Data synchronization implementation method in cross-machine-room distributed scene

Abstract

The invention relates to the technical field of cross-machine-room data synchronization, and discloses a method for realizing data synchronization in a cross-machine-room distributed scene, which comprises the following steps of: S1, defining a synchronization target, and determining the type of data needing to be synchronized and the real-time requirement of synchronization; s2, message queues are established, and a message queue cluster is deployed in each machine room and used for temporarily storing data change messages to be synchronized; s3, data change capture: capturing a data change request sent to a local Redis cluster or a database by a source end application by utilizing a cache proxy service or database trigger technology; and S4, message publishing: converting the captured data change request into a message format, and publishing the data change request to a local message queue cluster. The invention provides a data synchronization implementation method in a cross-machine-room distributed scene, and solves the problems of high network delay, data inconsistency and low synchronization efficiency in cross-machine-room data synchronization.

Description

Technical Field

[0001] The present invention relates to the technical field of cross-data center data synchronization, and particularly to a method for realizing data synchronization in a cross-data center distributed scenario. Background Art

[0002] A data center is a standardized telecommunications professional-level computer room environment established by the telecommunications department using existing Internet communication lines and bandwidth resources. It mainly provides server hosting, leasing, and related value-added services for enterprises and governments. The data center is not only the center for data storage but also the center for data circulation. It is located in the place where data exchange is most concentrated in the Internet network and provides secure and reliable professional server hosting services. With the rapid development of the Internet, the amount of enterprise data has increased sharply, and distributed systems have become the mainstream architecture for processing large amounts of data. Cross-data center distributed systems improve the reliability and scalability of the system by dispersing data storage in data centers in different geographical locations;

[0003] However, there are some problems in cross-data center data synchronization:

[0004] 1. Data centers in different geographical locations are connected by dedicated lines, but long-distance transmission still causes relatively high network latency, affecting the real-time performance of data synchronization;

[0005] 2. During the cross-data center data synchronization process, due to network latency and synchronization failure reasons, data inconsistency is likely to occur, affecting the correctness of business logic;

[0006] 3. Traditional synchronization methods often adopt full or incremental synchronization. As the amount of data increases, the synchronization time and resource consumption increase significantly.

[0007] To solve the above problems, a method for realizing data synchronization in a cross-data center distributed scenario is proposed in this application. Summary of the Invention

[0008] Based on the technical problems existing in the background art, the present invention proposes a method for realizing data synchronization in a cross-data center distributed scenario.

[0009] A method for realizing data synchronization in a cross-data center distributed scenario proposed by the present invention includes the following steps:

[0010] S1. Define the synchronization target, and clarify the data types to be synchronized and the real-time requirements for synchronization;

[0011] S2. Establish a message queue, and deploy a message queue cluster in each data center for temporarily storing data change messages to be synchronized;

[0012] S3. Data change capture: Use cache proxy services or database trigger technologies to capture data change requests sent by source applications to the local Redis cluster or database.

[0013] S4. Message publishing: Convert the captured data change requests into message formats and publish them to the local message queue cluster.

[0014] S5. Message listening and parsing: The consistency service listens to the local message queue cluster, obtains messages and parses their contents to extract the data change information that needs to be synchronized.

[0015] S6. Data synchronization: Based on the message content, perform the same data change operations on the Redis clusters or databases of all remote data centers in the data center list.

[0016] S7. Synchronization result verification: Verify the synchronization results through a verification mechanism to ensure data consistency.

[0017] S8. Monitoring and optimization: Establish a monitoring system to monitor the data synchronization process in real time.

[0018] Preferably, S1 includes the following steps:

[0019] Step 1: Requirement analysis: Communicate with business requirement parties to clarify which data types need to be synchronized across data centers, such as database tables and Redis key-value pairs.

[0020] Step 2: Real-time evaluation: Evaluate the real-time requirements of synchronization according to the business scenario to determine whether second-level, minute-level or lower-frequency synchronization is required.

[0021] Step 3: Documentation: Organize key information such as synchronization targets, data types, and real-time requirements into documents for reference in subsequent steps.

[0022] Preferably, S2 includes the following steps:

[0023] Step 1: Cluster planning: Plan the scale and configuration of the message queue cluster according to the hardware resources, network conditions, and expected data volume of the data center.

[0024] Step 2: Environment deployment: Install and configure message queue software, such as Kafka and RabbitMQ, in the selected data center to ensure the stable operation of the cluster.

[0025] Step 3: Performance testing: Perform performance testing on the deployed message queue cluster to verify whether key indicators such as throughput and latency meet the synchronization requirements.

[0026] Preferably, S3 includes the following steps:

[0027] Step 1: Technology Selection. Based on the data type and the source - side application architecture, select an appropriate capture technology, such as the publish - subscribe mechanism of Redis, database triggers, or change data capture tools.

[0028] Step 2: Integrated Development. Integrate the capture technology into the source - side application to ensure that data change events can be accurately captured.

[0029] Step 3: Testing and Verification. Test the capture function in the development environment to ensure that all data change scenarios that need to be synchronized are covered.

[0030] Preferably, step S4 includes the following steps:

[0031] Step 1: Message Formatting. Convert the captured data change information into a format supported by the message queue, such as JSON or XML.

[0032] Step 2: Message Publishing. Write code to publish the formatted message to the local message queue cluster.

[0033] Step 3: Logging. Record the detailed information of message publishing, including message content and publishing time, for subsequent problem troubleshooting.

[0034] Preferably, step S5 includes the following steps:

[0035] Step 1: Listener Configuration. Configure a message listener in the consistency service, specifying the message queue cluster and topic to be listened to.

[0036] Step 2: Message Retrieval. The listener retrieves messages from the message queue and performs preliminary processing on them, such as deduplication and sorting.

[0037] Step 3: Content Parsing. Perform detailed parsing on the message content to extract the data change information that needs to be synchronized, such as the change records of database tables and the updated values of Redis keys.

[0038] Preferably, step S6 includes the following steps:

[0039] Step 1: Target Location. Based on the computer room list and synchronization policy, determine the target computer room and target storage system for data synchronization, such as a Redis cluster or a database.

[0040] Step 2: Data Modification. Write code to perform data modification operations on the target storage system, such as insert, update, or delete.

[0041] Step 3: Batch Processing and Asynchronous Execution. According to the data volume and real - time requirements, adopt batch processing to reduce the number of network transmissions, and use asynchronous execution to reduce the impact of the synchronization process on the business system.

[0042] Preferably, S7 includes the following steps:

[0043] Step 1: Checksum algorithm selection. Select a suitable checksum algorithm according to the data type, such as MD5, CRC.

[0044] Step 2: Checksum execution. Perform a checksum on the synchronized data to ensure consistency with the source data.

[0045] Step 3: Error handling. For cases where the checksum is inconsistent or the synchronization fails, record the error information and decide whether to resynchronize according to the retry policy.

[0046] Preferably, S8 includes the following steps:

[0047] Step 1: Monitoring system setup. Establish a data synchronization monitoring system, including data collection, storage, and display functions.

[0048] Step 2: Metric monitoring. Real-time monitor various metrics during the synchronization process, such as synchronization latency, success rate, error rate.

[0049] Step 3: Data analysis. Regularly analyze the monitoring data to identify potential problems and bottlenecks.

[0050] Step 4: Policy adjustment. Dynamically adjust the synchronization policy according to the analysis results, such as adjusting the batch size, optimizing the network configuration, to optimize the synchronization performance.

[0051] The above technical solutions of the present invention have the following beneficial technical effects:

[0052] 1. Reducing the impact of network latency: Through the buffering effect of the message queue, the impact of network latency in direct cross-data center synchronization is reduced.

[0053] 2. Ensuring data consistency: Through the checksum mechanism and the retry mechanism, the consistency and integrity during the data synchronization process are ensured.

[0054] 3. Improving synchronization efficiency: By adopting the batch processing and asynchronous execution strategies, the data synchronization efficiency is significantly improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is a flowchart of a method for implementing data synchronization in a cross-data center distributed scenario proposed by the present invention. DETAILED DESCRIPTION

[0056] To make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings. It should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present invention. In addition, in the following description, the descriptions of well-known structures and technologies are omitted to avoid unnecessarily confusing the concepts of the present invention.

[0057] As Figure 1 shown, a method for implementing data synchronization in a cross-data center distributed scenario proposed by the present invention includes the following steps:

[0058] S1. Define the synchronization target, and clarify the data types to be synchronized and the real-time requirements for synchronization;

[0059] S2. Establish a message queue, and deploy a message queue cluster in each data center for temporarily storing data change messages to be synchronized;

[0060] S3. Data change capture, using a cache proxy service or database trigger technology to capture data change requests sent by the source application to the local Redis cluster or database;

[0061] S4. Message publishing, converting the captured data change requests into a message format and publishing them to the local message queue cluster;

[0062] S5. Message listening and parsing, the consistency service listens to the local message queue cluster, obtains the messages and parses their contents, and extracts the data change information to be synchronized;

[0063] S6. Data synchronization, based on the message content, perform the same data change operations on the Redis clusters or databases of all remote data centers in the data center list;

[0064] S7. Synchronization result verification, verifying the synchronization result through a verification mechanism to ensure data consistency;

[0065] S8. Monitoring and optimization, establish a monitoring system to monitor the data synchronization process in real time.

[0066] In a specific embodiment, S1 includes the following steps:

[0067] Step 1: Requirement analysis, communicate with the business requirement party to clarify which data types need to be synchronized across data centers, such as database tables, Redis key-value pairs;

[0068] Step 2: Real-time evaluation, evaluate the real-time requirements for synchronization according to the business scenario, and determine whether second-level, minute-level or lower-frequency synchronization is required;

[0069] Step 3: Document recording, organize the key information of the synchronization target, data types, and real-time requirements into a document for reference in subsequent steps.

[0070] In a specific embodiment, S2 includes the following steps:

[0071] Step 1: Cluster planning. According to the hardware resources, network conditions, and expected data volume of the computer room, plan the scale and configuration of the message queue cluster;

[0072] Step 2: Environment deployment. Install and configure message queue software, such as Kafka and RabbitMQ, in the selected computer room to ensure the stable operation of the cluster;

[0073] Step 3: Performance testing. Perform performance testing on the deployed message queue cluster to verify whether key indicators such as throughput and latency meet the synchronization requirements.

[0074] In a specific embodiment, S3 includes the following steps:

[0075] Step 1: Technology selection. According to the data type and source-side application architecture, select appropriate capture technologies, such as the publish and subscribe mechanisms of Redis, database triggers, or change data capture tools;

[0076] Step 2: Integrated development. Integrate the capture technology into the source-side application to ensure that data change events can be accurately captured;

[0077] Step 3: Test verification. Test the capture function in the development environment to ensure that all data change scenarios that need to be synchronized can be covered.

[0078] In a specific embodiment, S4 includes the following steps:

[0079] Step 1: Message formatting. Convert the captured data change information into a format supported by the message queue, such as JSON or XML;

[0080] Step 2: Message publishing. Write code to publish the formatted message to the local message queue cluster;

[0081] Step 3: Log recording. Record the detailed information of message publishing, including message content and publishing time, for subsequent problem troubleshooting.

[0082] In a specific embodiment, S5 includes the following steps:

[0083] Step 1: Listener configuration. Configure message listeners in the consistency service, specifying the message queue cluster and topic to be listened to;

[0084] Step 2: Message acquisition. The listener obtains messages from the message queue and performs preliminary processing on them, such as deduplication and sorting;

[0085] Step 3: Content Parsing. Parse the message content in detail to extract the data change information that needs to be synchronized, such as the change records of the database table and the updated values of the Redis keys.

[0086] In a specific embodiment, S6 includes the following steps:

[0087] Step 1: Target Location. Determine the target computer room and target storage system for data synchronization according to the computer room list and synchronization policy, such as the Redis cluster and the database.

[0088] Step 2: Data Change. Write code to perform data change operations on the target storage system, such as insert, update, and delete.

[0089] Step 3: Batch Processing and Asynchronous Execution. According to the data volume size and real-time requirements, adopt batch processing to reduce the number of network transmissions, and use asynchronous execution to reduce the impact of the synchronization process on the business system.

[0090] It should be noted that during the data synchronization process, batch processing is adopted to improve the synchronization efficiency. For batch processing, a sliding window algorithm is used to dynamically adjust the batch size to adapt to network conditions and data volume changes. The core of the sliding window algorithm lies in dynamically adjusting the window size according to the current network latency and data processing speed to balance the synchronization latency and throughput.

[0091] The sliding window size adjustment formula can be expressed as:

[0092]

[0093] Where WindowSize new and WindowSize old represent the new and old window sizes respectively; α is the adjustment factor used to control the adjustment speed; AvgLatency target is the target average latency; AvgLatency current is the current average latency.

[0094] In a specific embodiment, S7 includes the following steps:

[0095] Step 1: Checksum Algorithm Selection. Select a suitable checksum algorithm according to the data type, such as MD5 and CRC.

[0096] Step 2: Checksum Execution. Check the synchronized data to ensure that it is consistent with the source data.

[0097] Step 3: Error Handling. For cases where the checksum is inconsistent or the synchronization fails, record the error information and decide whether to resynchronize according to the retry policy.

[0098] In a specific embodiment, S8 includes the following steps:

[0099] Step 1: Build a monitoring system, establish a data synchronization monitoring system, including data collection, storage, and display functions;

[0100] Step 2: Monitor metrics, monitor various metrics during the synchronization process in real time, such as synchronization latency, success rate, and error rate;

[0101] Step 3: Analyze data, regularly analyze the monitored data to identify potential problems and bottlenecks;

[0102] Step 4: Adjust strategies, dynamically adjust the synchronization strategy according to the analysis results, such as adjusting the batch size and optimizing the network configuration, to optimize the synchronization performance.

[0103] The principle of the data synchronization implementation method of this application: First, by defining the synchronization target, it is clear which data types need to be synchronized across data centers and the real-time requirements of the synchronization, providing a clear direction for the subsequent steps. Then, a message queue is established as a temporary storage and transmission medium for data changes. A message queue cluster is deployed in each data center, effectively alleviating the impact of network latency on the synchronization process and improving the scalability and fault tolerance of the system. At the data source, a data change capture mechanism, such as a cache proxy service or database trigger technology, is used to capture in real time the data change requests sent by the source application to the local Redis cluster or database. These change requests are then converted into message format and published to the local message queue cluster, realizing the asynchronous processing of data changes and reducing the performance impact on the source application. Next, the consistency service, as the core of the synchronization process, is responsible for listening to the messages in the local message queue cluster, obtaining and parsing the message content, and extracting the data change information that needs to be synchronized. This step ensures the integrity and accuracy of the data during transmission. Based on the parsed message content, the data synchronization process is then carried out, and corresponding data change operations are performed on the Redis clusters or databases of all remote data centers in the data center list. During this process, batch processing and asynchronous execution strategies can be adopted to optimize the synchronization efficiency, reduce the number of network transmissions and synchronization time. To ensure data consistency, through a verification mechanism, the synchronized data is verified to ensure it is exactly the same as the source data. For cases where the synchronization fails, error information is recorded and a retry mechanism is triggered until the data is successfully synchronized. Finally, the monitoring and optimization mechanism provides continuous guarantee for the entire synchronization process. By establishing a monitoring system, various metrics during the data synchronization process, such as synchronization latency, success rate, and error rate, are monitored in real time, and the synchronization strategy is dynamically adjusted according to the monitoring results to optimize the synchronization performance. This step not only ensures the stability and reliability of the synchronization process but also provides data support for future optimization and expansion.

[0104] It should be understood that the above specific embodiments of the present invention are only for illustrative explanation or interpretation of the principles of the present invention, and do not constitute a limitation on the present invention. Therefore, any modifications, equivalent replacements, improvements, etc. made without departing from the spirit and scope of the present invention shall be included within the protection scope of the present invention. In addition, the appended claims of the present invention are intended to cover all variations and modifications that fall within the scope and boundaries of the appended claims, or equivalent forms of such scope and boundaries.

Claims

1. A method for implementing data synchronization in a cross-computer-room distributed scenario, characterized in that It includes the following steps: S1. Define the synchronization target, clarify the data types to be synchronized and the real-time requirements for synchronization; S2. Establish a message queue, deploy a message queue cluster in each computer room to temporarily store data change messages to be synchronized; S3. Data change capture, use cache proxy services or database trigger technologies to capture data change requests sent by source applications to the local Redis cluster or database; S4. Message publishing, convert the captured data change requests into message formats and publish them to the local message queue cluster; S5. Message listening and parsing, the consistency service listens to the local message queue cluster, obtains messages and parses their contents, and extracts the data change information to be synchronized; S6. Data synchronization, based on the message content, perform the same data change operations on the Redis clusters or databases of all remote computer rooms in the computer room list; S7. Synchronization result verification, verify the synchronization result through a verification mechanism to ensure data consistency; S8. Monitoring and optimization, establish a monitoring system to monitor the data synchronization process in real time.

2. The data synchronization implementation method in a cross-computer room distributed scenario according to claim 1, wherein, In S1, it includes the following steps: Step 1: Requirement analysis, communicate with business requirement parties to clarify which data types need to be synchronized across computer rooms, such as database tables, Redis key-value pairs; Step 2: Real-time evaluation, evaluate the real-time requirements for synchronization according to the business scenario, and determine whether second-level, minute-level or lower-frequency synchronization is required; Step 3: Documentation, organize key information such as synchronization targets, data types, and real-time requirements into documents for reference in subsequent steps.

3. The data synchronization implementation method in a cross-computer room distributed scenario according to claim 2, characterized in that, In S2, it includes the following steps: Step 1: Cluster planning, plan the scale and configuration of the message queue cluster according to the hardware resources, network conditions and expected data volume of the computer room; Step 2: Environment deployment, install and configure message queue software such as Kafka and RabbitMQ in the selected computer room to ensure the stable operation of the cluster; Step 3: Performance testing, perform performance testing on the deployed message queue cluster to verify whether key indicators such as throughput and latency meet synchronization requirements.

4. A method for implementing data synchronization in a cross-computer room distributed scenario according to claim 3, characterized in that, In S3, it includes the following steps: Step 1: Technology selection, select appropriate capture technologies according to data types and source application architectures, such as Redis publish and subscribe mechanisms, database triggers or change data capture tools; Step 2: Integrated development, integrate the capture technology into the source application to ensure that data change events can be accurately captured; Step 3: Test verification, test the capture function in the development environment to ensure that all data change scenarios to be synchronized can be covered.

5. A method for implementing data synchronization in a cross-computer room distributed scenario according to claim 4, characterized in that, In S4, it includes the following steps: Step 1: Message formatting, convert the captured data change information into formats supported by the message queue, such as JSON and XML; Step 2: Message publishing, write code to publish the formatted messages to the local message queue cluster; Step 3: Log recording, record the detailed information of message publishing, including message content and publishing time, for subsequent problem troubleshooting.

6. A method for implementing data synchronization in a cross-computer room distributed scenario according to claim 5, characterized in that, In S5, it includes the following steps: Step 1: Listener configuration, configure a message listener in the consistency service, specify the message queue cluster and topic to be listened to; Step 2: Message Acquisition. The listener retrieves messages from the message queue and performs preliminary processing on them, such as deduplication and sorting. Step 3: Content Parsing. The message content is parsed in detail to extract the data change information that needs to be synchronized, such as the change records of database tables and the updated values of Redis keys.

7. A method for implementing data synchronization in a cross-computer room distributed scenario according to claim 6, characterized in that, S6 includes the following steps: Step 1: Target Location. Based on the computer room list and synchronization policy, determine the target computer room and target storage system for data synchronization, such as Redis clusters, databases. Step 2: Data Change. Write code to perform data change operations on the target storage system, such as insert, update, delete. Step 3: Batch Processing and Asynchronous Execution. According to the data volume and real-time requirements, use batch processing to reduce the number of network transmissions, and use asynchronous execution to reduce the impact of the synchronization process on the business system.

8. A method for implementing data synchronization in a cross-computer room distributed scenario according to claim 7, characterized in that, S7 includes the following steps: Step 1: Verification Algorithm Selection. Select a suitable verification algorithm according to the data type, such as MD5, CRC. Step 2: Verification Execution. Verify the synchronized data to ensure it is consistent with the source data. Step 3: Error Handling. For cases where the verification is inconsistent or the synchronization fails, record the error information and decide whether to resynchronize according to the retry policy.

9. A method for implementing data synchronization in a cross-computer room distributed scenario according to claim 8, characterized in that, S8 includes the following steps: Step 1: Monitoring System Setup. Establish a data synchronization monitoring system, including data collection, storage, and display functions. Step 2: Metric Monitoring. Real-time monitor various metrics during the synchronization process, such as synchronization latency, success rate, error rate. Step 3: Data Analysis. Regularly analyze the monitoring data to identify potential problems and bottlenecks. Step 4: Policy Adjustment. Dynamically adjust the synchronization policy according to the analysis results, such as adjusting the batch size, optimizing the network configuration, to optimize the synchronization performance.

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