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Görsel yapay zekâ ile üretilmiştir

Yapay zekâ ile çevrildi. Henüz topluluk tarafından incelenmedi. İngilizcesini oku Bu bölüm İngilizcede değişti ve henüz yeniden çevrilmedi. (48)

Data Architect kursunun 7. bölümü olan Ölçek İçin Tasarlamak bölümündeki 4 modülden 1.‘i ve 2. Kısım’ın başlangıcı. Her modül kendi başına okunabilir. Sonraki: Sıcak Noktalar: Sahiplik, Üst Kayıt ve Arama Çarpıklığı

Bu modülün yanıtladığı soru: Veri büyüdükçe ne bozulur ve ne zaman?

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. ⟲

Burada görmekten tasarlamaya geçiyoruz. 1. Kısım’da işi, veri modelini, kurumsal sistem haritasını, yönetişimi ve yetkiyi görmeyi öğrendik. Şimdi o mimariye ağırlık ekliyoruz. Sorduğum ilk soru: bu ne kadar büyüyecek?

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 (yeni sekmede açılır)). 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 (yeni sekmede açılır)). 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. ⟲

Büyük Veri Hacmi Cetvelim

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 (yeni sekmede açılır)). 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. ⟲

Cetvelin bir milyondan başlaması tesadüf değil. Bir sonraki bölümün gösterdiği gibi, platformun kendi sorgu hesabı da “ilk milyon kayıtta” değişir. Ama tam sayılar asıl mesele değil. Asıl mesele: hacim tasarımı değiştirir.

Büyütmek için tıklayın

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 (yeni sekmede açılır)). ⟲

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

Bugün Ne Kadar Büyük Olduğunu Sormayın

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. ⟲

Bu yüzden Steve’in Sıfır Sayısı’nı (Steve’s Number of Zeros) öğretiyorum. Sahte bir kesinlikle başlamayın. Doğru büyüklük mertebesini bulun. On bin, yüz bin, bir milyon, on milyon, yüz milyon? Her fazladan sıfır mimariyi değiştirebilir.

Şimdi Alt Kayıtları Çarpın

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. ⟲

Ve Çekirdeğe Koymadığımız Veriler

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. ⟲

Yavaşlayan Yalnızca Sorgular Değil

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. ⟲

Temiz Modeller Pahalı Modellere Dönüşebilir

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. ⟲

Ortağa göre iş emri satır kalemi raporlamasının her zaman Perakende Ortağı’na (Retail Partner) ihtiyaç duyduğunu ve temiz modelde ortağın iki adım uzakta olduğunu düşünün: satır kalemi → iş emri → ortak. Yeterli hacimde, ortak tanımlayıcısının yönetilen bir kopyasını doğrudan satır kalemine koymak için bir neden olabilir. Artık çoğaltma yarattım ve onunla birlikte sorumluluk da üstlendim. Nasıl doldurulacak? Değişebilir mi? Nasıl doğru kalacak? Hangi kopya esas? Bunlar gerçek maliyetler ve işin ihtiyaç duyduğu performansı elde etmek için bazen kontrollü çoğaltmayı kabul ederim. Normalleştirmeye doğru tasarlayın, sonra hacim size bir neden verdiğinde bilinçli olarak geri adım atın. Kurması daha kolay diye denormalize etmeyin; faydayı ve kabul ettiğiniz maliyeti açıklayabildiğiniz için denormalize edin.

Çekirdek CRM’i Veri Ambarınıza Dönüştürmeyin

Gördüğüm bir başka hacim hatası daha var. İş birimi raporlar istiyor, bu yüzden operasyonel CRM’e sürekli geçmiş ekleniyor, ekleniyor, ta ki gerekçe “raporlama için” olana kadar. O noktada sorarım: operasyonel bir sistemi analitik bir sisteme mi dönüştürüyoruz? Bunlar iki farklı iş yükü. Steve Trucking’in bugünün teslimatlarını yürütmek için Salesforce’a ihtiyacı var: müşteri hizmetleri bugünün durumuna, planlama bugünün rotalarına, ortaklar bugünün ilerlemesine ihtiyaç duyar. Beş yıllık teslimat performansını bölgeye, ortağa, kamyona, havaya, ekibe ve teslimat aralığına göre karşılaştırıp finans verileriyle çaprazlayan bir analist analitik bir iş yükü çalıştırıyordur. Salesforce operasyonel veriler üzerinde rapor üretebilir. Bu, her tarihsel analitik sorunun operasyonel depoya ait olduğu anlamına gelmez. Steve Trucking’in zaten Snowflake’i var. Bu yüzden sorun: hangi soruları operasyonel sistem yanıtlamalı, hangileri analitik bir platforma ait? 3. Kısım veri ambarlarını derinlemesine ele alır.

Raporlama anlık görüntüleri de bir veri ambarı stratejisi değildir. Düzleştirilmiş rapor nesneleri, anlık görüntü kayıtları, rapor kolaylığı için çoğaltılan değerler: bazen geçerlidir. Mimari bir soruyu ertelemenin yolu da olabilirler. Zaten var olan milyonlarca kayıt üzerinde rapor alabilmek için milyonlarca kayıt daha oluşturuyorsam, bunun nerede biteceğini bilmek isterim. Kimse veri ambarı konuşmak istemediği için operasyonel kayıt üretmeye devam etmeyin.

Hacmin Bir Biçimi Vardır

İki sistemin her birinde 20 milyon kayıt olabilir ve çok farklı davranabilirler. Biri, neredeyse hiç güncellenmeyen, genellikle bilinen bir kimlikle tek tek okunan ve paylaşımı basit 20 milyon tarihsel kayıttır. Diğeri, sürekli güncellenen, sahipliği karmaşık, çok raporlanan, çok kullanıcılı, ağır üst-alt yığılmaları ve sık entegrasyonları olan 20 milyon aktif kayıttır. Bu aynı sorun değil. Bu yüzden kayıt sayısı tek başına yetmez. Ne kadar hızlı büyüyor? Ne sıklıkla değişiyor? Nasıl sorgulanıyor, paylaşılıyor, ilişkilendiriliyor? Ne kadar süre aktif kalıyor? Bir seferde ne kadarı geliyor veya değişiyor? Silindiğinde ne oluyor? “Salesforce 20 milyon kaydı kaldırabilir” bir yanıt değil. İlginç soru şu: ne yapan 20 milyon kayıt?

Yapay Zekâ Daha Fazla Sıfır Ekleyecek

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. ⟲

Sınavın ötesinde: yapay zekânın değiştirdikleri. Yapay zekâ, sistemlerin veri oluşturma ve getirme hızını ciddi biçimde artırabilir. İnsan işlemlerinin tarihsel hızının gelecekteki hacmi öngördüğünü varsaymak yerine, ajanların ürettiği hacmi açıkça modelleyin.

Steve Trucking: Mimari Şimdiden Değişiyor

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. ⟲

Büyütmek için tıklayın

Follow the model, and the zeros multiply. ⟲

Mimarın Burada Karar Verdikleri

• Bugünün Sıfır Sayısı’nın nasıl göründüğü, beklenen büyüme ve tüm saklama süresi dahil; yalnızca bugünkü hacmin çalışıp çalışmadığı değil.
• Hangi temiz yapıların normalleştirilmiş kaldığı ve hacmin nerede kontrollü denormalizasyonu haklı çıkardığı.
• Hangi verinin Salesforce’un operasyonel çekirdeğinde kaldığı ve hangisinin özel bir depoya, analitik bir platforma veya başka bir sisteme ait olduğu.
• Bir raporlama gereksiniminin gerçekten operasyonel mi olduğu, yoksa işin analitik bir mimariye ihtiyaç duyduğunun işareti mi olduğu.
• Ajanlar gibi yeni üreticilerin, kayıtları yarının sorunu olmadan önce hacim varsayımlarını nasıl değiştirdiği.

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. ⟲

Daha Fazlası

• Salesforce Help, Data and File Storage Allocations (yeni sekmede açılır): 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 (yeni sekmede açılır) (release 264, archived copy (yeni sekmede açılır)), checked 2026-10-20. ⟲
• Salesforce Developers, Best Practices for Deployments with Large Data Volumes (yeni sekmede açılır), Winter ‘27, checked 2026-11-01. ⟲
• Salesforce Architects, The Salesforce Platform: Transformed for Tomorrow (yeni sekmede açılır), checked 2026-11-01. ⟲
• Salesforce, Best Practices for Deployments with Large Data Volumes, Winter ‘16 edition (yeni sekmede açılır) (archived copy (yeni sekmede açılır)) (release 198), checked 2026-10-20. ⟲
• Salesforce, Developer Limits and Allocations Quick Reference, Winter ‘16 edition (yeni sekmede açılır) (archived copy (yeni sekmede açılır)) and Winter ‘27 edition (yeni sekmede açılır) (archived copy (yeni sekmede açılır)), checked 2026-10-20. ⟲
• Salesforce Developers, SOAP API End-of-Life Policy (yeni sekmede açılır), checked 2026-11-01. ⟲
• Salesforce Developers, Execution Governors and Limits (yeni sekmede açılır), Apex Developer Guide, Winter ‘27, checked 2026-11-01. ⟲
• Salesforce Developers, Working with Very Large SOQL Queries (yeni sekmede açılır), 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. ⟲
• Steve Trucking senaryo özeti, 4. kısım (hacimler, İngilizce).