🔥 The DB Grill 🔥

Oh, this is just wonderful. Another award. I was just telling the board we need to allocate more of our budget towards celebrating vendor press releases. It’s a real morale booster, especially for the accounts payable team.

I’m so thrilled to see Elastic named a Leader. "Leader" is one of my favorite words. It has such a reassuring ring to it, right up there with "enterprise-grade," "unlimited," and "price available upon request." It tells me that we’re not just buying a product; we’re buying a relationship. A very, very expensive relationship where we pay for their leadership, and in return, they lead us to new and creative ways to expand our annual commitment.

And from the IDC MarketScape, no less! I always find these reports so clarifying. They cut through the noise with their beautiful, easy-to-understand charts. It’s almost as if the complexity of our entire security and observability stack can be reduced to a single dot on a 2x2 grid. And I'm sure the cost of getting a favorable position on that grid has absolutely no impact on the final license fee. That would be cynical.

What I truly admire is the focus on Extended Detection and Response. The word "extended" is just brilliant from a business perspective. It implies that what we have now is insufficient, incomplete. It creates a need we didn't even know we had. It’s not just detection; it’s extended detection. I assume this is followed by extended implementation timelines, extended training for our already-overburdened engineers, and, my personal favorite, an extended invoice.

Let’s just do some quick back-of-the-napkin math here. I’m sure their ROI calculator, which I’m positive was built by the marketing department, shows a 500% return in the first six months. That’s adorable. Let’s try my calculator, which I call "Reality."

Let's assume the sticker price for this "Leader" solution is a modest, completely hypothetical $500,000 per year. A bargain for leadership!

• Migration & Integration: We have to move our data from the last "Leader" solution we were sold. That requires consultants. Let’s be conservative and call that a one-time fee of $250,000. They always know a "preferred partner" to recommend.
• Training: Our team of 15 engineers will need to be re-skilled. That's a week of lost productivity per engineer, plus the training course fees. Let's ballpark that at a gentle $75,000.
• Infrastructure Overhead: This "solution" doesn't run on hopes and dreams. It runs on servers. Whether on-prem or in the cloud, that data ingestion and processing costs real money. I’ll just pencil in a 30% uplift on our cloud bill. So, another $300,000 annually.
• The "Oh, You Need That Module?" Surcharge: The feature that actually sold us on the platform will, inevitably, be in the "Titanium++ Enterprise Max" tier, which wasn't on the initial quote. Add another $150,000.

So, our "modest" $500,000 solution actually has a True First-Year Cost of $1,275,000. And an ongoing annual cost of $950,000, assuming they don't hit us with the standard 10% "cost of living" price hike next year.

It's an investment in a synergistic, future-proof platform that breaks down silos.

I love that. We’re not just spending money; we’re investing in synergy. And by "breaking down silos," they mean creating one, giant, inescapable silo with their name on it. The vendor lock-in is so tight, it’s practically a feature. Once our data is in their proprietary format, getting it out would be more expensive than just paying whatever they ask for at renewal. It’s a brilliant business model, really. I have to admire the sheer audacity.

So, with a TCO of nearly $1.3 million against our projected annual profit of... well, let's just say this "leadership" will lead us straight into Chapter 11. But we’ll have a very well-monitored bankruptcy. We'll be able to detect and respond to our own financial collapse in real-time. That’s the kind of ROI you just can’t put a price on.

Honestly, congratulations to Elastic. Keep up the great work. We’ll be sure to send a fruit basket to your sales team. A very small one. From last season.

Alright, settle down, kids. Let me put down my coffee—the real kind, brewed in a pot that's been stained brown since the Reagan administration—and take a look at this... this press release from the future.

"Elastic named a Leader in The Forrester Wave™: Cognitive Search Platforms, Q4 2025."

Well, isn't that just precious? A "Leader." I've been a leader in my fantasy football league three times and all it got me was a cheap plastic trophy and the obligation to buy the first round. And they've won an award for the end of 2025? My goodness. Back in my day, you had to actually, you know, finish the quarter before you got a gold star for it. We called that "auditing." These days, I guess you just call it synergy.

"Cognitive Search." Oh, you have to forgive an old man. We had a simpler term for that back on the System/370: a program that works. The idea that the machine is "thinking"... Listen, I've seen a CICS transaction get stuck in a loop that printed gibberish to the console for six hours straight. The only thing that machine was "thinking" about was the heat death of the universe.

They talk about semantic understanding and vector search like they've split the atom all over again. It's adorable. You're telling me you can turn a sentence into a string of numbers to find... other, similar strings of numbers? Groundbreaking. We were doing that with DB2 in '85. It wasn't called "AI-powered vector similarity," it was called a "well-designed indexing strategy" written by a guy in a short-sleeved button-down who actually understood the data. You didn't ask the machine to understand the "vibe" of your query. You wrote a proper SQL statement, maybe threw in a LIKE clause with a few wildcards, and you got your answer. If you were slow, the system administrator walked over to your desk and asked if you were trying to boil the processor.

...a platform that intelligently surfaces the most relevant information...

You want to see "relevant information surfaced intelligently"? Try dropping a tray of 80-column punch cards for the quarterly payroll run. You'll see a team of five COBOL programmers "intelligently surface" every single one of those cards into the correct order with a level of focus and terror you startup folks have never experienced. That's a high-availability, fault-tolerant, human-powered sorting algorithm.

I'm sure this "Forrester Wave™"—and you just know they paid a handsome fee for that little ™ symbol—is filled with all sorts of metrics.

• Query latency measured in milliseconds. We measured things in "number of coffee breaks you could take while the batch job ran."
• Unprecedented scale. We called that adding another tape library. It was the size of a small car and sounded like a jet taking off, but it worked. You learn a lot about data integrity when you know a single speck of dust on a magnetic tape can wipe out a month's worth of financial records. You kids and your "ephemeral cloud storage" have no idea of the physical dread.
• Intuitive user interface. Is it more intuitive than a 3270 green screen? Because on that screen, you knew exactly where you were. You were in hell. But it was a consistent, predictable hell.

So congratulations, Elastic. You've successfully reinvented a well-indexed VSAM file, slapped a marketing budget on it that could fund a small country, and got some analyst who's never had to degauss a tape to call you a "Leader." It's the same cycle, over and over. Hierarchical, network, relational, object-oriented, NoSQL, and now this... "Cognitive." It's all just new hats on the same old data retrieval problems.

The more things change, the more I have to explain why the old way worked just fine. Now if you'll excuse me, I think I hear a mainframe calling my name. Probably forgot its own boot sequence again. They get forgetful in their old age. Just like the rest of us.

Ah, another dispatch from the front lines of... 'innovation'. One must commend the author for their seasonal metaphor. While they busy themselves with pumpkin spice lattes and so-called AI-powered worlds, it seems the crisp autumn air has done little to clear the fog of theoretical misunderstanding. It is, in its own way, a masterpiece of missing the point.

How delightful to see the term “modernization” used so liberally. It’s a wonderfully flexible term, much like the database schemas they seem to adore. One can't help but admire the sheer audacity of presenting a lack of data integrity as a feature. 'Flexible, data-driven future' is a charming euphemism for an anarchic free-for-all where referential integrity goes to die. I suppose when you've never been required to normalize a database to third normal form, the entire concept of a rigorous, predictable structure must seem like a "legacy bottleneck." Edgar Codd must be spinning in his grave at a velocity that would shatter a mainframe.

I was particularly taken with the Wells Fargo case study. Building an "operational data store" to "jumpstart its mainframe modernization" is a truly inspired solution. It’s akin to addressing a crack in a building's foundation by applying a fresh coat of paint to the exterior and calling it architecture. They've created "reusable APIs" and "curated data products" to handle millions of transactions with sub-second service. Fascinating. One wonders, what of the 'I' in ACID? Isolation? Merely a suggestion, I presume? The consistency of that data, pulled from a monolithic mainframe and served up through this... thing... must be a marvel of 'eventual' accuracy.

And then we have CSX, ensuring "business continuity" with their Cluster-to-Cluster Sync. It's a bold move, I'll grant them that. They've discovered, decades later, the fundamental challenges of distributed systems. Eric Brewer's CAP theorem is not so much a theorem to these folks as it is a quaint historical footnote. They speak of this synchronization as if it were a solved problem, a simple toggle switch. Did they opt for Consistency or Availability during that 'few hours' of migration? The paper is silent on this, which is telling. Clearly they've never read Stonebraker's seminal work on the trade-offs therein; they probably think he's a craft brewer from Portland.

The "success" of Intellect Design is perhaps the most revealing:

This transformation reengineered the platform's core components, resulting in an 85% reduction in onboarding workflow times...

An 85% reduction! Staggering. It begs the question: what foundational principles of data validation, transaction atomicity, and durable state management were jettisoned to achieve such speed? It’s like boasting that you can build a house in a day because you've decided to omit the foundation, load-bearing walls, and roof. Their "long-term vision" of an "AI-driven service" built upon such a base sounds less like a vision and more like a fever dream.

But the true pièce de résistance is Bendigo Bank. Reducing migration time from 80 hours to just five minutes using "generative AI." Five minutes! It takes my graduate students longer than that to properly define a primary key. The mind reels at the sheer, unadulterated hubris. What sort of 'migration' is this? A glorified copy-paste operation guided by a large language model that can't even perform basic arithmetic consistently? The epistemological chaos this must introduce into their core banking system is a thing of terrible beauty.

I must commend the author and the engineers featured. It takes a special kind of bravery to ignore fifty years of established computer science. They are not building on the shoulders of giants; they are tap-dancing on their graves. What a vibrant and utterly terrifying world they inhabit, where papers go unread and fundamental truths are reinvented, poorly, for marketing purposes.

Thank you for sharing this. It has been an illuminating, if profoundly depressing, read. I shall now return to my relational algebra, and I can cheerfully promise I will not be visiting the "Customer Success Stories hub" ever again.

Ah, yes. A veritable bildungsroman of the modern developer. One must commend the author for their candor in documenting, with such painstaking detail, a journey from blissful ignorance to what now passes for competence. It reads like a charming parable on the perils of eschewing a formal education for the fleeting wisdom of a blog post.

It is particularly delightful to see the author’s first “mistake” was, in fact, attempting to apply the foundational principles of database normalization.

I built my schema like I was still working in SQL– every entity in its own collection, always referencing instead of embedding, and absolutely no data duplication. It felt safe because it was familiar.

Familiar? My dear boy, it felt “safe” because it was the result of Dr. Codd’s revolutionary work to eliminate data redundancy and the ensuing update, insertion, and deletion anomalies! To cast aside decades of established relational theory as mere “old habits” is… well, it’s a bold choice. He then discovers “embedding,” which he hails as a “cheat code.” A cheat code, it seems, that deactivates the ‘C’ in ACID. He was astonished to find that duplicating data everywhere led to consistency issues. One imagines Archimedes being similarly surprised when, upon jumping into his tub, the water level rose. Eureka, indeed.

Then we come to the performance section, a truly harrowing account of one man’s battle with a query planner. He bravely admits to scattering indexes about his collections like a toddler flinging paint at a canvas, hoping a masterpiece might emerge by sheer chance. His great epiphany? That an index must actually match the query it is intended to accelerate. Groundbreaking. Clearly they’ve never read Stonebraker’s seminal work on query optimization; I suppose that’s not covered in a lunch-break Skill Badge. His subsequent discovery of the aggregation framework—the idea that one might perform data transformations within the database itself—is treated with the reverence of discovering fire. It is a concept so radical, so utterly foreign, that one can only assume his prior experience involved piping raw data through a labyrinth of shell scripts.

The chapter on reliability is perhaps my favorite. His initial strategy was, and I quote, to “wait for something to break, then figure out why.” An approach he later enhanced by turning the server “off and on again.” One is left breathless by the sheer audacity. We have wrestled with Brewer’s CAP Theorem for over two decades, meticulously balancing consistency, availability, and partition tolerance in distributed systems, and this brave pioneer’s contribution is a power cycle. To learn, years into his journey, that one should monitor latency and replication lag is not a sign of growing wisdom; it is a sign that he has finally found the dashboard of the car he has been driving blindfolded.

And now, with the “fundamentals” apparently mastered, he is free to explore Vector Search and gen AI. It’s a bit like a student who, having finally learned that dividing by zero is problematic, immediately declares themselves ready to tackle Riemannian geometry. The confidence is admirable, if profoundly misplaced.

In the end, this whole saga serves as a rather depressing validation of my deepest fears. We have replaced rigorous, principled computer science education with a series of digital merit badges one can earn while chewing on a sandwich. We’ve swapped Codd’s twelve rules for a dozen bullet points in a blog post. This entire journey of “discovery” is little more than a slow, painful, and entirely avoidable rediscovery of problems solved a half-century ago.

Ah, well. At least the résumés will look impressive. One more for the pile.

Alright, let's take a look at this... he says, putting on a pair of blue-light filtering glasses that are clearly not prescription. Oh, a "scaleup" benchmark for MariaDB. How delightful. The tl;dr says "scaleup is better for range queries than for point queries." Fantastic. So you've performance-tuned your database for bulk data exfiltration. I'm sure the attackers who lift your entire user table will send a thank-you note for making their job so efficient.

Let's dig into the "methodology," and I'm using that term very loosely.

You've got an AMD EPYC server, which is fine, but you've built it on... SW RAID 0? Are you kidding me? RAID 0? You've intentionally engineered a system with zero fault tolerance. One NVMe drive gets a bad block and your entire database vaporizes into digital confetti. This isn't a high-performance configuration; it's a data-loss speedrun. You're benchmarking how fast you can destroy evidence after a breach.

And you "compiled MariaDB from source." Oh, that fills me with confidence. I'm sure you personally vetted the entire toolchain, every dependency, and ran a full static analysis to ensure there were no trojans in your make process, right? Of course you didn't. You ran curl | sudo bash on some obscure PPA to get your dependencies and now half your CPU cores are probably mining Monero for a teenager in Minsk. Hope that custom build was worth the backdoor.

But my favorite part? You just posted a link to your my.cnf file. Publicly. On the internet. You've just handed every attacker on the planet a detailed schematic of your database's configuration. Every buffer size, every timeout, every setting. They don't need to probe your system for weaknesses; you've published the goddamn blueprint. Why not just post the root password while you're at it? It would "save time," which seems to be the main engineering principle here, considering you skipped 10 of the 42 microbenchmarks. What were in those 10 tests you conveniently omitted? The ones that test privilege escalation? The ones that stress the authentication plugins? The ones that would have triggered the buffer overflows? This isn't a benchmark; it's a curated highlight reel.

Now for the "results," where every chart is a roadmap to a new CVE. Your big takeaway is that performance suffers from mutex contention. You say "mutex contention" like it's a quirky performance bottleneck. I say "uncontrolled resource consumption leading to a catastrophic denial-of-service vector." You see a high context switch rate; I see a beautiful timing side-channel attack waiting to happen. An attacker doesn't need to crash your server; they just need to craft a few dozen queries that target these "hot points" you've so helpfully identified, and they can grind your entire 48-core beast to a halt. Your fancy EPYC processor will be so busy fighting itself for locks that it won't have time to, you know, reject a fraudulent transaction.

The problem appears to be mutex contention.

It appears to be? You're not even sure? You've just published a paper advertising a critical flaw in your stack, and your root cause analysis is a shrug emoji. This is not going to fly on the SOC 2 audit. "Our system crashes under load." "Why?" "¯\(ツ)/¯ Mutexes, probably."

Let's talk about random-points_range=1000. You found that a SELECT with a large IN-list scales terribly. Shocking. You've discovered that throwing a massive, complex operation at the database makes it... slow. This isn't a discovery; it's a well-known vector for resource exhaustion attacks. Any half-decent WAF would block a query with an IN-list that long, because it's either an amateur developer or someone trying to break things. You're not testing scaleup; you're writing a "how-to" guide for crippling InnoDB with a single line of SQL.

And the write performance... oh, the humanity. The only test that scales reasonably is a mix of reads and writes. Everything else involving DELETE, INSERT, or UPDATE falls apart after a handful of clients. So, your database is great as long as nobody... you know... changes anything. The moment you have actual users creating and modifying data, the whole thing devolves into a lock-and-contention nightmare.

The worst result is from update-one which suffers from data contention as all updates are to the same row. A poor result is expected here.

You expected a poor result on a hot-row update? Then what was the point? To prove that a race condition... is a race condition? That single hot row could be a global configuration flag, a session counter, or an inventory count for your last "revolutionary" new product. You've just confirmed that your architecture is fundamentally incapable of handling high-frequency updates to critical data without collapsing.

So let me summarize your findings for you: You've built a fragile, insecure, single-point-of-failure system with a publicly documented configuration. Its performance bottlenecks are textbook DoS vectors, its write-path is a house of cards, and you've optimized it for the one thing you should be preventing: mass data reads.

This isn't a benchmark. This is a pre-mortem for the data breach you're going to have next quarter. Good luck explaining "relative QPS" to the regulators.

Ah, wonderful. I've just finished reading this announcement, and I must say, it's a masterpiece of modern enterprise storytelling. Truly. The way they describe a "reimagined search experience" is so inspiring. It makes me want to reimagine our budget, perhaps by removing the line item for "products that describe themselves as an 'experience'."

It's just so thoughtful of them to solve a problem I wasn't aware we had. Our old search box was so pedestrian, merely finding things. This new one doesn't just find results, it "understands intent." I can already see the purchase order: one line for the software, and a second, much larger line, for the on-call philosopher required to explain what "intent" costs us per query.

I'm particularly impressed by the architecture. It's not just one vendor, you see. That would be far too simple. This is a beautiful collaboration between MongoDB, Pureinsights, and now Voyage AI. It’s like a corporate supergroup. We get the privilege of funding their collaboration, and in return, we get three different invoices, three different support numbers, and a "seamless UI" that likely requires a "certified integration partner" at $450 an hour to make it, you know, actually seamless.

The quote from the Vice President is a particular highlight.

“As organizations look to move beyond traditional keyword search, they need solutions that combine speed, relevance, and contextual understanding,”

He's absolutely right. And as a CFO, I need solutions that combine speed, relevance, and a price that doesn't require us to liquidate the office furniture. He cleverly omitted that last part. An oversight, I'm sure.

Let's do some quick, back-of-the-napkin math on the true cost of this "transformational" journey.

• The "Foundation": MongoDB Atlas. Let's be generous and call it a $250,000 annual commitment for the enterprise tier they'll insist we need.
• The "Orchestration Layer": Pureinsights. I assume "orchestration" is the line item right below "synergy" and just above "miscellaneous vendor fees." That sounds like at least another $100,000 for their secret sauce.
• The "AI Elevation": Voyage AI is in "private preview," which is my favorite kind of preview. It's code for “the price isn't on the website because we want to see how much is in your wallet first.” Let's pencil in a conservative $75,000 for this "enhancement."
• The Inevitable Consultants: You can't just "ingest and enrich" terabytes of our legacy content with the push of a button. That's a six-month, two-consultant project. At their rates, that's a cool $300,000.
• The "Bring Your Own LLM" Surprise: The article mentions integrating with models like GPT-4. How delightful. It’s like buying a luxury car and then being told the gasoline is not included and its price fluctuates based on the length of your sentences. Let’s just call that a running, uncapped operational expense of… all the money we have left.

So, for the low, low price of $725,000 for the first year—before we've even calculated a single generative query—we can have a search bar that provides "smarter, semantically aware responses." I am quite sure the response from our shareholders will be "semantically aware" as well.

They say this is "built for users everywhere," with adaptability for language and tone. I love features that sound like checkboxes on a sales call but manifest as change-orders on an invoice. "Oh, you wanted the AI to be 'concise' and not just 'verbose'? That's a different service tier."

They promise an AI-powered experience that will bring "intelligent discovery to your own data." And for that price, it had better discover a hidden oil reserve under the data center.

So yes, thank you for this article. It's a fantastic reminder that while our developers are searching for answers, I'll be searching for the quarter-million dollars that mysteriously vanished into the "cloud-native, enterprise-ready" ether.

This isn't a search solution. It's a business model. And we're the product.

Ah, yes. Another wonderfully insightful article about a "new reality" in the database world. I do so appreciate being kept abreast of these exciting market opportunities. It's always a thrill to learn that a technology we've relied on for years has suddenly decided its business model needed more... spice. And by spice, I of course mean "unforeseen and unbudgeted expenditures." This is my favorite kind of innovation.

It’s truly a testament to the vibrancy of the tech sector. One day, you have a perfectly functional, performant, and, most importantly, predictably priced piece of infrastructure. The next, you’re reading a blog post that serves as a polite, corporate-approved invitation to a financial knife fight.

The timing is always impeccable. Just after we’ve finalized the quarterly budgets, a new crop of vendors emerges from the woodwork, their PowerPoint decks gleaming. They’ve seen our Redis-related distress signal and are here to rescue us with their "next-generation, fully-compatible, drop-in replacement." I admire their proactive spirit. They don't just sell software; they sell salvation.

Of course, I like to do a little "Total Cost of Ownership" exercise. The vendors love that term, so I use it too. It’s fun for everyone.

Let’s take their proposed solution. The annual license seems... reasonable. At first glance. A mere $150,000. They call it the 'foundation of our new partnership.' I call it the cover charge.

The real magic happens when we calculate the True Cost™:

• The "Seamless Migration": This is my favorite line item. I'm told our team of 12 senior engineers can handle it. The vendor's 'solution architect'—a charmingly optimistic fellow—estimates it will take "a few sprints." I've learned to translate that. At a blended rate of $150/hour per engineer, for a project that will actually take six months of fighting with obscure APIs and data consistency models, that’s a simple... let's see... carry the one... ah, a $1.7 million investment in lost productivity and direct labor. Seamless!

• The Essential Consultants: Naturally, our team won't actually be able to do it alone. We’ll need the vendor’s "Professional Services" team to "ensure a smooth transition." Their rate is a modest $450/hour. They assure me they are worth it, and that we'll need a team of three for at least three months. That adds a tidy $648,000. They're not consultants; they're more like very expensive emotional support animals for our panicking DevOps team.

• Training & Certification: We can't have our people using this revolutionary new system without being fully "synergized with the new paradigm," can we? The "Enterprise Training Package" is only $50,000. A bargain to ensure our staff can operate the money pit we've just purchased.

So, the vendor’s proposed $150k solution actually has a first-year cost of $2,548,000.

They presented me with a chart promising a 300% ROI in the first 18 months. I’m still trying to figure out what the 'R' in their 'ROI' stands for, but I'm reasonably certain it isn't "Return." According to my napkin, for this to break even, it would need to independently discover cold fusion and start selling energy back to the grid.

And the pricing model, oh, the pricing model! It’s a masterpiece of abstract art. It's not just per-CPU or per-user. It's a complex algorithm based on vCPU cores, gigabytes of RAM, number of API calls made on a Tuesday, and, I suspect, the current phase of the moon. This isn't a pricing model; it's a riddle designed to ensure no one in procurement can ever accurately forecast costs. It’s a variable-rate mortgage on our data.

"Our multi-vector pricing ensures you only pay for what you use, providing maximum value and scalability!"

It’s just so thoughtful. They've given us the gift of vendor lock-in. After investing over two and a half million dollars just to get off the last platform, we'll be so financially and technically entangled with this new one that we'd sooner sell the office furniture than attempt another migration.

Honestly, at this point, I'm starting to think our Q3 strategic initiative should be replacing our entire database stack with a series of well-organized filing cabinets and a very fast intern. The upfront costs for steel and manila folders seem, by comparison, refreshingly transparent.

Alright, settle down, grab your kombucha. I just read the latest dispatch from the engineering-as-marketing department, and it’s a real piece of work. “How we built vector search in a relational database.” You can almost hear the triumphant orchestral score, can’t you? It starts with the bold proclamation that vector search has become table stakes. Oh, you don’t say? Welcome to two years ago, glad you could make it. The rest of us have been living with the fallout while you were apparently discovering fire.

The whole premise is just... chef’s kiss. They were surprised to find no existing papers on implementing a vector index inside a transactional, disk-based relational database. Shocked, I tell you! It’s almost as if people who design high-performance, in-memory graph algorithms weren’t thinking about the glacial pace of B-tree I/O and ACID compliance. It’s like being surprised your race car doesn’t have a tow hitch. They’re different tools for different jobs, you absolute titans of innovation.

And the tone! This whole, “we had to invent everything from scratch” routine. I remember meetings just like this. Someone scribbles a diagram on a whiteboard, reinvents a concept from a 1998 research paper, and the VP of Engineering declares it novel solutions. What they’re really saying is, “Our core architecture is fundamentally unsuited for this workload, but the roadmap says we have to ship it, so we built a skyscraper of hacks on top of it.”

They spend half the article giving a condescendingly simple explanation of HNSW, complete with a little jab at us poor mortals trapped in our "cursed prison of flesh." Real cute. Then they explain that HNSW is a mostly static data structure and doesn't fit in RAM. Again, groundbreaking stuff. This is the database equivalent of a car company publishing a whitepaper titled, "Our Discovery: Engines Require Fuel."

But this is where it gets good. This is where you see the scar tissue. Their grand design philosophy is that a vector index should behave like any other index.

We don’t think this is a reasonable approach when implementing a vector index for a relational database. Beyond pragmatism, our guiding light behind this implementation is ensuring that vector indexes in a PlanetScale MySQL database behave like you’d expect any other index to behave.

I can tell you exactly how that meeting went. The engineers proposed the easy way: “It’s approximate anyway, a little eventual consistency never hurt anyone.” And then marketing and sales had a collective aneurysm, shrieking about ACID compliance until the engineers were forced into this corner. This "guiding light" wasn't a moment of philosophical clarity; it was a surrender to the sales deck.

So what’s the solution to this problem they "discovered"? A glorious, totally-not-over-engineered Hybrid Vector Search. It’s part in-memory HNSW, part on-disk blobs in InnoDB. And my favorite part is their "research" into alternatives. They mention the SPANN paper and say, "It is not clear to us why HNSW was not evaluated in the paper." Translation: “We already had an HNSW implementation from a hack week project and we weren’t about to throw it out.” Then they dismiss a complex clustering algorithm in favor of random sampling, because "the law of large numbers ensures that our random sampling is representative." That’s the most academic-sounding way of saying, “We tried the right way, it was too hard, and this was good enough to pass the benchmark tests marketing wanted.”

And now for the main event. The part where they admit their entire foundation is made of quicksand. They lay out, in excruciating detail, why appending data to a blob in InnoDB is a performance catastrophe. It’s a beautiful, eloquent explanation of why a B-tree is the wrong tool for this job. And then they discover… LSM trees! They write a love letter to LSMs, explaining how they’re a "match made in heaven" for this exact problem. You can feel the hope, the excitement!

And then, the punchline. They can’t use it.

Because their customers are on InnoDB and forcing them to switch would be an "unacceptable barrier to adoption." So instead of using the right tool, they decided to build a clattering, wheezing, duct-taped emulation of an LSM tree… on top of a B-tree. This isn’t engineering; it’s a dare. It’s building a submarine out of screen doors because you’ve already got a surplus of screen doors.

From there, it’s just a cavalcade of complexity to paper over this original sin. We don’t just have an index; we have a swarm of background maintenance jobs to keep the whole thing from collapsing.

• Splits, because our fake-append-only system makes lists too long.
• Reassignments, because the splits mess up the data placement.
• Merges, because the reassignments leave behind mountains of stale, versioned garbage.
• Defragments, which is basically admitting the (head_vector_id, sequence) hack creates so much fragmentation you need another janitor to clean up after the other janitors.

They call this the LIRE protocol. We used to call it "technical debt containment." Every one of these background jobs is a new lock, a new race condition, a new way for the database to fall over at 3 AM. And the solution for making the in-memory part crash-resilient? A custom Write Ahead Log, on top of InnoDB’s WAL. It’s WALs all the way down! They even admit they have to pause all the background jobs to compact this thing. I can just picture the SREs' faces when they read that. "So, the self-healing slows down… to heal itself?"

Look, it’s a monumental achievement in over-engineering. They’ve successfully built a wobbly, seven-layer Jenga tower of compensations to make their relational database do something it was never designed to do, all while pretending it was a principled philosophical choice.

So, bravo. You did it. You shipped the feature on the roadmap. It’s a testament to what you can accomplish with enough bright engineers, a stubborn architectural constraint, and a complete disregard for operational simplicity.

Try it out. Happy (approximate) firefighting

Oh, wow. Thank you. Thank you for this. I was just thinking to myself, “You know what my Tuesday morning needs? Another revolutionary manifesto on search that promises a beautiful, unified future.” It’s truly a gift.

It's just so reassuring to learn that after we all scrambled to rewrite our infrastructure for vector search, the “game-changing” solution to everything, it “quickly became clear that vector embeddings alone were not enough.” You don’t say! Who could have possibly predicted that a system trained on the entire internet might not know what our company-specific SKU XF-87B-WHT is? I, for one, am shocked. It’s not like any of us who got paged at 2 AM because semantic search was returning results for “white blouses” instead of the specific refrigerator part a customer was searching for could have seen this coming.

I especially love the detailed history of how the market "reacted." It's so validating.

For lexical-first search platforms, the main challenge was to add vector search features... On the other hand, vector-first search platforms faced the challenge of adding lexical search.

This is my favorite part. It’s so beautiful. So you’re telling me that everyone built half a solution and is now frantically bolting on the other half? This gives me immense confidence in the maturity of the ecosystem. It reminds me of my last big project, the "simple" migration to a NoSQL database that couldn't do joins, which we solved by… adding a separate relational database to handle the joins. Seeing history repeat itself with such elegance is just… chef’s kiss.

And the new acronyms! RRF! RSF! I can’t wait to spend three sprints implementing one, only to be told in a planning meeting that the other one is now considered table stakes and we need to pivot immediately. I'm already clearing a space on my arm for my next tattoo, right next to my "SOAP forever" and "I survived the great Zookeeper migration of '18" ink.

The section on choosing a solution is a masterpiece of offering two equally terrible options. Let me see if I've got this straight:

• Option A: Separate Indexes. I get more "freedom" and "control." This is a lovely euphemism for “you now have two completely different distributed systems to maintain, monitor, and scale independently.” My on-call rotation just started weeping. I can already taste the cold coffee at 3 AM, trying to figure out why our score normalization function is throwing NaN and tanking the entire search page.
• Option B: A Combined Index. This is "easier to manage" but has "less mature keyword capabilities." Fantastic. So I get a magical black box that’s simple and elegant, right up until the moment it isn’t. And when it breaks, I'm at the mercy of whatever limited tuning knobs the native implementation provides. "Native" is my favorite marketing term. It’s Latin for “works perfectly until it’s your turn on the pager, at which point you discover the source code is a mystery wrapped in an enigma.”

And then, the grand finale. MongoDB, our benevolent savior, has solved it all by adding vector search to their existing platform, creating a unified architecture. Oh, a single, unified platform to support both operational and AI workloads? Where have I heard that before? It sounds suspiciously like the "one database to rule them all" pitch I heard right before I spent a month untangling a decade of tech debt that had been lovingly migrated into a single, monolithic nightmare. A "flexible, AI-ready foundation that grows with them" sounds exactly like what my last CTO said before he left for a competitor and we had to deal with the sharding crisis.

This was a fantastic read. Truly. I'm going to print it out and put it on the wall, right next to the "Reasons I Need a Vacation" list. Anyway, I’m unsubscribing now, but best of luck with your revolution.

Ah, yes. Another masterpiece. It's always so refreshing to read a thoughtful piece that begins with the classic "two hard problems" joke. It lets me know we're in the hands of a true practitioner, someone who has clearly never had to deal with the actual three hard problems of production systems: DNS propagation, expired TLS certificates, and a junior engineer being given root access on a Friday afternoon.

I'm particularly inspired by the breezy confidence with which "caching" is presented as a fundamental strategy. It's so elegant in theory. Just a simple key-value store that makes everything magically faster. It gives me the same warm, fuzzy feeling I get when a project manager shows me a flowchart where one of the boxes just says "AI/ML."

I can already see the change request now. It'll be a one-line ticket: "Implement new distributed caching layer for performance." And it will come with a whole host of beautiful promises.

My favorite, of course, will be the "zero-downtime" migration. It's my favorite phrase in the English language, a beautiful little lie we tell ourselves before the ritual sacrifice of a holiday weekend. I can already picture the game plan: a "simple" feature flag, a "painless" data backfill script, and a "seamless" cutover.

And I can also picture myself, at 3:15 AM on the Sunday of Memorial Day weekend, watching that "seamless" cutover trigger a thundering herd of cache misses that saturates every database connection and grinds the entire platform to a halt. The best part will be when we find out the new caching client has a subtle memory leak, but we won't know that for sure because the monitoring for it is still a story in the backlog, optimistically titled:

TODO: Add Prometheus exporters for NewShinyCacheThingy.

Oh, the monitoring! That’s the most forward-thinking part of these grand designs. The dashboards will be beautiful—full of green squares and vanity metrics like "Cache Hit Ratio," which will be a solid 99.8%. Of course, the 0.2% of misses will all be for the primary authentication service, but hey, that's a detail. The important thing is that the big number on the big screen looks good for the VPs. We'll get an alert when the system is well and truly dead, probably from a customer complaining on Twitter, which remains the most reliable end-to-end monitoring tool ever invented.

This whole proposal, with its clean lines and confident assertions, reminds me of my laptop lid. It’s a graveyard of vendor stickers from databases and platforms that were also going to solve one simple problem. There’s my shiny foil sticker for RethinkDB, right next to the holographic one from CoreOS, and let's not forget good old GobblinDB, which promised "petabyte-scale ingestion with ACID guarantees." They all looked fantastic in the blog posts, too.

So please, keep writing these. They're great. They give the developers a sense of purpose and the architects a new set of buzzwords for their slide decks.

You worry about cache invalidation. I'll be here, writing the post-mortem.