🚀 Revolutionizing Large-Scale Data Processing in Salesforce: Meet QueryCursors (Beta | Summer ’25) 🚀

Calling all Salesforce developers! If you’ve ever battled heap limits, governor limits, or the complexities of Batch Apex while processing massive datasets, this one’s for you. Salesforce is leveling up Apex with QueryCursors—a game-changing feature designed to simplify and supercharge large-scale data operations. Let’s dive in!

🔍 The Challenge: Handling Big Data in Apex

Processing large datasets in Salesforce has always been a tightrope walk. Traditional approaches like Batch Apex require meticulous chunking of data, managing statefulness, and dancing around governor limits. And let’s not forget the dreaded Too many query rows error! But what if there was a way to streamline this process, making it faster, simpler, and more scalable?

🛠️ Enter QueryCursors: The Future of Efficient Data Processing

QueryCursors introduce a cursor-based mechanism for SOQL queries, allowing developers to fetch and process records in chunks without the overhead of traditional batch jobs. Here’s why this is a big deal:

✨ Key Features

🚀 Cursor-Based SOQL Execution: Process records incrementally, reducing memory overhead.
📦 Serializable State: Save cursor positions mid-operation for fault-tolerant workflows.
💲 Total Record Count: Instantly access the size of your dataset—no more separate count queries!
📍 Chunk Fetching by Position: Grab records from specific offsets (e.g., fetchFrom(5000, 200)) for flexible pagination.
⚡ Direct SOQL Integration: Create cursors directly from queries—no wrapper classes needed.

💡 Why QueryCursors Beat Batch Apex

Batch Apex	QueryCursors
Requires implementing `Database.Batchable`	Created directly from SOQL
State management complexity	Built-in serialization for seamless pauses/resumes
Overhead for small datasets	Optimized for any dataset size
Manual row counting	Total record count included

Translation: Less boilerplate, more power. 🧫

🎯 Use Cases Where QueryCursors Shine

Data Migrations: Process millions of records without hitting heap limits.
Real-Time Reporting: Stream chunks of data to external systems on demand.
Complex Transformations: Pause/resume long-running operations effortlessly.
Governor Limit Avoidance: Fetch only what you need, when you need it.


## 🛠️ Putting QueryCursors into Action: A Code Example  
Here’s how to use QueryCursors with `Queueable` to process records in chunks of 200, perfect for archiving or deleting stale data:  


public class QueryChunkingQueuable implements Queueable {
    private Database.Cursor locator;
    private Integer position;

    public QueryChunkingQueuable() {
        // Initialize the cursor with a SOQL query
        locator = Database.getCursor('SELECT Id FROM Contact WHERE LastActivityDate = LAST_N_DAYS:400');
        position = 0; // Start at the beginning of the dataset
    }

    public void execute(QueueableContext ctx) {
        // Fetch 200 records starting from the current position
        List<Contact> scope = locator.fetch(position, 200);
        position += scope.size(); // Update the cursor position

        // Process records (e.g., archive, delete, or transform)
        // ...

        // Re-queue the job if more records remain
        if (position < locator.getNumRecords()) {
            System.enqueueJob(this);
        }
    }
}

Salesforce QueryCursor