Database compatibility testing method and system

Abstract

The invention discloses a database compatibility test method and system, which can structurally store data from different database systems, test scenes and compatibility indexes by defining a multi-dimensional array, cover key indexes such as data integrity, consistency, transaction processing, query performance and the like, and ensure that the test is comprehensive and systematic. A cluster containing a plurality of database instances is constructed, and the same test data set is configured, so that the consistency of a test environment is ensured, the accuracy and comparability of a test result are improved, a preset compatibility test suite is automatically executed, the test efficiency is greatly improved, manual intervention and errors are reduced, multi-dimensional array data are deeply analyzed, compatibility differences can be accurately identified, and the test efficiency is improved. According to the scheme, the performance bottleneck is positioned, potential problems are identified, the problems are found and solved in the development, upgrading or integration stage in time, risks are avoided, in addition, the scheme supports on-demand extension of array dimensions, test suites and data sets can be customized and adjusted, more requirements can be flexibly met, and high adaptability is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of database compatibility, and particularly to a method and system for database compatibility testing. Background Art

[0002] With the rapid development of information technology, database systems play a crucial role in enterprise applications, cloud computing, big data analysis and other fields. Due to differences in design concepts, architectures, functional characteristics, etc. among different database systems, they may exhibit different compatibility and performance in actual applications. To ensure that database systems can meet the requirements of specific application scenarios, it is particularly important to conduct database compatibility testing.

[0003] Traditional database compatibility testing methods often rely on manually writing and executing test cases. This method is not only time-consuming and laborious, but also difficult to comprehensively cover all possible test scenarios and compatibility metrics. In addition, with the continuous update and upgrade of database systems, traditional testing methods often struggle to adapt to rapidly changing requirements. Summary of the Invention

[0004] In view of this, the present invention proposes a method and system for database compatibility testing, which can effectively solve the defects of the prior art that it is difficult to comprehensively cover all possible test scenarios and compatibility metrics and difficult to adapt to rapidly changing requirements.

[0005] The technical solution of the present invention is implemented as follows:

[0006] A method for database compatibility testing, comprising:

[0007] Defining a multi-dimensional array for storing compatibility data of different database systems under different test conditions, where each dimension of the multi-dimensional array represents a different database system, test scenario, and compatibility metric;

[0008] Constructing a cluster, the cluster containing multiple database instances, each instance representing a different database system, and each instance being configured with the same test data set;

[0009] Executing a preset compatibility test suite on each database instance in the cluster, the test suite including compatibility metrics, and the compatibility metrics including data integrity, data consistency, transaction processing, and query performance;

[0010] During the execution of the test, recording the compatibility performance of each database instance in each test scenario and filling this data into the corresponding position in the multi-dimensional array;

[0011] Analyze the data in the multidimensional array to identify compatibility differences, performance bottlenecks, and potential compatibility issues between different database systems.

[0012] As a further optional solution of the database compatibility testing method, define a multidimensional array, specifically including:

[0013] Define the structure of the multidimensional array based on the database system, test scenario, and compatibility metrics to obtain a three-dimensional array;

[0014] Initialize each element of the three-dimensional array;

[0015] Define a calculation formula for each compatibility metric, and the calculation formula is used to calculate the corresponding compatibility metric based on the assumed database performance parameters and test scenarios;

[0016] According to the above calculation formula, traverse each element of the three-dimensional array, and calculate the corresponding compatibility metric values according to the differences in the database system and test scenarios;

[0017] Fill the calculated compatibility metric values into the three-dimensional array.

[0018] As a further optional solution of the database compatibility testing method, construct a cluster, the cluster contains multiple database instances, each instance represents a different database system, and each instance is configured with the same test data set, specifically including:

[0019] Determine the number and type of database instances included in the cluster;

[0020] According to the business requirements, design a test data set containing various data types;

[0021] Create a table structure corresponding to the test data set in each database instance;

[0022] Use the database SQL script to import the test data into the tables of each database instance.

[0023] As a further optional solution of the database compatibility testing method, execute a preset compatibility test suite on each database instance in the cluster, and the test suite includes compatibility metrics, and the compatibility metrics include data integrity, data consistency, transaction processing, and query performance, specifically including:

[0024] Obtain the test data in the tables of each database instance;

[0025] Use an iterator to traverse each database instance;

[0026] For each database instance, traverse all test scenarios in the test suite;

[0027] Execute the test data corresponding to the current test scenario on the current database instance;

[0028] Extract compatibility metrics based on the execution results of the test data, where the compatibility metrics include data integrity, data consistency, transaction processing, and query performance.

[0029] As a further optional solution of the database compatibility testing method, during the execution of the test, record the compatibility performance of each database instance under each test scenario, and fill these data into the corresponding positions in the multi-dimensional array, specifically including:

[0030] Determine the current database instance and test scenario;

[0031] Execute the test and obtain the metric values;

[0032] Check the structure of the metric values and fill them into the corresponding positions in the multi-dimensional array according to the structure of the metric values.

[0033] As a further optional solution of the database compatibility testing method, analyze the data in the multi-dimensional array to identify compatibility differences, performance bottlenecks, and potential compatibility issues between different database systems, specifically including:

[0034] Traverse the multi-dimensional array and calculate the compatibility difference index for all database systems for each test scenario;

[0035] Calculate the performance bottleneck index for each metric of each database system;

[0036] Calculate the potential compatibility issue index for each test scenario and metric of each database system;

[0037] Based on the compatibility difference index, performance bottleneck index, and potential compatibility issue index, identify compatibility differences, performance bottlenecks, and potential compatibility issues between different database systems.

[0038] As a further optional solution of the database compatibility testing method, the method further includes:

[0039] Generate a detailed database compatibility test report based on the identification results of compatibility differences, performance bottlenecks, and potential compatibility issues between different database systems. The report includes the compatibility scores, main compatibility issues, and improvement suggestions for each database system.

[0040] A database compatibility testing system, comprising:

[0041] A data storage module, used to define a multi-dimensional array, where each dimension of the multi-dimensional array is respectively used to store data of different database systems, test scenarios, and compatibility metrics;

[0042] A database cluster module, used to construct a cluster, the cluster contains multiple database instances, each instance represents a different database system, and each instance is configured with the same test data set;

[0043] A test execution module, used to execute a preset compatibility test suite on each database instance in the database cluster, the test suite contains multiple test scenarios, and each test scenario is designed with compatibility metrics for evaluating data integrity, data consistency, transaction processing, and query performance;

[0044] A data recording module, used to record the compatibility performance of each database instance in each test scenario during the test execution, and fill the performance data into the corresponding positions in the multi-dimensional array;

[0045] A data analysis module, used to analyze the data in the multi-dimensional array to identify compatibility differences, performance bottlenecks, and potential compatibility issues between different database systems.

[0046] A computing device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where when the processor executes the computer program, it implements the steps of any one of the above database compatibility test methods.

[0047] A computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of any one of the above database compatibility test methods.

[0048] The beneficial effects of the present invention are as follows: By defining a multi-dimensional array to structurally store compatibility data, it is possible to systematically organize and record data from different database systems, test scenarios, and compatibility metrics, ensuring the comprehensiveness and systematicness of the tests. Covering multiple key compatibility metrics such as data integrity, data consistency, transaction processing, and query performance, the test results are more comprehensive and have high reference value. Constructing a cluster containing multiple database instances and configuring the same test data set ensures the consistency of the test environment, thereby improving the accuracy and comparability of the test results. Automatically executing the preset compatibility test suite significantly improves the test efficiency and reduces the possibility of manual intervention and errors. Conducting in-depth analysis of the data in the multi-dimensional array can accurately identify the compatibility differences between different database systems. By analyzing the performance bottlenecks, performance problems existing in the database system can be located, potential compatibility problems can be identified, which helps to discover and solve problems in a timely manner during the development, upgrade, or integration phase of the database system, avoiding potential risks and costs. Support for expanding the dimensions of the multi-dimensional array according to actual needs to adapt to the requirements of more database systems, test scenarios, or compatibility metrics. The test suite and test data set can be customized and adjusted according to actual needs, making the test plan more flexible and highly adaptable. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0050] Figure 1 It is a flowchart of a database compatibility test method of the present invention;

[0051] Figure 2 It is a schematic diagram of the composition of a database compatibility test system of the present invention;

[0052] Figure 3 It is a schematic diagram of the composition of a computing device of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0053] The following will clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0054] Reference Figures 1 to 3, a database compatibility testing method, comprising:

[0055] Define a multi-dimensional array for storing compatibility data of different database systems under different test conditions, each dimension of the multi-dimensional array representing a different database system, test scenario, and compatibility metric respectively;

[0056] Construct a cluster, the cluster containing multiple database instances, each instance representing a different database system, and each instance being configured with the same test data set;

[0057] Execute a preset compatibility test suite on each database instance in the cluster, the test suite including compatibility metrics, the compatibility metrics including data integrity, data consistency, transaction processing, and query performance;

[0058] During the test execution, record the compatibility performance of each database instance under each test scenario, and fill this data into the corresponding position in the multi-dimensional array;

[0059] Analyze the data in the multi-dimensional array to identify compatibility differences, performance bottlenecks, and potential compatibility issues between different database systems.

[0060] In this embodiment, by defining a multi-dimensional array to structurally store compatibility data, it is possible to systematically organize and record data from different database systems, test scenarios, and compatibility metrics, ensuring the comprehensiveness and systematicness of the test, covering multiple key compatibility metrics such as data integrity, data consistency, transaction processing, and query performance, making the test results more comprehensive and having a high reference value. Constructing a cluster containing multiple database instances and configuring the same test data set ensures the consistency of the test environment, thereby improving the accuracy and comparability of the test results. Automatically executing a preset compatibility test suite significantly improves the test efficiency, reduces the possibility of manual intervention and errors. Analyzing the data in the multi-dimensional array can accurately identify compatibility differences between different database systems. By analyzing performance bottlenecks, performance problems existing in the database system can be located, and potential compatibility issues can be identified, which helps to discover and solve problems in a timely manner during the development, upgrade, or integration phase of the database system, avoiding potential risks and costs. Support expanding the dimensions of the multi-dimensional array according to actual needs to adapt to the requirements of more database systems, test scenarios, or compatibility metrics. The test suite and test data set can be customized and adjusted according to actual needs, making the test plan more flexible and having a high adaptability.

[0061] Preferably, the defining a multi-dimensional array specifically includes:

[0062] Define the structure of a multi-dimensional array based on the database system, test scenario, and compatibility metrics to obtain a three-dimensional array;

[0063] Initialize each element of the three-dimensional array;

[0064] Define a calculation formula for each compatibility metric, where the calculation formula is used to calculate the corresponding compatibility metric based on the assumed database performance parameters and test scenario;

[0065] According to the above calculation formula, traverse each element of the three-dimensional array, and calculate the corresponding compatibility metric value according to the differences in the database system and test scenario;

[0066] Fill the calculated compatibility metric values into the three-dimensional array.

[0067] In this embodiment, by defining the structure of the three-dimensional array based on the database system, test scenario, and compatibility metrics, the systematic and structured storage of test data is realized. This storage method not only facilitates the organization and management of data, but also helps subsequent data analysis and mining; initializing each element of the three-dimensional array ensures that the empty state of the array before filling data is consistent, avoiding calculation errors or data inconsistency problems caused by uninitialized elements; traversing each element of the three-dimensional array according to the calculation formula and calculating the corresponding compatibility metric value according to the differences in the database system and test scenario, this automated calculation method significantly improves the test efficiency and reduces the time and cost of manual calculation; filling the calculated compatibility metric values into the three-dimensional array ensures the integrity and consistency of the test data. This filling method not only facilitates subsequent data analysis and comparison, but also helps to discover compatibility differences and potential problems between different database systems; through the unified calculation formula and data filling method, this technical solution helps to promote the standardization and regularization process of database compatibility testing, which not only improves the comparability of test results, but also helps to promote the interoperability and compatibility between different database systems.

[0068] It should be noted that a calculation formula is defined for each compatibility metric, and the calculation formula includes:

[0069] Success Rate (SR):

[0070] SR = BaseSR + (DBModifier + SceneModifier) × RandomFactor;

[0071] Among them, BaseSR is the basic success rate (e.g., 0.95), DBModifier and SceneModifier are the correction factors for the database system and the test scenario respectively, and RandomFactor is a random number between -0.01 and 0.01, which is used to simulate the randomness in the experiment;

[0072] Average Response Time (ART):

[0073] ART = BaseART + (DBComplexity + SceneLoad) × ScaleFactor;

[0074] Among them, BaseART is the basic average response time (e.g., 100 milliseconds), DBComplexity and SceneLoad are the coefficients of the database complexity and the test scenario load respectively, and ScaleFactor is a scaling factor used to adjust the size of the response time;

[0075] Error Rate (ER):

[0076] ER = BaseER + (DBErrorPropensity + SceneStress) × ErrorFactor;

[0077] Among them, BaseER is the basic error rate (e.g., 0.01), DBErrorPropensity and SceneStress are the error propensity of the database system and the stress coefficient of the test scenario respectively, and ErrorFactor is a factor used to adjust the error rate.

[0078] Preferably, constructing a cluster, the cluster includes multiple database instances, each instance represents a different database system, and each instance is configured with the same test data set, specifically including:

[0079] Determine the number and type of database instances included in the cluster;

[0080] According to the business requirements, design a test data set including various data types;

[0081] Create a table structure corresponding to the test data set in each database instance;

[0082] Use the database SQL script to import the test data into the tables of each database instance.

[0083] In this embodiment, by using the same test data set in each database instance, the consistency of test conditions can be ensured, which facilitates the comparison of the performance and characteristics of different database systems when processing the same data. The centralized management of the test data set simplifies the test process and reduces the time and cost of preparing data separately for each instance; it allows different types of database instances, such as relational databases and non-relational databases, to be included in the cluster, thus providing the ability to comprehensively evaluate multiple database systems; by comparing the performance of different database instances on the same test data set, performance bottlenecks and potential optimization points can be identified, which helps database administrators and developers to carry out targeted performance tuning work; using database SQL scripts to import test data ensures the accuracy and consistency of the data, reducing the risk of errors caused by manual operations. In addition, the scripted data import process also facilitates data backup and recovery, enhancing the reliability of the data.

[0084] Preferably, a preset compatibility test suite is executed for each database instance in the cluster. The test suite includes compatibility metrics, and the compatibility metrics include data integrity, data consistency, transaction processing, and query performance. Specifically, it includes:

[0085] Obtain the test data in the tables of each database instance;

[0086] Use an iterator to traverse each database instance;

[0087] For each database instance, traverse all test scenarios in the test suite;

[0088] Execute the test data corresponding to the current test scenario on the current database instance;

[0089] Extract the compatibility metrics according to the execution results of the test data. The compatibility metrics include data integrity, data consistency, transaction processing, and query performance.

[0090] In this embodiment, through tests covering multiple aspects such as data integrity, data consistency, transaction processing, and query performance, a comprehensive database compatibility assessment can be provided, which helps identify potential differences and limitations of different database instances when processing various tasks; using an iterator to traverse each database instance and automatically execute all test scenarios in the test suite significantly improves the test efficiency. Automated testing reduces the need for manual intervention, reduces the risk of human errors, and makes the test process more repeatable and reliable; according to the execution results of the test data, compatibility metrics can be accurately extracted, which helps quantify the performance of the database instance, making it easier to identify performance bottlenecks and potential problems; the modular design of the test suite makes it relatively simple to add new test scenarios or metrics. As database technology and business requirements evolve, the test suite can be easily updated to adapt to new compatibility requirements; by comparing the performance of different database instances under the same test scenario, performance differences can be identified, which helps database administrators and developers carry out targeted optimization work to improve the overall performance of the database system.

[0091] It should be noted that the data integrity test specifically includes:

[0092] Insertion test: Insert test data into each database instance and record the results of the insertion operation to verify whether the inserted data is stored in the database completely and without defects;

[0093] Update test: Perform update operations on some data in the database and record the update results to verify whether the updated data is consistent with the expectations;

[0094] Deletion test: Delete some data from the database and record the results of the deletion operation to verify whether the deletion operation is successful and does not affect other data.

[0095] The data consistency test specifically includes:

[0096] Concurrent operation test: Simultaneously execute concurrent read and write operations on the same data set on multiple clients to verify whether the database instance can correctly handle concurrent operations and maintain data consistency;

[0097] Transaction test: Execute transaction operations containing multiple steps, such as transfer operations, to verify the atomicity, consistency, isolation, and durability of the transaction.

[0098] The transaction processing test specifically includes:

[0099] Transaction rollback test: Trigger an error during the execution of a transaction and verify whether the transaction can be correctly rolled back to the state before the transaction started;

[0100] Transaction Commit Test: Execute successful transaction operations and verify whether the transaction can be correctly committed and the data is persistently stored in the database.

[0101] Query Performance Test, specifically including:

[0102] Benchmark Test: Use standard benchmark tools (such as TPC-C, TPC-H) to perform performance tests on the database instance, record and compare metrics such as query response time and throughput of different database instances;

[0103] Complex Query Test: Execute complex queries that include multiple table joins, subqueries, and aggregate functions to verify the performance and accuracy of the database instance in handling complex queries.

[0104] Preferably, during the execution of the test, record the compatibility performance of each database instance in each test scenario and fill these data into the corresponding positions in the multi-dimensional array, specifically including:

[0105] Determine the current database instance and test scenario;

[0106] Execute the test and obtain the metric values;

[0107] Check the structure of the metric values and fill them into the corresponding positions in the multi-dimensional array according to the structure of the metric values.

[0108] In this embodiment, by determining the current database instance and test scenario, the compatibility performance of each instance in a specific scenario can be accurately traced and recorded, which ensures the accuracy and traceability of the data; after the test is executed, the metric values will be automatically obtained and filled into the corresponding positions in the multi-dimensional array according to the structure of the metric values. This automated data filling process reduces manual intervention, improves the efficiency and accuracy of data processing; using a multi-dimensional array to store test data makes the data more structured, easier to manage and analyze. This storage method not only facilitates the rapid retrieval and comparison of data, but also helps to discover compatibility differences and potential problems between different database instances; the process of automatically executing the test and recording data significantly improves the test efficiency, reduces the test cycle and cost. At the same time, through accurate data recording and filling, the accuracy and reliability of the test results are ensured.

[0109] It should be noted that the current database instance and test scenario are determined. Assume that the i-th database instance and the j-th test scenario are being processed currently. The test cases corresponding to the j-th test scenario are executed on the i-th database instance, and the metric values returned after the execution of the test cases are collected. These metric values may be a single numerical value, or a list or array containing multiple specific metrics. According to the design of the test cases, it is determined whether the returned metric values contain multiple specific metrics (i.e., whether the third dimension k exists). If the metric value is a single numerical value, directly fill this value into the multi-dimensional array. If the metric value is a list or array containing multiple specific metrics, traverse this list or array and fill the value of each specific metric into the multi-dimensional array.

[0110] Preferably, analyzing the data in the multi-dimensional array to identify compatibility differences, performance bottlenecks, and potential compatibility issues between different database systems specifically includes:

[0111] Traverse the multi-dimensional array and calculate the compatibility difference index of all database systems for each test scenario;

[0112] Calculate the performance bottleneck index for each metric of each database system;

[0113] Calculate the potential compatibility issue index for each test scenario and metric of each database system;

[0114] Based on the compatibility difference index, performance bottleneck index, and potential compatibility issue index, identify the compatibility differences, performance bottlenecks, and potential compatibility issues between different database systems.

[0115] In this embodiment, by traversing the multi-dimensional array and calculating the compatibility difference index of all database systems for each test scenario, the compatibility differences between different database systems can be accurately identified, which helps database administrators and developers understand the compatibility degree between different systems; calculating the performance bottleneck index for each metric of each database system can accurately locate the performance bottlenecks of each system on different metrics, which helps optimize the database configuration, improve system performance, and ensure that the database can meet business requirements; by calculating the potential compatibility issue index for each test scenario and metric of each database system, potential compatibility issues can be warned in advance, which helps avoid compatibility issues in actual applications and reduce maintenance costs; it provides comprehensive data analysis support, including the calculation of compatibility difference index, performance bottleneck index, and potential compatibility issue index. These indexes not only help identify problems but also provide strong data support for database optimization and decision-making.

[0116] It should be noted that the compatibility difference index is used to measure the compatibility differences between different database systems under the same test scenario. The calculation formula is: where V(i,j) represents the metric value of the i-th database system under the j-th test scenario, represents the average value of the metric values of all database systems under the j-th test scenario;

[0117] The performance bottleneck index is used to identify the performance bottlenecks in each database system. The calculation formula is: PBI(i,k) = (Max(V(i,:,k)) - V(i,worst_j,k)) / (Max(V(i,:,k)) - Min(V(i,:,k))), where V(i,:,k) represents the array of the k-th metric values of the i-th database system under all test scenarios, V(i,worst_j,k) represents the k-th metric value of the i-th database system under the worst_j-th test scenario (i.e., the scenario with the worst performance of this metric), and Max and Min respectively represent taking the maximum value and the minimum value;

[0118] The potential compatibility issue index is used to identify potential compatibility issues, that is, those metric values that deviate significantly from the expected or industry standards. The calculation formula is: PCII(i,j,k) = |V(i,j,k) - E(k)| / σ(k), where V(i,j,k) represents the value of the k-th metric of the i-th database system under the j-th test scenario, E(k) represents the industry standard value or expected value of the k-th metric, and σ(k) represents the standard deviation of the k-th metric across all database systems and test scenarios.

[0119] Preferably, the method further includes:

[0120] Generating a detailed database compatibility test report based on the identification results of the compatibility differences, performance bottlenecks, and potential compatibility issues between different database systems. The report includes the compatibility scores, main compatibility issues, and improvement suggestions for each database system.

[0121] In this embodiment, the report contains the compatibility scores of each database system, which provides an intuitive comparison standard for users and helps to quickly understand the compatibility levels between different database systems; by listing the main compatibility issues faced by each database system, the report helps users accurately locate the problems, so that targeted measures can be taken to solve them; the improvement suggestions in the report are obtained based on in-depth analysis of the test data, aiming to help users optimize the database configuration, improve system performance, and reduce potential compatibility issues.

[0122] A database compatibility test system includes:

[0123] A data storage module, used to define a multi-dimensional array, where each dimension of the multi-dimensional array is respectively used to store data of different database systems, test scenarios, and compatibility metrics;

[0124] A database cluster module, used to construct a cluster, the cluster contains multiple database instances, each instance represents a different database system, and each instance is configured with the same test data set;

[0125] A test execution module, used to execute a preset compatibility test suite on each database instance in the database cluster, the test suite contains multiple test scenarios, and each test scenario is designed with compatibility metrics for evaluating data integrity, data consistency, transaction processing, and query performance;

[0126] A data recording module, used to record the compatibility performance of each database instance in each test scenario during the test execution, and fill these performance data into the corresponding positions in the multi-dimensional array;

[0127] A data analysis module, used to analyze the data in the multi-dimensional array to identify compatibility differences, performance bottlenecks, and potential compatibility problems between different database systems.

[0128] A computing device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the steps of any one of the above database compatibility test methods are implemented.

[0129] A computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of any one of the above database compatibility test methods are implemented.

[0130] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A database compatibility testing method, characterized in that: include: Define a multidimensional array for storing compatibility data of different database systems under different test conditions, where each dimension of the multidimensional array represents a different database system, test scenario, and compatibility index; Building a cluster, wherein the cluster includes multiple database instances, each instance represents a different database system, and each instance is configured with the same test data set; Executing a preset compatibility test suite on each database instance in the cluster, the test suite including compatibility indicators including data integrity, data consistency, transaction processing and query performance; During the test execution, the compatibility performance of each database instance in each test scenario is recorded, and these data are filled into the corresponding position in the multidimensional array; The data in the multidimensional array is analyzed to identify compatibility differences, performance bottlenecks, and potential compatibility issues between different database systems.

2. A database compatibility testing method according to claim 1, characterized in that: The definition of a multidimensional array specifically includes: Define the structure of the multidimensional array according to the database system, test scenario and compatibility index to obtain a three-dimensional array; Initialize each element of the three-dimensional array; A calculation formula is defined for each compatibility indicator, where the calculation formula is used to calculate the corresponding compatibility indicator based on the assumed database performance parameters and test scenarios; According to the above calculation formula, each element of the three-dimensional array is traversed, and the corresponding compatibility index value is calculated according to the different database systems and test scenarios; Fill the calculated compatibility index values ​​into a three-dimensional array.

3. A database compatibility testing method according to claim 2, characterized in that: The construction of a cluster includes multiple database instances, each instance represents a different database system, and each instance is configured with the same test data set, specifically including: Determine the number and type of database instances included in the cluster; Design test data sets containing various data types according to business needs; Create a table structure corresponding to the test dataset in each database instance; Using the database SQL scripts, import the test data into the tables of each database instance.

4. A database compatibility testing method according to claim 3, characterized in that: The pre-set compatibility test suite is executed on each database instance in the cluster, and the test suite includes compatibility indicators, and the compatibility indicators include data integrity, data consistency, transaction processing and query performance, specifically including: Get the test data in the table of each database instance; Use an iterator to iterate over each database instance; For each database instance, iterate over all test scenarios in the test suite; Execute the test data corresponding to the current test scenario on the current database instance; Compatibility indicators are extracted based on the execution results of the test data, and the compatibility indicators include data integrity, data consistency, transaction processing, and query performance.

5. A database compatibility testing method according to claim 4, characterized in that: During the test execution, the compatibility performance of each database instance in each test scenario is recorded, and these data are filled into the corresponding position in the multidimensional array, specifically including: Determine the current database instance and test scenario; Execute the test and obtain the indicator value; Check the structure of the indicator value and fill it into the corresponding position in the multidimensional array according to the structure of the indicator value.

6. A database compatibility testing method according to claim 5, characterized in that: The analyzing of the data in the multidimensional array to identify compatibility differences, performance bottlenecks and potential compatibility issues between different database systems specifically includes: Traverse the multidimensional array and calculate the compatibility difference index of all database systems for each test scenario; Calculate the performance bottleneck index for each indicator of each database system; Calculate the potential compatibility issue index for each test scenario and indicator of each database system; Based on the compatibility difference index, performance bottleneck index and potential compatibility problem index, the compatibility differences, performance bottlenecks and potential compatibility problems between different database systems can be identified.

7. A database compatibility testing method according to claim 6, characterized in that: The method further comprises: Based on the identification results of compatibility differences, performance bottlenecks, and potential compatibility issues between different database systems, a detailed database compatibility test report is generated, which includes the compatibility score of each database system, major compatibility issues, and improvement suggestions.

8. A database compatibility testing system, characterized in that: include: A data storage module, used to define a multidimensional array, each dimension of the multidimensional array is used to store data of different database systems, test scenarios and compatibility indicators; A database cluster module, used to construct a cluster, wherein the cluster includes multiple database instances, each instance represents a different database system, and each instance is configured with the same test data set; A test execution module, configured to execute a preset compatibility test suite on each database instance in the database cluster, wherein the test suite includes a plurality of test scenarios, each of which is designed with compatibility indicators for evaluating data integrity, data consistency, transaction processing, and query performance; A data recording module, used to record the compatibility performance of each database instance in each test scenario during the test execution process, and fill these performance data into the corresponding position in the multidimensional array; The data analysis module is used to analyze the data in the multidimensional array to identify compatibility differences, performance bottlenecks and potential compatibility issues between different database systems.

9. A computing device, characterized in that The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the database compatibility testing method described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by the processor, the steps of the database compatibility testing method described in any one of claims 1 to 7 are implemented.

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