Introduction
Efficient SQL queries are essential for optimal performance in Oracle databases. This article focuses on key practices for query optimization, with practical examples tailored for Oracle environments.
1. Use Indexes Effectively
Indexes are a powerful tool in Oracle for speeding up data retrieval. Oracle supports various types of indexes, including B-Tree, Bitmap, and Function-Based Indexes.
Example: Creating a B-Tree Index
CREATE INDEX idx_employee_name ON Employees(Name);
Why It Matters: Without an index, Oracle performs a full table scan, which is slower. The index allows Oracle to locate rows more efficiently.
2. Avoid Using Functions on Indexed Columns
Using functions on indexed columns prevents Oracle from utilizing the index, leading to a full table scan.
Bad Practice
SELECT * FROM Employees
WHERE UPPER(Name) = 'ALICE';
Good Practice
SELECT * FROM Employees
WHERE Name = 'Alice';
Why It Matters: Keep indexed columns unaltered to allow the optimizer to use the index effectively.
3. Use Oracle’s Execution Plans
Oracle’s execution plans provide detailed insights into how queries are executed, helping identify inefficiencies.
Example: Viewing Execution Plan
EXPLAIN PLAN FOR
SELECT e.Name, d.DepartmentName
FROM Employees e
JOIN Departments d
ON e.DepartmentID = d.DepartmentID
WHERE e.Salary > 50000;
SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY);
Look For:
- Full table scans: Indicate missing or ineffective indexes.
- High-cost operations: Optimize joins, filters, or aggregations.
4. Use Bind Variables
Bind variables improve performance by allowing Oracle to reuse execution plans, reducing hard parsing.
Example: Using Bind Variables
VARIABLE salary_threshold NUMBER;
EXEC :salary_threshold := 50000;
SELECT Name, Department
FROM Employees
WHERE Salary > :salary_threshold;
Why It Matters: Reduces CPU and memory usage by avoiding repeated parsing for similar queries.
5. Partition Large Tables
Partitioning splits a large table into smaller, manageable pieces, improving query performance and scalability.
Example: Range Partitioning
CREATE TABLE Orders (
OrderID INT,
OrderDate DATE,
TotalAmount NUMBER
)
PARTITION BY RANGE (OrderDate) (
PARTITION p2021 VALUES LESS THAN (TO_DATE('2022-01-01', 'YYYY-MM-DD')),
PARTITION p2022 VALUES LESS THAN (TO_DATE('2023-01-01', 'YYYY-MM-DD'))
);
Querying a Partitioned Table
SELECT * FROM Orders
WHERE OrderDate BETWEEN TO_DATE('2021-01-01', 'YYYY-MM-DD') AND TO_DATE('2021-12-31', 'YYYY-MM-DD');
Why It Matters: Oracle scans only the relevant partition instead of the entire table, reducing I/O.
6. Use Materialized Views for Complex Queries
Materialized views store precomputed query results, speeding up execution for repeated queries.
Example: Creating a Materialized View
CREATE MATERIALIZED VIEW EmployeeStats
AS
SELECT Department, AVG(Salary) AS AvgSalary
FROM Employees
GROUP BY Department;
Query the View:
SELECT * FROM EmployeeStats;
Why It Matters: Materialized views reduce computation time for complex aggregations and joins.
7. Monitor Query Performance with AWR
Oracle’s Automatic Workload Repository (AWR) helps identify slow queries and bottlenecks.
Generating an AWR Report
EXEC DBMS_WORKLOAD_REPOSITORY.CREATE_SNAPSHOT;
-- Query AWR data
SELECT * FROM DBA_HIST_SQLTEXT WHERE SQL_TEXT LIKE '%Employees%';
Why It Matters: AWR provides detailed insights into resource-intensive queries and helps identify optimization opportunities.
Summary of Best Practices
Best Practice | Why It Helps |
---|---|
Use indexes effectively | Speeds up data retrieval. |
Avoid functions on indexed columns | Ensures indexes are used efficiently. |
Use execution plans | Identifies inefficiencies in query execution. |
Use bind variables | Reduces hard parsing and improves plan reuse. |
Partition large tables | Improves performance for large datasets. |
Use materialized views | Speeds up repeated execution of complex queries. |
Monitor with AWR | Provides insights into resource-intensive queries. |
Conclusion
By following these Oracle-specific best practices, you can optimize SQL queries, reduce execution time, and enhance overall database performance. Start implementing these tips in your Oracle environment to see significant improvements!
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Top comments (1)
Caveat: I am MSSQL rather than Oracle so your mileage may differ. More indexes can affect write speed so only add them if there is a real performance impact on read.
Only add the required unique columns to the index; this will allow an index-seek. More columns could result in an index search which will be slower. MSSQL has a concept of covering index where other important data from the row is replicated into the index but is not part of the key. This can save you from a row lookup (once the record is identified, you still need to read it) but it comes at the cost of storage and extended write times
Set your indexes with the highest cardinality items first (first name, last name, title); this should allow a faster seek as there are fewer records to go through.
Your data is stored on disk using a clustered index. This will often be the same as the primary key but doesn't have to be. Make sure your clustered key is made of immutable fields where possible as this will prevent from moving in the indexes and therefore disk location.
You made no mention of page fill factor. A table will get written to an 8K page. When it reaches a fill threshold (say 75%) the underlying engine with split the page and create a 2nd page and copy half the data to it with pointers between the leaves (hence a b-tree index). There is a time cost to this page split and your database now takes up 16K on disk even though the actual data is still just 6K. Tables that get sequential writes can afford higher fill factors (save space, less leaves to traverse) as you rarely insert or delete records into the middle of sequences (e.g. orders). Tables where the clustered key is not sequential (e.g. product code) should have lower fill factors to enable more space to add extra records (more frequent but smaller leaf splits) .
Correct data types, scale and precision can also have a significant impact on both database size and index performance (one of many reasons why I don't like code-first schema designs). Using a Nvarchar(1000) when the actual data never exceeds varchar(20).
Your suggestion for not using functions on indexed fields is correct but the example was poor. If your database collation is case sensitive the two queries will return different results. A better example is when looking for dates. If you want every record in the last 30 days, put the calculation on the date you are looking for rather than on the record date.
MSSQL doesn't have the concept of fixed parameters in general queries but does cache query plans for stored procedures. On older versions (pre 2021 I think) you can only have one cached plan. If the procedure can accept vastly different parameters (e.g. date range from 1 week to 1 decade) the cached plan may not be the best strategy for all variations. MSSQL can be 'tricked' by copying the input parameters into local variables and using these instead; this is a concept called parameter sniffing.
As well as case sensitivity, another aspect of collation is how null values are treated: some will put them at the top of the index while others will put them at the end. This can have a significant impact on the amount of index rewrite especially for records that have an end date that starts off NULL and eventually gets filled in. If the collation is wrong and the application can withstand it, you might better with a dummy end date of 31-12-2999 so that at least it doesn't move in the index.
Where you have a large number of deletes or changes in indexed fields a regular defrag of the indexes will also help both performance as database size.