Let's be direct. Anyone can learn to write Apex code that saves a record. A quick Trailhead module, some copy-pasting from a forum, and voilà, it works. But here’s the hard truth: code that simply *works* is a time bomb waiting to explode in your production org.
I’ve been in this game for a long time, and the biggest difference between a junior developer and a senior architect isn't the complexity of the code they can write. It's their obsession with efficiency. It's about writing code that not only works today but will continue to work flawlessly when you have ten times the data and a hundred times the users. Are you building a solid foundation or a house of cards?
This isn't just about theory. This is about survival. Your job as a Salesforce developer isn't just to build features; it's to protect the platform. And that starts with mastering efficient Apex Programming.
It’s All About Governor Limits, Isn't It?
Before we get into the nitty-gritty, we have to talk about the elephant in the room: governor limits. New developers often see them as a punishment, a set of arbitrary rules designed to make their lives difficult. That's the wrong way to look at it.
Think of Salesforce as a massive, high-end apartment building. You, your company, and thousands of other companies are all tenants on the same server infrastructure. Governor limits are the building rules. You can't run a generator in your living room or have a rock concert at 3 AM because it would disrupt everyone else. Salesforce's limits—on SOQL queries, DML statements, CPU time—are there for the exact same reason. They ensure that one tenant's poorly written code doesn't bring the entire building to a screeching halt.
Your goal isn't to "get around" these limits. It's to write code that operates so efficiently it never even comes close to them. That's what a professional does.
The Cardinal Sin: SOQL and DML in Loops
If there is one mistake that screams "amateur," this is it. I see it constantly, and it's the fastest way to crash your system. Placing a SOQL query or a DML statement (like `insert`, `update`, or `delete`) inside a `for` loop is a non-negotiable "don't."
Why? Because that code will execute the query or the database operation for every single record in the loop. If your trigger fires with one record, it works. If a user uses the Data Loader to update 200 records, your code tries to run 200 separate queries. The result? You’ll instantly hit the governor limit of 100 SOQL queries per transaction and the whole thing fails. It's brittle, it's inefficient, and it's completely avoidable.
Let's look at a painfully common bad example. Imagine you want to update the related contacts every time an account's shipping address changes.
The Wrong Way
trigger AccountTrigger on Account (after update) { for (Account acc : Trigger.new) { // This query runs for EVERY account in the trigger. Bad! List<Contact> contactsToUpdate = [SELECT Id, MailingStreet FROM Contact WHERE AccountId = :acc.Id]; for (Contact con : contactsToUpdate) { con.MailingStreet = acc.ShippingStreet; } // This DML statement runs for EVERY account's list of contacts. Also bad! update contactsToUpdate; }}If you update 150 accounts, this code attempts 150 SOQL queries and 150 DML statements. It will fail spectacularly. It’s not scalable.
The Right Way: Bulkification
The correct approach is to think in collections. You gather all your IDs first, run one single query, process the results, and then perform one single DML operation. This is the core principle of bulkification.
trigger AccountTrigger on Account (after update) { // 1. Create a set of all account IDs from the trigger context Set<Id> accountIds = Trigger.newMap.keySet(); // 2. Run ONE query to get all related contacts at once List<Contact> contactsToUpdate = [SELECT Id, AccountId, MailingStreet FROM Contact WHERE AccountId IN :accountIds]; // Create a map for easy access Map<Id, Account> newAccountMap = Trigger.newMap; // 3. Loop through the results in memory for (Contact con : contactsToUpdate) { // Check if the account exists in the trigger's context if (newAccountMap.containsKey(con.AccountId)) { Account parentAccount = newAccountMap.get(con.AccountId); con.MailingStreet = parentAccount.ShippingStreet; } } // 4. Perform ONE DML operation on the entire list if (!contactsToUpdate.isEmpty()) { update contactsToUpdate; }}See the difference? This code performs exactly one SOQL query and one DML statement, whether you update one account or 200. This is efficient. This is professional. This code will survive in production.
Bulkification Isn't Just a Suggestion; It's a Commandment
The example above is a simple demonstration of bulkification, but the mindset should apply to everything you build. Your triggers and Apex classes must be designed from the ground up with the assumption that they will run on a batch of records, not just one.
This means you need to become very comfortable with a few key tools:
- Sets: Perfect for storing unique IDs to use in a SOQL `WHERE` clause.
- Lists: The standard collection for holding the records you get back from a query or that you plan to perform DML on.
- Maps: These are your secret weapon. A `Map<Id, sObject>` is incredibly powerful. It lets you get a specific record from a collection using its ID without having to loop through the whole list again. `Trigger.newMap` and `Trigger.oldMap` are prime examples of this. Use them.
Always ask yourself: "What would happen if this code had to process 200 records at once?" If the answer involves hitting a governor limit, you need to go back to the drawing board.
Thinking Beyond Your Code: Apex and the Wider Ecosystem
Great Apex Programming doesn't exist in a vacuum. Your code is part of a larger system. An efficient backend is the foundation for a high-performance user interface and reliable automation. A slow Apex method can have ripple effects across the entire user experience.
Efficient Apex for Lightning Web Components (LWC)
You can build the most beautiful, user-friendly UI with Lightning Web Components (LWC), but if the Apex controller method it calls is slow, the user experience will be terrible. Users don't care that your LWC code is clean; they care that the spinner on their screen disappears quickly.
When you write an `@AuraEnabled` method, all the principles of bulkification still apply. But there's another tool you must use: caching. If you have a method that just fetches data and doesn't change it, you should be using `(cacheable=true)`.
@AuraEnabled(cacheable=true)public static List<Contact> getContacts(Id accountId) { return [SELECT Name, Email, Phone FROM Contact WHERE AccountId = :accountId LIMIT 10];}This simple addition tells the Lightning Data Service that it can cache the results on the client side. If the user performs an action that calls this method again with the same parameters, it can return the data from the cache instantly instead of making another round-trip to the server. It's a simple change that makes your UI feel significantly faster.
Integrating with Data Cloud
Now let's talk about big data. When you start working with a platform like Data Cloud, the scale of data changes completely. You're no longer dealing with thousands of records; you're dealing with millions or even billions. In this environment, inefficient Apex isn't just slow—it's completely non-functional.
I remember a project where a team was tasked with processing records ingested from Data Cloud. They wrote a trigger that tried to make a callout for each individual record as it was created. The system fell over instantly. You can't think on a per-record basis when you're dealing with that volume. Your Apex must be designed for asynchronous, bulk processing. This means using Queueable Apex or Batch Apex to handle these records in manageable chunks, respecting governor limits every step of the way.
The Role of Apex in Salesforce Automation
Flow is an incredibly powerful tool for Salesforce Automation. But sometimes, you hit a wall where you need more complex logic than Flow can provide. That's when you call an Invocable Apex method.
When you write an Invocable Method, you have to remember that it could be called from a record-triggered Flow that is running on a batch of 200 records. The Flow engine automatically bulkifies the calls to your method. Your method will receive a `List` of inputs, and it must be prepared to process that entire list efficiently. If your Invocable Method has a SOQL query inside a loop, you'll bring down the entire Flow, and the business process it was automating will fail.
When to Call Salesforce Einstein AI
Making callouts to external services, including powerful platforms like Salesforce Einstein AI for predictions or analysis, also requires careful planning. Callouts have their own governor limits, and they are often time-consuming.
The rule is the same: no callouts in a loop. If you need to get predictions for a batch of records, your first step should be to aggregate all the data you need. Then, if the API supports it, make a single, bulk callout with all the records. If it doesn't, you'll need to use an asynchronous pattern (like Queueable Apex) to process the callouts one by one without freezing the user's transaction.
Writing Clean, Maintainable Code
Efficiency isn't just about speed; it's also about maintainability. Code that is hard to read is hard to debug and improve. Six months from now, you (or worse, someone else on your team) will have to look at your code. Will they understand it, or will they curse your name?
The Power of Helper Classes and Separation of Concerns
Your trigger file should be practically empty. It should do one thing: delegate the work to a handler class based on the trigger context (`isInsert`, `isUpdate`, etc.).
All your business logic, your queries, and your processing should live in a separate Apex class. This makes your code infinitely easier to read, manage, and, most importantly, test. Don't cram everything into the trigger file. It's a recipe for disaster.
Meaningful Naming Conventions
This sounds basic, but it's critical. What's easier to understand? `List<Account> aList` or `List<Account> accountsToBill`? `Map<Id, Opportunity> oMap` or `Map<Id, Opportunity> oppsWithHighValueLineItems`? Be descriptive. Your future self will thank you.
Don't Forget Your Test Classes
Writing tests isn't a chore you do at the end; it's part of the development process. And your tests should also be efficient. Use the `@TestSetup` annotation to create all your test records once. This makes your tests run faster and keeps your logic clean.
Furthermore, you can and should write assertions against your governor limits. The `Limits` class is your friend. You can check how many queries your code used and assert that it's within an acceptable range.
// In your test methodTest.startTest(); // Call your code here Integer queriesUsed = Limits.getQueries();Test.stopTest();// Assert that your code was efficientSystem.assertEquals(1, queriesUsed, 'The code used more than one SOQL query!');This forces you to hold yourself accountable for writing efficient code.
It's a Craft, Not Just a Job
Look, writing truly efficient, scalable, and maintainable Apex Programming code is a craft. It takes discipline and a commitment to best practices. It's about more than just making the code work; it's about building solutions that are durable and professional.
So, the next time you open the Developer Console, don't just ask, "Will this work?" Ask, "Will this scale? Is this efficient? Is this something a professional would build?" Master bulkification. Keep your code clean. Understand how your Apex fits into the bigger picture with LWC, Salesforce Automation, and Data Cloud. That's how you go from being just another coder to being a truly valuable developer on the Salesforce platform.
