{"id":19712,"date":"2025-08-29T09:51:38","date_gmt":"2025-08-29T04:21:38","guid":{"rendered":"https:\/\/vinish.dev\/?p=19712"},"modified":"2025-08-29T14:55:08","modified_gmt":"2025-08-29T09:25:08","slug":"select-oracle-23ai","status":"publish","type":"post","link":"https:\/\/vinish.dev\/select-oracle-23ai","title":{"rendered":"How to Use SELECT Queries in Oracle Database 23ai"},"content":{"rendered":"\n

Working with a database is all about getting useful information out of it. Oracle Database 23ai<\/a> gives you a powerful way to do this with the SELECT<\/strong> statement. This tutorial explains in detail how to write SELECT queries, step by step, in a way even a beginner can understand. You will also see how Oracle 23ai brings new features like JSON querying and vector search into SELECT statements, making data retrieval more intelligent.<\/p>\n\n\n\n

Preparing Sample Data<\/h2>\n\n\n\n

Before we start writing queries, let us create some tables<\/a> and insert data<\/a> so that the examples in this tutorial return real results.<\/p>\n\n\n\n

-- Create employees table\nCREATE TABLE employees (\n  employee_id NUMBER PRIMARY KEY,\n  first_name VARCHAR2(50),\n  last_name VARCHAR2(50),\n  hire_date DATE,\n  salary NUMBER(10,2),\n  department_id NUMBER,\n  commission_pct NUMBER\n);\n\n-- Insert sample employees\nINSERT INTO employees VALUES (1, 'John', 'Smith', DATE '2020-01-15', 8000, 10, NULL);\nINSERT INTO employees VALUES (2, 'Alice', 'Johnson', DATE '2019-03-10', 12000, 20, 0.10);\nINSERT INTO employees VALUES (3, 'Michael', 'Brown', DATE '2021-06-20', 4500, 10, NULL);\nINSERT INTO employees VALUES (4, 'Sophia', 'Davis', DATE '2022-11-05', 6000, 30, 0.05);\nINSERT INTO employees VALUES (5, 'David', 'Wilson', DATE '2021-02-18', 9500, 20, NULL);\n\n-- Create departments table\nCREATE TABLE departments (\n  department_id NUMBER PRIMARY KEY,\n  department_name VARCHAR2(100)\n);\n\n-- Insert sample departments\nINSERT INTO departments VALUES (10, 'Sales');\nINSERT INTO departments VALUES (20, 'HR');\nINSERT INTO departments VALUES (30, 'IT');\n\nCOMMIT;\n<\/pre>\n\n\n\n
We now have an employees<\/strong> table with five rows and a departments<\/strong> table with three rows. These will be used throughout the tutorial.<\/p>\n\n\n\n
Selecting All Columns<\/h2>\n\n\n\n
If you want to see everything from a table, use the asterisk symbol *<\/code>. This returns all columns in the table.<\/p>\n\n\n\n
SELECT * \nFROM employees;\n<\/pre>\n\n\n\n
Result:<\/strong><\/p>\n\n\n\n
EMPLOYEE_ID<\/th>FIRST_NAME<\/th>LAST_NAME<\/th>HIRE_DATE<\/th>SALARY<\/th>DEPARTMENT_ID<\/th>COMMISSION_PCT<\/th><\/tr><\/thead>
1<\/td>John<\/td>Smith<\/td>15-JAN-20<\/td>8000<\/td>10<\/td>NULL<\/td><\/tr>
2<\/td>Alice<\/td>Johnson<\/td>10-MAR-19<\/td>12000<\/td>20<\/td>0.10<\/td><\/tr>
3<\/td>Michael<\/td>Brown<\/td>20-JUN-21<\/td>4500<\/td>10<\/td>NULL<\/td><\/tr>
4<\/td>Sophia<\/td>Davis<\/td>05-NOV-22<\/td>6000<\/td>30<\/td>0.05<\/td><\/tr>
5<\/td>David<\/td>Wilson<\/td>18-FEB-21<\/td>9500<\/td>20<\/td>NULL<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
Selecting Specific Columns<\/h2>\n\n\n\n
To view only chosen details, list the column names after SELECT.<\/p>\n\n\n\n
SELECT first_name, last_name, hire_date \nFROM employees;\n<\/pre>\n\n\n\n
Result:<\/strong><\/p>\n\n\n\n
FIRST_NAME<\/th>LAST_NAME<\/th>HIRE_DATE<\/th><\/tr><\/thead>
John<\/td>Smith<\/td>15-JAN-20<\/td><\/tr>
Alice<\/td>Johnson<\/td>10-MAR-19<\/td><\/tr>
Michael<\/td>Brown<\/td>20-JUN-21<\/td><\/tr>
Sophia<\/td>Davis<\/td>05-NOV-22<\/td><\/tr>
David<\/td>Wilson<\/td>18-FEB-21<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
Using WHERE to Filter Data<\/h2>\n\n\n\n
The WHERE<\/code> clause narrows down rows to those that meet a condition.<\/p>\n\n\n\n
SELECT first_name, last_name, salary \nFROM employees\nWHERE salary > 5000;\n<\/pre>\n\n\n\n
Result:<\/strong><\/p>\n\n\n\n
FIRST_NAME<\/th>LAST_NAME<\/th>SALARY<\/th><\/tr><\/thead>
John<\/td>Smith<\/td>8000<\/td><\/tr>
Alice<\/td>Johnson<\/td>12000<\/td><\/tr>
Sophia<\/td>Davis<\/td>6000<\/td><\/tr>
David<\/td>Wilson<\/td>9500<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
Sorting Results with ORDER BY<\/h2>\n\n\n\n
The ORDER BY<\/code> clause organizes results. You can sort in ascending (ASC<\/code>, default) or descending (DESC<\/code>) order.<\/p>\n\n\n\n
SELECT first_name, last_name, salary \nFROM employees\nORDER BY salary DESC;\n<\/pre>\n\n\n\n
Result:<\/strong><\/p>\n\n\n\n
FIRST_NAME<\/th>LAST_NAME<\/th>SALARY<\/th><\/tr><\/thead>
Alice<\/td>Johnson<\/td>12000<\/td><\/tr>
David<\/td>Wilson<\/td>9500<\/td><\/tr>
John<\/td>Smith<\/td>8000<\/td><\/tr>
Sophia<\/td>Davis<\/td>6000<\/td><\/tr>
Michael<\/td>Brown<\/td>4500<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
Using Aliases for Readability<\/h2>\n\n\n\n
Aliases rename columns or tables in the result for clarity.<\/p>\n\n\n\n
SELECT first_name AS fname, last_name AS lname, salary AS monthly_pay\nFROM employees;\n<\/pre>\n\n\n\n
Result:<\/strong><\/p>\n\n\n\n
FNAME<\/th>LNAME<\/th>MONTHLY_PAY<\/th><\/tr><\/thead>
John<\/td>Smith<\/td>8000<\/td><\/tr>
Alice<\/td>Johnson<\/td>12000<\/td><\/tr>
Michael<\/td>Brown<\/td>4500<\/td><\/tr>
Sophia<\/td>Davis<\/td>6000<\/td><\/tr>
David<\/td>Wilson<\/td>9500<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
Combining Conditions with AND and OR<\/h2>\n\n\n\n
Conditions can be combined to make queries more precise.<\/p>\n\n\n\n
SELECT first_name, department_id, salary\nFROM employees\nWHERE department_id = 10 AND salary > 6000;\n<\/pre>\n\n\n\n
Result:<\/strong><\/p>\n\n\n\n
FIRST_NAME<\/th>DEPARTMENT_ID<\/th>SALARY<\/th><\/tr><\/thead>
John<\/td>10<\/td>8000<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
Aggregating Data with GROUP BY<\/h2>\n\n\n\n
The GROUP BY<\/code> clause groups rows that have the same values and allows use of aggregate functions like COUNT<\/code>, SUM<\/code>, AVG<\/code>, MIN<\/code>, and MAX<\/code>.<\/p>\n\n\n\n
SELECT department_id, AVG(salary) AS avg_salary\nFROM employees\nGROUP BY department_id;\n<\/pre>\n\n\n\n
Result:<\/strong><\/p>\n\n\n\n
DEPARTMENT_ID<\/th>AVG_SALARY<\/th><\/tr><\/thead>
10<\/td>6250<\/td><\/tr>
20<\/td>10750<\/td><\/tr>
30<\/td>6000<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
Filtering Groups with HAVING<\/h2>\n\n\n\n
When using GROUP BY<\/code>, the HAVING<\/code> clause filters results after aggregation.<\/p>\n\n\n\n
SELECT department_id, COUNT(*) AS total_employees\nFROM employees\nGROUP BY department_id\nHAVING COUNT(*) > 1;\n<\/pre>\n\n\n\n
Result:<\/strong><\/p>\n\n\n\n
DEPARTMENT_ID<\/th>TOTAL_EMPLOYEES<\/th><\/tr><\/thead>
10<\/td>2<\/td><\/tr>
20<\/td>2<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
Joining Multiple Tables<\/h2>\n\n\n\n
SELECT queries often need data from more than one table. Joins combine rows from two or more tables using related columns.<\/p>\n\n\n\n
SELECT e.first_name, d.department_name\nFROM employees e\nJOIN departments d\nON e.department_id = d.department_id;\n<\/pre>\n\n\n\n
Result:<\/strong><\/p>\n\n\n\n
FIRST_NAME<\/th>DEPARTMENT_NAME<\/th><\/tr><\/thead>
John<\/td>Sales<\/td><\/tr>
Alice<\/td>HR<\/td><\/tr>
Michael<\/td>Sales<\/td><\/tr>
Sophia<\/td>IT<\/td><\/tr>
David<\/td>HR<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
Subqueries in SELECT<\/h2>\n\n\n\n
A subquery is a query inside another query. It can provide data for filtering.<\/p>\n\n\n\n
SELECT first_name, last_name\nFROM employees\nWHERE salary > (SELECT AVG(salary) FROM employees);\n<\/pre>\n\n\n\n
Result:<\/strong><\/p>\n\n\n\n
FIRST_NAME<\/th>LAST_NAME<\/th><\/tr><\/thead>
Alice<\/td>Johnson<\/td><\/tr>
David<\/td>Wilson<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
SELECT with DISTINCT<\/h2>\n\n\n\n
DISTINCT<\/code> removes duplicates from results.<\/p>\n\n\n\n
SELECT DISTINCT department_id\nFROM employees;\n<\/pre>\n\n\n\n
Result:<\/strong><\/p>\n\n\n\n
DEPARTMENT_ID<\/th><\/tr><\/thead>
10<\/td><\/tr>
20<\/td><\/tr>
30<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
SELECT and NULL Handling<\/h2>\n\n\n\n
NULL represents missing or unknown values. To check for NULL, use IS NULL<\/code> or IS NOT NULL<\/code>.<\/p>\n\n\n\n
SELECT first_name, commission_pct\nFROM employees\nWHERE commission_pct IS NULL;\n<\/pre>\n\n\n\n
Result:<\/strong><\/p>\n\n\n\n
FIRST_NAME<\/th>COMMISSION_PCT<\/th><\/tr><\/thead>
John<\/td>NULL<\/td><\/tr>
Michael<\/td>NULL<\/td><\/tr>
David<\/td>NULL<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
Limiting Results with FETCH<\/h2>\n\n\n\n
Oracle allows limiting rows with FETCH FIRST<\/code> syntax.<\/p>\n\n\n\n
SELECT first_name, last_name, salary\nFROM employees\nORDER BY salary DESC\nFETCH FIRST 3 ROWS ONLY;\n<\/pre>\n\n\n\n
Result:<\/strong><\/p>\n\n\n\n
FIRST_NAME<\/th>LAST_NAME<\/th>SALARY<\/th><\/tr><\/thead>
Alice<\/td>Johnson<\/td>12000<\/td><\/tr>
David<\/td>Wilson<\/td>9500<\/td><\/tr>
John<\/td>Smith<\/td>8000<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
Using SELECT for JSON Data in Oracle 23ai<\/h2>\n\n\n\n
Oracle 23ai enhances working with JSON directly inside tables. Suppose you store product details in a JSON column:<\/p>\n\n\n\n
CREATE TABLE products (\n  product_id NUMBER PRIMARY KEY,\n  product_data JSON\n);\n\nINSERT INTO products VALUES (\n  1,\n  '{\"name\":\"Laptop\",\"specs\":{\"cpu\":\"Intel i7\",\"ram\":\"16GB\"}}'\n);\n\nINSERT INTO products VALUES (\n  2,\n  '{\"name\":\"Phone\",\"specs\":{\"cpu\":\"Snapdragon\",\"ram\":\"8GB\"}}'\n);\n<\/pre>\n\n\n\n
You can query JSON with simple operators:<\/p>\n\n\n\n
SELECT
  JSON_VALUE(product_data, '$.name')         AS product_name,
  JSON_VALUE(product_data, '$.specs.cpu')    AS cpu_type
FROM products;
<\/pre>\n\n\n\n
Result:<\/strong><\/p>\n\n\n\n
PRODUCT_NAME<\/th>CPU_TYPE<\/th><\/tr><\/thead>
Laptop<\/td>Intel i7<\/td><\/tr>
Phone<\/td>Snapdragon<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
SELECT with Vector Search in Oracle 23ai<\/h2>\n\n\n\n
Oracle 23ai introduces vector data types<\/strong> for AI-powered search. This is useful when finding similar items based on embeddings.<\/p>\n\n\n\n
-- Create table with VECTOR column
CREATE TABLE documents (
  doc_id     NUMBER PRIMARY KEY,
  content    CLOB,
  embedding  VECTOR(3)
);

-- Insert multiple rows using MULTIVALUE INSERT (Oracle 23ai feature)
INSERT INTO documents (doc_id, content, embedding)
VALUES 
  (1, 'Oracle Database 23ai introduces AI features', TO_VECTOR('[0.1, 0.8, 0.5]')),
  (2, 'Learn how to run SQL queries effectively', TO_VECTOR('[0.2, 0.7, 0.6]'));

COMMIT;
<\/pre>\n\n\n\n
Now you can run a similarity search:<\/p>\n\n\n\n
SELECT doc_id, content,
       VECTOR_DISTANCE(embedding, TO_VECTOR('[0.1, 0.75, 0.55]')) AS similarity
FROM documents
ORDER BY similarity ASC
FETCH FIRST 2 ROWS ONLY;
<\/pre>\n\n\n\n
Result:<\/strong><\/p>\n\n\n\n
\"Oracle<\/figure>\n\n\n\n
SELECT with Analytical Functions<\/h2>\n\n\n\n
Analytical functions let you perform calculations across sets of rows while still showing detailed data.<\/p>\n\n\n\n
SELECT first_name, salary,\n       RANK() OVER (ORDER BY salary DESC) AS rank_position\nFROM employees;\n<\/pre>\n\n\n\n
Result:<\/strong><\/p>\n\n\n\n
FIRST_NAME<\/th>SALARY<\/th>RANK_POSITION<\/th><\/tr><\/thead>
Alice<\/td>12000<\/td>1<\/td><\/tr>
David<\/td>9500<\/td>2<\/td><\/tr>
John<\/td>8000<\/td>3<\/td><\/tr>
Sophia<\/td>6000<\/td>4<\/td><\/tr>
Michael<\/td>4500<\/td>5<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
SELECT with CASE Expressions<\/h2>\n\n\n\n
CASE provides conditional logic inside SELECT.<\/p>\n\n\n\n
SELECT first_name, salary,\n       CASE \n         WHEN salary > 10000 THEN 'High'\n         WHEN salary BETWEEN 5000 AND 10000 THEN 'Medium'\n         ELSE 'Low'\n       END AS salary_category\nFROM employees;\n<\/pre>\n\n\n\n
Result:<\/strong><\/p>\n\n\n\n
FIRST_NAME<\/th>SALARY<\/th>SALARY_CATEGORY<\/th><\/tr><\/thead>
John<\/td>8000<\/td>Medium<\/td><\/tr>
Alice<\/td>12000<\/td>High<\/td><\/tr>
Michael<\/td>4500<\/td>Low<\/td><\/tr>
Sophia<\/td>6000<\/td>Medium<\/td><\/tr>
David<\/td>9500<\/td>Medium<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
Conclusion<\/h2>\n\n\n\n
The SELECT statement in Oracle Database 23ai remains the most important tool for extracting meaningful information. From simple column retrieval to advanced AI-driven queries with JSON and vector data, SELECT adapts to both traditional business needs and modern AI-powered applications. With these skills, you can analyze data faster, design smarter queries, and take full advantage of the innovations in Oracle 23ai.<\/p>\n\n\n\n
\n\n\n\n
Note: All the SQL examples provided in this tutorial were tested and verified on Oracle Database 23ai<\/strong> using Oracle SQL Developer version 24<\/strong>.<\/em><\/p>\n\n\n\n
See also:<\/h3>\n\n\n\n
    \n
  1. How to Install Oracle Database 23ai on Windows<\/a><\/li>\n\n\n\n
  2. Installing Oracle SQL Developer on Windows<\/a><\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"
    Working with a database is all about getting useful information out of it. Oracle Database 23ai gives you a powerful way to do this with the SELECT statement. This tutorial explains in detail how to write SELECT queries, step by step, in a way even a beginner can understand. You will also see how Oracle […]<\/p>\n","protected":false},"author":1,"featured_media":19715,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1819,1807],"tags":[],"class_list":["post-19712","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-oracle-23ai","category-oracle-sql"],"blocksy_meta":[],"yoast_head":"\nHow to Use SELECT Queries in Oracle 23ai • Vinish.Dev<\/title>\n<meta name=\"description\" content=\"Learn how to use SELECT queries in Oracle Database 23ai with examples, including JSON and vector search features.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/vinish.dev\/select-oracle-23ai\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" 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