🔥 The DB Grill 🔥

Ah, a twentieth-anniversary retrospective. How... quaint. It's always a pleasure to read these little trips down memory lane. It gives one a moment to pause, reflect, and run the numbers on just how a business model that is "sometimes misunderstood" has managed to persevere. Let me see if I can help clear up any of that "misunderstanding."

I must applaud your two decades of dedication to the craft. It's truly a masterclass. Not in database management, of course, but in the subtle art of financial extraction. You've perfected the perplexing pricing paradigm, a truly innovative approach where the initial quote is merely the cover charge to get into a very, very expensive nightclub. And once you're in, darling, every drink costs more than the last, and the bouncer has your car keys.

The claim that your model has "worked" is, I suppose, technically true. It has worked its way into our budgets with the precision of a surgeon and the subtlety of a sledgehammer. Let's do some quick, back-of-the-napkin math on your latest proposal, shall we? I like to call this the "True Cost of Ownership" calculation, or as my team now calls it, the "Patricia Goldman Panic-Inducing Profit-and-Loss Projection."

• Your "Affordable" License Fee: We'll call this the "bait." A tidy little number.
• The "Simple" Migration: You quoted 3 months. My engineers are telling me it's a 9-month monolithic migration misery. I'll multiply their salary and benefits by three, add a 50% "optimism tax" for inevitable delays, and then factor in the therapy they'll need.
• Essential Training: Or, as I see it, the cost of re-educating a perfectly competent team to navigate your uniquely convoluted ecosystem. Let's just pencil in the price of a small island for that.
• The Inevitable Consultants: Ah, my favorite part. You can't just use the product. You need the High Priests of Percona, the certified "Gurus of Go-Live" who bill by the minute and speak in riddles. Their fee appears to be calculated based on the gravitational pull of Jupiter on a Tuesday.

So, when I add it all up—the bait, the migration misery, the re-education camps, and the consultant's new yacht—your "cost-effective solution" will, by my estimation, achieve a negative 400% ROI and cost us roughly the same as our entire Q3 revenue. A spectacular achievement in budget-busting.

From the beginning, Percona has followed a model that is sometimes misunderstood, occasionally questioned…

Misunderstood? Questioned? Oh, no, my dear. I understand it perfectly. It's the "open-door" prison model. You champion the "freedom of open source" which is marvelous—it gives us the freedom to enter. But once we're in, your proprietary monitoring tools, your bespoke patches, and your labyrinthine support contracts create a vendor lock-in so powerful it makes Alcatraz look like a petting zoo. The cost to leave becomes even more catastrophic than the cost to stay. It's splendidly, sinfully smart.

So, congratulations on 20 years. Twenty years of perfecting a sales pitch that promises a sports car and delivers a unicycle with a single, perpetually flat tire… and a mandatory, 24/7 maintenance plan for the air inside it.

Your platform isn’t a database solution; it’s a long-term liability I can’t amortize.

Ah, another dispatch from the front lines. One must applaud the author's enthusiasm for tackling such a... pedestrian topic as checkpoint tuning. It's utterly charming to see the practitioner class rediscover the 'D' in ACID after a decade-long infatuation with simply losing data at "web scale". One gets the sense they've stumbled upon a foundational concept and, bless their hearts, decided to write a "how-to" guide for their peers.

It's a valiant, if misguided, effort. This frantic obsession with "tuning" is, of course, a symptom of a much deeper disease: a profound and willful ignorance of first principles. They speak of "struggling with poor performance" and "huge wastage of server resources" as if these are novel challenges, rather than the predictable, mathematically guaranteed outcomes of building systems on theoretical quicksand.

So it’s time to reiterate the importance again with more details, especially for new users.

Especially for new users. How wonderful. Perhaps a primer on relational algebra or the simple elegance of Codd's rules would be a more suitable starting point, but I suppose one must learn to crawl before one can learn to ignore the giants upon whose shoulders they stand.

This entire exercise in knob-fiddling is a tacit admission of failure. It’s a desperate attempt to slap bandages on a system whose designers were so preoccupied with Availability and Partition Tolerance that they forgot Consistency was, in fact, a desirable property. They chanted Brewer's CAP theorem like a mantra, conveniently forgetting it’s a theorem about trade-offs, not a license to commit architectural malpractice. Now they're trying to clumsily bolt Durability back on with configuration flags. It's like trying to make a canoe seaworthy by adjusting the cup holders.

One can't help but pity them. They are wrestling with the ghosts of problems solved decades ago. If only they'd crack open a proceedings from a 1988 SIGMOD conference, they'd find elegant solutions that don't involve blindly adjusting max_wal_size. But why read a paper when you can cargo-cult a blog post? So much more... accessible.

Their entire approach is a catalogue of fundamental misunderstandings:

• They treat a physical implementation detail—the flushing of dirty buffers to disk—as a primary tuning vector, rather than a consequence of a properly designed logical model.
• They celebrate their ability to mitigate I/O storms, never once asking why their write patterns are so pathological in the first place. Clearly they've never read Stonebraker's seminal work on the inherent trade-offs in storage architectures.
• They're so focused on the checkpoint interval that they completely miss the forest for the trees. They spend weeks tuning checkpoints but can't be bothered to ensure their data model even remotely adheres to, say, Codd's Third Normal Form. It's like meticulously polishing the hubcaps on a car with no engine.

I shall watch this with academic amusement. I predict, with a confidence bordering on certainty, that this meticulously "tuned" system will experience catastrophic, unrecoverable data corruption during a completely foreseeable failure mode. The post-mortem will, no doubt, blame a "suboptimal checkpoint_timeout setting" rather than the true culprit: the hubris of believing you can build a robust system while being utterly ignorant of the theory that underpins it.

Now, if you'll excuse me, I must return to my grading. The youth, at least, are still teachable. Sometimes.

Well, this was a delightful read. Truly. I must applaud the courage it takes to publish what is essentially a pre-mortem for a future catastrophic data breach. It’s not often you see a company document its own negligence with such enthusiasm and pretty graphs.

It’s genuinely heartwarming to see a focus on solving the “inverse scaling problem.” It’s a bold choice to prioritize the performance of your reporting dashboard while your entire real-time data ingestion pipeline becomes a welcome mat for every threat actor this side of the Caucuses. The business intelligence team will have beautiful, real-time charts showing exactly how fast their customer data is being exfiltrated. Progress.

Replacing a "fragile" pipeline is a noble goal. Of course, you’ve simply replaced a system you understood with a third-party black box. That’s not fragility, that’s just outsourcing your vulnerabilities. It’s a fantastic strategy for plausible deniability when the auditors show up. "It wasn't our code that was insecure, it was Tinybird's!" A classic. I’m sure your legal team is thrilled.

And the move to a "real-time ingestion pipeline" for one of the "world's largest live entertainment platforms"... magnificent. I can already see the CVEs lining up. Let’s just brainstorm for a moment, shall we?

• A real-time pipeline is just a synonym for a zero-day-ready attack surface. You’re not just ingesting data; you’re ingesting payloads. What’s your sanitization and validation process for data that, by your own admission, needs to be processed instantly? Spoiler: it’s probably a single, terrified regex.
• What kind of data are we talking about? User activity? Ticket purchases? PII? I love the casual glossing over of the data payload. It’s like describing a missile by talking about its beautiful paint job.
• I’m sure this "Tinybird" has a fantastically robust and secure API. And I’m sure the credentials to access it are stored securely, not just sitting in a plaintext environment variable on a forgotten CI/CD runner. The attack surface you’ve introduced is, and I say this with professional admiration, vast.

The focus on business reporting is the chef's kiss. It demonstrates a clear, unadulterated focus on metrics that matter to the business, while completely ignoring the metrics that matter to your CISO—who I assume is now chain-smoking in a dark room.

...better business meant worse reporting.

Let me correct that for you: better business meant a juicier target. You haven't solved the problem; you’ve just made the blast radius larger. Imagine the fun an attacker could have with a real-time data stream. Forget simple data theft; we're talking about real-time data manipulation. A little BirdQL injection—or whatever proprietary, surely-un-fuzzable query language this thing uses—and suddenly you’re selling phantom tickets or giving everyone front-row seats.

I can't wait to see the SOC 2 audit for this. It'll be a masterpiece of creative writing. How do you prove change management on a system designed to be a magical black box? How do you assert data integrity when you’re just yeeting JSON blobs into the void and hoping for the best? This architecture doesn’t just fail a SOC 2 audit; it makes the auditors question their career choices.

So, congratulations. You’ve replaced a rickety wooden bridge with a beautiful, high-speed, structurally unsound suspension bridge, and you’ve written a lovely blog post about how much faster the cars are going.

That was a fun read! I will now be adding "Tinybird" to my vulnerability scanner’s dictionary and recommending my clients treat it as actively hostile. I look forward to never reading this blog again.

Ah, another dispatch from the front lines of industry, where the wheel is not only reinvented, but proudly unveiled as a heptagon. It seems Oracle has finally, in the year of our Lord 2026, managed to implement a fraction of the SQL-92 standard. One must applaud the sheer velocity of this innovation. I can only assume the working group is communicating via carrier pigeon.

The premise is that we can now enforce business rules in the database using assertions, thereby placing the burden on ACID's 'C' instead of its 'I'. A noble goal, to be sure. It's a concept we've understood for, oh, about thirty years. Let's see how our plucky practitioners have managed to manifest this ancient wisdom.

They begin by creating a simple table, and then, with bated breath, attempt to write a perfectly reasonable assertion using a GROUP BY and a HAVING COUNT. This is, of course, the most direct, logical, and mathematically sound way to express the constraint: "for every shift, the count of on-call doctors must not be less than one."

And what is the result of this bold foray into declarative integrity?

ORA-08661: Aggregates are not supported.

Perfection. One simply has to marvel at the audacity. They've implemented 'assertions' that cannot handle the most fundamental of assertions: an aggregate. COUNT() is apparently a bridge too far, a piece of computational esoterica beyond the ken of this new "AI Database". What, precisely, is the 'AI' doing? Counting the licensing fees?

But fear not! Our intrepid blogger offers a "more creative way" to express this. I always shudder when an engineer uses the word 'creative'. It's typically a prelude to a gross violation of first principles. And this... this is a masterpiece of the form. A tortured, nested NOT EXISTS monstrosity that reads like a logic problem written by a first-year undergraduate after a particularly long night.

“There must not exist any doctor who belongs to a shift that has no on-call doctor”

This is what passes for elegance? This is their substitute for a simple HAVING COUNT(...) < 1? Codd must be spinning in his grave. The principle of Integrity Independence, Rule 10, was meant to free the application programmer from such Byzantine contortions! The database is supposed to be intelligent enough to manage its own integrity without the user having to perform logical gymnastics. Clearly, they've never read his seminal work, A Relational Model of Data for Large Shared Data Banks. It's only fifty-odd years old; perhaps it hasn't been indexed by their search engine yet.

And the mechanism behind this grand illusion? An "internal change tracking table" that is, by the author's own gleeful admission, a thinly veiled reimplementation of materialized view logs from 1992. Bravissimo! It only took them thirty-four years to rediscover their own work and present it as progress. They've built this entire baroque locking and tracking mechanism—this proprietary enq: AN lock, these ORA$SA$TE_ tables—all to circumvent a problem that has a known, elegant, and mathematically proven solution: Serializable Isolation.

Let's be clear. This entire Rube Goldberg machine exists because their implementation of Isolation, the 'I' in ACID, is so profoundly inadequate. Instead of providing true serializability to prevent write-skew, they've bolted a complex, opaque, and incomplete feature onto the side of the engine. It's a classic case of treating the symptoms because the disease—a weak isolation model—is too difficult to cure. Clearly they've never read Stonebraker's seminal work on concurrency control, or they'd understand they're just building a poor man's version of predicate locking. It's as if they read Brewer's CAP Theorem and decided that 'Consistency' was something you could just approximate with enough temporary tables and proprietary lock modes.

So here we are, with a list of "three solutions":

• The correct, theoretically sound one (Serializable isolation), which is noted with a casual aside, "not available in Oracle Database".
• The application-level hack (explicit locking), which pushes consistency logic out of the database, where it has no business being.
• And now, this new, "pragmatic" solution: a partially implemented, thirty-year-old standard that can't perform a basic COUNT.

It's... endearing, in a way. Like watching a toddler attempt to build a load-bearing wall out of LEGOs. You've tried so very hard, and you've certainly built something. Keep at it. Perhaps in another thirty years, you'll discover SUM(). We in academia will be waiting. Now, if you'll excuse me, I have actual research to attend to.

Alright, one of the junior devs, bless his heart, sent me this... blog post. Said it was a "deep dive" into how computers work. I’ve seen deeper dives in the office coffee pot. He's reading a book called "Three Easy Pieces," which is your first red flag. Nothing in this business is easy, kid. Not when you've had to restore a corrupted VSAM file from a tape backup that's been sitting in a warehouse in Poughkeepsie for five years. But fine, let's see what "brilliance" the youth have discovered this week.

It's cute, watching them discover the "process" like it's some lost city of gold. They draw their little diagrams of the stack and the heap and talk about it with such reverence. "A living process in memory," they call it, quoting some sci-fi nonsense about "hydration." Give me a break. Back in my day, we didn't have fancy "hydration." We had the WORKING-STORAGE SECTION in COBOL and a fixed memory partition on the System/370. You got what you were allocated, and if your batch job overran it, you didn't get a polite "segmentation fault." You got a binder full of hexadecimal core dump printouts on green bar paper, and you liked it. This whole "stack vs. heap" debate feels like two toddlers arguing over building blocks when I was building skyscrapers with JCL.

And the hero worship over this fork() and exec() song and dance is just baffling. The blog author breathlessly calls this two-step process "unmatched" and the "gold standard." Are you kidding me? You're telling me the peak of operating system design is to create an entire, exact clone of a running program—memory, file handles, the works—only to immediately throw it all away to load a different program? That’s not brilliant design; that’s like buying a brand-new car just to use its radio. We called that 'wasteful' in the 80s. A simple SUBMIT command in JCL did the same thing without all the theatrics, and it was a hell of a lot more efficient. DB2 didn't have to copy itself every time it spawned a utility process.

Then they act like I/O redirection is some kind of dark magic.

The 'wc' program writes to standard output as usual, unaware that its output is now going to a file instead of the screen.

Unaware? It’s not sentient, kid, it’s a program. And this "magic" is something we perfected decades ago. Ever hear of a JCL DD statement? //SYSOUT DD DSN=... We could route anything to anywhere—a dataset, a printer, a different terminal, a tape drive. It was explicit, powerful, and declared right up front in the job card. We didn't rely on this shell game of closing and reopening file descriptors, hoping the OS gives you the right number. You kids reinvented the Data Definition statement, made it more fragile, and are now patting yourselves on the back for its "simplicity."

I had to chuckle when he mentioned the author's nostalgia for Turbo Pascal in the 1990s. The 90s? That was practically yesterday! That was the era of GUI nonsense and client-server fluff. We were debugging CICS transactions with command-line debuggers on 3270 green-screen terminals while he was watching a call stack in a cozy IDE. The fact that he thinks the 90s are "30 years ago" ancient history tells you everything you need to know.

And the best part, the absolute kicker, is the final paragraph. After pages of praising this design as a work of timeless genius from the "UNIX gods," he mentions a paper from 2019 that calls fork() a "clever hack" that has become a "liability." Finally! The children are learning. It only took them fifty-five years to catch up to what any mainframe guy could have told you in 1985 over a cup of lukewarm Sanka. It is a hack. It was always a hack.

Mark my words, this whole house of cards built on "simple" and "elegant" hacks is going to come tumbling down. One day soon, all this distributed, containerized, forked-and-exec'd nonsense will collapse under its own complexity. And when it does, you'll all come crawling back to the rock-solid, transactional integrity of a real database on a real machine. I'll be waiting. I’ve still got the manuals.

Oh, this is just a masterpiece of investigative work. Truly. I haven't seen this level of inspired, reckless curiosity since I watched a junior developer discover you could query the production database with SELECT * without a LIMIT clause. You've peeled back the onion on your HVAC system and, to absolutely no one's surprise, found a rotten, insecure core.

It's truly admirable how you identified a critical flaw in the manufacturer's support process—namely, that their entire authentication model is based on the honor system. You didn't just find a workaround; you performed a successful social engineering penetration test and then, in a stroke of genius, published the exploit and the target's direct line. Chef's kiss. Why bother with phishing emails when you can just tell people to lie? It's a bold strategy for brute-forcing the human firewall, I'll give you that. I'm sure Durastar's legal and compliance teams are thrilled to see their trade secrets being handed out by a support engineer to a man who successfully spoofed his identity as "some guy from Indiana."

And the "feature" you uncovered! Oh, it's just beautiful. It’s not an undocumented feature, my friend; it's a pre-installed CVE. You're celebrating that your climate control system, a critical piece of infrastructure for your home, operates on a stateful inference engine with an unauthenticated, non-standard input vector.

...learn the set point by tracking the 24V thermostat’s calls for heating over time.

So, let me get this straight. The system's core logic is based on guessing. It's a black box algorithm that's trying to predict user intent based on a noisy, binary signal. What could possibly go wrong? You think you're getting "smoothing," but what you've found is a perfect vector for a denial-of-service attack. A malicious actor with access to your "smart" thermostat—which, let's be honest, is probably an IoT device with the security posture of a wet paper bag—could just send a few irregular pulses on that 24V line.

• Congratulations, you've just confused the inference engine. Does it ramp to 100% and burn out the compressor?
• Does it shut down entirely, letting your pipes freeze in the winter?
• Or does it just oscillate wildly, a physical manifestation of a logic bomb, until the hardware gives up?

You've connected this whole Rube Goldberg machine to Home Assistant, no less. That's fantastic. So now the attack surface isn't just your thermostat; it's every other insecure IoT gadget on your network. Your smart lightbulb gets compromised, and now a hacker in a Romanian basement is using it to pivot and send precisely timed signals to your heat pump to induce catastrophic failure. You were worried about noise and inefficiency; I'm worried about your house being declared a superfund site after the refrigerant leaks.

And you think this will ever pass an audit? Let's just run through a quick SOC 2 readiness check, shall we?

• Access Control: Failed. You proved their support authentication is nonexistent.
• Change Management: There is no documentation for this "feature," so its behavior is undefined and uncontrollable. Any firmware update could change it without warning.
• Risk Assessment: You've gleefully documented a single point of failure that can cause thousands of dollars in physical damage, and you're calling it a "closely guarded secret."
• Security Monitoring: What logs does this "inference engine" produce? None? Shocking.

You're lamenting the lack of a standard protocol. A standard! How quaint. You think a standard would save you? A standard is just a common set of attack vectors we've all agreed upon.

You didn't find a clever hack; you found the smoking crater where a security design review was supposed to be.

Alright, settle down, class. Alex is here to translate this... optimistic piece of technical fiction into what it actually means for those of us who carry the pager. I’ve seen this blog post before, just with a different logo at the top. It's the same story every time, and it always ends with me getting a phone call that starts with, "So, a weird thing happened..."

Here’s my operational translation of this work of art:

• It’s adorable how they describe rebuilding a replica like you’re just making a copy of a file. They conveniently omit the part where kicking off a “physical backup” on your primary node during peak traffic causes an I/O storm that makes the entire application feel like it's running on a dial-up modem. The primary starts sweating bullets, replication lag for the other replicas starts climbing into the thousands of seconds, and suddenly your High-Availability setup looks suspiciously like a Single-Point-of-Failure that's having a panic attack.

• This whole dance is always performed under the banner of a "Zero-Downtime" operation. This is my favorite marketing term. It has the same relationship to reality as a stock photo of a server room has to our actual server room. What it really means is "zero-downtime, provided the process completes in the 120 seconds we estimated, not the seven hours it will actually take, and doesn't trigger a cascading failure that requires us to take everything down anyway to 'ensure data integrity'." It’s not downtime, it's an 'unscheduled data consistency event'.

• I love the casual, hand-wavy dismissal of the one tool that might actually fix this without a full rebuild:

  ...when pt-table-sync is not an option. Let me tell you why it’s "not an option." It’s not an option because the table is 4 terabytes, the checksum would take three days to run, it would lock rows and kill production performance, and the last time someone ran it, it filled up the disk with binary logs and crashed the primary. It’s not an “option” because it’s a landmine, and you’re telling us to go play in the field next to it instead.

• Notice what’s completely missing? Any mention of monitoring. This blog post starts after the disaster. It assumes you magically discovered the corruption. In the real world, you discover replica drift when a customer calls support to complain that the report they just pulled is missing the last six hours of sales data. Why? Because the replica they were routed to has been silently broken for a week, and the only check we have is a basic replication_is_running ping that glows a happy green while the data rots from the inside out.

• So here’s the screenplay for how this plays out. It’ll be 3 AM on the Saturday of Memorial Day weekend. The logical backup you’re forced to run will be 90% complete when a network hiccup causes the connection to drop. The import on the new replica will then fail with a cryptic foreign key constraint violation because a background job on the primary deleted a row that your backup thought existed. Your entire "simple" process is now shot. And I’ll be sitting here, staring at the terminal glow, adding another sticker to my museum of dead databases right next to my prized RethinkDB one.

Thanks for the whitepaper. Now, if you'll excuse me, I have to go write an alert for the "solution" you just proposed.

Alright, settle down, kids. Let me get my reading glasses. Someone forwarded me this... article... from "CedarDB." Sounds like a brand of mulch. Let's see what the latest revolution is this week.

"Why you should care about strings."

Oh, good heavens. We're starting there? Are we? I haven't seen a headline that basic since the manual for my first Commodore PET. Back in my day, we didn't "care" about strings, we feared them. We had fixed-length COBOL records defined with a PICTURE clause, and you got your 80 characters, and you liked it. If you wanted variable length, you prayed to the VSAM gods and prepared for a week of debugging pointer errors in Assembler. These kids act like they just discovered that people write things down.

"...roughly 50% of data is stored as strings. This is largely because strings are flexible and convenient: you can store almost anything as astring."

You don't say. You can also use a wrench to hammer a nail. Doesn't make it a good idea. This isn't a feature; it's a confession. It's admitting your entire user base has the data discipline of a toddler with a box of crayons. Storing UUIDs as text? I've got JCL scripts older and smarter than that. We were storing structured data in hierarchical databases before your parents met.

And of course, some professor is quoted. “In database systems, you don’t primarily compress data to reduce size, but to improve query performance.” Deep. Truly profound. We figured that out around 1983 when we realized swapping tapes for the monthly payroll run took longer than the heat death of the universe. Smaller data meant fewer tapes to mount. It's not about "better bandwidth-utilization," you slide-deck jockey, it's about not having to call Barry from operations to go find reel 7B in a salt mine in Kansas.

So, let's get to the meat of it. Dictionary Compression. They explain it like they're unveiling the secrets of the pyramids. Storing unique values and replacing them with integers. Welcome to 1985, fellas. DB2 was doing this while you were still learning to use a fork.

The attentive reader may have noticed two things. First, we store the offsets to the strings... Second, our dictionary data is lexicographically ordered.

The attentive reader? Son, the comatose reader noticed that. If you don't store offsets, it's not a dictionary, it's a grocery list. And if you don't sort it, you can't binary search, which is the whole point. This is like a chef proudly announcing his secret technique for boiling water is to apply heat.

And then they get to the big reveal. The "problem" with dictionaries. They don't work well on high-cardinality data. No kidding. That's why we had DBAs, not just a button that says "compress." You had to actually know your data. What a concept.

So, what's their silver bullet? FSST. Fast Static Symbol Table. It replaces common substrings with tokens. My God, they've reinvented Huffman coding and given it a marketing budget. "In FSST-Lingo, substrings are called 'symbols' and tokens are called 'codes'." How precious. It's still just a lookup table, you've just made it for pieces of strings instead of whole ones. Congratulations on inventing a fractal dictionary.

And the best part, the absolute chef's kiss of this whole thing, is when they realize that querying this FSST-encoded gibberish is a nightmare.

One naive way to evaluate this query on the data would be to decompress each compressed string and then perform a string comparison. This is quite slow.

Ya think? So what's the revolutionary solution they landed on after all this brainpower?

The key idea is to create a dictionary from the input data and then use FSST to compress only the dictionary.

Stop the presses. Hold the phone. You went through all that, just to land on... compressing the dictionary? You built a whole new engine, wrote a blog post, and your grand innovation is to add a second layer of compression to the thing you just said had problems? And then, you have the nerve to footnote that DuckDB did it six months ago. You're not even the first kid on the block to rediscover this "new" idea!

Let's look at these benchmarks. They're so proud.

• "Enabling FSST reduces the storage size... by 20%..."
• "Cold runs... query runtimes improve by up to 40%..."

That's lovely. Now let's look at the fine print, shall we?

• "For the ClickBench queries... the runtime for hot runs is higher by up to 2.8x."

So let me get this straight. You made things faster when you have to go fetch the data from the digital equivalent of the tape library. But once the data is actually in memory, where real work gets done, your "improvement" makes the query almost three times slower. You've optimized for the one-off report that the CEO asks for once a year, while kneecapping the operational queries that run a thousand times a second. Brilliant. Absolutely brilliant. As they say, "there’s no free lunch, you can’t beat physics." No, but you can apparently build a really slow car and brag about its fuel efficiency when it's being towed.

Their proposed solution? "...one way to improve the performance... might be to cache decompressed strings in the buffer manager."

Just add another layer of caching. The answer to and cause of all of life's problems. So now we have the original data, a dictionary of the data, an FSST-compressed dictionary of the data, the FSST symbol table, and now you want to add a cache of the decompressed FSST-compressed dictionary of the data. This isn't a database system; it's a Russian nesting doll of lookup tables. At some point, you're spending more CPU cycles figuring out how to read the data than actually reading it.

It's always the same. Every decade, a new generation of bright-eyed programmers stumbles out of university, reinvents a concept from a 40-year-old IBM System R paper, gives it a four-letter acronym, and writes a blog post like this. They talk about trade-offs and resource usage like it's some new revelation. We've been making these trade-offs since our servers had less memory than your wristwatch.

They end by asking me to download the community version. I think I'll pass. Now if you'll excuse me, I've got a tape library that needs dusting. At least I know it works.

Oh, fantastic. Just what my Q3 budget needed: the ability to “Explore with AI” from a browser tab. I can already hear the cha-ching of a thousand GPU instances spinning up because a marketing intern asked our new data platform, “What’s the meaning of life, but like, for our Q2 sales numbers?” Truly, a revolutionary leap forward for our expense reports.

And what’s this little gem? “Time Series and Playgrounds bring back the flexibility of Classic, improved for Forward.” Improved. Let me translate that from Vendor-ese to English: “We deprecated the old version you were perfectly happy with, deliberately removed features, and are now graciously selling them back to you as an ‘improvement’ in our new, mandatory platform.” It’s not an upgrade; it’s a ransom note with better kerning. This whole “Forward” business smells less like progress and more like a forced march off a financial cliff.

They sell you on the dream of writing SQL in a browser, as if our engineers are just dying to trade their finely-tuned local environments for a text box that probably hangs if you look at it wrong. But let’s not get distracted by the shiny features. Let’s do some quick back-of-the-napkin math, shall we? I like to call this the Goldman TCO—the True Cost of Obfuscation.

First, the sticker price. They’ll show you a lovely, simple pricing page. Probably something with smiling cartoon animals and tiers named “Sprout,” “Grow,” and “Basically Your Entire Series B Funding.” But that’s just the cover charge.

The real party starts here:

• The “Forward” Migration: Oh, it’s just a simple migration, they’ll say. My numbers say that’s a minimum of 600 engineer-hours to untangle every API call, every data pipeline, and every dashboard we painstakingly built on “Classic.” At a blended rate, that’s a cool $90,000 in productivity we can just light on fire. And that’s before we bring in the inevitable consultants they “highly recommend” to ensure a “seamless transition.” Add another $75,000 for a team of people named Chad who will spend six weeks producing a 200-slide PowerPoint about our “data journey.”
• The AI Fun Tax: That little “Explore with AI” button isn’t free, darling. It’s powered by what I’m sure are called “Synaptic Thought Units” or some other nonsense, billed per thousandth of a second. The “Playground” is where my developers get to discover, through trial and error, exactly how to rack up a $50,000 overage bill in a single afternoon while “prototyping innovative solutions.”
• Retraining & Unlearning: The team now has to be trained on the “new, improved” workflow. That’s another week of lost productivity for the entire data team. We’ll call it a $40,000 team-building exercise in collective frustration.

So, let’s tally it up. A modest base license of, say, $100,000 a year, plus $165,000 in migration and consulting fees, plus a conservative $50,000 AI “oopsie” fund, plus $40,000 in retraining. Our “simple browser-based solution” is now a $355,000 Year-One adventure.

They’ll promise a 300% ROI, based on “developer velocity” and “synergistic data insights.” My napkin here shows a 300% increase in our cloud services bill and a Q4 where we have to choose between paying for this “Playground” and keeping the lights on. I wonder how you amortize “synergy” on a balance sheet. It certainly doesn’t pay the bills.

Anyway, this has been a profoundly enlightening two minutes. Thank you, Tinybird, for the reminder that the most valuable data tool I have is the delete button. I’ll be cheerfully unsubscribing now.

Alright, team, gather 'round for the all-hands on our new salvation, the PlanetScale MCP server. I've read the announcement, and my eye has developed a brand new twitch. They say it'll bring our database "directly into our AI tools," which sounds just as reassuring as bringing a toddler into a server room. Here are just a few of my favorite highlights from this brave new future.

• So, let me get this straight. We're connecting a Stochastic Parrot directly to our production database. The same technology that confidently hallucinates API calls and invents library functions now gets to play with customer data. I’m particularly excited for the execute_write_query permission. The blog post kindly includes this little gem:

  We advise caution when giving LLMs write access to any production database. Ah, yes, "caution." I remember "caution." It’s what we were told right before that "simple" ALTER TABLE migration in '22 locked the entire user table for six hours during peak traffic. Giving a glorified autocomplete bot write-access feels less like a feature and more like a creative way to file for bankruptcy.

• I’m very comforted by the "Safe and intelligent query execution." Specifically, the "Destructive query protection" that blocks UPDATE or DELETE statements without a WHERE clause. That’s fantastic. It will definitely stop a cleverly worded prompt that generates DELETE FROM users WHERE is_active = true;. It has a WHERE clause, so it's totally safe, right? We're not eliminating human error; we're just outsourcing it to a machine that can make mistakes faster and at a scale we can't even comprehend. This isn't a safety net; it's a safety net with a giant, AI-shaped hole in the middle.

• My favorite new workflow enhancement is the "Human confirmation for DDL." It says any schema change will "prompt the LLM to request human confirmation." Wonderful. So my job, as a senior engineer with a decade of experience watching databases catch fire, is now to be a human CAPTCHA for a language model that thinks adding six new JSONB columns to a billion-row table is a "quick optimization." My pager is about to be replaced by a Slack bot asking, "Are you sure you want to drop index_users_on_email? Pretty please?" at 2 AM.

• And of course, the promise of letting everyone else in on the fun. "Use natural language to learn about your data." I can already picture it: the marketing team asking, "Just pull me a quick list of all users and everything they've ever clicked on," which the AI helpfully translates into a full table scan that grinds our read replicas to a fine powder. I have PTSD from junior developers writing N+1 queries. Now we're giving the entire company a tool to invent N+Infinity queries on the fly. What could possibly go wrong?

• Ultimately, this is just another layer. Another API, another set of credentials, another point of failure in a chain that's already begging to break. We’re not solving the problem of complex database interactions; we're just trading a set of well-understood, predictable SQL problems for a new set of opaque, non-deterministic AI problems. When this breaks, who do I file a ticket with? The protocol? The model? Myself, for thinking this time would be different?

Anyway, I’ve got to go update my resume. It seems "AI Query Babysitter" is a new and exciting job title.