Help Center/
GaussDB/
Best Practices/
Best Practices for Index Design/
Best Practices for Index Design (Distributed Instances)/
Preparations
Updated on 2025-09-04 GMT+08:00
Preparations
Create a table in the current database and insert data at the million-row scale.
Machine configurations: 8-core CPU; 32 GB of memory
-- Create the test_table table. gaussdb=# CREATE TABLE test_table ( id SERIAL PRIMARY KEY,name VARCHAR(100),email VARCHAR(100),created_at TIMESTAMP); CREATE TABLE -- Insert a million data records into the table. gaussdb=# INSERT INTO test_table (name,email,created_at) select 'User_' || i,'User_' || i || '@example.com',NOW() - (i * INTERVAL '1 minute') FROM generate_series(1, 1000000) AS i; INSERT 0 1000000 -- Create another table. gaussdb=# CREATE TABLE sales_records (record_id BIGSERIAL PRIMARY KEY,region_id INT NOT NULL,store_id INT NOT NULL,product_id INT NOT NULL,sale_date DATE NOT NULL,amount DECIMAL(12,2) NOT NULL,is_refund BOOLEAN DEFAULT false); -- Insert 2 million data records. gaussdb=# INSERT INTO sales_records (region_id, store_id, product_id, sale_date, amount) SELECT (random()*9)::INT + 1,(random()*99)::INT + 1,(random()*499)::INT + 1,current_date - (random()*1095)::INT,(random()*9900)::DECIMAL + 100 FROM generate_series(1,2000000); INSERT 0 2000000
Parent topic: Best Practices for Index Design (Distributed Instances)
Feedback
Was this page helpful?
Provide feedbackThank you very much for your feedback. We will continue working to improve the documentation.See the reply and handling status in My Cloud VOC.
The system is busy. Please try again later.
For any further questions, feel free to contact us through the chatbot.
Chatbot