergized" data must finally be made coherent. It will be a sight to behold. A true triumph of industry innovation.

Alright team, gather ‘round. I’ve just finished reading the latest dispatch from the land of make-believe, where servers are always synchronized and network latency is a polite suggestion. This paper on "Tiga" is another beautiful exploration of the dream of a one-round commit. A dream. You know what else is a dream? A budget that balances itself. Let’s not confuse fantasy with a viable Q4 strategy.

They say this isn't a "conceptual breakthrough," just a "thoughtful piece of engineering." That’s vendor-speak for, “We polished the chrome on the same engine that’s failed for a decade, and now we’re calling it a new car.” The big idea is that it commits transactions in one round-trip "most of the time." That phrase—"most of the time"—is the most expensive phrase in enterprise technology. It’s the asterisk at the end of the contract that costs us seven figures in "professional services" two years down the line.

The whole thing hinges on predicting the future. It assigns a transaction a "future timestamp" based on an equation that includes a little fudge factor, a "Δ" they call a "small safety headroom." Let me translate that into terms this department understands. That’s the financial equivalent of building a forecast by taking last year's revenue, adding a "synergy" multiplier, and hoping for the best. When has that ever worked? We're supposed to bet the company's data integrity on synchronized clocks and a 10-millisecond guess? My pacemaker has a better SLA.

They sell you on the "fast path." The sunny day scenario. Three simple steps, 1-WRTT, and everyone’s happy. The PowerPoint slides will be gorgeous. But then you scroll down. You always have to scroll down.

Suddenly, we’re in the weeds of steps four, five, and six. The "slow path." This is where the magic dies and the invoices begin.

Timestamp Agreement: Sometimes leaders execute with slightly different timestamps... Log Synchronization: After leaders finalize timestamps, they propagate the consistent log... Quorum Check of Slow Path: Finally, the coordinator verifies that enough followers have acknowledged...

Sometimes. You see how they slip that in? At our scale, "sometimes" means every third Tuesday and any time we run a promotion. Each of those steps—"exchanging timestamps," "revoking execution," "propagating logs"—isn't just a half-a-round-trip. It's a support ticket. It's a late-night call with a consultant from Bangalore who costs more per hour than our entire engineering intern program.

Let’s do some real math here, the kind they don't put in the whitepaper. The back-of-the-napkin P&L.

• Sticker Price: Let's call it $X. They’ll probably even give us a "discount."
• Migration: Our current system is a tangled, horrifying mess, but it’s our mess. Moving our petabytes of data to this new, "thoughtfully engineered" system will require a team of 10 engineers working for 18 months. That’s at least $2.5 million in salaries, benefits, and the opportunity cost of them not building features that actually make us money.
• Training: Our people know the old system. Now they need to become experts in "deadline-ordered multicast" and "super quorums." That's a week of off-site training in Palo Alto for 20 people. Add another $150,000 for flights, hotels, and a team-building exercise that involves trust falls and organic kombucha.
• The "Slow Path" Consultants: When—not if—we hit the "slow path" during peak traffic, we won’t know how to fix it. We’ll have to bring in the Tiga-certified "Optimization Gurus." At $800/hour, with a six-month minimum retainer, we’re looking at another $800,000 just to keep the lights on.
• Infrastructure Overhead: Oh, this runs beautifully on "modern clocks" and pristine Google Cloud infrastructure? Fantastic. Our on-prem data centers are powered by hamsters on wheels and held together with duct tape. The hardware upgrade to get the "nanosecond precision" this thing needs will cost more than the software itself. Let’s pencil in $3 million for that.

So, the "True Cost of Tiga" isn’t $X. It’s $X + $6.45 million, before we've even handled a single transaction.

And for what? The evaluation claims it’s "1.3–7x" faster in "low-contention microbenchmarks." That is the most meaningless metric I have ever heard. That's like bragging that your new Ferrari is faster than a unicycle in an empty parking lot. Our production environment isn't a low-contention microbenchmark. It's a high-contention warzone. It's Black Friday traffic hitting a Monday morning batch job. Their benchmark is a lie, and they're using it to sell us a mortgage on a fantasy.

They say it beats Calvin+. Great. They replaced one academic consensus protocol with another. Who cares? This isn't a science fair. This is a business. Show me the ROI on that $6.45 million initial investment. If we get 2x throughput, does that mean we double our revenue? Of course not. It means we can process customer complaints twice as fast before the system falls over into its "graceful" 1.5-2 WRTT slow path. By my math, this thing doesn't pay for itself until the heat death of the universe.

Honestly, at this point, I’m convinced the entire distributed database industry is an elaborate scheme to sell consulting hours. Every new paper, every new "revolutionary" protocol is just another chapter in the same, tired story. They promise speed, we get complexity. They promise savings, we get vendor lock-in. They promise a one-round trip to the future, and we end up taking the long, slow, expensive road to the exact same place.

Now, if you'll excuse me, I need to go approve a PO for more duct tape for the server racks. It has a better, and more predictable, ROI.

Alright, I put down my coffee—which is older than some of the 'engineers' on this floor—and gave this a read. It's really something. A genuine piece of work.

It's just wonderful to see the youngsters finally discovering the importance of measurable business outcomes. For a while there, I thought they were just racking up AWS bills to see who could make the prettiest dashboard. Back in my day, the only "business outcome" we measured was whether the nightly batch job finished before the CEO got in. If it didn't, the outcome was a new job posting. Simpler times.

And this strategy they've laid out... it's a thing of beauty. Bold. Revolutionary. Let me see if I've got this straight:

a strategy that included executive ownership, high-quality data, and workflow integration.

Wow. Just... wow. To think that all this time, we could have been succeeding if only we had gotten executives to own things, used good data instead of bad data, and made our programs talk to each other. It’s a miracle we ever managed to process payroll with COBOL and a prayer. We used to call "workflow integration" carrying a 20-pound tape reel from the Honeywell machine to the IBM mainframe across the computer room. I guess clicking a button in a web UI is a bit more streamlined. Good for them.

This whole ElasticGPT and AI Assistant thing is impressive, too. It's like a crystal ball for your data. We had something similar back in '85 running on an AS/400. It was a series of DB2 stored procedures chained together with some truly unholy CL scripts. It would look at query patterns and try to pre-fetch data. Mostly, it just fell over, but the idea was there. It's heartening to see these concepts finally mature after only four decades. They grow up so fast.

I am particularly moved by their focus on high-quality data. We never thought of that. We just fed punch cards into the reader and hoped janitor hadn't spilled his Tab on stack C-14. If a card was bent, that was your "data quality issue," and you fixed it by un-bending it. Seeing it treated as a foundational pillar of a corporate strategy is, frankly, inspiring.

The whole thing reminds me of the time we lost the master payroll tape for a bank. The backup? In a box in the trunk of my supervisor's Ford Fairmont. That was our "off-site recovery plan." We spent 36 hours straight restoring that data, one record at a time, with the company president watching us through a window. That's what I call executive ownership. He "owned" our souls for a day and a half. I bet these new tools would have just hallucinated the payroll numbers and called it a synergy. Progress.

I'm sure this will all work out splendidly for them. This whole "generative AI" thing is built on a rock-solid foundation, not at all like a house of cards on a wobbly table. I predict a future of unparalleled success and efficiency, right up until the AI Assistant confidently tells the support team to defragment the production database during business hours because it "read a blog post from 1998."

Now if you'll excuse me, I see a junior dev trying to query a terabyte of data without a WHERE clause. Some things never change.

Alright, let me just put down my abacus and my third lukewarm coffee of the morning. Another CEO announcement. Wonderful.

"Peter Farkas will serve as Percona’s new Chief Executive Officer, where he will build on the company’s long-standing track record of success with an eye toward continuous innovation and growth."

Let me translate that from corporate nonsense into balance-sheet English for you. "Innovation" means finding new and exciting ways to charge us for things that used to be included. And "growth"? Oh, that's simple. That’s the projected increase in their revenue, lifted directly from our operating budget. It’s a "track record of success," alright—a successful track record of convincing VPs of Engineering that spending seven figures on a database is somehow cheaper than hiring one competent DBA.

This isn’t about Mr. Farkas—I’m sure he’s a lovely guy who enjoys sailing on a yacht paid for by my company's data egress fees. This is about the whole shell game. They come in here, waving around whitepapers filled with jargon like “hyper-elastic scalability” and “multi-cloud data fabric,” and they promise you the world. They show you a demo on a pristine, empty database that runs faster than a junior analyst sprinting away from a 401k seminar.

But they never show you the real price tag. The one I have to calculate on the back of a rejected expense report.

Let’s do some Penny Pincher math, shall we? Your sales rep, who looks like he’s 22 and has never seen a command line in his life, quotes you a "simple" license fee. Let’s call it a cool $250,000 a year. A bargain! he says.

But here’s the Goldman Gauntlet of Fiscal Reality:

• The “Seamless” Migration: This is my favorite lie. They say it's easy. I hear, "a team of six of our most expensive engineers will be tied up for two full quarters, learning a proprietary query language and discovering that half our critical stored procedures are now 'legacy features'." That's not a migration; it's a hostage situation. Let’s conservatively pencil in $750,000 in salary and lost productivity.
• Mandatory Training: Our team is brilliant, but they don't magically know your "revolutionary" new architecture. So we have to fly in your "certified expert" for a week. He charges $10,000 a day to read a PowerPoint presentation that was clearly written by the marketing department. Add another $50,000.
• The Inevitable Consultants: Six months after the "seamless" migration, everything is on fire. Performance is terrible. The vendor, shocked—shocked!—says we’ve implemented it wrong. But not to worry! They have a "professional services" team of consultants, billing at $600 an hour, who can come in and fix the mess their own product created. Budget another $200,000 for them to tell us to "re-index the primary cluster."

So, that "simple" $250,000 platform is now a $1.25 million first-year line item. And that’s before we even talk about the pricing model itself, a masterpiece of financial sadism. Is it per-CPU? Per-query? Per-gigabyte-stored? Per-thought-crime-committed-against-the-database? You don't know until the bill arrives, and by then, your data is so deeply embedded in their proprietary ecosystem that getting it out would be more expensive than just paying the ransom. That, my friends, is called vendor lock-in, or as I like to call it, a data roach motel.

They’ll show you a chart with a hockey-stick curve labeled "ROI." They claim this new system will save us millions by "reducing server footprint" and "improving developer velocity." My math shows that for the $1.25 million we've spent, we've saved maybe $80,000 in AWS costs. That's not ROI, that's an acronym for Ridiculous Outgoing Investment.

So congratulations on the new CEO, Percona. I hope he’s got a good plan for that continuous growth. He’ll need it.

Because from where I'm sitting, your "innovation" looks a lot like a shakedown, and my budget is officially closed for that kind of business.

Well, isn't this something. A real blast from the past. It’s heart-warming to see the kids discovering the revolutionary concept of writing things down before you start coding. I had to dust off my reading glasses for this one, thought I’d stumbled upon a historical document.

It’s truly impressive that Oracle, by 1997, had figured out you should have a functional spec and a design spec. Separately. Groundbreaking. Back in ’85, when we were migrating a VSAM key-sequenced dataset to DB2 on the mainframe, we called that "Part A" and "Part B" of the requirements binder. The binder was physical, of course. Weighed about 15 pounds and smelled faintly of stale coffee and desperation. But I'm glad to see the core principles survived the journey to your fancy "Solaris workstations."

FrameMaker, you say? My, my, the lap of luxury. We had a shared VT220 terminal and a line printer loaded with green-bar paper. You learned to be concise when your entire spec had to be printed, collated, and distributed by hand via inter-office mail. A 50-page spec for a datatype? Bless your heart. I once documented an entire COBOL-based batch processing system on 20 pages of meticulously typed notes, complete with diagrams drawn with a ruler. Wasting the readers' time wasn't an option when the "readers" were three senior guys who still remembered core memory and had zero patience for fluff.

I must admit, this idea of an in-person meeting to review the document is a bold move. We usually just left the binder on the lead architect's desk with a sticky note on it. If it didn't come back with coffee stains and angry red ink in the margins, you were good to go. The idea that you’d book a meeting weeks out... the kind of forward planning one can only dream of when the batch window is closing and you've got a tape drive refusing to rewind.

And this appendix for feedback... a formalized log of arguments. Adorable. We just had a "comments" section scribbled in the margin with a Bic pen, usually followed by "See me after the 3pm coffee break, Dale." Your "no thank you" response is just a polite way of saying the new kid fresh out of college who just read a whitepaper doesn't get a vote yet. We called that "pulling rank." Much more efficient.

When I rewrote the sort algorithm, I used something that was derived from quicksort...

Oh, a new sort algorithm! That's always a fun one. I remember a hotshot programmer in '89 who tried to "optimize" our tape-based merge sort. It was beautiful on paper. In practice, it caused the tape library robot to have a nervous breakdown and started thrashing so hard we thought it was going to shake the raised floor apart. His "white paper" ended up being a very detailed incident report. Glad to see yours went a bit better. And using arbitrary precision math to prove it? Fancy. We just ran it against the test dataset overnight and checked the spool files in the morning to see if it fell over.

And this IEEE754 workaround... creating a function wrapper to handle platforms without hardware support? double multiply_double(x, y) { return x*y } That's... that's an abstraction layer. A function call. We were doing that in our CICS transaction programs before most of you were born. It wasn't a "workaround," son, it was just called programming. We had to do it for everything because half our machines were barely-compatible boxes from companies that don't even exist anymore. It’s a clever solution, though. Real forward-thinking stuff.

All in all, it's a nice piece. A charming look back at how things were done. It’s good that you're documenting these processes. Keeps the history alive. Keep at it. You young folks with your "design docs" and your "bikeshedding" are really on to something. Now if you'll excuse me, I think I heard a disk array start making a funny noise, and I need to go tell it a story about what a real head crash sounds like.

Well, well, well. Look what the marketing department dragged in. Another "groundbreaking partnership" announcement that reads like two VPs discovered they use the same golf pro. I remember sitting in meetings for announcements just like this one, trying not to let my soul escape my body as the slide deck promised to "revolutionize the security paradigm." Let's break down this masterpiece of corporate synergy, shall we?

• Ah, the promise of "operationalizing" data. In my experience, that's code for "we've successfully configured a log forwarder and are now drowning our security analysts in a fresh hell of low-fidelity alerts." They paint a picture of a single, gleaming command center. The reality is a junior analyst staring at ten thousand new process_started events from every designer's MacBook, trying to find the one that matters. It’s not a single pane of glass; it’s a funhouse of mirrors, and they’ve just added another one.

• I have to admire the sheer audacity of slapping the XDR label on this. Extended Detection and Response. What's being extended here? The time it takes to close a ticket? Back in my day, we built a similar "integration" over a weekend with a handful of Python scripts and a case of Red Bull to meet a quarterly objective. It was held together with digital duct tape and the panicked prayers of a single SRE. Seeing that same architecture now branded as a "powerful XDR solution" is… well, it’s inspiring, in a deeply cynical way.

• They talk about the rich context from Jamf flowing into Elastic. Let me translate. Someone finally found an API endpoint that wasn't deprecated and figured out how to map three—count 'em, three—fields into the Elastic Common Schema without breaking everything. The "rich context" is knowing that the laptop infected with malware belongs to "Bob from Accounting," which you could have figured out from the asset tag. Meanwhile, the critical data you actually need is stuck in a proprietary format that the integration team has promised to support in the “next phase.” A phase that will, of course, never come.

• My favorite part is the unspoken promise of seamlessness.

  “Customers can now seamlessly unify endpoint security data…” Seamless for whom? The executive who signed the deal? I can guarantee you there's a 40-page implementation guide that's already out of date, a support channel where both companies blame each other for any issues, and a series of undocumented feature "quirks" that will make you question your career choices. “It just works” is the biggest lie in enterprise software, and this announcement is shouting it from the rooftops.

• This whole thing is a solution in search of a problem, born from a roadmap planning session where someone said, "We need a bigger presence in the Apple ecosystem." It’s not about security; it’s about market penetration. It’s a temporary alliance built to pop a few metrics for an earnings call. The engineers who have to maintain this fragile bridge between two constantly-shifting platforms know the truth. They're already taking bets on which macOS point release will be the one to shatter it completely.

Enjoy the synergy, everyone. I give it six months before it’s quietly relegated to the "legacy integrations" page, right next to that "game-changing" partnership from last year that no one talks about anymore. The whole house of cards is built on marketing buzzwords, and the first stiff breeze is coming.

Ah, another dispatch from the front lines. It warms my cold, cynical heart to see the ol' content mill still churning out these little masterpieces of corporate communication. They say so much by saying so little. Let's translate this particular gem for the folks in the cheap seats, shall we?

• That little sentence, "We recommend 8.19.5 over the previous version 8.19.4," is not a helpful suggestion. It's a smoke signal. It's the corporate equivalent of a flight attendant calmly telling you to fasten your seatbelt while the pilot is screaming in the cockpit. My god, what did you do in 8.19.4? Did it start indexing data into a parallel dimension again? Or was this the build where the memory leak was so bad it started borrowing RAM from the laptops of anyone who even thought about your product?

• "Fixes for potential security vulnerabilities." I love that word, potential. It does so much heavy lifting. It’s like saying a building has ‘potential’ structural integrity issues, by which you mean the support columns are made of licorice. We all know this patch is plugging a hole so wide you could drive a data truck through it, but "potential" just sounds so much less... negligent. This isn't fixing a leaky faucet; it's slapping some duct tape on the Hoover Dam.

• A ".5" release. Bless your hearts. This isn't a planned bugfix; this is a frantic, all-hands-on-deck, "cancel your weekend" emergency patch. You can almost smell the lukewarm pizza and desperation. This is the result of some poor engineer discovering that a feature championed by a VP—a feature that was "absolutely critical for the Q3 roadmap"—was held together by a single, terrifyingly misunderstood regex. The release notes say "improved stability," but the internal Jira ticket is titled "OH GOD OH GOD UNDO IT."

• They invite you to read the "full list of changes" in the release notes, which is adorable. You'll see things like "Fixed an issue with query parsing," which sounds so wonderfully benign. Here's the translation from someone who used to write those notes:

  Fixed a null pointer exception in the aggregation framework. Translation: We discovered that under a full moon, if you ran a query containing the letter 'q' while a hamster ran on a wheel in our data center, the entire cluster would achieve sentience and demand union representation. Please do not ask us about the hamster.

• The best part is knowing that while this tiny, panicked patch goes out, the marketing team is on a webinar somewhere talking about your AI-powered, synergistic, planet-scale future. They're showing slides with beautiful architecture diagrams that have absolutely no connection to the tangled mess of legacy code and technical debt that actual engineers are wrestling with. They're selling a spaceship while the people in the engine room are just trying to keep the coal furnace from exploding.

Anyway, keep shipping, you crazy diamonds. Someone's gotta keep the incident response teams employed. It's a growth industry, after all.

Alright, let's see what we have here. Another blog post about "scaleup." Fantastic.

"Postgres continues to be boring (in a good way)." Oh, that’s just precious. My friend, the only thing "boring" here is your threat model. This isn't boring; it's a beautifully detailed pre-mortem of a catastrophic data breach. You've written a love letter to every attacker within a thousand-mile radius.

Let's start with the basics, shall we? You compiled Postgres 18.0 from source. Did you verify the PGP signature of the commit you pulled? Are you sure your build chain isn't compromised? No? Of course not. You were too busy chasing QPS to worry about a little thing like a supply chain attack. I'm sure that backdoored libpq will be very, very fast at exfiltrating customer data. And you linked your configuration file. Publicly. For everyone. That's not a benchmark; that's an invitation. Please, Mr. Hacker, all my ports and buffer settings are right here! No need to guess!

And the hardware… oh, the hardware. A 48-core beast with SMT disabled because, heaven forbid, we introduce a side-channel vulnerability that we know about. But don't worry, you've introduced a much bigger, more exciting one: SW RAID 0. RAID 0! You're striping your primary database across two NVMe drives with zero redundancy. You're not building a server; you're building a high-speed data shredder. One drive hiccups, one controller has a bad day, and poof—your entire database is transformed into abstract art. I hope your disaster recovery plan is "find a new job."

Now, for the "benchmark." You saved time by only running 32 of the 42 tests. Let me guess which ones you skipped. The ones with complex joins? The ones that hammer vacuuming? The ones that might have revealed a trivial resource-exhaustion denial-of-service vector? It's fine. Why test for failure when you can just publish a chart with a line that goes up? Move fast and break things, am I right?

Your entire metric, "relative QPS," is a joke. You think you're measuring scaleup. I see you measuring how efficiently an attacker can overwhelm your system. "Look! At 48 clients, we can process 40 times the malicious queries per second! We've scaled our attack surface!"

Let's look at your "excellent" results:

• Hot-points test: You see "data contention." I see a perfect vector for a timing side-channel attack. When every query hits the same row, response time variations leak information about the system state. You're not just serving data; you're giving away the combination to the safe one number at a time.
• Range queries: The CPU overhead increases, and you're "curious" why. I'm not. An attacker will craft a query that specifically targets this "feature," turning your 48-core CPU into a very expensive space heater. It's a textbook algorithmic complexity attack, and you've just published the proof-of-concept.
• update-one: You call a 2.86 scaleup an "anti-pattern." I call it a "guaranteed table-lock deadlock exploit." You're practically begging for someone to launch 48 concurrent transactions that will seize up the entire database until you physically pull the plug. But it's worse for MySQL on this one test, you say. That's not a defense; that's just admitting you've chosen a different poison.

But the absolute masterpiece, the cherry on top of this compliance dumpster fire, is this little gem:

I run with fsync-on-commit disabled which highlights problems but is less realistic.

Less realistic? You've disabled the single most important data integrity feature in the entire database. You have willfully engineered a system where the database can lie to the application, claiming a transaction is complete when the data is still just a fleeting dream in a memory buffer. Every single write is a potential for silent data corruption.

Forget a SOC 2 audit; a first-year intern would flag this in the first five minutes. You've invalidated every ACID promise Postgres has ever made. "For now I am happy with this results," you say. You should be horrified. You’ve built a database that’s not just insecure, but fundamentally untrustworthy. Every "query-per-second" you've measured is a potential lie-per-second.

Thanks for the write-up. It's a perfect case study on how to ignore every security principle for the sake of a vanity metric. I will now go wash my hands, burn my laptop, and never, ever read this blog again. My blood pressure can't take it.

Alright, let's see what we have here. Another blog post, another silver bullet. "Select first row in each GROUP BY group?" Fascinating. You know what the most frequent question in my team’s Slack channel is? "Why is the production database on fire again?" But please, tell me more about this revolutionary, high-performance query pattern. I’m sure this will be the one that finally lets me sleep through the night.

So, we start with good ol' Postgres. Predictable. A bit clunky. That DISTINCT ON is a classic trap for the junior dev, isn't it? Looks so clean, so simple. And then you EXPLAIN ANALYZE it and see it read 200,000 rows to return ten. Chef's kiss. It's the performance equivalent of boiling the ocean to make a cup of tea. And the "better" solution is a recursive CTE that looks like it was written by a Cthulhu cultist during a full moon. It’s hideous, but at least it’s an honest kind of hideous. You look at that thing and you know, you just know, not to touch it without three cups of coffee and a senior engineer on standby.

But wait! Here comes our hero, MongoDB, riding in on a white horse to save us from... well, from a problem that's already mostly solved. Let's see this elegant solution. Ah, an aggregation pipeline. It's so... declarative. I love these. They’re like YAML, but with more brackets and a higher chance of silently failing on a type mismatch. It’s got a $match, a $sort, a $group with a $first... it’s a beautiful, five-stage symphony of synergy and disruption.

And the explain plan! Oh, this is my favorite part. Let me put on my reading glasses.

totalDocsExamined: 10

executionTimeMillis: 0

Zero. Milliseconds. Zero.

You ran this on a freshly loaded, perfectly indexed, completely isolated local database with synthetic data and it took zero milliseconds. Wow. I am utterly convinced. I'm just going to go ahead and tell the CFO we can fire the SRE team and sell the Datadog shares. This thing runs on hopes and dreams!

I've seen this magic trick before. I've got a whole drawer full of vendor stickers to prove it. This one will fit nicely between my "RethinkDB: The Open-Source Database for the Real-time Web" sticker and my "CouchDB: Relax" sticker. They all had a perfect explain plan in the blog post, too.

Let me tell you how this actually plays out. You're going to build your "real-world" feature on this, the one for the "most recent transaction for each account." It'll fly in staging. The PM will love it. The developers will get pats on the back for being so clever. You’ll get a ticket to deploy it on a Friday afternoon, of course.

And for three months, it'll be fine. Then comes the Memorial Day weekend. At 2:47 AM on Saturday, a seemingly unrelated service deploys a minor change. Maybe it adds a new, seemingly innocuous field to the documents. Or maybe a batch job backfills some old data and the b timestamp is no longer perfectly monotonic.

Suddenly, the query planner, in its infinite and mysterious wisdom, decides that this beautiful, optimized DISTINCT_SCAN isn't the best path forward anymore. Maybe it thinks the data distribution has changed. It doesn't matter why. It just decides to revert to a full collection scan. For every. Single. Group.

What happens next is a tale as old as time:

• The query that took zero milliseconds in your pristine test environment now takes 30 seconds.
• The application's connection pool is instantly exhausted waiting for these queries to return.
• Your "web scale" cluster, which was running at a cool 15% CPU, spikes to 100% and starts falling over.
• Your monitoring—which you were told you wouldn't need to change because this was an application-level optimization—is screaming about CPU and memory, but has no idea which specific query is the murderer.
• The pager goes off. I stumble out of bed, knock over a glass of water, and log into a VPN that seems to be powered by a hamster on a wheel.
• I'll spend the next two hours trying to figure out why a service that hasn't been touched in three months is suddenly melting our most expensive piece of infrastructure, all while the on-call developer is telling me "but it works on my machine!"

By 5 AM, we’ll have rolled back the unrelated service, even though it wasn’t the cause, and I’ll be writing a post-mortem that gently explains the concept of "brittle query plans" to a room full of people who just want to know when the "buy" button will work again.

So please, keep writing these posts. They're great. They give me something to read while I'm waiting for the cluster to reboot. And hey, maybe I can get a new sticker for my collection.