Database data monitoring method and device, storage medium and electronic equipment
By calculating the growth rate of database data volume and combining it with alarm thresholds, the problem of inaccurate monitoring of database data volume changes in existing technologies is solved, and more efficient anomaly detection is achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- INDUSTRIAL AND COMMERCIAL BANK OF CHINA
- Filing Date
- 2023-03-23
- Publication Date
- 2026-06-16
Smart Images
Figure CN116225878B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of big data technology, and more specifically, to a method, apparatus, storage medium, and electronic device for monitoring database data. Background Technology
[0002] Changes to critical parameter tables in the production database can impact business operations. For example, batch processing or manual business operations can cause sudden increases or decreases in the data volume of database tables. For instance, deleting a parameter table for a specific region in batches can cause a sudden increase or decrease in the data volume of the database table, potentially leading to errors in business functions. To monitor changes to parameter tables and detect problems earlier than the business side, we can monitor these parameter tables in the database. If we detect sudden increases or decreases in the data volume of database tables due to batch processing or manual business operations that could cause errors in business functions, we can issue an alert.
[0003] In related technologies, routine monitoring of databases can be mainly divided into several categories:
[0004] (1) "Personalized performance capacity" performs cyclic statistics on the amount of data in the table and makes alarm threshold judgment based on the absolute value of the data. However, this monitoring method is not intuitive enough for monitoring data changes and cannot quickly and accurately detect abnormal changes in data through monitoring configuration. Furthermore, the maintenance of absolute values depends on manual judgment and experience.
[0005] (2) "Application Diagnosis - Database Query" performs online detailed queries on database tables and outputs detailed results based on conditions. However, detailed output is suitable for scenarios with relatively small amounts of data. If there is too much data, it will cause great performance pressure.
[0006] Therefore, current database monitoring methods, particularly for monitoring data changes in sensitive tables, lack intuitiveness. They cannot monitor relative growth rates, and for data with dynamic characteristics, they cannot quickly and accurately detect abnormal changes through monitoring configurations. Furthermore, threshold-based alarm modes based on absolute values present challenges in setting monitoring metrics. Maintaining absolute values relies on manual judgment and experience, requiring specific analysis for different scenarios, making setup difficult and inaccurate.
[0007] There is currently no effective solution to the above problems. Summary of the Invention
[0008] This invention provides a method, apparatus, storage medium, and electronic device for monitoring database data, to at least solve the technical problem in related technologies where monitoring changes in database data volume by manually setting data volume thresholds results in low accuracy.
[0009] According to one aspect of the present invention, a method for monitoring database data is provided, comprising: acquiring monitoring configuration data, wherein the monitoring configuration data includes at least: a statistical period, a target time period for monitoring, an alarm type, and an alarm threshold corresponding to the alarm type; based on the monitoring configuration data, calculating the data volume in a target data table in a target database to obtain a data volume set, wherein the data volume set includes: data volume at M times within the target time period, wherein the data volume is the data volume associated with target data in the target data table, the interval between two adjacent times in the M times is the statistical period, and M is an integer greater than 1; based on the M data volumes in the data volume set, calculating the growth rate of the data volume at each time moment to obtain a first growth rate set, wherein the first growth rate set includes M growth rates; and monitoring the data volume in the target data table based on the first growth rate set and the alarm threshold corresponding to the alarm type.
[0010] Further, based on the M data quantities in the data quantity set, the growth rate of the data quantity at each time moment is calculated to obtain a first growth rate set, including: determining the growth rate of the data quantity at each time moment based on the data quantity at each time moment and the data quantity at the previous time moment adjacent to that time moment, to obtain M growth rates; and determining the first growth rate set based on the M growth rates.
[0011] Furthermore, based on the first growth rate set and the alarm threshold corresponding to the alarm type, the data volume in the target data table is monitored, including: determining target statistical data based on the first growth rate set and statistical dimensions, wherein the statistical dimensions are determined by a preset statistical duration; and monitoring the data volume in the target data table based on the target statistical data and the alarm threshold corresponding to the alarm type.
[0012] Furthermore, the alarm type includes at least one of the following: maximum value warning, average value warning; the target statistical data includes at least one of the following: maximum growth rate, average growth rate; determining the target statistical data based on the first growth rate set and the statistical dimension includes: determining a second growth rate set based on the first growth rate set and the statistical dimension, wherein the second growth rate set includes: N growth rates within a preset statistical period corresponding to the statistical dimension, where N is an integer greater than or equal to 1 and N is less than M; when the alarm type is the maximum value warning, determining the maximum growth rate in the second growth rate set and using the maximum growth rate as the target statistical data; when the alarm type is the average value warning, determining the average growth rate of the data volume associated with the statistical dimension based on the second growth rate set and using the average growth rate as the target statistical data.
[0013] Furthermore, based on the target statistical data and the alarm threshold corresponding to the alarm type, the data volume in the target data table is monitored, including: when the alarm type is the maximum value warning, determining whether the maximum growth rate exceeds the alarm threshold associated with the maximum value warning, and sending a first alarm message when the maximum growth rate exceeds the alarm threshold associated with the maximum value warning; when the alarm type is the average number warning, determining whether the average growth rate exceeds the alarm threshold associated with the average number warning, and sending a second alarm message when the average growth rate exceeds the alarm threshold associated with the average number warning.
[0014] Furthermore, each alarm type is associated with multiple alarm thresholds, the multiple alarm thresholds correspond to different alarm levels, and each alarm level sends different first alarm information.
[0015] Furthermore, the monitoring configuration data also includes: a data table identifier for the target data table, filtering conditions for filtering the target data in the target data table, and, based on the monitoring configuration data, statistically analyzing the data volume in the target data table in the target database to obtain a data volume set, including: at each time point in the target time period, filtering the target data in the target data table based on the data table identifier and the filtering conditions to obtain data table data for each time point; statistically analyzing the data volume of the data table data at each time point to obtain M data volumes; and determining the data volume set based on the M data volumes.
[0016] According to another aspect of the present invention, a database data monitoring device is also provided, comprising: an acquisition unit, configured to acquire monitoring configuration data, wherein the monitoring configuration data includes at least: a statistical period, a target time period for monitoring, an alarm type, and an alarm threshold corresponding to the alarm type; a statistics unit, configured to, based on the monitoring configuration data, count the amount of data in a target data table in a target database to obtain a data volume set, wherein the data volume set includes: the amount of data at M times within the target time period, wherein the data volume is the amount of data associated with target data in the target data table, the interval between two adjacent times in the M times is the statistical period, and M is an integer greater than 1; a calculation unit, configured to, based on the M data volumes in the data volume set, calculate the growth rate of the data volume at each time moment to obtain a first growth rate set, wherein the first growth rate set includes M growth rates; and a monitoring unit, configured to, based on the first growth rate set and the alarm threshold corresponding to the alarm type, monitor the amount of data in the target data table.
[0017] Furthermore, the calculation unit includes: a first determining subunit, configured to determine the growth rate of the data volume at each time point based on the data volume at each time point and the data volume at the previous time point adjacent to that time point, thereby obtaining M growth rates; and a second determining subunit, configured to determine the first set of growth rates based on the M growth rates.
[0018] Furthermore, the monitoring unit includes: a third determining subunit, used to determine target statistical data based on the first growth rate set and statistical dimensions, wherein the statistical dimensions are determined by a preset statistical duration; and a monitoring subunit, used to monitor the amount of data in the target data table based on the target statistical data and the alarm threshold corresponding to the alarm type.
[0019] Further, the alarm type includes at least one of the following: maximum value warning, average value warning; the target statistical data includes at least one of the following: maximum growth rate, average growth rate; the third determining subunit includes: a first determining module, used to determine a second growth rate set based on the first growth rate set and the statistical dimension, wherein the second growth rate set includes: N growth rates within a preset statistical period corresponding to the statistical dimension, where N is an integer greater than or equal to 1 and N is less than M; a second determining module, used to determine the maximum growth rate in the second growth rate set when the alarm type is the maximum value warning, and use the maximum growth rate as the target statistical data; a third determining module, used to determine the average growth rate of the data volume associated with the statistical dimension based on the second growth rate set when the alarm type is the average value warning, and use the average growth rate as the target statistical data.
[0020] Furthermore, the monitoring subunit includes: a first judgment module, configured to, when the alarm type is the maximum value warning, determine whether the maximum growth rate exceeds the alarm threshold associated with the maximum value warning, and send a first alarm message if the maximum growth rate exceeds the alarm threshold associated with the maximum value warning; and a second judgment module, configured to, when the alarm type is the average number warning, determine whether the average growth rate exceeds the alarm threshold associated with the average number warning, and send a second alarm message if the average growth rate exceeds the alarm threshold associated with the average number warning.
[0021] Furthermore, each alarm type is associated with multiple alarm thresholds, the multiple alarm thresholds correspond to different alarm levels, and each alarm level sends different first alarm information.
[0022] Furthermore, the monitoring configuration data also includes: a data table identifier for the target data table, filtering conditions for filtering the target data in the target data table, and a statistics unit, including: a filtering subunit, used to filter the target data in the target data table based on the data table identifier and the filtering conditions at each time point in the target time period to obtain data table data at each time point; a statistics subunit, used to count the data volume of the data table data at each time point to obtain M data volumes; and a fourth determination subunit, used to determine the data volume set based on the M data volumes.
[0023] According to another aspect of the present invention, an electronic device is also provided, including: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform a database data monitoring method of any of the above-mentioned methods by executing the executable instructions.
[0024] According to another aspect of the present invention, a computer-readable storage medium is also provided, which stores a computer program, wherein a method for monitoring database data of any of the above-mentioned items is provided when the computer program is running, which controls the device where the computer-readable storage medium is located to execute the database data.
[0025] In this invention, monitoring configuration data is acquired, including at least the statistical period, the target time period for monitoring, the alarm type, and the alarm threshold corresponding to the alarm type. Based on the monitoring configuration data, the data volume in the target data table of the target database is statistically analyzed to obtain a data volume set. This data volume set includes: data volume at M times within the target time period, where each data volume is the data volume associated with the target data in the target data table, and the interval between any two adjacent times within the M times is the statistical period, where M is an integer greater than 1. Based on the M data volumes in the data volume set, the growth rate of the data volume at each time point is calculated to obtain a first growth rate set, which includes M growth rates. Based on the first growth rate set and the alarm threshold corresponding to the alarm type, the data volume in the target data table is monitored. This solves the technical problem in related technologies where monitoring database data volume changes for anomalies by manually setting data volume thresholds results in low accuracy. In this invention, the changes in the data volume of the database table are monitored by the growth rate of the data volume at each moment. This avoids the low accuracy of monitoring abnormal changes in the database data volume by comparing the data volume of the data table with manually set data volume thresholds in related technologies. This achieves the technical effect of improving the accuracy of monitoring abnormal changes in the database data volume. Attached Figure Description
[0026] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:
[0027] Figure 1 This is a flowchart of an optional database data monitoring method according to an embodiment of the present invention;
[0028] Figure 2 This is a flowchart of an optional method for determining a first set of growth rates according to an embodiment of the present invention;
[0029] Figure 3 This is a schematic diagram of an optional database data monitoring device according to an embodiment of the present invention;
[0030] Figure 4 This is a schematic diagram of an electronic device according to an embodiment of the present invention. Detailed Implementation
[0031] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.
[0032] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.
[0033] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, database data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation portals are provided for users to choose to authorize or refuse.
[0034] Example 1
[0035] According to an embodiment of the present invention, an optional method embodiment for monitoring database data is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.
[0036] Figure 1 This is a flowchart of an optional database data monitoring method according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:
[0037] Step S101: Obtain monitoring configuration data, wherein the monitoring configuration data includes at least: statistical period, target time period for monitoring, alarm type, and alarm threshold corresponding to the alarm type.
[0038] The aforementioned monitoring configuration data can be collected through the preset interface of the target system. The aforementioned monitoring configuration data may include, but is not limited to: statistical period, target time period for monitoring, alarm type, alarm threshold corresponding to alarm type, statistical start time, statistical end time, data table identifier of target data table in the target database being monitored, and filtering conditions for filtering target data in the target data table.
[0039] Specifically, the statistical period mentioned above can be a preset duration, including but not limited to: 5 minutes, 10 minutes, 30 minutes, 60 minutes, 480 minutes, etc.; the target time period mentioned above can be the time period from the start time of the statistics to the end time of the statistics, including but not limited to: the data statistics start time T_start (which can be 0 o'clock by default) to the data statistics end time T_end (which can be 24 o'clock by default); the alarm types mentioned above can include but are not limited to: maximum value warning, average number warning, etc., wherein each alarm type can include one or more alarm thresholds, each alarm threshold can correspond to different alarm levels, and different alarm prompt information can be sent according to different alarm levels.
[0040] Step S102: Based on the monitoring configuration data, count the amount of data in the target data table in the target database to obtain a data volume set. The data volume set includes: the amount of data at M times within the target time period, the amount of data is the amount of data associated with the target data in the target data table, the interval between two adjacent times in the M times is the statistical period, and M is an integer greater than 1.
[0041] Based on the monitoring configuration data described above, within the target time period, the amount of data in the target data table of the target database is counted according to the statistical period described above. It should be noted that in this embodiment, the target data in the target data table can also be filtered based on the filtering conditions in the monitoring configuration data, and the data obtained after filtering is used as the amount of data in the target data table described above.
[0042] To filter target data in the target database, you can use database statements. For example, you can filter the status column of the target data table (to filter out data with a status of success) or the region code column (to filter out data from region A).
[0043] Specifically, in this embodiment, the target system's background can record the statistical period data volumes S_t0, S_t1, S_t2...: from the time the input conditions take effect, the parameter table records the total data volume S_t0, S_t1, S_t2... at each statistical period time t0, t1, t2... within the statistical start time to statistical end time. This statistical data is generated based on the database, and based on the input table name and filtering conditions, the database backup is prioritized for statistics, reducing the performance impact on the primary database.
[0044] Step S103: Based on the M data quantities in the data quantity set, calculate the growth rate of the data quantity at each time point to obtain the first growth rate set, wherein the first growth rate set includes M growth rates.
[0045] In this embodiment, the growth rate of the data volume at each time moment can be determined by calculating the difference between the data volume at each time moment and the data volume at the previous time moment adjacent to that time moment, and then calculating the ratio of the difference to the data volume at the previous time moment. M growth rates are obtained, and the above-mentioned first growth rate set is composed of the M growth rates.
[0046] Step S104: Based on the first growth rate set and the alarm threshold corresponding to the alarm type, monitor the amount of data in the target data table.
[0047] The alarm types mentioned above may include, but are not limited to, maximum value warning, average value warning, etc. Each alarm type may include one or more alarm thresholds, and each alarm threshold may correspond to different alarm levels. Different alarm prompts may be sent according to different alarm levels to monitor the amount of data in the target data table.
[0048] Through the above steps, this embodiment monitors the changes in the data volume of the database table by measuring the growth rate of the data volume at each moment. This avoids the low accuracy of related technologies that rely on cyclically calculating and comparing the data volume of the table with manually set data volume thresholds to monitor abnormal changes in the database data volume. This significantly improves the accuracy of monitoring abnormal changes in the database data volume. Furthermore, it solves the problem of low accuracy in related technologies that rely on manually setting data volume thresholds to monitor abnormal changes in database data volume.
[0049] Figure 2 This is a flowchart of an optional method for determining a first set of growth rates according to an embodiment of the present invention, such as... Figure 2 The steps for calculating the growth rate of the data at each time point based on M data points in the data set, to obtain the first set of growth rates, include:
[0050] Step S201: Based on the data volume at each time point and the data volume at the previous time point adjacent to that time point, determine the growth rate of the data volume at each time point to obtain M growth rates;
[0051] Step S202: Determine the first set of growth rates based on M growth rates.
[0052] In this embodiment, the growth rate of the data volume at each time moment can be determined by calculating the difference between the data volume at each time moment and the data volume at the previous time moment adjacent to that time moment, and then calculating the ratio of the difference to the data volume at the previous time moment. M growth rates can then be obtained.
[0053] For example, the instantaneous parameter deviations (corresponding to the growth rates mentioned above) △S_t1, △S_t2, △S_t3... can be calculated:
[0054] △S_tn=(S_tn-S_tn-1) / S_tn-1*100%, n>=1;
[0055] (Example: △S_t1=(S_t1-S_t0) / S_t0*100%).
[0056] The instantaneous parameter deviation under the same filtering conditions can be saved as a table, as shown in Table 1. Each table data has a time tn and a parameter deviation magnitude △S_tn.
[0057] Table 1
[0058]
[0059] By calculating the data volume of the target data table in the target database at each time step and the data volume of the previous time step adjacent to that time step, the technical effect of accurately obtaining the data volume changes of the database table is achieved.
[0060] Optionally, based on the first growth rate set and the alarm threshold corresponding to the alarm type, the data volume in the target data table is monitored, including: determining the target statistical data based on the first growth rate set and the statistical dimension, wherein the statistical dimension is determined by a preset statistical duration; and monitoring the data volume in the target data table based on the target statistical data and the alarm threshold corresponding to the alarm type.
[0061] The aforementioned statistical dimensions are determined by a preset statistical duration. For example, if the preset statistical duration is 1 hour, the statistical dimension can be an hourly dimension; if the preset statistical duration is 1 day, the statistical dimension can be a daily dimension; if the preset statistical duration is 1 week, the statistical dimension can be a weekly dimension; if the preset statistical duration is 1 month, the statistical dimension can be a monthly dimension, and so on.
[0062] The aforementioned target statistical data can be either the maximum growth rate or the average growth rate. In this embodiment, the maximum growth rate or the average growth rate within the preset statistical period can be determined based on the preset statistical period of the statistical dimension. The amount of data in the target data table can be monitored by monitoring whether the target statistical data meets the alarm threshold corresponding to the alarm type. In this embodiment, the amount of data in the target data table is monitored based on the target statistical data and the alarm threshold corresponding to the alarm type, thereby achieving the technical effect of improving the accuracy and precision of detecting abnormal changes in the database.
[0063] Optionally, the alarm type includes at least one of the following: maximum value warning, average value warning; the target statistical data includes at least one of the following: maximum growth rate, average growth rate; the target statistical data is determined based on the first set of growth rates and the statistical dimension, including: determining the second set of growth rates based on the first set of growth rates and the statistical dimension, wherein the second set of growth rates includes: N growth rates within a preset statistical period corresponding to the statistical dimension, where N is an integer greater than or equal to 1 and N is less than M; when the alarm type is maximum value warning, the maximum growth rate in the second set of growth rates is determined and the maximum growth rate is used as the target statistical data; when the alarm type is average value warning, the average growth rate of the data volume associated with the statistical dimension is determined based on the second set of growth rates and the average growth rate is used as the target statistical data.
[0064] In this embodiment, if the above-mentioned statistical dimension is an hourly dimension, the growth rate of each hour can be calculated based on the M growth rates in the first growth rate set to obtain the above-mentioned second growth rate set. When the alarm type is maximum value warning, the maximum growth rate in the second growth rate set is determined and the maximum growth rate is used as the target statistical data. When the alarm type is average number warning, the average growth rate of the data volume associated with the statistical dimension is determined based on the second growth rate set and the average growth rate is used as the target statistical data.
[0065] If the above statistical dimension is a daily dimension, the growth rate for each day can be calculated based on the M growth rates in the first growth rate set to obtain the above second growth rate set. When the alarm type is maximum value warning, the maximum growth rate in the second growth rate set is determined and the maximum growth rate is used as the target statistical data. When the alarm type is average warning, the average growth rate of the data volume associated with the statistical dimension is determined based on the second growth rate set and the average growth rate is used as the target statistical data.
[0066] If the above statistical dimension is a weekly dimension, the growth rate in each week can be calculated based on the M growth rates in the first growth rate set to obtain the above second growth rate set. When the alarm type is maximum value warning, the maximum growth rate in the second growth rate set is determined and the maximum growth rate is used as the target statistical data. When the alarm type is average warning, the average growth rate of the data volume associated with the statistical dimension is determined based on the second growth rate set and the average growth rate is used as the target statistical data.
[0067] If the above statistical dimension is a monthly dimension, the growth rate for each week can be calculated based on the M growth rates in the first growth rate set. When the alarm type is maximum value warning, the maximum growth rate in the second growth rate set can be determined and the maximum growth rate can be used as the target statistical data. When the alarm type is average warning, the average growth rate of the data volume associated with the statistical dimension can be determined based on the second growth rate set and the average growth rate can be used as the target statistical data.
[0068] For example, the maximum parameter deviation (corresponding to the maximum growth rate) under each dimension (corresponding to the statistical dimensions mentioned above) can be calculated, specifically including:
[0069] (1) Output the maximum parameter deviation △S_max_hour within one hour: You can take the table data of tn in the data table (corresponding to the target data table above) within the interval [T_system-1h,T_system] 1 hour before the current system time T_system, and take the maximum parameter deviation △S_tn in the table as △S_max_hour.
[0070] △S_max_hour=max(△S_t1,△S_t2,△S_t3,△S_t4....S_tn);
[0071] (T_system-1h<△S_t1<△S_t2...<△S_tn <T_system)。
[0072] (2) Output the maximum parameter deviation △S_max_day within a day: You can take the table data [T_system-24h,T_system] within the 24-hour interval before the current system time T_system, and take the maximum value △S_tn in the table (or the second growth rate set) as △S_max_day.
[0073] (3) Similarly, define the maximum parameter deviation within a week △S_max_week and the maximum parameter deviation within a month △S_max_mouth.
[0074] Calculate the average parameter deviation for each dimension (corresponding to the average growth rate mentioned above), which may specifically include:
[0075] (1) Output the average parameter deviation △S_avg_hour within one hour: Take the table data of tn in the interval [T_system-1h, T_system] 1 hour before the current system time T_system in the data table, add up the parameter deviations in the table (second growth rate set) and divide by the number to obtain the statistical average, and output the average value as △S_avg_hour.
[0076] △S_avg_hour=(△S_t1+△S_t2+△S_t3+△S_t4+....+S_tn) / n;
[0077] (T_system-1h<△S_t1<△S_t2...<△S_tn <T_system)。
[0078] (2) Output the average parameter deviation △S_avg_day within a day: Take the table data of tn in the interval [T_system-24h,T_system] before the current system time T_system, add up the parameter deviations in the table (second growth rate set) and divide by the number to obtain the statistical average, and output the average value as △S_avg_day.
[0079] (3) Similarly, define the maximum parameter deviation within a week △S_avg_week and the maximum parameter deviation within a month △S_avg_mouth.
[0080] Optionally, based on the target statistical data and the alarm threshold corresponding to the alarm type, the data volume in the target data table is monitored, including: when the alarm type is a maximum value warning, determining whether the maximum growth rate exceeds the alarm threshold associated with the maximum value warning, and sending a first alarm message if the maximum growth rate exceeds the alarm threshold associated with the maximum value warning; when the alarm type is an average number warning, determining whether the average growth rate exceeds the alarm threshold associated with the average number warning, and sending a second alarm message if the average growth rate exceeds the alarm threshold associated with the average number warning.
[0081] When the alarm type is maximum value warning, it can be determined whether the maximum growth rate exceeds the alarm threshold associated with the maximum value warning. If the maximum growth rate exceeds the alarm threshold associated with the maximum value warning, the first alarm information is sent.
[0082] For example: Optional alarm parameter type 1: Parameter deviation absolute value alarm (corresponding to the maximum value warning mentioned above).
[0083] In this embodiment, system users can configure the absolute value of parameter deviation alarms and match alarm levels:
[0084] (1) For example, if a 5% absolute alarm value is configured and the alarm level is set to Level 1, then when the parameter deviation ΔS_tn of tn is 7% at a certain moment, the Level 1 alarm condition will be triggered, and an email notification will be sent to the Level 1 monitoring configurator (which may include front-line maintenance personnel). At other times, if ΔS_tn is less than 5%, no alarm will be generated.
[0085] (2) For example, if an absolute alarm value of 10% is configured and the alarm level is set to level 2, then when the parameter deviation ΔS_tn of tn is 14% at a certain moment, the level 2 alarm condition will be triggered, and email and SMS notifications will be sent to the level 2 monitoring configurator (which may include front-line maintenance personnel and their superiors). At other times, if ΔS_tn is less than 10%, no alarm will be generated.
[0086] When the alarm type is average warning, it is determined whether the average growth rate exceeds the alarm threshold associated with the average warning. If the average growth rate exceeds the alarm threshold associated with the average warning, a second alarm message is sent. The alarm threshold associated with the average warning can be an alarm range.
[0087] For example: Alarm parameter type 2: Alarm based on the average parameter deviation (corresponding to the average warning mentioned above). In this embodiment, system users can configure deviation alarms based on the average parameter deviation and match alarm levels. The dimension of the average parameter deviation can be selected (hour, day, week, month, etc.).
[0088] (1) For example, if a 20% deviation alarm based on the average parameter deviation is configured, and the alarm level is set to level 1, with the average parameter deviation dimension being hours, then the average parameter deviation ΔS_avg_hour in hours is calculated, and the interval alarms are obtained as (-∞, -|S_avg_hour*1.2|) and (|S_avg_hour*1.2|, +∞). If the parameter deviation ΔS_tn at time t is within the alarm interval, a level 1 alarm is generated. Depending on performance requirements, ΔS_avg_hour can be calculated once per hour, and the alarm can be matched based on the most recently calculated ΔS_avg_hour.
[0089] (2) For example, if a 40% deviation alarm based on the average parameter deviation is configured, and the alarm level is set to level 2, with the average parameter deviation dimension being days, then based on the calculated average parameter deviation ΔS_avg_day in daily units, the interval alarms are (-∞, -|S_avg_day*1.4|) and (|S_avg_hour*1.4|, +∞). If the parameter deviation ΔS_tn at time t is within the alarm interval, a level 2 alarm is generated. Depending on performance requirements, ΔS_avg_day can be calculated hourly (or daily, without limitation), and alarms can be matched based on the most recently calculated ΔS_avg_day.
[0090] By triggering alarms based on different alarm types, the technical effect of improving the accuracy of monitoring data tables in the target database is achieved.
[0091] Optionally, each alarm type may be associated with multiple alarm thresholds, and the alarm levels corresponding to the multiple alarm thresholds may be different, with each alarm level sending a different first alarm message.
[0092] In this embodiment, the alarm parameter deviations (corresponding to the alarm thresholds mentioned above) △S_error_1, △S_error_2, △S_error_3... and the alarm levels T_error_1, T_error_2, T_error_3... corresponding to the alarm parameter deviations are explained below. Each alarm type is associated with multiple alarm thresholds, and the alarm levels corresponding to these multiple alarm thresholds are different. The first alarm message sent for each alarm level is also different.
[0093] 1. Alarm Parameter Type 1: Absolute Value Alarm of Parameter Deviation (corresponding to the maximum value warning mentioned above). In this embodiment, system users can configure the absolute value of the parameter deviation alarm and match the alarm level:
[0094] (1) For example, if a 5% absolute alarm value is configured and the alarm level is set to Level 1, then when the parameter deviation ΔS_tn of tn is 7% at a certain moment, the Level 1 alarm condition will be triggered, and an email notification will be sent to the Level 1 monitoring configurator (which may include front-line maintenance personnel). At other times, if ΔS_tn is less than 5%, no alarm will be generated.
[0095] (2) For example, if an absolute alarm value of 10% is configured and the alarm level is set to level 2, then when the parameter deviation ΔS_tn of tn is 14% at a certain moment, the level 2 alarm condition will be triggered, and email and SMS notifications will be sent to the level 2 monitoring configurator (which may include front-line maintenance personnel and their superiors). At other times, if ΔS_tn is less than 10%, no alarm will be generated.
[0096] 2. Alarm Parameter Type 2: Alarm based on the average parameter deviation (corresponding to the average warning mentioned above). In this embodiment, system users can configure deviation alarms based on the average parameter deviation and match alarm levels. The dimension of the average parameter deviation can be selected (hour, day, week, month, etc.).
[0097] (1) For example, if a 20% deviation alarm based on the average parameter deviation is configured, and the alarm level is set to level 1, with the average parameter deviation dimension being hours, then the average parameter deviation ΔS_avg_hour in hours is calculated, and the interval alarms are obtained as (-∞, -|S_avg_hour*1.2|) and (|S_avg_hour*1.2|, +∞). If the parameter deviation ΔS_tn at time t is within the alarm interval, a level 1 alarm is generated. Depending on performance requirements, ΔS_avg_hour can be calculated once per hour, and the alarm can be matched based on the most recently calculated ΔS_avg_hour.
[0098] (2) For example, if a 40% deviation alarm based on the average parameter deviation is configured, and the alarm level is set to level 2, with the average parameter deviation dimension being days, then the alarm intervals (-∞, -|S_avg_day*1.4|) and (|S_avg_hour*1.4|, +∞) can be derived based on the calculated average parameter deviation ΔS_avg_day in daily units. If the parameter deviation ΔS_tn at time t is within the alarm interval, a level 2 alarm is generated. Depending on performance requirements, ΔS_avg_day can be calculated hourly or daily; there is no limitation here. Alarms can be matched based on the most recently calculated ΔS_avg_day.
[0099] This embodiment monitors and issues early warnings for changes in the data tables of the target database based on the maximum or average growth rate under statistical dimensions. This avoids the low monitoring accuracy caused by performing cyclical statistics on the data volume within the data table and determining alarm thresholds based on the absolute value of the data volume in related technologies. This achieves the technical effect of improving the accuracy of monitoring changes in the data volume within the data table.
[0100] Optionally, the monitoring configuration data also includes: the data table identifier of the target data table, the filtering conditions for filtering the target data in the target data table, and, based on the monitoring configuration data, the data volume of the target data table in the target database is counted to obtain a data volume set, including: at each time point in the target time period, the target data in the target data table is filtered based on the data table identifier and filtering conditions to obtain the data table data at each time point; the data volume of the data table data at each time point is counted to obtain M data volumes; and based on the M data volumes, the data volume set is determined.
[0101] The aforementioned monitoring configuration data can be collected through the target interface of the target system, for example:
[0102] (1) Select the database to be monitored (you can prioritize the backup database to reduce the impact on the performance and capacity of the main database).
[0103] (2) Input the table name of the target object to be monitored for deviation (i.e. growth rate). You can filter the results by inputting database statements, including but not limited to: filtering the status column in the table field (filtering out data with a status of success), filtering the region code column (filtering out data from region A), etc.
[0104] (3) Set the statistical period T_cycle. The default value is 5 minutes, but it can also be 10 minutes, 30 minutes, 60 minutes, 480 minutes, etc.
[0105] (4) Data statistics start time T_start (can be set to 0 o'clock by default) and data statistics end time T_end (can be set to 24 o'clock by default).
[0106] (5) You can also collect whether to process daily data EVERYDAY_KEY separately (the subsequent processing of the average and maximum values can be determined by this option to decide whether to take data from different days for joint calculation. If not, only data belonging to the same day can be processed each time).
[0107] (6) It can also collect parameter deviation alarm threshold I and alarm level, and set alarm notify person and notification method according to different levels to distinguish alarm urgency.
[0108] (7) An optional alarm parameter type 1: parameter deviation absolute value alarm.
[0109] (8) An optional alarm parameter type 2: alarm based on the average deviation of parameters.
[0110] By performing multi-level monitoring of the target data tables in the target database, the technical effect of accurately monitoring abnormal changes in the data tables was achieved.
[0111] In this embodiment, monitoring results for monitoring the amount of data in the target data table can also be output, specifically including:
[0112] Output 1: Output parameter deviation (growth rate) line graph, with the horizontal axis representing the statistical period and the number axis representing the percentage of parameter deviation.
[0113] Output 2: A table showing the maximum and average deviations of the output parameters, as shown in Table 2. This table can be batch maintained and updated by the backend after configuring the monitoring metrics (i.e., monitoring configuration data), making it convenient for users to refer to the following metrics when setting monitoring thresholds.
[0114] Table 2
[0115] Statistical period This hour Today This week this month Maximum parameter deviation △S_max_hour △S_max_day △S_max_week △S_max_mouth Parameter deviation average △S_avg_hour △S_avg_day △S_avg_week △S_avg_mouth
[0116] Output 3: Notify the contact person of the parameter deviation that has reached the alarm threshold and the corresponding time via SMS or email, based on the alarm level.
[0117] Through this embodiment, 1) the difficulty of setting up parameter change monitoring can be simplified by transforming absolute changes in data into relative changes in data deviation, simplifying the specific monitoring configuration for different scenarios, maintaining a unified dimension, and simplifying the evaluation of monitoring elements; 2) data change trends can be intuitively displayed by collecting historical parameter deviations, making it easier to make basic trend judgments on key data; 3) the severity of problems in database tables can be differentiated through multi-level monitoring of deviation, distinguishing occasional minor changes from major data anomalies, tiered monitoring, alleviating monitoring pressure, and ensuring the identification rate of risk incidents; 4) multiple monitoring parameter setting methods are available, which can be based on the instantaneous value of parameter deviation or the average value of parameter deviation, covering more monitoring threshold setting scenarios and achieving accurate monitoring of changes in database data volume.
[0118] Example 2
[0119] Embodiment 2 of this application provides an optional database data monitoring device, wherein each implementation unit in the monitoring device corresponds to each implementation step in Embodiment 1.
[0120] Figure 3 This is a schematic diagram of an optional database data monitoring device according to an embodiment of the present invention, such as... Figure 3 As shown, there are acquisition unit 31, statistics unit 32, calculation unit 33 and monitoring unit 34.
[0121] Specifically, the acquisition unit 31 is used to acquire monitoring configuration data, wherein the monitoring configuration data includes at least: statistical period, target time period for monitoring, alarm type, and alarm threshold corresponding to the alarm type.
[0122] The statistics unit 32 is used to count the amount of data in the target data table in the target database based on the monitoring configuration data, and obtain a data set. The data set includes: the amount of data at M times within the target time period, the amount of data is the amount of data in the target data table associated with the target data, the interval between two adjacent times in the M times is the statistical period, and M is an integer greater than 1.
[0123] The calculation unit 33 is used to calculate the growth rate of the data volume at each time step based on M data volumes in the data volume set, and obtain a first growth rate set, wherein the first growth rate set includes M growth rates;
[0124] The monitoring unit 34 is used to monitor the amount of data in the target data table based on the first growth rate set and the alarm threshold corresponding to the alarm type.
[0125] In the database data monitoring device provided in Embodiment 2 of this application, monitoring configuration data can be acquired by the acquisition unit 31. The monitoring configuration data includes at least: a statistical period, a target time period for monitoring, an alarm type, and an alarm threshold corresponding to the alarm type. The statistical unit 32, based on the monitoring configuration data, calculates the data volume in the target data table of the target database to obtain a data volume set. This data volume set includes: data volume at M times within the target time period, where each data volume is the data volume associated with the target data in the target data table. The interval between two adjacent times within the M times is the statistical period, and M is an integer greater than 1. The calculation unit 33 calculates the growth rate of the data volume at each time based on the M data volumes in the data volume set, obtaining a first growth rate set. This first growth rate set includes M growth rates. The monitoring unit 34 monitors the data volume in the target data table based on the first growth rate set and the alarm threshold corresponding to the alarm type. This solves the technical problem in related technologies where monitoring database data volume changes for abnormalities by manually setting data volume thresholds results in low accuracy. In this embodiment, the changes in the data volume of the database table are monitored by the growth rate of the data volume at each moment. This avoids the low accuracy of monitoring abnormal changes in the database data volume by comparing the data volume of the data table with manually set data volume thresholds in related technologies. This achieves the technical effect of improving the accuracy of monitoring abnormal changes in the database data volume.
[0126] Optionally, in the database data monitoring device provided in Embodiment 2 of this application, the calculation unit includes: a first determining subunit, used to determine the growth rate of the data volume at each time based on the data volume at each time and the data volume at the previous time adjacent to that time, to obtain M growth rates; and a second determining subunit, used to determine a first set of growth rates based on the M growth rates.
[0127] Optionally, in the database data monitoring device provided in Embodiment 2 of this application, the monitoring unit includes: a third determining subunit, used to determine target statistical data based on a first growth rate set and a statistical dimension, wherein the statistical dimension is determined by a preset statistical duration; and a monitoring subunit, used to monitor the amount of data in the target data table based on the target statistical data and the alarm threshold corresponding to the alarm type.
[0128] Optionally, in the database data monitoring device provided in Embodiment 2 of this application, the alarm type includes at least one of the following: maximum value warning, average value warning, and the target statistical data includes at least one of the following: maximum growth rate, average growth rate. The third determining subunit includes: a first determining module, used to determine a second growth rate set based on a first growth rate set and a statistical dimension, wherein the second growth rate set includes N growth rates within a preset statistical period corresponding to the statistical dimension, where N is an integer greater than or equal to 1 and N is less than M; a second determining module, used to determine the maximum growth rate in the second growth rate set when the alarm type is a maximum value warning, and use the maximum growth rate as the target statistical data; and a third determining module, used to determine the average growth rate of the data volume associated with the statistical dimension based on the second growth rate set when the alarm type is an average value warning, and use the average growth rate as the target statistical data.
[0129] Optionally, in the database data monitoring device provided in Embodiment 2 of this application, the monitoring subunit includes: a first judgment module, used to determine whether the maximum growth rate exceeds the alarm threshold associated with the maximum value warning when the alarm type is maximum value warning, and to send a first alarm message when the maximum growth rate exceeds the alarm threshold associated with the maximum value warning; and a second judgment module, used to determine whether the average growth rate exceeds the alarm threshold associated with the average number warning when the alarm type is average number warning, and to send a second alarm message when the average growth rate exceeds the alarm threshold associated with the average number warning.
[0130] Optionally, in the database data monitoring device provided in Embodiment 2 of this application, there are multiple alarm thresholds associated with each alarm type, and the alarm levels corresponding to the multiple alarm thresholds are different, and the first alarm information sent by each alarm level is different.
[0131] Optionally, in the database data monitoring device provided in Embodiment 2 of this application, the monitoring configuration data further includes: a data table identifier of the target data table, filtering conditions for filtering the target data in the target data table, and a statistical unit, including: a filtering subunit, used to filter the target data in the target data table based on the data table identifier and filtering conditions at each moment in the target time period to obtain the data table data at each moment; a statistical subunit, used to count the data volume of the data table data at each moment to obtain M data volumes; and a fourth determination subunit, used to determine the data volume set based on the M data volumes.
[0132] The aforementioned database data monitoring device may also include a processor and a memory. The aforementioned acquisition unit 31, statistics unit 32, calculation unit 33 and monitoring unit 34 are all stored in the memory as program units, and the processor executes the aforementioned program units stored in the memory to realize the corresponding functions.
[0133] The aforementioned processor contains a kernel, which retrieves the corresponding program units from memory. One or more kernels can be configured, and by adjusting kernel parameters, the changes in the data volume of the database tables can be monitored based on the data volume growth rate at each moment. This avoids the low accuracy issues of related technologies that rely on cyclically counting data table data volumes and comparing them with manually set data volume thresholds to monitor abnormal data volume changes. Therefore, this method significantly improves the accuracy of monitoring abnormal data volume changes in the database.
[0134] The aforementioned memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.
[0135] According to another aspect of the present invention, an electronic device is also provided, including: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform a database data monitoring method of any of the above-mentioned methods by executing the executable instructions.
[0136] According to another aspect of the present invention, a computer-readable storage medium is also provided, which stores a computer program, wherein a method for monitoring database data of any of the above-mentioned items is provided when the computer program is running, which controls the device where the computer-readable storage medium is located to execute the database data.
[0137] Figure 4 This is a schematic diagram of an electronic device according to an embodiment of the present invention, such as... Figure 4 As shown, an embodiment of the present invention provides an electronic device 40, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it implements a database data monitoring method for any of the above-mentioned features.
[0138] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.
[0139] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.
[0140] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.
[0141] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.
[0142] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.
[0143] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.
[0144] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.
Claims
1. A method for monitoring database data, characterized in that, include: Acquire monitoring configuration data, wherein the monitoring configuration data includes at least: statistical period, target time period for monitoring, alarm type, alarm threshold corresponding to the alarm type, wherein the alarm type includes at least: maximum value warning, average number warning, each alarm type includes one or more alarm thresholds, each alarm threshold corresponds to an alarm level, and send corresponding alarm prompt information according to each alarm level; Based on the monitoring configuration data, the amount of data in the target data table in the target database is counted to obtain a data volume set. The data volume set includes: the data volume at M times within the target time period. The data volume is the data volume of the target data associated with the target data in the target data table. The interval between two adjacent times in the M times is the statistical period, and M is an integer greater than 1. Based on the M data quantities in the data quantity set, the growth rate of the data quantity at each time moment is calculated to obtain a first growth rate set, wherein the first growth rate set includes M growth rates; Based on the first growth rate set and the alarm threshold corresponding to the alarm type, the amount of data in the target data table is monitored; The monitoring configuration data further includes: a data table identifier for the target data table, filtering conditions for filtering the target data in the target data table, and, based on the monitoring configuration data, statistically analyzing the data volume in the target data table in the target database to obtain a data volume set, including: at each time point in the target time period, filtering the target data in the target data table based on the data table identifier and the filtering conditions to obtain data table data for each time point; statistically analyzing the data volume of the data table data at each time point to obtain M data volumes; and determining the data volume set based on the M data volumes.
2. The monitoring method according to claim 1, characterized in that, Based on the M data quantities in the data quantity set, the growth rate of the data quantity at each time moment is calculated to obtain a first growth rate set, including: Based on the data volume at each time point and the data volume at the previous time point adjacent to that time point, the growth rate of the data volume at each time point is determined, resulting in M growth rates; Based on the M growth rates, the first set of growth rates is determined.
3. The monitoring method according to claim 1, characterized in that, Based on the first growth rate set and the alarm threshold corresponding to the alarm type, the data volume in the target data table is monitored, including: Based on the first set of growth rates and statistical dimensions, target statistical data are determined, wherein the statistical dimensions are determined by a preset statistical duration; Based on the target statistical data and the alarm threshold corresponding to the alarm type, the amount of data in the target data table is monitored.
4. The monitoring method according to claim 3, characterized in that, The alarm types include at least one of the following: maximum value warning, average value warning; the target statistical data includes at least one of the following: maximum growth rate, average growth rate; the target statistical data is determined based on the first set of growth rates and statistical dimensions, including: Based on the first set of growth rates and the statistical dimension, a second set of growth rates is determined, wherein the second set of growth rates includes N growth rates within a preset statistical period corresponding to the statistical dimension, where N is an integer greater than or equal to 1 and N is less than M; When the alarm type is the maximum value warning, the maximum growth rate in the second growth rate set is determined, and the maximum growth rate is used as the target statistical data. When the alarm type is the average warning, the average growth rate of the data volume associated with the statistical dimension is determined based on the second growth rate set, and the average growth rate is used as the target statistical data.
5. The monitoring method according to claim 4, characterized in that, Based on the target statistical data and the alarm threshold corresponding to the alarm type, the data volume in the target data table is monitored, including: When the alarm type is the maximum value warning, determine whether the maximum growth rate exceeds the alarm threshold associated with the maximum value warning, and if the maximum growth rate exceeds the alarm threshold associated with the maximum value warning, send the first alarm information; When the alarm type is the average warning, it is determined whether the average growth rate exceeds the alarm threshold associated with the average warning, and if the average growth rate exceeds the alarm threshold associated with the average warning, a second alarm message is sent.
6. The monitoring method according to claim 5, characterized in that, Each alarm type is associated with multiple alarm thresholds, and the alarm levels corresponding to the multiple alarm thresholds are different. The first alarm information sent by each alarm level is different.
7. A device for monitoring database data, characterized in that, include: The acquisition unit is used to acquire monitoring configuration data, wherein the monitoring configuration data includes at least: statistical period, target time period for monitoring, alarm type, and alarm threshold corresponding to the alarm type, wherein the alarm type includes at least: maximum value warning and average number warning, each alarm type includes one or more alarm thresholds, each alarm threshold corresponds to an alarm level, and corresponding alarm prompt information is sent according to each alarm level; The statistics unit is used to count the amount of data in the target data table in the target database based on the monitoring configuration data, and obtain a data set. The data set includes the data amount at M times within the target time period. The data amount is the amount of data associated with the target data in the target data table. The interval between two adjacent times in the M times is the statistical period, and M is an integer greater than 1. A calculation unit is configured to calculate the growth rate of the data volume at each time moment based on M data volumes in the data volume set, thereby obtaining a first growth rate set, wherein the first growth rate set includes M growth rates; The monitoring unit is used to monitor the amount of data in the target data table based on the first growth rate set and the alarm threshold corresponding to the alarm type; The monitoring configuration data further includes: a data table identifier for the target data table, filtering conditions for filtering the target data in the target data table, and a statistics unit, comprising: a filtering subunit, used to filter the target data in the target data table based on the data table identifier and the filtering conditions at each time point in the target time period to obtain data table data at each time point; a statistics subunit, used to count the data volume of the data table data at each time point to obtain M data volumes; and a fourth determination subunit, used to determine the data volume set based on the M data volumes.
8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the database data monitoring method according to any one of claims 1 to 6.
9. An electronic device, characterized in that, It includes one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the database data monitoring method according to any one of claims 1 to 6.