Decentralized copy control method for distributed database

Abstract

The invention belongs to the technical field of distributed databases, and particularly relates to a decentralized copy control method of a distributed database, which comprises the following steps: step 1, copy management: determining the number and storage positions of copies according to data volume and system load requirements, and reasonably distributing data copies to different nodes; 2, replica consistency: selecting a consistency model to balance system performance and data consistency requirements; 3, synchronizing the copies: determining a data synchronization strategy between the copies, and adjusting according to the real-time requirement of the system; 4, conflict detection and resolution: detecting data conflict by using a timestamp, a version number or a logic clock mechanism, and designing a conflict resolution strategy; and 5, replica recovery: monitoring the state of each node, quickly detecting replica faults, and recovering data from other replicas. The stability, reliability and performance of the distributed database system can be effectively improved, and the distributed database system adapts to constantly changing service requirements.

Description

Technical Field

[0001] The present invention belongs to the technical field of distributed databases, and in particular relates to a decentralized copy control method for a distributed database. Background Art

[0002] A distributed database is a database system that stores data on multiple computer nodes, which can be located in the same or different locations. Its main features include data distribution, data redundancy, node independence, and the choice of consistency model. Distributed database systems have advantages such as high availability, scalability, performance optimization, geographical distribution, and flexibility, and are suitable for scenarios such as big data processing, distributed applications, and geographically distributed systems. Key technologies include data sharding, replica management, consistency protocols, synchronization mechanisms, and load balancing, which help ensure data consistency, availability, and system performance.

[0003] When the existing decentralized replica control methods are applied to distributed databases, the data recovery speed of stage failures is slow, the load between nodes is uneven, and the database system is not convenient for horizontal and vertical expansion, resulting in poor data access efficiency and overall system performance. To this end, we propose a decentralized replica control method for distributed databases to solve the above problems. Summary of the invention

[0004] The purpose of the present invention is to provide a decentralized copy control method for a distributed database, which can effectively improve the stability, reliability and performance of the distributed database system and adapt to changing business needs.

[0005] The technical solution adopted by the present invention is as follows:

[0006] A decentralized replica control method for a distributed database, the control method comprising the following steps:

[0007] Step 1. Copy management: Determine the number and storage location of copies based on the data volume and system load requirements, and distribute data copies to different nodes in a reasonable manner;

[0008] Step 2. Replica consistency: Select a consistency model to balance system performance and data consistency requirements;

[0009] Step 3. Replica synchronization: Determine the data synchronization strategy between replicas and adjust it according to the real-time requirements of the system;

[0010] Step 4. Conflict detection and resolution: Use timestamps, version numbers, or logical clock mechanisms to detect data conflicts and design conflict resolution strategies;

[0011] Step 5. Replica recovery: monitor the status of each node, quickly detect replica failures, and recover data from other replicas;

[0012] Step 6. Replica load balancing: monitor the load of each node in real time, dynamically adjust the replica location according to the load, and migrate replicas to achieve load balancing;

[0013] Step 7. Security and permission control: Encrypt the copy data to achieve fine-grained permission control;

[0014] Step 8. Monitoring and log management: Real-time monitoring of the operating status of the database system, including performance indicators, error logs, and operation logs, and designing log recording and management mechanisms;

[0015] Step 9. Scalability and maintainability: Support horizontal and vertical expansion of the system to cope with changes in business needs and growth in data volume, and design a system architecture that is easy to maintain and upgrade.

[0016] In a preferred embodiment, the replica management also includes evaluating current and future data volumes, predicting growth trends, analyzing system load requirements, including read and write operation frequencies and performance requirements, sharding large data sets, independently storing and managing each shard, selecting a distribution strategy to balance the load and improve data access efficiency, copying the original data to selected replica nodes, verifying the copied data, allocating each data shard to different nodes, and configuring the network to optimize communication between replicas.

[0017] In a preferred solution, the replica consistency also includes evaluating the system's requirements for data consistency, including the trade-offs between consistency, availability, and partition tolerance, analyzing the types and frequencies of read and write operations in the system, evaluating the advantages and disadvantages of different consistency models, selecting a consistency model based on business needs, including strong consistency, eventual consistency, and read-write consistency, and selecting a distributed consistency protocol, including Paxos, Raft, two-phase commit, and three-phase commit.

[0018] In a preferred solution, the replica synchronization also includes evaluating the system's real-time requirements for data synchronization based on business needs, analyzing the frequency and scale of data changes in the system to determine a suitable synchronization strategy, selecting a synchronization strategy based on real-time requirements and system performance, the synchronization strategy including synchronous replication, asynchronous replication, and semi-synchronous replication, configuring specific parameters of the selected synchronization strategy, including synchronization interval, batch size, selecting data transmission protocols including TCP, UDP, and HTTP, designing data compression and encryption mechanisms, capturing data changes on the primary replica, including insert, update, and delete operations, packaging the changed data into transmission units, transmitting the data to the target replica node through the selected transmission protocol, unpacking the received data changes at the receiving end, applying the changed data to the target replica, using timestamps, version numbers, or logical clocks to detect conflicts in the data application process, resolving data conflicts according to predetermined strategies, monitoring the data transmission process in real time, quickly detecting transmission failures, and performing fault recovery, retransmitting data, or rolling back operations when transmission fails or data application fails.

[0019] In a preferred embodiment, the conflict detection and resolution also includes generating a timestamp for each data operation, recording the specific time when the operation occurs, maintaining a version number for each data object, incrementing the version number for each operation, using a logical clock or a vector clock to record the order and dependencies of the operations, capturing the changes and attaching the timestamp, version number or logical clock information when the data operation occurs, propagating the data changes and the accompanying conflict detection information to other replica nodes, comparing the timestamp, version number or logical clock of the new changes with the current data at the target replica node to detect whether there is a conflict, using a version vector to record the operation history of each replica on the data, resolving conflicts by comparing version vectors, designing a customized conflict resolution strategy based on specific business needs, and handling it according to the predetermined conflict resolution strategy when a conflict is detected.

[0020] In a preferred solution, the replica recovery also includes real-time monitoring of the status of each node through a heartbeat mechanism and health check, setting fault detection thresholds, including response timeout and error rate, quickly capturing abnormal signals, analyzing system logs and error logs, determining faulty nodes and fault types, isolating the faulty nodes from the system, automatically switching to backup nodes or replicas, recovering the data of the faulty nodes from other replicas, selecting a replica of the latest data based on the latest consistency information, synchronizing data from the recovery source node to the faulty node, synchronizing metadata and status information, and restarting the faulty node after data recovery is completed.

[0021] In a preferred solution, the replica load balancing also includes deploying real-time monitoring tools, including Prometheus and Grafana, monitoring the load indicators of the CPU, memory, network IO, and disk IO of each node, regularly collecting and recording the load data of each node to form a historical load data set, identifying nodes with excessively high or low loads by setting thresholds and anomaly detection algorithms, analyzing the collected load data, evaluating the load conditions of each node, identifying hot spots with excessively high loads, determining the demand for load balancing, dynamically adjusting strategies based on real-time load conditions, allocating new requests to nodes with lower loads based on the load balancing strategy, dynamically adjusting the resource allocation of each node, and performing replica migration when the load is unbalanced, migrating the load from hot spots to nodes with lower loads. After replica migration, re-evaluating the load conditions of each node to ensure that the migration achieves the goal of balanced load.

[0022] In a preferred embodiment, the security and permission control also includes selecting encryption algorithms, including AES and RSA, using a secure key management system to generate, store and manage encryption keys, encrypting data copies at static, using TLS / SSL protocol to encrypt data during network transmission, decrypting data using keys when reading data, and encrypting data before storing it when writing data, using role-based access control or attribute-based access control to define permissions for different user roles, and allocating corresponding access permissions based on user roles and business needs.

[0023] In a preferred embodiment, the monitoring and log management also includes monitoring the error log of the database system, recording and monitoring the user operation log, setting alarm rules, using SMS, email, and system notifications to promptly notify relevant personnel to handle abnormal situations, using a centralized log management system to collect and manage the logs of each node, and classifying and storing different types of logs.

[0024] In a preferred solution, the scalability and maintainability also include upgrading the hardware resources of existing nodes, including increasing CPU, memory, and disk capacity, optimizing the resource configuration of existing nodes, adopting a modular design to divide the system into independent modules, and adopting a microservice architecture to divide the system functions into multiple independent services.

[0025] The technical effects achieved by the present invention are:

[0026] By properly managing replicas and replica recovery mechanisms, the system can quickly recover data when a node fails, ensuring the continuous availability of the system. Selecting an appropriate consistency model can balance the requirements of data consistency and system performance in different business scenarios, ensuring data accuracy and reliability. Replica synchronization and load balancing mechanisms can optimize system performance and reduce uneven loads between nodes, thereby improving the overall system's response speed and processing capabilities.

[0027] Detecting and resolving data conflicts through mechanisms such as timestamps, version numbers, and logical clocks can reduce the impact of data conflicts and ensure data consistency and accuracy. It supports horizontal and vertical expansion of the system and can adapt to the growth of data volume and changes in business needs, making the system have good scalability. Through encryption and permission control mechanisms, it protects the confidentiality and integrity of data and prevents unauthorized access and data leakage.

[0028] Real-time monitoring of the operating status of the database system and effective log recording and management can promptly detect and handle abnormal situations in the system to ensure system stability. By evaluating the amount of data and system load requirements, the number of copies and storage locations are optimized to improve data access efficiency and the overall performance of the system. The use of modular design and microservice architecture makes the system easier to maintain and upgrade, improving the maintainability and flexibility of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a schematic diagram of a decentralized copy control method of a distributed database of the present invention. DETAILED DESCRIPTION

[0030] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0031] See also Figure 1 As shown, the present invention provides a decentralized copy control method for a distributed database, and the control method includes the following steps:

[0032] Step 1. Copy management: Determine the number and storage location of copies based on the data volume and system load requirements, and distribute data copies to different nodes in a reasonable manner;

[0033] Step 2. Replica consistency: Select a consistency model to balance system performance and data consistency requirements;

[0034] Step 3. Replica synchronization: Determine the data synchronization strategy between replicas and adjust it according to the real-time requirements of the system;

[0035] Step 4. Conflict detection and resolution: Use timestamps, version numbers, or logical clock mechanisms to detect data conflicts and design conflict resolution strategies;

[0036] Step 5. Replica recovery: monitor the status of each node, quickly detect replica failures, and recover data from other replicas;

[0037] Step 6. Replica load balancing: monitor the load of each node in real time, dynamically adjust the replica location according to the load, and migrate replicas to achieve load balancing;

[0038] Step 7. Security and permission control: Encrypt the copy data to achieve fine-grained permission control;

[0039] Step 8. Monitoring and log management: Real-time monitoring of the operating status of the database system, including performance indicators, error logs, and operation logs, and designing log recording and management mechanisms;

[0040] Step 9. Scalability and maintainability: Support horizontal and vertical expansion of the system to cope with changes in business needs and growth in data volume, and design a system architecture that is easy to maintain and upgrade.

[0041] Replica management also includes evaluating current and future data volumes, predicting growth trends, analyzing the system's load requirements, including read and write operation frequency and performance requirements, sharding large data sets, storing and managing each shard independently, selecting distribution strategies to balance the load and improve data access efficiency, copying the original data to the selected replica nodes, verifying the copied data, allocating each data shard to different nodes, and configuring the network to optimize communication between replicas.

[0042] Replica consistency also includes evaluating the system's needs for data consistency, including the trade-offs between consistency, availability, and partition tolerance, analyzing the types and frequency of read and write operations in the system, evaluating the advantages and disadvantages of different consistency models, and selecting a consistency model based on business needs. Consistency models include strong consistency, eventual consistency, and read-write consistency. Selecting a distributed consistency protocol includes Paxos, Raft, two-phase commit, and three-phase commit.

[0043] Replica synchronization also includes evaluating the system's real-time requirements for data synchronization based on business needs, analyzing the frequency and scale of data changes in the system to determine the appropriate synchronization strategy, selecting a synchronization strategy based on real-time requirements and system performance, and synchronization strategies include synchronous replication, asynchronous replication, and semi-synchronous replication. Configuring specific parameters of the selected synchronization strategy, including synchronization interval, batch size, selecting data transmission protocols, including TCP, UDP, and HTTP, designing data compression and encryption mechanisms, capturing data changes on the primary replica, including insert, update, and delete operations, packaging the changed data into transmission units, transmitting the data to the target replica node through the selected transmission protocol, unpacking the received data changes at the receiving end, applying the changed data to the target replica, using timestamps, version numbers, or logical clocks to detect conflicts in the data application process, resolving data conflicts according to predetermined strategies, monitoring the data transmission process in real time, quickly detecting transmission failures, and performing fault recovery, retransmitting data, or rolling back operations when transmission fails or data application fails.

[0044] Conflict detection and resolution also includes generating a timestamp for each data operation, recording the specific time when the operation occurred, maintaining a version number for each data object, incrementing the version number for each operation, using logical clocks or vector clocks to record the order and dependencies of operations, capturing changes and attaching timestamps, version numbers or logical clock information when data operations occur, propagating data changes and accompanying conflict detection information to other replica nodes, and comparing the timestamp, version number or logical clock of the new changes with the current data at the target replica node to detect whether there is a conflict. Version vectors are used to record the operation history of each replica on the data, and conflicts are resolved by comparing version vectors. Customized conflict resolution strategies are designed based on specific business needs, and when conflicts are detected, they are handled according to the predetermined conflict resolution strategy.

[0045] Replica recovery also includes real-time monitoring of the status of each node through heartbeat mechanisms and health checks, setting fault detection thresholds, including response timeouts and error rates, quickly capturing abnormal signals, analyzing system logs and error logs, determining faulty nodes and fault types, isolating faulty nodes from the system, automatically switching to backup nodes or replicas, recovering data on the faulty node from other replicas, selecting a replica of the latest data based on the latest consistency information, synchronizing data from the recovery source node to the faulty node, synchronizing metadata and status information, and restarting the faulty node after data recovery is complete.

[0046] Replica load balancing also includes deploying real-time monitoring tools, including Prometheus and Grafana, to monitor the CPU, memory, network IO, and disk IO load indicators of each node, regularly collect and record the load data of each node to form a historical load data set, and identify nodes with excessively high or low loads by setting thresholds and anomaly detection algorithms. The collected load data is analyzed to evaluate the load conditions of each node, identify hot spots with excessive loads, determine the demand for load balancing, and dynamically adjust strategies based on real-time load conditions. According to the load balancing strategy, new requests are allocated to nodes with lower loads, and resource allocation of each node is dynamically adjusted. When the load is unbalanced, replica migration is performed to migrate the load from hot spots to nodes with lower loads. After replica migration, the load conditions of each node are re-evaluated to ensure that the migration achieves the goal of balanced load.

[0047] Security and permission control also includes selecting encryption algorithms, including AES and RSA, using a secure key management system to generate, store and manage encryption keys, encrypting data copies at rest, using TLS / SSL protocols to encrypt data during network transmission, decrypting data using keys when reading data, encrypting data before storing it when writing data, using role-based access control or attribute-based access control, defining permissions for different user roles, and assigning corresponding access permissions based on user roles and business needs.

[0048] Monitoring and log management also includes monitoring the error logs of the database system, recording and monitoring user operation logs, setting alarm rules, using SMS, emails, and system notifications to promptly notify relevant personnel to handle abnormal situations, using a centralized log management system to collect and manage logs of each node, and classifying and storing different types of logs.

[0049] Scalability and maintainability also include upgrading the hardware resources of existing nodes, including increasing CPU, memory, and disk capacity, optimizing the resource configuration of existing nodes, adopting a modular design to divide the system into independent modules, and adopting a microservice architecture to divide system functions into multiple independent services.

[0050] In the present invention, by properly managing replicas and replica recovery mechanisms, the system can quickly recover data when a node fails, ensuring the continuous availability of the system. Selecting an appropriate consistency model can balance the requirements of data consistency and system performance in different business scenarios, ensuring data accuracy and reliability. The replica synchronization and load balancing mechanism can optimize system performance and reduce uneven loads between nodes, thereby improving the response speed and processing capacity of the overall system.

[0051] Detecting and resolving data conflicts through mechanisms such as timestamps, version numbers, and logical clocks can reduce the impact of data conflicts and ensure data consistency and accuracy. It supports horizontal and vertical expansion of the system and can adapt to the growth of data volume and changes in business needs, making the system have good scalability. Through encryption and permission control mechanisms, it protects the confidentiality and integrity of data and prevents unauthorized access and data leakage.

[0052] Real-time monitoring of the operating status of the database system and effective log recording and management can promptly detect and handle abnormal situations in the system to ensure system stability. By evaluating the amount of data and system load requirements, the number of copies and storage locations are optimized to improve data access efficiency and the overall performance of the system. The use of modular design and microservice architecture makes the system easier to maintain and upgrade, improving the maintainability and flexibility of the system.

[0053] The above is only a preferred embodiment of the present invention. It should be noted that, for those skilled in the art, several improvements and modifications can be made without departing from the principles of the present invention, and these improvements and modifications should also be considered as the protection scope of the present invention. The structures, devices and operating methods not specifically described and explained in the present invention shall be implemented according to the conventional means in the art unless otherwise specified and limited.

Claims

1. A decentralized replica control method for a distributed database, characterized in that: The control method comprises the following steps: Step 1. Copy management: Determine the number and storage location of copies based on the data volume and system load requirements, and distribute data copies to different nodes in a reasonable manner; Step 2. Replica consistency: Select a consistency model to balance system performance and data consistency requirements; Step 3. Replica synchronization: Determine the data synchronization strategy between replicas and adjust it according to the real-time requirements of the system; Step 4. Conflict detection and resolution: Use timestamps, version numbers, or logical clock mechanisms to detect data conflicts and design conflict resolution strategies; Step 5. Replica recovery: monitor the status of each node, quickly detect replica failures, and recover data from other replicas; Step 6. Replica load balancing: monitor the load of each node in real time, dynamically adjust the replica location according to the load, and migrate replicas to achieve load balancing; Step 7. Security and permission control: Encrypt the copy data to achieve fine-grained permission control; Step 8. Monitoring and log management: Real-time monitoring of the operating status of the database system, including performance indicators, error logs, and operation logs, and designing log recording and management mechanisms; Step 9. Scalability and maintainability: Support horizontal and vertical expansion of the system to cope with changes in business needs and growth in data volume, and design a system architecture that is easy to maintain and upgrade.

2. The decentralized copy control method of a distributed database according to claim 1, characterized in that: The replica management also includes evaluating current and future data volumes, predicting growth trends, analyzing system load requirements, including read and write operation frequencies and performance requirements, sharding large data sets, independently storing and managing each shard, selecting distribution strategies to balance loads and improve data access efficiency, copying original data to selected replica nodes, verifying the copied data, allocating each data shard to different nodes, and configuring the network to optimize communication between replicas.

3. The decentralized copy control method of a distributed database according to claim 1, characterized in that: The replica consistency also includes evaluating the system's requirements for data consistency, including the trade-offs between consistency, availability, and partition tolerance, analyzing the types and frequencies of read and write operations in the system, evaluating the pros and cons of different consistency models, and selecting a consistency model based on business needs. Consistency models include strong consistency, eventual consistency, and read-write consistency. Distributed consistency protocols are selected. Consistency protocols include Paxos, Raft, two-phase commit, and three-phase commit.

4. The decentralized copy control method of a distributed database according to claim 1, characterized in that: The replica synchronization also includes evaluating the system's real-time requirements for data synchronization based on business needs, analyzing the frequency and scale of data changes in the system to determine a suitable synchronization strategy, selecting a synchronization strategy based on real-time requirements and system performance, the synchronization strategy including synchronous replication, asynchronous replication, and semi-synchronous replication, configuring specific parameters of the selected synchronization strategy, including synchronization interval, batch size, selecting data transmission protocols including TCP, UDP, and HTTP, designing data compression and encryption mechanisms, capturing data changes on the primary replica, including insert, update, and delete operations, packaging the changed data into transmission units, transmitting the data to the target replica node via the selected transmission protocol, unpacking the received data changes at the receiving end, applying the changed data to the target replica, using timestamps, version numbers, or logical clocks to detect conflicts in the data application process, resolving data conflicts according to predetermined strategies, monitoring the data transmission process in real time, quickly detecting transmission failures, and performing fault recovery, retransmitting data, or rolling back operations when transmission fails or data application fails.

5. The decentralized copy control method of a distributed database according to claim 1, characterized in that: The conflict detection and resolution also includes generating a timestamp for each data operation, recording the specific time when the operation occurs, maintaining a version number for each data object, incrementing the version number for each operation, using a logical clock or a vector clock to record the order and dependencies of the operations, capturing the changes and attaching the timestamp, version number or logical clock information when the data operation occurs, propagating the data changes and the accompanying conflict detection information to other replica nodes, comparing the timestamp, version number or logical clock of the new changes and the current data at the target replica node to detect whether there is a conflict, using a version vector to record the operation history of each replica on the data, resolving conflicts by comparing version vectors, designing a customized conflict resolution strategy based on specific business needs, and handling the conflict according to the predetermined conflict resolution strategy when a conflict is detected.

6. The decentralized copy control method of a distributed database according to claim 1, characterized in that: The replica recovery also includes real-time monitoring of the status of each node through a heartbeat mechanism and health check, setting fault detection thresholds, including response timeout and error rate, quickly capturing abnormal signals, analyzing system logs and error logs, determining faulty nodes and fault types, isolating faulty nodes from the system, automatically switching to backup nodes or replicas, recovering data of the faulty node from other replicas, selecting a replica of the latest data based on the latest consistency information, synchronizing data from the recovery source node to the faulty node, synchronizing metadata and status information, and restarting the faulty node after data recovery is completed.

7. The decentralized copy control method of a distributed database according to claim 1, characterized in that: The replica load balancing also includes deploying real-time monitoring tools, including Prometheus and Grafana, monitoring the load indicators of the CPU, memory, network IO, and disk IO of each node, regularly collecting and recording the load data of each node to form a historical load data set, identifying nodes with excessively high or low loads by setting thresholds and anomaly detection algorithms, analyzing the collected load data, evaluating the load conditions of each node, identifying hot spots with excessively high loads, determining the demand for load balancing, dynamically adjusting strategies based on real-time load conditions, allocating new requests to nodes with lower loads based on the load balancing strategy, dynamically adjusting the resource allocation of each node, and performing replica migration when the load is unbalanced, migrating the load from hot spots to nodes with lower loads. After replica migration, re-evaluating the load conditions of each node to ensure that the migration achieves the goal of balanced load.

8. The decentralized copy control method of a distributed database according to claim 1, characterized in that: The security and permission control also includes selecting encryption algorithms, including AES and RSA, using a secure key management system to generate, store and manage encryption keys, encrypting data copies at rest, using TLS / SSL protocol to encrypt data during network transmission, decrypting data using keys when reading data, encrypting data before storing it when writing data, using role-based access control or attribute-based access control to define permissions for different user roles, and allocating corresponding access permissions based on user roles and business needs.

9. The decentralized copy control method of a distributed database according to claim 1, characterized in that: The monitoring and log management also includes monitoring the error log of the database system, recording and monitoring the user operation log, setting alarm rules, using SMS, email, and system notifications to promptly notify relevant personnel to handle abnormal situations, using a centralized log management system to collect and manage the logs of each node, and classifying and storing different types of logs.

10. The decentralized copy control method of a distributed database according to claim 1, characterized in that: The scalability and maintainability also include upgrading the hardware resources of existing nodes, including increasing CPU, memory, and disk capacity, optimizing the resource configuration of existing nodes, adopting a modular design to divide the system into independent modules, and adopting a microservice architecture to divide system functions into multiple independent services.

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