Quando il volume cambia tutto

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Immagine generata con l'IA

Tradotto con l'IA. Non ancora rivisto dalla comunità. Leggi in inglese Questa sezione è cambiata in inglese e non è ancora stata tradotta di nuovo. (48)

Modulo 1 di 4 del capitolo Progettare per la scala, capitolo 7 del corso Data Architect, e inizio della Parte 2. Ogni modulo si regge da solo. Successivo: Punti caldi: squilibrio di titolarità, di padre e di ricerca

La domanda a cui risponde questo modulo: Cosa si rompe quando i dati crescono, e quando?

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. ⟲

È qui che passiamo dal vedere al progettare. Nella Parte 1 abbiamo imparato a vedere il business, il modello dei dati, il panorama aziendale, la governance e l’autorità. Ora mettiamo peso su quell’architettura. E la prima domanda che faccio è: quanto diventerà grande?

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 (si apre in una nuova scheda)). 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 (si apre in una nuova scheda)). 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. ⟲

Il mio metro per i grandi volumi di dati

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 (si apre in una nuova scheda)). 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. ⟲

Non è un caso che il metro parta da un milione. I calcoli delle query della piattaforma stessa cambiano al “primo milione di record”, come mostra il prossimo capitolo. Ma il numero esatto non è la lezione. La lezione è: il volume cambia il progetto.

Clicca per ingrandire

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 (si apre in una nuova scheda)). ⟲

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 (si apre in una nuova scheda)). 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 (si apre in una nuova scheda)). 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 (si apre in una nuova scheda) (archived copy (si apre in una nuova scheda)) and Winter ‘27 (si apre in una nuova scheda) (archived copy (si apre in una nuova scheda)); Salesforce Limits Quick Reference Guide, Winter ‘16 (si apre in una nuova scheda) (archived copy (si apre in una nuova scheda)), and Salesforce Developer Limits and Allocations Quick Reference, Winter ‘27 (si apre in una nuova scheda) (archived copy (si apre in una nuova scheda)); Data and File Storage Allocations (si apre in una nuova scheda) (10 GB “starting in late March 2019”); Execution Governors and Limits (si apre in una nuova scheda), Winter ‘27. ⟲

Non chiedere quanto è grande oggi

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. ⟲

Per questo insegno il Numero di Zeri di Steve (Steve’s Number of Zeros). Non fissarti subito su una falsa precisione. Trova l’ordine di grandezza giusto. Diecimila, centomila, un milione, dieci milioni, cento milioni? L’architettura può cambiare ogni volta che aggiungiamo uno zero.

Ora moltiplica i figli

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. ⟲

E i dati che non abbiamo messo nel 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. ⟲

Non sono solo le query a rallentare

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. ⟲

I modelli puliti possono diventare modelli costosi

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. ⟲

Mettiamo che i report per partner sulle voci dell’ordine di lavoro abbiano continuamente bisogno del partner retail (Retail Partner), che nel modello pulito sta a due salti di distanza: voce → ordine di lavoro → partner. Con abbastanza volume può esserci un motivo per mettere una copia governata dell’identificativo del partner direttamente sulla voce. Ho creato dati duplicati, e questo crea una responsabilità. Come viene popolata? Può cambiare? Come la manteniamo esatta? Quale copia fa fede? Sono costi reali, e a volte accetto una duplicazione controllata per ottenere le prestazioni di cui il business ha bisogno. Progetta verso la normalizzazione, poi fai un passo indietro di proposito quando il volume te ne dà un motivo. Non denormalizzare perché è più facile da costruire; denormalizza perché sai spiegare il beneficio e il costo che accetti.

Non trasformare il CRM principale nel tuo data warehouse

C’è un altro errore di volume che vedo. Il business vuole report, quindi si continua ad aggiungere dati storici nel CRM operativo, e poi ancora, finché la giustificazione diventa “ci servono per i report”. È lì che chiedo se abbiamo trasformato il sistema operativo in un sistema analitico. Sono carichi di lavoro diversi. Steve Trucking ha bisogno di Salesforce per gestire le consegne di oggi: l’assistenza clienti ha bisogno dello stato di oggi, chi pianifica dei giri di oggi, il partner dell’avanzamento di oggi. Un analista che confronta cinque anni di prestazioni di consegna per regione, partner, camion, meteo, squadra e fascia oraria, incrociati con la finanza, ha un carico di lavoro analitico. Salesforce sa fare report sui dati operativi. Questo non significa che ogni domanda analitica storica appartenga all’archivio operativo. Steve Trucking ha già Snowflake. Quindi chiediti: a quali domande deve rispondere il sistema operativo, e quali appartengono alla piattaforma analitica? La Parte 3 tratta il data warehouse in profondità.

E gli snapshot dei report non sono una strategia di data warehouse. Oggetti di report appiattiti, record snapshot, valori copiati per facilitare i report: a volte legittimi. Possono anche essere un modo per rimandare la domanda di architettura. Se creo milioni di record in più per poter fare report sui milioni che ho già, voglio sapere dove finisce. Non continuare a creare record operativi perché nessuno vuole fare la conversazione sul data warehouse.

Il volume ha una forma

Due sistemi possono avere ciascuno 20 milioni di record e comportarsi in modo molto diverso. Uno ha 20 milioni di record storici, quasi mai aggiornati, di solito letti uno alla volta tramite un ID noto, con poca complessità di condivisione. L’altro ha 20 milioni di record attivi, aggiornati di continuo, con titolarità complessa, tanti report, tanti utenti, grandi concentrazioni padre-figlio e integrazioni frequenti. Non è lo stesso problema. Il numero di record da solo non basta. Quanto in fretta cresce? Quanto spesso cambia? Come viene interrogato, condiviso, collegato? Per quanto tempo deve restare attivo? Quanto ne arriva, o ne cambia, in una volta? Cosa succede quando lo eliminiamo? “Salesforce regge 20 milioni di record” non è una risposta. La domanda interessante è: venti milioni di record che fanno cosa?

L’IA aggiungerà altri zeri

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. ⟲

Oltre l’esame: cosa cambia l’IA. L’IA può aumentare enormemente il ritmo con cui i sistemi creano e recuperano dati. Modella esplicitamente il volume generato dagli agenti, invece di supporre che i ritmi storici delle transazioni umane prevedano il volume futuro.

Steve Trucking: l’architettura sta già cambiando

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. ⟲

Clicca per ingrandire

Follow the model, and the zeros multiply. ⟲

Cosa decide qui un architetto

• Com’è oggi il Numero di Zeri, con la crescita prevista e per tutto il periodo di conservazione, e non solo se il volume di oggi funziona.
• Quali strutture pulite restano normalizzate, e dove il volume giustifica una denormalizzazione controllata.
• Quali dati restano nel core operativo di Salesforce, e quali appartengono ad archivi specializzati, piattaforme analitiche o altri sistemi.
• Se un requisito di report è davvero operativo, o il segnale che il business ha bisogno di un’architettura analitica.
• Come nuovi produttori di dati, come gli agenti, cambiano le ipotesi di volume prima che i loro record diventino il problema di domani.

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. ⟲

Per approfondire

• Salesforce Help, Data and File Storage Allocations (si apre in una nuova scheda): 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 (si apre in una nuova scheda) (release 264, archived copy (si apre in una nuova scheda)), checked 2026-10-20. ⟲
• Salesforce Developers, Best Practices for Deployments with Large Data Volumes (si apre in una nuova scheda), Winter ‘27, checked 2026-11-01. ⟲
• Salesforce Architects, The Salesforce Platform: Transformed for Tomorrow (si apre in una nuova scheda), checked 2026-11-01. ⟲
• Salesforce, Best Practices for Deployments with Large Data Volumes, Winter ‘16 edition (si apre in una nuova scheda) (archived copy (si apre in una nuova scheda)) (release 198), checked 2026-10-20. ⟲
• Salesforce, Developer Limits and Allocations Quick Reference, Winter ‘16 edition (si apre in una nuova scheda) (archived copy (si apre in una nuova scheda)) and Winter ‘27 edition (si apre in una nuova scheda) (archived copy (si apre in una nuova scheda)), checked 2026-10-20. ⟲
• Salesforce Developers, SOAP API End-of-Life Policy (si apre in una nuova scheda), checked 2026-11-01. ⟲
• Salesforce Developers, Execution Governors and Limits (si apre in una nuova scheda), Apex Developer Guide, Winter ‘27, checked 2026-11-01. ⟲
• Salesforce Developers, Working with Very Large SOQL Queries (si apre in una nuova scheda), 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. ⟲
• Il brief di Steve Trucking, sezione 4 (volumi).