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Modul 1 dari 4 dalam bab Merancang untuk Skala, bab 7 kursus Data Architect, dan awal Bagian 2. Setiap modul dapat dibaca secara mandiri. Berikutnya: Titik Panas: Skew Kepemilikan, Induk, dan Lookup
Pertanyaan yang dijawab modul ini: Apa yang rusak ketika data tumbuh, dan kapan?
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. ⟲
Di sinilah kita beralih dari melihat ke merancang. Di Bagian 1 kita belajar melihat bisnis, model data, lanskap perusahaan, tata kelola, dan otoritas. Sekarang kita memberi arsitektur itu beban. Pertanyaan pertama saya: seberapa besar ini akan tumbuh?
Built for Transactions
Section titled “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 (terbuka di tab baru)). 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 (terbuka di tab baru)). 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. ⟲
Penggaris Volume Data Besar Saya
Section titled “Penggaris Volume Data Besar Saya ”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 (terbuka di tab baru)). 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. ⟲
Bukan kebetulan penggaris ini dimulai dari satu juta. Seperti yang ditunjukkan bab berikutnya, perhitungan kueri platform sendiri juga berubah pada “satu juta rekaman pertama”. Tetapi angka pastinya bukan intinya. Intinya: volume mengubah desain.
Every zero changes what you notice. A rule of thumb, not a limit. ⟲
The Platform Keeps Growing
Section titled “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 (terbuka di tab baru)). ⟲
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 (terbuka di tab baru)). 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 (terbuka di tab baru)). 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 (terbuka di tab baru) (archived copy (terbuka di tab baru)) and Winter ‘27 (terbuka di tab baru) (archived copy (terbuka di tab baru)); Salesforce Limits Quick Reference Guide, Winter ‘16 (terbuka di tab baru) (archived copy (terbuka di tab baru)), and Salesforce Developer Limits and Allocations Quick Reference, Winter ‘27 (terbuka di tab baru) (archived copy (terbuka di tab baru)); Data and File Storage Allocations (terbuka di tab baru) (10 GB “starting in late March 2019”); Execution Governors and Limits (terbuka di tab baru), Winter ‘27. ⟲
Jangan Tanya Seberapa Besar Hari Ini
Section titled “Jangan Tanya Seberapa Besar Hari Ini ”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. ⟲
Itulah sebabnya saya mengajarkan Jumlah Nol ala Steve (Steve’s Number of Zeros). Jangan mulai dengan presisi palsu. Temukan besaran yang benar. Sepuluh ribu, seratus ribu, satu juta, sepuluh juta, seratus juta? Setiap nol tambahan bisa mengubah arsitektur.
Sekarang Kalikan Anak-anaknya
Section titled “Sekarang Kalikan Anak-anaknya ”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. ⟲
Dan Data yang Tidak Kita Masukkan ke Core
Section titled “Dan Data yang Tidak Kita Masukkan ke 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. ⟲
Bukan Hanya Kueri yang Melambat
Section titled “Bukan Hanya Kueri yang Melambat ”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. ⟲
Model yang Bersih Bisa Menjadi Model yang Mahal
Section titled “Model yang Bersih Bisa Menjadi Model yang Mahal ”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. ⟲
Misalkan pelaporan item baris perintah kerja per mitra selalu membutuhkan Mitra Ritel (Retail Partner), dan di model yang bersih itu berjarak dua lompatan: item baris → perintah kerja → mitra. Pada volume yang cukup, mungkin ada alasan untuk menaruh salinan terkelola dari pengenal mitra langsung di item baris. Sekarang saya menciptakan duplikasi, dan saya ikut menanggung tanggung jawabnya. Bagaimana nilainya diisi? Bisakah ia berubah? Bagaimana ia tetap akurat? Salinan mana yang otoritatif? Itu biaya nyata, dan saya kadang menerima duplikasi terkendali untuk mendapatkan kinerja yang dibutuhkan bisnis. Rancang ke arah normalisasi, lalu mundur dengan sengaja ketika volume memberi Anda alasan. Jangan melakukan denormalisasi karena lebih mudah dibangun; lakukan karena Anda bisa menjelaskan manfaatnya dan biaya yang Anda terima.
Jangan Jadikan CRM Inti sebagai Gudang Data Anda
Section titled “Jangan Jadikan CRM Inti sebagai Gudang Data Anda ”Ada kesalahan volume lain yang saya lihat. Bisnis menginginkan laporan, jadi riwayat terus ditambahkan ke CRM operasional, dan terus lagi, sampai alasannya menjadi “untuk pelaporan”. Pada titik itu saya bertanya: apakah kita mengubah sistem operasional menjadi sistem analitis? Itu dua beban kerja yang berbeda. Steve Trucking membutuhkan Salesforce untuk menjalankan pengiriman hari ini: layanan pelanggan butuh status hari ini, penjadwalan butuh rute hari ini, mitra butuh progres hari ini. Seorang analis yang membandingkan kinerja pengiriman lima tahun menurut wilayah, mitra, truk, cuaca, kru, dan jendela pengiriman, lalu menyilangkannya dengan data keuangan, menjalankan beban kerja analitis. Salesforce bisa membuat laporan dari data operasional. Itu tidak berarti setiap pertanyaan analitis historis termasuk dalam penyimpanan operasional. Steve Trucking sudah memiliki Snowflake. Jadi tanyakan: pertanyaan mana yang harus dijawab sistem operasional, dan mana yang termasuk platform analitis? Bagian 3 membahas gudang data secara mendalam.
Snapshot pelaporan juga bukan strategi gudang data. Objek laporan yang diratakan, rekaman snapshot, nilai yang diduplikasi demi kemudahan laporan: kadang itu sah. Itu juga bisa menjadi cara menunda pertanyaan arsitektur. Jika saya membuat jutaan rekaman lagi hanya agar bisa melaporkan jutaan rekaman yang sudah ada, saya ingin tahu di mana ini berakhir. Jangan terus membuat rekaman operasional karena tidak ada yang mau membicarakan gudang data.
Volume Punya Bentuk
Section titled “Volume Punya Bentuk ”Dua sistem bisa masing-masing punya 20 juta rekaman dan berperilaku sangat berbeda. Yang satu berisi 20 juta rekaman historis yang hampir tidak pernah diperbarui, biasanya dibaca satu per satu lewat ID yang diketahui, dengan berbagi yang sederhana. Yang lain berisi 20 juta rekaman aktif yang terus diperbarui, dengan kepemilikan rumit, banyak laporan, banyak pengguna, penumpukan induk-anak yang berat, dan integrasi yang sering. Itu bukan masalah yang sama. Jadi jumlah rekaman saja tidak cukup. Seberapa cepat ia tumbuh? Seberapa sering berubah? Bagaimana ia dikueri, dibagikan, dihubungkan? Berapa lama ia tetap aktif? Berapa banyak yang masuk atau berubah sekaligus? Apa yang terjadi saat dihapus? “Salesforce bisa menangani 20 juta rekaman” bukan jawaban. Pertanyaan yang menarik adalah: 20 juta rekaman yang melakukan apa?
AI Akan Menambah Lebih Banyak Nol
Section titled “AI Akan Menambah Lebih Banyak Nol ”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. ⟲
Di luar ujian: apa yang diubah AI. AI dapat sangat meningkatkan laju sistem membuat dan mengambil data. Modelkan volume yang dihasilkan agen secara eksplisit, alih-alih menganggap laju historis transaksi manusia memprediksi volume di masa depan.
Steve Trucking: Arsitekturnya Sudah Berubah
Section titled “Steve Trucking: Arsitekturnya Sudah Berubah ”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. ⟲
Follow the model, and the zeros multiply. ⟲
Yang Diputuskan Arsitek di Sini
Section titled “Yang Diputuskan Arsitek di Sini ”- Seperti apa Jumlah Nol hari ini, termasuk pertumbuhan yang diharapkan dan seluruh masa retensi, bukan hanya apakah volume hari ini berjalan.
- Struktur bersih mana yang tetap ternormalisasi, dan di mana volume membenarkan denormalisasi yang terkendali.
- Data mana yang tetap di core operasional Salesforce, dan mana yang termasuk penyimpanan khusus, platform analitis, atau sistem lain.
- Apakah sebuah kebutuhan laporan benar-benar operasional, atau tanda bahwa bisnis membutuhkan arsitektur analitis.
- Bagaimana produsen baru seperti agen mengubah asumsi volume sebelum rekaman mereka menjadi masalah esok hari.
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. ⟲
Jelajahi
Section titled “Jelajahi ”- Salesforce Help, Data and File Storage Allocations (terbuka di tab baru): 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 (terbuka di tab baru) (release 264, archived copy (terbuka di tab baru)), checked 2026-10-20. ⟲
- Salesforce Developers, Best Practices for Deployments with Large Data Volumes (terbuka di tab baru), Winter ‘27, checked 2026-11-01. ⟲
- Salesforce Architects, The Salesforce Platform: Transformed for Tomorrow (terbuka di tab baru), checked 2026-11-01. ⟲
- Salesforce, Best Practices for Deployments with Large Data Volumes, Winter ‘16 edition (terbuka di tab baru) (archived copy (terbuka di tab baru)) (release 198), checked 2026-10-20. ⟲
- Salesforce, Developer Limits and Allocations Quick Reference, Winter ‘16 edition (terbuka di tab baru) (archived copy (terbuka di tab baru)) and Winter ‘27 edition (terbuka di tab baru) (archived copy (terbuka di tab baru)), checked 2026-10-20. ⟲
- Salesforce Developers, SOAP API End-of-Life Policy (terbuka di tab baru), checked 2026-11-01. ⟲
- Salesforce Developers, Execution Governors and Limits (terbuka di tab baru), Apex Developer Guide, Winter ‘27, checked 2026-11-01. ⟲
- Salesforce Developers, Working with Very Large SOQL Queries (terbuka di tab baru), 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. ⟲
- Ringkasan Steve Trucking, bagian 4 (volume), bahasa Inggris.