GridGain Aggregation Query

GridGain is a memory based Distributed database that supports rich aggregate query operations. It can perform aggregate query operations through the SQL Query language. The following are several common aggregation queries supported by GridGain: 1. Count: Counts the number of records that meet the specified conditions. For example, counting the number of employees with salaries greater than 5000 in the employee table: SELECT COUNT(*) FROM employees WHERE salary > 5000; 2. Sum: Calculate the total number of record fields that meet the specified conditions. For example, calculating the total number of products sold in the order table: SELECT SUM(quantity) FROM orders; 3. Average: Calculate the average value of the record fields that meet the specified conditions. For example, calculating the average salary in an employee table: SELECT AVG(salary) FROM employees; 4. Min/Max: Finds the minimum/maximum values of fields in records that meet the specified conditions. For example, to find the employees with the highest and lowest salaries in the employee table: SELECT MAX(salary) FROM employees; SELECT MIN(salary) FROM employees; 5. Group by: Groups records based on specified fields, and then performs aggregation operations on each group. For example, calculating the number of employees in each department: SELECT department, COUNT(*) FROM employees GROUP BY department; 6. Having: In the Group by query, filter the grouping results. For example, calculate the number of employees in each department with a salary exceeding 5000, and then only return departments with a quantity greater than 2: SELECT department, COUNT(*) FROM employees WHERE salary > 5000 GROUP BY department HAVING COUNT(*) > 2; For the aggregation query operation in the above example, assume there is a table called "employees" that contains the following columns: id, name, department, and salary, and has a certain amount of sample data. It should be noted that when aggregating queries through GridGain, more efficient query operations can be achieved by configuring caching, and cluster resources can be utilized to improve query performance during distributed deployment.