How to efficiently count the number of node services in MYSQL tree structure and ensure data consistency in Java?

Java efficiently counts the number of nodes in MySQL tree structure and data consistency strategy

This article discusses how to efficiently count the number of services of each node in the MySQL tree structure and ensure data consistency, especially when multiple service modules are updated concurrently. Suppose the database table contains id , type (province city and county level), parentId and num (business quantity) fields.

challenge:

1. How to quickly update num value of a node and respond to changes in the number of business of the lower nodes?
2. How to ensure data consistency when multiple business modules are updated concurrently?

Solution:

1. Database design optimization: The existing database is designed reasonably and clearly expresses the tree structure relationship.

2. Recursive update strategy: Use stored procedures or custom functions to implement recursive updates. When num value of a leaf node (such as a county) changes, the function recursively updates num values ​​of all its ancestor nodes (district, city, province). This function should contain transaction control to ensure atomic operations.

Sample stored procedure (MySQL):

 DELIMITER //

CREATE PROCEDURE update_node_num(IN nodeId INT)
BEGIN
  DECLARE done INT DEFAULT FALSE;
  DECLARE parentId INT;
  DECLARE currentNum INT;
  DECLARE cursor_children CURSOR FOR SELECT id, parentId FROM your_table WHERE parentId = nodeId;
  DECLARE CONTINUE HANDLER FOR NOT FOUND SET done = TRUE;

  OPEN cursor_children;

  read_loop: LOOP
    FETCH cursor_children INTO nodeId, parentId;
    IF done THEN
      LEAVE read_loop;
    END IF;

    -- Recursively call itself CALL update_node_num(nodeId);

  END LOOP;
  CLOSE cursor_children;

  -- Update the num value of the current node (calculate the sum of the child nodes)
  SELECT SUM(num) INTO currentNum FROM your_table WHERE parentId = nodeId;
  UPDATE your_table SET num = currentNum WHERE id = nodeId;

END //

DELIMITER ;

3. Java code implementation:

Java code calls the above stored procedures and performs necessary exception handling and transaction management. For example, use Spring JDBC or MyBatis framework to simplify database operations.

4. Data consistency guarantee:

• Transaction management: All database update operations should be included in database transactions to ensure atomicity and consistency. If any update fails, the entire transaction rolls back.
• Optimistic lock: Before the update operation, num value of the node can be read and version checked during update to prevent concurrent update conflicts.
• Database lock: For high concurrency scenarios, you can consider using database row locks or table locks, but use them with caution to avoid performance problems caused by lock competition.

5. Batch update optimization: For batch updates, you can consider using batch update statements or asynchronous task queues to improve efficiency.

Summarize:

By combining optimized database design, recursive update stored procedures and transaction management in Java code, we can efficiently count the number of services of each node in the MySQL tree structure, and effectively ensure data consistency, and maintain data accuracy even in high concurrency environments. Choosing optimistic lock or pessimistic lock depends on the degree of concurrency and performance requirements of the specific application scenario. Asynchronous task queues can be used to handle batch updates that are not real-time required.

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