Ownership, Parent and Lookup Skew

🌐 Also in 19 languages

Español · 日本語 · Português (Brasil) · Français · Deutsch · Italiano · 한국어 · 简体中文 · Português (Portugal) · 繁體中文 · Bahasa Indonesia · Türkçe · Polski · Tiếng Việt · ไทย · Українська · Español (México) · Français (Canada) · Deutsch (Schweiz)

Image generated with AI

Module 2 of 4 in the chapter Designing for Scale, chapter 7 of the Data Architect course. Each module stands on its own. Previous: When Volume Changes Everything · Next: Volume Meets the Sharing Model

The question this module answers: What happens when too many records point at one thing?

Large-data-volume problems aren’t always about having too many records. Sometimes they’re about too many records connected to the same thing: one owner, one account, one parent, one lookup value. Think about traffic. A city can have millions of cars and still work. The trouble starts when everyone takes the same freeway exit at 5 p.m. The total matters, and so does the concentration. I call these concentrations hot spots; Salesforce calls them data skew. There are several kinds, and one idea underneath them all: too much activity converging on one point in the model.

Parent-Child Skew

The easiest to picture. One account with 100 contacts: nothing unusual. A thousand: probably fine. Ten thousand: now I’m paying attention. My design rule is no more than about 10,000 children under one parent, and Salesforce’s guidance draws the same line: “a large number of child records (10,000 or more) with a single parent account” is a common cause of poor performance (Parent-Child Data Skew (opens in a new tab)). Salesforce’s own example shows why. Put 300,000 contacts under one account. A user who can see one of those contacts gets implicit access to the account. When that user loses access to the contact, Salesforce has to check the other 299,999 contacts before it can decide whether to remove the user’s access to the account. Do that for many users at once, and it gets expensive. Salesforce also names one way to take part of that cost away: where your security requirements allow, set the child object to Controlled by Parent, so there are no parent implicit shares to maintain (same source). In practice, it’s the most common skew I see, and I’ve seen users notice visible lag at around 20,000 children under one parent. Where somebody feels it depends on what the system is doing. That’s why 10,000 is a design threshold, not a promise that record 10,001 breaks anything. The real question is: why are all these records pointing at the same parent?

Click to enlarge

Step 1 of 2: every line points at one account. That’s the hot spot.

The “Unassigned” Account, and Account Pools

The classic case is contacts with no real account. Somebody decides every contact needs an account, so they create Unassigned Contacts and put every contact without an account there. Problem solved, until there are 300,000 contacts under it. We created an artificial hot spot because the model required us to put everything somewhere, and the relationship doesn’t even carry business meaning. It worked beautifully at 500 records. Then the Number of Zeros caught up with it.

What do you do when you’ve got a million contacts under it? I’ve seen child accounts by state, one unhandled-contacts account per state. I’ve seen an automatic version: whenever an account reaches 10,000, create another one. It can be a little awkward, but it spreads the load.

One answer is an account pool: Unassigned Contacts 001, 002, 003 and so on, with the children spread across them, each kept under the threshold. It works, and it’s Salesforce’s own advice: “Distribute child records so that no parent has more than 10,000 child records,” and for contacts without real accounts, “set up several dummy accounts and distribute the contacts among them” (Best Practices for Deployments with Large Data Volumes: General (opens in a new tab), Winter ‘27). But those aren’t real accounts; they’re architecture artifacts. Users will ask who “Unassigned Contacts 017” is and why it’s in their search results. So when I use a pool, I hide the placeholder parents from normal business experiences as much as I can. It’s a recurring trade-off: sometimes scale forces a structure that exists for the platform, not the business. When it does, don’t pretend it’s business data. Name it, document it, hide it where appropriate.

Click to enlarge

Step 2 of 2: the same contacts, spread across a pool. Nothing moved but where they point.

Master-Detail at Volume

Contacts under an account are a lookup. Master-detail is a different decision, and it’s one you make in the data model, long before the records arrive. Here’s the question I ask in class: would an invoice ever have more than 10,000 line items? No, it never would. So an invoice and its lines can be master-detail all day long. Now ask the same question of every master-detail relationship you’re about to build: at the volume we expect, will one parent ever pass 10,000 children?

Salesforce draws the same line: “As a best practice, don’t exceed 10,000 child records for a master-detail relationship” (Considerations for Object Relationships (opens in a new tab)). Master-detail is the strongest relationship Salesforce gives you, and that’s exactly why the number matters. The detail record has no owner of its own; its access is controlled by the parent. Delete the parent, and every child goes with it. A child can’t move to another parent unless you allowed reparenting. Roll-up summaries on the parent have to keep up as the children change. And saving a child can lock the parent, which is why Salesforce’s loading advice is to group child records by their parent “to minimize locking conflicts” (Loading Data from the API (opens in a new tab)). With ten children, none of that is noticeable. With a hundred thousand, every one of them is.

It’s also hard to undo. A master-detail relationship can only be converted to a lookup when the parent has no roll-up summary fields, and converting it changes the child’s sharing to Public Read/Write (same source). By the time volume tells you it was the wrong choice, the relationship may be holding up reports, sharing and automation. Count the children before you choose the relationship.

Steve Trucking has one of these decisions in its model. Each Retail Partner sends a Manifest (Manifest__c, a custom object) for each distribution center, for each working day. Manifest is custom, so master-detail to the partner’s account is allowed, and it’s tempting: the manifest would inherit the partner’s sharing, which is exactly the isolation we want. Now count. Our largest partner has nine or more DCs: nine manifests a working day, about 2,700 a year. Past 10,000 in under four years, under one parent, and every manifest revised on the day, so every revision would touch the partner’s account. That’s why the model makes Manifest a lookup and handles partner access through sharing. The invoice stays small; the manifests keep coming.

Click to enlarge

Step 1 of 2: master-detail, where the parent stays small.

Click to enlarge

Step 2 of 2: a lookup, where the children keep coming.

Watch Account with its contacts and opportunities too. It isn’t master-detail, but it behaves like a pseudo master-detail, with implicit sharing to the parent, which is why the 10,000 guidance shows up there as well. None of this means never use master-detail at scale. It means don’t choose a strongly coupled relationship and then act surprised that the records are strongly coupled.

Test knowledge. Know the 10,000-children guidance for master-detail, and what master-detail brings with it at volume: no owner on the detail (access controlled by the parent), cascade delete, no reparenting unless allowed, roll-up summaries, and parent locking when children are saved. Converting to a lookup needs no roll-up summaries on the master.

Ownership Skew

Now the owner. I ask the room: who owns a million records? The admin? Is the admin sitting there creating a million records? Usually it’s the integration user or the data load user. The records come in, and nobody knows who to give them to. We create an integration user, it creates records, nobody thinks about ownership (“it’s just the integration user”), and a year later that user owns 2 million records. Salesforce calls it ownership data skew when one user owns more than 10,000 records of an object, and the classic example is all the unassigned leads parked on a dummy user (Ownership Data Skew (opens in a new tab)). That doesn’t mean a user can never own more. It means understanding the consequences: ownership isn’t a Created By stamp. It drives the role hierarchy, sharing rules and recalculation: behind the scenes, Salesforce keeps share records for the owner’s records above them in the role hierarchy. Attach millions of records to one owner who sits deep in the sharing model, and changing anything about that owner gets expensive.

The guidance matches what I teach in class. If a few users must own a very large number of records, give them no role; if they need one, put them in a separate role at the top of the hierarchy and don’t move them, and keep them out of public groups used as sources for sharing rules (same source). One catch with the top role: that user inherits access to all the data owned by or shared with the users below them. You could spread the records across a bunch of users instead, but for an integration user or a data load user, that typically isn’t worth the trouble.

We’re isolating the hot spot from the structures that trigger recalculation. Imagine a user who owns 5 million records being moved in a reorganization. That isn’t moving a box on the org chart; Salesforce may have to re-evaluate access across 5 million records. At this scale, organizational changes become data operations.

Test knowledge. Know the skew thresholds and the fixes: ownership skew when one user owns more than 10,000 records of an object (no role, or a separate role at the top that doesn’t move, and out of public groups used by sharing rules); parent-child skew at about 10,000 children under one parent.

Who Should Own Machine-Created Records?

Who created a record and who owns it aren’t the same question. Redrock sends 500,000 deliveries through the API, and the integration user creates them. Should it own them forever? Maybe, but not just because it created them. And now agents create records too.

My position: it depends on whether ownership matters for sharing. The integration or agent user is a valid user and can own records. If ownership drives access in that object (through the role hierarchy or sharing rules), assign the records to the owner the business requires: a real user, a queue where the object supports one, or a dedicated owner user. If ownership doesn’t drive access, keeping the agent or integration user as owner may be the better choice, because the audit trail leads straight back to whatever created the record. Either way, decide what Owner means for this object, rather than mechanically reassigning every machine-created record because people usually own records. And remember that Created By is still there: reassigning ownership doesn’t lose the fact that the integration created it. Created By and Owner answer different questions. Making one field answer both is how you get a bad model.

Lookup Skew

A lookup can be a hot spot too: millions of records pointing at the same location, category, reference record or configuration record. Salesforce’s own training defines it: “Lookup skew happens when a very large number of records are associated with a single record in the lookup object” (Large Data Volumes: Design Your Data Model (opens in a new tab)). The danger isn’t “lots of lookups.” It’s concentration combined with activity: if millions of active records reference one record, and transactions keep touching that relationship, operations start colliding over it. That’s record locking, which chapter 11 covers. For now, recognize the shape: many active records, one heavily referenced record, worth inspecting.

Not Every Skew Is Equally Dangerous

Don’t memorize “10,000 = bad.” Fifty thousand historical records pointing at a reference value that never changes, with no sharing role and almost no concurrent activity, may be fine. Twenty thousand active records under a parent that changes often, drives sharing, feeds roll-ups and gets hit by several integrations at once is a very different story. Volume has shape, and so does skew. How many records converge there? How often do they change? Does the parent change? Does the relationship drive sharing or locking? Do integrations update the children concurrently? Does moving the owner or parent trigger recalculation? The number tells me where to look; the behavior tells me how worried to be.

Steve Trucking: Where Are the Hot Spots?

At five years we projected about 9 million work orders. Suppose someone says, “Let’s have the integration user own every delivery.” Nine million records, one owner. Does ownership matter for sharing deliveries? If not, a dedicated high-volume owner can work, isolated from the role hierarchy and sharing-rule groups as above. If it does, one integration owner is the wrong business model: the integration creates the record; that doesn’t mean it should own it.

Then the Retail Partner. Every work order carries its partner, deliberately, because partner isolation matters. But if one partner is 40% of the business, five years of deliveries is about 3.6 million work orders under one partner account. Does that make the relationship wrong? No. The business relationship is real, and I’m not going to invent 400 fake Redrock accounts because I don’t like the count. But I am going to look for a split the business would recognize. Each partner has real distribution centers, so a child account per DC under the partner spreads the work orders nine or more ways while every account still means something. Add archiving completed deliveries after the claims window, and the active children per parent stay low.

That’s the difference between the two fixes. “Unassigned Contacts” was already artificial, so splitting it into a pool loses no meaning. Redrock is a real partner; splitting it into Redrock 001, 002 and 003 would corrupt the model. Splitting it into its real DCs doesn’t. Scale fixes still have to preserve meaning. Sometimes the answer is a different relationship, sharing strategy, query pattern, storage model or archive policy, never a fake identity for a real business entity.

Click to enlarge

Inspect, not bad. Concentration matters, not the total.

Find the Hot Spots on the Diagram

Here’s an exercise I like. Take the data model and look at the arrows, not just the objects. For each relationship, ask: how many records can pile up on this side of the arrow? Then ask the same about Owner. Now imagine three years, five, ten. Where do the records concentrate? That’s often where tomorrow’s problem is hiding.

Beyond the exam: what AI changes. A person might create 20 records a day; an agent could create thousands. Give an agent its own user, let every record stay owned by it, and ownership skew appears far faster than it ever did with people. Agent ownership isn’t wrong, but decide what Owner means for agent-created records before the volume makes the default part of the architecture.

What an Architect Decides Here

• Where parent-child, ownership or lookup relationships concentrate records, and whether they’re active enough to become hot spots.
• Whether machine-created records stay owned by the integration or agent user, or are reassigned because ownership drives access.
• How high-volume owners are isolated from the role hierarchy and sharing-rule groups.
• When an artificial parent can be pooled, and when a real relationship needs a meaningful split instead.
• Whether each master-detail relationship stays under about 10,000 children per parent at projected volume, or should be a lookup.

A million records spread across thousands of owners and parents behaves very differently from a million records pointing at one place. Look for the hot spots early: owners, parents, lookups, anywhere the Number of Zeros converges. The goal isn’t to eliminate every large relationship; some represent the business correctly. It’s to know where the traffic is going before you open the freeway, because once millions of records are heading for the same exit, changing the road gets much harder.

Explore

• Salesforce Developers, Best Practices for Deployments with Large Data Volumes, Loading Data from the API (opens in a new tab) (Winter ‘27): group child records by parent to minimize locking conflicts, checked 2026-11-01.
• Salesforce Help, Considerations for Object Relationships (opens in a new tab): no more than 10,000 child records for a master-detail relationship; converting master-detail to lookup, checked 2026-11-01.
• Salesforce Developers, Designing Record Access for Enterprise Scale (Winter ‘27): Ownership Data Skew (opens in a new tab) and Parent-Child Data Skew (opens in a new tab), checked 2026-11-01.
• Salesforce Developers, Best Practices for Deployments with Large Data Volumes: General (opens in a new tab) (Winter ‘27), the 10,000-record owner and parent guidance, checked 2026-11-01.
• Trailhead, Large Data Volumes: Design Your Data Model (opens in a new tab): parent-child, ownership and lookup skew, checked 2026-11-01.
• Salesforce engineering blog, Managing Lookup Skew to Avoid Record Lock Exceptions (opens in a new tab) (2013; older, but still the clearest explanation; checked 2026-11-01).
• SteveTechArc, Salesforce Documents by Release: these guides, once a year from Winter ‘16, each with an archived copy.