🔥 The DB Grill 🔥

Ah, yes. I must confess, a student forwarded me this… artefact. I found it utterly charming, in the way one finds a child's crayon drawing of a supernova charming. The enthusiasm is palpable, even if the grasp of first principles is, shall we say, developmental.

It is truly a testament to the relentless march of progress that the industry has, after decades of fervent effort, independently rediscovered the concept of a database management system. One must applaud this brave author for their courageous stance: that the system designed specifically to manage and secure data should be… well, the system that manages and secures the data. A truly novel concept for the Web 3.0 paradigm, I'm sure.

"...always enforce row-level access control (RLAC) for LLM database access."

It's as if a toddler, having just discovered object permanence, has penned a stirring manifesto on the subject. “Objects continue to exist,” he declares, “even when you cannot see them!” Yes, my dear boy, they do. We've known this for some time. We built entire logical frameworks around the idea. They're called "views" and "access control lists." Perhaps you've heard of them?

The author's breathless warning against trusting an "inference layer" for security is particularly delightful. It's a magnificent, chrome-plated sledgehammer of a term for what we have always called the "application layer." And for fifty years, the fundamental axiom has been to never, ever trust the application layer. To see this wisdom repackaged as a hot-take for the Large Language Model era is a brand of intellectual recycling so profound it verges on performance art.

I can only imagine the conversations that led to this epiphany:

• “Persephone, what if we just… put the rules… in the database?”
• “Jaxon, you maverick! That’s crazy enough to work! We’ll be disrupting the entire data security middleware-as-a-service ecosystem!”

Clearly they've never read Stonebraker's seminal work on INGRES, let alone Codd's original papers. The ghost of Edgar F. Codd must be weeping with joy that his relational model, with its integrated, non-subvertible data sublanguage, is finally being vindicated against the horrors of… checks notes… a Python script with an API key. This isn't just a failure to adhere to Codd's rules; it's a profound ignorance that they even exist.

They speak of these modern systems as if the laws of computer science were suspended in their presence. The CAP theorem, it seems, is no longer a theorem but a gentle suggestion one can "innovate" around. They chase Availability and Partition Tolerance with such rabid glee that they forget that Consistency applies to security policies, too. The "C" in ACID isn't just for financial transactions; it's the very bedrock of reliability. When you outsource your access control to a stateless, probabilistic text generator, you haven't embraced eventual consistency, you've achieved accidental anarchy.

But one must not be too harsh. It's difficult to find the time to read those dusty old papers when you're so busy shipping product and A/B testing button colors.

It's heartening to see the industry has finally completed the first chapter of the textbook. I shall await their thoughts on third normal form with bated breath.

Well, isn't this just a hoot. Stumbled across this little gem while my pot of coffee was brewing—you know, the real kind, not the pod-based dishwater you kids drink. "How Tipalti mastered Elasticsearch performance with AutoOps." Mastered. That's a strong word. It's the kind of word you use when you've been keeping a system online for three weeks without a core dump, I suppose. Bless your hearts. Let's break down this... masterpiece.

• Let me get this straight. You've invented something called "AutoOps" to automatically manage your database. Groundbreaking. Back in 1987, we had something similar. It was a series of JCL scripts chained together by a guy named Stan who drank too much coffee and slept in the data center. It ran nightly batch jobs to re-index VSAM files and defragment disk packs the size of wedding cakes. The only difference is our automation notified us by printing a 300-page report on green bar paper, not by sending a "cool" little alert to your chat program.

• You're mighty proud of taming this "Elasticsearch" thing. A database so "resilient" it can't decide who its own master is half the time. A split-brain? We didn't have "split-brains" with our mainframes. We had sysadmins with actual brains who designed systems that didn't need to have a committee meeting every time a network cable got jostled. You talk about performance tuning? Try optimizing a COBOL program to reduce physical I/O reads from a tape drive that took 20 minutes to rewind. Your "sharding strategy" is just a new name for partitioning, a concept we perfected in DB2 while your parents were still trying to figure out the VCR.

• This whole article reads like you're surprised that a database needs maintenance. Shocking! You mean you can't just throw unstructured data into a schema-less bucket indefinitely without it slowing down? Color me unimpressed. We called that "planning." It involved data dictionaries, normalization, and weeks of design meetings to ensure we didn't end up with a digital junk drawer. You call it a "data lake"; I call it a swamp that needs an automated backhoe you've dubbed "AutoOps" just to keep from sinking.

• The hubris of claiming you've "mastered" performance because you fiddled with some JVM heap sizes and automated a few cron jobs is... well, it's adorable, really. Performance mastery isn't about setting up alerts for high CPU usage. It's about recovering a corrupted customer database from the one DLT tape backup that didn't get chewed up by the drive, all while the VP of Finance is breathing down your neck. You haven't mastered performance until you've had to explain data remanence on a magnetic platter to a federal auditor.

You built a robot to babysit your toddler. We built a battleship and taught the crew discipline.

Anyway, this has been a real trip down memory lane. It's comforting to know that for all your serverless, cloud-native, hyper-converged nonsense, you're all just re-learning the same lessons we figured out on punch cards.

Don't worry, I won't be subscribing. I have a COBOL program that's been running since 1992 that probably needs its semi-annual check-up.

Ah, a truly fascinating piece of work. I must applaud your diligence in meticulously measuring the performance of various MySQL versions. It’s a wonderfully academic exercise, a real love letter to the purity of raw throughput. It’s so... focused. So beautifully oblivious.

It’s especially bold to start your baseline with MySQL 5.6.51. A classic! I mean, who needs security patches? They just add CPU overhead, as your data so clearly shows. Using a version that went End-of-Life over three years ago is a brilliant move. It’s like testing the crash safety of modern cars by comparing them to a Ford Pinto. Sure, the new ones are slower, but they have this pesky feature called "not exploding on impact." You’ve essentially benchmarked a ghost, a digital phantom riddled with more known vulnerabilities than a politician’s promises. I can almost hear the CVEs whispering from the great beyond.

And the dedication to compile from source! A true artisan. This isn't some pre-packaged, vendor-vetted binary. Oh no. This is bespoke, hand-crafted software. I'm sure you audited every line of the millions of lines of C++ for potential buffer overflows, and verified the cryptographic signatures of every dependency in the toolchain, right? Right? Or did you just git clone and pray? Because from where I'm sitting, you've just created a beautiful, artisanal supply chain attack vector. It’s a unique little snowflake of a target.

I’m also smitten with your choice of lab equipment. An ASUS ExpertCenter! It’s so… approachable. I’m sure that consumer-grade hardware has all the necessary out-of-band management and physical security controls one would expect. It’s not like an attacker could just walk away with your "server" under their arm. The choice of a fresh-off-the-presses Ubuntu 24.04 is another masterstroke—nothing says "stable and secure" like an OS that's barely old enough to have its first zero-day discovered.

But my favorite part, the real chef’s kiss, is your commitment to radical transparency.

The my.cnf files are here. All files I saved from the benchmark are here and the spreadsheet is here.

Why make attackers work for it? This isn’t just open source; it’s open infrastructure. You've laid out the complete architectural blueprint for anyone who might want to, say, craft a perfectly tuned denial-of-service attack, or perhaps exploit a specific configuration setting you've enabled. It’s an act of profound generosity. Here are the keys to the kingdom, please don't rifle through the drawers.

The benchmark itself is a masterpiece of sterile-room engineering.

• A single table.
• 50 million clean, predictable rows.
• Simple, repetitive queries.

It's like testing a bank vault's integrity by politely asking the door to open. You haven't benchmarked a database; you've benchmarked a best-case scenario that exists only in a PowerPoint presentation. Throw some malformed UTF-8 at it. Try a UNION-based SQL injection. See how fast it is when it’s trying to fend off a polymorphic attack string designed to bypass web application firewalls. I have a few I could lend you.

Your grand conclusion that regressions are from "new CPU overheads" is simply breathtaking. You're telling me that adding features, hardening code, implementing mitigations for speculative execution attacks, and generally making the software less of a security dumpster fire... uses more CPU? Groundbreaking. It’s a revelation. You’ve discovered that armor is, in fact, heavier than cloth.

I can just picture the SOC 2 audit for this setup. "So, for your evidence of vulnerability management, you're presenting a benchmark of an EOL, unpatched database, compiled ad-hoc from source, on a desktop computer, with the configuration files published on the internet?" The silence in that room would be deafening.

Honestly, thank you for this. You've perfectly demonstrated how to optimize for a single metric while completely ignoring the landscape of fire and ruin that is modern cybersecurity.

This isn't a benchmark; it's a bug bounty speedrun where you've given everyone a map and a head start.

Alright, settle down, kids, let ol' Rick pour himself a cup of lukewarm coffee from the pot that's been stewing since dawn and have a look at this... this manifesto. I have to hand it to you, the sheer enthusiasm is something to behold. It almost reminds me of the wide-eyed optimism we had back in '88 when we thought X.25 packet switching was going to solve world hunger.

I must say, this idea of a "converged datastore" is truly a monumental achievement. A real breakthrough. You've managed to unify structured and unstructured data into one cohesive... thing. It's breathtaking. Back in my day, we had a similar, albeit less glamorous, technology for this. We called it a "flat file." Sometimes, if we were feeling fancy, we'd stuff everything into a DB2 table with a few structured columns and one massive BLOB field. We were just decades ahead of our time, I suppose. We didn't call it a "cognitive memory architecture," though. We called it "making it work before the batch window closed."

And the central premise here, that AI agents don't just query data but inhabit it... that's poetry, pure and simple. It paints a beautiful picture. It's the same beautiful picture my manager painted when he said our new COBOL program would "live and breathe the business logic." In reality, it just meant it had access to a VSAM file and would occasionally dump a core file so dense it would dim the lights on the whole floor. This idea of an agent having "persistent state" is just adorable. You mean... you're storing session data? In a table? Welcome to 1995, we're glad to have you.

I'm especially impressed by the "five core principles." Let's see here...

• Unified context in a "cohesive document structure." You've discovered denormalization! Congratulations! All us old-timers who were forced to learn third normal form until our knuckles bled are just tickled pink to see you toss it all out for one giant, unmanageable JSON blob. The maintenance programmer who has to deal with this in five years will surely thank you.
• Semantic intelligence with Atlas Vector Search. A fancy index. Got it. We used to dream of having enough processing power to do a LIKE '%string%' query without bringing the whole mainframe to its knees. Now you can do it with... meaning. I'm sure the CPU cycles it burns will generate enough heat to keep the data center toasty through the winter.
• Autonomous reasoning using an "event-driven architecture." This one almost made me spit out my coffee. You're talking about triggers. We had triggers in the '90s. A change in this table kicks off a procedure over there. You've just given it a cool name and hooked it up to a chatbot that probably costs more per hour than my first house.

And this architectural diagram... a masterpiece of marketing. So many boxes, so many arrows. It's a beautiful sight. It's got the same aspirational quality as the flowcharts we used to draw on whiteboards for systems that would never, ever get funded. You've got your "Data Integration Layer," your "Agentic AI Layer," your "Business Systems Layer"... It's just incredible. We had three layers: the user's green screen, the CICS transaction server, and the mainframe humming away in a refrigerated room the size of a gymnasium. Seemed to work just fine.

The fundamental shift from relational to document-based data architecture represents more than a technical upgrade—it's an architectural revolution...

A revolution! My goodness. Codd is spinning in his grave so fast you could hook him up to a generator and power a small city. You took a data structure designed to prevent redundancy and ensure integrity, and you replaced it with a text file that looks like it was assembled by a committee. I'm looking at this Figure 4 example, and it's a thing of beauty. A single, monolithic document holding everything. It's magnificent. What happens when you need to add one tiny field to the customerPreferences? Do you have to read and rewrite the entire 50KB object? Brilliant. That'll scale wonderfully. It reminds me of the time we had to update a field on a magnetic tape record. You'd read a record, update it in memory, write it to a new tape, and then copy the rest of the millions of records over. You've just reinvented the tape-to-tape update for the cloud generation. Bravo.

Your claim of "sub-second response times for vector searches across billions of embeddings" is also quite a thing. I remember when getting a response from a cross-continental query in under 30 seconds was cause for a champagne celebration. Of course, that was over a 9600 baud modem, but the principle is the same. The amount of hardware you must be throwing at this "problem" must be staggering.

So let me just say, I'm truly, genuinely impressed. You've taken the concepts of flat files, triggers, denormalization, and session state, slapped a coat of "AI-powered cognitive agentic" paint on them, and sold it as the future. It's the kind of bold-faced confidence I haven't seen since the NoSQL evangelists promised me I'd never have to write a JOIN again, right before they invented their own, less-efficient JOIN.

I predict this will all go swimmingly. Right up until the first time one of these "cohesive" mega-documents gets corrupted and you lose the customer, their policy, all their claims, and the AI's entire "memory" in one fell swoop. The ensuing forensic analysis of that unfathomable blob of text will be a project for the ages. They'll probably have to call one of us old relics out of retirement to figure out how to parse it.

Now if you'll excuse me, I think I have a box of punch cards in the attic that's more logically consistent than that JSON example. I'm going to go lie down.

Ah, here we go. It’s “surprising” that a brand-new, completely idle cluster is writing to its logs like a hyperactive day trader who’s just discovered caffeine and futures. Surprising to whom, exactly? The marketing department? The new hires who still believe the slide decks? Because I can promise you, it wasn’t surprising to anyone who sat in the Q3 planning meetings for "Project Cohesion" back in the day.

This write-up is a classic. It’s a beautifully crafted piece of technical archeology, trying to explain away a fundamental design choice that was made in a panic to meet a conference deadline. You see, when you bolt a state machine onto a system that was never designed for it and then decide the only way for it to know what its friends are doing is by screaming into the void every 500 milliseconds, you get what they politely call “a significant amount of writes.”

We called it "architectural scar tissue."

They say the effect became “much more spectacular after MySQL version 8.4.” Spectacular. That’s a word, alright. It’s the kind of word a project manager uses when the performance graphs look like an EKG during a heart attack. “The latency is… spectacular!” It’s not a bug, you see, it’s just a very dramatic and unforeseen feature. A consequence of that next-generation group communication protocol we were all so excited about. The one that, under the hood, was basically a series of increasingly desperate shell scripts held together with duct tape and the vague hope that network latency would one day be solved by magic.

This whole article is a masterclass in corporate doublespeak. It’ll “explain why it happens and how to address it.” Let me translate.

• Why it happens: Because the "cluster" isn't so much a cohesive unit as it is a bunch of helper daemons playing a very loud, very panicked game of telephone. Every node needs to constantly check if its neighbors are still alive, if their configurations have changed, if the primary sneezed, and if the quorum is thinking about ordering pizza. And where does all this chatter go? Straight into the binary log, the database’s one and only diary, which is now filled with the system’s own neurotic, internal monologue.

• How to address it: By tweaking six obscure variables with names like group_replication_unseeable_frobnostication_level that the documentation swears you should never touch unless guided by a support engineer who has signed a blood pact with the original developer. You’re not fixing the problem; you’re just turning down the volume on the smoke alarm while the fire continues to smolder.

I love the pretense that this is all some fascinating, emergent behavior of a complex system. It’s not. It’s the direct, predictable result of prioritizing a bullet point on a feature matrix over sound engineering. I seem to recall a few whiteboards covered in warnings about this exact kind of metadata churn. Those warnings were cheerfully erased to make room for the new marketing slogan. Something about “effortless scale” or “autonomous operation,” I think. Turns out “autonomous” just meant it would find new and creative ways to thrash your I/O all on its own, no user intervention required.

This effect became much more spectacular after MySQL version 8.4.

You have to admire the honesty, buried as it is. That’s the version where "Project Chimera" finally got merged—the one that stitched three different management tools together and called it a unified control plane. The result is a system that has to write to its own log to tell itself what it’s doing. It's the database equivalent of leaving sticky notes all over your own body to remember your name.

So, by all means, read the official explanation. Learn the proper incantations to make the cluster a little less chatty. But don’t for a second think this is just some quirky side effect. It’s the ghost of a thousand rushed stand-ups, a monument to the roadmap that a VP drew on a napkin.

It’s good they’re finally documenting it, I suppose. It’s brave, really. Almost as brave as putting it into production. Good luck with that. You’re gonna need it.

Oh, goody. Another "comprehensive guide" to a "game-changing" feature that promises to solve scaling for good. I’m getting flashbacks to that NoSQL migration in ‘18 that was supposed to be “just a simple data dump and restore.” My eye is still twitching from that one. Let’s see what fresh hell this new benchmark report is promising to save us from, shall we?

• First, I love the honesty in admitting the “considerable setup overhead, complex parameter tuning, and the cost of experimentation.” It’s refreshing. It’s like a restaurant menu that says, “This dish is incredibly expensive and will probably give you food poisoning, but look at the pretty picture!” You’re telling me that to even start testing this, I have to navigate a new universe of knobs and levers? Fantastic. I can already taste the 3 AM cold pizza while I try to figure out why our staging environment costs more than my rent.

• Ah, the benchmark numbers. “90–95% accuracy with less than 50ms of query latency.” That’s beautiful. Truly. It reminds me of the performance specs for that distributed graph database we tried last year. It was also incredibly fast… on the vendor’s perfectly curated, read-only dataset that bore zero resemblance to our actual chaotic, write-heavy production traffic. I’m sure these numbers will hold up perfectly once we introduce our dataset, which is less “pristine Amazon reviews” and more “a decade of unstructured garbage fire user input.”

• Let’s all welcome the Grand Unifying Configuration Nightmare™, a brand-new set of interconnected variables guaranteed to make my on-call shifts a living nightmare. Before, I just had to worry about indexing and shard keys. Now I get to play a fun game of Blame Roulette with quantization, dimensionality, numCandidates, and search node vCPUs. The next time search latency spikes, the war room is going to be a blast. “Was it the binary quantization rescoring step? Or did Dave just breathe too hard on the sharding configuration again?”

• My absolute favorite part of any performance guide is the inevitable, galaxy-brained solution to performance bottlenecks:

  Scaling out the number of search nodes or increasing available vCPUs is recommended to resolve these bottlenecks and achieve higher QPS. Truly revolutionary. You’re telling me that if something is slow, I should… throw more money at it? Groundbreaking. This is the “Have You Tried Turning It Off and On Again?” of cloud infrastructure. I can’t wait to explain to finance that our "cost-effective" search solution requires us to double our cluster size every time we add a new feature filter.

• And the pièce de résistance: the hidden trade-offs. We’re told binary quantization is more cost-effective, but whoopsie, it “can have higher latency” when you ask for a few hundred candidates. That’s not a footnote; that’s a landmine. This is the kind of "gotcha" that works perfectly in a benchmark but brings the entire site to its knees during a Black Friday traffic spike. It’s the database equivalent of a car that gets great mileage, but only if you never drive it over 30 mph.

Anyway, this was a fantastic read. Thanks so much for outlining all the new and exciting ways my weekends will be ruined. I’ll be sure to file this guide away in the folder I’ve labeled “Things That Will Inevitably Page Me on a Holiday.” Now if you’ll excuse me, I’m going to go stare at a wall for an hour.

Thanks for the post! I will be sure to never, ever read this blog again.

Ah, yes, another paper set to appear in VLDB'25. It's always a treat to see what the academic world considers "production-ready." I must commend the authors of "Cabinet" for their ambition. It takes a special kind of bravery to build an entire consensus algorithm on a foundation of, shall we say, creatively interpreted citations.

It's truly magnificent how they kick things off by "revisiting" the scalability of consensus. They claim majority quorums are the bottleneck, a problem that was… solved years ago by flexible quorums. But I admire the dedication to ignoring prior art. It's a bold strategy. Why muddy the waters with established, secure solutions when you can invent a new, more complex one? And the motivation! Citing Google Spanner as having quorums of hundreds of nodes—that’s not just wrong, it’s a work of art. It’s like describing a bank vault by saying it’s secured with a child's diary lock. This level of foundational misunderstanding isn't a bug; it's a feature, setting the stage for the glorious security theatre to come.

And the algorithm itself! Oh, it's a masterpiece of unnecessary complexity. Dynamically adjusting node weights based on "responsiveness." I love it. You call it a feature for "fast agreement." I call it the 'Adversarially-Controlled Consensus Hijacking API.'

Let's play this out, shall we?

• An attacker wants to get their malicious node into the "cabinet." What do they do? Simple. They just run a little targeted DDoS or introduce network latency to the other, legitimate nodes.
• Suddenly, their compromised, lightning-fast node looks wonderfully "responsive" because it's not doing any actual work or, you know, running pesky security checks.
• The system, in its infinite wisdom, rewards this behavior by giving the attacker's node more weight. More say. More power.

You haven't built a consensus algorithm; you've built a system that allows for Denial-of-Service-to-Privilege-Escalation. It's a CVE speedrun, and frankly, I'm impressed. And the justification for this? The assumption that fast nodes are reliable? Based on a 2004 survey? My god. In 2004, the biggest threat was pop-up ads. Basing a modern distributed system's trust model on security assumptions from two decades ago is… well, it’s certainly a choice.

But the true genius, the part that will have SOC 2 auditors weeping into their compliance checklists, is the implementation. You're telling me this weight redistribution happens for every consensus instance and the metadata—the W_clock and weight values—is stored with every single message and log entry?

"The result is weight metadata stored with every message. Uff."

"Uff" is putting it mildly. You've just created a brand new, high-value target for injection attacks inside your replication log. An attacker no longer needs to corrupt application data; they can aim to corrupt the consensus metadata itself. A single malformed packet that tricks a leader into accepting a bogus weight assignment could permanently compromise the integrity of the entire cluster. Imagine trying to explain to an auditor: "Yes, the fundamental trust and safety of our multi-million dollar infrastructure is determined by this little integer that gets passed around in every packet. We're sure it's fine." This architecture isn't just a vulnerability; it's a signed confession.

And then, the punchline. The glorious, spectacular punchline in Section 4.1.3. After building this entire, overwrought, CVE-riddled machine for weighted consensus, you admit that for leader election, you just... set the quorum size to n-t. Which is, and I can't stress this enough, exactly how flexible quorums work.

You've built a Rube Goldberg machine of attack surfaces and performance overhead, only to have it collapse into a less efficient, less secure, and monumentally more confusing implementation of the very thing you ignored in your introduction. All that work ensuring Q2 quorums intersect with each other—a problem Raft's strong leader already mitigates—was for nothing. It’s like putting ten deadbolts and a laser grid on your front door, then leaving the back door wide open with a sign that says "Please Don't Rob Us."

So you've created a system that's slower, more complex, and infinitely more vulnerable than the existing solution, all to solve a problem that you invented by misreading a Wikipedia page about Spanner.

This isn't a consensus algorithm. It's a bug bounty program waiting for a sponsor.

Oh, bravo. A truly remarkable piece of... prose. I must commend the author's enthusiasm for tackling such a complex problem as "threat hunting" using the digital equivalent of a child's toy chest. One simply dumps all the misshapen blocks of data in, shakes it vigorously, and hopes a castle comes out. It’s a fantastically flexible approach, I’ll grant you that.

It is positively pioneering to see such a courageous disregard for decades of established data management theory. The choice to build this entire edifice upon what is, charitably, a distributed document store is a masterstroke of pragmatism. Why bother with the tedious ceremony of normalization or the rigid structures of a relational model when you can simply have a delightfully denormalized, JSON-formatted free-for-all? Codd’s twelve rules? I suppose they’re more like Codd’s Twelve Suggestions to the modern practitioner. A quaint historical document, really.

And the "rules"! The sheer, unadulterated genius of it all. To craft what is essentially a sophisticated grep command and call it a "detection rule" is a testament to the industry's boundless creativity. It's a brilliant brute-force ballet.

"...effective threat hunting and detection rules in Elastic Security..."

One has to admire the audacity. Instead of designing a system with inherent integrity and verifiable consistency, the solution is to pour ever more computational power into sifting through the resulting chaos. Who needs a proper query planner when you have more CPUs? It’s a philosophy that truly captures the spirit of the age.

I was particularly taken with the implicit architectural decisions. It's a rather brave choice, I daresay, to so casually cast aside Consistency in favor of Availability and Partition Tolerance. The CAP theorem, it seems, has been solved not with careful trade-offs, but with a shrug and a cheerful acceptance of eventual consistency. “The threat might have happened, and the data might be there, and it might be correct… eventually.” It’s a bold stance. One must wonder if the authors have ever encountered the concept of ACID properties, or if they simply found them too... well, acidic for their palate. The "Isolation" and "Consistency" guarantees are, after all, dreadful impediments to scalability.

It’s all so wonderfully innovative. It’s a shame, really. This entire class of problem, managing and querying vast datasets with integrity, was largely explored in the late 1980s. But I suppose nobody reads papers anymore. Clearly they've never read Stonebraker's seminal work on federated databases, or they would have realized they're simply re-implementing—and rather poorly, I might add—concepts we found wanting thirty years ago. My minor quibbles, to be sure, are just the pedantic ramblings of an old formalist:

• The complete abdication of a relational schema in favor of a "schema-on-read" approach, which is a charming way of saying "an unmanageable mess."
• The reliance on full-text search as a substitute for a structured, performant query language.
• The fundamental architectural choice to prioritize availability over the one thing that matters in a security context: data consistency.

Still, one mustn't stifle such creative spirit with tiresome formalism and a demand for theoretical rigor. Keep up the good work! I shall make a point of never reading your blog again, lest I be tempted to send you a reading list.

Cheerfully,

Dr. Cornelius "By The Book" Fitzgerald Professor of Computer Science (and Keeper of the Relational Flame)

Alright, team, gather 'round. I’ve just finished reading this… inspirational piece of literature from our friends at CockroachDB and CedarDB, titled "Better Together." And I must say, it’s a compelling argument. A compelling argument for me to start stress-testing the company's liquidation procedures.

They paint this heart-wrenching picture of a poor, overworked database struggling with an "innocent looking query." Oh, the humanity! A query that has the sheer audacity to ask for our top 10 products, their sales figures, and inventory levels. This isn't an "innocent query," this is a Tuesday morning report. If our current system chokes on a top-10 list, we don't need a new database, we need to fire the person who bought the last one. Probably the same V.P. of 'Synergistic Innovation' who approved this blog post.

But let's play their game. Let's pretend we're in this apocalyptic scenario where we can't figure out what our best-selling widget is. The solution, apparently, is not one, but two new database systems, because "Better Together" is just marketing speak for "Neither of our products could do the whole job alone."

They conveniently forget to include the price tag in this little fairy tale, so let me get out my trusty napkin and a red pen. I call this exercise "Calculating the True Cost of an Engineer's Fever Dream."

Let's assume the sticker price for this dynamic duo is a "modest" $500,000 a year in licensing. A bargain, I'm sure. But that's just the cover charge to get into the nightclub of financial ruin.

• The Great Data Migration Pilgrimage: We have to move terabytes of data. This will not be done by cheerful elves in the dead of night. No, this will be done by consultants. Consultants who bill at $400 an hour and communicate exclusively in acronyms. Let's budget a cool $1 million for them to inevitably copy-paste a schema incorrectly, bringing sales to a screeching halt for three days during the Q4 rush.
• The "Re-Education" Camp: Our entire engineering team, who are already overworked, now have to become experts in two esoteric new systems. That's a month of lost productivity for a dozen engineers, plus the cost of the training itself. Let's call that $250,000 in opportunity cost and fees. Suddenly, they're not shipping features; they're learning the "nuances" of a "distributed SQL paradigm." Fabulous.
• The "Better Together" Integration Tax: These two systems don't just magically hold hands and sing Kumbaya. Oh no. You need a dedicated team to write, maintain, and inevitably debug the custom glue code that holds this monstrosity together. That’s two new senior engineers we have to hire, assuming we can even find people with "CockroachDB and CedarDB" on their LinkedIn profiles. That’s another $400,000 a year, easy.

So, let's tally that up. Our initial, "innocent" $500k investment is actually a $2.15 million hole in my Year 1 budget. And for what? So a product manager can get his top-10 list 0.8 seconds faster? My back-of-the-napkin ROI calculation on that is... let's see... carry the one... ah, yes: negative infinity.

They talk about how this query is "challenging for industry-leading transactional database systems."

Take the innocent task of finding the the 10 top-grossing items, along with how much we sold, how much money they made, what we usually charge per unit...

This isn't a challenge; it's a sales pitch built on a manufactured crisis. They are selling us a billion-dollar hammer for a thumbtack, and telling us our existing hammer is fundamentally broken. They're not selling a solution; they're selling vendor lock-in, squared. Once we're on two proprietary systems, our negotiating power for renewal drops to approximately zero. They'll have us.

So here is my prediction if we approve this. Q1, we sign the deal. Q2, the consultants arrive and commandeer the good conference room. Q3, the migration fails twice, corrupting our staging environment. Q4, we finally "go live," just as they announce a 30% price hike for Year 2. The year after that, we're explaining to shareholders why our "Strategic Data Initiative" has the same annual budget as a small European nation and our primary business is now generating bug reports for two different companies.

So, no. We will not be making our databases "Better Together." We will be keeping our cash "Better in Our Bank Account." Now if you'll excuse me, I need to go deny a request for new office chairs. Those things are expensive.

Ah, another wonderfully detailed exploration into the esoteric arts of database distribution. It’s always a delight to see engineers so passionate about shard rebalancing and data movement. I, too, am passionate about movement—specifically, the movement of our entire annual IT budget into the pockets of a single, smiling vendor. This piece on integrating Citus with a pernicious Patroni is a masterpiece of technical optimism, a love letter to complexity that conveniently forgets to mention the invoices that follow.

They speak of "various other Citus distribution models" with such glee, as if they’re discussing different flavors of ice cream and not profoundly permanent, multi-million-dollar architectural decisions. Each "model" is just another chapter in the "How to Guarantee We Need a Specialist Consultant" handbook. I can practically hear the sales pitch now: “Oh, you chose the hash distribution model? Excellent! For just a modest uplift, our professional services team can help you navigate the inevitable performance hotspots you’ll discover in six months.”

The article’s focus on the mechanics of shard rebalancing is particularly… illuminating. It’s presented as a powerful feature, a solution. But from my seat in the finance department, “rebalancing” is a euphemism for “an unscheduled, high-stakes, data-shuffling fire drill that will consume your best engineers for a week and somehow still result in a surprise egress fee on your cloud bill.” They call it elasticity; I call it a recurring, unbudgeted expense.

Let’s perform some of my patented, back-of-the-napkin math on the True Cost of Ownership for one of these devious database darlings, shall we?

• The Bait: Let’s say the vendor quotes us a cool $200,000 annual license. They’ll produce a beautiful deck showing how it replaces $250,000 in legacy hardware and licensing, promising a $50,000 ROI in year one. How prudent! How fiscally responsible!
• The Switch:
  • Migration Mayhem: They’ll say it's "mostly compatible." That "mostly" will cost us four senior engineers for nine months. At a blended rate of $175k/year, that’s a $525,000 personnel cost, not to mention the opportunity cost of what they should have been building.
  • Training Tribute: Your existing team can’t just use this thing. Oh no. They need to be certified. That’s a $10,000 per head "bootcamp" for five people. Another $50,000 gone.
  • Consultant Caravan: The migration will inevitably hit a "unique environmental snag." The vendor’s top-tier, platinum-plated "solutions architect" will need to fly in. Their rate is a paltry $6,000 a day, and they’ll need a minimum of 20 days. That's a $120,000 ransom to get our own data working in their system.
  • The "Rebalancing" Incident: Six months post-launch, we hit a scaling issue. The promised auto-magic doesn’t work. The vendor, with a sympathetic frown, informs us we need another "engagement" with their experts to re-architect our sharding strategy. Add another $80,000.

So, that fantastic $50,000 ROI has, in reality, become a Year One cash bonfire of $775,000. We haven’t saved $50,000; we’ve spent three-quarters of a million dollars for the privilege of being utterly and completely locked into their proprietary "distribution models." And once your data is sharded across their celestial plane, trying to migrate off it is like trying to un-bake a cake. It’s not a migration; it’s a complete company-wide rewrite.

In this follow-up post, I will discuss various other Citus distribution models.

It’s just so generous of them to detail all the different, intricate ways they plan to make our infrastructure so specialized that no one else on the planet can run it. What they call "high availability," I see as a high-cost hostage situation. They're not selling a database; they're selling a dependence. A wonderfully, fantastically, financially ruinous dependence.

Honestly, at this point, I'm starting to think a room full of accountants with abacuses would have better uptime and a more predictable TCO. At least their pricing model is transparent.