The Idea That Was Supposed to Fail
Why DirectFlash and Evergreen//One suddenly look a lot smarter in a world of NAND and DRAM price shocks Dmitry Gorbatov Mar 20, 2026 Important Note for my readers: Writing this piece took me a lot longer than I normally spend on a post. It took a lot of reading and research. Many articles and blogs were written on the subject before NAND and DRAM costs went crazy. The dry-humor version is that the storage industry spent years insisting flash was just disk with better manners, and then acted surprised when the underlying physics eventually asked to speak with management. Now, let’s get to it. I can still picture the room. It wasn’t anything special — just another corporate competitive training session, the kind you’ve sat through many times if you’ve spent enough years in enterprise tech. This was at NetApp, in 2015 or 2016, back when flash was still a question mark. Not if, but how. The industry had not fully committed yet, and everyone was trying to figure out what role it would play. The presenter clicked to the next slide, paused for a second, and said something that stuck with me in a way most of those sessions never do: “Pure Storage is crazy! They’re building their own flash modules. That’s stupid. It’s not sustainable. They won’t survive.” It wasn’t said for effect. There was no dramatic pause afterward, no attempt to persuade. It was delivered as a simple, almost obvious conclusion. And to be fair, it felt obvious. Because the entire storage industry operated on a shared assumption: you didn’t build components, you assembled them. You relied on a mature ecosystem of suppliers who specialized in drives, storage controllers, and memory, and you focused your differentiation on software features and integration. That was the efficient path. That was the scalable path. That was how serious companies behaved. What Pure was proposing at the time — what would later become Everpure — felt like a deviation from that logic. Building your own flash modules didn’t just introduce complexity; it seemed to reject the economic advantages of the broader supply chain. It looked like a risk without a clear payoff. So the conclusion made sense. Until it didn’t. Looking Back, Differently If I think back to that training session now, I do not really see it as a moment where someone was foolish. I see it as a moment where the industry was trapped inside the logic of its own assumptions. If you believe flash should look like disk, then building your own flash modules sounds silly. If you believe storage is just a sequence of refresh cycles, then a model built around non-disruptive evolution sounds unnecessary. If you believe component pricing will keep trending in the right direction forever, then architectural efficiency feels like an academic luxury. But once those assumptions start to crack, the logic changes. And when it changes, the things that once looked eccentric start to look oddly prescient. A Change You Don’t Notice Right Away For years, nothing about that statement felt particularly worth revisiting. The industry moved forward in predictable ways. Flash became mainstream. Performance improved. Density increased. Vendors competed on features, benchmarks, and price points. The conversations most of us had with customers followed familiar patterns. If anything, the abstraction layers built around flash made things easier to consume. SSDs behaved like faster disks — and that was good enough. There is a reason they showed up in familiar HDD form factors. The industry was trying to preserve the old world while sneaking in a new medium. Keep the slots. Keep the enclosures. Keep the assumptions. Change as little as possible. That made adoption easier, but it also buried the problem. Because flash is not a disk. It never was. It does not behave like one, and it does not particularly enjoy being treated like one. The only reason the illusion worked is because the industry built a fairly elaborate translation layer to maintain it. That translation layer is where the story really starts. The Trick That Made Flash Look Simple When commodity SSDs became the standard way to bring flash into enterprise storage, they depended on a piece of internal firmware called the Flash Translation Layer, or FTL. Its job was deceptively simple: make raw NAND look like a disk. That sounds harmless enough until you think about what that actually requires. NAND cannot just overwrite data in place the way the rest of the stack would like it to. It has to handle erase cycles, wear leveling, garbage collection, bad block management, and the constant translation between logical addresses and physical locations on the media. So every SSD became its own little self-contained world, complete with its own controller, its own metadata tables, and its own DRAM to keep track of everything. In other words, every drive became a tiny independent computer, making local decisions in isolation. That design solved the adoption problem. It did not solve the architecture problem. For a while, the tradeoff seemed worth it. The drives were fast enough, the packaging was familiar, and the whole system kept pretending that flash was just a much nicer version of disk. But what looked neat and modular at small scale turned out to be awkward and expensive at enterprise scale. And that is where the “stupid” decision begins to look a lot smarter. What Commodity SSDs Actually Drag Along With Them The more I researched this topic (and believe me I did), the more I realized how much of the industry got comfortable with an abstraction that was doing a lot of quiet damage. Commodity SSDs carry four structural inefficiencies that matter much more today than they did when pricing was stable. Trapped DRAM. Every SSD maintains its own mapping tables, so large-scale systems end up carrying a remarkable amount of DRAM inside the drives themselves. That memory is necessary for the SSD to function, but it does not really help the array think globally. It is duplicated overhead, repeated again and again, drive by drive. In a petabyte-scale system, that is not a rounding error. It is cost, power, and complexity hiding in plain sight. Unpredictable Latency. Garbage collection inside a traditional SSD happens when the drive decides it needs to happen. When that occurs, the drive may become temporarily less responsive, and in an array full of independent drives, those little stalls start to show up as tail-latency spikes. The system is always vulnerable to one drive having a private crisis at exactly the wrong time. Write Amplification. Because the SSD does not really understand the workload or the data structures above it, it moves data more often than necessary. More movement means more writes. More writes mean more wear. More wear means the media gets consumed faster than it should. Over-provisioning. Every SSD holds back some raw capacity for its own housekeeping and spare-cell management, but that reserved space is siloed. The array cannot use it intelligently across the system because each drive is managing its own private affairs. None of this sounded especially dramatic when NAND kept getting cheaper and the economics of flash kept improving. It sounded like engineering trivia. The sort of thing infrastructure people argue about while everyone else waits for the quote. Today it is not trivia. Today it is exposure. Why AI Made This Suddenly Everyone’s Problem For years, one of the quiet assumptions in enterprise IT was that storage capacity would continue to become cheaper and more abundant over time. Not perfectly, not smoothly, but predictably enough that the inefficiencies of the underlying architecture could be tolerated. That assumption is now not only under pressure, it is getting decimated. AI did not just create a new category of interesting workloads. It created a global appetite for silicon that is large enough to bend supply curves. The cute part of AI is easy to mock. The cat kicking the T-Rex. The surreal generated videos. The deepfakes that make you look twice and then sigh a little for civilization. But behind every one of those outputs is a less funny reality: extraordinary consumption of DRAM, NAND, GPUs, and supporting infrastructure. The novelty at the edge is powered by very serious resource demand at the core. And that demand is landing directly on the components enterprise storage depends on. This is the part customers are beginning to feel in ways that are no longer abstract. Expansion quotes do not look as comfortable as they once did. Refresh cycles feel more expensive. Delivery windows stretch. Budgets built on assumptions from even two years ago suddenly need more explaining than anyone wanted. There is a tendency to call this inflation because that is the easiest word available. It is not really inflation. It is supply and demand, with a side of semiconductor reality. And that matters, because a traditional SSD array is exposed to both sides of the problem at once. It is exposed to NAND because that is the medium you are buying, and it is exposed to DRAM because every SSD drags its own DRAM overhead along for the ride. When those two markets tighten at the same time, the cost of the architecture gets hit twice. That is not just a technical nuance. That is economics. Revisiting the “Stupid” Decision This is where the old training-room comment starts to age badly. Because what looked like unnecessary vertical integration was really a decision to stop pretending flash was a disk and start treating it like what it actually is: semiconductor media with very specific physical behaviors that should be managed at the system level, not hidden inside dozens of drives. That is the DirectFlash idea in plain English. Take the Flash Translation Layer out of the individual drive. Pull media management into the operating environment. Let Purity manage flash globally instead of leaving each device to improvise its own local strategy. That changes more than performance charts. It means metadata no longer has to be duplicated and trapped inside every SSD. It means wear leveling can happen across the full system instead of inside the borders of a single device. It means bad block handling, garbage collection, and data placement can be coordinated with global context. It means the platform can see the difference between data that should live together and data that should not, which dramatically reduces unnecessary movement and lowers write amplification. And when write amplification drops, the economics change. The NAND lasts longer. The useful life of the media extends. Lower-endurance flash, like QLC, becomes viable for serious enterprise use because the software is smart enough not to abuse it. The system extracts more useful work from the same raw silicon. That is not just clever engineering. That is insulation from volatility. The reason this matters now is that DirectFlash changes the ratio between the silicon you buy and the value you get from it. If the rest of the market is paying more for NAND and more for DRAM, an architecture that reduces trapped DRAM, minimizes wasted writes, extends media life, and packs far more capacity into far denser modules is not just elegant. It is economically defensive. This is where the old “they build their own flash” criticism misses the point. Building your own flash modules was never the point by itself. The point was controlling the relationship between software and media well enough to eliminate the inefficiencies the commodity model had normalized. Why Purity Is the Real Story DirectFlash makes for a good visual. It is a module. You can point to it. You can talk about density and reliability and the fact that a 150TB module can do work that would have required a small army of traditional devices not all that long ago. But the real story is Purity Operating Environment, i.e. software. Purity is where the architectural bet pays off. It is what turns raw NAND into a coordinated system instead of a pile of politely disagreeing SSDs. Because Purity sees the entire media pool, it can write more intelligently. It can group data with similar expected lifespans together, so that when a snapshot or a temporary workload disappears, whole regions of storage can be retired cleanly instead of forcing background reshuffling of still-live data. That reduces unnecessary churn. Less churn means fewer writes. Fewer writes mean longer media life. Because Purity sees when a NAND die is busy with an erase or program cycle, it can avoid letting that become a host-visible performance problem. RAID-3D and system-level awareness allow the platform to reconstruct data from parity rather than simply waiting for a busy drive to get its act together. The end result is deterministic performance rather than a roulette wheel of occasional latency spikes. Because Purity owns media management globally, the over-provisioning and spare resources are no longer trapped in per-drive silos. The system can use them strategically. I know that all of this sounds a bit scientific, and to be fair, it is. I did spend over 7 years working for Everpure and a few weeks researching for this post. I wanted to sit with that science for a bit. Where the Economics Start to Matter The moment component pricing becomes unpredictable, architecture stops being an engineering preference and starts becoming a financial strategy. That is the part that matters most to customers right now. A traditional buying model assumes that at some point you will hit a refresh cycle, a capacity wall, or a migration event that forces a purchase whether the market timing is good or terrible. You buy when you have to buy. If NAND is expensive, that is unfortunate. If DRAM is expensive too, even better, because apparently the universe enjoys symmetry. That is what makes the combination of DirectFlash and Evergreen so important. DirectFlash reduces the amount of waste, duplication, and premature wear in the system. Evergreen removes the old habit of tying innovation to forklift replacement. Controllers evolve. Capacity can be consolidated into denser modules over time. Data stays in place. The customer is not forced into rebuying the whole environment every few years just to remain current. That already changes the economics. But it still leaves one more question: who is carrying the price risk? And this is where Evergreen//One matters more than ever. The Part I Actually Wanted to Get To Evergreen//One is not just a consumption model. It is not just a nicer way to finance storage. It is a mechanism for moving volatility away from the customer. That is the conclusion I wanted to earn, not just declare. When NAND and DRAM prices start climbing, most traditional models push that turbulence straight into the customer’s planning cycle. The customer eats the increase, absorbs the uncertainty, and tries to explain to the business why the infrastructure line now behaves like it has a gambling problem. Evergreen//One changes that relationship. The customer consumes capacity as a service. Everpure owns the burden of the underlying hardware lifecycle, the media strategy, and the ongoing optimization. DirectFlash makes that model stronger because the platform is structurally more efficient with the silicon it uses. It needs less trapped DRAM, wastes fewer writes, extends media life, and supports denser modules that deliver more usable capacity per unit of power, space, and raw media. Purity compounds that advantage with data reduction, ongoing software improvements, and smarter system-wide media management. Put differently, Everpure is in a much better position to absorb and manage component volatility than a customer buying boxes on a refresh schedule. That is the real price protection story. Not some magical promise that economics no longer apply. They do. NAND still costs what NAND costs. DRAM still costs what DRAM costs. Physics remains annoyingly undefeated. The difference is who is exposed to that volatility, how much inefficiency is built into the system before the customer ever sees it, and whether the operating model gives the customer a stable runway instead of a quarterly surprise. DirectFlash reduces the waste. Evergreen removes the forced disruption. Evergreen//One shifts the risk. That combination is a lot more interesting than it sounded in that room 11 years ago. The Part I Didn’t Appreciate Then What I did not understand sitting in that room 11 years ago was that some decisions are made for futures that have not arrived yet. The market eventually caught up to the architecture. That does not happen often enough in enterprise tech to ignore when it does. DirectFlash was never interesting just because it was different. It was interesting because it removed layers of inherited inefficiency that the rest of the market had accepted as normal. And in a period where NAND and DRAM pricing are under pressure, removing inefficiency is no longer just a performance story. It is a protection story. That is why this matters now. Not because it makes for a clever slide. Because it gives customers a more predictable way forward when the underlying component markets are anything but predictable. And in the current environment, that might be the most practical definition of innovation there is. Appreciate you reading. Dmitry Gorbatov © 2025 Dmitry Gorbatov | #dmitrywashere
Cooking with “Enterprise Data Cloud”: Recipes for your Datacenter
September 24 | Register Now! Looking for practical ways to streamline your datacenter or hybrid cloud environment and run it more reliably? Following up from last September’s episode, “Cloud - A Place or a Strategy? Unpacking the Everpure Enterprise Data Cloud” (EDC) with JD Wallace, host Andrew Miller invites Michael Sasse (Principal Technology Strategist with Everpure for 11 years) to discuss how to make EDC real for YOU in the midst of the many industry changes from even just 12 months ago. We’ll explore: Changing customer needs: What Michael has heard as he discusses EDC with customers, including how speed and efficiency matters more than ever and the risk of being replaced if you’re perceived as the “department of no”. What EDC is: If you love dogs, you’ll love this (join us to see why). We’ll look at how EDC is driving the simplicity that FlashArray™ brought to the storage industry throughout the datacenter and cloud. Cooking the EDC souffle (making It real): We’ll look at the building blocks of EDC from a technical perspective — things like Fleets, Topology Groups, Presets, observability, governance, automation and more. Of course, underpinning all of this is Evergreen//One™. Presenting to the (exec) table: Think of this as delivering the meal. Having reviewed the building blocks, we’ll look at the outcomes both you and your executives care about. Think of this as 1) who is leveraging EDC, 2) how has it helped (measurable outcomes), and 3) why does it matter? Even better, we’ll discuss the EDC Blueprint: how Everpure is linking our products and features to outcomes that your CIO and CTO care about. September 24 | Register Now!
Using Pure1 AI Copilot to Assess CVE Exposure and Get Upgrade Recommendations
Fleet Security Assessment The Pure1 Fleet Security Assessment provides a fleet-wide view of the security posture of FlashArray and FlashBlade systems by correlating each asset's current software version against Everpure's curated CVE database. Determine whether an asset is exposed to a known CVE A storage administrator can: Log into Pure1. Navigate to Fleet Security Assessment. View all registered arrays within the fleet. For each array, Pure1 automatically identifies: Current Purity version Product family (FlashArray / FlashBlade) Array Name Array ID Serial Number Pure1 compares the running Purity version against the Everpure Product Security Advisory database and determines: Whether the version is affected by known CVEs. Severity of each vulnerability (Critical, High, Medium, Low). Whether a fix is available. Whether a workaround exists. Whether the CVE affects the customer's specific product. Selecting an individual array allows the administrator to review: CVE ID CVSS Score Severity Description Affected component Security Bulletin Fixed version Mitigation guidance This enables administrators to quickly prioritize remediation activities based on risk across their entire storage estate. Using AI Copilot AI Copilot provides a conversational interface to the same security intelligence, allowing administrators to ask questions in natural language rather than navigating multiple dashboards. Examples include: "Is array FA-01 exposed to any critical security vulnerabilities?" Another example: "Which arrays in my fleet are exposed to critical CVEs?" Administrators could also ask: "Show me every FlashBlade affected by OpenSSH vulnerabilities." or "Which arrays are running unsupported Purity versions?" Intelligent Upgrade Recommendations Rather than simply identifying vulnerable arrays, AI Copilot can also recommend an upgrade. AI Copilot will go beyond security and determine whether the array is actually ready for upgrade. Example prompt: "Can I safely upgrade FA-01 to Purity 6.9.4?" AI evaluates: Hardware compatibility Controller generation Installed DirectFlash modules Current Purity version Intermediate upgrade requirements Known upgrade restrictions Health status Replication configuration SafeMode Supportability Current alerts Capacity utilization Pending hardware failures Replication synchronization Outstanding upgrade blockers And determines the most appropriate version based on the customer's environment based on customer’s Current Purity version Current LLR Current ER Security vulnerabilities Feature requirements Hardware support Known software defects Current advisories Upgrade path complexity Engineering recommendations Customer support policy Example: Users could ask: "What version should I upgrade to?" Fleet-wide Upgrade Planning AI could also answer strategic questions such as: "Show every array requiring a security upgrade."or "Which arrays should be upgraded in the next 30 days?" Conclusion The integration of Pure1 Fleet Security Assessment and AI Copilot fundamentally transforms storage security management from a reactive, manual task into a proactive, data-driven strategy. By providing deep visibility into CVE exposure across the entire fleet and delivering intelligent, context-aware Purity upgrade recommendations, Pure1 empowers administrators to mitigate risks before they can be exploited. This shift to strategic fleet planning ensures that storage environments remain resilient, compliant, and optimized, allowing organizations to focus on innovation rather than crisis management.
From CSI to Snapshots: Seamless VM Management on Red Hat OpenShift
September 22 | Register Now Red Hat OpenShift Virtualization is an increasingly popular option for organizations migrating workloads (often from VMware) to a new hypervisor. Rather than being an afterthought, storage is actually a critical element of any OpenShift Virtualization deployment. Join this webinar for a set of short demos of Everpure FlashArray™ with Red Hat OpenShift. We'll go from the ground up: connecting the Container Storage Interface (CSI) to storage, spinning up a virtual machine (VM) from a template, and protecting the VM with instant, array-based snapshots. These demos will show how naturally FlashArray fits into an OpenShift environment. Get connected: See how to set up the CSI with the Portworx® operator and define both the StorageCluster and the StorageClass. Run and protect: We’ll demonstrate both VM provisioning in OpenShift Virtualization plus how to create instant, space-efficient snapshots. Register Now!FlashArray Meets Nutanix, Independently Scale Compute and Storage
September 24 | Register Now Traditional hyperconverged infrastructure ties compute and storage together, forcing you to add nodes you don't need just to gain capacity. This webinar shows infrastructure and storage admins how pairing Everpure FlashArray with the Nutanix Cloud Platform over NVMe/TCP breaks that dependency, delivering external, high-performance block storage to Acropolis Hypervisor (AHV) clusters. You'll walk through the reference architecture in detail, including how the two platforms connect and how data services like VM-level snapshots, replication, and SafeMode ransomware protection layer beneath your Nutanix workloads. The result is an architecture you can scale on your terms, expanding compute and storage independently while maintaining the enterprise data services and non-disruptive operations your workloads depend on. Key takeaways: How FlashArray connects to Nutanix AHV over NVMe/TCP, and what the end-to-end reference architecture looks like in practice Why disaggregating compute and storage eliminates node overprovisioning and lowers TCO without sacrificing performance How to extend enterprise data services — snapshots, replication, and ransomware-resistant immutable snapshots — beneath your Nutanix workloads What non-disruptive upgrades and independent scaling mean for day-2 operations and long-term capacity planning Register NowActionable Assessments: Prepare Your Infrastructure for Oracle 26ai
August 20 | Register Now As organizations adopt Oracle AI Database 26ai with capabilities like AI vector search, understanding whether their existing data infrastructure can support these new workloads becomes increasingly important, while keeping costs under control. Join us as we explore how to analyze peak workload concurrency, optimize storage redundancy, and map advanced feature dependencies. Attendees will walk away with an actionable framework for evaluating their own environment, including access to a complimentary, privacy-first Everpure readiness assessment to help automate the planning process. Key takeaways: See your true workload and capture a consolidated, hour-by-hour view of peak demand. Crush storage bloat and learn how real-world environments are safely shrinking their storage footprints. Learn how to align infrastructure for active features like TDE, HCC, and Smart Scan without over-buying hardware. Secure your assessment and get exact sizing recommendations. Register Now!
The Art of Sizing: The Seven Signals That Help Decide Oracle 26ai Readiness
The Art of Sizing — Series Categories: Databases · Oracle · AI and Machine Learning By Thomas Stutesman, Principal Field Solutions Architect, Everpure A migration readiness scorecard turns raw Oracle AWR telemetry into seven plain-language signals — and shows you exactly where a lift-and-shift would carry yesterday's problems into tomorrow's platform. The migration everyone is planning for Across the industry, many organizations are now looking to move to Oracle AI Database 26ai. The release promises autonomous efficiency, AI-driven insight, and native vector search — but for most teams the path forward is clouded by one uncomfortable question: is our current infrastructure sized for tomorrow, or are we carrying the weight of yesterday's inefficiencies? The 26ai Migration Readiness scorecard is one of the helpful tools Everpure has created to make that migration easier. It takes the telemetry you already have — your Oracle Automatic Workload Repository (AWR) reports — and translates it into a clear, consistent read on where each database stands, so you move with evidence instead of assumptions. This is the first idea in our Art of Sizing series: for a decade, cheap flash and fast cores let us over-provision and "throw hardware at it." Oracle 26ai — with real vector workloads and a hardware market where components cost more and take longer to arrive — ends that era. Disciplined sizing is back, and it starts with reading the right signals. What the scorecard shows Point the assessment at an estate and it produces a single view: every database, scored across seven signals, rolled up into a combined readiness score. In one illustrative example across eight databases, the combined readiness score was 118 out of 168 — with 0 Ready, 7 Caution, and 1 Risk. None were a clean lift-and-shift: seven needed validation and tuning, and one needed remediation before it could move. Each database is scored across the seven signals with a simple traffic light, and the lights roll up into a score out of 21 — seven signals, three points each: Ready — 3 points — inside the healthy threshold Caution — 2 points — worth validating Risk — 1 point — attend to before you move A score of 18–21 is favorable, 13–17 means review needed, and below 13 flags remediation. Think of it as a current-state indicator that tells you where the risk concentrates — not a migration certification. A red flag doesn't mean "cannot migrate." It means the problem will follow you into 26ai — or get worse — if you size the new environment from the old box instead of from the evidence. The seven signals, one by one These are the value items to worry about when sizing for and moving to 26ai. For each, here is what it reads from AWR, why it drives your target design, what a red is telling you, and how Everpure helps you act on it. Signal 01 — System Capacity Reads DB Time against available CPU cores, average active sessions, and host CPU pressure. This sets the compute floor for the target and drives per-core Oracle licensing — size to the old ceiling and you inherit the old bottleneck. On top of that, 26ai's vector and embedding work adds fresh CPU demand. Red means: the source is already CPU-bound; moving as-is relocates the constraint. You need real headroom, not a like-for-like footprint. How Everpure helps: the assessment reports peak (not averaged) CPU demand per database and translates it into a right-sized core count — so the target is provisioned for the real workload plus deliberate headroom, and licensing is planned rather than guessed. Signal 02 — SQL & Parse Reads hard-parse rate, literal (non-bind) SQL, library-cache and cursor-sharing behaviour. 26ai changes optimizer behaviour, so heavy hard-parsing burns CPU, pressures the shared pool, and invites plan regressions on the new release. Red means: application-level SQL that will keep burning CPU or regress on cutover. Plan-stability work (SQL Plan Management, baselines) and shared-pool sizing belong before the move. How Everpure helps: because the assessment looks at the whole estate, not one instance, it surfaces shared SQL/parse patterns across databases — so one remediation effort (often "add bind variables") protects many migrations and lowers the CPU you have to size and license for. Signal 03 — Wait Profile Shows how DB Time splits across CPU, I/O, concurrency, and commit/log waits. It tells you what kind of bottleneck you are actually sizing for — a commit-bound database and an I/O-bound one need very different target designs. Red means: DB Time is dominated by a problematic wait (log file sync, buffer busy, latch/enqueue). Sizing CPU or storage without addressing it just moves the pain. How Everpure helps: Everpure maps the dominant wait class to the right lever — low, predictable write latency on FlashArray for commit/log waits; CPU or memory changes for concurrency waits — so the target attacks the real bottleneck instead of over-buying everywhere. Signal 04 — I/O Profile Reads read/write IOPS, throughput, block size, and latency. This is the direct input to storage sizing, and it separates latency-sensitive OLTP from bandwidth-driven scan/DW workloads — which size very differently. Averages hide the peak windows that actually test the array. Red means: real I/O demand the target tier must sustain. Under-provision here and everything above it — waits, capacity, response time — degrades. How Everpure helps: the assessment moves beyond averages to hour-by-hour peak-window analysis and turns it into a concrete IOPS/throughput/latency target for FlashArray — sub-millisecond and consistent, so the design performs at peak, not just on paper. Signal 05 — Memory Reads SGA/PGA sizing, buffer-cache behaviour, and Oracle's own memory advisories. Right-sizing memory on the target avoids trading RAM savings for a flood of avoidable physical I/O — and under-sized memory quietly inflates the I/O profile, so the two signals move together. Red means: memory is under-provisioned and driving physical reads that should be cache hits. Size SGA/PGA to demand rather than copying the old config. How Everpure helps: Everpure correlates memory pressure with observed I/O, so recommendations account for both together — enough memory to cut needless physical reads, and storage sized for what genuinely reaches disk. Signal 06 — Temp & Work Reads temp tablespace usage, sort/hash spills, PGA work-area activity, and multipass operations. Analytics, reporting, and AI-adjacent workloads live or die on temp and PGA — and 26ai vector operations add sort/compute patterns you have not sized for before. Red means: large sorts/hashes are spilling to temp; without PGA and temp sizing, the workload is slow on day one regardless of platform. How Everpure helps: the assessment quantifies spill behaviour and feeds it into PGA and temp-tier sizing, while FlashArray's consistent low latency keeps unavoidable spill cheap rather than catastrophic. Signal 07 — Segment Risk Reads large or fast-growing segments, chaining, LOBs, partitioning, and use of features like TDE, Hybrid Columnar Compression, and Smart Scan. These are the objects that don't migrate cleanly — reorg candidates, feature-compatibility items, and capacity-growth landmines. Red means: schema-level cleanup, not just capacity: objects that need reorg, or that depend on features the target must be configured to support from day one. How Everpure helps: Everpure auto-detects active features (TDE, HCC, Smart Scan) so the target is configured for them without over-provisioning, and replaces full clones and legacy triple-copy redundancy with space-efficient snapshots and external redundancy to reclaim capacity. From evidence to a right-sized migration The scorecard is the front door. Behind it is an assessment built to turn these seven signals into decisions you can defend to procurement and operations alike. It captures every instance for an ecosystem view, replaces multi-day averages with peak-window analysis, and translates raw metrics into specific storage and configuration recommendations for a 26ai target. It runs privacy-first — hostnames and SQL identifiers are masked, and only the actionable results are shared. Sometimes the biggest finding is what you don't need to buy. One prospect expected to buy 200 TB. The workload needed 18 TB. The gap was legacy ASM high-redundancy (three data copies) plus full clones for dev/test. Moving to Oracle-recommended external redundancy on Everpure and space-efficient snapshots reclaimed roughly two-thirds of the footprint and eliminated over 180 TB of wasted capacity — exactly the kind of trap that nameplate-based sizing would have locked in. From there, a right-sized design lands on a platform built for database density: Everpure FlashArray//XL R5 delivers sub-millisecond, consistent latency and industry-leading data reduction that absorbs vector growth economically. Pure Fusion presets encode your sizing discipline once so every 26ai environment is provisioned the same way, and Pure1 adds fleet-wide performance, capacity forecasting, and application-level context. A sizing-first path to 26ai Assess. Send your AWR reports; the 26ai Readiness Assessment is generated automatically, with identifiers masked. Interpret the signals. Reds are the pre-migration punch list; cautions are the validate list. Watch for fleet-wide patterns. Remediate reds first. Fix CPU, SQL/parse, dominant waits, and segment issues before cutover. Right-size the target. Size compute, memory, temp/PGA, and storage to the measured peak — and strip out redundancy and clone inflation. Provision consistently. Use Pure Fusion presets so every environment is built to the same standard. Validate with a pilot. Confirm behaviour against a representative pilot and target telemetry before scaling the wave. Summary Oracle 26ai brings back the art of sizing. The 26ai Migration Readiness scorecard reads seven signals from your AWR data — System Capacity, SQL & Parse, Wait Profile, I/O Profile, Memory, Temp & Work, and Segment Risk — so you can remediate the reds, right-size the target to your real workload, and move to 26ai with confidence instead of guesswork. Know where you stand — send us an AWR report, and Everpure's Oracle experts will run the 26ai Readiness Assessment on your environment and walk you through the seven signals, your remediation punch list, and a right-sized target design. The readiness score is an AWR-derived, current-state indicator to guide investigation and planning. It is not a migration certification, a final target-platform sizing, or a target-array headroom calculation — those decisions require workload requirements, target telemetry, compatibility checks, and a representative pilot. Database figures shown are illustrative. Oracle is a trademark of Oracle Corporation. © 2026 Everpure, Inc.The Art of Sizing: Breaking the Myths of Oracle Compression
If you work in storage or databases, you have probably heard the pitch: turn on Oracle compression, send fewer bytes, save space, reduce I/O, and lower cost. That sounds great on a slide. In the real world, it is often the opposite. This installment of The Art of Sizing breaks down one of the most persistent myths in enterprise infrastructure: that Oracle host-based compression is automatically a win. We are going to walk through the major compression types, where they help, where they hurt, and why the wrong compression decision can actually create more cost, more network traffic, more I/O, and more work for both storage admins and DBAs. The goal is not to say compression is bad. The goal is to size it correctly, understand where it belongs, and avoid paying premium dollars to make your systems do extra work. Why this myth survives Compression has a good reputation for a reason. Historically, it solved real problems. Storage was expensive, bandwidth was limited, and shrinking data was often the simplest path to efficiency. That logic still holds in some places. But in Oracle environments, especially transactional ones, the story gets more complicated. Oracle is not just writing datafiles. It is writing redo, managing undo, reorganizing blocks, updating symbol tables, and sometimes re-processing data later for deeper compression. That means a “smaller data footprint” does not always equal a smaller infrastructure burden. Sometimes it just shifts the burden somewhere else. First, let’s separate the compression families Not all compression is the same, and not all of it behaves the same way in Oracle. 1. General lossless compression This is the classic world of ZIP, GZIP, LZ77, LZ78, DEFLATE, ZSTD, and similar algorithms. The point is simple: reduce size without losing information. These methods are excellent for files, backups, archives, and many data services. Modern storage platforms use fast versions of these ideas in ways that are largely invisible to the application. 2. Lossy compression Think JPEG, MP3, MPEG, and H.264. These formats intentionally throw away some data in exchange for dramatic size reduction. They are incredibly effective for media, but they are not relevant for Oracle datafiles because databases generally require exact fidelity. 3. Oracle database compression This is where the confusion starts. Oracle has several different compression approaches, each with different behavior, licensing implications, and performance trade-offs: - Basic Table Compression - Advanced Row Compression - Advanced Index Compression - SecureFiles LOB Compression - Hybrid Columnar Compression - RMAN Backup Compression - Automatic Data Optimization and ILM-driven background re-compression Lumping all of those together under “Oracle compression” is one of the fastest ways to make bad architecture decisions. The big myth: compressed writes mean less work Here is the myth in plain English: If Oracle compresses the data before it sends it to storage, the network carries fewer bytes, the array writes less data, and the whole system gets more efficient. What that myth ignores is the full lifecycle of an Oracle write. In active transactional systems, Oracle prioritizes commit latency. That means the redo stream is written first, and it is written uncompressed. Later, as blocks fill and thresholds are crossed, Oracle may compress or re-compress them in memory. That structural change can generate additional redo. If data is later pushed into deeper formats through background optimization or archive-style compression, the system may read, process, and write the same data again. So yes, one part of the path may get smaller. But the total system effort often gets bigger. Figure 1: The life of a compressed I/O inside Oracle (OLTP write path). Notice that data is NOT compressed when it first travels to storage as redo, that write amplification happens at the threshold-hit step, and that this is where the footprint can start to grow. How to actually see it happening This is the part both DBAs and storage admins care about: how do you know Oracle is revisiting blocks, delaying compression, and generating extra work after the commit already succeeded? The threshold delay, in plain English With Advanced Row Compression, Oracle does not usually compress every row the moment it is inserted. Instead, rows are typically written into the block uncompressed first so Oracle can keep transactional latency low. Oracle keeps watching the remaining free space in that 8KB block. Once the block crosses an internal fullness threshold, Oracle goes back, builds or updates the symbol table, batch-compresses the block in memory, and then has to account for that structural change. That delayed work is what I mean by the threshold delay. So the timeline looks more like this: user writes data Oracle writes redo for durability commit returns quickly the block stays in buffer cache the block fills further threshold is crossed Oracle compresses or re-compresses the block in memory additional redo and block maintenance activity can follow That is why the system can look quiet at commit time and then busy again later. How you can tell Oracle is "redoing" things You are usually not looking for one giant smoking gun. You are looking for a pattern where post-commit activity does not line up with the simple story of "we wrote it once and moved on." Common signs include: redo generation that seems higher than expected for the amount of business data changed continued redo and log write activity after the original insert burst is over CPU spikes around block maintenance rather than just around user SQL periodic write bursts that do not line up cleanly with front-end transaction volume maintenance-window bandwidth spikes when colder data is being reworked into deeper compression formats storage-side churn where the array still sees a lot of activity even though the data was supposedly "compressed already" At the database layer, the giveaway is often the mismatch between application change volume and the total work observed in redo, background activity, and later data movement. At the storage layer, the giveaway is seeing traffic patterns that look like read-process-write loops rather than a single smooth write path. Why the database can get larger after a few days This confuses a lot of people because they expect compression to make the footprint immediately smaller and keep it smaller. Figure 2: The background ADO/HCC re-compression path. Days or weeks after the initial write, background jobs read cold data off the array, re-process it on the host, and write it back, so segments can grow from extra redo, undo, rewritten copies, and unreclaimed space. But Oracle compression can create delayed growth behaviors for several reasons: new rows may land uncompressed first and only later be reorganized recompression work can generate extra redo and undo background optimization jobs may read old blocks, reorganize them, and write new versions back out old extents may not be reclaimed immediately even after data is moved or rewritten free space inside segments may become fragmented in ways that do not instantly shrink the physical files if data is updated repeatedly, blocks can split, migrate, or be rewritten in ways that increase segment size before any long-term savings appear So what looks like "compression should have made this smaller" can become "the system created more structures, more history, and more rewritten copies before it settled down." For storage admins, this often shows up as a database that writes out one size on day one and then consumes more logical or physical space over the next several days as Oracle continues block maintenance, redo generation, archive activity, and background reorganization. The practical operator lesson If you want to understand whether compression is helping or hurting, do not just compare the first write size to the final stored size. Instead, look at the full lifecycle: initial redo volume later redo spikes buffer-cache and CPU behavior around block fullness archive log growth background maintenance windows segment growth over time instead of only at load completion storage bandwidth and write churn several days after the original ingest That is where the threshold delay becomes visible. That is where the myth breaks. Why host-based compression can cost more money This is where The Art of Sizing matters most. Compression is often sold as a capacity story, but in Oracle it can quickly become a licensing and CPU story. Advanced Row Compression and SecureFiles LOB Compression are paid features. That means you are not just paying in cycles, you may be paying in Oracle licensing. And if compression overhead pushes CPU consumption higher, you may end up needing to license more cores just to preserve the performance you had before. That is a brutal trade: - You pay for the compression feature. - You spend host CPU running the compression feature. - You may need more licensed cores because of the compression feature. - You still do not eliminate redo overhead. At that point, “saving space” can become one of the most expensive optimizations in the stack. Why it can create more network traffic This is the part that surprises people. On transactional writes, the redo stream still moves as uncompressed change data so Oracle can preserve low-latency commit behavior. That means the initial transactional path does not magically shrink just because the table eventually lands in a compressed state. Then the hidden traffic starts: - secondary redo generated when blocks are compressed or re-compressed - additional log activity to track structural changes - background movement when colder data is reorganized into deeper compression formats - read-process-write cycles for jobs like ADO or HCC-related maintenance For the SAN, that can mean less of a neat “compressed payload” story and more of a churn story. Why it can create more I/O Storage admins know this instinctively: once a system starts revisiting the same data repeatedly, the theoretical savings usually get eaten by operational noise. That is what can happen here. A write is not always a single write anymore. It can become: - the original transactional activity - redo logging for durability - later in-memory compression work - secondary redo for compression state changes - future background read-and-rewrite operations for deeper compression That is not reduced work. That is redistributed work, with extra steps. For busy OLTP systems, that redistribution can show up as more write amplification, more jitter, and more performance variance than people expected when they first heard the word “compression.” Why it creates more operational work Compression decisions do not just affect hardware. They create administrative drag. DBAs have to understand which compression mode is active, what is licensed, what is free, what silently triggered usage, and how it affects redo, CPU, and maintenance windows. Storage admins have to explain why the array still sees redo churn, why bandwidth spikes appear during data reorganization, and why dedupe or downstream efficiency may not look the way a simplified Oracle story suggested. And everyone gets more work when performance troubleshooting starts with a bad assumption. A quick breakdown of the Oracle compression types Basic Table Compression Good for bulk loads and relatively static datasets. It is not a magic answer for active transactional workloads because standard ongoing DML does not benefit the same way. Advanced Row Compression This is the big one in OLTP discussions. It supports active transactional operations, but it is also where deferred compression, block threshold behavior, secondary redo, and paid licensing can combine into a very expensive surprise. Advanced Index Compression Useful in the right indexing scenarios, especially with repetitive keys. This is more targeted and usually not the villain in the story, but it still needs to be understood separately from table compression. SecureFiles LOB Compression Can reduce footprint for large objects like documents, JSON, XML, and similar content, but it pushes work onto host CPU and can throttle ingestion performance when volumes are high. Hybrid Columnar Compression Very powerful for analytics, archival, and cold data patterns. It is not designed like OLTP row compression, and it often belongs in a very different conversation. When used through background movement or deep reorganization, it can generate substantial read-process-rewrite churn. RMAN Backup Compression A separate discussion from live transactional compression. Useful when applied deliberately, but some algorithms can also introduce licensing implications. The sizing lesson This is the heart of the series. Do not size from the brochure claim. Size from the full path of work. When evaluating compression, ask these questions: - What happens on the initial write path? - What happens to redo? - What CPU tax lands on the host? - Does this feature introduce licensing cost? - Will background maintenance create bursts of read-write churn later? - Is the data really a good fit for host-side database compression, or would array-level reduction be cleaner? If you do not answer those questions, you are not sizing compression. You are just hoping it behaves the way marketing described it. Where compression often belongs instead For many environments, especially where modern storage platforms provide inline reduction, the cleaner design is to let the database do database work and let the array do storage work. Figure 3: Myth vs reality, where should compression live? Host-side compression adds CPU, redo, and license cost, while array-level compression keeps host writes normal and delivers predictable I/O with global dedupe. That changes the equation: - less host CPU consumed by compression logic - fewer surprises tied to paid database options - fewer extra redo side effects from re-compression behavior - more predictable storage-side efficiency - a simpler operational model for both DBAs and storage teams That does not mean every Oracle compression feature is wrong. It means compression should be placed where it creates the least total system friction. And in many real-world environments, that is not at the host. Final thought: compression is not free just because it saves space Compression can absolutely be part of a smart architecture. But if you only measure saved capacity and ignore processor cost, network churn, redo behavior, maintenance overhead, and operational complexity, you can easily end up paying more to store less. That is the myth this post is here to break. In The Art of Sizing, the best design is not the one with the smallest number on a capacity chart. It is the one that delivers the best total outcome across cost, performance, simplicity, and operational sanity. And when it comes to Oracle compression, that usually starts with asking a harder question: Is this actually reducing work, or just moving it somewhere more expensive? Coming next In the next installment, we will look at the relationship between compression and encryption in Oracle, and why that combination can further change what the storage team sees and what the database team pays for.Enterprise Data Cloud: Managing Data, Not Just Storage
August 6 | Register Now Infrastructure teams have always managed applications. AI requires them to manage data. That's a different challenge, and most platforms weren't built for it. The Enterprise Data Cloud architecture from Everpure bridges that gap, bringing data intelligence into the same platform infrastructure teams already manage, so infrastructure and data teams are finally working from a single, unified view. In this session, we'll dig into what it means to manage a platform built for both operational performance and AI readiness, and the changes when infrastructure can finally gain visibility into its data, not just storage. Key takeaways: Why the shift from application-centric to data-first infrastructure changes how platforms need to be managed How Everpure brings data intelligence into the operational layer without adding tools, teams, or complexity What shared visibility across infrastructure and data teams actually unlocks for AI initiatives How to manage a platform that serves every workload, from archive to AI Register Now!The Art of Sizing: When Your "Safe" Standby Database Starts Hurting Production
In earlier posts in The Art of Sizing, the focus was on what happens when Oracle systems create their own instability through design shortcuts that seem harmless at first. This post extends that same idea into Data Guard, where a standby database that looks like passive insurance can become part of the foreground performance problem when it is undersized or poorly observed. That is the trap with synchronous disaster recovery. The standby is not just sitting there waiting for a failover. In MAX AVAILABILITY or MAX PROTECTION with SYNC AFFIRM, the primary commit path is directly dependent on the standby receiving redo, writing it to the standby redo log, and acknowledging that write before user sessions are released. When that standby server is short on CPU, struggling on storage, or starved for memory, the latency does not stay isolated on the remote side. It propagates backward into production as log file sync pain, commit stalls, and application slowdown. Why this problem is easy to miss The diagnostic challenge is that the evidence is often sitting on the standby side, but a physical standby in Active Data Guard read-only mode does not behave like a normal local AWR source. Standard local AWR reporting is not enough, and if Remote Management Framework is not already configured, the team can be in the middle of an incident without the standby visibility they actually need. That is what makes this a sizing topic rather than just a monitoring topic. If the standby participates in the commit path, then its storage behavior, CPU headroom, and buffer pressure are part of the production design whether teams acknowledge that or not. How to expose standby performance in Oracle 19c In Oracle 19c, the practical answer is to enable RMF so the primary can collect standby performance snapshots remotely and store them safely in the primary SYSAUX tablespace. The configuration sequence below is the key setup step that makes standby AWR reporting usable during a real production event. -- 1. Enable Management Pack Access on both instances ALTER SYSTEM SET control_management_pack_access='DIAGNOSTIC+TUNING' SCOPE=BOTH; -- 2. Register Nodes and Establish Topology on Primary EXEC DBMS_UMF.configure_node('NODE_PRIMARY', 'PRIMARY'); EXEC DBMS_UMF.configure_node('NODE_STANDBY', 'STANDBY'); EXEC DBMS_UMF.create_topology('ADG_AWR_TOPOLOGY'); -- 3. Link Remote Topology and Enable AWR Service EXEC DBMS_UMF.register_node( 'ADG_AWR_TOPOLOGY', 'NODE_STANDBY', 'DB_LINK_TO_STANDBY', 'DB_LINK_TO_PRIMARY', 'AS_NODE', 'TRUE' ); EXEC DBMS_WORKLOAD_REPOSITORY.register_remote_database( node_name => 'NODE_STANDBY' ); Once the standby is registered, reports can be generated from the primary by running awrrpti.sql and selecting the standby DBID from the menu. What to watch for in a standby AWR report The most useful way to read a standby AWR during a production slowdown is to correlate primary symptoms with standby evidence. The issue usually presents itself as one of a few recognizable patterns. Primary production symptom Standby AWR red flag Likely meaning Spike in log file sync and high SYNC transport lag High log file parallel write on the standby, especially above roughly 5 to 10 ms The standby storage tier is underperforming and slowing standby redo log flushes. Primary LGWR stalls on LNS wait on send Standby host CPU utilization near 100 percent or a high load average The standby is CPU-starved and RFS processes are not acknowledging packets quickly enough. Commit stalls appear at particular hours High ASH on the standby from user reporting activity Heavy Active Data Guard reporting is taking CPU and buffer cache away from recovery work. Flush delays and transport buffer saturation High free buffer waits or checkpoint completed during MRP Buffer cache is too small or DBWR is saturated on the standby. The sizing lesson underneath the incident The bigger lesson is that standby design is not a secondary hardware conversation. In synchronous architectures, the primary database is only as fast as the weakest link in the standby path. That means a disaster recovery platform should not be treated as a low-priority landing zone built from slower storage, thinner CPU allocation, or loosely governed reporting workloads. If it participates in commit acknowledgment, it participates in production performance. The practical operating principles are straightforward: Keep the standby infrastructure performance-symmetric with production where synchronous protection is required. If Active Data Guard is used for reporting, govern those read workloads so they cannot starve RFS or MRP activity. Enable RMF-based standby AWR collection before there is a crisis, not during one. Final thought A standby database is supposed to be your safety net. But in synchronous Data Guard, a poorly sized or poorly monitored standby can become part of the outage story itself. That is the real point here: availability architecture is still performance architecture, and the standby is still part of the sizing equation. That is also why this topic belongs in The Art of Sizing series. Good sizing is not just about capacity. It is about understanding which components quietly sit inside the critical path, and making sure they are designed, monitored, and governed accordingly. Sources The Art of Sizing Data Guard and the Hidden Cost of Small Redo Decisions
