Method, apparatus, storage medium and electronic device for analyzing storage performance of a device

Abstract

The present application discloses a method, apparatus, storage medium, and electronic device for analyzing the storage performance of a device, relating to the field of fintech. The method includes: obtaining data tables in a plurality of disk drives of a target device to obtain multiple groups of data tables; respectively extracting target feature information from each group of data tables to obtain multiple pieces of target feature information; classifying the multiple pieces of target feature information according to attribute information to obtain multiple groups of target feature information, and calculating performance data through the feature values in each group of target feature information to obtain multiple pieces of performance data; and determining an analysis result of the storage performance of the target device according to the multiple pieces of performance data. Through the present application, the problems in the related art that in the process of analyzing the storage performance of a device manually, the analysis efficiency is low and the accuracy of the analysis result is low are solved.

Description

Technical Field

[0001] The present application relates to the field of fintech, and in particular, to a method, device, storage medium, and electronic device for analyzing the storage performance of a device. Background Art

[0002] In large computer devices, the core storage device of a computer host usually consists of multiple disk drives. Operation and maintenance personnel need to regularly analyze the performance of multiple disk drives, which can play a reference and guiding role in the hardware maintenance of computer devices. However, there are a large number of types of disk drive performance indicators. If the performance data of various disk drives is exported on a monthly basis, the amount of performance data will exceed one million. Therefore, if only relying on manual use of computer software for data screening, statistical collation, and result analysis, problems such as low analysis efficiency and incorrect data analysis will occur due to the overly large amount of data.

[0003] In view of the problems of low analysis efficiency and low accuracy of analysis results in the process of analyzing the storage performance of a device by manual means in the related art, no effective solution has been proposed yet. Summary of the Invention

[0004] The present application provides a method, device, storage medium, and electronic device for analyzing the storage performance of a device to solve the problems of low analysis efficiency and low accuracy of analysis results in the process of analyzing the storage performance of a device by manual means in the related art.

[0005] According to one aspect of the present application, a method for analyzing the storage performance of a device is provided. The method includes: obtaining data tables in multiple disk drives of a target device to obtain multiple groups of data tables, where each group of data tables includes multiple characteristic information, and each characteristic information includes attribute information and a characteristic value; respectively extracting target characteristic information in each group of data tables to obtain multiple target characteristic information, where the target characteristic information is the characteristic information representing the performance of the disk drive in the data table; classifying the multiple target characteristic information according to the attribute information to obtain multiple groups of target characteristic information, and calculating performance data through the characteristic values in each group of target characteristic information to obtain multiple performance data; and determining the storage performance analysis result of the target device according to the multiple performance data.

[0006] Optionally, each group of data tables includes the following data tables: the system performance data table of the disk drive, the performance data tables of each node of the disk drive, and the performance data tables of each port of the disk drive. The ways to extract the target feature information from each group of data tables to obtain multiple pieces of target feature information include at least one of the following: extracting the first target data from the system performance data table of the disk drive and using the first target data as the target feature information, where the first target data includes at least one of the following: the number of port requests, throughput, and response time information; extracting the second target data from the performance data tables of each node of the disk drive and using the second target data as the target feature information, where the second target data includes at least one of the following: the number of port requests of each node, the throughput of each node, and the cache data occupancy rate of each node; extracting the third target data from the performance data tables of each port of the disk drive and using the third target data as the target feature information, where the third target data includes at least one of the following: port throughput.

[0007] Optionally, calculate performance data through the feature values in each group of target feature information. The multiple pieces of performance data obtained include: calculating the metric values of the feature values in each group of target feature information to obtain multiple metric values, where the metric values are at least one of the following: maximum value and average value; using the attribute information corresponding to each group of target feature information and the metric values as a piece of performance data to obtain multiple pieces of performance data.

[0008] Optionally, obtain the data tables in multiple disk drives of the target device according to the first time period, and extract the target feature information from multiple groups of data tables according to the second time period, where the second time period is greater than the first time period.

[0009] Optionally, before determining the storage performance analysis result of the target device according to multiple pieces of performance data, the method further includes: obtaining the previous historical performance data with the same attribute information as each piece of performance data, and comparing the performance data with the previous historical performance data to obtain the change trend information of the performance data, where the previous historical performance data is the performance data determined within the previous second time period.

[0010] Optionally, before determining the storage performance analysis result of the target device according to multiple pieces of performance data, the method further includes: determining whether the metric value included in each piece of performance data is greater than the corresponding threshold; when the metric value is greater than the threshold, generating an alarm message.

[0011] Optionally, determining the storage performance analysis result of the target device according to multiple pieces of performance data includes: generating the storage performance analysis result of the target device according to multiple pieces of performance data, the change trend information of each piece of performance data, and the alarm message.

[0012] Optionally, determining the storage performance analysis result of the target device according to multiple performance data includes: generating a chart corresponding to the multiple performance data by the Ggplot function method; generating a document by the Office function method with the multiple performance data and the chart, and using the document as the storage performance analysis result of the target device.

[0013] According to another aspect of the present application, there is provided a storage performance analysis device for a device. The device includes: a first acquisition unit configured to acquire data tables in multiple disk drives of a target device to obtain multiple groups of data tables, where each group of data tables includes multiple feature information, and each feature information includes attribute information and a feature value; a first extraction unit configured to respectively extract target feature information from each group of data tables to obtain multiple target feature information, where the target feature information is the feature information characterizing the performance of the disk drive in the data table; a first calculation unit configured to classify the multiple target feature information according to the attribute information to obtain multiple groups of target feature information, and calculate performance data through the feature values in each group of target feature information to obtain multiple performance data; a first determination unit configured to determine the storage performance analysis result of the target device according to the multiple performance data.

[0014] According to another aspect of the embodiments of the present invention, there is also provided a non-volatile storage medium, where the non-volatile storage medium includes a stored program, and when the program runs, it controls the device where the non-volatile storage medium is located to execute a storage performance analysis method for a device.

[0015] According to another aspect of the embodiments of the present invention, there is also provided an electronic device including one or more processors and a memory; the memory stores computer-readable instructions, and the processor is configured to run the computer-readable instructions, where when the computer-readable instructions run, they execute a storage performance analysis method for a device.

[0016] Through this application, the following steps are adopted: obtaining data tables in multiple disk drives of a target device to obtain multiple groups of data tables, where each group of data tables includes multiple feature information, and each feature information includes attribute information and a feature value; respectively extracting target feature information in each group of data tables to obtain multiple target feature information, where the target feature information is the feature information characterizing the performance of the disk drive in the data table; classifying the multiple target feature information according to the attribute information to obtain multiple groups of target feature information, and calculating performance data through the feature values in each group of target feature information to obtain multiple performance data; determining the storage performance analysis result of the target device according to the multiple performance data. This solves the problems of low analysis efficiency and low accuracy of the analysis result in the process of analyzing the storage performance of a device manually in the related art. By collecting data of the disk drive, analyzing and processing the collected data to obtain performance data, and determining a performance analysis report through the performance data, the effect of accurately and efficiently analyzing the performance of the disk drive can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings constituting a part of this application are used to provide a further understanding of this application. The schematic embodiments of this application and their descriptions are used to explain this application and do not constitute an improper limitation to this application. In the drawings:

[0018] Figure 1 is a flowchart of a method for analyzing the storage performance of a device according to an embodiment of this application;

[0019] Figure 2 is a comparison chart of the average number of requests of 3 disk drives for 3 months made using the Ggplot function according to an embodiment of this application;

[0020] Figure 3 is a comparison chart of the back-end and overall IO throughput of a disk drive made using the Ggplot function according to an embodiment of this application;

[0021] Figure 4 is a schematic diagram of a device for analyzing the storage performance of a device according to an embodiment of this application;

[0022] Figure 5 is a schematic diagram of an electronic device according to an embodiment of this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0023] It should be noted that, without conflict, the embodiments in this application and the features in the embodiments can be combined with each other. The following will refer to the drawings and combine the embodiments to detail this application.

[0024] To enable those skilled in the art to better understand the solution of this application, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of this application.

[0025] It should be noted that the terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily need to be used to describe a specific order or sequence. It should be understood that such data used in appropriate cases can be interchanged so as to implement the embodiments of this application described here. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0026] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for display, data for analysis, etc.) involved in this disclosure are all information and data authorized by the user or fully authorized by all parties.

[0027] It should be noted that the method, device, storage medium, and electronic device for analyzing the storage performance of a device determined in this disclosure can be used in the financial field and can also be used in any field other than the financial field. The application field of the method, device, storage medium, and electronic device for analyzing the storage performance of a device determined in this disclosure is not limited.

[0028] According to an embodiment of this application, a method for analyzing the storage performance of a device is provided.

[0029] Figure 1 is a flowchart of the method for analyzing the storage performance of a device provided according to an embodiment of this application. As Figure 1 shown, the method includes the following steps:

[0030] Step S101, obtain data tables in multiple disk drives of a target device to obtain multiple groups of data tables, where each group of data tables includes multiple feature information, and each feature information includes attribute information and a feature value.

[0031] Specifically, a disk drive is a storage device that uses a disk drive as a recording medium. It can serve as the core storage device of a computer's host system and store the performance data of the disk drive. When analyzing the performance of a disk drive, it is necessary to obtain the performance data of the disk drive. At this time, performance data collection can be performed through disk drive performance capacity monitoring software. The disk drive performance capacity monitoring software is composed of three functional modules and implements a monitoring and management solution for file systems, database systems, SAN networks, and disk drive arrays. The disk drive performance capacity monitoring software is an open storage infrastructure management system. It is designed to support a flexible storage infrastructure, meet on-demand storage requirements, and mainly uses the monitoring and management function of the disk drive array in the disk drive performance capacity monitoring software during the process of obtaining the disk drive data table. Through the disk drive performance capacity monitoring software, the workload of managing complex storage infrastructures can be reduced, storage capacity utilization can be improved, and management efficiency can be enhanced.

[0032] It should be noted that data collection can be performed by obtaining and storing multiple data tables according to the first time period. For example: obtain a disk drive's multiple data tables containing performance data representing the disk drive every 5 minutes and store the data table information separately. Among them, the multiple data tables can include: the overall system performance data table of the disk drive, the performance data tables of each node of the disk drive, and the performance data tables of each front-end port of the disk drive, etc.

[0033] It should be noted that each data table contains multiple characteristic information. The characteristic information can be the performance data of the disk drive. Each characteristic information is composed of attribute information and a characteristic value and they correspond one by one. For example: the characteristic information can be that the throughput of Disk Drive No. 1 at a certain moment is 2000 MB / s.

[0034] The following is a partial program for obtaining the data table in a disk drive:

[0035] SPD1_nodes<-read.csv("PerfReport_SPD1-75HNH81_Nodes.csv",header=TRUE,sep=",");

[0036] SPD2_nodes<-read.csv("PerfReport_SPD2-75HMA11_Nodes.csv",header=TRUE,sep=",");

[0037] SPD3_nodes<-read.csv("PerfReport_SPD3-75HNN61_Nodes.csv",header=TRUE,sep=",");

[0038] All data information in the three disk drives SPD1, SPD2, and SPD3 can be obtained through the above program.

[0039] SPD1_NODE0 <- SPD1_nodes[which(SPD1_nodes$Node == 'Node 0'),];

[0040] SPD1_NODE1 <- SPD1_nodes[which(SPD1_nodes$Node == 'Node 1'),];

[0041] SPD2_NODE0 <- SPD2_nodes[which(SPD2_nodes$Node == 'Node 0'),];

[0042] SPD2_NODE1 <- SPD2_nodes[which(SPD2_nodes$Node == 'Node 1'),];

[0043] SPD3_NODE0 <- SPD3_nodes[which(SPD3_nodes$Node == 'Node 0'),];

[0044] SPD3_NODE1 <- SPD3_nodes[which(SPD3_nodes$Node == 'Node 1'),].

[0045] All data information corresponding to two nodes in each of the three disk drives SPD1, SPD2, and SPD3 can be obtained through the above program.

[0046] Step S102: Extract the target feature information from each group of data tables respectively to obtain multiple pieces of target feature information. Among them, the target feature information is the feature information representing the performance of the disk drive in the data table.

[0047] Specifically, each disk drive can contain multiple data tables, and the amount of feature information in each data table is very large. Therefore, it is necessary to select the target feature information suitable for performance analysis for the performance analysis of the disk drive. In order to reduce the analysis frequency and workload, the data tables can be extracted according to the second time period. For example, the data tables stored in this month are obtained once a month.

[0048] Further, in the overall system performance data table of the disk drive, by obtaining target feature information such as the number of IO requests, throughput, and response time, after extracting the target feature information, the target feature information can be calculated to obtain performance data, and the overall system performance of the disk drive can be analyzed. In addition, other target feature information can be selected according to the needs of performance analysis. The type of target feature information is not limited in this embodiment.

[0049] Step S103: Classify multiple pieces of target feature information according to attribute information to obtain multiple groups of target feature information, and calculate performance data through the feature values in each group of target feature information to obtain multiple pieces of performance data.

[0050] In an alternative embodiment, all data tables obtained this month can be classified according to attribute information and sorted by time. Among them, the attribute information can be: disk drive number, data table name, project name, etc. For example, the throughput data in the overall system performance data table of the 1st disk drive. Classify all target feature information with the same attribute information into one group, and calculate the feature values in this group to obtain performance data. For example: calculate the average value and the maximum value for all feature values in this group, and set the average value and the maximum value as the performance data corresponding to this table. Then, the overall system performance of the 1st disk drive this month can be characterized by this performance data.

[0051] Step S104: Determine the storage performance analysis result of the target device according to multiple pieces of performance data.

[0052] Specifically, after obtaining multiple pieces of performance data, the performance analysis result can be quickly obtained through the compilation method of the R language. It should be noted that the R language has a powerful data visualization function. Through packages such as ggplot2 for drawing various charts and packages such as xtable, flextable, and officer for making word, it is possible to generate the required disk drive performance capacity analysis report with one click, so as to intuitively display the performance index situation of the disk drive.

[0053] The storage performance analysis method of the device provided by the embodiment of the present application obtains data tables in multiple disk drives of a target device to obtain multiple groups of data tables. Each group of data tables includes multiple feature information, and each feature information includes attribute information and a feature value. Respectively extract the target feature information in each group of data tables to obtain multiple target feature information, where the target feature information is the feature information representing the performance of the disk drive in the data table. Classify the multiple target feature information according to the attribute information to obtain multiple groups of target feature information, and calculate performance data through the feature values in each group of target feature information to obtain multiple performance data. Determine the storage performance analysis result of the target device according to the multiple performance data. This solves the problems of low analysis efficiency and low accuracy of the analysis result in the process of analyzing the storage performance of the device by manual means in the related art. By collecting data of the disk drive, analyzing and processing the collected data to obtain performance data, and determining a performance analysis report through the performance data, the effect of accurately and efficiently analyzing the performance of the disk drive can be achieved.

[0054] Optionally, in order to conveniently and accurately analyze the performance of the disk drive, in the storage performance analysis method of the device provided by the embodiment of the present application, each group of data tables includes the following data tables: the system performance data table of the disk drive, the performance data tables of each node of the disk drive, and the performance data tables of each port of the disk drive. The method of respectively extracting the target feature information in each group of data tables to obtain multiple target feature information includes at least one of the following: Extract the first target data in the system performance data table of the disk drive and use the first target data as the target feature information, where the first target data includes at least one of the following: the number of port requests, throughput, and response time information; Extract the second target data in the performance data tables of each node of the disk drive and use the second target data as the target feature information, where the second target data includes at least one of the following: the number of port requests of each node, the throughput of each node, and the cache data occupancy rate of each node; Extract the third target data in the performance data tables of each port of the disk drive and use the third target data as the target feature information, where the third target data includes at least one of the following: port throughput.

[0055] Specifically, the target feature information extracted from the system performance data table of the disk drive can be: the number of port requests, throughput, and response time information. Among them, the number of port requests is the number of times the entire ports of the disk drive are requested, the throughput is the size of the information received and sent, and the response time is the response time between the request being sent and the response. The above three target feature information are important performance indicators for measuring the overall state of the disk drive. The overall performance of the disk drive can be analyzed by calculating the above target feature information.

[0056] The target feature information extracted from the performance data tables of each node of the disk drive can also be: the number of port requests of each node, the throughput of each node, and the cache data occupancy rate of each node. For example, each disk drive has two nodes, and the two nodes can run together. When one node fails, the other node runs all requests alone. The cache data occupancy of the node is to put the data with high usage frequency into the cache, so that data access can be performed efficiently when in use. The above three pieces of target feature information are important performance indicators for measuring each node of the disk drive. The performance of each node of the disk drive can be analyzed by calculating the above target feature information.

[0057] The target feature information extracted from the performance data tables of each port of the disk drive can also be: port throughput. The data in the performance data tables of each port of the disk drive is very large, with a quantity at the million level. The performance of each node of the disk drive can be analyzed by calculating the port throughput. In this embodiment, by obtaining the target feature information, the effect of conveniently and accurately analyzing the performance of the disk drive is achieved.

[0058] Optionally, in order to obtain the performance data of the disk drive, in the storage performance analysis method of the device provided in the embodiment of the present application, performance data is calculated through the feature values in each group of target feature information, and multiple pieces of performance data are obtained, including: calculating the index values of the feature values in each group of target feature information to obtain multiple index values, where the index values are at least one of the following: maximum value and average value; taking the attribute information corresponding to each group of target feature information and the index value as a piece of performance data to obtain multiple pieces of performance data.

[0059] Specifically, after classification according to the attribute information, it is necessary to calculate the multiple feature values corresponding to the attribute information of each category, and data calculation and processing can be performed through the R language. It should be noted that the R language is a complete software system for data processing, calculation, and mapping. Its functions include: a data storage and processing system, array operation tools, a complete and coherent statistical analysis tool, a strong statistical mapping function, a simple and powerful programming language, which can manipulate the input and output of data, implement branches and loops, and users can customize functions.

[0060] By using various functional methods in the R language, data transformation, analysis, table merging, etc. can be performed on the data tables collected by the disk drive performance capacity monitoring software (the overall system performance data table of the disk drive, the performance data tables of each node of the disk drive, and the performance data table of the front-end ports of the disk drive), so as to achieve the purpose of analyzing the performance capacity of all disk drives. Among them, the functional methods for data transformation can use matrix, as.character, apply, etc.; the functional methods for data analysis can use max, min, mean, summarise, group, ifelse, which, etc.; the functional methods for table merging can use data.frame, unite, fletable, etc.

[0061] In an alternative embodiment, the port request quantity of each node stored this month can be calculated through an R language program to obtain the maximum value of the request quantity of each node in each disk drive, and the obtained maximum value is used as performance data.

[0062] The following is an alternative program for obtaining the maximum value of the port request quantity of each node:

[0063] SPD1_NODE0_IO request quantity <- matrix(SPD1_NODE0[,34]);

[0064] SPD1_NODE0_IO request quantity_MAX <- max(SPD1_NODE0_IO request quantity);

[0065] SPD1_NODE1_IO request quantity <- matrix(SPD1_NODE1[,34]);

[0066] SPD1_NODE1_IO request quantity_MAX <- max(SPD1_NODE1_IO request quantity);

[0067] SPD2_NODE0_IO request quantity <- matrix(SPD2_NODE0[,34]);

[0068] SPD2_NODE0_IO request quantity_MAX <- max(SPD2_NODE0_IO request quantity);

[0069] SPD2_NODE1_IO request quantity <- matrix(SPD2_NODE1[,34]);

[0070] SPD2_NODE1_IO request quantity_MAX <- max(SPD2_NODE1_IO request quantity);

[0071] SPD3_NODE0_IO request quantity <- matrix(SPD3_NODE0[,34]);

[0072] SPD3_NODE0_IO request quantity_MAX <- max(SPD3_NODE0_IO request quantity);

[0073] SPD3_NODE1_IO request quantity <- matrix(SPD3_NODE1[,34]);

[0074] SPD3_NODE1_IO request quantity_MAX <- max(SPD3_NODE1_IO request quantity);

[0075] NODE_IO request quantity peak value <- max(SPD1_NODE0_IO request quantity_MAX,

[0076] SPD1_NODE1_IO request quantity_MAX, SPD2_NODE0_IO request quantity_MAX,

[0077] SPD2_NODE1_IO request quantity_MAX, SPD3_NODE0_IO request quantity_MAX,

[0078] SPD3_NODE1_IO request quantity_MAX).

[0079] Through the above program, the maximum value of the port request quantity of each node in this month can be obtained, and the port attribute information corresponding to the maximum value can be obtained. In this embodiment, by calculating performance data such as the maximum value and the average value, the data used to characterize the performance of the disk drive is obtained, laying a data foundation for further analyzing the storage performance of the device.

[0080] Optionally, in the storage performance analysis method of the device provided in the embodiment of the present application, data tables in multiple disk drives of the target device are obtained according to the first time period, and target feature information in multiple groups of data tables is extracted according to the second time period, where the second time period is greater than the first time period.

[0081] Specifically, in order to obtain the performance data of the disk drive at different times, it is necessary to obtain the disk drive data in a relatively short period. Therefore, the data tables in the disk drive are obtained according to the first time period, and the data tables are stored. After storing a certain number of data tables, data analysis and calculation are performed centrally, and the target feature information in multiple groups of data tables is extracted according to the second time period.

[0082] For example, the data table in the disk drive is obtained every five minutes and stored, and the target feature information in the data table is extracted and calculated every month to obtain the disk drive performance information for that month. Through the acquisition at different frequencies in this embodiment, a data foundation is laid for further performance analysis.

[0083] Optionally, in order to obtain the change trend of the performance data, in the storage performance analysis method of the device provided in the embodiment of the present application, before determining the storage performance analysis result of the target device according to multiple performance data, the method further includes: obtaining the previous historical performance data with the same attribute information as each performance data, and comparing the performance data with the previous historical performance data to obtain the change trend information of the performance data, where the previous historical performance data is the performance data determined within the previous second time period.

[0084] Specifically, after calculating the performance data, each performance data is compared with the historical performance data determined within the previous second time period to obtain the change trend of the performance data. For example, the maximum value of the port requests of each node this month is compared one by one with the maximum value of the port requests of each node last month to obtain the change trend of the port requests.

[0085] The following is an optional program for obtaining historical performance data:

[0086] NODE_last_month_peak <- read.csv("last_month_data_NODE.csv", header = TRUE, sep = ",")

[0087] The historical performance data can be obtained through the above program.

[0088] It should be noted that after obtaining all the performance data for this month, all the performance data for this month is stored as new historical performance data, and after obtaining the performance data for the next month, it is compared with the performance data for the next month. Through the comparison of the data for this month with the historical data in this embodiment, the effect of confirming the change trend of the data is achieved.

[0089] Optionally, in order to display abnormal performance data, in the storage performance analysis method of the device provided in the embodiment of the present application, before determining the storage performance analysis result of the target device according to multiple performance data, the method further includes: judging whether the index value included in each performance data is greater than the corresponding threshold; when the index value is greater than the threshold, generating an alarm message.

[0090] Specifically, each performance data has a corresponding warning value. After the performance data exceeds the warning value, an alarm message for the target feature information corresponding to the performance data will be generated in the disk drive performance analysis result to perform abnormal data alarm. For example, when the maximum value of the port request quantity of a certain node is 5000 and the warning value is 2000, an alarm will be displayed in the performance analysis result corresponding to the node, and the display method is shown in Table 1:

[0091] Table 1

[0092] Feature Device Node Maximum value Occurrence time Warning value Remark Request quantity SPD1 CL1 5000 10 / 1 / 20 / 1:45 2000 Maximum value anomaly

[0093] In this embodiment, the alarm information is published through a table, achieving the effect of alarming and prompting abnormal data.

[0094] Optionally, in the storage performance analysis method of the device provided in the embodiment of the present application, determining the storage performance analysis result of the target device according to multiple performance data includes: generating the storage performance analysis result of the target device according to multiple performance data, the change trend information of each performance data, and the alarm information.

[0095] Specifically, in the case of obtaining all performance data, the change trend obtained by comparing the performance data with historical performance data, and the alarm information, the above information is comprehensively generated into the final performance analysis result, and part of the table content is displayed graphically. Table 2 is a performance analysis result table corresponding to an optional overall system performance data table of the disk drive, as shown in Table 2:

[0096] Table 2

[0097]

[0098] Table 3 is a performance analysis result table corresponding to an optional performance data table of each node of the disk drive, as shown in Table 3:

[0099] Table 3

[0100]

[0101] Table 4 is a performance analysis result table corresponding to an optional performance data table of the front-end port of the disk drive, as shown in Table 4:

[0102] Table 4

[0103] Feature Device Maximum value Occurrence time Average value Warning value Remark Request quantity SPD1 391 10 / 1 / 20 / 1:45 36 1200 - Request quantity SPD2 391 10 / 1 / 20 / 1:45 36 1200 - Request quantity SPD3 630 10 / 1 / 20 / 1:45 33 1200 -

[0104] In this embodiment, a disk drive performance data table is generated through various performance data and alarm data, achieving the effect of analyzing the disk drive performance data through the performance data table.

[0105] Optionally, in the storage performance analysis method of the device provided in the embodiments of the present application, determining the storage performance analysis result of the target device according to multiple performance data includes: generating a chart corresponding to the multiple performance data through the Ggplot function method; generating a document by combining the multiple performance data and the chart through the Office function method, and using the document as the storage performance analysis result of the target device.

[0106] It should be noted that the R language has a powerful function of visualizing data. Various types of charts can be made into corresponding charts through the ggplot2 function for plotting, and functions such as xtable, flextable, and officer for making word can be used to generate word files. The performance result report can be automatically generated through the program, realizing one-key generation of the required disk drive performance capacity analysis report, so as to intuitively display the performance index situation of the disk drive. Figure 2 It is an optional comparison table of the average number of requests of 3 disk drives in the past 3 months made using the Ggplot function. As Figure 2 shown, the differences in the number of requests between the three disk drives and the change trend of the number of requests for each disk drive can be clearly seen through the bar chart.

[0107] Figure 3 It is an optional comparison chart of the disk drive backend and the overall IO throughput made using the Ggplot function. As Figure 3 shown, the two curves in the figure are the backend throughput and the overall throughput respectively, which can be obtained by plotting through the following program Figure 3 .

[0108] The following is a partial generation program for an optional comparison chart of the disk drive backend and the overall IO throughput:

[0109] docx<-body_add_fpar(x=docx,dn1,style="Normal");

[0110] dn2<-fpar("1. Each disk drive subsystem performance index daily line (xx / xx / xxxx)",fp_t=fp_text(color="black",font.size=15,font.family="KaiTi"));

[0111] docx<-body_add_fpar(x=docx,dn2,style="Normal");

[0112] SPD1_18_time<-SPD1_18[,2];

[0113] SPD1_18_backend_IO <- SPD1_18[, 18];

[0114] SPD1_18_total_IO <- SPD1_18[, 36];

[0115] SPD1_18_backend_data_rate <- SPD1_18[, 21];

[0116] SPD1_18_total_data_rate <- SPD1_18[, 48];

[0117] SPD1_18_response_time <- SPD1_18[, 51];

[0118] SPD1_18_time <- strptime(c(SPD1_18_time), "%Y-%m-%d %H:%M:%S");

[0119] SPD1_18_1 <- data.frame(SPD1_18_time, SPD1_18_backend_IO, SPD1_18_total_IO);

[0120] SPD1_18_2 <- data.frame(SPD1_18_time, SPD1_18_backend_data_rate, SPD1_18_total_data_rate);

[0121] SPD1_18_3 <- data.frame(SPD1_18_time, SPD1_18_response_time);

[0122] SPD1_18_1_p <- ggplot(SPD1_18_1) + geom_line(aes(x = SPD1_18_1[, 1], y = SPD1_18_1[, 2]), color = "red") + geom_line(aes(x = SPD1_18_1[, 1], y = SPD1_18_1[, 3]), color = "blue") + labs(x = "Date", y = "IO_Rate", title = "SPD1 Backend and Total IO_Rate Comparison") + theme(plot.title =

[0123] element_text(hjust = 0.5)) + theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 1)) +

[0124] scale_x_datetime(date_labels = "%H:%M", date_breaks = ("1 hour")) + scale_y_continuous(breaks = seq(15000, 300000, 15000));

[0125] SPD1_18_2_p <- ggplot(SPD1_18_2) + geom_line(aes(x = SPD1_18_2[, 1], y = SPD1_18_2[, 2]), color = "red") + geom_line(aes(x = SPD1_18_2[, 1], y = SPD1_18_2[, 3]), color = "blue") + labs(x = "Date", y = "Data_Rate", title = "SPD1 Back-end and Overall Data_Rate Comparison") + theme(plot.title = element_text(hjust = 0.5)) + theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 1)) + scale_x_datetime(date_labels = "%H:%M", date_breaks = ("1 hour")) + scale_y_continuous(breaks = seq(0, 10000, 2000));

[0126] SPD1_18_3_p <- ggplot(SPD1_18_3) + geom_line(aes(x = SPD1_18_3[, 1], y = SPD1_18_3[, 2]), color = "red") + labs(x = "Date", y = "Resonse Time", title = "SPD1 Total Response Time") + theme(plot.title = element_text(hjust = 0.5)) + theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 1)) + scale_x_datetime(date_labels = "%H:%M", date_breaks = ("1 hour")) + scale_y_continuous(breaks = seq(0, 0.5, 0.05)).

[0127] After obtaining multiple performance data and charts in this embodiment, the charts and data in this embodiment are made into a result report by means of Office functions, achieving the effect of obtaining the performance analysis results of the disk drive.

[0128] It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0129] The embodiment of the present application also provides a storage performance analysis device for a device. It should be noted that the storage performance analysis device for the device in the embodiment of the present application can be used to execute the storage performance analysis method for the device provided in the embodiment of the present application. The following introduces the storage performance analysis device for the device provided in the embodiment of the present application.

[0130] Figure 4 is a schematic diagram of the storage performance analysis device for the device provided in the embodiment of the present application. As Figure 4 shown, the device includes:

[0131] The first acquisition unit 10 is used to acquire data tables in multiple disk drives of the target device to obtain multiple groups of data tables, where each group of data tables includes multiple feature information, and each feature information includes attribute information and a feature value;

[0132] The extraction unit 20 is used to extract target feature information from each group of data tables respectively to obtain multiple target feature information, where the target feature information is the feature information representing the performance of the disk drive in the data table;

[0133] The calculation unit 30 is used to classify the multiple target feature information according to the attribute information to obtain multiple groups of target feature information, and calculate performance data through the feature values in each group of target feature information to obtain multiple performance data;

[0134] The determination unit 40 is used to determine the storage performance analysis result of the target device according to the multiple performance data.

[0135] The storage performance analysis device of the equipment provided by the embodiment of the present application obtains data tables in multiple disk drives of a target device through a first acquisition unit 10, and obtains multiple groups of data tables. Each group of data tables includes multiple feature information, and each feature information includes attribute information and a feature value. An extraction unit 20 extracts target feature information from each group of data tables respectively, and obtains multiple pieces of target feature information. The target feature information is the feature information characterizing the performance of the disk drive in the data table. A calculation unit 30 classifies the multiple pieces of target feature information according to the attribute information, obtains multiple groups of target feature information, and calculates performance data through the feature values in each group of target feature information, obtaining multiple pieces of performance data. A determination unit 40 determines the storage performance analysis result of the target device according to the multiple pieces of performance data. This solves the problems of low analysis efficiency and low accuracy of the analysis result in the process of analyzing the storage performance of the equipment by manual means in the related art. By collecting data of the disk drive, analyzing and processing the collected data to obtain performance data, and determining a performance analysis report through the performance data, the effect of accurately and efficiently analyzing the performance of the disk drive can be achieved.

[0136] Optionally, in the storage performance analysis device of the equipment provided by the embodiment of the present application, the extraction unit 20 includes: a first extraction module, configured to extract first target data from the system performance data table of the disk drive, and use the first target data as the target feature information, where the first target data includes at least one of the following: the number of port requests, throughput, and response time information; a second extraction module, configured to extract second target data from the performance data tables of each node of the disk drive, and use the second target data as the target feature information, where the second target data includes at least one of the following: the number of port requests of each node, the throughput of each node, and the cache data occupancy rate of each node; a third extraction module, configured to extract third target data from the performance data tables of each port of the disk drive, and use the third target data as the target feature information, where the third target data includes at least one of the following: port throughput.

[0137] Optionally, in the storage performance analysis device of the equipment provided by the embodiment of the present application, the calculation unit 30 includes: a calculation module, configured to calculate the index values of the feature values in each group of target feature information, obtaining multiple index values, where the index values are at least one of the following: the maximum value and the average value; a first acquisition module, configured to use the attribute information corresponding to each group of target feature information and the index value as a piece of performance data, obtaining multiple pieces of performance data.

[0138] Optionally, in the storage performance analysis device of the equipment provided in the embodiments of the present application, the device further includes: a second acquisition unit, configured to acquire data tables in multiple disk drives of a target device according to a first time period, and extract target feature information in multiple groups of data tables according to a second time period, where the second time period is greater than the first time period.

[0139] Optionally, in the storage performance analysis device of the equipment provided in the embodiments of the present application, the device further includes: a comparison unit, configured to acquire a previous historical performance data with the same attribute information as each performance data, and compare the performance data with the previous historical performance data to obtain change trend information of the performance data, where the previous historical performance data is the performance data determined within the previous second time period.

[0140] Optionally, in the storage performance analysis device of the equipment provided in the embodiments of the present application, the device further includes: a judgment unit, configured to judge whether an index value included in each performance data is greater than a corresponding threshold; a generation unit, configured to generate an alarm message when the index value is greater than the threshold.

[0141] Optionally, in the storage performance analysis device of the equipment provided in the embodiments of the present application, the determination unit 40 includes: a first generation module, configured to generate a storage performance analysis result of the target device according to multiple performance data, change trend information of each performance data, and the alarm message.

[0142] Optionally, in the storage performance analysis device of the equipment provided in the embodiments of the present application, the determination unit 40 includes: a second generation module, configured to generate a chart corresponding to multiple performance data by using the Ggplot function method; a third generation module, configured to generate a document by using the Office function method with multiple performance data and the chart, and use the document as the storage performance analysis result of the target device.

[0143] The above-mentioned storage performance analysis device of the equipment includes a processor and a memory. The above-mentioned first acquisition unit 10, extraction unit 20, calculation unit 30, determination unit 40, etc. are all stored in the memory as program units, and the processor executes the above-mentioned program units stored in the memory to implement corresponding functions.

[0144] The processor includes a kernel, and the kernel retrieves the corresponding program unit from the memory. One or more kernels can be set, and by adjusting the kernel parameters, the problems of low analysis efficiency and low accuracy of the analysis result in the process of analyzing the storage performance of the equipment in the related art by manual means are solved.

[0145] The memory may include non-permanent memory in the form of computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0146] Embodiments of the present invention provide a computer-readable storage medium, on which a program is stored, and when the program is executed by a processor, the storage performance analysis method of the device is implemented.

[0147] Embodiments of the present invention provide a processor for running a program, wherein when the program runs, the storage performance analysis method of the device is executed.

[0148] As Figure 5 shown, embodiments of the present invention provide an electronic device. The electronic device 50 includes a processor, a memory, and a program stored on the memory and executable on the processor. When the processor executes the program, the following steps are implemented: obtaining data tables in multiple disk drives of a target device to obtain multiple groups of data tables, wherein each group of data tables includes multiple feature information, and each feature information includes attribute information and a feature value; respectively extracting target feature information in each group of data tables to obtain multiple target feature information, wherein the target feature information is the feature information characterizing the performance of the disk drive in the data table; classifying the multiple target feature information according to the attribute information to obtain multiple groups of target feature information, and calculating performance data through the feature values in each group of target feature information to obtain multiple performance data; determining the storage performance analysis result of the target device according to the multiple performance data. The device herein may be a server, a PC, a PAD, a mobile phone, etc.

[0149] This application also provides a computer program product, which, when executed on a data processing device, is adapted to execute a program initialized with the following method steps: obtaining data tables in multiple disk drives of a target device to obtain multiple groups of data tables, wherein each group of data tables includes multiple feature information, and each feature information includes attribute information and a feature value; respectively extracting target feature information in each group of data tables to obtain multiple target feature information, wherein the target feature information is the feature information characterizing the performance of the disk drive in the data table; classifying the multiple target feature information according to the attribute information to obtain multiple groups of target feature information, and calculating performance data through the feature values in each group of target feature information to obtain multiple performance data; determining the storage performance analysis result of the target device according to the multiple performance data.

[0150] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of an all-hardware embodiment, an all-software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0151] The present application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in Figure 1 one or more of the processes or multiple processes and / or blocks Figure 1 one or more of the blocks or multiple blocks.

[0152] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device implements the functions specified in Figure 1 one or more of the processes or multiple processes and / or blocks Figure 1 one or more of the blocks or multiple blocks.

[0153] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one or more of the processes or multiple processes and / or blocks Figure 1 one or more of the blocks or multiple blocks.

[0154] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and a memory.

[0155] The memory may include non-permanent memory in a computer-readable medium, in the form of random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash memory (flash RAM). The memory is an example of a computer-readable medium.

[0156] A computer-readable medium includes both permanent and non-permanent, removable and non-removable media and can implement information storage by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tapes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transitory medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer-readable medium does not include transitory computer-readable media such as modulated data signals and carrier waves.

[0157] It should also be noted that the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.

[0158] The above are only embodiments of the present application and are not used to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims

1. A method for analyzing the storage performance of a device, characterized in that, Including: Obtain data tables in multiple disk drives of a target device to get multiple groups of data tables. Each group of data tables includes multiple feature information, and each feature information includes attribute information and a feature value. Among them, the collection of the multiple feature information is performed through disk drive performance capacity monitoring software; Extract target feature information from each group of data tables respectively to obtain multiple target feature information. Among them, the target feature information is the feature information representing the performance of the disk drive in the data table; Classify the multiple target feature information according to the attribute information to obtain multiple groups of target feature information, and calculate performance data through the feature values in each group of the target feature information to obtain multiple performance data; Determine the storage performance analysis result of the target device according to the multiple performance data. Among them, the storage performance analysis result is obtained through the method compiled by R language.

2. The method according to claim 1, wherein Each group of data tables includes the following data tables: The system performance data table of the disk drive, the performance data tables of each node of the disk drive, and the performance data tables of each port of the disk drive. The ways of extracting target feature information from each group of data tables respectively include at least one of the following: Extract the first target data in the system performance data table of the disk drive, and use the first target data as the target feature information. Among them, the first target data includes at least one of the following: the number of port requests, throughput, and response time information; Extract the second target data in the performance data tables of each node of the disk drive, and use the second target data as the target feature information. Among them, the second target data includes at least one of the following: the number of port requests of each node, the throughput of each node, and the cache data occupancy rate of each node; Extract the third target data in the performance data tables of each port of the disk drive, and use the third target data as the target feature information. Among them, the third target data includes at least one of the following: port throughput.

3. The method according to claim 1, characterized in that Calculating performance data through the feature values in each group of the target feature information to obtain multiple performance data includes: Calculate the index values of the feature values in each group of the target feature information to obtain multiple index values. Among them, the index value is at least one of the following: maximum value and average value; Use the attribute information corresponding to each group of the target feature information and the index value as a performance data to obtain multiple performance data.

4. The method according to claim 1, characterized in that Obtain data tables in multiple disk drives of the target device according to the first time period, and extract the target feature information in the multiple groups of data tables according to the second time period. Among them, the second time period is greater than the first time period.

5. The method according to claim 4, wherein Before determining the storage performance analysis result of the target device according to the multiple performance data, the method further includes: Obtain the previous historical performance data with the same attribute information as each performance data, and compare the performance data with the previous historical performance data to obtain the change trend information of the performance data. Among them, the previous historical performance data is the performance data determined in the previous second time period.

6. The method according to claim 5, wherein Before determining the storage performance analysis result of the target device based on the multiple performance data, the method further includes: judging whether the metric value included in each of the performance data is greater than the corresponding threshold value; when the metric value is greater than the threshold value, generating an alarm message.

7. The method according to claim 6, wherein Determining the storage performance analysis result of the target device according to the multiple performance data includes: generating the storage performance analysis result of the target device according to the multiple performance data, the change trend information of each of the performance data, and the alarm message.

8. The method according to claim 1, wherein Determining the storage performance analysis result of the target device according to the multiple performance data includes: generating a chart corresponding to the multiple performance data by means of the Ggplot function method; generating a document by means of the Office function method with the multiple performance data and the chart, and using the document as the storage performance analysis result of the target device.

9. A storage performance analysis device for a device, characterized in that, including: a first obtaining unit, configured to obtain data tables in a plurality of disk drives of a target device, to obtain multiple groups of data tables, wherein each group of data tables includes a plurality of feature information, and each feature information includes attribute information and a feature value, and wherein the plurality of feature information is collected by a disk drive performance capacity monitoring software; a first extracting unit, configured to respectively extract target feature information in each group of data tables, to obtain a plurality of target feature information, wherein the target feature information is feature information in the data tables that characterizes the performance of the disk drive; a first calculating unit, configured to classify the plurality of target feature information according to the attribute information, to obtain multiple groups of target feature information, and calculate performance data by using the feature values in each group of the target feature information, to obtain a plurality of performance data; a first determining unit, configured to determine the storage performance analysis result of the target device according to the plurality of performance data, wherein the storage performance analysis result is obtained by means of R language compilation.

10. A non-volatile storage medium, characterized in that, The non-volatile storage medium includes a stored program, wherein when the program runs, it controls the device where the non-volatile storage medium is located to execute the storage performance analysis method of the device according to any one of claims 1 to 8.

11. An electronic device, characterized in that, including one or more processors and a memory, the memory is configured to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the storage performance analysis method of the device according to any one of claims 1 to 8.

Images