Applying SOLID Principles for Salesforce Scalability

Hello #Connection #SalesforceInterview #Question #2025 #Question: You have a high-volume Salesforce org where millions of records are processed daily. Your team notices performance issues with triggers, batch jobs, and integrations. How would you analyze and optimize the performance of these components while ensuring scalability? #Expected Answer: To optimize the performance of a high-volume Salesforce org, I would take a multi-layered approach, addressing triggers, batch jobs, and integrations separately while ensuring overall scalability. 1. Trigger Optimization: Bulkification: Ensure all triggers handle bulk operations using Trigger.New, Trigger.Old, and Maps/Sets for efficient processing. One Trigger per Object: Implement a Trigger Handler framework to centralize logic and prevent recursion issues. Use Asynchronous Processing: Offload heavy processing (e.g., API calls, complex calculations) to Queueable, Future, or Batch Apex. Selective Queries & Indexing: Use indexed fields, WHERE clauses, and avoid full table scans. Leverage Skinny Tables if necessary. Avoid DML inside Loops: Batch DML operations to avoid exceeding limits. 2. Batch Jobs Optimization: Reduce Query Load: Use incremental processing (query only new/updated records). Implement Selective SOQL filters using indexed fields. Tune Batch Size: Experiment with scopeSize (e.g., 200 for optimal performance). Monitor governor limits via Limits.getDMLStatements() & Limits.getQueryRows(). Parallel Processing: Use Queueable Apex or Parallel Batch Jobs for non-dependent operations. Implement Chaining but avoid overloading Queueable limits. Use Platform Events or CDC (Change Data Capture): For real-time processing instead of polling-based batch jobs. 3. Integration Performance (APIs & External Systems): Optimize Callouts: Use Continuation (for LWC) or Queueable (for Apex) for long-running external API calls. Implement caching (Custom Settings, Platform Cache) for static data to reduce API calls. Governor Limits Management: Reduce API calls by batching requests (e.g., Composite API, GraphQL). Use Asynchronous Apex (Future, Queueable) for non-critical operations. Streaming APIs for Real-Time Data: Implement Streaming API, Platform Events, or Pub/Sub API instead of periodic polling. 4. Monitoring & Troubleshooting: Apex Execution Logs & Debugging: Analyze logs using Event Monitoring, Apex Replay Debugger, or Log Analyzer. Use System.debug(Limits.getHeapSize()) to check memory consumption. Performance Monitoring: Use Salesforce Optimizer, Lightning Usage App, and Einstein Recommendations. Enable Debug Logs, Governor Limits Monitoring, and Transaction Security Policies. Query Performance: Run SOQL queries in Developer Console to check execution time. Use Query Plan Tool to identify indexing needs.

“𝗝𝘂𝘀𝘁 𝗮𝗱𝗱 𝗮 𝘀𝘄𝗶𝘁𝗰𝗵 𝘀𝘁𝗮𝘁𝗲𝗺𝗲𝗻𝘁” 𝗧𝗵𝗮𝘁’𝘀 𝗻𝗼𝘁 𝘀𝗰𝗮𝗹𝗮𝗯𝗹𝗲. 𝗜𝘁’𝘀 𝘁𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗱𝗲𝗯𝘁. 𝗧𝗵𝗲 𝗢𝗽𝗲𝗻/𝗖𝗹𝗼𝘀𝗲𝗱 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲 𝗲𝘅𝗶𝘀𝘁𝘀 𝗳𝗼𝗿 𝗮 𝗿𝗲𝗮𝘀𝗼𝗻 👉 Let’s talk about something 𝗺𝗮𝗻𝘆 𝗦𝗮𝗹𝗲𝘀𝗳𝗼𝗿𝗰𝗲 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿𝘀 overlook when building scalable features: extensibility. You’ve built a feature. It works. Business loves it. A month later, a new requirement arrives - and you’re back inside the same class, adding more if/else/switch logic. What began as a clean solution becomes a bowl of spaghetti. The root cause? Your code was designed to be 𝗰𝗵𝗮𝗻𝗴𝗲𝗱 - not 𝗲𝘅𝘁𝗲𝗻𝗱𝗲𝗱. Here’s why: The Open/Closed Principle (from the SOLID Principles) states that 𝙨𝙤𝙛𝙩𝙬𝙖𝙧𝙚 𝙨𝙝𝙤𝙪𝙡𝙙 𝙗𝙚 𝙤𝙥𝙚𝙣 𝙛𝙤𝙧 𝙚𝙭𝙩𝙚𝙣𝙨𝙞𝙤𝙣 𝙗𝙪𝙩 𝙘𝙡𝙤𝙨𝙚𝙙 𝙛𝙤𝙧 𝙢𝙤𝙙𝙞𝙛𝙞𝙘𝙖𝙩𝙞𝙤𝙣. It helps avoid ripple effects every time a new requirement shows up. Instead of hard-coding every case, imagine building behaviour through abstraction: Strategy patterns, interfaces, dynamic flows, handler registries, and metadata-driven decisions. Key Benefits of using OCP in Apex: ✅ Clean Separation: Responsibilities are isolated ✅ Lower Risk: Legacy functionality remains untouched ✅ Fewer Merge Conflicts: No need to modify existing logic ✅ Testability: Each extension can be tested independently ✅ Reusability: Same abstractions support future use cases ✅ Easy Scalability: Add new features without touching old code The Reality: We often treat Apex classes as one-size-fits-all containers. But without extensibility, we trade short-term wins for long-term maintenance nightmares. Best Practices for OCP in Apex: 🔹 Avoid business logic in conditionals—delegate behaviour 🔹 Use metadata or object config to influence behaviour 🔹 Embrace the Strategy or Factory pattern where needed 🔹 Design classes to depend on abstractions 🔹 Think extension before modification Ready to level up? Here's what you need to focus on: → Learn the Open/Closed Principle inside-out → Reflect on where you're violating it today → Refactor your next feature using abstraction → Study common extension strategies in Apex → Share and document reusable patterns with your team What’s your biggest challenge in applying the Open/Closed Principle in Apex? Do you usually extend or modify existing logic in your org? 👇 Let’s discuss in the comments below

When an Account owner changes in Salesforce, business users often expect all related records (Contacts, Cases, Opportunities, Orders, Invoices, etc.) to follow the new owner. But this is not standard behaviour for custom objects, and even some standard ones too. There are common ways to approach this — multiple Flows, object-specific triggers, or scheduled jobs. Each works, but they tend to be either hard to maintain, fragmented, or not real-time. I wanted a design that was scalable, maintainable, and declarative where possible. Here’s what I built: 1 - A record-triggered Flow, which detects the Account ownership change. 2 - The Flow invokes a single Apex method that performs the ownership cascade. 3 - A Custom Metadata Type defines which objects are included, and which lookup field ties them to the Account. - The Apex dynamically queries and updates the related records in a bulk-safe way. This approach isn’t the only valid one. You could use separate triggers on each child object, or even solve access concerns with Territory Management or sharing rules. But in this case, explicit ownership needed to change, and I wanted to avoid scattering logic across multiple places. What makes this design valuable is how it balances trade-offs: • Configurable: adding or removing objects is a metadata update, not a code change. • Bulk-safe: it can handle a single update or a large batch without hitting limits. • Separation of concerns: Flow handles orchestration, Apex handles logic. • Hybrid approach: declarative where possible, programmatic where necessary. Lesson learned: the best Salesforce solutions often come from combining declarative tools with programmatic techniques, rather than forcing one approach. By using metadata to control Apex behaviour and letting Flow handle orchestration, you get something that is scalable, flexible, and still admin-friendly. #Salesforce #SalesforceArchitect #SalesforceFlow #Apex #CustomMetadata #SolutionArchitecture #Automation #ClicksNotCode #LowCode #ProCode #SalesforceConsultant #SystemDesign

A while back, I was working on an automation where a case update needed to trigger multiple downstream actions — updating entitlement records, sending an escalation email, and syncing data to an external system. Everything was working fine… until it wasn’t. During a high-volume day, the system started throwing errors: “CPU Time Limit Exceeded.” The root cause surprised even me: A simple email alert inside a record-triggered Flow was delaying the whole transaction — and because everything ran synchronously, nothing could move forward until the email was processed. That’s when I realized something important: Synchronous automation has limits. Scalability needs asynchronous design. Instead of trying to “optimize” the Flow further, I redesigned the entire solution: 1️⃣ Shifted Email Alerts to Platform Events Instead of sending the email directly, the Flow published a Platform Event, which triggered a separate asynchronous Flow to send the notification. 2️⃣ Moved the External System Callout to Queueable Apex Instead of blocking the transaction with a callout, I queued the integration using Queueable Apex, allowing Salesforce to handle it in the background. 3️⃣ Reduced CPU Load by Splitting Logic Into Subflows Each subflow handled only one type of operation, making the main Flow much lighter. 4️⃣ Added Monitoring and Retry Logic Platform Event subscribers were configured with fault paths, so failures didn’t get lost — they triggered retry logic automatically. After redesigning the automation, the same process that previously failed under load began handling 20,000+ case updates per hour without a single CPU timeout. That day taught me something about Salesforce architecture: > “If your automation struggles under pressure, it’s not a Flow problem — it’s a design pattern problem.” Since then, my rule is simple: Use synchronous automation for user-facing logic. Use asynchronous automation for everything else. #Salesforce

At some point, Salesforce stops being “just a CRM”. It starts out simple. Fast. Drag-and-drop. But if you’re successful with it? It will become mission-critical infrastructure. That’s the Salesforce @ scale dilemma. → Quick wins lead to more departments wanting in → More custom apps, more integrations, more complexity → Every small change becomes risky, slow, and expensive Salesforce’s greatest strength, flexibility, starts to work against you. Imagine a company that starts with a Sales department using Sales Cloud. Then Marketing joins. Then the Service department wants Service Cloud. Then... why not do an Experience Cloud? Before you know it, 10 users become 1,000. The result? – Innovation slows significantly. – Operating cost rise –“Easy changes” now require dev teams and massive testing Why? Because they treated Salesforce like a CRM app… Instead of what it really is at scale: An enterprise platform. If you’re serious about growth with Salesforce, treat it seriously from day one: ✅ Architecture before implementation ✅ Scalability, performance, and security from the first process ✅ A Center of Excellence (CoE) to guide strategic decisions ✅ Platform thinking, not app thinking Salesforce is no longer the thing you just install and run. It’s the foundation of your business tech stack. And it needs to be treated with the same level of planning, governance, and respect. What do you think? Is this change inevitable? Or can a company commit to platform thinking from day one?

𝗙𝗿𝗼𝗺 𝗪𝗼𝗿𝗸𝗶𝗻𝗴 𝗖𝗼𝗱𝗲 𝘁𝗼 𝗦𝗰𝗮𝗹𝗮𝗯𝗹𝗲 𝗖𝗼𝗱𝗲: 𝗠𝘆 𝗔𝗽𝗲𝘅 𝗝𝗼𝘂𝗿𝗻𝗲𝘆 💻⚡ Early in my Apex career, I learned a hard lesson: code that works with test data often fails with real data. The difference? Scalability. Here are the game-changing practices that transformed my approach: 🎯 𝗕𝘂𝗹𝗸𝗶𝗳𝘆 𝗘𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴 Your trigger isn't handling one record - it's potentially handling thousands. Always code for 200+ records. 🔄 𝗠𝗮𝘀𝘁𝗲𝗿 𝗖𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗼𝗻𝘀 & 𝗠𝗮𝗽𝘀 Reduce SOQL queries and DML operations by smartly using Sets, Lists, and Maps. Fewer governor limit hits, happier code. 🏗️ 𝗢𝗻𝗲 𝗧𝗿𝗶𝗴𝗴𝗲𝗿 𝗣𝗲𝗿 𝗢𝗯𝗷𝗲𝗰𝘁 Keep your logic clean with a trigger framework that separates concerns and makes maintenance predictable. ⚡ 𝗘𝗺𝗯𝗿𝗮𝗰𝗲 𝗔𝘀𝘆𝗻𝗰𝗵𝗿𝗼𝗻𝗼𝘂𝘀 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 Use @future, Queueable, and Batch Apex for heavy operations. Don't make users wait for what can run in the background. 🧪 𝗧𝗲𝘀𝘁 𝗪𝗶𝘁𝗵 𝗥𝗲𝗮𝗹 𝗦𝗰𝗲𝗻𝗮𝗿𝗶𝗼𝘀 Quality tests aren't about coverage - they're about simulating real business volume and edge cases. 𝗧𝗵𝗲 𝗥𝗲𝗮𝗹𝗶𝘁𝘆 𝗖𝗵𝗲𝗰𝗸: Code that ignores these principles might pass deployment today, but will likely fail when your business grows tomorrow. What's the most valuable scaling lesson you've learned in Apex? Share your wisdom below! 👇 #Salesforce #Apex #BestPractices #SoftwareDevelopment #CodingStandards #TechTips