A method and device for optimizing docking parameters, an electronic device, and a storage medium

Abstract

The application discloses a method and device for optimizing docking parameters, electronic equipment and a storage medium. The method comprises the following steps: obtaining an initial database performance index of a target database; combining all parameter values of each docking parameter item of all target auxiliary systems into multiple groups of parameter combinations; obtaining a database performance index corresponding to each parameter combination; subtracting the initial database performance index of the target database from each database performance index respectively to obtain a database change value corresponding to each parameter combination; screening a parameter combination corresponding to a database change value with a number of occurrences meeting a preset requirement as an effective parameter combination; calculating a significance score corresponding to each effective parameter combination; screening multiple high-quality effective parameter combinations to form a high-quality combination set based on the significance score corresponding to each effective parameter combination; calculating an importance value of each selected effective parameter combination in the high-quality combination set; and adjusting each docking parameter item according to an effective parameter combination with the largest importance value.

Description

Technical Field

[0001] This application relates to the field of system parameter adjustment technology, and in particular to a method and apparatus for optimizing docking parameters, electronic equipment, and storage medium. Background Technology

[0002] Databases are the core infrastructure of information systems, providing strong technical support for digital transformation. To enable databases to provide better services, they are typically integrated with multiple auxiliary systems such as monitoring software and SQL analysis software.

[0003] Since the specific settings of the connection parameters between each auxiliary system and the database will affect the performance of the database and the auxiliary systems, after connecting each auxiliary system to the database using the initial connection parameters, the staff will continuously adjust the connection parameters and observe the performance changes of the database and each auxiliary system in order to make the overall performance of the database and each auxiliary system reach the expected level as much as possible.

[0004] However, the existing methods are not only inefficient, but also, because they are adjusted based on experience and changes in system performance, the overall performance of the database and various auxiliary systems is difficult to achieve as expected. Summary of the Invention

[0005] In view of the shortcomings of the prior art, this application provides a method, apparatus, electronic device, and storage medium for optimizing docking parameters, so as to solve the problem that the prior art cannot efficiently adjust the overall performance of the system to the expected level.

[0006] To achieve the above objectives, this application provides the following technical solution:

[0007] The first aspect of this application provides a method for optimizing docking parameters, including:

[0008] Obtain the initial database performance metrics of the target database;

[0009] All parameter values ​​of each docking parameter item of all target auxiliary systems docked with the target database are combined to obtain multiple sets of parameter combinations; wherein, each set of parameter combinations includes one parameter value of each docking parameter item;

[0010] Obtain the database performance metrics corresponding to each of the parameter combinations; wherein, the database performance metrics corresponding to each parameter combination are the performance metrics of the target database when each of the parameter items is adjusted to the parameter combination;

[0011] Subtract the initial database performance index of the target database from the database performance index corresponding to each of the parameter combinations to obtain the database change value corresponding to each parameter combination.

[0012] The parameter combinations corresponding to the database change values ​​whose occurrence frequency meets the preset requirements are selected as valid parameter combinations.

[0013] Calculate the significance score corresponding to each of the effective parameter combinations; wherein, the significance score corresponding to each effective parameter combination is obtained by summing the database change value corresponding to the effective parameter combination and the system change value corresponding to the effective parameter combination;

[0014] Based on the significance scores corresponding to each effective parameter combination, multiple high-quality effective parameter combinations are selected to form a high-quality combination set.

[0015] Calculate the importance value of each effective parameter combination in the set of high-quality combinations;

[0016] The effective parameter combination with the highest importance value is determined as the optimal parameter combination, and each docking parameter item is adjusted according to the optimal parameter combination.

[0017] Optionally, in the above-described method for optimizing docking parameters, the parameter combinations corresponding to the database change values ​​whose frequency of occurrence meets preset requirements are considered as effective parameter combinations, including:

[0018] Count the number of times each database change value appears in the database change values ​​corresponding to each of the parameter combinations;

[0019] A histogram of the change values ​​in the database is plotted with the change values ​​on the x-axis and the number of occurrences of the change values ​​on the y-axis.

[0020] According to the set minimum sliding window and sliding step size, multiple data windows are taken in the plotted histogram of transformation values; wherein, the sliding step size is smaller than the minimum sliding window.

[0021] Target data windows are selected from each of the data windows; wherein the ratio of the sum of the occurrences of each database change value in the target data window to the sum of the occurrences of all database change values ​​is greater than a preset percentage.

[0022] Each parameter combination corresponding to the database change value in each target data window is formed into a combination set, and the intersection of each combination set is obtained to obtain multiple effective parameter combination sets; wherein, the parameter combination in the effective parameter combination set is the effective parameter combination.

[0023] Optionally, in the above-described method for optimizing docking parameters, the step of selecting multiple high-quality effective parameter combinations to form a high-quality combination set based on the significance scores corresponding to each effective parameter combination includes:

[0024] The high-quality combination set is formed by selecting the effective parameter combinations whose significance scores rank first among the preset percentages from each set of effective parameter combinations.

[0025] Optionally, in the above-described method for optimizing docking parameters, calculating the importance value of each effective parameter combination in the set of high-quality combinations includes:

[0026] The total number of categories of each key dimension parameter combination and the total number of categories of secondary dimension parameter combinations in the high-quality combination set are counted under all dimension division methods.

[0027] Count the number of occurrences of each category of key dimension parameter combination and each category of secondary dimension parameter combination;

[0028] For each effective parameter combination, the difference between the percentage of database change values ​​for the key dimension parameter combination category and the percentage of database change values ​​for the secondary dimension parameter combination category under each dimension partitioning method is calculated to obtain the importance value of the effective parameter combination in the set of high-quality combinations under each dimension partitioning method. Specifically, the percentage of database change values ​​for the key dimension parameter combination category is equal to the sum of the ratios of the corresponding database change values ​​of each effective parameter combination containing the key dimension parameter combination category to the frequency of occurrence of the key dimension parameter combination category, multiplied by the ratio of the frequency of occurrence of the key dimension parameter combination category to the total number of categories. Similarly, the percentage of database change values ​​for the secondary dimension parameter combination category is equal to the sum of the ratios of the corresponding database change values ​​of each effective parameter combination containing the secondary dimension parameter combination category to the frequency of occurrence of the secondary dimension parameter combination category, multiplied by the ratio of the frequency of occurrence of the secondary dimension parameter combination category to the total number of categories.

[0029] Optionally, in the above-described method for optimizing docking parameters, after determining the effective parameter combination with the highest importance value as the optimal parameter combination, the method further includes:

[0030] Based on the dimension division method corresponding to the maximum value among the importance values ​​of the optimal parameter combination, the key parameter items and non-key parameter items among the docking parameter items are determined.

[0031] A second aspect of this application provides a device for optimizing docking parameters, comprising:

[0032] The first acquisition unit is used to acquire the initial database performance metrics of the target database;

[0033] The combination unit is used to combine all parameter values ​​of each docking parameter item of all target auxiliary systems docked with the target database to obtain multiple sets of parameter combinations; wherein, each set of parameter combinations includes one parameter value of each docking parameter item;

[0034] The second acquisition unit is used to acquire the database performance indicators corresponding to each of the parameter combinations; wherein, the database performance indicators corresponding to the parameter combinations are the performance indicators of the target database when each of the parameter items is adjusted to the parameter combinations;

[0035] The change value calculation unit is used to subtract the initial database performance index of the target database from the database performance index corresponding to each of the parameter combinations to obtain the database change value corresponding to each of the parameter combinations.

[0036] The first filtering unit is used to filter the parameter combinations corresponding to the database change values ​​whose occurrence frequency meets the preset requirements, as valid parameter combinations.

[0037] The scoring unit is used to calculate the significance score corresponding to each of the effective parameter combinations; wherein, the significance score corresponding to each effective parameter combination is obtained by summing the database change value corresponding to the effective parameter combination and the system change value corresponding to the effective parameter combination;

[0038] The second screening unit is used to select multiple high-quality effective parameter combinations to form a high-quality combination set based on the significance scores corresponding to each effective parameter combination.

[0039] An importance calculation unit is used to calculate the importance value of each effective parameter combination in the set of high-quality combinations.

[0040] The parameter adjustment unit is used to determine the effective parameter combination with the highest importance value as the optimal parameter combination, and adjust each of the docking parameter items according to the optimal parameter combination.

[0041] Optionally, in the above-mentioned docking parameter optimization device, the first screening unit includes:

[0042] The first statistical unit is used to count the number of times each database change value appears in the database change values ​​corresponding to each of the parameter combinations;

[0043] The plotting unit is used to plot a histogram of the transformation values ​​with the changed values ​​in the database on the horizontal axis and the number of occurrences of the changed values ​​in the database on the vertical axis.

[0044] A window value-taking unit is used to take multiple data windows in the plotted transform value histogram according to a set minimum sliding window and sliding step size; wherein the sliding step size is smaller than the minimum sliding window.

[0045] A window filtering unit is used to filter out a target data window from each of the data windows; wherein the ratio of the sum of the occurrence times of each database change value in the target data window to the sum of the occurrence times of all database change values ​​is greater than a preset quantity percentage.

[0046] The set filtering unit is used to form a set of parameters corresponding to each database change value in each target data window, and to find the intersection of each set of parameters to obtain multiple sets of effective parameter combinations; wherein, the parameter combination in the set of effective parameter combinations is the effective parameter combination.

[0047] Optionally, in the above-mentioned docking parameter optimization device, the second screening unit includes:

[0048] The second screening subunit selects the effective parameter combinations that rank first in the preset percentage based on their significance scores from each set of effective parameter combinations to form the high-quality combination set.

[0049] Optionally, in the above-mentioned docking parameter optimization device, the importance calculation unit includes:

[0050] The second statistical unit is used to count the total number of categories of each key dimension parameter combination and the total number of categories of secondary dimension parameter combination in the high-quality combination set under all dimension division methods.

[0051] The third statistical unit is used to count the number of occurrences of each category of key dimension parameter combination and each category of secondary dimension parameter combination.

[0052] The importance value calculation unit is used to calculate, for each effective parameter combination, the difference between the proportion of database change values ​​of the key dimension parameter combination category and the proportion of database change values ​​of the secondary dimension parameter combination category in each of the dimensional division methods, to obtain the importance value of the effective parameter combination in the set of high-quality combinations under each of the dimensional division methods; wherein, the proportion of database change values ​​of the key dimension parameter combination category is equal to the sum of the ratios of the corresponding database change values ​​of each effective parameter combination containing the key dimension parameter combination category to the occurrence frequency of the key dimension parameter combination category, multiplied by the ratio of the occurrence frequency of the key dimension parameter combination category to the total number of categories; the proportion of database change values ​​of the secondary dimension parameter combination category is equal to the sum of the ratios of the corresponding database change values ​​of each effective parameter combination containing the secondary dimension parameter combination category to the occurrence frequency of the secondary dimension parameter combination category, multiplied by the ratio of the occurrence frequency of the secondary dimension parameter combination category to the total number of categories.

[0053] Optionally, the above-mentioned docking parameter optimization device further includes:

[0054] The dimension determination unit is used to determine the key parameter items and non-key parameter items in each of the docking parameter items according to the dimension division method corresponding to the maximum value among the importance values ​​of each of the optimal parameter combinations.

[0055] A third aspect of this application provides an electronic device, comprising:

[0056] Memory and processor;

[0057] The memory is used to store programs;

[0058] The processor is used to execute the program, which, when executed, is specifically used to implement the docking parameter tuning method as described in any of the above.

[0059] A fourth aspect of this application provides a computer storage medium for storing a computer program, which, when executed, implements the docking parameter tuning method as described in any of the preceding claims.

[0060] This application provides a method for optimizing docking parameters, obtaining the initial database performance metrics of the target database. Then, all parameter values ​​of each docking parameter item of all target auxiliary systems docking with the target database are combined to obtain multiple parameter combinations. Each parameter combination includes one parameter value for each docking parameter item. Next, the database performance metrics corresponding to each parameter combination are obtained. These metrics represent the performance metrics of the target database when each parameter item is adjusted to a parameter combination. Then, the initial database performance metrics of the target database are subtracted from the database performance metrics corresponding to each parameter combination to obtain the database change values ​​corresponding to each parameter combination. This allows for the selection of parameter combinations with database change values ​​that meet preset frequency requirements as valid parameter combinations, thereby filtering out invalid parameter combinations, effectively narrowing the analysis scope, and reducing the amount of data for subsequent analysis. Next, the significance score corresponding to each valid parameter combination is calculated. This score is obtained by summing the database change values ​​and system change values ​​corresponding to the valid parameter combinations. Finally, based on the significance scores of each valid parameter combination, multiple high-quality valid parameter combinations are selected to form a high-quality combination set. Next, the importance value of each effective parameter combination in the set of high-quality combinations is calculated. Finally, the effective parameter combination with the highest importance value is determined as the optimal parameter combination, and each docking parameter item is adjusted according to the optimal parameter combination. This automatically traverses all cases, filters out the parameter combination that makes the overall performance of the database and various auxiliary systems approach or reach the optimal level, and adjusts accordingly to quickly bring the overall performance of the database and various auxiliary systems to the expected level. Attached Figure Description

[0061] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0062] Figure 1 A flowchart illustrating a method for optimizing docking parameters provided in an embodiment of this application;

[0063] Figure 2 A flowchart illustrating a method for filtering effective parameter combinations provided in this application embodiment;

[0064] Figure 3 A flowchart illustrating a method for calculating importance values ​​provided in an embodiment of this application;

[0065] Figure 4A schematic diagram of the architecture of a docking parameter optimization device provided in an embodiment of this application;

[0066] Figure 5 This is a schematic diagram of the architecture of an electronic device provided in an embodiment of this application. Detailed Implementation

[0067] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0068] In this application, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0069] This application provides a method for optimizing docking parameters, such as... Figure 1 As shown, it includes the following steps:

[0070] S101. Obtain the initial database performance metrics of the target database.

[0071] The target database refers to the specified database whose docking parameters need to be adjusted. The initial database performance metrics of the target database usually refer to the performance metrics of the target database after docking with the auxiliary system, but before the docking parameters are adjusted.

[0072] Optionally, database performance metrics can be a specific performance metric of the database, the sum of multiple performance metrics, the weighted sum of multiple performance metrics, or the weighted average of multiple performance metrics, etc.

[0073] In order to obtain the changes in database performance after adjusting the parameters, it is necessary to first obtain the initial database performance indicators of the target database.

[0074] S102. Combine all parameter values ​​of each docking parameter item of all target auxiliary systems in the target database to obtain multiple sets of parameter combinations.

[0075] The target auxiliary system refers to the auxiliary system that requires parameter adjustments. The interface parameters refer to the parameters that connect the auxiliary system to the database; these parameters affect the performance of both the database and the auxiliary system.

[0076] It should be noted that a target auxiliary system may have one or more docking parameter items, and there may be multiple target auxiliary systems, thus requiring adjustment of multiple docking parameter items. Each docking parameter item typically has a corresponding value range, meaning there are multiple selectable parameter values. Therefore, selecting different values ​​for different docking parameter items results in a corresponding parameter selection method. To consider all parameter selection methods, this embodiment combines all parameter values ​​for all docking parameter items to obtain multiple parameter combinations. Thus, each parameter combination includes one parameter value from each docking parameter item.

[0077] S103. Obtain the database performance metrics corresponding to each parameter combination.

[0078] Among them, the database performance index corresponding to the parameter combination is the performance index of the target database when the individual parameter items are adjusted to the parameter combination.

[0079] It should be noted that, due to the relatively large number of docking parameters and their values, the number of parameter combinations obtained is usually quite large. Therefore, in order to reduce the number of parameter combinations for subsequent precise analysis and improve processing efficiency, in this embodiment, parameter combinations that are clearly unlikely to achieve optimal overall performance will be prioritized and eliminated. Therefore, it is necessary to first determine the database performance indicators corresponding to each parameter combination so that preliminary screening can be performed based on these indicators.

[0080] S104. Subtract the initial database performance index of the target database from the database performance index corresponding to each parameter combination to obtain the database change value corresponding to each parameter combination.

[0081] S105. Filter the parameter combinations corresponding to database change values ​​that meet the preset requirements in terms of frequency of occurrence, and use them as valid parameter combinations.

[0082] It should be noted that different parameter combinations may result in the same database change value, meaning a single database change value may appear multiple times. Since parameter combinations with frequently occurring database change values ​​typically bring the overall performance of the database and auxiliary system closer to the optimal overall performance, the optimal parameter combinations are among these. Conversely, parameter combinations that produce database changes with only one or a few parameter combinations are considered extreme. These combinations usually result in large database changes but low auxiliary system performance, or small database changes but high auxiliary system performance, leading to relatively low overall performance. Therefore, in this embodiment, parameter combinations corresponding to database change values ​​that meet a preset frequency requirement are selected as valid parameter combinations, while relatively extreme parameter combinations are discarded.

[0083] Optionally, in another embodiment of this application, one specific implementation of step S105 is as follows: Figure 2 As shown, it includes the following steps:

[0084] S201. Count the number of times each database change value appears in the database change values ​​corresponding to each parameter combination.

[0085] S202. Plot a histogram of the changes in the database, with the changes in the database values ​​on the x-axis and the number of times the changes in the database values ​​occur on the y-axis.

[0086] S203. According to the set minimum sliding window and sliding step size, take multiple data windows in the plot of the histogram of the transformed values.

[0087] The sliding step size is smaller than the minimum sliding window size. Typically, the sliding step size is much smaller than the minimum sliding window size to facilitate subsequent identification of which database changes are key to selecting a data window as the target data window. These database changes are the frequently occurring database changes we are looking for.

[0088] Therefore, specifically, the size of each data window is the same as the minimum sliding window, meaning that each data window contains the same number of database change values. Furthermore, because the sliding step size is smaller than the minimum sliding window, adjacent data windows will contain some of the same database change values.

[0089] S204. Select the target data window from the various data windows.

[0090] Specifically, the ratio of the sum of the occurrences of each database change value in the target data window to the sum of the occurrences of all database change values ​​is greater than a preset percentage. In other words, the target data window is a data window that contains database change values ​​that occur frequently. Therefore, it is filtered to facilitate the subsequent selection of the database change values ​​that occur most frequently in this data window.

[0091] S205. Combine the parameters corresponding to the database changes in each target data window into a set, and find the intersection of the sets to obtain multiple sets of valid parameter combinations.

[0092] Among them, the parameter combinations in the set of effective parameter combinations are the effective parameter combinations.

[0093] Specifically, since a database change value corresponds to multiple parameter combinations, for each database change value in each target data window, all the corresponding parameters are combined to form a combination set, thus obtaining the combination set corresponding to each database change value, and then the intersection of these sets is calculated.

[0094] It should be noted that the target data window is the data window with the highest total frequency of occurrences of various database change values. Since there is some overlap in the data of various target data windows, if a database change value appears in various combination sets, it means that the database change value makes the data window in which it appears meet the filtering conditions of the target data window. Therefore, its frequency of occurrence is relatively high, and this is exactly the database change value to be filtered. Thus, the parameter combinations in the combination set corresponding to this database change value are the effective parameter combinations to be found, and the corresponding combination set is the effective parameter combination set.

[0095] S106. Calculate the significance score corresponding to each effective parameter combination.

[0096] The significance score corresponding to the effective parameter combination is obtained by summing the database change value and the system change value corresponding to the effective parameter combination. Optionally, considering that different auxiliary systems have different levels of importance, when calculating the significance score, the database change value corresponding to the effective parameter combination can be added to the product of the system change value corresponding to the effective parameter combination and the weight coefficient of the corresponding target auxiliary system, and the sum can be used as the significance score.

[0097] Optionally, since an effective parameter combination can affect multiple target auxiliary systems, the significance score can be the sum of the database change value plus the product of the change value of each target auxiliary system and its corresponding weight coefficient, or it can be the significance score of an effective parameter combination for each target auxiliary system, i.e., an effective parameter combination corresponds to multiple significance scores.

[0098] Among them, the system change value corresponding to the effective parameter combination refers to the change value of the auxiliary system's performance before and after adjusting the docking parameter items to the effective parameter combination. Therefore, its specific calculation process is the same as the calculation process of the database change value.

[0099] Since the preceding considerations only addressed the impact on the database, after effectively reducing the number of parameter combinations, it is necessary to further consider the impact on both the database and the auxiliary system. The optimal parameter combination must significantly affect the performance of both the database and the auxiliary system. Therefore, in this embodiment, by summing the data change values ​​reflecting the impact on database performance and the system change values ​​reflecting the impact on the auxiliary system performance—that is, by equally dividing the impact based on significance—the magnitude of the influence of each effective parameter combination is determined. This identifies the effective parameters that significantly affect both the database and the auxiliary system, further reducing the number of parameter combinations that need to be processed subsequently.

[0100] S107. Based on the significance scores corresponding to each effective parameter combination, select multiple high-quality effective parameter combinations to form a high-quality combination set.

[0101] Specifically, the results can be sorted from highest to lowest according to the significance score, and the top N combinations of valid adoptions corresponding to the significance scores can be selected to form a high-quality combination set.

[0102] Optionally, in step 105, the following steps are adopted: Figure 2 When the implementation shown is implemented, that is, when the set of effective parameter combinations is determined, a specific implementation of step S107 in this application embodiment includes:

[0103] Each effective parameter combination set is selected from the various effective parameter combination sets, and the effective parameter combinations that rank first in the preset percentage based on their significance scores are selected to form a high-quality combination set.

[0104] S108. Calculate the importance value of each effective parameter combination in the high-quality combination set.

[0105] Among them, the higher the degree of an effective parameter combination is compared with other effective parameter combinations, the more optimal the parameter combination is. Therefore, in this embodiment, the importance value of each effective parameter combination in the set of high-quality combinations is calculated, and then the selection is performed based on the importance value.

[0106] Optionally, in another embodiment of this application, one specific implementation of step S108 is as follows: Figure 3 As shown, it includes the following steps:

[0107] S301. Calculate the total number of categories of each key dimension parameter combination and the total number of categories of each secondary dimension parameter combination in the high-quality combination set under all dimension division methods.

[0108] It should be noted that since different docking parameters have varying degrees of impact on overall performance, prioritizing the adjustment of more critical docking parameters can lead to faster performance improvements. Furthermore, when adjusting parameters, resource consumption or other limitations may sometimes necessitate adjusting only the more critical docking parameters to achieve the expected overall performance. Because critical parameters are of high importance, and the values ​​of various parameter combinations differ across parameters, they also influence the calculation of the importance of each parameter combination. Therefore, it is necessary to identify the most critical parameters.

[0109] In this embodiment, the docking parameters are divided into key parameter items and secondary parameter items. Since it is unknown which parameter item is the key parameter item, it is also unknown how to divide the parameter items into dimensions. Therefore, in this embodiment, all dimension division methods are considered first. Then, the total number of categories for each key dimension parameter combination and the total number of categories for each secondary dimension parameter combination, as well as the frequency of occurrence of each category, are counted under each dimension division method. Specifically, under a given dimension division method, the way each parameter value of each key parameter item is taken constitutes a key dimension parameter combination category, and the way each parameter value of each secondary parameter item is taken constitutes a secondary dimension parameter combination category. For example, there are a total of 5 docking parameters, and the set of high-quality combinations contains three parameter combinations: (1, 2, 3, 4, 5), (1, 2, 7, 8, 9), and (1, 3, 6, 7, 8). When considering only one dimension partitioning method, and where the first two docking parameters are key parameters and the last three are secondary parameters, the key dimension parameter combination categories are (1, 2) and (1, 3), so the total number of categories is 2. (1, 2) appears twice, while (1, 3) appears once. Correspondingly, the secondary dimension parameter combination categories include (3, 4, 5), (7, 8, 9), and (6, 7, 8).

[0110] S302. Count the occurrence of each type of key dimension parameter combination and each type of secondary dimension parameter combination.

[0111] S303. For each effective parameter combination, calculate the difference between the proportion of database change values ​​of the key dimension parameter combination category and the proportion of database change values ​​of the secondary dimension parameter combination category under each dimension division method, and obtain the importance value of the effective parameter combination in the high-quality combination set under each dimension division method.

[0112] The percentage of database change values ​​for key dimension parameter combination categories is equal to the sum of the ratios of the corresponding database change values ​​for each valid parameter combination that includes the key dimension parameter combination category to the number of occurrences of the key dimension parameter combination category, multiplied by the ratio of the number of occurrences of the key dimension parameter combination category to the total number of categories.

[0113] The percentage of database changes for a secondary dimension parameter combination category is equal to the sum of the ratios of the corresponding database changes for each valid parameter combination that includes the secondary dimension parameter combination category to the number of occurrences of the secondary dimension parameter combination category, multiplied by the ratio of the number of occurrences of the secondary dimension parameter combination category to the total number of categories.

[0114] It should be noted that for a valid parameter combination, the key dimension parameter combination categories it contains may also be included in other valid parameter combinations. The more valid parameter combinations it contains, the higher the importance of that key dimension parameter combination category within the set of valid high-quality combinations, and the higher its corresponding database change value, the more important it is. Secondary dimension parameter combination categories behave in the opposite way. Therefore, the calculation of importance values ​​needs to consider the frequency percentage of each category and the sum of the database change values.

[0115] S109. Determine the effective parameter combination with the highest importance value as the optimal parameter combination, and adjust each docking parameter item according to the optimal parameter combination.

[0116] Specifically, one approach is to directly adjust each docking parameter to the value in the optimal parameter combination. Alternatively, one can gradually adjust each docking parameter according to the optimal parameter combination until the overall performance meets expectations.

[0117] Optionally, in another embodiment of this application, after determining the effective parameter combination with the highest importance value as the optimal parameter combination, it may further include:

[0118] Based on the dimension division method corresponding to the maximum value among the importance values ​​of the optimal parameter combination, the key parameter items and non-key parameter items in each docking parameter item are determined.

[0119] Since the adjustment process usually requires adjusting the key parameters first, and the dimension division method corresponding to the highest importance value is the most accurate dimension division method, the key parameter items and non-key parameter items in each docking parameter item are determined according to this method, so that the parameters can be adjusted in the subsequent dimension division method.

[0120] This application provides a method for optimizing docking parameters, starting with obtaining the initial database performance metrics of the target database. Then, all parameter values ​​of each docking parameter item of all target auxiliary systems docking with the target database are combined to obtain multiple parameter combinations. Each parameter combination includes one parameter value for each docking parameter item. Next, the database performance metrics corresponding to each parameter combination are obtained. These metrics represent the performance metrics of the target database when each parameter item is adjusted to a parameter combination. Then, the initial database performance metrics of the target database are subtracted from the database performance metrics corresponding to each parameter combination to obtain the database change values ​​corresponding to each parameter combination. This allows for the selection of parameter combinations with database change values ​​that meet preset frequency requirements as valid parameter combinations, thereby filtering out invalid parameter combinations, effectively narrowing the analysis scope, and reducing the amount of data for subsequent analysis. Next, the significance score corresponding to each valid parameter combination is calculated. This score is obtained by summing the database change values ​​and system change values ​​corresponding to the valid parameter combinations. Finally, based on the significance scores of each valid parameter combination, multiple high-quality valid parameter combinations are selected to form a high-quality combination set. Next, the importance value of each effective parameter combination in the set of high-quality combinations is calculated. Finally, the effective parameter combination with the highest importance value is determined as the optimal parameter combination, and each docking parameter item is adjusted according to the optimal parameter combination. This automatically traverses all cases, filters out the parameter combination that makes the overall performance of the database and various auxiliary systems approach or reach the optimal level, and adjusts accordingly to quickly bring the overall performance of the database and various auxiliary systems to the expected level.

[0121] Another embodiment of this application provides a device for optimizing docking parameters, such as... Figure 4 As shown, it includes:

[0122] The first acquisition unit 401 is used to acquire the initial database performance metrics of the target database.

[0123] Combination unit 402 is used to combine all parameter values ​​of each docking parameter item of all target auxiliary systems in the docking target database to obtain multiple sets of parameter combinations.

[0124] One set of parameters includes one parameter value for each docking parameter item.

[0125] The second acquisition unit 403 is used to acquire the database performance metrics corresponding to each parameter combination. The database performance metrics corresponding to each parameter combination are the performance metrics of the target database when each parameter item is adjusted to the parameter combination.

[0126] The change value calculation unit 404 is used to subtract the initial database performance index of the target database from the database performance index corresponding to each parameter combination to obtain the database change value corresponding to each parameter combination.

[0127] The first filtering unit 405 is used to filter parameter combinations corresponding to database change values ​​that meet preset requirements in terms of frequency of occurrence, and to select them as valid parameter combinations.

[0128] The scoring calculation unit 406 is used to calculate the significance score corresponding to each effective parameter combination. The significance score for each effective parameter combination is obtained by summing the database change value corresponding to the effective parameter combination and the system change value corresponding to the effective parameter combination.

[0129] The second screening unit 407 is used to select multiple high-quality effective parameter combinations to form a high-quality combination set based on the significance scores corresponding to each effective parameter combination.

[0130] Importance calculation unit 408 is used to calculate the importance value of each effective parameter combination in the high-quality combination set.

[0131] The parameter adjustment unit 409 is used to determine the effective parameter combination with the highest importance value as the optimal parameter combination, and adjust each docking parameter item according to the optimal parameter combination.

[0132] Optionally, in another embodiment of the docking parameter optimization device provided in this application, the first screening unit includes:

[0133] The first statistical unit is used to count the number of times each database change value appears in the database change values ​​corresponding to each parameter combination.

[0134] The plotting unit is used to draw a histogram of the transformed values ​​with the changed values ​​in the database on the x-axis and the number of times the changed values ​​in the database appear on the y-axis.

[0135] The window value selection unit is used to select multiple data windows in the histogram of transformed values ​​according to the set minimum sliding window and sliding step size. The sliding step size is smaller than the minimum sliding window.

[0136] The window filtering unit is used to filter out the target data window from various data windows. Specifically, the ratio of the sum of the occurrences of each database change value in the target data window to the sum of the occurrences of all database change values ​​must be greater than a preset percentage.

[0137] The set filtering unit is used to combine the parameter combinations corresponding to each database change value in each target data window into a set, and then find the intersection of the various set combinations to obtain multiple sets of valid parameter combinations. The parameter combinations in the set of valid parameter combinations are considered valid parameter combinations.

[0138] Optionally, in another embodiment of the docking parameter optimization device provided in this application, the second screening unit includes:

[0139] The second screening sub-unit selects the effective parameter combinations that rank first in the preset percentage based on their significance scores from each set of effective parameter combinations to form a high-quality combination set.

[0140] Optionally, in another embodiment of the docking parameter optimization device provided in this application, the importance calculation unit includes:

[0141] The second statistical unit is used to count the total number of categories of each key dimension parameter combination and the total number of categories of secondary dimension parameter combinations in the high-quality combination set under all dimension division methods.

[0142] The third statistical unit is used to count the occurrence frequency of each type of key dimension parameter combination and each type of secondary dimension parameter combination.

[0143] The importance value calculation unit is used to calculate, for each effective parameter combination, the difference between the percentage of database change values ​​for the key dimension parameter combination category and the percentage of database change values ​​for the secondary dimension parameter combination category under each dimension partitioning method. This difference yields the importance value of the effective parameter combination within the high-quality combination set under each dimension partitioning method. Specifically, the percentage of database change values ​​for the key dimension parameter combination category is equal to the sum of the ratios of the corresponding database change values ​​of each effective parameter combination containing the key dimension parameter combination category to the frequency of occurrence of the key dimension parameter combination category, multiplied by the ratio of the frequency of occurrence of the key dimension parameter combination category to the total number of categories. Similarly, the percentage of database change values ​​for the secondary dimension parameter combination category is equal to the sum of the ratios of the corresponding database change values ​​of each effective parameter combination containing the secondary dimension parameter combination category to the frequency of occurrence of the secondary dimension parameter combination category, multiplied by the ratio of the frequency of occurrence of the secondary dimension parameter combination category to the total number of categories.

[0144] Optionally, in another embodiment of this application, the device for optimizing docking parameters further includes:

[0145] The dimension determination unit is used to determine the key and non-key parameter items in each docking parameter item according to the dimension division method corresponding to the maximum value of each importance value of the optimal parameter combination.

[0146] It should be noted that the specific working process of each unit provided in the above embodiments of this application can be referred to the implementation process of the corresponding steps in the above method embodiments, and will not be repeated here.

[0147] Another embodiment of this application provides an electronic device, such as... Figure 5 As shown, it includes:

[0148] Memory 501 and processor 502.

[0149] The memory 501 is used to store the program.

[0150] The processor 502 is used to execute the program stored in the memory 501. When the program is executed, it is specifically used to implement the tuning method of docking parameters as provided in any of the above embodiments.

[0151] Another embodiment of this application provides a computer storage medium for storing a computer program, which, when executed, is used to implement the docking parameter tuning method provided in any of the above embodiments.

[0152] Computer storage media, including both permanent and non-permanent, removable and non-removable media, can store information using any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0153] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0154] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for optimizing docking parameters, characterized in that, include: Obtain the initial database performance metrics of the target database; All parameter values ​​of each docking parameter item of all target auxiliary systems docked with the target database are combined to obtain multiple sets of parameter combinations; wherein, each set of parameter combinations includes one parameter value of each docking parameter item; Obtain the database performance metrics corresponding to each of the parameter combinations; wherein, the database performance metrics corresponding to each parameter combination are the performance metrics of the target database when each of the parameter items is adjusted to the parameter combination; Subtract the initial database performance index of the target database from the database performance index corresponding to each of the parameter combinations to obtain the database change value corresponding to each parameter combination. The parameter combinations corresponding to the database change values ​​whose occurrence frequency meets the preset requirements are selected as valid parameter combinations. Calculate the significance score corresponding to each of the effective parameter combinations; wherein, the significance score corresponding to each effective parameter combination is obtained by summing the database change value corresponding to the effective parameter combination and the system change value corresponding to the effective parameter combination; Based on the significance scores corresponding to each effective parameter combination, multiple high-quality effective parameter combinations are selected to form a high-quality combination set. Calculate the importance value of each effective parameter combination in the high-quality combination set; wherein, the importance of each effective parameter combination in the high-quality combination set is the difference between the proportion of database change values ​​of the key dimension parameter combination category and the proportion of database change values ​​of the secondary dimension parameter combination category under each dimension division method; the proportion of database change values ​​of the key dimension parameter combination category is equal to the sum of the ratios of the corresponding database change values ​​of each effective parameter combination containing the key dimension parameter combination category to the occurrence frequency of the key dimension parameter combination category, multiplied by the ratio of the occurrence frequency of the key dimension parameter combination category to the total number of categories; the proportion of database change values ​​of the secondary dimension parameter combination category is equal to the sum of the ratios of the corresponding database change values ​​of each effective parameter combination containing the secondary dimension parameter combination category to the occurrence frequency of the secondary dimension parameter combination category, multiplied by the ratio of the occurrence frequency of the secondary dimension parameter combination category to the total number of categories; The effective parameter combination with the highest importance value is determined as the optimal parameter combination, and each docking parameter item is adjusted according to the optimal parameter combination.

2. The method according to claim 1, characterized in that, The parameter combinations corresponding to the database change values ​​whose frequency of occurrence meets the preset requirements are considered as valid parameter combinations, including: Count the number of times each database change value appears in the database change values ​​corresponding to each of the parameter combinations; A histogram of the change values ​​in the database is plotted with the change values ​​on the x-axis and the number of occurrences of the change values ​​on the y-axis. According to the set minimum sliding window and sliding step size, multiple data windows are taken in the plotted histogram of transformation values; wherein, the sliding step size is smaller than the minimum sliding window. Target data windows are selected from each of the data windows; wherein the ratio of the sum of the occurrences of each database change value in the target data window to the sum of the occurrences of all database change values ​​is greater than a preset percentage. Each parameter combination corresponding to the database change value in each target data window is formed into a combination set, and the intersection of each combination set is obtained to obtain multiple effective parameter combination sets; wherein, the parameter combination in the effective parameter combination set is the effective parameter combination.

3. The method according to claim 2, characterized in that, Based on the significance scores corresponding to each of the effective parameter combinations, a set of high-quality effective parameter combinations is selected, including: The high-quality combination set is formed by selecting the effective parameter combinations whose significance scores rank first among the preset percentages from each set of effective parameter combinations.

4. The method according to claim 1, characterized in that, The calculation of the importance value of each effective parameter combination in the high-quality portfolio set includes: The total number of categories of each key dimension parameter combination and the total number of categories of secondary dimension parameter combinations in the high-quality combination set are counted under all dimension division methods. Count the occurrence frequency of each category of key dimension parameter combination and each category of secondary dimension parameter combination; For each effective parameter combination, the difference between the percentage of database change values ​​for the key dimension parameter combination category and the percentage of database change values ​​for the secondary dimension parameter combination category under each dimension partitioning method is calculated to obtain the importance value of the effective parameter combination in the set of high-quality combinations under each dimension partitioning method. Specifically, the percentage of database change values ​​for the key dimension parameter combination category is equal to the sum of the ratios of the corresponding database change values ​​of each effective parameter combination containing the key dimension parameter combination category to the frequency of occurrence of the key dimension parameter combination category, multiplied by the ratio of the frequency of occurrence of the key dimension parameter combination category to the total number of categories. Similarly, the percentage of database change values ​​for the secondary dimension parameter combination category is equal to the sum of the ratios of the corresponding database change values ​​of each effective parameter combination containing the secondary dimension parameter combination category to the frequency of occurrence of the secondary dimension parameter combination category, multiplied by the ratio of the frequency of occurrence of the secondary dimension parameter combination category to the total number of categories.

5. The method according to claim 4, characterized in that, After determining the effective parameter combination with the highest importance value as the optimal parameter combination, the method further includes: Based on the dimension division method corresponding to the maximum value among the importance values ​​of the optimal parameter combination, the key parameter items and non-key parameter items among the docking parameter items are determined.

6. A device for optimizing docking parameters, characterized in that, include: The first acquisition unit is used to acquire the initial database performance metrics of the target database; The combination unit is used to combine all parameter values ​​of each docking parameter item of all target auxiliary systems docked with the target database to obtain multiple sets of parameter combinations; wherein, each set of parameter combinations includes one parameter value of each docking parameter item; The second acquisition unit is used to acquire the database performance indicators corresponding to each of the parameter combinations; wherein, the database performance indicators corresponding to the parameter combinations are the performance indicators of the target database when each of the parameter items is adjusted to the parameter combinations; The change value calculation unit is used to subtract the initial database performance index of the target database from the database performance index corresponding to each of the parameter combinations to obtain the database change value corresponding to each of the parameter combinations. The first filtering unit is used to filter the parameter combinations corresponding to the database change values ​​whose occurrence frequency meets the preset requirements, as valid parameter combinations. The scoring unit is used to calculate the significance score corresponding to each of the effective parameter combinations; wherein the significance score corresponding to each effective parameter combination is obtained by summing the database change value corresponding to the effective parameter combination and the system change value corresponding to the effective parameter combination; The second screening unit is used to select multiple high-quality effective parameter combinations to form a high-quality combination set based on the significance scores corresponding to each effective parameter combination. An importance calculation unit is used to calculate the importance value of each effective parameter combination in the high-quality combination set. The importance of each effective parameter combination in the high-quality combination set is the difference between the percentage of database change values ​​for key dimension parameter combination categories and the percentage of database change values ​​for secondary dimension parameter combination categories under each dimension division method. The percentage of database change values ​​for key dimension parameter combination categories is equal to the sum of the ratios of the corresponding database change values ​​of each effective parameter combination containing the key dimension parameter combination category to the frequency of occurrence of the key dimension parameter combination category, multiplied by the ratio of the frequency of occurrence of the key dimension parameter combination category to the total number of categories. The percentage of database change values ​​for secondary dimension parameter combination categories is equal to the sum of the ratios of the corresponding database change values ​​of each effective parameter combination containing the secondary dimension parameter combination category to the frequency of occurrence of the secondary dimension parameter combination category, multiplied by the ratio of the frequency of occurrence of the secondary dimension parameter combination category to the total number of categories. The parameter adjustment unit is used to determine the effective parameter combination with the highest importance value as the optimal parameter combination, and adjust each of the docking parameter items according to the optimal parameter combination.

7. The apparatus according to claim 6, characterized in that, The first filtering unit includes: The first statistical unit is used to count the number of times each database change value appears in the database change values ​​corresponding to each of the parameter combinations; The plotting unit is used to plot a histogram of the transformation values ​​with the changed values ​​in the database on the horizontal axis and the number of occurrences of the changed values ​​in the database on the vertical axis. A window value-taking unit is used to take multiple data windows in the plotted transform value histogram according to a set minimum sliding window and a sliding step size; wherein the sliding step size is smaller than the minimum sliding window. A window filtering unit is used to filter out a target data window from each of the data windows; wherein the ratio of the sum of the occurrence times of each database change value in the target data window to the sum of the occurrence times of all database change values ​​is greater than a preset quantity percentage. The set filtering unit is used to form a set of parameters corresponding to each database change value in each target data window, and to find the intersection of each set of parameters to obtain multiple sets of valid parameter combinations; wherein, the parameter combination in the set of valid parameter combinations is the valid parameter combination.

8. The apparatus according to claim 7, characterized in that, The second filtering unit includes: The second screening subunit selects the effective parameter combinations that rank first in the preset percentage based on their significance scores from each set of effective parameter combinations to form the high-quality combination set.

9. An electronic device, characterized in that, include: Memory and processor; The memory is used to store programs; The processor is used to execute the program, which, when executed, is specifically used to implement the docking parameter tuning method as described in any one of claims 1 to 5.

10. A computer storage medium, characterized in that, Used to store a computer program, which, when executed, is used to implement the method for optimizing docking parameters as described in any one of claims 1 to 5.

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