Getting Started with Cursors: An Overview

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Getting Started with Cursors: An Overview

In the world of relational databases, operations are typically performed on sets of data. SQL (Structured Query Language) is fundamentally designed around set-based logic. Queries like SELECT, INSERT, UPDATE, and DELETE usually operate on multiple rows simultaneously based on specified conditions. This set-based approach is highly efficient and is one of the cornerstones of relational database performance.

However, there are situations where processing data row by individual row becomes necessary. Imagine needing to perform a complex calculation on each record that depends on the previous one, or needing to call a separate stored procedure for every single entry in a result set, or performing intricate validation logic that cannot be easily expressed in a single SQL statement. For these scenarios, databases provide a mechanism called a cursor.

This article serves as a comprehensive introduction to database cursors. We will explore what they are, why and when you might (cautiously) use them, their lifecycle, different types, performance implications, alternatives, and best practices. By the end, you should have a solid understanding of cursors and their place – or often, their lack thereof – in modern database development.

1. What is a Database Cursor? The Fundamental Concept

At its core, a database cursor is a control structure or a pointer that allows for traversal over the records in a database result set, one row at a time. Think of it like a bookmark in a book or a pointer iterating through elements in an array in procedural programming.

When you execute a standard SELECT statement, the database system retrieves all the qualifying rows and returns them as a single unit – a result set. You don’t typically interact with individual rows within that set at the SQL level after the query executes (though your application layer might iterate over the fetched results).

A cursor, however, allows you to:

  1. Define a result set: Specify a SELECT statement whose results the cursor will operate on.
  2. Open the cursor: Execute the SELECT statement and position the cursor before the first row.
  3. Fetch rows: Retrieve rows one by one (or in small blocks) from the result set, moving the cursor pointer accordingly.
  4. Process the current row: Perform operations (like updates, deletes, or complex logic) based on the data in the currently fetched row.
  5. Close the cursor: Release the current result set and associated locks.
  6. Deallocate the cursor: Remove the cursor definition and release all memory resources.

This row-by-row processing capability stands in stark contrast to the default set-based nature of SQL.

Analogy:

Imagine you have a spreadsheet full of customer data.
* Set-based operation: “Increase the credit limit by 10% for all customers in California.” This is like applying a formula to the entire ‘State’ column filtered for ‘CA’. Fast and efficient.
* Cursor-based operation: “Go to the first customer row. Check their purchase history. If they meet complex criteria X, Y, and Z (which might involve checking other tables or calling functions), then update their status. Now, move to the next customer row and repeat.” This is like manually moving down the spreadsheet, row by row, performing checks and actions for each one.

2. Why Use Cursors? Identifying the Use Cases (Handle with Care!)

Given that set-based operations are generally far more efficient, why do cursors even exist? They address specific scenarios where set-based logic is either extremely complex, inefficient in its own way, or simply not possible.

Here are some common, though often debatable, reasons for using cursors:

  • Complex Row-Dependent Logic: Performing actions on a row that depend directly on the values or calculations performed on the previous row processed. While window functions (LAG, LEAD) can handle many such cases now, truly intricate sequential dependencies might still necessitate a cursor.
  • Calling Stored Procedures or Functions Iteratively: Executing a separate stored procedure or complex function for each row in a result set. For instance, processing each order in an Orders table by calling a detailed ProcessOrder procedure. Caveat: Often, the procedure itself could be rewritten to handle a set of IDs.
  • Row-by-Row Validation: Implementing extremely complex validation rules that cannot be expressed easily using CHECK constraints or simple WHERE clauses, requiring procedural logic for each row before an update or insert.
  • Data Auditing or Archiving with Complex Rules: Iterating through records to decide, based on complex criteria per row, whether to archive, delete, or log specific information.
  • Sequential Processing Requirements: Situations where the order of processing is critical and cannot be guaranteed or easily managed with set-based approaches alone (though ORDER BY helps, the processing step itself might need to be sequential).
  • Database Administration Tasks: Cursors are sometimes used by DBAs for maintenance scripts, like iterating through all tables in a database to perform an action (e.g., rebuild indexes, gather statistics) or checking permissions on various objects.
  • Migrating or Transforming Data with Intricate Logic: During complex data migrations, sometimes row-by-row processing is needed to transform data based on rules that are difficult to implement in bulk.

Crucial Warning: Before resorting to a cursor, always exhaust possibilities for achieving the same result using set-based operations, temporary tables, window functions, or other more efficient techniques. Cursors should generally be considered a last resort due to their significant performance implications.

3. The Cursor Lifecycle: Step-by-Step Mechanics

Understanding how cursors work involves knowing their lifecycle. This typically involves five key steps, implemented using specific SQL commands that vary slightly between database systems (like SQL Server’s T-SQL, Oracle’s PL/SQL, PostgreSQL’s PL/pgSQL, MySQL’s procedural language).

Step 1: DECLARE the Cursor

This step defines the cursor, giving it a name and associating it with a SELECT statement. You also often specify cursor characteristics here, such as its sensitivity to data changes and its scrolling capabilities (more on types later).

  • Purpose: Name the cursor and define the data it will iterate over.
  • Syntax (Conceptual):
    sql
    DECLARE cursor_name [CURSOR_OPTIONS] CURSOR
    FOR
    SELECT column1, column2, ...
    FROM table_name
    WHERE conditions
    [ORDER BY ...];
  • CURSOR_OPTIONS might include keywords like INSENSITIVE, SCROLL, DYNAMIC, FORWARD_ONLY, STATIC, KEYSET, etc., depending on the RDBMS.

Step 2: OPEN the Cursor

This step executes the SELECT statement defined in the DECLARE phase and populates the cursor’s result set. It positions an internal pointer before the first row of this result set.

  • Purpose: Execute the query and make the result set available for fetching.
  • Syntax (Conceptual):
    sql
    OPEN cursor_name;
  • For certain cursor types (like static), this step might involve creating a temporary copy of the data.

Step 3: FETCH Rows from the Cursor

This is the core of the row-by-row processing. The FETCH statement retrieves the next available row from the cursor’s result set and advances the internal pointer to the following row. The data from the fetched row is typically stored in local variables for processing.

  • Purpose: Retrieve a single row (or a block of rows with some syntax variations) and move the cursor pointer.
  • Syntax (Conceptual):
    sql
    FETCH [NEXT | PRIOR | FIRST | LAST | ABSOLUTE n | RELATIVE n] -- Scroll options depend on cursor type
    FROM cursor_name
    INTO @variable1, @variable2, ...; -- Store column values into variables
  • Looping: Fetch is almost always used within a loop structure (e.g., WHILE, LOOP). The loop continues as long as FETCH successfully retrieves a row.
  • Status Check: It’s crucial to check the status of the FETCH operation after each execution. Different database systems have different ways to report this (e.g., @@FETCH_STATUS in SQL Server, cursor_name%FOUND or cursor_name%NOTFOUND in Oracle/PostgreSQL). The loop terminates when there are no more rows to fetch.

Step 4: CLOSE the Cursor

Once processing is complete (i.e., the fetch loop finishes), the cursor should be closed. This releases the current result set and any locks associated with the cursor rows (the specific locks depend on the cursor type and transaction isolation level). However, the cursor definition still exists and can potentially be reopened.

  • Purpose: Release the active result set and associated locks.
  • Syntax (Conceptual):
    sql
    CLOSE cursor_name;
  • Importance: Failing to close cursors can hold locks longer than necessary, impacting concurrency, and potentially keep resources allocated.

Step 5: DEALLOCATE the Cursor

This final step removes the cursor definition completely and releases all memory and system resources associated with it. Once deallocated, the cursor cannot be reopened unless it is declared again.

  • Purpose: Remove the cursor definition and free all associated resources.
  • Syntax (Conceptual):
    sql
    DEALLOCATE cursor_name;
  • Importance: Essential for good resource management, especially for cursors declared at broader scopes (though local scope is preferred). Prevents resource leaks.

4. A Simple Cursor Example (SQL Server T-SQL)

Let’s illustrate the lifecycle with a practical, albeit somewhat contrived, example using SQL Server’s T-SQL syntax. Imagine we have a Products table and we want to iterate through each product, check its InventoryCount, and if it’s below a threshold (e.g., 10), log a message or perform some action (here, we’ll just print).

“`sql
— Sample Table and Data (for context)
— CREATE TABLE Products (
— ProductID INT PRIMARY KEY IDENTITY,
— ProductName VARCHAR(100),
— InventoryCount INT,
— UnitPrice DECIMAL(10, 2)
— );
— INSERT INTO Products (ProductName, InventoryCount, UnitPrice) VALUES
— (‘Laptop’, 15, 1200.00),
— (‘Mouse’, 5, 25.00),
— (‘Keyboard’, 8, 75.00),
— (‘Monitor’, 20, 300.00);

— Start of the cursor logic
DECLARE @ProdID INT;
DECLARE @ProdName VARCHAR(100);
DECLARE @InvCount INT;
DECLARE @LowStockThreshold INT = 10;

— 1. DECLARE the cursor
DECLARE ProductCursor CURSOR FORWARD_ONLY STATIC — Specify options (Forward-only, Static for this example)
FOR
SELECT ProductID, ProductName, InventoryCount
FROM Products
WHERE InventoryCount IS NOT NULL; — Selecting specific columns needed

— 2. OPEN the cursor
OPEN ProductCursor;

— 3. FETCH the first row
FETCH NEXT FROM ProductCursor INTO @ProdID, @ProdName, @InvCount;

— Check @@FETCH_STATUS to see if there are any rows at all
— @@FETCH_STATUS = 0: Fetch successful
— @@FETCH_STATUS = -1: Fetch failed or row beyond result set
— @@FETCH_STATUS = -2: Row fetched previously was deleted (Keyset cursors)

— Loop through all rows as long as the fetch is successful
WHILE @@FETCH_STATUS = 0
BEGIN
— Process the current row (stored in variables)
PRINT ‘Processing Product: ‘ + @ProdName + ‘ (ID: ‘ + CAST(@ProdID AS VARCHAR) + ‘) with Inventory: ‘ + CAST(@InvCount AS VARCHAR);

IF @InvCount < @LowStockThreshold
BEGIN
    PRINT '   -> Low Stock Alert for ' + @ProdName;
    -- In a real scenario, you might:
    -- INSERT INTO LowStockLog (ProductID, LogDate) VALUES (@ProdID, GETDATE());
    -- EXEC GenerateReorderRequest @ProductID = @ProdID;
END

-- 3. FETCH the next row
FETCH NEXT FROM ProductCursor INTO @ProdID, @ProdName, @InvCount;

END; — End of WHILE loop

— 4. CLOSE the cursor
CLOSE ProductCursor;

— 5. DEALLOCATE the cursor
DEALLOCATE ProductCursor;

PRINT ‘Cursor processing finished.’;

— End of the cursor logic
“`

Explanation:

  1. Variables: We declare variables (@ProdID, @ProdName, @InvCount) to hold the data fetched from each row.
  2. DECLARE: We define ProductCursor, specifying it should be FORWARD_ONLY (can only fetch next) and STATIC (uses a snapshot of the data taken when opened). The SELECT statement defines the data it operates on.
  3. OPEN: The SELECT query is executed, and the results are prepared for fetching.
  4. Initial FETCH: We perform one FETCH NEXT before the loop to load the first row’s data.
  5. WHILE Loop: The loop continues as long as @@FETCH_STATUS is 0 (indicating the last FETCH was successful).
  6. Processing: Inside the loop, we use the values stored in the variables (@ProdID, @ProdName, @InvCount) to perform our logic (printing messages, checking stock).
  7. Subsequent FETCH: At the end of the loop body, we FETCH NEXT again to get the next row for the next iteration. This updates the variables and @@FETCH_STATUS.
  8. CLOSE: After the loop finishes (because @@FETCH_STATUS is no longer 0), we close the cursor, releasing the result set.
  9. DEALLOCATE: Finally, we remove the cursor definition.

5. Types of Cursors: Sensitivity and Scrollability

Cursors aren’t monolithic; they come in different flavors, primarily defined by two characteristics:

  • Sensitivity: How the cursor reacts to changes (inserts, updates, deletes) made to the underlying base tables after the cursor has been opened.
  • Scrollability: The ability to move backward or jump to specific positions within the result set, not just forward.

The exact terminology and behavior can vary slightly between RDBMS vendors (SQL Server, Oracle, PostgreSQL, MySQL), but the general concepts are similar. The ISO SQL standard defines some types, but implementations often have specific nuances.

Common Cursor Types (using SQL Server terminology as a base, concepts apply broadly):

  • FORWARD_ONLY:

    • Scrollability: Can only move forward using FETCH NEXT. Cannot move backward or jump. This is the default in many systems if not specified.
    • Sensitivity: Can be STATIC, KEYSET, or DYNAMIC in sensitivity (SQL Server links FORWARD_ONLY with these).
      • If not specified otherwise, often behaves like DYNAMIC (sensitive to changes) but without the ability to scroll backward, making it more efficient than a fully DYNAMIC SCROLL cursor.
    • Performance: Generally the most performant type due to reduced overhead.

  • STATIC (or INSENSITIVE):

    • Scrollability: Usually scrollable (FETCH NEXT, PRIOR, FIRST, LAST, ABSOLUTE, RELATIVE).
    • Sensitivity: Insensitive to changes. The cursor operates on a temporary snapshot of the data as it existed when the cursor was OPENed. Changes made to the base tables (updates, deletes, inserts) by other users (or even the same session outside the cursor) are not reflected in the fetched data. Rows inserted after the cursor opened are not visible.
    • Performance: Can be resource-intensive, especially for large result sets, as it requires copying the data (often to a temporary work table like tempdb in SQL Server). Fetching can be fast once the copy is made. Good for reporting or analysis where a stable view of the data is needed.

  • KEYSET-DRIVEN:

    • Scrollability: Usually scrollable.
    • Sensitivity: Partially sensitive. The membership (which rows belong to the cursor) and the order of rows are fixed when the cursor is opened. It achieves this by storing the unique keys of all rows in the result set (hence “keyset”) in a temporary table.
      • Updates: Changes to non-key columns in the base tables are visible when the row is fetched.
      • Deletes: Attempts to fetch a row that has been deleted from the base table after the cursor opened will fail (e.g., @@FETCH_STATUS = -2 in SQL Server). The “hole” is detectable.
      • Inserts: Rows inserted into the base tables after the cursor was opened are not visible through the cursor (because their keys weren’t in the initial keyset).
    • Performance: Moderate overhead. Less than STATIC if only keys are stored and rows are fetched directly, but more than basic FORWARD_ONLY.

  • DYNAMIC:

    • Scrollability: Usually scrollable.
    • Sensitivity: Fully sensitive. The cursor reflects all changes (inserts, updates, deletes) made to the underlying data by any user while the cursor is open, regardless of when the change occurred relative to the fetch. The result set, row order, and values are dynamic.
    • Performance: Typically the most resource-intensive and slowest type. The database must constantly detect changes that affect the cursor’s result set every time a FETCH occurs. This can lead to significant locking and performance overhead. Use with extreme caution.

  • SCROLL:

    • This isn’t always a distinct type but rather an option that enables fetching in directions other than just NEXT. You can use FETCH PRIOR, FETCH FIRST, FETCH LAST, FETCH ABSOLUTE n (fetch the nth row), FETCH RELATIVE n (fetch n rows forward or backward from the current position).
    • Often combined with sensitivity types (e.g., STATIC SCROLL, KEYSET SCROLL, DYNAMIC SCROLL). FORWARD_ONLY cursors are inherently non-scrollable.

Choosing the Right Type:

  • If you only need to iterate forward once and don’t need to see changes made after opening, use FORWARD_ONLY STATIC (or just FORWARD_ONLY if its default sensitivity is acceptable and efficient). This is often the best performance choice if a cursor is truly needed.
  • If you need a stable, unchanging view of the data for the duration of the cursor’s life and potentially need to scroll, use STATIC (or INSENSITIVE). Be mindful of temp space usage.
  • If you need to see updates to existing rows and detect deletions, but don’t need to see new rows inserted after opening, and need scrollability, KEYSET might be appropriate.
  • If you absolutely must see all committed changes (inserts, updates, deletes) as they happen, and need scrollability, use DYNAMIC, but be prepared for potential performance hits and locking issues.
  • Avoid scrollable cursors (STATIC, KEYSET, DYNAMIC with scroll options) unless backward or random access is essential, as they generally have higher overhead than FORWARD_ONLY.

6. Cursor Scope and Visibility

Where and how a cursor is declared affects how long it persists and where it can be accessed. The most common scopes are:

  • Local Cursors:
    • Declared within a batch, stored procedure, or trigger.
    • Their scope is limited to that specific batch, procedure, or trigger execution.
    • They are implicitly deallocated when the batch, procedure, or trigger finishes.
    • This is the preferred scope for cursors as it ensures automatic cleanup and limits potential resource contention. Cursor names must be unique within their scope (e.g., within the procedure).
  • Global Cursors (Supported in some RDBMS like SQL Server):
    • Declared with a GLOBAL keyword.
    • Their scope is the entire database connection.
    • They persist until explicitly deallocated or until the connection is closed.
    • They can be referenced by any batch or stored procedure executed on that connection after declaration.
    • Generally discouraged. Global cursors make resource management much harder, increase the risk of conflicts (if multiple procedures try to use the same global cursor), and can hold resources for the duration of a connection. Avoid unless there’s a very specific, well-understood need.

Understanding scope is crucial for preventing resource leaks. Always aim for the narrowest possible scope (Local) and ensure cursors are explicitly closed and deallocated even within that scope, especially if errors might occur before the natural end of the batch or procedure.

7. Performance Considerations and Drawbacks: The Big Caveat

This is arguably the most critical section. While cursors provide row-by-row processing capabilities, they come with significant performance drawbacks compared to set-based operations.

  • Increased Network Traffic: Instead of fetching a whole result set at once, each FETCH often involves communication between the client/application logic (even if it’s a stored procedure) and the database engine’s storage layer. This round-trip communication for every single row is inherently less efficient.
  • Row-by-Row Processing Overhead: The database engine is optimized for set operations. Forcing it to process data one row at a time incurs overhead for each row: locating the row, fetching it, managing the cursor position, checking status, etc.
  • Locking and Concurrency Issues: Cursors, especially updatable or sensitive ones (DYNAMIC, KEYSET), often need to hold locks on the rows they are processing or might process. Holding locks for the duration of a loop that processes rows one by one can block other users trying to access the same data for much longer than a quick set-based update would. This severely impacts concurrency. Static cursors might lock less but have overhead elsewhere.
  • Resource Consumption:
    • Memory: Cursors require memory to store their definition, state, and sometimes the result set data or keys.
    • Temporary Storage (tempdb): STATIC and KEYSET cursors often rely heavily on temporary database storage (like tempdb in SQL Server or temporary tablespaces in Oracle) to store the snapshot data or keys. This can lead to I/O contention and fill up temporary storage if not managed carefully, impacting the entire server.
  • Code Complexity and Maintainability: Cursor logic (declare, open, fetch loop, status check, close, deallocate, variable handling) is significantly more verbose and complex than equivalent set-based SQL statements. This makes the code harder to write, read, debug, and maintain.
  • Optimizer Limitations: The database query optimizer is highly sophisticated at finding efficient execution plans for set-based operations. It has far fewer optimization opportunities when dealing with procedural row-by-row logic inside a cursor loop.

In summary: Cursors often trade perceived logical simplicity (for the developer thinking procedurally) for significant performance and scalability problems.

8. Alternatives to Cursors: Thinking Set-Based

Given the drawbacks, it’s essential to consider alternatives before implementing a cursor. Most tasks that initially seem to require cursors can often be solved more efficiently using other SQL constructs or approaches.

  • Set-Based Operations (The Gold Standard):
    • Always try to rewrite the logic using standard UPDATE, INSERT, DELETE statements with appropriate WHERE clauses, JOINs, and subqueries.
    • Example: Instead of looping through products to update prices based on category, use:
      sql
      UPDATE Products
      SET UnitPrice = UnitPrice * 1.10
      WHERE CategoryID IN (SELECT CategoryID FROM Categories WHERE CategoryName = 'Electronics');
  • Temporary Tables / Table Variables:
    • If complex intermediate processing is needed, break the problem down.
    • Load the initial data set into a temporary table or table variable.
    • Perform manipulations on the temporary table using set-based operations (multiple UPDATE statements, etc.).
    • Join the final temporary table back to the base tables if needed for the final update or insert.
  • Window Functions (Modern SQL Powerhouse):
    • Functions like ROW_NUMBER(), RANK(), DENSE_RANK(), NTILE(), LAG(), LEAD(), SUM() OVER (...), AVG() OVER (...) allow calculations across a set of table rows that are somehow related to the current row, without collapsing the rows.
    • Example: Calculating running totals, finding the previous/next row’s value, ranking items within categories – tasks that might have previously tempted developers to use cursors – are often easily solved with window functions.
      sql
      -- Example: Get each product and the price of the *next* most expensive product
      SELECT
      ProductName,
      UnitPrice,
      LEAD(UnitPrice, 1, 0) OVER (ORDER BY UnitPrice DESC) AS PriceOfNextMostExpensive
      FROM Products;
  • Recursive CTEs (Common Table Expressions):
    • Useful for processing hierarchical data (like organizational charts or parts explosions) or certain types of iterative sequences that can be modeled relationally. They provide a set-based way to handle some recursive or iterative problems.
  • CASE Expressions:
    • Complex conditional logic within an update or select can often be handled using CASE statements, avoiding the need to process rows individually based on conditions.
    • Example: Updating status based on multiple conditions:
      sql
      UPDATE Orders
      SET OrderStatus = CASE
      WHEN ShippedDate IS NOT NULL THEN 'Shipped'
      WHEN PaymentReceivedDate IS NOT NULL THEN 'Processing'
      ELSE 'Pending'
      END
      WHERE OrderStatus <> 'Cancelled';
  • Application-Level Processing:
    • Sometimes, the complex logic is better suited to the application layer (e.g., in C#, Java, Python).
    • Fetch the necessary data set (perhaps filtered) to the application.
    • Iterate through the results in the application code using native loops.
    • Perform the complex logic, potentially making calls to external services or using application libraries.
    • If updates are needed, either send individual updates back (less ideal) or collect the changes and perform a batch update. This can sometimes be more efficient than a database cursor, especially if the logic doesn’t heavily involve further database lookups within the loop.
  • Bulk Operations / ETL Tools:
    • For large data transformations or migrations, dedicated ETL (Extract, Transform, Load) tools or database bulk loading utilities are almost always vastly superior to cursors.

The key takeaway is to “think in sets.” Try to phrase your problem in terms of operations on entire groups of rows that meet certain criteria.

9. Platform-Specific Syntax Examples (Brief Comparison)

While the concepts are similar, the exact syntax for cursors varies across major RDBMSs.

  • SQL Server (T-SQL):
    sql
    DECLARE @var1 INT;
    DECLARE cursor_name CURSOR [LOCAL | GLOBAL] [FORWARD_ONLY | SCROLL]
    [STATIC | KEYSET | DYNAMIC | FAST_FORWARD]
    [READ_ONLY | SCROLL_LOCKS | OPTIMISTIC]
    FOR select_statement;
    OPEN cursor_name;
    FETCH NEXT FROM cursor_name INTO @var1, ...;
    WHILE @@FETCH_STATUS = 0
    BEGIN
    -- process
    FETCH NEXT FROM cursor_name INTO @var1, ...;
    END
    CLOSE cursor_name;
    DEALLOCATE cursor_name;

    • Uses @@FETCH_STATUS for loop control.
    • FAST_FORWARD is an optimized FORWARD_ONLY, READ_ONLY, STATIC cursor.

  • Oracle (PL/SQL):

    • Implicit Cursors: Used automatically for single-row SELECT INTO or for DML statements (INSERT, UPDATE, DELETE). Attributes like SQL%FOUND, SQL%NOTFOUND, SQL%ROWCOUNT available.
    • Explicit Cursors: Defined by the developer.
      sql
      DECLARE
      var1 table.column1%TYPE;
      CURSOR cursor_name IS select_statement;
      BEGIN
      OPEN cursor_name;
      LOOP
      FETCH cursor_name INTO var1, ...;
      EXIT WHEN cursor_name%NOTFOUND; -- Loop control attribute
      -- process
      END LOOP;
      CLOSE cursor_name;
      END;
      / -- Execute block
    • Uses attributes like cursor_name%FOUND, cursor_name%NOTFOUND, cursor_name%ISOPEN.
    • Also supports CURSOR FOR LOOP which simplifies iteration (implicitly opens, fetches, closes).
    • Supports REF CURSORs (cursor variables) for passing cursors between procedures/functions.

  • PostgreSQL (PL/pgSQL):
    sql
    DECLARE
    var1 integer; -- Or use record types
    cursor_name CURSOR [(options)] FOR select_statement;
    -- Or use unbound cursor variables: cur_var REFCURSOR;
    BEGIN
    OPEN cursor_name; -- Or: OPEN cur_var FOR select_statement;
    LOOP
    FETCH cursor_name INTO var1, ...; -- Or: FETCH cur_var INTO ...
    EXIT WHEN NOT FOUND; -- Special variable 'FOUND'
    -- process
    END LOOP;
    CLOSE cursor_name; -- Or: CLOSE cur_var;
    END;
    $$ LANGUAGE plpgsql;

    • Uses the special boolean variable FOUND for loop control after FETCH.
    • Strong support for REFCURSOR (reference cursors) for dynamic queries and returning result sets from functions.
    • Often uses FOR record_variable IN query LOOP ... END LOOP; which is a more concise cursor loop construct.

  • MySQL (Stored Procedures):
    “`sql
    DECLARE var1 INT;
    DECLARE done INT DEFAULT FALSE; — Handler flag
    DECLARE cursor_name CURSOR FOR select_statement;
    DECLARE CONTINUE HANDLER FOR NOT FOUND SET done = TRUE; — Handler for loop exit

    OPEN cursor_name;

    read_loop: LOOP
    FETCH cursor_name INTO var1, …;
    IF done THEN
    LEAVE read_loop;
    END IF;
    — process
    END LOOP;

    CLOSE cursor_name;
    — No explicit DEALLOCATE needed for procedure cursors; handled at end of scope.
    ``
    * Uses a handler (    DECLARE CONTINUE HANDLER FOR NOT FOUND) to set a flag whenFETCHruns out of rows.
    * Requires explicit loop labeling (    read_loop) andLEAVE` statement.

Always consult the specific documentation for your RDBMS for the precise syntax, available options, and default behaviors.

10. Best Practices for Using Cursors (If You Absolutely Must)

If, after careful consideration, a cursor is deemed the only viable solution, follow these best practices to mitigate the negative impacts:

  1. Avoid If Possible: This can’t be stressed enough. Exhaust all set-based alternatives first.
  2. Minimize Use: Use cursors only for the specific part of the logic that absolutely requires row-by-row processing. Combine with set-based operations where possible.
  3. Keep the Cursor’s SELECT Statement Simple: Avoid complex joins, subqueries, or functions within the cursor’s SELECT statement itself. Fetch necessary IDs or base data, and perform lookups inside the loop if needed (though this also has costs).
  4. Fetch Only Necessary Columns: Don’t SELECT *. Specify only the columns you actually need inside the loop to reduce data transfer and memory overhead.
  5. Use the Least Resource-Intensive Type: Prefer FORWARD_ONLY and READ_ONLY if you don’t need to scroll or update through the cursor. STATIC or FAST_FORWARD (SQL Server) are often good choices if sensitivity isn’t needed. Avoid DYNAMIC unless absolutely necessary.
  6. Minimize Logic Inside the Loop: Keep the processing logic within the FETCH loop as lean and fast as possible. Avoid lengthy operations or complex queries inside the loop.
  7. Close and Deallocate Promptly: Always explicitly CLOSE and DEALLOCATE cursors as soon as they are no longer needed, even if using local cursors (especially important in error handling blocks).
  8. Use Local Scope: Declare cursors with LOCAL scope (the default in most modern contexts) to ensure they are automatically cleaned up if the procedure/batch exits unexpectedly. Avoid GLOBAL cursors.
  9. Beware of Nested Cursors: Opening a cursor inside the loop of another cursor usually leads to terrible performance (O(N*M) complexity). Look for ways to restructure using joins or temporary tables.
  10. Test Thoroughly: Performance test cursor-based solutions under realistic data volumes and concurrency loads. Compare their performance against set-based alternatives.
  11. Understand Locking: Be aware of the locking behavior of the chosen cursor type and isolation level and how it impacts concurrency. READ_ONLY cursors generally lock less. Updatable cursors (FOR UPDATE) or sensitive cursors hold more restrictive locks for longer.

11. Conclusion: A Powerful Tool, Used Sparingly

Database cursors are a specialized tool in the SQL arsenal, providing the necessary mechanism for procedural, row-by-row processing within a database environment predominantly designed for set-based operations. They allow developers to tackle problems involving complex sequential logic, iterative procedure calls, or detailed row-level validation that might be difficult or impossible to express using standard set-based SQL alone.

However, this capability comes at a significant cost. Cursors typically introduce performance bottlenecks due to increased network traffic, row-by-row processing overhead, potential locking and concurrency issues, and higher resource consumption (memory, temporary storage). They also increase code complexity, making solutions harder to write, understand, and maintain.

Therefore, the cardinal rule when considering cursors is avoidance. Always strive to find a set-based solution first, leveraging the power of SQL’s relational algebra, temporary tables, window functions, or application-level logic. Cursors should be reserved as a last resort for those niche scenarios where no efficient set-based alternative exists.

If you must use a cursor, do so judiciously. Understand the different types and their implications, choose the least resource-intensive option that meets your needs (often FORWARD_ONLY, READ_ONLY, STATIC), keep the logic lean, manage resources carefully by closing and deallocating promptly, and test performance rigorously. By understanding both the mechanics and the pitfalls of cursors, you can make informed decisions and write more efficient, scalable, and maintainable database code.

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