🔥 The DB Grill 🔥

Ah, a "detailed cost analysis." How wonderfully quaint. It's truly a breath of fresh air to see someone focusing on the real priorities, like shaving a few cents off a terabyte-scan, while completely ignoring the trivial, multi-million-dollar cost of a catastrophic data breach. It shows a certain... focus.

I must commend you on your bold, almost artistic decision to completely ignore the concept of a threat model. Comparing Tinybird and ClickHouse Cloud on price is like comparing two different models of fish tanks based on their water efficiency, while cheerfully overlooking the fact that both are filled with piranhas and you plan to store your company's bearer tokens inside. A truly inspired choice.

Your focus on "billing mechanisms" is particularly delightful. While you’re calculating the cost per query, I’m calculating the attack surface of your billing portal. Can I trigger a denial-of-wallet attack by running an infinite query? Can I glean metadata about your data volumes from billing logs? You see a spreadsheet; I see a data exfiltration side-channel. It's all about perspective, isn't it?

And the "real-world use case scenarios"! My absolute favorite part. Let's paint a picture of these scenarios, shall we?

• You're ingesting user activity logs. Translation: a rich, centralized repository of PII, browser fingerprints, and IP addresses, just waiting for a single leaky API key to end up on a public GitHub repo.
• You're building a real-time analytics dashboard. How lovely! A direct, high-speed pipeline from your production database to an under-secured frontend, ripe for a cross-site scripting attack that hijacks an admin session.
• You're using Tinybird's famous APIs to publish data. Ah, yes, the "feature" where you turn your database into a publicly-accessible web server. What could possibly go wrong? I’m sure your SQL sanitization on that dynamic endpoint is absolutely perfect. Flawless, even.

It's impressive, really. You’ve managed to write an entire article about adopting a third-party, managed data platform without once whispering the cursed words: SOC 2, GDPR, data residency, IAM policies, or vulnerability scanning. It’s like publishing a guide to skydiving that focuses exclusively on the fashion-forward design of the parachutes.

...to help you choose the right managed ClickHouse solution.

This is my favorite line. The "right" solution. You've given your readers a comprehensive guide on choosing between being compromised via a supply-chain attack on Vendor A versus a zero-day in the web console of Vendor B. You’re not choosing a database; you’re choosing your future CVE number. Will it be a classic SQL injection, or are we aiming for something more exotic, like a deserialization bug in their proprietary data ingestion format? The suspense is killing me.

Honestly, bringing this cost analysis to a security review would be hilarious. We wouldn't even need to open the document. The sheer fact that your decision-making framework is based on "billing mechanisms" instead of "least privilege principles" tells me everything I need to know. This architecture would fail a SOC 2 audit so hard, the auditors would bill you for emotional damages.

This is a fantastic article if your goal is to explain to your future CISO, with charts and graphs, precisely which budget-friendly decision led to the company's name being the top post on a hacker forum.

Heh. Well, well, well. I just finished my cup of coffee—the kind that could strip paint, not one of your half-caf soy lattes—and stumbled across this... this masterpiece of modern analysis. A truly breathtaking bit of bean-counting, son. You've compiled every version from source, you've got your little my.cnf files all lined up, and you've even connected via a socket to avoid the dreaded SSL. My, how clever. It’s a level of meticulousness that warms my old, cynical heart.

It’s just wonderful to see you kids rediscover the scientific method to arrive at a conclusion that we, the greybeards of the server room, knew in our bones: "progress" is just another word for "more layers of abstraction that slow things down."

You’ve produced a lovely little table here, all full of pretty colors. It's a real work of art.

The summary is: ...modern MySQL only gets ~60% of the throughput relative to 5.6 because modern MySQL has more CPU overhead

Oh, you don't say? More CPU overhead? You mean to tell me that after a decade of piling on features that nobody asked for—JSON support, window functions, probably an integration with a blockchain somewhere—the thing actually got slower? I am shocked. Shocked, I tell you.

Back in my day, if you shipped a new version of the payroll system that ran 40% slower, you weren't writing a blog post. You were hand-typing your resume after being walked out of the building by a man named Gus who hadn't smiled since the Truman administration. We didn't have "CPU overhead." We had 8 kilobytes of memory to work with and a stack of punch cards that had to be perfect, or the whole run was shot. You learned efficiency real quick when a typo meant staying until 3 AM re-punching a card.

I must commend your rigorous testing on the l.i0 step. A clean insert into a table with a primary key. A foundational, fundamental function. And the throughput drops by 40%. It’s a bold strategy, to make the most basic operation of your database perform like it's calculating pi on an abacus. We had that figured out on DB2 on a System/370 back in '85. It was called a "batch job," and I assure you, the next version didn't make it slower.

But let’s not be entirely negative! Your chart clearly shows a massive improvement in l.x, creating secondary indexes. A 2.5x speedup! Hallelujah! So, while the initial data load crawls and the queries gasp for air, you can build the scaffolding for your slow-as-molasses lookups faster than ever before. It’s like putting racing stripes on a hearse. A triumph of modern engineering, to be sure.

And the query performance... ah, the queries.

• qr100... regression.
• qp100... bigger regression.
• qr500... regression.
• qp500... bigger regression.
• ...and so on.

It’s a veritable parade of performant poppycock. You're telling me that with an 8-core processor and 32 GIGABYTES of RAM—a comical amount of power, by the way; we used to run an entire bank on a machine with less memory than your phone's weather app—it chokes this badly? What are all those CPU cycles doing? Are they thinking about their feelings? Contemplating the futility of existence? We used to write COBOL that was more efficient than this, and COBOL is just a series of angry shouts at the machine.

It's just the same old story. Every few years, a fresh-faced generation comes along, reinvents the flat tire, and calls it a paradigm shift. They add so many digital doodads and frivolous features that the core engine, the thing that's supposed to just store and retrieve data, gets buried under a mountain of cruft.

So thank you, kid. Thank you for this wonderfully detailed, numerically sound confirmation of everything I've been muttering into my coffee for the last twenty years. You’ve put data to my disappointment.

Now if you'll excuse me, I think I hear a tape drive calling my name. At least when that breaks, you can fix it with a well-placed kick.

Alright team, let's huddle up. I’ve just finished reading the latest magnum opus from the "let's solve a wrench problem with a particle accelerator" school of thought. It seems MongoDB and their new friend Dataworkz want to save us from flight delays using an "agentic voice assistant." It’s a compelling narrative, I'll give them that. Now, let me get my reading glasses and my red pen and translate this marketing pamphlet into a language we understand: Generally Accepted Accounting Principles.

• First, let's admire the sheer, breathtaking complexity of this "solution." We're not just buying a database; we're funding a tech-stack party where MongoDB, Dataworkz, Google Cloud, and Voyage AI are all on the guest list, and we’re paying the open bar tab. They call it "seamless data integration"; I call it a five-headed subscription hydra. My napkin math puts the base licensing for this Rube Goldberg machine at a cool $500k annually. But wait, there's more! We'll need a "Systems Integrator"—let's call them 'Consultants-R-Us'—to bolt this all together, another $300k. Then we have to retrain our entire ground crew to talk to a box instead of, you know, their supervisor. Add $150k for training and lost productivity. Our "True First-Year Cost" isn't a line item; it's a cool million dollars before a single bag is loaded.

• They dangle a very specific carrot: a 15-minute delay on an A321 costs about €3,030. What a wonderfully precise, emotionally resonant number. Let's play with it. Using our $1 million "all-in" first-year cost, we would need to prevent roughly 330 of these exact 15-minute delays just to break even. Not shorter delays, not delays caused by weather or catering, but specifically the ones a ground crew member could have solved if only they’d asked their phone where the APU was. They tout "data-driven insights," but the most crucial insight is that we're more likely to see a unicorn tow a 747 than we are to see a positive ROI on this venture.

• My absolute favorite feature is the "meticulously logged" audit trail where "each session is represented as a single JSON document." How thoughtful. They’re not just selling us a database; they're selling us a data landfill. Every question, every checklist confirmation, every time someone coughs near the microphone—it's all stored forever in their proprietary BSON format. This isn't an audit trail; it's a data hostage situation. The storage costs will balloon exponentially, and just wait until you see the egress fees when our analytics team wants to, God forbid, actually analyze this mountain of JSON logs in a different system.

By providing immediate access to comprehensive and contextualized information, the solution can significantly reduce the training time and cognitive load for ground crews...

• Ah, the "reduced cognitive load" argument. That's my signal to check for my wallet. This is a classic vendor trick, promising soft, unquantifiable benefits to distract from the hard, quantifiable costs. What is the line item for "cognitive load" on our P&L? I'll wait. This is a solution built for a quiet library, not a deafeningly loud, chaotic airport tarmac with jet engines screaming and baggage carts beeping. The number of times the "natural language processing" mistakes "chock the wheels" for "shock the seals" will be a source of endless operational comedy and zero efficiency.

• Finally, let’s talk about vendor lock-in, or as they call it, an "AI-optimized data layer (ODL) foundation." How charming. By vectorizing our proprietary manuals and embedding them into their ecosystem, they ensure that untangling ourselves from this platform will be more complex and expensive than manually rewriting every single one of our safety regulations. We’re not buying a tool; we’re entering a long-term, one-sided marriage where the prenup was written by their lawyers, and we’re already paying for a very expensive couples therapist masquerading as "technical support."

It's a lovely presentation, really. A for effort. Now, if you'll excuse me, I'm going to go approve the PO for a new set of laminated checklists and a box of walkie-talkies. Let's talk about solutions that actually fit on a balance sheet.

Alright, settle down, kids. Let me put down my coffee—the kind that's brewed strong enough to dissolve a spoon, not your half-caff-soy-latte-with-a-sprinkle-of-existential-dread—and take a look at this... this bulletin.

Oh, this is precious. MyDumper "takes backup integrity to the next level" by... creating checksums.

Next level. Bless your hearts.

You know what we called checksums back in my day? We called it Tuesday. That wasn't a "feature," it was the bare-minimum entry fee for not getting hauled into the data center manager's office to explain why the entire company's payroll vanished into the ether. We were doing parity checks on data transfers when the only "cloud" was the plume of smoke coming from the lead system architect's pipe.

This whole article reads like someone just discovered fire and is trying to patent it. "The last thing you want is to discover your data is corrupted during a critical restore." Ya think? That's like saying the last thing a pilot wants to discover is that the wings were an optional extra. This isn't some profound insight, it's the fundamental premise of the entire job. A job, I might add, that used to involve wrestling with reel-to-reel tape drives the size of a small refrigerator.

You want to talk about backup integrity? Let me tell you about integrity. Integrity is running a 12-hour batch job written in COBOL, fed into the mainframe on a stack of punch cards you prayed was in the right order. Integrity is physically carrying a set of backup tapes in a lead-lined briefcase to an off-site vault because "off-site" meant a different building, not just another availability zone. We had a physical, plastic ring we had to put on the tape reel to allow it to be written to. No ring, no write. You kids and your 'immutable storage' probably think that's a life hack.

This often-overlooked feature […]

"Often-overlooked." Of course it's overlooked! You're all too busy disrupting synergy in your open-plan offices to read the manual. We had manuals. Binders, three inches thick, filled with glorious dot-matrix printouts. You read them. Cover to cover. Or you were fired. There were no "often-overlooked" features, only "soon-to-be-unemployed" DBAs.

This "MyDumper" tool... cute name. Sounds friendly. We had tools with names like IEBGENER, ADABAS, and CICS. They sounded like industrial machinery because that's what they were. They didn't have a -M option. They had 300 pages of JCL (Job Control Language) that you had to get exactly right, or the entire system would just sit there, blinking a single, mocking green cursor at you from across the room.

You're celebrating a checksum on a logical dump. We were validating tape headers, checking block counts, and running restores to a test LPAR on a different machine just to be sure. And we did it all through a 3270 terminal that rendered text in one color: searing green on soul-crushing black.

So, it's wonderful that your newfangled tools are finally catching up to the basic principles we established on DB2 and IMS back in 1985. It really is. Keep exploring those command-line flags. You're doing great. Maybe next month you'll write another breathless post about the "revolutionary" concept of transaction logging.

Just try not to hurt yourselves. The adults need the systems to stay up. Now if you'll excuse me, I have to go explain to a DevOps intern why they can't just rm -rf the archive logs. Again.

Oh, this is just delightful. "SQLite when used with WAL doesn’t do fsync unless specified." You say that like it's a fun performance trivia fact and not the opening sentence of a future incident post-mortem that will be studied by security students for a decade. It’s not a feature, it's a bug bounty waiting to be claimed. You’ve gift-wrapped a race condition and called it "optimized for concurrency."

Let me translate this from 'move fast and break things' developer-speak into a language that a CISO, or frankly any adult with a functioning sense of object permanence, can understand. What you're celebrating is a database that essentially pinky-promises it wrote your data to disk. The operating system, bless its heart, is told "Hey, just, you know, get to this whenever you feel like it. No rush. I'm sure a sudden power loss or kernel panic won't happen in the next few hundred milliseconds."

I can already see the meeting with the auditors.

"So, walk me through your transaction logging for critical security events. Let's say, for example, an administrator revokes a user's credentials after detecting a breach."

"Well," you'll say, shuffling your feet, "our system immediately processes the request and commits it to the write-ahead log with blazing speed!"

"And that log is durable? It's physically on disk?"

"...It's... specified... to be written. Eventually. We've decided that data integrity is more of a philosophical concept than a hard requirement. We're an agile shop."

You haven't built a database, you've built a Schrödinger's commit. The transaction is both saved and not saved until the moment a GCP zone goes down, at which point you discover it was most definitely not saved. Every single state-changing operation is a potential time-travel exploit for an attacker. Imagine this:

• An attacker triggers a high-value, fraudulent transaction.
• Your brilliant intrusion detection system spots it and logs the event, locking their account.
• The attacker, knowing you treat fsync as an optional extra, simply triggers a denial-of-service attack that crashes the server.

Poof. The machine reboots. The fraudulent transaction? It might have made it to the log file before the OS got around to flushing it. The account lockout and the security log entry? Whoops, they were still floating in a buffer somewhere. To the rest of the system, it never happened. This isn't just a data loss issue; it's a state-confusion vulnerability that allows an attacker to effectively roll back your security measures.

And don't even get me started on compliance. You think you're passing a SOC 2 audit with this? The auditor will take one look at your "ephemeral-by-default" data layer and start laughing. They'll ask for evidence of your data integrity controls (CC7.1), and you'll show them a link to a blog post about how you bravely turned them off for a 5% performance gain on a benchmark you ran on your laptop.

This entire architecture is built on the hope that nothing ever goes wrong. And in the world of security, "hope" is not a strategy; it's a liability. Every single feature you build on top of this flimsy foundation is another potential CVE. User authentication? Potential account takeover via state rollback. Financial ledgers? A great way to invent money. Audit trails? You mean the optional suggestion box?

So, thank you for this fascinating little tidbit. It's always nice to read a short, concise confession of architectural negligence. I'll be sure to file this away under "Companies I Will Never, Ever Work For or Trust With a Single Byte of PII." Anyway, I'm sure this was very enlightening for someone. I, however, will not be reading this blog again. I have to go wash my hands. Thoroughly.

Well now, isn't this just a delightful piece of literature. I had to pour myself a fresh cup of coffee—and something a little stronger to go in it—just to properly appreciate the artistry here. It’s always a treat to see the old gang putting on a brave face.

It starts strong, right out of the gate, positioning the open source project as just one of the options. The audacity is... well, it's admirable. It’s like a cover band explaining why their version of "Stairway to Heaven," complete with a kazoo solo, is actually the definitive one. You're not just getting ClickHouse, you're getting the Tinybird experience.

I particularly love the promise of "simpler deployment." I remember those meetings. That phrase is a masterpiece of corporate poetry. It beautifully glosses over the teetering Jenga tower of Kubernetes operators, custom Ansible playbooks, and that one critical shell script nobody's dared to touch since Kevin left. “It’s simple!” they’d say. “You just run the bootstrap command.” They always neglect to mention the bootstrap command summons a Cthulhu of dependencies that devours your VPC for breakfast. Simple, indeed.

And the promise of "more features"… oh, bless their hearts. This is my favorite part. It’s a bold strategy, bolting a new dashboard onto a race car engine and calling it a luxury sedan. Let's be honest about what those "features" usually are:

• A UI that looks spectacular in screenshots, as long as you don't actually click on anything that needs to load data.
• That proprietary data ingestion API that was definitely "production-ready" three roadmaps ago and is still considered to be in "active beta."
• The "intelligent" query caching layer that occasionally decides to return results from last Tuesday, just to keep you on your toes.

But the real kicker, the line that truly brought a tear to my eye, is "fewer infrastructure headaches."

...fewer infrastructure headaches.

That is, without a doubt, one of the finest sentences ever assembled in the English language. It’s like trading a leaky faucet for a pipe that’s sealed behind a concrete wall. Sure, you don’t see the leak anymore, but good luck when the whole foundation starts getting damp. You're just swapping the headaches you know for a whole new universe of proprietary, black-box headaches that you can't Google the answer to. I'm sure the support team loves explaining why the "magic" isn't working, and that no, you can't have shell access to just see what's going on. We all remember what happened with the great shard rebalancing incident of '22, don't we? Good times.

Honestly, though, it's a great effort. You can really feel the ambition. Keep shipping, you crazy diamonds. It takes real courage to sell people a pre-built ship while gently hiding the fact that you’re still frantically patching the hull below the waterline.

Stay scrappy.

-Jamie "Vendetta" Mitchell

Alright, let’s pull up a chair. I’ve just been sent another one of these… thought leadership pieces. This one’s a real page-turner. "Think PostgreSQL with JSONB can replace a document database?" Oh, honey, that’s adorable. It’s like asking if my son’s lemonade stand can replace the Coca-Cola Company. It’s a tempting idea, sure, if your goal is to go bankrupt with extra steps.

Let's dig into this fiscal tragedy masquerading as a technical deep-dive. They start with a "straightforward example." That’s vendor-speak for, “Here’s a scenario so sterilized and perfect it will never happen in the real world, but it makes our charts look pretty.” They load up a hundred thousand orders, each with ten items, and what's this? They’re generating random data with /dev/urandom piped through base64. Fantastic. We're not just wasting CPU cycles, we're doing it with panache. I can already see the AWS bill for this little science fair project.

And look at this wall of text they call a query result. What am I looking at? The encrypted launch codes for a defunct Soviet satellite? This isn’t data; it’s a cry for help. I’m paying for storage on this, by the way. Every single one of these gibberish characters is a tiny debit against my Q4 earnings.

Now for the juicy part, the part they always gloss over in the sales pitch: the execution plan. The first query, the "good" relational one, reads eight pages. Eight pages. In my world, that’s not a performance metric; it's an itemized receipt for wasted resources. Four for the index, four for the table. Simple enough. But then they get clever. They decide to "improve" things by cramming everything into a JSONB column to get that sweet, sweet data locality. They want to be just like MongoDB, isn't that cute?

So they run their little update and vacuum commands—cha-ching, cha-ching, that’s the sound of billable compute hours—and what happens? To get the same data out, the page count goes from eight… to ten.

Let me repeat that for the MBAs in the back. Their "optimization" resulted in a 25% increase in I/O for a single lookup. If one of my department heads came to me with a 25% cost overrun on a core business function, they wouldn't be optimizing a database; they’d be optimizing their LinkedIn profile.

But it gets better. They reveal the dark secret behind this magic trick: a mechanism called TOAST. It sounds warm and comforting, doesn't it? Let me tell you what TOAST is. TOAST is the hidden resort fee on your hotel bill. It's the "convenience charge" for using your own credit card. It’s a system designed to take something that should be simple—storing data—and turn it into a byzantine nightmare of hidden tables, secret indexes (pg_toast_10730420_index, really rolls off the tongue), and extra lookups. You thought you bought a single, elegant solution, but you actually bought a timeshare in a relational database pretending to be something it's not.

This execution plan reveals the actual physical access to the JSONB document... no data locality at all.

There it is. The whole premise is a lie. It's the Fyre Festival of database architectures. You're promised luxury villas on the beach, and you end up with relational tables in a leaky tent.

Now, let's do some real CFO math, the back-of-the-napkin kind they don’t teach you at Stanford.

• Migration Cost: They casually mention alter table and update. For one hundred thousand records. Do you know what that looks like on our multi-terabyte production database? That’s not a script; that’s a three-week project requiring two senior DBAs, a project manager to tell them they’re behind schedule, and a catering budget for all the late-night pizza. Estimate: $85,000.
• Consultant Fees: When this inevitably grinds to a halt because a developer tries to query for the second item in the array and accidentally reads the entire database into memory—which they helpfully demonstrate reads half a million pages—who do we call? The PostgreSQL gurus. The ones who charge $500 an hour to look at our EXPLAIN ANALYZE output and say, "Yep, you’re TOASTed." Estimate: A recurring $150,000 per year, forever.
• Training & Rework: Every engineer who was told "it's just like a document database" now has to unlearn that and learn the thirty-seven exceptions and hidden gotchas of the TOAST protocol. That’s productivity down the drain. Estimate: Lost opportunity cost of $250,000 in the first year alone.

So the "true" cost of this "free" optimization is a cool half-a-million dollars just to get worse performance. The ROI on this project isn't just negative; it's a black hole that sucks money out of the budget and light out of my soul.

They conclude with this masterpiece of corporate doublespeak: "PostgreSQL’s JSONB offers a logical data embedding, but not physical, while MongoDB provides physical data locality." Translation: "Our product can wear a costume of the thing you actually want, but underneath, it’s still the same old thing, just slower and more confusing." Then they have the audacity to plug a conference. Sell me the problem, then sell me a ticket to the solution. That's a business model I can almost respect.

So, no. We will not be replacing our document database with a relational database in a cheap Halloween costume. I’ve seen better-structured data in my grandma’s recipe box.

My budget is closed.

(Leans back in a creaking, ergonomic-nightmare of a chair, stained with coffee from the Reagan administration. Squints at the screen over a pair of bifocals held together with electrical tape.)

Well, look at this. The kids have discovered that if you try to make a relational database act like something it's not, it still acts like a relational database. Groundbreaking stuff. It's a real barn-burner of an article, this one. "Think PostgreSQL with JSONB can replace a document database? Be careful." You don't say. Next, you'll tell me that my station wagon can't win the Indy 500 just because I put a racing stripe on it.

Back in my day, we didn't have "domain-driven aggregates." We had a master file on a tape reel and a transaction file on another. You read 'em both, you wrote a new master file. We called it a "batch job," and it was written in COBOL. If you wanted "data that is always queried together" to be in the same place, you designed your record layouts on a coding form, by hand, and you didn't whine about it. You kids and your fancy "document models"... you've just reinvented the hierarchical database, but with more curly braces and a worse attitude. IMS/DB was doing this on mainframes when your CEO was still learning how to use a fork.

So this fella goes through all this trouble to prove a point. He loads up a million rows of nonsense by piping /dev/urandom into base64. Real cute. We had a keypunch machine and a stack of 80-column cards. Our test data had structure, even if it was just EBCDIC gibberish. You learn respect for data when you can drop it on your foot.

And the big "gotcha"? He discovers TOAST.

In PostgreSQL, however, the same JSON value may be split into multiple rows in a separate TOAST table, only hiding the underlying index traversal and joins.

Let me get this straight. You took a bunch of related data, jammed it into a single column to avoid having a second table with a foreign key, and the database... toasted it by splitting it up and storing it in... a second table with an internal key. And this is presented as a shocking exposé?

Son, we called this "overflow blocks" in DB2 back in 1985. When a VARCHAR field got too big, the system would dutifully stick the rest of it somewhere else and leave a pointer. It wasn't magic, it was just sensible engineering. You're acting like you've uncovered a conspiracy when all you've done is read the first chapter of the manual. The database is just cleaning up your mess behind the scenes, and you're complaining about the janitor's methods. This whole song and dance with pageinspect and checking B-Tree levels to "prove" there's an index... of course there's an index! How else did you think it was going to find the data chunks? Wishful thinking? Synergy?

The best part is this line right here: "the lookup to a TOAST table is similar to the old N+1 problem with ORMs." You kids are adorable. You think the "N+1 problem" is some new-fangled issue from these object-relational mappers. We called it "writing a shitty, row-by-row loop in your application code." We didn't write a blog post about it; we just took away your 3270 terminal access until you learned how to write a proper join.

So after all that, the performance is worse. Reading the "embedded" document is slower than the honest, god-fearing JOIN on two properly normalized tables. The buffer hits go up. The query plan looks like a spaghetti monster cooked up by a NodeJS developer on a Red Bull bender. And the final conclusion is... drumroll please...

"If your objective is to simulate MongoDB and use a document model to improve data locality, JSONB may not be the best fit."

You have spent thousands of words, generated gigabytes of random data, and meticulously analyzed query plans to arrive at the stunning conclusion that a screwdriver makes a lousy hammer. Congratulations. You get a gold star. We've known this since Codd himself laid down the law. You're treating Rule #8 on data independence like you just discovered it on some ancient scroll, but we were living it while you were still trying to figure out how to load a program from a cassette tape.

This whole fad is just history repeating itself. In the 90s, it was object databases. In the 2000s, it was shoving everything into giant XML columns. Now it's JSONB. And I'll tell you what happens next, because I've seen this movie before. In about three to five years, there will be a new wave of blog posts. They'll be titled "The Great Un-JSONing: Migrating from JSONB back to a Relational Model." A whole new generation of consultants will make a fortune untangling this mess, writing scripts to parse these blobs back into clean, normalized tables. And I'll be right here, cashing my pension checks and laughing into my Sanka.

Now if you'll excuse me, I've got a backup tape from '98 that needs to be restored. It's probably got a more sensible data model on it than this.

Ah, yes. A new missive from the... front lines. One must admire the sheer bravery of our industry colleagues. While we in academia concern ourselves with the tedious trifles of logical consistency, formal proofs, and the mathematical purity of the relational model, they are out there tackling the real problems. Truly, it's a triumph of pragmatism.

I must commend the authors for their laser-like focus on "cost-aware resource configuration." It's a breathtakingly innovative perspective. For decades, we were under the foolish impression that "database optimization" referred to arcane arts like query planning, index theory, or achieving at least the Third Normal Form without weeping. How quaint we must seem! It turns out, the most profound optimization is simply telling the cloud provider to use a slightly smaller virtual machine. Who knew the path to performance was paved with accounting?

It’s particularly heartening to see such a dedicated effort to micromanage the physical layer for a "Relational Database Service." I'm sure Ted Codd would be simply tickled to see his Rule 8, Physical Data Independence—the one that explicitly states applications should be insulated from how data is physically stored and accessed—treated as a charming historical footnote. Clearly, the modern interpretation is:

The application should be intimately and anxiously aware of its underlying vCPU count and memory allocation at all times, lest it incur an extra seventy-five cents in hourly charges.

This piece is a testament to the modern ethos. Why waste precious engineering cycles understanding workload characteristics, schema design, or transaction isolation levels when you can simply click a button in the "AWS Compute Optimizer"? The name itself is a masterwork of seductive simplicity. It implies that compute is the problem, not, say, an unindexed, billion-row table join that brings the system to its knees. It’s not your N+1 query, my dear boy, it’s the instance type!

One has to appreciate the elegant sidestepping of the industry's... let's call it a casual relationship with the ACID properties. The focus on resource toggling is so all-consuming that one gets the impression that Atomicity, Consistency, Isolation, and Durability are now features you can scale up or down depending on your budget. Perhaps we can achieve "Eventual Consistency" with our quarterly earnings report as well?

It's this kind of thinking that leads to such bold architectural choices. They speak of scaling as if the CAP theorem is merely a friendly suggestion from Dr. Brewer, rather than an immutable law of distributed systems. But why let theoretical impossibilities get in the way of five-nines availability and a lean cloud bill? I'm sure the data will sort itself out. Eventually.

This whole approach displays a level of intellectual freedom that is, frankly, staggering. It's the kind of freedom that comes from a blissful ignorance of the foundational literature.

• The courage to treat a relational database like a volatile key-value store.
• The ingenuity to solve a data problem with a hardware solution.
• The audacity to call it "optimization."

Clearly, they've never read Stonebraker's seminal work on Ingres, or they'd understand that a database is more than just a well-funded process consuming memory. But why would they? There are no stock options in reading forty-year-old papers, are there?

So, let us applaud this work. It is a perfect artifact of our time. A time of immense computational power, wielded with the delicate, nuanced understanding of a toddler with a sledgehammer. Keep up the good work, practitioners. Your charming efforts are a constant source of... material for my undergraduate lectures on what not to do. Truly, you are performing a great service.

Ah, marvelous. They've finally bestowed upon MySQL the grand title of "Long-Term Support." One must applaud the sheer audacity. It’s akin to celebrating that a bridge you've been building for two decades might, at long last, stop wobbling in a stiff breeze. "Great news for all of us who value stability," they say. One presumes the previous thirty years were just a whimsical experiment in managed chaos.

This entire spectacle is a symptom of a deeply pernicious trend. They speak of an "enterprise-ready platform" as if it were some new-found treasure, a revolutionary concept just discovered. What, precisely, were they offering before? A hobbyist's plaything? It seems the "enterprise" has become a synonym for "we'll promise not to break your mission-critical systems for at least a few fiscal quarters." How reassuring.

The very need for an "LTS" release exposes the intellectual bankruptcy of the modern development cycle. A database system, if designed with even a modicum of rigor, should be stable by its very nature. Its principles should be axiomatic, not subject to the fleeting whims of quarterly feature sprints. But no, they bolt on "innovations" that would make Edgar Codd turn in his grave, then act surprised when the whole precarious Jenga tower needs a "stabilization" release.

I can only imagine the sort of "features" this new, stable platform will enshrine:

• More unstructured JSON blobs masquerading as "columns"? A delightful violation of First Normal Form, treating the relational model not as a mathematical foundation, but as a quaint, decorative suggestion.
• Newfangled "serverless" connectors that abstract away the very notion of a transaction's lifecycle? Because who needs ACID properties when you have infinite scalability and a cloud bill to match? Atomicity is so... restrictive, isn't it?
• Perhaps they've found a new, exciting way to ignore Codd's Rule 3 on the systematic treatment of nulls? They've been trying for decades; one must admire the persistence.

They speak of predictability. What is predictable is their flagrant disregard for the fundamentals. They speak of "availability" and "scalability," chanting mantras they picked up from some dreadful conference keynote. Clearly, they've never grappled with the implications of the CAP theorem; they simply treat Consistency as the awkward guest at the party they hope will leave early so the real fun can begin.

"a more predictable, enterprise-ready platform"

This isn't innovation; it's an apology. It's a tacit admission that their previous work was a series of frantic sprints away from sound computer science principles. It's the inevitable result of a culture where no one reads the papers anymore. You can practically hear the product managers asking, "Why bother with isolation levels when we can just throw more pods at it?" Clearly, they've never read Stonebraker's seminal work on the architecture of database systems, or they'd understand they are solving yesterday's problems with tomorrow's over-engineered and fundamentally unsound solutions.

So, let them have their "LTS" release. Let the industry celebrate this monument to its own short-sightedness. I shall be in my office, re-reading Codd's 1970 paper, and quietly weeping for a field that has mistaken marketing cycles for progress. Enterprise-ready, indeed. Hmph.