🔥 The DB Grill 🔥

Alright, gather 'round, folks, because here we go again. MongoDB, the undisputed champion of convincing people that eventual consistency is a feature, is apparently now guaranteeing consistent and durable write operations. Oh, really? Because last I checked, that was the baseline expectation for anything calling itself a database, not some revolutionary new parlor trick. They’re doing this with... wait for it... write-ahead logging! My word, has anyone informed the relational database world, which has only been doing that since, oh, the dawn of time? And they flush the journal to disk! I'm genuinely shocked, truly. I thought Mongo just kinda, whispered data into the ether and hoped for the best.

Then, they trot out the "synchronous replication to a quorum of replicas" and the claim that "replication and failover are built-in and do not require external tools." Yes, because every other modern database system requires you to hire a team of dedicated medieval alchemists to conjure up a replica set. Imagine that, a database that replicates itself without needing a separate enterprise-grade forklift and a team of consultants for every single failover. The audacity! And to set it up, you just... start three mongod instances. It’s almost like they're trying to make it sound complicated when it's just, you know, how these things work.

But here’s where the innovation truly blossoms. To "experiment with replication," they ran it in a lab with Docker Compose. A lab! With Docker Compose! Groundbreaking. But the networks were too perfect, you see. So, they had to bring out the big guns: tc and strace. Yes, the tools every seasoned sysadmin has had in their kit since forever are now being wielded like enchanted artifacts to "inject some artificial latencies." Because simulating reality is apparently a Herculean task when your core product struggles with it natively. They’re manually adding network delays and disk sync delays just to prove a point about... well, about how slow things can get when you force them to be slow. Who knew? It's like rigging a race so your slowest runner looks like they're trying really hard to finish last.

They write to the primary and read from each node to "explain the write concern and its consequences for latency." You mean, if I write something and don't wait for it to be replicated, I might read an old value? Stop the presses! The fundamental trade-off between consistency and availability, re-discovered in a Docker container with tc and strace! And bless their hearts, they even provided the Dockerfile and docker-compose.yml because setting up a basic three-node replica set in containers is apparently rocket science that requires bespoke NET_ADMIN and SYS_PTRACE capabilities. I particularly enjoyed the part where they inject a 50 millisecond fdatasync delay. Oh, the horror! My goodness, who would have thought that writing to disk takes time?

Then they discover that if you set w=0—that's "write to no one, tell no one"—your writes are fast, but your reads are "stale." Imagine! If you tell a system not to wait for acknowledgement, it, get this, doesn't wait for acknowledgement, and then other nodes might not have the data yet. This isn't just an introduction, it's a profound, spiritual journey into the heart of distributed systems. And the pièce de résistance: "the client driver is part of the consensus protocol." My sides. So, my Node.js driver running on some budget server in Ohio is actively participating in a Raft election? I thought it just sent requests. What a multi-talented piece of software.

Finally, they switch to w=1, journal=false and proudly announce that this "reduces write latency to just the network time," but with the caveat that "up to 100 milliseconds of acknowledged transactions could be lost" if the Linux instance crashes. But if the MongoDB instance fails, "there is no data loss, as the filesystem buffers remain intact." Oh, good, so as long as your kernel doesn't panic, your data's safe. It's a "feature," they say, for "IoT scenarios" where "prioritizing throughput is crucial, even if it means accepting potential data loss during failures." Sounds like a fantastic business requirement to build upon. "Sure, we're losing customer orders, but boy, are we losing them fast!"

In summary, after all this groundbreaking lab work, what do we learn? MongoDB allows you to balance performance and durability. You mean, like every single database ever built? They’ve essentially reinvented the wheel, added some shiny Docker paint, and called it a masterclass in distributed systems. My prediction? Someone, somewhere, will read this, excitedly deploy w=1, journal=false to "prioritize throughput," and then come crying to Stack Overflow when their "IoT" data vanishes into the digital ether. But hey, at least they’ll have the docker compose up --build command handy for the next time they want to watch their data disappear.

Alright, gather 'round, folks, because the titans of database research have dropped another bombshell! We're talking about the earth-shattering revelations from Postgres 18 beta2 performance! And let me tell you, when your main takeaway is 'up to 2% less throughput' on a benchmark step you had to run for 10 times longer because you apparently still can't figure out how long to run your 'work in progress' steps, well, that's just riveting stuff, isn't it? It’s not a benchmark, it’s a never-ending science fair project.

And this 'tl;dr' summary? Oh, it's a masterpiece of understatement. We've got our thrilling 2% decline in one corner, dutifully mimicking previous reports – consistency, at least, in mediocrity! Then, in the other corner, a whopping 12% gain on a single, specific benchmark step that probably only exists in this particular lab's fever dreams. They call it 'much better,' I call it grasping at straws to justify the whole exercise.

The 'details' are even more glorious. A single client, cached database – because that's exactly how your high-traffic, real-world systems are configured, right? No contention, no network latency, just pure, unadulterated synthetic bliss. We load 50 million rows, then do 160 million writes, 40 million more, then create three secondary indexes – all very specific, very meaningful operations, I'm sure. And let's not forget the thrilling suspense of 'waiting for N seconds after the step finishes to reduce variance.' Because nothing says 'robust methodology' like manually injecting idle time to smooth out the bumps.

Then we get to the alphabet soup of benchmarks: l.i0, l.x, qr100, qp500, qr1000. It's like they're just mashing the keyboard and calling it a workload. My personal favorite is the 'SLA failure' if the target insert rate isn't sustained during a synthetic test. News flash: an SLA failure that only exists in your test harness isn't a failure, it's a toy. No actual customer is calling you at 3 AM because your qr100 benchmark couldn't hit its imaginary insert rate.

And finally, the crowning achievement: relative QPS, meticulously color-coded like a preschooler's art project. Red for less than 0.97, green for greater than 1.03. So, if your performance changes by, say, 1.5% in either direction, it's just 'grey' – which, translated from corporate-speak, means "don't look at this, it's statistically insignificant noise we're desperately trying to spin." Oh, and let's not forget the glorious pronouncement: "Normally I summarize the summary but I don't do that here to save space." Because after pages of highly specific, utterly meaningless numerical gymnastics, that's where we decide to be concise.

So, what does this groundbreaking research mean for you, the actual developer or DBA out there? Absolutely nothing. Your production Postgres instance will continue to operate exactly as it did before, blissfully unaware of the thrilling 2% regression on a synthetic query in a cached environment. My prediction? In the next beta, they'll discover a 0.5% gain on a different, equally irrelevant metric, and we'll have to sit through this whole song and dance again. Just deploy the damn thing and hope for the best, because these 'insights' certainly aren't going to save your bacon.

Oh, Percona PMM! The all-seeing eye for your MySQL empire, except apparently, it's got a rather nasty blind spot – and a convenient memory wipe when it comes to past breaches. Because, of course, the very first thing they want you to know is that 'no evidence this vulnerability has been exploited in the wild, and no customer data has been exposed.' Right. Because if a tree falls in the forest and you don't have enough logs to parse its fall, did it even make a sound? It's the corporate equivalent of finding a gaping hole in your security fence and proudly declaring, 'Don't worry, we haven't seen any sheep escape yet!' Bless their hearts for such optimistic denial.

But let's not dwell on their admirable faith in invisible, unlogged non-events. The real gem here is that this 'vulnerability has been discovered in all versions of Percona Monitoring and Management.' All of them! Not just some obscure build from 2017 that nobody uses, but the entire family tree of their supposedly robust, enterprise-grade monitoring solution. It's almost impressive in its comprehensive lack of foresight.

And where does this monumental oversight originate? Ah, 'the way PMM handles input for MySQL services and agent actions.' So, basically, it trusts everyone? It's like building a secure vault and then leaving the key under the mat labeled 'please sanitize me.' And naturally, it's by 'abusing specific API endpoints.' Because why design a secure API with proper authentication and input validation when you can just throw some JSON at the wall and hope it doesn't accidentally reveal your grandma's maiden name? This isn't some cutting-edge, nation-state zero-day. This sounds like 'we forgot to validate the user input' level stuff, for a tool whose entire purpose is to monitor the most sensitive parts of your infrastructure. The very thing you deploy to get a handle on risk is, itself, a walking, talking risk assessment failure.

So, what's next? They'll patch it, of course. They'll issue a stern, somber release about 'lessons learned' and 'commitment to security' – probably with some newly minted corporate jargon about 'strengthening our security posture through proactive vulnerability management frameworks.' And then, sometime next year, we'll get to do this exact same cynical dance when their next 'revolutionary' feature, designed to give you 'unprecedented insights into your database performance,' turns out to be broadcasting your entire database schema on a public Slack channel. Just another glorious day in the never-ending parade of 'trust us, we're secure' software.

Alright, so we're kicking off with "recent reads" that are actually "listens." Fantastic start, really sets the tone for the kind of precision and rigorous analysis we can expect. It’s like a tech startup announcing a "groundbreaking new feature" that’s just a slightly re-skinned version of something that’s been around for five years. But hey, "series name," right? Corporate speak for "we didn't bother updating the template."

First up, the "Billion Dollar Whale." Oh, the shock and fury that a Wharton grad—a Wharton grad, mind you, the pinnacle of ethical business acumen!—managed to con billions out of a developing nation. Who could have ever predicted that someone from an elite institution might be more interested in personal enrichment than global well-being? And "everyone looked away"—banks, regulators, governments. Yes, because that's not the entire operating model of modern finance, is it? We build entire platforms on the principle of looking away, just with prettier dashboards and more blockchain. The "scale" was shocking? Please. The only shocking thing is that anyone's still shocked by it. This entire system runs on grift, whether it’s a Malaysian sovereign wealth fund or a VC-funded startup promising to "disrupt" an industry by simply overcharging for a basic service.

Then, for a complete tonal shift, we drift into the tranquil, emotionally resonant world of Terry Pratchett's final novel. Because when you’re done being infuriated by real-world financial malfeasance, the obvious next step is to get misty-eyed over a fictional witch whose soul almost got hidden in a cat. It’s like a corporate agile sprint: big, messy, systemic problem, then a quick, sentimental "retrospective" to avoid actually addressing the core issues. And the high praise for Pratchett's writing, even with Alzheimer's, compared to "most writers at their best." It's the literary equivalent of saying, "Our legacy system, despite being held together by duct tape and prayer, still outperforms your shiny new microservices architecture." Always good for a laugh, or a tear, depending on how much coffee I've had.

But let's pivot to the real gem: David Heinemeier Hansson, or DHH as the cool kids say. Now apparently a "young Schwarzenegger with perfect curls"—because nothing screams "cutting-edge tech thought leader" like a six-hour interview that's essentially a self-congratulatory monologue. Six hours! That's not an interview, that's a hostage situation for Lex Fridman. "Communist" to "proper capitalist"? "Strong opinions, loosely held"? That’s not authenticity, folks, that's just a finely tuned ability to pivot to whatever gets you maximum engagement and speaking fees. It's the ultimate "agile methodology" for personal branding.

And the tech takes! Ruby "scales," he says! Citing Shopify handling "over a million dynamic requests per second." Dynamic requests, mind you. Not actual resolved transactions, not sustained throughput under load, just "requests." It’s the kind of success metric only an executive or a "thought leader" could love. Ruby is a "luxury language" that lets developers "move fast, stay happy, and write expressive code." Translate that for me: "We want to pay top dollar for engineers who enjoy what they do, regardless of whether the underlying tech is actually efficient or just comfortable. And if it's slow, blame the database, because developer time is obviously more valuable than server costs." Spoken like a true champion of the enterprise budget.

And the AI bit: using it as a "tutor, a pair programmer, a sounding board." So, basically, an expensive rubber duck that costs compute cycles. But "vibe coding"? That’s where he draws the line? Not the six-hour, self-congratulatory podcast, but the "vibe coding" that feels "hollow" and like skills are "evaporating." Heaven forbid you lose your "muscle memory" while the AI does the actual thinking. Because programming isn't just a job, it's a craft! A bespoke, hand-stitched artisan craft that requires "hands on the keyboard" even when a machine could do it faster. It's like insisting on hand-cranking your car because "muscle memory" is knowledge, even though the electric starter is clearly superior.

So, what have we learned from this insightful journey through financial crime, fictional feline souls, and tech bros who've apparently solved coding by not "vibe coding"? Absolutely nothing. Except maybe that the next "disruptive" tech will still manage to funnel billions from somewhere, make a few people very rich, be lauded by a six-hour podcast, and then we'll all be told it's a "luxury experience" that lets us "move fast" towards... well, towards the next big scam. Cheers.

Ah, yes, the age-old mystery: "Are your database read operations unexpectedly slowing down as your workload scales?" Truly, a profound question for the ages. I mean, who could possibly expect that more people trying to access more data at the same time might lead to, you know, delays? It's not like databases have been doing this for decades, or that scaling issues are the very bedrock of half the industry's consultants. "Bottlenecks that aren’t immediately obvious," they say. Right, because the first place anyone looks when their system is sluggish is usually the coffee machine, not the database getting hammered into submission.

Then we get to the good stuff: "Many organizations running PostgreSQL-based systems." Shocking! Not MySQL, not Oracle, but PostgreSQL! The sheer audacity of these organizations to use a widely adopted, open-source database and then experience, gasp, scaling challenges. And what's the culprit? "Many concurrent read operations access tables with numerous partitions or indexes." So, in other words, they're using a database... like a database? With data structures designed for performance and partitioning for management? My word, it’s almost as if the system is being utilized!

But wait, there's a villain in this tale, a true architectural betrayal: these operations can "even exhaust PostgreSQL’s fast path locking mechanism." Oh, the horror! Exhaustion! It sounds less like a technical limitation and more like PostgreSQL has been up all night watching cat videos and just needs a good nap. And when this poor mechanism finally collapses into a heap, what happens? The system is "forcing the system to use shared memory locks." Forcing! As if PostgreSQL is being dragged kicking and screaming into a dark alley of less-optimal lock management. It’s almost as if it’s a designed fallback mechanism for when the fast path isn't feasible, rather than some catastrophic, unforeseen failure. I'm sure the next sentence, tragically cut short, was going to reveal that "The switch... will invariably lead to a 'revolutionary' new caching layer that just shoves more hardware at the problem, or a whitepaper recommending you buy more RAM. Because when in doubt, just add RAM. It's the silicon equivalent of a participation trophy for your database.

Alright, gather ‘round, folks, because we’ve got another groundbreaking revelation from the bleeding edge of distributed systems theory! Apparently, after a rigorous two-hour session of two “experts” reading a paper for the first time live on camera—because nothing says “scholarly rigor” like a real-time, unedited, potentially awkward book club—they’ve discovered something truly revolutionary: the F-threshold fault model is outdated! My word, stop the presses! I always assumed our distributed systems were operating on 19th-century abacus logic, but to find out the model of faults is a bit too simple? Who could have possibly imagined such a profound insight?

And what a way to deliver this earth-shattering news! A two-hour video discussion where one of the participants asks us to listen at 1.5x speed because they "sound less horrible." Confidence inspiring, truly. I’m picturing a room full of engineers desperately trying to debug a critical production outage, and their lead says, "Hold on, I need to check this vital resource, but only if I can double its playback speed to avoid unnecessary sonic unpleasantness." And then there's the pun, "F'ed up, for F=1 and N=3." Oh, the sheer intellectual power! I’m sure universities worldwide are already updating their curricula to include a mandatory course on advanced dad jokes in distributed systems. Pat Helland must be quaking in his boots, knowing his pun game has been challenged by such linguistic virtuosos.

So, the core argument, after all this intellectual gymnastics, is that machines don't fail uniformly. Shocking! Who knew that a server rack in a scorching data center might be more prone to issues than one chilling in an arctic vault? Or that software updates, those paragons of perfect execution, might introduce new failure modes? It’s almost as if the real world is… complex. And to tackle this mind-bending complexity, this paper, which they admit doesn't propose a new algorithm, suggests a "paradigm shift" to a "probabilistic approach based on per-node failure probabilities, derived from telemetry and predictive modeling." Ah, yes, the classic "trust the black box" solution! We don’t need simple, understandable guarantees when we can have amorphous "fault curves (p_u)" that are never quite defined. Is p_u 1% per year, per month, per quorum formation? Don't worry your pretty little head about the details, just know the telemetry will tell us! It’s like being told your car is safe because the dashboard lights up with a "trust me, bro" indicator.

And then they dive into Raft, that bastion of safety, and declare it’s only "99.97% safe and live." What a delightful piece of precision! Did they consult a crystal ball for that number? Because later, they express utter confusion about what "safe OR live" vs. "safe AND live" even means in the paper. It seems their profound academic critique hinges on a fundamental misunderstanding of what safety and liveness actually are in consensus protocols. My goodness, if you can’t tell the difference between "my system might lose data OR it might just stop responding" versus "my system will always be consistent and always respond," perhaps you should stick to annotating grocery lists. The paper even claims "violating quorum intersection invariants triggers safety violations"—a statement so hilariously misguided it makes me question if they’ve ever actually read the Paxos family of protocols. Quorum intersection is a mathematical guarantee, not some probabilistic whim!

But wait, there's more! The paper suggests "more nodes can make things worse, probabilistically." Yes, because adding more unreliable components to a system, with poorly understood probabilistic models, definitely could make things worse. Truly, the intellectual bravery to state the obvious, then immediately provide no explanation for it.

In the end, after all the pomp and circumstance, the lengthy video, the undefined p_us, and the apparent confusion over basic distributed systems tenets, the blog post’s author essentially shrugs and admits the F-abstraction they initially mocked might actually be quite useful. They laud its simplicity and the iron-clad safety guarantees it provides. So, the great intellectual journey of discovering a "paradigm shift" concludes with the realization that, actually, the old way was pretty good. It’s like setting off on an epic quest to find a revolutionary new form of wheeled transport, only to return with a slightly scuffed but perfectly functional bicycle, declaring it to be "not bad, really."

My prediction? This "HotOS 2025" paper, with its 77 references validating its sheer volume of reading, will likely grace the bottom of many academic inboxes, perhaps serving as a handy coaster for coffee cups. And its grand "paradigm shift" will gently settle into the dustbin of "interesting ideas that didn't quite understand what they were trying to replace." Pass me a beer, I need to go appreciate the simple, non-probabilistic guarantee that my fridge will keep it cold.

Oh, excellent, another intrepid pioneer has strapped a jetpack onto a tricycle and declared it the future of intergalactic travel. "Tinybird Code as a Claude Code sub-agent." Right, because apparently, the simple act of writing code is far too pedestrian these days. We can't just build things; we have to build things with AI, and then we have to build our AI with other AI, which then acts as a "sub-agent." What's next, a meta-agent overseeing the sub-agent's existential dread? Is this a software development lifecycle or a deeply recursive inception dream?

The sheer, unadulterated complexity implied by that title is enough to make a seasoned DBA weep openly into their keyboard. We're not just deploying applications; we're attempting to "build, deploy, and optimize analytics-powered applications from idea to production" with two layers of AI abstraction. I'm sure the "idea" was, in fact, "let's throw two trendy tech names together and see what sticks to the wall." And "production"? My guess is "production" means it ran without immediately crashing on the author's personal laptop, perhaps generating a CSV file with two rows of sample data.

"Optimize analytics-powered applications," they say. I'm picturing Claude Code spitting out 15 different JOIN clauses, none of them indexed, and Tinybird happily executing them at the speed of light, only for the "optimization" to be the sub-agent deciding to use SELECT * instead of SELECT ID, Name. Because, you know, AI. The real measure of success here will be whether this magnificent Rube Goldberg machine can generate a PowerPoint slide deck about itself without human intervention.

"Here's how it went." Oh, I'm sure it went phenomenally well, in the sense that no actual business value was generated, but a new set of buzzwords has been minted for future conference talks. My prediction? Within six months, this "sub-agent" will have been silently deprecated, probably because it kept trying to write its own resignation letter in Python, and someone will eventually discover that a simple pip install and a few lines of SQL would've been 100 times faster, cheaper, and infinitely less prone to an existential crisis.

Oh, hold the phone, folks, we've got a groundbreaking bulletin from the front lines of database innovation! CedarDB, in a stunning display of self-awareness, has apparently just stumbled upon the earth-shattering realization that turning an academic research project into something people might actually, you know, use is "no trivial task." Truly, the depths of their sagacity are unfathomable. I mean, who would've thought that transitioning from a university sandbox where "success" means getting a paper published to building something a paying customer won't immediately throw their monitor at would involve differences? It's almost as if the real world has demands beyond theoretical elegance!

They're "bringing the fruits of the highly successful Umbra research project to a wider audience." "Fruits," you say? Are we talking about some kind of exotic data-mango, or are these the same bruised apples everyone else is trying to pass off as revolutionary? And "Umbra," which sounds less like a performant database and more like a moody indie band or a particularly bad shade of paint, apparently "undoubtedly always had the potential" to be "highly performant production-grade." Ah, potential, the sweet siren song of every underfunded, overhyped academic pet project. My grandma had the potential to be an astronaut; it doesn't mean she ever left her armchair.

The real kicker? They launched a year ago and were "still figuring out the differences between building a research system at university, and building a system for widespread use." Let that sink in. They started a company, presumably with actual venture capital, and then decided it might be a good idea to understand what a "production workload" actually entails. It's like opening a Michelin-star restaurant and then admitting your head chef just learned what an oven is. The sheer audacity to present this as a "learning journey" rather than a colossal miscalculation is, frankly, breathtaking. And after a year of this enlightening journey, what's their big takeaway? "Since then, we have learned a lot." Oh, the pearls of wisdom! Did they learn that disks are involved? That queries sometimes finish, sometimes don't? Perhaps that customers prefer data not to spontaneously combust? My prediction? Next year, they'll publish an equally profound blog post titled "We Discovered That People Like Databases That Don't Crash Every Tuesday." Truly, the future of data is in such capable, self-discovering hands.

Alright, gather 'round, folks, because I've just stumbled upon a headline that truly redefines "data integrity." "SQLite WAL has checksums, but on corruption it drops all the data and does not raise error." Oh, excellent. Because nothing instills confidence quite like a safety mechanism that, upon detecting an issue, decides the most efficient course of action is to simply wipe the slate clean and then not tell you about it. It's like having a smoke detector that, when it smells smoke, immediately sets your house on fire to "resolve" the problem, then just sits there silently while your life savings go up in digital flames.

Checksums, you say? That's just adorable. It's security theater at its finest. We've got the mechanism to detect a problem, but the prescribed response to that detection is akin to a surgeon finding a tumor and deciding the most prudent step is to perform an immediate, unscheduled full-body amputation. And then the patient just... doesn't wake up, with no explanation. No error? None whatsoever? So, you're just happily humming along, querying your database, thinking everything's just peachy, while in the background, SQLite is playing a high-stakes game of digital Russian roulette with your "mission-critical" data. One bad bit flip, one cosmic ray, one overly aggressive vacuum job, and poof! Your customer records, your transaction logs, your meticulously curated cat picture collection – all just gone. Vaporized. And the best part? You won't know until you try to access something that's no longer there, at which point the "solution" has already been elegantly implemented.

I can just hear the meeting where this was conceptualized: "Well, we could raise an error, but that might be... disruptive. Users might get confused. We should strive for a seamless, 'self-correcting' experience." Self-correcting by erasing everything. It's not a bug, it's a feature! A feature for those who truly believe in the minimalist approach to data retention. My prediction? Within five years, some cutting-edge AI startup will laud this as a revolutionary "zero-latency data purging mechanism" for "proactive compliance with GDPR's Right to Be Forgotten." Just try to remember what you wanted to forget, because SQLite already took care of it. Silently.