When Volume Changes Everything

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Module 1 of 4 in the chapter Designing for Scale, chapter 7 of the Data Architect course, and the start of Part 2. Each module stands on its own. Next: Hot Spots: Ownership, Parent and Lookup Skew

The question this module answers: What breaks as data grows, and when?

A design can be perfectly reasonable at 70,000 records and completely wrong at 70 million. That’s one of the things that makes data architecture difficult. The object is the same. The fields are the same. The relationships are the same. The business requirement may even be the same. But add a few zeros and the architecture changes.

This is where we move from See to Design. In Part 1, we learned to see the business, the data model, the enterprise landscape, governance and authority. Now we’re going to put weight on that architecture. And the first question I ask is: how big does this get?

Built for Transactions

Salesforce runs some of the largest CRM implementations in the world, and its core database handles “upwards of 1.1 trillion transactions per month.” Salesforce’s own architects describe that database as “optimized for high-performance transactional workloads on structured data” (The Salesforce Platform: Transformed for Tomorrow (opens in a new tab)). It’s built for that: transactional work on structured data. And every record comes with a lot of built-in capabilities and functionality that are part of the core Salesforce platform: the object model, sharing and security, pages, reports, automation, search. All of it runs on a multitenant database, where Salesforce writes the SQL, not you.

I once taught this class at Dreamforce to a room with a lot of database administrators from Oracle and SQL Server, and of course they were asking, “Why doesn’t Salesforce act this way? Why doesn’t Salesforce do this?” By the end they were smiling, because they’d understood why. Chapter 3 described Salesforce as a multitenant, metadata-driven platform: the apartment building. We’re not tuning our own dedicated database. Salesforce’s storage, indexing, sharing, locking, query optimization and resource protections are designed for many tenants at once. That’s not bad; it’s why we get so much capability without running database infrastructure. But it means the architect needs to understand the platform’s shape.

Salesforce says it plainly in its own guide: as data sets get larger, “the time required for certain operations grows,” and the architect’s choices change that time “by several orders of magnitude” (Best Practices for Deployments with Large Data Volumes (opens in a new tab)). That’s not a weakness. It’s the design. At small volumes you can sometimes ignore the platform’s shape. At large volumes, it reminds you it’s there, and we’ll see that very clearly with selectivity and indexes in the next chapter. That’s why the architect has to know how big this gets.

The same guide ends with the other half of the story. Salesforce is “a robust environment in which native and custom applications can scale to large volumes of data very quickly while continuing to perform well,” and it names the two ways you get there: make queries selective, and reduce the amount of active data. Millions of records can perform well on Salesforce. They perform well because somebody designed for them. That’s what this part of the course is about.

My Large Data Volume Ruler

When I’m teaching large data volumes, I use a rough ruler. It’s not a Salesforce limit. The numbers are general figures discussed in the community, and Salesforce itself calls a large data volume “an imprecise, elastic term”: tens of thousands of users, tens of millions of records, or hundreds of gigabytes of storage (Best Practices for Deployments with Large Data Volumes (opens in a new tab)). And when I say records, I mean records in a single object, not across all your objects.

Like every number in this course, the ruler has a date on it. More on that in a moment.

At about 1 million records in an object, there’s just a little bit of drag as dashboards and reports show up. Not a lot, depending on how you filter. Around 10 million, there’s some effect, but it’s still performing. Heading toward 50 million, you start to notice it: list views taking two, three, four seconds, reports and dashboards taking time. Past 50 million, I like to say you’re driving a convertible with your head out the window, and your hair is blown back. Don’t stick your head out the window: past 50 million you’ve got to be streamlined, and you’ve got to know what you’re doing. And once we’re talking about 100 million records or more, I start asking a different question: should these records be here at all? I’ve heard of people running a hundred million or more in an object. But if you’re at 50 million today, you’re going to 100, 150, 200 million in the years ahead. If you’re not archiving today, when will you? If somebody tells me they have 150 million records in an object and almost nobody ever looks at the old ones, that’s not an operational object any more. That’s an audit log.

It isn’t a coincidence that the ruler starts at a million. The platform’s own query math changes at “the first million records,” as the next chapter shows. The exact number isn’t the lesson, though. The lesson is: volume changes the design.

Click to enlarge

Every zero changes what you notice. A rule of thumb, not a limit.

The Platform Keeps Growing

Salesforce ships three releases a year. Over time the platform grows, some limits are raised, and systems get faster. So I don’t want you to treat any number in this course as if it were permanent, including mine. Salesforce keeps every release’s edition of its guides online, which means we can do something useful: put ten years side by side. Here’s Winter ‘16, from late 2015, next to Winter ‘27, from 2026.

	Winter ‘16 (2015)	Winter ‘27 (2026)
Big objects	Didn’t exist yet	“Consistent performance, whether you have 1 million records, 100 million, or even 1 billion”
Skinny table columns	100	200
Index selectivity thresholds	Capped at 1 million records (standard) and 333,333 (custom)	No published cap, and the optimizer can add an index on its own
Minimum data storage	1 GB per org, or 20 MB per user	10 GB per org, plus storage per user
Bulk API batches a day	5,000	15,000
Parent-to-child levels in one SOQL query	1	5
Apex heap per transaction	6 MB (12 MB asynchronous)	10 MB (25 MB asynchronous)

That’s the upward direction: limits raised, caps removed, new kinds of storage, and an optimizer that now “recognizes that an index can improve performance for frequently run queries” and adds one (Working with Very Large SOQL Queries (opens in a new tab)).

Now look at what didn’t move in those ten years. The definition of a large data volume is word for word the same: “tens of thousands of users, tens of millions of records, or hundreds of gigabytes.” The advice to avoid having any user own more than 10,000 records is the same. And an Apex transaction still gets 100 SOQL queries, 50,000 records and 10 seconds of CPU (Execution Governors and Limits (opens in a new tab)). Those are the house rules of the apartment building, the guardrails that keep one tenant from hurting the others, and they’ve barely moved.

Salesforce is open about the pace, too. It ships three releases a year, each with a new API version, and it commits to “supporting each API version for a minimum of 3 years from the date of first release,” with at least a year’s notice before support for a version ends (SOAP API End-of-Life Policy (opens in a new tab)). In practice versions live much longer: as I write this, version 41.0, from Winter ‘18, is still supported. If you’re new, you don’t need the history. But it does change decisions. The org you inherit was designed under older limits, and some of its workarounds may not be needed any more. The integration you build today is pinned to an API version with a clock on it. And study material written a few releases ago may still teach yesterday’s numbers.

So when you read a number in this course, read the date next to it. The specific limits will keep changing, mostly upward, and Salesforce will keep pivoting. The thinking won’t: know your Number of Zeros, keep the active data small, make your queries selective, and check the current edition of the guide before you commit to a number. That’s why I teach the ruler as a ruler, not a law. For now, it’s a good place to start.

The sources, both editions: Best Practices for Deployments with Large Data Volumes, Winter ‘16 (opens in a new tab) (archived copy (opens in a new tab)) and Winter ‘27 (opens in a new tab) (archived copy (opens in a new tab)); Salesforce Limits Quick Reference Guide, Winter ‘16 (opens in a new tab) (archived copy (opens in a new tab)), and Salesforce Developer Limits and Allocations Quick Reference, Winter ‘27 (opens in a new tab) (archived copy (opens in a new tab)); Data and File Storage Allocations (opens in a new tab) (10 GB “starting in late March 2019”); Execution Governors and Limits (opens in a new tab), Winter ‘27.

Don’t Ask How Big It Is Today

One of the easiest mistakes is sizing an architecture from today’s data. Steve Trucking has about 1 million deliveries a year. If I only ask “can Salesforce handle one million deliveries?”, I’m asking the wrong question. Steve Trucking expects about 30% annual growth: 1.0 million in year one, 1.3 in year two, 1.69 in year three, about 2.2 in year four, about 2.86 in year five. That’s annual volume. If we keep all five years, we’re not designing for 2.86 million deliveries; we’re designing for roughly 9.04 million. And that’s before we ask how much history already exists.

That’s why I teach Steve’s Number of Zeros. Don’t obsess over false precision early. Get the magnitude right. Ten thousand, a hundred thousand, a million, ten million, a hundred million? The architecture can change every time we add a zero.

Now Multiply the Children

The delivery isn’t the only record. Steve Trucking averages about 1.6 line items a delivery, so over five years that’s about 14.5 million work order line items. And there are around eight status changes a delivery: about 72.3 million status events. The requirement sounded like “track deliveries.” The architecture has to deal with tens of millions of related records. One million parents with eight children each is not a one-million-record problem. You have to follow the model.

And the Data We Didn’t Put in Core

Checkpoint 1 also gave us four to five million photos a year, and FleetLink produces about 200,000 GPS readings a day: roughly 73 million a year before growth. That’s why we decided not to put every GPS reading, every photo or every status change into the Salesforce operational model. We hadn’t even started this chapter, and the Number of Zeros was already telling us something. It’s like telling the doctor, “It hurts when I raise my arm,” and the doctor says, “Then don’t raise your arm.” Sometimes the best way to solve a large-data-volume problem is don’t create it in the first place.

Queries Aren’t the Only Thing That Slows Down

When people hear large data volumes, they think “queries get slow.” Yes, and the next chapter is all about query and retrieval. But it’s bigger than that. As volume grows, I care about sharing calculations, ownership, record locking, parent-child relationships, list views, related lists, reports, search results, batch processing, integrations, data loads, deletes, archiving, backups and recovery. Even a harmless-looking decision gets expensive when it’s repeated millions of times.

That’s the lesson: what works today may fail tomorrow. Maybe nobody notices at 50,000 records, or at 500,000. Then growth pushes the object through several million, and an old design assumption becomes the bottleneck. The system didn’t randomly go bad. The architecture crossed the scale where that decision mattered.

Clean Models Can Become Expensive Models

My starting position is still: design clean first, then back off when necessary. Understand the entities and relationships; don’t duplicate data because it’s convenient. But normalization is a principle, not a rule. If a report or operational query constantly has to walk several relationships across tens of millions of records to get one value, at some scale I may copy that value closer to where it’s used. That’s denormalization.

Say partner-scoped reports on work order line items constantly need the Retail Partner, which in the clean model sits two hops away: line item → work order → partner. At enough scale, there may be a reason to put a governed copy of the partner identifier directly on the line item. Now I’ve created duplicate data, and that creates responsibility. How is it populated? Can it change? How do I keep it correct? Which copy is authoritative? Those are real costs, and sometimes I accept controlled duplication to get the performance the business needs. Design toward normalization, then back off deliberately when scale gives you a reason. Don’t denormalize because it’s easier to build; denormalize because you can explain the benefit and the cost you’re accepting.

Don’t Make Core CRM Your Warehouse

There’s another scale mistake I see. The business wants reporting, so we keep adding historical data to the operational CRM, then more, until the justification becomes “we need it for reporting.” That’s when I ask whether we’ve turned the operational system into the analytical system. Those are different workloads. Steve Trucking needs Salesforce to run today’s deliveries: the agent needs today’s status, the dispatcher today’s routes, the partner today’s progress. An analyst comparing five years of delivery performance by region, partner, truck, weather, crew and delivery window, joined with finance, is an analytical workload. Salesforce can report on operational data. That doesn’t mean every historical analytical question belongs in the operational store. Steve Trucking already has Snowflake. So ask: which questions must the operational system answer, and which belong on the analytical platform? Part 3 covers the warehouse properly.

And reporting snapshots aren’t a warehouse strategy. Flattened reporting objects, snapshot records, copies of values that make reports easier: sometimes legitimate. They can also be a way of postponing the architectural question. If I’m creating millions of extra records so I can report on the millions I already have, I want to know where that ends. Don’t keep manufacturing operational records because nobody wants to have the warehouse conversation.

Volume Has Shape

Two systems can each have 20 million records and behave very differently. One holds 20 million historical records, almost never updated, usually fetched one at a time by a known identifier, with little sharing complexity. The other holds 20 million active records, constantly updated, with complex ownership, heavy reporting, many users, large parent-child concentrations and frequent integrations. Not the same problem. So record count alone isn’t enough. How fast is it growing? How often does it change? How is it queried, shared and related? How long must it stay active? How much arrives, or changes, at once? What happens when we delete it? “Salesforce can handle 20 million records” isn’t an answer. The interesting question is: twenty million records doing what?

AI Is Going to Add More Zeros

Agents can create data very quickly: interactions, tasks, summaries, recommendations, logs, intermediate results, events, audit information. Maybe that’s useful. But AI doesn’t make storage free, or sharing calculations, or indexing, and it certainly doesn’t make useless history valuable. If an agent creates ten records where a human process used to create one, our volume assumptions may be wrong before the business itself grows. I’m not yet seeing agents generate more records in the orgs I work with, but it’s the natural progression of more and more data being tracked.

Beyond the exam: what AI changes. AI can dramatically increase the rate at which systems create and retrieve data. Model agent-generated volume explicitly rather than assuming historical human transaction rates will predict future scale.

Steve Trucking: the Architecture Is Already Changing

At the end of Part 1 we made several decisions that are now reinforcing each other. The work order is central. The current status lives in the org, and the history of every status change is published to the warehouse, Snowflake. Photos live outside the core. Raw GPS stays with FleetLink. At five years of 30% growth, we’re approaching 9 million work orders, 14.5 million line items and 72.3 million status events, and hundreds of millions of GPS readings if we’d copied them all.

Was keeping raw GPS out of the operational model just an integration decision? No; it was a scale decision too. Was putting photos off-core just a storage decision? No; scale again. And the status history? It has real business value, so we keep it. But at 72.3 million events, keeping it outside the operational database is the difference between an org that carries its history and an org that’s buried by it. The question that remains for what we do keep in core: how long should those records stay active? That’s module 4.

Click to enlarge

Follow the model, and the zeros multiply.

What an Architect Decides Here

• What the Number of Zeros looks like today, at expected growth, and across the retention period, not merely whether today’s volume works.
• Which clean structures stay normalized, and where controlled denormalization is justified by scale.
• Which data stays in the operational Salesforce core and which belongs in specialized storage, analytical platforms or other systems.
• Whether a reporting requirement is truly operational, or a sign the business needs an analytical architecture.
• How new producers such as agents change volume assumptions before their records become tomorrow’s problem.

Large data volume architecture isn’t about being afraid of big numbers. Salesforce runs very large implementations. One million records isn’t automatically a problem; neither is ten million or a hundred million. But every zero should make me ask another question: how will we find them, who owns them, how are they shared, what are they related to, how often do they change, how long do we need them, and where should they live? In Part 1 we learned to recognize the right entities and relationships. Now we make sure those bones can carry the weight, because what works today may fail tomorrow.

Explore

• Salesforce Help, Data and File Storage Allocations (opens in a new tab): 10 GB of data storage since late March 2019, checked 2026-11-01.
• Salesforce, Best Practices for Deployments with Large Data Volumes, Winter ‘27 edition (opens in a new tab) (release 264, archived copy (opens in a new tab)), checked 2026-10-20.
• Salesforce Developers, Best Practices for Deployments with Large Data Volumes (opens in a new tab), Winter ‘27, checked 2026-11-01.
• Salesforce Architects, The Salesforce Platform: Transformed for Tomorrow (opens in a new tab), checked 2026-11-01.
• Salesforce, Best Practices for Deployments with Large Data Volumes, Winter ‘16 edition (opens in a new tab) (archived copy (opens in a new tab)) (release 198), checked 2026-10-20.
• Salesforce, Developer Limits and Allocations Quick Reference, Winter ‘16 edition (opens in a new tab) (archived copy (opens in a new tab)) and Winter ‘27 edition (opens in a new tab) (archived copy (opens in a new tab)), checked 2026-10-20.
• Salesforce Developers, SOAP API End-of-Life Policy (opens in a new tab), checked 2026-11-01.
• Salesforce Developers, Execution Governors and Limits (opens in a new tab), Apex Developer Guide, Winter ‘27, checked 2026-11-01.
• Salesforce Developers, Working with Very Large SOQL Queries (opens in a new tab), Apex Developer Guide, Winter ‘27, checked 2026-11-01.
• SteveTechArc, Salesforce Documents by Release: the guides cited here, once a year from Winter ‘16 to today, each with an archived copy.
• The Steve Trucking brief, section 4 (volumes).