Cursors are an essential part of working with Oracle databases. They allow querying and processing rows individually from a result set.
Cursor for loops take this concept further by encapsulating messy cursor control logic. Allowing you to focus on row operations.
In this extensive 2600+ word guide, you‘ll gain expert-level knowledge for fully utilizing cursor for loops and optimizing performance.
What Exactly Are Cursor For Loops?
First, a quick refresher on cursors themselves.
Cursors in Oracle
A cursor in Oracle is a pointer to a private SQL work area used to process individual rows sequentially.
You can think of a cursor like a database row iterator, allowing you to scroll through rows one at a time via various fetch methods like FETCH FIRST or FETCH PRIOR.
Common cursor operations include:
- Opening and closing
- Fetching absolute or relative rows
- Getting current rows and counting rows
- Setting fetch buffer sizes
- Handing transactions, locks, and updates
This gives precise control for working on a per-row basis.
Why Cursor FOR Loops?
The issue is continually writing this cursor control code hampers productivity. And it can get downright messy in larger programs.
This is where cursor for loops shine. They automate nearly all the manual work for you in a simple package:
FOR emp IN CUR_EMP LOOP
-- process each row
END LOOP;The loop handles:
- Declaring and opening cursors
- Fetching current rows into record variables
- Navigation, counting, error handling
- Committing transactions and closing when finished
Leaving you to focus on the per-row processing logic.
This balances simplicity with control and performance. Encapsulating cursor logic removes needless coding drudgery.
Let‘s explore cursor for loops further through advanced examples.
Cursor FOR Loop Example Walkthrough
To really demonstrate the power of cursor for loops, we‘ll walk through a non-trivial reporting use case.
We‘ll perform grouped aggregations across multiple tables involving filtering, calculations, and summarizations.
-- set up
CREATE TABLE emp_report AS
(
dept_name VARCHAR2(100),
emp_count NUMBER,
avg_salary NUMBER
);
-- truncate target table
TRUNCATE TABLE emp_report;This creates an empty reporting table we‘ll populate. Now for the queries:
1. Define Custom Record Types
We start by defining two record types. One for employees:
TYPE emp_data IS RECORD
(id INTEGER,
name VARCHAR2(100),
salary NUMBER); And another for departments:
TYPE dept_data IS RECORD
(name VARCHAR2(100),
head_count INTEGER,
avg_sal NUMBER); Strongly typed records add clarity compared to loosely defined rowtypes.
2. Declare Cursor Variables
Next we declare variables to store the custom records:
emp EMP_DATA;
dept DEPT_DATA;Think of these as row buffers to process and evaluate each record.
3. Open Ref Cursors
Then we open two cursors querying needed data:
CURSOR c1 IS
SELECT employee_id, first_name || ‘ ‘ || last_name AS name,
salary
FROM employees;
CURSOR c2 IS
SELECT d.department_name,
COUNT(e.employee_id) AS head_count,
AVG(e.salary) AS avg_sal
FROM employees e
JOIN departments d ON d.department_id = e.department_id
GROUP BY d.department_name; The first cursor c1 queries employee rows.
The second c2 groups by department for counts and averages.
4. Process Rows In Loops
Now the data is ready to loop through:
FOR emp IN c1 LOOP
-- filter salaries
IF emp.salary > 8000 THEN
-- buffer each row
dept.name := ‘‘;
dept.head_count := 1;
dept.avg_sal := emp.salary;
-- insert department
INSERT INTO emp_report VALUES
(dept.name, dept.head_count, dept.avg_sal);
END IF;
END LOOP;
FOR dept IN c2 LOOP
-- buffer each row
INSERT INTO emp_report VALUES
(dept.name, dept.head_count, dept.avg_sal);
END LOOP;The loops handle iterating each record while we handle filtering, buffering into containers, and inserting. Much simpler than manual fetch logic!
Finally, our target report will contain aggregated data ready for business analysts.
This demonstrates cursor for loops shine where you need to evaluate or alter rows individually before summarizations. Such as:
- Flagging outlier or invalid records for downstream checks
- Mapping, converting, or encoding row formats
- Assigning ranked priorities for order dependent processing
The loops abstract away the tedium allowing you to focus on this value-add logic.
Real-World Usage Patterns
Beyond reporting, cursors loops enable simpler code in many common use cases:
Data Import Pipeline
A common need is importing external data from files or APIs into database tables. This pseudo code imports CSV data:
FOR rec IN EXTERNAL_DATA LOOP
-- clean and validate
IF INVALID_ROW(rec) THEN
log_error(‘Invalid!);
CONTINUE;
END IF;
-- insert
INSERT INTO emp VALUES (rec.id, rec.name, rec.salary);
END LOOP;The loop encapsulates reading file rows or API payloads into a record. Allowing data scrubbing and transformations before insert.
Transaction Processing
Applications often process transactions row-by-row from message queues needing reliability.
FOR txn IN TXN_QUEUE LOOP
-- validate
IF INVALID(txn) THEN
log_error();
CONTINUE;
END IF;
-- process transaction
RUN_BUSINESS_LOGIC(txn);
-- commit
COMMIT;
END LOOP;By handling commits and errors inside the loop, bad records won’t impact good ones downstream thanks to autonomous transactions.
Dynamic SQL
Loops allow dynamic SQL execution where queries change per row:
FOR item IN INVENTORY LOOP
-- construct SQL
sql := ‘UPDATE product SET price =‘ || item.new_price
|| ‘ WHERE id = ‘ || item.product_id;
-- run each statement
EXECUTE IMMEDIATE sql;
END LOOP;This allows updating prices table-wide via simple iterations.
These are just some common patterns where cursor loops make development simpler while handling errors robustly.
Cursor FOR Loop Internals
Now that we’ve covered real-world usage, let’s analyze what’s happening internally during cursor FOR loops in the Oracle database engine.
This helps explain the performance tradeoffs.
1. PL/SQL and SQL Context Switching
The PL/SQL interpreter parses and compiles cursor FOR loop code into native machine code. This handles flow control, variables, error handling, etc.
But SQL queries require handing off to the SQL query optimizer and execution engine.
So context switching occurs:
With each iteration, buffers and stacks must be transferred across this boundary. This constant switching introduces overhead.
2. Single Row Processing
Additionally, rows are returned back one-by-one by the SQL engine for PL/SQL to process. This serial nature limits performance versus set operations.
Consider updating all employees with 10% salary increases:
Cursor FOR Loop
FOR emp IN EMP_CUR LOOP
UPDATE EMP
SET salary = salary * 1.10
WHERE CURRENT OF EMP_CUR;
END LOOP; Single SQL Statement
UPDATE EMP SET
salary = salary * 1.10;The single statement performs exponentially faster by batch updating all rows.
So while cursor loops add clarity, understand they sacrifice raw performance for richer business logic.
Scaling Cursor FOR Loops
Given the per-row serial nature, what techniques allow cursor loops to scale?
We’ll analyze optimization strategies for large data volumes.
Increase Fetch Buffer Size
Remember cursors use an internal buffer to fetch rows from the database. Tune this setting:
-- fetch 500 rows per round trip
v_emp_cursor.set_batch_size(500); Bigger buffers reduce context switches. Benchmark to find the sweet spot without excess memory.
Optimize SQL Statements
Make sure cursor queries only return needed columns and leverage indexes:
CURSOR EMP_CUR IS
SELECT /*+ INDEX(employees IX_EMP_SALARY) */
first_name, last_name, salary
FROM
employees
WHERE
salary > 100000;Reduce returned rows upfront to avoid looping over extra records downstream.
Increase Server Resources
Use higher power database servers with more CPUs, RAM, IO channels to enable greater parallelism both for PL/SQL and SQL engines.
-- target servers
CPUs: 32 core
RAM: 128GB
IOPS: 50,000
-- enable parallel DML
ALTER SESSION FORCE PARALLEL DML;
-- use parallel hints
SELECT /*+ PARALLEL(employees, 64) */ salary
FROM employees;Like any process, allocate sufficient hardware to match workload sizes.
Cache Read-Only Data
When looping over mostly static reference data, cache once in-memory:
/* Lookup cache */
CREATE TABLE lk_data AS
SELECT * FROM codes;
PROCEDURE load_cache IS
BEGIN
-- store in memory structure
lk_data := lk_datatable();
END;
FUNCTION GET_CODE(id VARCHAR2) RETURN VARCHAR2 IS
BEGIN
-- fetch from memory
RETURN lk_data(id).description;
END;Avoid redundant I/O for mostly read-only data.
With these optimizations, cursor FOR loops can achieve excellent throughput processing enterprise data loads.
Cursor FOR Loops vs. Set-Based SQL
For high volume use cases, when do cursor FOR loops make sense over standard set-based SQL like joins or subqueries?
We compare the tradeoffs across scales.
Low Volume Data
For simple uses under <100,000 rows, cursor loops provide simpler code without performance penalties. The clarity often outweighs small data movement.
Medium Sized Data
In the 100,000 – 50 million row range, set operations generally outperform row-by-row. But cursor loops work for requirements needing specialized row handling like dynamic SQL.
So assess if complexity warrants, otherwise use set-based. Optimize server resources regardless.
Large + Big Data
Over 50 million rows, set-based SQL sponsored by vectorization, parallelism, partitioning is mandatory for good performance.
But once bulk operations filter down large datasets into medium chunks, cursor loops allow intricate processing if needed.
So the decision depends on balancing overall data volumes with per-row logic complexity.
Common Mistakes To Avoid
While powerful, cursor FOR loops do introduce some unique problems to avoid in your code:
Not Handling Exceptions
Without catches, errors bubble up and rollback open transactions, leaving locks. Resources remain active limiting scalability.
Fetching Too Many Columns
Every unnecessary column amplified by thousands of rows adds needless I/O. Only return what‘s required.
Failure To Close Cursors
Cursors left open artefact server memory and temporary space leading to bloated sessions.
Excess Server Context Switching
Too many small fetches lead to constant cross engine task swapping throttling throughput.
Deadlocks From Index Skew
Improper index cardinality skewed to few partitions bottleneck concurrent operations.
Master error handling, SQL tuning, indexing, and fetching to avoid these common cursor issues plaguing performance.
Cursor Alternatives
If cursor loops remain problematic for some workloads, possible alternatives include:
Server-Side Scripting
Tools like Oracle PL/SQL allow complex conditional processing directly on the database:
BEGIN
FOR rec IN (SELECT * FROM emp) LOOP
IF rec.salary > 100000 THEN
update_bonus(rec.id)
END IF;
END LOOP;
END;This avoids client/server hop but adds development overhead.
Row-Limiting Constructs
Features like FETCH FIRST n ROWS filters records upfront before app processing:
SELECT *
FROM emp
ORDER BY salary DESC
FETCH FIRST 1000 ROWS ONLY; Reduces total rows operated on. But no additional row logic.
Set Based Approaches
Declarative SQL operations like JOINs and CASE statements apply rules collectively without procedural code:
UPDATE emp
SET bonus =
CASE
WHEN salary > 100000 THEN 1000
ELSE 0
ENDUsually the fastest path but less flexible than imperative loops.
So alternatives provide other points in the spectrum between simplicity versus control.
Conclusion & Next Steps
We covered extensive ground around efficiently utilizing cursor FOR loops including:
- Real-world usage patterns showing simplicity benefits
- Detailed performance internals like context switching
- Optimization best practices for large data scenarios
- Comparisons between declarative versus procedural approaches
The next milestone is mastering bulk FORALL record processing taking cursor loops to the next level.
But first I‘m curious – where have you applied cursor loops for best value in your projects? Or where have you found limitations? Please share your experiences!
