WO2018228049A1 - Database performance index monitoring method, apparatus and device, and storage medium - Google Patents

Database performance index monitoring method, apparatus and device, and storage medium Download PDF

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Publication number
WO2018228049A1
WO2018228049A1 PCT/CN2018/083703 CN2018083703W WO2018228049A1 WO 2018228049 A1 WO2018228049 A1 WO 2018228049A1 CN 2018083703 W CN2018083703 W CN 2018083703W WO 2018228049 A1 WO2018228049 A1 WO 2018228049A1
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Prior art keywords
performance indicator
value
current
performance
reference threshold
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PCT/CN2018/083703
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French (fr)
Chinese (zh)
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张卫中
陈亚殊
黄伟星
顾怡婷
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平安科技（深圳）有限公司
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Publication of WO2018228049A1 publication Critical patent/WO2018228049A1/en

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/30—Monitoring
    • G06F11/3003—Monitoring arrangements specially adapted to the computing system or computing system component being monitored
    • G06F11/302—Monitoring arrangements specially adapted to the computing system or computing system component being monitored where the computing system component is a software system
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/30—Monitoring
    • G06F11/32—Monitoring with visual or acoustical indication of the functioning of the machine
    • G06F11/323—Visualisation of programs or trace data
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/30—Monitoring
    • G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment
    • G06F11/3409—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment for performance assessment
    • G06F11/3419—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment for performance assessment by assessing time
    • G06F11/3423—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment for performance assessment by assessing time where the assessed time is active or idle time
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/30—Monitoring
    • G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment
    • G06F11/3452—Performance evaluation by statistical analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/30—Monitoring
    • G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment
    • G06F11/3466—Performance evaluation by tracing or monitoring
    • G06F11/3476—Data logging
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2201/00—Indexing scheme relating to error detection, to error correction, and to monitoring
    • G06F2201/80—Database-specific techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2201/00—Indexing scheme relating to error detection, to error correction, and to monitoring
    • G06F2201/81—Threshold
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2201/00—Indexing scheme relating to error detection, to error correction, and to monitoring
    • G06F2201/865—Monitoring of software

Definitions

  • the present invention relates to the field of database performance monitoring technologies, and in particular, to a method, device, device and storage medium for monitoring database performance indicators.
  • Database-based application systems are very widely used in enterprise applications, and the application system response is slow due to database performance problems. Therefore, it is necessary to collect statistical information on the performance of the database in daily operation and maintenance.
  • the existing APM analysis tool can provide the performance index value of the database, it lacks the analysis and judgment of the performance index value, so that it is inconvenient to conduct trend analysis and abnormal judgment.
  • the embodiment of the present application provides a method, a device, a device, and a storage medium for monitoring database performance indicators, which can perform centralized analysis of multi-dimensional data on performance indexes of a database, so as to perform trend analysis and abnormal judgment.
  • an embodiment of the present application provides a method for monitoring a database performance indicator, where the method includes: creating an association relationship between a performance indicator of a database and a database dimension; and collecting sub-performance corresponding to each performance indicator at different dimensions.
  • the indicator value is obtained by accumulating the sub-performance index values of the same performance indicator at different dimensions to obtain the current performance index value of the performance indicator; and obtaining a plurality of historical performance index values corresponding to each performance indicator, where the historical performance indicator value is
  • the current performance indicator value corresponds to the current performance indicator value of each performance indicator and a preset algorithm to analyze the current performance indicator value of the performance indicator to obtain an analysis result.
  • the embodiment of the present application provides a monitoring device for database performance indicators, where the device includes a creating unit for creating a relationship between a performance index of a database and a database dimension, and an acquiring unit for collecting each performance.
  • the accumulating unit is used to accumulate the sub-performance index value of the same performance indicator in different dimensions to obtain the current performance index value of the performance indicator;
  • the acquiring unit is used to acquire each a plurality of historical performance index values corresponding to the performance indicators, where the historical performance index values correspond to the current performance indicator values;
  • the analyzing unit is configured to analyze the historical performance index values and preset algorithms according to each performance indicator The current performance indicator value of the performance indicator to obtain the analysis result.
  • an embodiment of the present application provides a computer readable storage medium having one or more programs, the one or more programs being executable by one or more processors to implement the following steps: The relationship between the performance index of the database and the database dimension is created; the sub-performance index value corresponding to each performance indicator at different dimensions is collected; and the sub-performance index values of the same performance indicator at different dimensions are accumulated to obtain the performance index. a current performance indicator value; obtaining a plurality of historical performance indicator values corresponding to each performance indicator, where the historical performance indicator value corresponds to the current performance indicator value; and the historical performance indicator value and the pre-determination according to each performance indicator An algorithm is provided to analyze the current performance index value of the performance indicator to obtain an analysis result.
  • an embodiment of the present application provides a monitoring device for database performance indicators, where the device includes: a memory and a processor; the memory is configured to store at least one computer program; the processor reads the memory The computer program performs the following operations: creating a relationship between the performance index of the database and the database dimension; collecting sub-performance index values corresponding to each performance indicator at different dimensions; and sub-performance indicators of the same performance indicator at different dimensions The values are accumulated to obtain the current performance indicator value of the performance indicator; and the plurality of historical performance indicator values corresponding to each performance indicator are obtained, where the historical performance indicator value corresponds to the current performance indicator value; The historical performance indicator value and a preset algorithm analyze the current performance indicator value of the performance indicator to obtain an analysis result.
  • the monitoring method of the database performance index in the embodiment of the present application can perform multi-dimensional centralized analysis on the performance index of the database, so as to perform trend analysis and abnormal judgment.
  • FIG. 1 is a schematic flowchart of a method for monitoring a database performance indicator provided by an embodiment of the present application
  • FIG. 2 is a schematic diagram of a sub-flow of S105 in FIG. 1;
  • FIG. 3 is a schematic diagram of a sub-flow of S203 in FIG. 2;
  • FIG. 4 is a schematic flowchart of a method for monitoring a database performance indicator according to another embodiment of the present application.
  • FIG. 5 is a monitoring diagram of CPU usage time provided by an embodiment of the present application.
  • FIG. 6 is a monitoring graph of I/O waiting time provided by an embodiment of the present application.
  • FIG. 8 is a schematic block diagram of a monitoring device for database performance indicators according to an embodiment of the present application.
  • Figure 9 is a schematic block diagram of the analysis unit of Figure 8.
  • Figure 10 is a schematic block diagram of the judging unit of Figure 9;
  • FIG. 11 is a schematic block diagram of a monitoring device for database performance indicators according to another embodiment of the present application.
  • FIG. 12 is a schematic block diagram of a monitoring device for database performance indicators provided by an embodiment of the present application.
  • FIG. 1 is a schematic flowchart of a method for monitoring a database performance indicator according to an embodiment of the present application. As shown in the figure, the monitoring method of the database performance indicator includes steps S101 to S105.
  • the performance indicator includes a CPU (Central Processing Unit) usage time, an I/O (input/output, input/output port) waiting time, and a lock waiting time.
  • CPU Central Processing Unit
  • I/O input/output, input/output port
  • lock waiting time a time period for the performance indicators of the database.
  • the performance indicators of the database are not limited thereto, and may also include disk usage, memory usage, and the like.
  • Dimensions are organized hierarchies in the database that describe the classification of data. These categories and structures describe a collection of similar members that can be analyzed based on these sets of members. As in the embodiment of the present application, the dimensions may include SQL, EVENT, OBJECT, and USER.
  • SQL is a structured query statement
  • EVENT is an event
  • OBJECT is an object
  • objects in the database can be tables, stored procedures, functions, views, etc.
  • USER is the user.
  • the dimension can also include an INSTANCE instance.
  • the relationship between the performance metrics of the database and the database dimensions you can generate them through the table structure.
  • the CPU usage time, I/O wait time, and Lock wait time can be used as column items of the table
  • the database dimensions SQL, EVENT, OBJECT, and USER are used as row items of the table, thereby associating the database dimension with the performance indicator.
  • the relationship between the performance index and the dimension of the database is not limited to the form of the table structure.
  • each performance indicator has a corresponding sub-performance index value at different dimensions.
  • the sub-performance indicator values corresponding to the CPU usage time at different dimensions include SQL, EVENT, OBJECT, and USER respectively.
  • Sub-performance index value; the sub-performance index value corresponding to the I/O waiting time at different dimensions includes the sub-performance index values corresponding to SQL, EVENT, OBJECT, and USER respectively; the sub-performance index values corresponding to the lock waiting time at different dimensions It includes sub-performance indicator values corresponding to SQL, EVENT, OBJECT, and USER respectively.
  • the current performance indicator value is used to indicate the current running performance of the database, which is characterized by collecting the sum of the sub-performance index values corresponding to each performance indicator at different dimensions.
  • the current performance index values of different performance indicators are obtained according to the relationship between the performance index and the dimension of the created database and the sub-performance index values corresponding to each performance index collected in different dimensions.
  • the collected CPU usage time is accumulated in the dimension SQL, EVENT, OBJECT, and USER sub-performance index values to obtain the current performance index value of the CPU usage time, and the current performance indicator value is marked as X1;
  • the waiting time is accumulated in the dimension SQL, EVENT, OBJECT, and USER sub-performance indicator values to obtain the current performance index value of the I/O waiting time, and the current performance indicator value is marked as X2;
  • the collected lock waiting time is in the dimension SQL.
  • the EVENT, OBJECT, and USER sub-performance index values are accumulated to obtain the current performance index value of the Lock waiting time, and the current performance index value is marked as X3.
  • the current performance indicator value represents the performance indicator value of the current time database, such as the performance index value of the database at 3:3 AM in 2017.
  • the historical performance indicator value corresponds to the current performance indicator value.
  • the historical performance indicator value is the sum of the sub-performance indicator values of the predetermined number of each performance indicator at different dimensions. For example, the sub-performance index value corresponding to each performance index of each performance indicator at 11:00 am between 2/3 and 3/3 in 2017 is collected, and the historical performance index of the preset quantity is obtained according to the obtained sub-performance index value. value.
  • FIG. 2 is a schematic diagram of a sub-flow of step S105. As shown in the figure, step S105 includes steps S201 to S203.
  • each performance indicator includes 29 historical performance index values.
  • the average and standard deviation of the CPU usage time are calculated according to the 29 historical performance index values of the CPU usage time, and the average value corresponding to the CPU usage time is marked as ⁇ 1, and the corresponding standard deviation is marked as ⁇ 1; waiting according to I/O
  • the 29 historical performance index values of time are calculated to obtain the mean and standard deviation of the I/O waiting time, and the average value corresponding to the I/O waiting time is marked as ⁇ 2, and the corresponding standard deviation is marked as ⁇ 2; according to the lock waiting time
  • the mean value and standard deviation of the Lock waiting time are calculated for the 29 historical performance index values, and the mean value corresponding to the Lock waiting time is marked as ⁇ 3, and the corresponding standard deviation is marked as ⁇ 3.
  • the Six Sigma method uses a standard deviation of six times as a reference threshold range for determining whether each current performance index value is normal. It can be seen that the reference threshold range of the current performance index value X1 of the CPU usage time is ⁇ 1-6 ⁇ 1, ⁇ 1+6 ⁇ 1 ⁇ ; the reference threshold range of the current performance index value X2 of the I/O waiting time is ⁇ 2-6 ⁇ 2, ⁇ 2 +6 ⁇ 2 ⁇ ; The reference threshold range of the current performance index value X3 of the lock waiting time is ⁇ 3-6 ⁇ 3, ⁇ 3+6 ⁇ 3 ⁇ .
  • FIG. 3 is a schematic diagram of a sub-flow of step S203. As shown in the figure, S203 includes steps S301 to S303.
  • the current performance index value X1 of the CPU usage time is within the reference threshold range ⁇ 1-6 ⁇ 1, ⁇ 1+6 ⁇ 1 ⁇ , and whether the current performance index value X2 of the I/O waiting time is within the reference threshold range ⁇ 2-6 ⁇ 2 , within ⁇ 2+6 ⁇ 2 ⁇ , and whether the current performance index value X3 of the lock waiting time is within the reference threshold range ⁇ 3-6 ⁇ 3, ⁇ 3+6 ⁇ 3 ⁇ .
  • the current performance index value X1 of the CPU usage time is within the reference threshold range ⁇ 1-6 ⁇ 1, ⁇ 1+6 ⁇ 1 ⁇ , that is, ⁇ 1-6 ⁇ 1 ⁇ X1 ⁇ 1+6 ⁇ 1, it is determined that the current performance index value is normal, otherwise the determination is performed.
  • the performance index value is abnormal; if the current performance index value X2 of the I/O waiting time is within the reference threshold range ⁇ 2-6 ⁇ 2, ⁇ 2+6 ⁇ 2 ⁇ , that is, ⁇ 2-6 ⁇ 2 ⁇ X2 ⁇ 2+6 ⁇ 2, the current performance index is determined.
  • the value is normal, otherwise it is determined that the performance index value is abnormal; if the current performance index value X3 of the Lock waiting time is within the reference threshold range ⁇ 3-6 ⁇ 3, ⁇ 3+6 ⁇ 3 ⁇ , it is determined that the current performance index value is normal, otherwise the performance index is determined. The value is abnormal.
  • the relationship between the performance index of the database and the database dimension is created, and the sub-performance index values corresponding to each performance indicator in different dimensions are collected, and then the sub-performance index values of the same performance indicator at different dimensions are obtained.
  • the current performance index value of the performance indicator is obtained, and the historical performance index values corresponding to each performance indicator are obtained, and the current performance index value corresponding to each performance indicator is analyzed according to the historical performance index value and a preset algorithm to obtain Analysis results.
  • the embodiment of the present application can perform multi-dimensional centralized analysis on performance indexes of the database for trend analysis and abnormal judgment.
  • FIG. 4 is a schematic flowchart of a method for monitoring database performance indicators according to another embodiment of the present application. As shown, the method includes S401 to S410.
  • steps S401 to S404 are the same as steps S101 to S104, and details are not described herein again.
  • steps S405 to S407 are the same as steps S201 to S203, and details are not described herein again.
  • the embodiment of the present application provides a monitoring graph of CPU usage time, I/O waiting time, and lock waiting time.
  • the current performance index value X1 of the CPU usage time obeys a normal distribution with a mathematical expectation of ⁇ 1 and a standard deviation of ( ⁇ 1) 2 with a reference threshold range of ⁇ 1-6 ⁇ 1, ⁇ 1+6 ⁇ 1 ⁇ ;
  • the current performance index value X2 of the O wait time obeys a normal distribution with a mathematical expectation of ⁇ 2 and a standard deviation of ( ⁇ 2) 2 , and its reference threshold range is ⁇ 2-6 ⁇ 2, ⁇ 2+6 ⁇ 2 ⁇ ; current performance index of Lock waiting time
  • the value X3 obeys a normal distribution with a mathematical expectation of ⁇ 3 and a standard deviation of ( ⁇ 3) 2 with a reference threshold range of ⁇ 3-6 ⁇ 3, ⁇ 3+6 ⁇ 3 ⁇ .
  • the mark corresponding to the current performance indicator value is on the corresponding monitoring graph.
  • the current performance index value X1 of the CPU usage time appears within the reference threshold range in the monitoring pattern as ⁇ 1-6 ⁇ 1, ⁇ 1+6 ⁇ 1 ⁇ , and thus the current performance index value X1 of the CPU usage time is normal.
  • the current performance index value X2 of the I/O waiting time appears in the monitoring graph, and the reference threshold range is outside the range of ⁇ 2-6 ⁇ 2, ⁇ 2+6 ⁇ 2 ⁇ , and is located on the right side of ⁇ 2+6 ⁇ 2.
  • the current performance indicator value X2 of the I/O wait time is abnormal, and the current performance indicator value is greater than the upper limit of the reference threshold range.
  • FIG. 5 the current performance index value X1 of the CPU usage time appears within the reference threshold range in the monitoring pattern as ⁇ 1-6 ⁇ 1, ⁇ 1+6 ⁇ 1 ⁇ , and thus the current performance index value X1 of the CPU usage time is normal.
  • the current performance index value X3 of the lock waiting time appears within the reference threshold range of the monitoring graph as ⁇ 3-6 ⁇ 3, ⁇ 3+6 ⁇ 3 ⁇ , and is located on the left side of ⁇ 3-6 ⁇ 3, thereby knowing the lock waiting time.
  • the current performance indicator value X3 is abnormal, and the current performance indicator value is less than the lower limit of the reference threshold range.
  • the relationship between the performance index of the database and the database dimension is created, and the sub-performance index values corresponding to each performance indicator in different dimensions are collected, and then the sub-performance index values of the same performance indicator at different dimensions are obtained.
  • the current performance index value of the performance indicator is obtained, and the historical performance index values corresponding to each performance indicator are obtained, and the current performance index value corresponding to each performance indicator is analyzed according to the historical performance index value and a preset algorithm to obtain Analysis results.
  • the embodiment of the present application displays the monitoring pattern corresponding to the performance indicator correspondingly and marks the current performance index on the monitoring graphic to perform multi-dimensional centralized analysis on the performance index of the database, so as to intuitively perform trend analysis and Abnormal judgment.
  • FIG. 8 is a schematic block diagram of a monitoring device for database performance indicators according to an embodiment of the present application.
  • the apparatus 50 in this embodiment as shown includes a creation unit 51, an acquisition unit 52, an accumulation unit 53, an acquisition unit 54, and an analysis unit 55.
  • the creating unit 51 is configured to create an association between the performance indicator of the database and the database dimension.
  • the collecting unit 52 is configured to collect sub-performance index values corresponding to each performance indicator at different dimensions.
  • the accumulating unit 53 is configured to accumulate the sub-performance index values of the same performance indicator at different dimensions to obtain the current performance index value of the performance indicator.
  • the obtaining unit 54 is configured to obtain a plurality of historical performance indicator values corresponding to each performance indicator, where the historical performance indicator value corresponds to the current performance indicator value.
  • the analyzing unit 55 is configured to analyze the current performance indicator value of the performance indicator according to the historical performance index value and the preset algorithm of each performance indicator to obtain an analysis result.
  • FIG. 9 is a schematic structural diagram of the analyzing unit 55 of FIG. As shown, the analysis unit 55 includes a calculation unit 551, a determination unit 552, and a determination unit 553.
  • the accumulating unit 551 is configured to calculate, according to the historical performance index value, a mean and a standard deviation corresponding to each performance indicator at different dimension information.
  • the determining unit 552 is configured to determine a reference threshold range of each current performance indicator value according to the mean, standard deviation, and Six Sigma method.
  • the determining unit 553 is configured to determine a current performance indicator value of the performance indicator according to the determined reference threshold range to obtain an analysis result.
  • FIG. 10 is a schematic structural diagram of the determining unit 553 of FIG. As shown in the figure, the judging unit 553 includes a collating unit 5531 and a judging unit 5532.
  • the checking unit 5531 is configured to determine whether the current performance indicator value of the performance indicator is within the reference threshold.
  • the determining unit 5532 is configured to determine that the current performance indicator value is normal if the current performance indicator value is within the reference threshold value, and determine the current performance if the current performance indicator value is not within the reference threshold range The indicator value is abnormal.
  • the relationship between the performance index of the database and the database dimension is created, and the sub-performance index values corresponding to each performance indicator in different dimensions are collected, and then the sub-performance index values of the same performance indicator at different dimensions are obtained.
  • the current performance index value of the performance indicator is obtained, and the historical performance index values corresponding to each performance indicator are obtained, and the current performance index value corresponding to each performance indicator is analyzed according to the historical performance index value and a preset algorithm to obtain Analysis results.
  • the embodiment of the present application can perform multi-dimensional centralized analysis on performance indexes of the database for trend analysis and abnormal judgment.
  • FIG. 11 is a schematic block diagram of a monitoring device for database performance indicators according to another embodiment of the present application.
  • the device 60 in the embodiment as shown in the figure includes a creating unit 61, an collecting unit 62, an accumulating unit 63, an obtaining unit 64, a calculating unit 65, a determining unit 66, a judging unit 67, a drawing unit 68, a display unit 69, and Marking unit 70.
  • the creating unit 61 is configured to create an association between the performance indicator of the database and the database dimension.
  • the collecting unit 62 is configured to collect sub-performance index values corresponding to each performance indicator at different dimensions.
  • the accumulating unit 63 is configured to accumulate the sub-performance index values of the same performance indicator at different dimensions to obtain the current performance index value of the performance indicator.
  • the obtaining unit 64 is configured to obtain a plurality of historical performance indicator values corresponding to each performance indicator, where the historical performance indicator value corresponds to the current performance indicator value.
  • the calculating unit 65 is configured to calculate, according to the historical performance indicator value, a mean and a standard deviation corresponding to each performance indicator at different dimension information.
  • the determining unit 66 is configured to determine a reference threshold range of each current performance indicator value according to the mean, standard deviation, and Six Sigma method.
  • the determining unit 67 is configured to determine a current performance indicator value of the performance indicator according to the determined reference threshold range to obtain an analysis result.
  • a rendering unit 68 is configured to plot a monitoring pattern for each performance indicator based on the mean, standard deviation, and six sigma methods.
  • the display unit 69 is configured to display a monitoring graph of each performance indicator.
  • the marking unit 70 is configured to mark the current performance indicator value on the monitoring graphic.
  • the relationship between the performance index of the database and the database dimension is created, and the sub-performance index values corresponding to each performance indicator in different dimensions are collected, and then the sub-performance index values of the same performance indicator at different dimensions are obtained.
  • the current performance index value of the performance indicator is obtained, and the historical performance index values corresponding to each performance indicator are obtained, and the current performance index value corresponding to each performance indicator is analyzed according to the historical performance index value and a preset algorithm to obtain Analysis results.
  • the embodiment of the present application displays the monitoring pattern corresponding to the performance indicator correspondingly and marks the current performance index on the monitoring graphic to perform multi-dimensional centralized analysis on the performance index of the database, so as to intuitively perform trend analysis and Abnormal judgment.
  • the units described in the embodiments of the present application may also be stored in a computer readable storage medium if they are implemented in the form of software functional modules and sold or used as separate products. Based on such understanding, the technical solution of the embodiments of the present application may be embodied in the form of a software product in essence or in the form of a software product stored in a storage medium, including a plurality of instructions.
  • a computer device (which may be a personal computer, server, or network device, etc.) is caused to perform all or part of the methods described in various embodiments of the present application.
  • the storage medium includes various media that can store program codes, such as a USB flash drive, a removable hard disk, a read only memory (ROM), a magnetic disk, or an optical disk.
  • ROM read only memory
  • embodiments of the present application are not limited to any particular combination of hardware and software.
  • the embodiment of the present application further describes a computer readable storage medium having one or more programs, the one or more programs being executable by one or more processors to implement the following steps: The relationship between the performance index of the database and the database dimension; collecting the sub-performance index values corresponding to each performance indicator at different dimensions; and accumulating the sub-performance index values of the same performance indicator at different dimensions to obtain the current performance index a performance indicator value; obtaining a plurality of historical performance indicator values corresponding to each performance indicator, where the historical performance indicator value corresponds to the current performance indicator value; and the historical performance indicator value and the preset according to each performance indicator
  • the algorithm analyzes the current performance indicator value of the performance indicator to obtain an analysis result.
  • the analyzing the current performance indicator value of the performance indicator according to the historical performance indicator value of each performance indicator and the preset algorithm to obtain the analysis result includes the following steps: calculating each performance indicator according to the historical performance indicator value. Corresponding mean and standard deviation; determining a reference threshold range of each current performance indicator value according to the mean, standard deviation, and Six Sigma method; determining a current performance indicator value of the performance indicator according to the determined reference threshold range for analysis result.
  • the determining, according to the determined reference threshold range, the current performance indicator value of the performance indicator to obtain an analysis result includes the following steps: determining whether a current performance indicator value of the performance indicator is within the reference threshold; If the current performance indicator value is within the reference threshold, the current performance indicator value is determined to be normal; if the current performance indicator value is not within the reference threshold, the current performance indicator value is determined to be abnormal.
  • the one or more programs may be executed by the one or more processors to implement the steps of: drawing a monitoring graphic corresponding to each performance indicator according to the mean, standard deviation, and six sigma method; displaying each a monitoring graph of a performance indicator; marking the current performance indicator value on the monitoring graph.
  • the performance indicator includes CPU usage time, I/O waiting time, and Lock waiting time, and the dimensions include SQL, EVENT, OBJECT, and USER.
  • FIG. 12 is a schematic block diagram of a monitoring device for database performance indicators according to an embodiment of the present application.
  • the device 80 includes one or several processors 81, a memory 82, one or several input devices 83, one or several output devices 84.
  • the processor 81, the input device 83, the output device 84, and the memory 82 are connected via a bus 85.
  • the input device 83 is for the user to input an operation command.
  • the input device 83 of the embodiment of the present application may include a keyboard, a mouse, a photoelectric input device, a sound input device, a touch input device, and the like.
  • Output device 84 is used to display a monitoring pattern for each performance indicator.
  • the output device 84 of the embodiment of the present application may include a display, a display screen, a touch screen, and the like.
  • the memory 82 is configured to store a computer program and data for enabling the monitoring device to perform a specific function and operation, for example, to store a program and data for enabling the monitoring device to implement database performance indicator detection.
  • the memory 82 of the embodiment of the present application may be It is a system memory, such as non-volatile (such as ROM, flash memory, etc.).
  • the memory 82 of the embodiment of the present application may also be an external memory outside the system, such as a magnetic disk, an optical disk, a magnetic tape, or the like.
  • the processor 81 is operative to execute computer programs and data stored in the memory 82 to perform the following operations:
  • the relationship between the performance index of the database and the database dimension is created; the sub-performance index value corresponding to each performance indicator at different dimensions is collected; and the sub-performance index values of the same performance indicator at different dimensions are accumulated to obtain the performance index.
  • a current performance indicator value obtaining a plurality of historical performance indicator values corresponding to each performance indicator, where the historical performance indicator value corresponds to the current performance indicator value; and the historical performance indicator value and the pre-determination according to each performance indicator
  • An algorithm is provided to analyze the current performance index value of the performance indicator to obtain an analysis result.
  • the analyzing the historical performance indicator value of the performance indicator according to the historical performance indicator value of each performance indicator and the preset algorithm to obtain the analysis result includes: calculating, according to the historical performance indicator value, each performance indicator Mean and standard deviation; determining a reference threshold range of each current performance indicator value according to the mean, standard deviation, and Six Sigma method; determining a current performance indicator value of the performance indicator according to the determined reference threshold range to obtain an analysis result.
  • determining the current performance indicator value of the performance indicator according to the determined reference threshold range to obtain the analysis result includes: determining whether a current performance indicator value of the performance indicator is within the reference threshold; if the current performance If the indicator value is within the reference threshold, it is determined that the current performance indicator value is normal; if the current performance indicator value is not within the reference threshold range, determining that the current performance indicator value is abnormal.
  • the processor 81 further performs: calculating a monitoring graphic corresponding to each performance indicator according to the mean, standard deviation, and six sigma method; displaying a monitoring graphic of each performance indicator; marking the monitoring graphic on the monitoring graphic The current performance indicator value is described.
  • performance indicators include CPU usage time, I/O wait time, and Lock wait time
  • dimensions include SQL, EVENT, OBJECT, and USER.
  • the disclosed terminal and method may be implemented in other manners.
  • the device device embodiments described above are merely illustrative.
  • the division of the unit is only a logical function division.
  • there may be another division manner for example, multiple units or components may be combined. Or it can be integrated into another system, or some features can be ignored or not executed.
  • the mutual coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interface, device device or unit, or an electrical, mechanical or other form of connection.
  • a person skilled in the art can understand that all or part of the steps of implementing the method of the foregoing embodiments may be completed by hardware, or may be instructed by a program to execute related hardware, and the program may be stored in a computer readable storage medium.
  • the storage medium mentioned above may be a read only memory, a magnetic disk or an optical disk or the like.
  • the steps in the method of the foregoing embodiment of the present application may be sequentially adjusted, merged, and deleted according to actual needs.
  • the units in the terminal in the embodiment of the present application may be combined, divided, and deleted according to actual needs.
  • each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.
  • the above integrated unit can be implemented in the form of hardware or in the form of a software functional unit.
  • the integrated unit if implemented in the form of a software functional unit and sold or used as a standalone product, may be stored in a computer readable storage medium.
  • the technical solution of the present application may be in essence or part of the contribution to the prior art, or all or part of the technical solution may be embodied in the form of a software product stored in a storage medium.
  • There are a number of instructions for causing a computer device (which may be a personal computer, terminal, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present application.

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Abstract

Disclosed are a database performance index monitoring method, apparatus and device, and a storage medium. The method comprises: collecting a performance sub-index value corresponding to each performance index of a database at different dimensions of the database; accumulating performance sub-index values of the same performance index at the different dimensions so as to obtain the current performance index value; and analysing, according to a historical performance index value of each performance index and a pre-set algorithm, the current performance index value so as to obtain an analysis result.

Description

数据库性能指标的监测方法、装置、设备及存储介质Method, device, device and storage medium for monitoring database performance indicators
本申请要求于2017年6月15日提交中国专利局、申请号为201710454882.2、发明名称为“一种数据库性能指标的监测方法、装置、计算机可读存储介质及设备”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。This application claims priority to Chinese patent application filed on June 15, 2017, the Chinese Patent Office, application number 201710454882.2, and the invention titled "A monitoring method, device, computer readable storage medium and device for database performance indicators" The entire content of which is incorporated herein by reference.
技术领域Technical field
本申请涉及数据库性能监控技术领域，尤其涉及一种数据库性能指标的监测方法、装置、设备及存储介质。The present invention relates to the field of database performance monitoring technologies, and in particular, to a method, device, device and storage medium for monitoring database performance indicators.
背景技术Background technique
基于数据库的应用系统在企业应用中非常广泛，由于数据库的性能问题导致应用系统响应慢等情况时有发生。因此，在日常的运维中对于数据库的性能状况做到定期收集统计信息是非常必要的。然而，现有的APM分析工具虽然可以提供数据库的性能指标值，但缺少对性能指标值的分析判断，从而不便进行趋势分析和异常判断。Database-based application systems are very widely used in enterprise applications, and the application system response is slow due to database performance problems. Therefore, it is necessary to collect statistical information on the performance of the database in daily operation and maintenance. However, although the existing APM analysis tool can provide the performance index value of the database, it lacks the analysis and judgment of the performance index value, so that it is inconvenient to conduct trend analysis and abnormal judgment.
发明内容Summary of the invention
本申请实施例提供一种数据库性能指标的监测方法、装置、设备及存储介质，其能够对数据库的性能指标进行多维度数据的集中分析，以便进行趋势分析和异常判断。The embodiment of the present application provides a method, a device, a device, and a storage medium for monitoring database performance indicators, which can perform centralized analysis of multi-dimensional data on performance indexes of a database, so as to perform trend analysis and abnormal judgment.
第一方面，本申请实施例提供了一种数据库性能指标的监测方法，该方法包括：创建数据库的性能指标与数据库维度之间的关联关系；采集每一性能指标在不同维度处所对应的子性能指标值；将同一性能指标在不同维度处的子性能指标值累加以得到该性能指标的当前性能指标值；获取每一性能指标所对应的多个历史性能指标值，所述历史性能指标值与所述当前性能指标值相对应；根据每一性能指标的所述历史性能指标值及预设算法分析该性能指标的当前性能指标值以得到分析结果。In a first aspect, an embodiment of the present application provides a method for monitoring a database performance indicator, where the method includes: creating an association relationship between a performance indicator of a database and a database dimension; and collecting sub-performance corresponding to each performance indicator at different dimensions. The indicator value is obtained by accumulating the sub-performance index values of the same performance indicator at different dimensions to obtain the current performance index value of the performance indicator; and obtaining a plurality of historical performance index values corresponding to each performance indicator, where the historical performance indicator value is The current performance indicator value corresponds to the current performance indicator value of each performance indicator and a preset algorithm to analyze the current performance indicator value of the performance indicator to obtain an analysis result.
第二方面，本申请实施例提供了一种数据库性能指标的监测装置，该装置包括创建单元，用于创建数据库的性能指标与数据库维度之间的关联关系；采集单元，用于采集每一性能指标在不同维度处所对应的子性能指标值；累加单元，用于将同一性能指标在不同维度处的子性能指标值累加以得到该性能指标的当前性能指标值；获取单元，用于获取每一性能指标所对应的多个历史性能指标值，所述历史性能指标值与所述当前性能指标值相对应；分析单元，用于根据每一性能指标的所述历史性能指标值及预设算法分析该性能指标的当前性能指标值以得到分析结果。In a second aspect, the embodiment of the present application provides a monitoring device for database performance indicators, where the device includes a creating unit for creating a relationship between a performance index of a database and a database dimension, and an acquiring unit for collecting each performance. The sub-performance indicator value corresponding to the indicator in different dimensions; the accumulating unit is used to accumulate the sub-performance index value of the same performance indicator in different dimensions to obtain the current performance index value of the performance indicator; the acquiring unit is used to acquire each a plurality of historical performance index values corresponding to the performance indicators, where the historical performance index values correspond to the current performance indicator values; the analyzing unit is configured to analyze the historical performance index values and preset algorithms according to each performance indicator The current performance indicator value of the performance indicator to obtain the analysis result.
第三方面，本申请实施例提供了一种计算机可读存储介质，所述存储介质有一个或者一个以上程序，该一个或者一个以上程序可被一个或者一个以上的处理器执行以实现以下步骤：创建数据库的性能指标与数据库维度之间的关联关系；采集每一性能指标在不同维度处所对应的子性能指标值；将同一性能指标在不同维度处的子性能指标值累加以得到该性能指标的当前性能指标值；获取每一性能指标所对应的多个历史性能指标值，所述历史性能指标值与所述当前性能指标值相对应；根据每一性能指标的所述历史性能指标值及预设算法分析该性能指标的当前性能指标值以得到分析结果。In a third aspect, an embodiment of the present application provides a computer readable storage medium having one or more programs, the one or more programs being executable by one or more processors to implement the following steps: The relationship between the performance index of the database and the database dimension is created; the sub-performance index value corresponding to each performance indicator at different dimensions is collected; and the sub-performance index values of the same performance indicator at different dimensions are accumulated to obtain the performance index. a current performance indicator value; obtaining a plurality of historical performance indicator values corresponding to each performance indicator, where the historical performance indicator value corresponds to the current performance indicator value; and the historical performance indicator value and the pre-determination according to each performance indicator An algorithm is provided to analyze the current performance index value of the performance indicator to obtain an analysis result.
第四方面，本申请实施例提供了一种数据库性能指标的监测设备，所述设备包括：存储器以及处理器；所述存储器用于存储至少一个计算机程序；所述处理器读取所述存储器中的计算机程序以执行以下操作：创建数据库的性能指标与数据库维度之间的关联关系；采集每一性能指标在不同维度处所对应的子性能指标值；将同一性能指标在不同维度处的子性能指标值累加以得到该性能指标的当前性能指标值；获取每一性能指标所对应的多个历史性能指标值，所述历史性能指标值与所述当前性能指标值相对应；根据每一性能指标的所述历史性能指标值及预设算法分析该性能指标的当前性能指标值以得到分析结果。In a fourth aspect, an embodiment of the present application provides a monitoring device for database performance indicators, where the device includes: a memory and a processor; the memory is configured to store at least one computer program; the processor reads the memory The computer program performs the following operations: creating a relationship between the performance index of the database and the database dimension; collecting sub-performance index values corresponding to each performance indicator at different dimensions; and sub-performance indicators of the same performance indicator at different dimensions The values are accumulated to obtain the current performance indicator value of the performance indicator; and the plurality of historical performance indicator values corresponding to each performance indicator are obtained, where the historical performance indicator value corresponds to the current performance indicator value; The historical performance indicator value and a preset algorithm analyze the current performance indicator value of the performance indicator to obtain an analysis result.
本申请实施例中的数据库性能指标的监测方法能够对数据库的性能指标进行多维度的集中分析，以便进行趋势分析和异常判断。The monitoring method of the database performance index in the embodiment of the present application can perform multi-dimensional centralized analysis on the performance index of the database, so as to perform trend analysis and abnormal judgment.
附图说明DRAWINGS
为了更清楚地说明本申请实施例技术方案，下面将对实施例描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图是本申请的一些实 施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings used in the description of the embodiments will be briefly described below. Obviously, the drawings in the following description are some embodiments of the present application, For the ordinary technicians, other drawings can be obtained based on these drawings without any creative work.
图1是本申请实施例提供的数据库性能指标的监测方法的流程示意图；1 is a schematic flowchart of a method for monitoring a database performance indicator provided by an embodiment of the present application;
图2是图1中S105的子流程示意图；2 is a schematic diagram of a sub-flow of S105 in FIG. 1;
图3是图2中S203的子流程示意图；3 is a schematic diagram of a sub-flow of S203 in FIG. 2;
图4是本申请另一实施例提供的数据库性能指标的监测方法的流程示意图；4 is a schematic flowchart of a method for monitoring a database performance indicator according to another embodiment of the present application;
图5是本申请实施例提供的CPU使用时间的监测图形；FIG. 5 is a monitoring diagram of CPU usage time provided by an embodiment of the present application; FIG.
图6是本申请实施例提供的I/O等待时间的的监测图形；6 is a monitoring graph of I/O waiting time provided by an embodiment of the present application;
图7是本申请实施例提供的Lock等待时间的监测图形；7 is a monitoring graph of Lock waiting time provided by an embodiment of the present application;
图8是本申请实施例提供的数据库性能指标的监测装置的示意性框图；FIG. 8 is a schematic block diagram of a monitoring device for database performance indicators according to an embodiment of the present application; FIG.
图9是图8中分析单元的示意性框图；Figure 9 is a schematic block diagram of the analysis unit of Figure 8;
图10是图9中判断单元的示意性框图；Figure 10 is a schematic block diagram of the judging unit of Figure 9;
图11是本申请另一实施例提供的数据库性能指标的监测装置的示意性框图；11 is a schematic block diagram of a monitoring device for database performance indicators according to another embodiment of the present application;
图12是本申请实施例提供的数据库性能指标的监测设备的示意性框图。FIG. 12 is a schematic block diagram of a monitoring device for database performance indicators provided by an embodiment of the present application.
具体实施方式detailed description
下面将结合本申请实施例中的附图，对本申请实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例是本申请一部分实施例，而不是全部的实施例。基于本申请中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本申请保护的范围。The technical solutions in the embodiments of the present application are clearly and completely described in the following with reference to the drawings in the embodiments of the present application. It is obvious that the described embodiments are a part of the embodiments of the present application, and not all of the embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments of the present application without departing from the inventive scope are the scope of the present application.
参见图1，图1是本申请实施例提供的一种数据库性能指标的监测方法的流程示意图。如图所示，该数据库性能指标的监测方法包括步骤S101～S105。Referring to FIG. 1, FIG. 1 is a schematic flowchart of a method for monitoring a database performance indicator according to an embodiment of the present application. As shown in the figure, the monitoring method of the database performance indicator includes steps S101 to S105.
S101，创建数据库的性能指标与数据库维度之间的关联关系。S101. Create an association between a performance indicator of the database and a database dimension.
具体地，在本申请实施例中，性能指标包括CPU(Central Processing Unit，中央处理器)使用时间、I/O(input/output，输入/输出端口)等待时间、Lock等待时间。可以理解地，数据库的性能指标并不局限于此，如还可以包括磁盘占用率、内存占用率等。维度是数据库中用来描述数据的分类的有组织层次结构，这些分类和结构描述了一些相似的成员集合，可将基于这些成员集合进行分析。如在本申请实施例中，维度可以包括SQL、EVENT、OBJECT、及USER等。其中，SQL为结构化查询语句，EVENT为事件，OBJECT为对象，数据库 中的对象可以是表、存储过程、函数、视图等，USER为用户。可以理解地，在一些其他实施例中，维度还可以包括INSTANCE实例。Specifically, in the embodiment of the present application, the performance indicator includes a CPU (Central Processing Unit) usage time, an I/O (input/output, input/output port) waiting time, and a lock waiting time. It can be understood that the performance indicators of the database are not limited thereto, and may also include disk usage, memory usage, and the like. Dimensions are organized hierarchies in the database that describe the classification of data. These categories and structures describe a collection of similar members that can be analyzed based on these sets of members. As in the embodiment of the present application, the dimensions may include SQL, EVENT, OBJECT, and USER. Among them, SQL is a structured query statement, EVENT is an event, OBJECT is an object, objects in the database can be tables, stored procedures, functions, views, etc., USER is the user. As can be appreciated, in some other embodiments, the dimension can also include an INSTANCE instance.
在创建数据库的性能指标与数据库维度之间的关联关系时，可以通过表结构来生成。如可以将CPU使用时间、I/O等待时间、Lock等待时间作为表的列项，将数据库维度SQL、EVENT、OBJECT、USER作为表的行项，从而实现将数据库维度和性能指标相关联。可以理解地，在其他一些实施例中，数据库的性能指标和维度的关联关系并不局限于表结构的形式。When you create the relationship between the performance metrics of the database and the database dimensions, you can generate them through the table structure. For example, the CPU usage time, I/O wait time, and Lock wait time can be used as column items of the table, and the database dimensions SQL, EVENT, OBJECT, and USER are used as row items of the table, thereby associating the database dimension with the performance indicator. It can be understood that in other embodiments, the relationship between the performance index and the dimension of the database is not limited to the form of the table structure.
S102，采集每一性能指标在不同维度处所对应的子性能指标值。S102. Collect sub-performance indicator values corresponding to each performance indicator at different dimensions.
具体地，如上所述，每一个性能指标在不同维度处对应有各自的子性能指标值，如CPU使用时间在不同维度处对应的子性能指标值包括SQL、EVENT、OBJECT、及USER分别对应的子性能指标值；I/O等待时间在不同维度处对应的子性能指标值包括SQL、EVENT、OBJECT、及USER分别对应的子性能指标值；Lock等待时间在不同维度处对应的子性能指标值包括SQL、EVENT、OBJECT、及USER分别对应的子性能指标值。Specifically, as described above, each performance indicator has a corresponding sub-performance index value at different dimensions. For example, the sub-performance indicator values corresponding to the CPU usage time at different dimensions include SQL, EVENT, OBJECT, and USER respectively. Sub-performance index value; the sub-performance index value corresponding to the I/O waiting time at different dimensions includes the sub-performance index values corresponding to SQL, EVENT, OBJECT, and USER respectively; the sub-performance index values corresponding to the lock waiting time at different dimensions It includes sub-performance indicator values corresponding to SQL, EVENT, OBJECT, and USER respectively.
S103，将同一性能指标在不同维度处的子性能指标值累加以得到该性能指标的当前性能指标值。S103. Accumulate the sub-performance indicator values of the same performance indicator at different dimensions to obtain a current performance indicator value of the performance indicator.
具体地，当前性能指标值用于指示数据库当前的运行性能，其通过采集每一个性能指标在不同维度处所对应的子性能指标值之和来表征。在本申请实施例中，根据创建的数据库的性能指标和维度的关联关系及采集到的每一性能指标在不同维度处所对应的子性能指标值来得到不同性能指标的当前性能指标值。即采集到的CPU使用时间在维度SQL、EVENT、OBJECT、USER处子性能指标值进行累加以得到CPU使用时间的当前性能指标值，并将该当前性能指标值标记为X1；采集到的I/O等待时间在维度SQL、EVENT、OBJECT、USER处子性能指标值进行累加以得到I/O等待时间的当前性能指标值，并将该当前性能指标值标记为X2；采集到的Lock等待时间在维度SQL、EVENT、OBJECT、USER处子性能指标值进行累加以得到Lock等待时间的当前性能指标值，并将该当前性能指标值标记为X3。该当前性能指标值表征当前时刻数据库的性能指标值，如2017年3/3上午11点的数据库的性能指标值。Specifically, the current performance indicator value is used to indicate the current running performance of the database, which is characterized by collecting the sum of the sub-performance index values corresponding to each performance indicator at different dimensions. In the embodiment of the present application, the current performance index values of different performance indicators are obtained according to the relationship between the performance index and the dimension of the created database and the sub-performance index values corresponding to each performance index collected in different dimensions. That is, the collected CPU usage time is accumulated in the dimension SQL, EVENT, OBJECT, and USER sub-performance index values to obtain the current performance index value of the CPU usage time, and the current performance indicator value is marked as X1; the collected I/O The waiting time is accumulated in the dimension SQL, EVENT, OBJECT, and USER sub-performance indicator values to obtain the current performance index value of the I/O waiting time, and the current performance indicator value is marked as X2; the collected lock waiting time is in the dimension SQL. The EVENT, OBJECT, and USER sub-performance index values are accumulated to obtain the current performance index value of the Lock waiting time, and the current performance index value is marked as X3. The current performance indicator value represents the performance indicator value of the current time database, such as the performance index value of the database at 3:3 AM in 2017.
S104，获取每一性能指标所对应的多个历史性能指标值，所述历史性能指标值与所述当前性能指标值相对应。S104. Acquire a plurality of historical performance indicator values corresponding to each performance indicator, where the historical performance indicator value corresponds to the current performance indicator value.
具体地，在本申请实施例中，历史性能指标值与当前性能指标值相对应。该历史性能指标值为预设数量的每一个性能指标在不同维度处的子性能指标值之和。如采集数据库2017年2/3～3/3之间每天上午11点每一性能指标在不同维度处所对应的子性能指标值，并根据获取的子性能指标值得到该预设数量的历史性能指标值。Specifically, in the embodiment of the present application, the historical performance indicator value corresponds to the current performance indicator value. The historical performance indicator value is the sum of the sub-performance indicator values of the predetermined number of each performance indicator at different dimensions. For example, the sub-performance index value corresponding to each performance index of each performance indicator at 11:00 am between 2/3 and 3/3 in 2017 is collected, and the historical performance index of the preset quantity is obtained according to the obtained sub-performance index value. value.
S105，根据每一性能指标的所述历史性能指标值及预设算法分析该性能指标的当前性能指标值以得到分析结果。S105. Analyze a current performance indicator value of the performance indicator according to the historical performance indicator value and a preset algorithm of each performance indicator to obtain an analysis result.
具体地，为了判断数据库的当前性指标值是否正常，在本申请实施例中，根据每一性能指标对应的历史性能指标值及预设算法来分析该性能指标的当前性能指标值以得到分析结果。参照图2，图2是步骤S105的子流程示意图。如图所示，步骤S105包括骤S201～S203。Specifically, in order to determine whether the current index value of the database is normal, in the embodiment of the present application, the current performance index value of the performance indicator is analyzed according to the historical performance index value and the preset algorithm corresponding to each performance indicator to obtain an analysis result. . Referring to FIG. 2, FIG. 2 is a schematic diagram of a sub-flow of step S105. As shown in the figure, step S105 includes steps S201 to S203.
S201，根据所述历史性能指标值计算每一性能指标对应的均值和标准差。S201. Calculate a mean value and a standard deviation corresponding to each performance indicator according to the historical performance index value.
具体地，如上所述，获取2017年2/3～3/3之间每天上午11点每一性能指标的历史性能指标值。由此可知，在此期间，每一个性能指标均包括29个历史性能指标值。如根据CPU使用时间的29个历史性能指标值计算得到CPU使用时间的均值和标准差，并将CPU使用时间对应的均值标记为μ1，将其对应的标准差标记为σ1；根据I/O等待时间的29个历史性能指标值计算得到I/O等待时间的均值和标准差，并将I/O等待时间对应的均值标记为μ2，将其对应的标准差标记为σ2；根据Lock等待时间的29个历史性能指标值计算得到Lock等待时间的均值和标准差，并将Lock等待时间对应的均值标记为μ3，将其对应的标准差标记为σ3。Specifically, as described above, the historical performance index value of each performance indicator at 11:00 am every day between 2/3 and 3/3 in 2017 is obtained. It can be seen that during this period, each performance indicator includes 29 historical performance index values. For example, the average and standard deviation of the CPU usage time are calculated according to the 29 historical performance index values of the CPU usage time, and the average value corresponding to the CPU usage time is marked as μ1, and the corresponding standard deviation is marked as σ1; waiting according to I/O The 29 historical performance index values of time are calculated to obtain the mean and standard deviation of the I/O waiting time, and the average value corresponding to the I/O waiting time is marked as μ2, and the corresponding standard deviation is marked as σ2; according to the lock waiting time The mean value and standard deviation of the Lock waiting time are calculated for the 29 historical performance index values, and the mean value corresponding to the Lock waiting time is marked as μ3, and the corresponding standard deviation is marked as σ3.
S202，根据所述均值、标准差及六西格玛法确定所述每一当前性能指标值的参考阈值范围。S202. Determine a reference threshold range of each current performance indicator value according to the mean, standard deviation, and six sigma method.
具体地，六西格玛法即采用六倍的标准差作为判定每一当前性能指标值是否正常的参考阈值范围。由此可知，CPU使用时间的当前性能指标值X1的参考阈值范围为{μ1-6σ1，μ1+6σ1}；I/O等待时间的当前性能指标值X2的参考阈值范围为{μ2-6σ2，μ2+6σ2}；Lock等待时间的当前性能指标值X3的参考阈值范围为{μ3-6σ3，μ3+6σ3}。Specifically, the Six Sigma method uses a standard deviation of six times as a reference threshold range for determining whether each current performance index value is normal. It can be seen that the reference threshold range of the current performance index value X1 of the CPU usage time is {μ1-6σ1, μ1+6σ1}; the reference threshold range of the current performance index value X2 of the I/O waiting time is {μ2-6σ2, μ2 +6σ2}; The reference threshold range of the current performance index value X3 of the lock waiting time is {μ3-6σ3, μ3+6σ3}.
S203，根据所确定的参考阈值范围判断该性能指标的当前性能指标值以得到分析结果。S203. Determine a current performance indicator value of the performance indicator according to the determined reference threshold range to obtain an analysis result.
具体地，根据步骤S202中得到的每一性能指标的当前性能指标值的参考阈值范围来确定当前性能指标值是否在参考阈值范围内以得到分析结果。进一步地，参照图3，图3是步骤S203的子流程示意图。如图所示，S203包括骤S301～S303。Specifically, according to the reference threshold range of the current performance indicator value of each performance indicator obtained in step S202, it is determined whether the current performance indicator value is within the reference threshold range to obtain an analysis result. Further, referring to FIG. 3, FIG. 3 is a schematic diagram of a sub-flow of step S203. As shown in the figure, S203 includes steps S301 to S303.
S301，判断该性能指标的当前性能指标值是否在所述参考阈值范围内。S301. Determine whether a current performance indicator value of the performance indicator is within the reference threshold.
具体地，判断CPU使用时间的当前性能指标值X1是否在参考阈值范围{μ1-6σ1，μ1+6σ1}内，判断I/O等待时间的当前性能指标值X2是否在参考阈值范围{μ2-6σ2，μ2+6σ2}内，以及判断Lock等待时间的当前性能指标值X3是否在参考阈值范围{μ3-6σ3，μ3+6σ3}内。Specifically, it is determined whether the current performance index value X1 of the CPU usage time is within the reference threshold range {μ1-6σ1, μ1+6σ1}, and whether the current performance index value X2 of the I/O waiting time is within the reference threshold range {μ2-6σ2 , within μ2+6σ2}, and whether the current performance index value X3 of the lock waiting time is within the reference threshold range {μ3-6σ3, μ3+6σ3}.
S302，若所述当前性能指标值在所述参考阈值范围内，则判定该当前性能指标值正常。S302. If the current performance indicator value is within the reference threshold, determine that the current performance indicator value is normal.
S303，若所述当前性能指标值不在所述参考阈值范围内，则判定该当前性能指标值异常。S303. If the current performance indicator value is not within the reference threshold, determine that the current performance indicator value is abnormal.
具体地，若CPU使用时间的当前性能指标值X1在参考阈值范围{μ1-6σ1，μ1+6σ1}内，即μ1-6σ1≤X1≤μ1+6σ1，则判定该当期性能指标值正常，否则判定该性能指标值异常；若I/O等待时间的当前性能指标值X2在参考阈值范围{μ2-6σ2，μ2+6σ2}内，即μ2-6σ2≤X2≤μ2+6σ2，则判定该当期性能指标值正常，否则判定该性能指标值异常；若Lock等待时间的当前性能指标值X3在参考阈值范围{μ3-6σ3，μ3+6σ3}内，则判定该当期性能指标值正常，否则判定该性能指标值异常。Specifically, if the current performance index value X1 of the CPU usage time is within the reference threshold range {μ1-6σ1, μ1+6σ1}, that is, μ1-6σ1≤X1≤μ1+6σ1, it is determined that the current performance index value is normal, otherwise the determination is performed. The performance index value is abnormal; if the current performance index value X2 of the I/O waiting time is within the reference threshold range {μ2-6σ2, μ2+6σ2}, that is, μ2-6σ2≤X2≤μ2+6σ2, the current performance index is determined. The value is normal, otherwise it is determined that the performance index value is abnormal; if the current performance index value X3 of the Lock waiting time is within the reference threshold range {μ3-6σ3, μ3+6σ3}, it is determined that the current performance index value is normal, otherwise the performance index is determined. The value is abnormal.
本申请实施例通过创建数据库的性能指标和数据库维度之间的关联关系，进而采集每一性能指标在不同维度处所对应的子性能指标值，然后将同一性能指标在不同维度处的子性能指标值累加以得到该性能指标的当前性能指标值，并获取每一性能指标所对应的多个历史性能指标值，根据历史性能指标值及预设算法分析每一性能指标对应的当前性能指标值以得到分析结果。本申请实施例能够对数据库的性能指标进行多维度的集中分析，以便进行趋势分析和异常判断。In the embodiment of the present application, the relationship between the performance index of the database and the database dimension is created, and the sub-performance index values corresponding to each performance indicator in different dimensions are collected, and then the sub-performance index values of the same performance indicator at different dimensions are obtained. The current performance index value of the performance indicator is obtained, and the historical performance index values corresponding to each performance indicator are obtained, and the current performance index value corresponding to each performance indicator is analyzed according to the historical performance index value and a preset algorithm to obtain Analysis results. The embodiment of the present application can perform multi-dimensional centralized analysis on performance indexes of the database for trend analysis and abnormal judgment.
参照图4，图4是本申请另一实施例提供的数据库性能指标的监测方法的示意流程图。如图所示，该方法包括S401～S410。Referring to FIG. 4, FIG. 4 is a schematic flowchart of a method for monitoring database performance indicators according to another embodiment of the present application. As shown, the method includes S401 to S410.
S401，创建数据库的性能指标与数据库维度之间的关联关系。S401. Create an association between a performance indicator of the database and a database dimension.
S402，采集每一性能指标在不同维度处所对应的子性能指标值。S402. Collect sub-performance index values corresponding to each performance indicator at different dimensions.
S403，将同一性能指标在不同维度处的子性能指标值累加以得到该性能指标的当前性能指标值。S403. Accumulate the sub-performance indicator values of the same performance indicator at different dimensions to obtain a current performance indicator value of the performance indicator.
S404，获取每一性能指标所对应的多个历史性能指标值，所述历史性能指标值与所述当前性能指标值相对应。S404. Acquire a plurality of historical performance indicator values corresponding to each performance indicator, where the historical performance indicator value corresponds to the current performance indicator value.
具体地，步骤S401～S404与步骤S101～S104相同，此处不再赘述。Specifically, steps S401 to S404 are the same as steps S101 to S104, and details are not described herein again.
S405，根据所述历史性能指标值计算每一性能指标对应的均值和标准差。S405. Calculate a mean value and a standard deviation corresponding to each performance indicator according to the historical performance indicator value.
S406，根据所述均值、标准差及六西格玛法确定所述每一当前性能指标值的参考阈值范围。S406. Determine a reference threshold range of each current performance indicator value according to the mean, standard deviation, and Six Sigma method.
S407，根据所确定的参考阈值范围判断该性能指标的当前性能指标值以得到分析结果。S407. Determine a current performance indicator value of the performance indicator according to the determined reference threshold range to obtain an analysis result.
具体地，步骤S405～S407与步骤S201～S203相同，此处不再赘述。Specifically, steps S405 to S407 are the same as steps S201 to S203, and details are not described herein again.
S408，根据所述均值、标准差及六西格玛法绘制每一性能指标对应的监测图形。S408: Draw a monitoring pattern corresponding to each performance indicator according to the mean value, the standard deviation, and the Six Sigma method.
S409，显示每一性能指标的监测图形。S409, displaying a monitoring graph of each performance indicator.
S410，在所述监测图形上标记所述该当前性能指标值。S410. Mark the current performance indicator value on the monitoring graphic.
具体地，参照图5至图7，是本申请实施例提供CPU使用时间、I/O等待时间及Lock等待时间的监测图形。如图所示，CPU使用时间的当前性能指标值X1服从一个数学期望为μ1、标准差为(σ1) 2的正态分布，其参考阈值范围为{μ1-6σ1，μ1+6σ1}；I/O等待时间的当前性能指标值X2服从一个数学期望为μ2、标准差为(σ2) 2的正态分布，其参考阈值范围为{μ2-6σ2，μ2+6σ2}；Lock等待时间的当前性能指标值X3服从一个数学期望为μ3、标准差为(σ3) 2的正态分布，其参考阈值范围为{μ3-6σ3，μ3+6σ3}。进一步地，将当前性能指标值对应的标记在对应的监测图形上。如图5所示，CPU使用时间的当前性能指标值X1出现监测图形中的参考阈值范围为{μ1-6σ1，μ1+6σ1}内，由此可知该CPU使用时间的当前性能指标值X1正常。如图6所示，I/O等待时间的当前性能指标值X2出现监测图形中的参考阈值范围为{μ2-6σ2，μ2+6σ2}外，并位于μ2+6σ2的右侧，由此可知该I/O等待时间的当前性能指标值X2异常，且该当前性能指标值大于参考阈值范围的上限。如图7所示，Lock等待时间的当前性能指标值X3出现监测图形中的参考阈值范围为{μ3-6σ3， μ3+6σ3}内，并位于μ3-6σ3的左侧，由此可知Lock等待时间的当前性能指标值X3异常，且该当前性能指标值小于参考阈值范围的下限。 Specifically, referring to FIG. 5 to FIG. 7 , the embodiment of the present application provides a monitoring graph of CPU usage time, I/O waiting time, and lock waiting time. As shown in the figure, the current performance index value X1 of the CPU usage time obeys a normal distribution with a mathematical expectation of μ1 and a standard deviation of (σ1) 2 with a reference threshold range of {μ1-6σ1, μ1+6σ1}; I/ The current performance index value X2 of the O wait time obeys a normal distribution with a mathematical expectation of μ2 and a standard deviation of (σ2) 2 , and its reference threshold range is {μ2-6σ2, μ2+6σ2}; current performance index of Lock waiting time The value X3 obeys a normal distribution with a mathematical expectation of μ3 and a standard deviation of (σ3) 2 with a reference threshold range of {μ3-6σ3, μ3+6σ3}. Further, the mark corresponding to the current performance indicator value is on the corresponding monitoring graph. As shown in FIG. 5, the current performance index value X1 of the CPU usage time appears within the reference threshold range in the monitoring pattern as {μ1-6σ1, μ1+6σ1}, and thus the current performance index value X1 of the CPU usage time is normal. As shown in FIG. 6, the current performance index value X2 of the I/O waiting time appears in the monitoring graph, and the reference threshold range is outside the range of {μ2-6σ2, μ2+6σ2}, and is located on the right side of μ2+6σ2. The current performance indicator value X2 of the I/O wait time is abnormal, and the current performance indicator value is greater than the upper limit of the reference threshold range. As shown in FIG. 7, the current performance index value X3 of the lock waiting time appears within the reference threshold range of the monitoring graph as {μ3-6σ3, μ3+6σ3}, and is located on the left side of μ3-6σ3, thereby knowing the lock waiting time. The current performance indicator value X3 is abnormal, and the current performance indicator value is less than the lower limit of the reference threshold range.
本申请实施例通过创建数据库的性能指标和数据库维度之间的关联关系，进而采集每一性能指标在不同维度处所对应的子性能指标值，然后将同一性能指标在不同维度处的子性能指标值累加以得到该性能指标的当前性能指标值，并获取每一性能指标所对应的多个历史性能指标值，根据历史性能指标值及预设算法分析每一性能指标对应的当前性能指标值以得到分析结果。本申请实施例通过将该性能指标对应的监测图形对应的显示出来并将当前性能指标对应的标记在监测图形上以实现对数据库的性能指标进行多维度的集中分析，以便直观地进行趋势分析和异常判断。In the embodiment of the present application, the relationship between the performance index of the database and the database dimension is created, and the sub-performance index values corresponding to each performance indicator in different dimensions are collected, and then the sub-performance index values of the same performance indicator at different dimensions are obtained. The current performance index value of the performance indicator is obtained, and the historical performance index values corresponding to each performance indicator are obtained, and the current performance index value corresponding to each performance indicator is analyzed according to the historical performance index value and a preset algorithm to obtain Analysis results. The embodiment of the present application displays the monitoring pattern corresponding to the performance indicator correspondingly and marks the current performance index on the monitoring graphic to perform multi-dimensional centralized analysis on the performance index of the database, so as to intuitively perform trend analysis and Abnormal judgment.
参照图8，图8是本申请实施例提供的一种数据库性能指标的监测装置的示意性框图。如图所示的本实施例中的装置50包括创建单元51、采集单元52、累加单元53、获取单元54、及分析单元55。Referring to FIG. 8, FIG. 8 is a schematic block diagram of a monitoring device for database performance indicators according to an embodiment of the present application. The apparatus 50 in this embodiment as shown includes a creation unit 51, an acquisition unit 52, an accumulation unit 53, an acquisition unit 54, and an analysis unit 55.
创建单元51，用于创建数据库的性能指标与数据库维度之间的关联关系。The creating unit 51 is configured to create an association between the performance indicator of the database and the database dimension.
采集单元52，用于采集每一性能指标在不同维度处所对应的子性能指标值。The collecting unit 52 is configured to collect sub-performance index values corresponding to each performance indicator at different dimensions.
累加单元53，用于将同一性能指标在不同维度处的子性能指标值累加以得到该性能指标的当前性能指标值。The accumulating unit 53 is configured to accumulate the sub-performance index values of the same performance indicator at different dimensions to obtain the current performance index value of the performance indicator.
获取单元54，用于获取每一性能指标所对应的多个历史性能指标值，所述历史性能指标值与所述当前性能指标值相对应。The obtaining unit 54 is configured to obtain a plurality of historical performance indicator values corresponding to each performance indicator, where the historical performance indicator value corresponds to the current performance indicator value.
分析单元55，用于根据每一性能指标的所述历史性能指标值及预设算法分析该性能指标的当前性能指标值以得到分析结果。The analyzing unit 55 is configured to analyze the current performance indicator value of the performance indicator according to the historical performance index value and the preset algorithm of each performance indicator to obtain an analysis result.
进一步地，参照图9，图9是图8中分析单元55的结构示意图。如图所示，分析单元55包括计算单元551、确定单元552、及判断单元553。Further, referring to FIG. 9, FIG. 9 is a schematic structural diagram of the analyzing unit 55 of FIG. As shown, the analysis unit 55 includes a calculation unit 551, a determination unit 552, and a determination unit 553.
累加单元551，用于根据所述历史性能指标值计算每一性能指标在不同维度信息处所对应的均值和标准差。The accumulating unit 551 is configured to calculate, according to the historical performance index value, a mean and a standard deviation corresponding to each performance indicator at different dimension information.
确定单元552，用于根据所述均值、标准差及六西格玛法确定所述每一当前性能指标值的参考阈值范围。The determining unit 552 is configured to determine a reference threshold range of each current performance indicator value according to the mean, standard deviation, and Six Sigma method.
判断单元553，用于根据所确定的参考阈值范围判断该性能指标的当前性能指标值以得到分析结果。The determining unit 553 is configured to determine a current performance indicator value of the performance indicator according to the determined reference threshold range to obtain an analysis result.
进一步地，参照图10，图10是图9中判断单元553的结构示意图。如图所 示，判断单元553包括核对单元5531、及判定单元5532。Further, referring to FIG. 10, FIG. 10 is a schematic structural diagram of the determining unit 553 of FIG. As shown in the figure, the judging unit 553 includes a collating unit 5531 and a judging unit 5532.
核对单元5531，用于判断该性能指标的当前性能指标值是否在所述参考阈值范围内。The checking unit 5531 is configured to determine whether the current performance indicator value of the performance indicator is within the reference threshold.
判定单元5532，用于若所述当前性能指标值在所述参考阈值范围内，则判定该当前性能指标值正常；若所述当前性能指标值不在所述参考阈值范围内，则判定该当前性能指标值异常。The determining unit 5532 is configured to determine that the current performance indicator value is normal if the current performance indicator value is within the reference threshold value, and determine the current performance if the current performance indicator value is not within the reference threshold range The indicator value is abnormal.
本申请实施例通过创建数据库的性能指标和数据库维度之间的关联关系，进而采集每一性能指标在不同维度处所对应的子性能指标值，然后将同一性能指标在不同维度处的子性能指标值累加以得到该性能指标的当前性能指标值，并获取每一性能指标所对应的多个历史性能指标值，根据历史性能指标值及预设算法分析每一性能指标对应的当前性能指标值以得到分析结果。本申请实施例能够对数据库的性能指标进行多维度的集中分析，以便进行趋势分析和异常判断。In the embodiment of the present application, the relationship between the performance index of the database and the database dimension is created, and the sub-performance index values corresponding to each performance indicator in different dimensions are collected, and then the sub-performance index values of the same performance indicator at different dimensions are obtained. The current performance index value of the performance indicator is obtained, and the historical performance index values corresponding to each performance indicator are obtained, and the current performance index value corresponding to each performance indicator is analyzed according to the historical performance index value and a preset algorithm to obtain Analysis results. The embodiment of the present application can perform multi-dimensional centralized analysis on performance indexes of the database for trend analysis and abnormal judgment.
参照图11，图11是本申请另一实施例提供的数据库性能指标的监测装置的示意性框图。如图所示的本实施例中的装置60包括创建单元61、采集单元62、累加单元63、获取单元64、计算单元65、确定单元66、判断单元67、绘制单元68、显示单元69、及标记单元70。Referring to FIG. 11, FIG. 11 is a schematic block diagram of a monitoring device for database performance indicators according to another embodiment of the present application. The device 60 in the embodiment as shown in the figure includes a creating unit 61, an collecting unit 62, an accumulating unit 63, an obtaining unit 64, a calculating unit 65, a determining unit 66, a judging unit 67, a drawing unit 68, a display unit 69, and Marking unit 70.
创建单元61，用于创建数据库的性能指标与数据库维度之间的关联关系.The creating unit 61 is configured to create an association between the performance indicator of the database and the database dimension.
采集单元62，用于采集每一性能指标在不同维度处所对应的子性能指标值。The collecting unit 62 is configured to collect sub-performance index values corresponding to each performance indicator at different dimensions.
累加单元63，用于将同一性能指标在不同维度处的子性能指标值累加以得到该性能指标的当前性能指标值。The accumulating unit 63 is configured to accumulate the sub-performance index values of the same performance indicator at different dimensions to obtain the current performance index value of the performance indicator.
获取单元64，用于获取每一性能指标所对应的多个历史性能指标值，所述历史性能指标值与所述当前性能指标值相对应。The obtaining unit 64 is configured to obtain a plurality of historical performance indicator values corresponding to each performance indicator, where the historical performance indicator value corresponds to the current performance indicator value.
计算单元65，用于根据所述历史性能指标值计算每一性能指标在不同维度信息处所对应的均值和标准差。The calculating unit 65 is configured to calculate, according to the historical performance indicator value, a mean and a standard deviation corresponding to each performance indicator at different dimension information.
确定单元66，用于根据所述均值、标准差及六西格玛法确定所述每一当前性能指标值的参考阈值范围。The determining unit 66 is configured to determine a reference threshold range of each current performance indicator value according to the mean, standard deviation, and Six Sigma method.
判断单元67，用于根据所确定的参考阈值范围判断该性能指标的当前性能指标值以得到分析结果。The determining unit 67 is configured to determine a current performance indicator value of the performance indicator according to the determined reference threshold range to obtain an analysis result.
绘制单元68，用于根据所述均值、标准差及六西格玛法绘制每一性能指标 的监测图形。A rendering unit 68 is configured to plot a monitoring pattern for each performance indicator based on the mean, standard deviation, and six sigma methods.
显示单元69，用于显示每一性能指标的监测图形。The display unit 69 is configured to display a monitoring graph of each performance indicator.
标记单元70，用于在所述监测图形上标记所述该当前性能指标值。The marking unit 70 is configured to mark the current performance indicator value on the monitoring graphic.
本申请实施例通过创建数据库的性能指标和数据库维度之间的关联关系，进而采集每一性能指标在不同维度处所对应的子性能指标值，然后将同一性能指标在不同维度处的子性能指标值累加以得到该性能指标的当前性能指标值，并获取每一性能指标所对应的多个历史性能指标值，根据历史性能指标值及预设算法分析每一性能指标对应的当前性能指标值以得到分析结果。本申请实施例通过将该性能指标对应的监测图形对应的显示出来并将当前性能指标对应的标记在监测图形上以实现对数据库的性能指标进行多维度的集中分析，以便直观地进行趋势分析和异常判断。In the embodiment of the present application, the relationship between the performance index of the database and the database dimension is created, and the sub-performance index values corresponding to each performance indicator in different dimensions are collected, and then the sub-performance index values of the same performance indicator at different dimensions are obtained. The current performance index value of the performance indicator is obtained, and the historical performance index values corresponding to each performance indicator are obtained, and the current performance index value corresponding to each performance indicator is analyzed according to the historical performance index value and a preset algorithm to obtain Analysis results. The embodiment of the present application displays the monitoring pattern corresponding to the performance indicator correspondingly and marks the current performance index on the monitoring graphic to perform multi-dimensional centralized analysis on the performance index of the database, so as to intuitively perform trend analysis and Abnormal judgment.
本申请实施例中所述的单元如果以软件功能模块的形式实现并作为独立的产品销售或使用时，也可以存储在一个计算机可读取存储介质中。基于这样的理解，本申请实施例的技术方案本质上或者说对现有技术做出贡献的部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质中，包括若干指令用以使得一台计算机设备(可以是个人计算机、服务器、或者网络设备等)执行本申请各个实施例所述方法的全部或部分。而该存储介质包括U盘、移动硬盘、只读存储器(ROM，Read Only Memory)、磁碟或者光盘等各种可以存储程序代码的介质。这样，本申请实施例不限制于任何特定的硬件和软件结合。The units described in the embodiments of the present application may also be stored in a computer readable storage medium if they are implemented in the form of software functional modules and sold or used as separate products. Based on such understanding, the technical solution of the embodiments of the present application may be embodied in the form of a software product in essence or in the form of a software product stored in a storage medium, including a plurality of instructions. A computer device (which may be a personal computer, server, or network device, etc.) is caused to perform all or part of the methods described in various embodiments of the present application. The storage medium includes various media that can store program codes, such as a USB flash drive, a removable hard disk, a read only memory (ROM), a magnetic disk, or an optical disk. Thus, embodiments of the present application are not limited to any particular combination of hardware and software.
相应的，本申请实施例还记载了一种计算机可读存储介质，该存储介质有一个或者一个以上程序，该一个或者一个以上程序可被一个或者一个以上的处理器执行以实现以下步骤：创建数据库的性能指标与数据库维度之间的关联关系；采集每一性能指标在不同维度处所对应的子性能指标值；将同一性能指标在不同维度处的子性能指标值累加以得到该性能指标的当前性能指标值；获取每一性能指标所对应的多个历史性能指标值，所述历史性能指标值与所述当前性能指标值相对应；根据每一性能指标的所述历史性能指标值及预设算法分析该性能指标的当前性能指标值以得到分析结果。Correspondingly, the embodiment of the present application further describes a computer readable storage medium having one or more programs, the one or more programs being executable by one or more processors to implement the following steps: The relationship between the performance index of the database and the database dimension; collecting the sub-performance index values corresponding to each performance indicator at different dimensions; and accumulating the sub-performance index values of the same performance indicator at different dimensions to obtain the current performance index a performance indicator value; obtaining a plurality of historical performance indicator values corresponding to each performance indicator, where the historical performance indicator value corresponds to the current performance indicator value; and the historical performance indicator value and the preset according to each performance indicator The algorithm analyzes the current performance indicator value of the performance indicator to obtain an analysis result.
优选地，所述根据每一性能指标的所述历史性能指标值及预设算法分析该性能指标的当前性能指标值以得到分析结果包括以下步骤：根据所述历史性能指标值计算每一性能指标对应的均值和标准差；根据所述均值、标准差及六西 格玛法确定所述每一当前性能指标值的参考阈值范围；根据所确定的参考阈值范围判断该性能指标的当前性能指标值以得到分析结果。Preferably, the analyzing the current performance indicator value of the performance indicator according to the historical performance indicator value of each performance indicator and the preset algorithm to obtain the analysis result includes the following steps: calculating each performance indicator according to the historical performance indicator value. Corresponding mean and standard deviation; determining a reference threshold range of each current performance indicator value according to the mean, standard deviation, and Six Sigma method; determining a current performance indicator value of the performance indicator according to the determined reference threshold range for analysis result.
优选地，所述根据所确定的参考阈值范围判断该性能指标的当前性能指标值以得到分析结果包括以下步骤：判断该性能指标的当前性能指标值是否在所述参考阈值范围内；若所述当前性能指标值在所述参考阈值范围内，则判定该当前性能指标值正常；若所述当前性能指标值不在所述参考阈值范围内，则判定该当前性能指标值异常。Preferably, the determining, according to the determined reference threshold range, the current performance indicator value of the performance indicator to obtain an analysis result includes the following steps: determining whether a current performance indicator value of the performance indicator is within the reference threshold; If the current performance indicator value is within the reference threshold, the current performance indicator value is determined to be normal; if the current performance indicator value is not within the reference threshold, the current performance indicator value is determined to be abnormal.
优选地，所述一个或者一个以上程序还可被所述一个或者一个以上的处理器执行以实现以下步骤：根据所述均值、标准差及六西格玛法绘制每一性能指标对应的监测图形；显示每一性能指标的监测图形；在所述监测图形上标记所述该当前性能指标值。Preferably, the one or more programs may be executed by the one or more processors to implement the steps of: drawing a monitoring graphic corresponding to each performance indicator according to the mean, standard deviation, and six sigma method; displaying each a monitoring graph of a performance indicator; marking the current performance indicator value on the monitoring graph.
优选地，所述性能指标包括CPU使用时间、I/O等待时间、及Lock等待时间，所述维度包括SQL、EVENT、OBJECT、及USER。Preferably, the performance indicator includes CPU usage time, I/O waiting time, and Lock waiting time, and the dimensions include SQL, EVENT, OBJECT, and USER.
参照图12，图12是本申请实施例提供的一种数据库性能指标的监测设备的示意性框图。如图12所示，该设备80包括一个或者若干个处理器81、存储器82、一个或者若干个输入装置83、一个或者若干个输出装置84。上述处理器81、输入装置83、输出装置84以及存储器82通过总线85连接。Referring to FIG. 12, FIG. 12 is a schematic block diagram of a monitoring device for database performance indicators according to an embodiment of the present application. As shown in FIG. 12, the device 80 includes one or several processors 81, a memory 82, one or several input devices 83, one or several output devices 84. The processor 81, the input device 83, the output device 84, and the memory 82 are connected via a bus 85.
输入装置83用于供用户输入操作指令。具体实现中，本申请实施例的输入装置83可包括键盘、鼠标、光电输入装置、声音输入装置、触摸式输入装置等。The input device 83 is for the user to input an operation command. In a specific implementation, the input device 83 of the embodiment of the present application may include a keyboard, a mouse, a photoelectric input device, a sound input device, a touch input device, and the like.
输出装置84用于显示每一性能指标的监测图形。具体实现中，本申请实施例的输出装置84可包括显示器、显示屏、触摸屏等。 Output device 84 is used to display a monitoring pattern for each performance indicator. In a specific implementation, the output device 84 of the embodiment of the present application may include a display, a display screen, a touch screen, and the like.
存储器82用于存储使监测设备实现特定功能及操作的计算机程序及数据，例如，用于存储使监测设备实现数据库性能指标检测的程序及数据；在具体实现中，本申请实施例的存储器82可以是系统存储器，比如，非易失性的(诸如ROM，闪存等)。具体实现中，本申请实施例的存储器82还可以是系统之外的外部存储器，比如，磁盘、光盘、磁带等。The memory 82 is configured to store a computer program and data for enabling the monitoring device to perform a specific function and operation, for example, to store a program and data for enabling the monitoring device to implement database performance indicator detection. In a specific implementation, the memory 82 of the embodiment of the present application may be It is a system memory, such as non-volatile (such as ROM, flash memory, etc.). In a specific implementation, the memory 82 of the embodiment of the present application may also be an external memory outside the system, such as a magnetic disk, an optical disk, a magnetic tape, or the like.
处理器81用于执行存储器82中存储的计算机程序及数据以执行如下操作：The processor 81 is operative to execute computer programs and data stored in the memory 82 to perform the following operations:
创建数据库的性能指标与数据库维度之间的关联关系；采集每一性能指标在不同维度处所对应的子性能指标值；将同一性能指标在不同维度处的子性能指标值累加以得到该性能指标的当前性能指标值；获取每一性能指标所对应的 多个历史性能指标值，所述历史性能指标值与所述当前性能指标值相对应；根据每一性能指标的所述历史性能指标值及预设算法分析该性能指标的当前性能指标值以得到分析结果。The relationship between the performance index of the database and the database dimension is created; the sub-performance index value corresponding to each performance indicator at different dimensions is collected; and the sub-performance index values of the same performance indicator at different dimensions are accumulated to obtain the performance index. a current performance indicator value; obtaining a plurality of historical performance indicator values corresponding to each performance indicator, where the historical performance indicator value corresponds to the current performance indicator value; and the historical performance indicator value and the pre-determination according to each performance indicator An algorithm is provided to analyze the current performance index value of the performance indicator to obtain an analysis result.
进一步地，所述根据每一性能指标的所述历史性能指标值及预设算法分析该性能指标的当前性能指标值以得到分析结果包括：根据所述历史性能指标值计算每一性能指标对应的均值和标准差；根据所述均值、标准差及六西格玛法确定所述每一当前性能指标值的参考阈值范围；根据所确定的参考阈值范围判断该性能指标的当前性能指标值以得到分析结果。Further, the analyzing the historical performance indicator value of the performance indicator according to the historical performance indicator value of each performance indicator and the preset algorithm to obtain the analysis result includes: calculating, according to the historical performance indicator value, each performance indicator Mean and standard deviation; determining a reference threshold range of each current performance indicator value according to the mean, standard deviation, and Six Sigma method; determining a current performance indicator value of the performance indicator according to the determined reference threshold range to obtain an analysis result.
进一步地，所述根据所确定的参考阈值范围判断该性能指标的当前性能指标值以得到分析结果包括：判断该性能指标的当前性能指标值是否在所述参考阈值范围内；若所述当前性能指标值在所述参考阈值范围内，则判定该当前性能指标值正常；若所述当前性能指标值不在所述参考阈值范围内，则判定该当前性能指标值异常。Further, determining the current performance indicator value of the performance indicator according to the determined reference threshold range to obtain the analysis result includes: determining whether a current performance indicator value of the performance indicator is within the reference threshold; if the current performance If the indicator value is within the reference threshold, it is determined that the current performance indicator value is normal; if the current performance indicator value is not within the reference threshold range, determining that the current performance indicator value is abnormal.
进一步地，所述处理器81还执行以下操作：根据所述均值、标准差及六西格玛法绘制每一性能指标对应的监测图形；显示每一性能指标的监测图形；在所述监测图形上标记所述该当前性能指标值。Further, the processor 81 further performs: calculating a monitoring graphic corresponding to each performance indicator according to the mean, standard deviation, and six sigma method; displaying a monitoring graphic of each performance indicator; marking the monitoring graphic on the monitoring graphic The current performance indicator value is described.
进一步地，所述性能指标包括CPU使用时间、I/O等待时间、及Lock等待时间，所述维度包括SQL、EVENT、OBJECT、及USER。Further, the performance indicators include CPU usage time, I/O wait time, and Lock wait time, and the dimensions include SQL, EVENT, OBJECT, and USER.
所属领域的技术人员可以清楚地了解到，为了描述的方便和简洁，上述描述的终端和单元的具体工作过程，可以参考前述方法实施例中的对应过程，在此不再赘述。A person skilled in the art can clearly understand that, for the convenience and brevity of the description, the specific working process of the terminal and the unit described above can be referred to the corresponding process in the foregoing method embodiment, and details are not described herein again.
在本申请所提供的几个实施例中，应该理解到，所揭露的终端和方法，可以通过其它的方式实现。例如，以上所描述的装置设备实施例仅仅是示意性的，例如，所述单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个单元或组件可以结合或者可以集成到另一个系统，或一些特征可以忽略，或不执行。另外，所显示或讨论的相互之间的耦合或直接耦合或通信连接可以是通过一些接口、装置设备或单元的间接耦合或通信连接，也可以是电的，机械的或其它的形式连接。In the several embodiments provided by the present application, it should be understood that the disclosed terminal and method may be implemented in other manners. For example, the device device embodiments described above are merely illustrative. For example, the division of the unit is only a logical function division. In actual implementation, there may be another division manner, for example, multiple units or components may be combined. Or it can be integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interface, device device or unit, or an electrical, mechanical or other form of connection.
本领域普通技术人员可以理解实现上述实施例的方法的全部或部分步骤可以通过硬件来完成，也可以通过程序来指令相关的硬件完成，所述的程序可以 存储于一种计算机可读存储介质中，上述提到的存储介质可以是只读存储器，磁盘或光盘等。并且，本申请上述实施例方法中的步骤可以根据实际需要进行顺序调整、合并和删减。本申请实施例终端中的单元可以根据实际需要进行合并、划分和删减。A person skilled in the art can understand that all or part of the steps of implementing the method of the foregoing embodiments may be completed by hardware, or may be instructed by a program to execute related hardware, and the program may be stored in a computer readable storage medium. The storage medium mentioned above may be a read only memory, a magnetic disk or an optical disk or the like. Moreover, the steps in the method of the foregoing embodiment of the present application may be sequentially adjusted, merged, and deleted according to actual needs. The units in the terminal in the embodiment of the present application may be combined, divided, and deleted according to actual needs.
另外，在本申请各个实施例中的各功能单元可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以是两个或两个以上单元集成在一个单元中。上述集成的单元既可以采用硬件的形式实现，也可以采用软件功能单元的形式实现。In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit.
所述集成的单元如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。基于这样的理解，本申请的技术方案本质上或者说对现有技术做出贡献的部分，或者该技术方案的全部或部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质中，包括若干指令用以使得一台计算机设备(可以是个人计算机，终端，或者网络设备等)执行本申请各个实施例所述方法的全部或部分步骤。The integrated unit, if implemented in the form of a software functional unit and sold or used as a standalone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be in essence or part of the contribution to the prior art, or all or part of the technical solution may be embodied in the form of a software product stored in a storage medium. There are a number of instructions for causing a computer device (which may be a personal computer, terminal, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present application.
以上为发明的优选实施例，而非对发明做任何形式上的限制。本领域的技术人员可在上述实施例的基础上施以各种等同的更改和改进，凡在权利要求范围内所做的等同变化或修饰，均应落入发明的包含范围之内。The above is a preferred embodiment of the invention, and is not intended to limit the invention in any way. A person skilled in the art can make various equivalent modifications and improvements to the above-described embodiments, and equivalent changes or modifications made within the scope of the claims should fall within the scope of the invention.

Claims (20)

  1. 一种数据库性能指标的监测方法，其包括：A method for monitoring database performance indicators, including:
    创建数据库的性能指标与数据库维度之间的关联关系；Create an association between the performance metrics of the database and the database dimensions;
    采集每一性能指标在不同维度处所对应的子性能指标值；Collecting sub-performance index values corresponding to each performance indicator at different dimensions;
    将同一性能指标在不同维度处的子性能指标值累加以得到该性能指标的当前性能指标值；Accumulating the sub-performance indicator values of the same performance indicator at different dimensions to obtain the current performance indicator value of the performance indicator;
    获取每一性能指标所对应的多个历史性能指标值，所述历史性能指标值与所述当前性能指标值相对应；Obtaining a plurality of historical performance indicator values corresponding to each performance indicator, where the historical performance indicator value corresponds to the current performance indicator value;
    根据每一性能指标的所述历史性能指标值及预设算法分析该性能指标的当前性能指标值以得到分析结果。And analyzing the current performance indicator value of the performance indicator according to the historical performance indicator value of each performance indicator and a preset algorithm to obtain an analysis result.
  2. 如权利要求1所述的方法，其中，所述根据每一性能指标的所述历史性能指标值及预设算法分析该性能指标的当前性能指标值以得到分析结果，包括：The method of claim 1, wherein the historical performance indicator value of each performance indicator and a preset algorithm analyze the current performance indicator value of the performance indicator to obtain an analysis result, including:
    根据所述历史性能指标值计算每一性能指标对应的均值和标准差；Calculating a mean value and a standard deviation corresponding to each performance indicator according to the historical performance index value;
    根据所述均值、标准差及六西格玛法确定所述每一当前性能指标值的参考阈值范围；Determining a reference threshold range of each current performance indicator value according to the mean, standard deviation, and Six Sigma method;
    根据所确定的参考阈值范围判断该性能指标的当前性能指标值以得到分析结果。Determining a current performance indicator value of the performance indicator according to the determined reference threshold range to obtain an analysis result.
  3. 如权利要求2所述的方法，其中，所述根据所确定的参考阈值范围判断该性能指标的当前性能指标值以得到分析结果，包括：The method of claim 2, wherein the determining the current performance indicator value of the performance indicator according to the determined reference threshold range to obtain an analysis result comprises:
    判断该性能指标的当前性能指标值是否在所述参考阈值范围内；Determining whether a current performance indicator value of the performance indicator is within the reference threshold;
    若所述当前性能指标值在所述参考阈值范围内，则判定该当前性能指标值正常；If the current performance indicator value is within the reference threshold, determining that the current performance indicator value is normal;
    若所述当前性能指标值不在所述参考阈值范围内，则判定该当前性能指标值异常。If the current performance indicator value is not within the reference threshold range, determining that the current performance indicator value is abnormal.
  4. 如权利要求2所述的方法，其中，所述方法还包括：The method of claim 2, wherein the method further comprises:
    根据所述均值、标准差及六西格玛法绘制每一性能指标对应的监测图形；Drawing a monitoring pattern corresponding to each performance indicator according to the mean value, standard deviation, and six sigma method;
    显示每一性能指标的监测图形；Display a monitoring graph for each performance indicator;
    在所述监测图形上标记所述该当前性能指标值。The current performance indicator value is marked on the monitoring graphic.
  5. 如权利要求1所述的方法，其中，所述性能指标包括CPU使用时间、I/O 等待时间、及Lock等待时间，所述维度包括SQL、EVENT、OBJECT、及USER。The method of claim 1 wherein said performance metrics include CPU usage time, I/O latency, and Lock latency, said dimensions including SQL, EVENT, OBJECT, and USER.
  6. 一种数据库性能指标的监测装置，其包括：A monitoring device for database performance indicators, comprising:
    创建单元，用于创建数据库的性能指标与数据库维度之间的关联关系；Create a unit to create an association between the performance metrics of the database and the database dimensions;
    采集单元，用于采集每一性能指标在不同维度处所对应的子性能指标值；An acquisition unit, configured to collect sub-performance index values corresponding to each performance indicator at different dimensions;
    累加单元，用于将同一性能指标在不同维度处的子性能指标值累加以得到该性能指标的当前性能指标值；An accumulating unit, configured to accumulate sub-performance index values of the same performance indicator at different dimensions to obtain a current performance indicator value of the performance indicator;
    获取单元，用于获取每一性能指标所对应的多个历史性能指标值，所述历史性能指标值与所述当前性能指标值相对应；An obtaining unit, configured to obtain a plurality of historical performance indicator values corresponding to each performance indicator, where the historical performance indicator value corresponds to the current performance indicator value;
    分析单元，用于根据每一性能指标的所述历史性能指标值及预设算法分析该性能指标的当前性能指标值以得到分析结果。The analyzing unit is configured to analyze the current performance indicator value of the performance indicator according to the historical performance index value of each performance indicator and a preset algorithm to obtain an analysis result.
  7. 如权利要求6所述的装置，其中，所述分析单元包括：The apparatus of claim 6 wherein said analyzing unit comprises:
    计算单元，用于根据所述历史性能指标值计算每一性能指标在不同维度处所对应的均值和标准差；a calculating unit, configured to calculate, according to the historical performance indicator value, a mean and a standard deviation corresponding to each performance indicator at different dimensions;
    确定单元，用于根据所述均值、标准差及六西格玛法确定所述每一当前性能指标值的参考阈值范围；a determining unit, configured to determine a reference threshold range of each current performance indicator value according to the mean, standard deviation, and Six Sigma method;
    判断单元，用于根据所确定的参考阈值范围判断该性能指标的当前性能指标值以得到分析结果。And a determining unit, configured to determine a current performance indicator value of the performance indicator according to the determined reference threshold range to obtain an analysis result.
  8. 如权利要求7所述的装置，其中，所述判断单元包括：The apparatus of claim 7, wherein the determining unit comprises:
    核对单元，用于判断该性能指标的当前性能指标值是否在所述参考阈值范围内；a checking unit, configured to determine whether a current performance indicator value of the performance indicator is within the reference threshold;
    判定单元，用于若所述当前性能指标值在所述参考阈值范围内，则判定该当前性能指标值正常；若所述当前性能指标值不在参考所述阈值范围内，则判定该当前性能指标值异常。a determining unit, configured to determine that the current performance indicator value is normal if the current performance indicator value is within the reference threshold value; and determine the current performance indicator if the current performance indicator value is not within the reference threshold range The value is abnormal.
  9. 如权利要求7所述的装置，其中，还包括：The apparatus of claim 7 further comprising:
    绘制单元，用于根据所述均值、标准差及六西格玛法绘制每一性能指标对应的监测图形；a drawing unit, configured to draw a monitoring graphic corresponding to each performance indicator according to the mean, standard deviation, and six sigma method;
    显示单元，用于显示每一性能指标的监测图形；a display unit for displaying a monitoring graph of each performance indicator;
    标记单元，用于在所述监测图形上标记所述该当前性能指标值。a marking unit, configured to mark the current performance indicator value on the monitoring graphic.
  10. 如权利要求6所述的装置，其中，所述性能指标包括CPU使用时间、I/O等待时间、及Lock等待时间，所述维度包括SQL、EVENT、OBJECT、及USER。The apparatus of claim 6, wherein the performance indicator comprises CPU usage time, I/O latency, and Lock latency, the dimensions including SQL, EVENT, OBJECT, and USER.
  11. 一种计算机可读存储介质，其中，所述存储介质有一个或者一个以上程序，该一个或者一个以上程序可被一个或者一个以上的处理器执行以实现以下步骤：创建数据库的性能指标与数据库维度之间的关联关系；采集每一性能指标在不同维度处所对应的子性能指标值；将同一性能指标在不同维度处的子性能指标值累加以得到该性能指标的当前性能指标值；获取每一性能指标所对应的多个历史性能指标值，所述历史性能指标值与所述当前性能指标值相对应；根据每一性能指标的所述历史性能指标值及预设算法分析该性能指标的当前性能指标值以得到分析结果。A computer readable storage medium, wherein the storage medium has one or more programs, the one or more programs being executable by one or more processors to implement the steps of: creating performance metrics and database dimensions of a database The relationship between the sub-performance indicators corresponding to each performance indicator at different dimensions; the sub-performance index values of the same performance indicator at different dimensions are added to obtain the current performance index value of the performance indicator; a plurality of historical performance indicator values corresponding to the performance indicator, where the historical performance indicator value corresponds to the current performance indicator value; and the current performance indicator value of each performance indicator and a preset algorithm are used to analyze the current performance indicator Performance indicator values for analysis results.
  12. 如权利要求11所述的计算机可读存储介质，其中，该一个或者一个以上程序可被一个或者一个以上的处理器执行根据每一性能指标的所述历史性能指标值及预设算法分析该性能指标的当前性能指标值以得到分析结果时，具体实现以下步骤：根据所述历史性能指标值计算每一性能指标对应的均值和标准差；根据所述均值、标准差及六西格玛法确定所述每一当前性能指标值的参考阈值范围；根据所确定的参考阈值范围判断该性能指标的当前性能指标值以得到分析结果。The computer readable storage medium of claim 11 wherein the one or more programs are executable by one or more processors to analyze the performance based on the historical performance indicator value and a predetermined algorithm for each performance indicator When the current performance indicator value of the indicator is used to obtain the analysis result, the following steps are specifically implemented: calculating a mean value and a standard deviation corresponding to each performance indicator according to the historical performance index value; determining each of the foregoing according to the mean value, the standard deviation, and the Six Sigma method a reference threshold range of the current performance indicator value; determining a current performance indicator value of the performance indicator according to the determined reference threshold range to obtain an analysis result.
  13. 如权利要求12所述的计算机可读存储介质，其中，该一个或者一个以上程序可被一个或者一个以上的处理器执行根据所确定的参考阈值范围判断该性能指标的当前性能指标值以得到分析结果时，具体实现以下步骤：判断该性能指标的当前性能指标值是否在所述参考阈值范围内；若所述当前性能指标值在所述参考阈值范围内，则判定该当前性能指标值正常；若所述当前性能指标值不在所述参考阈值范围内，则判定该当前性能指标值异常。The computer readable storage medium of claim 12, wherein the one or more programs are executable by one or more processors to determine a current performance indicator value of the performance indicator based on the determined reference threshold range for analysis The result is as follows: determining whether the current performance indicator value of the performance indicator is within the reference threshold; if the current performance indicator is within the reference threshold, determining that the current performance indicator is normal; If the current performance indicator value is not within the reference threshold range, determining that the current performance indicator value is abnormal.
  14. 如权利要求12所述的计算机可读存储介质，其中，该一个或者一个以上程序可被一个或者一个以上的处理器执行根据所确定的参考阈值范围判断该性能指标的当前性能指标值以得到分析结果之后，还实现以下步骤：根据所述均值、标准差及六西格玛法绘制每一性能指标对应的监测图形；显示每一性能指标的监测图形；在所述监测图形上标记所述该当前性能指标值。The computer readable storage medium of claim 12, wherein the one or more programs are executable by one or more processors to determine a current performance indicator value of the performance indicator based on the determined reference threshold range for analysis After the result, the following steps are further implemented: drawing a monitoring pattern corresponding to each performance indicator according to the mean, standard deviation, and six sigma method; displaying a monitoring pattern of each performance indicator; marking the current performance indicator on the monitoring graphic value.
  15. 如权利要求11所述的计算机可读存储介质，其中，所述性能指标包括CPU使用时间、I/O等待时间、及Lock等待时间，所述维度包括SQL、EVENT、OBJECT、及USER。The computer readable storage medium of claim 11 wherein the performance metrics comprise CPU usage time, I/O latency, and Lock latency, the dimensions comprising SQL, EVENT, OBJECT, and USER.
  16. 一种数据库性能指标的监测设备，其中，所述设备包括：存储器以及处 理器；所述存储器用于存储至少一个计算机程序；所述处理器读取所述存储器中的计算机程序以执行以下操作：创建数据库的性能指标与数据库维度之间的关联关系；采集每一性能指标在不同维度处所对应的子性能指标值；将同一性能指标在不同维度处的子性能指标值累加以得到该性能指标的当前性能指标值；获取每一性能指标所对应的多个历史性能指标值，所述历史性能指标值与所述当前性能指标值相对应；根据每一性能指标的所述历史性能指标值及预设算法分析该性能指标的当前性能指标值以得到分析结果。A monitoring device for database performance indicators, wherein the device comprises: a memory and a processor; the memory is for storing at least one computer program; the processor reads a computer program in the memory to perform the following operations: The relationship between the performance index of the database and the database dimension is created; the sub-performance index value corresponding to each performance indicator at different dimensions is collected; and the sub-performance index values of the same performance indicator at different dimensions are accumulated to obtain the performance index. a current performance indicator value; obtaining a plurality of historical performance indicator values corresponding to each performance indicator, where the historical performance indicator value corresponds to the current performance indicator value; and the historical performance indicator value and the pre-determination according to each performance indicator An algorithm is provided to analyze the current performance index value of the performance indicator to obtain an analysis result.
  17. 如权利要求16所述的监测设备，其中，所述处理器读取所述存储器中的计算机程序以执行根据每一性能指标的所述历史性能指标值及预设算法分析该性能指标的当前性能指标值以得到分析结果时，具体执行以下操作：根据所述历史性能指标值计算每一性能指标对应的均值和标准差；根据所述均值、标准差及六西格玛法确定所述每一当前性能指标值的参考阈值范围；根据所确定的参考阈值范围判断该性能指标的当前性能指标值以得到分析结果。The monitoring device of claim 16 wherein said processor reads a computer program in said memory to perform said historical performance indicator value and a predetermined algorithm for each performance indicator to analyze current performance of said performance indicator When the indicator value is used to obtain the analysis result, the following operations are specifically performed: calculating a mean value and a standard deviation corresponding to each performance indicator according to the historical performance index value; determining each current performance indicator according to the mean value, the standard deviation, and the Six Sigma method a reference threshold range of values; determining a current performance indicator value of the performance indicator according to the determined reference threshold range to obtain an analysis result.
  18. 如权利要求17所述的监测设备，其中，所述处理器读取所述存储器中的计算机程序以执行根据所确定的参考阈值范围判断该性能指标的当前性能指标值以得到分析结果时，具体执行以下操作：判断该性能指标的当前性能指标值是否在所述参考阈值范围内；若所述当前性能指标值在所述参考阈值范围内，则判定该当前性能指标值正常；若所述当前性能指标值不在所述参考阈值范围内，则判定该当前性能指标值异常。The monitoring device of claim 17, wherein said processor reads a computer program in said memory to perform a determination of a current performance indicator value of said performance indicator based on said determined reference threshold range to obtain an analysis result, Performing the following operations: determining whether the current performance indicator value of the performance indicator is within the reference threshold; if the current performance indicator value is within the reference threshold, determining that the current performance indicator is normal; If the performance indicator value is not within the reference threshold, it is determined that the current performance indicator value is abnormal.
  19. 如权利要求17所述的监测设备，其中，所述处理器读取所述存储器中的计算机程序以执行根据所确定的参考阈值范围判断该性能指标的当前性能指标值以得到分析结果之后，还执行以下操作：根据所述均值、标准差及六西格玛法绘制每一性能指标对应的监测图形；显示每一性能指标的监测图形；在所述监测图形上标记所述该当前性能指标值。The monitoring device of claim 17, wherein said processor reads a computer program in said memory to perform a determination of a current performance indicator value of said performance indicator based on said determined reference threshold range to obtain an analysis result, Performing the following operations: drawing a monitoring pattern corresponding to each performance indicator according to the mean, standard deviation, and six sigma method; displaying a monitoring pattern of each performance indicator; and marking the current performance indicator value on the monitoring graphic.
  20. 如权利要求16所述的监测设备，其中，所述性能指标包括CPU使用时间、I/O等待时间、及Lock等待时间，所述维度包括SQL、EVENT、OBJECT、及USER。The monitoring device of claim 16, wherein the performance indicator comprises CPU usage time, I/O latency, and Lock latency, the dimensions including SQL, EVENT, OBJECT, and USER.
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