Research on the Construction and Guardianship of Inner Mongolia Minzu Sports Resource Database in Cloud Computing Environment

The Mongolian population is mostly distributed in Inner Mongolia Autonomous Region, Ningxia, Qinghai, and the three northeastern provinces, etc., and the Inner Mongolia Autonomous Region has the largest proportion of the population, and most of them are distributed in the vast semi-agricultural and semi-pastoral areas in the eastern part of Inner Mongolia. They often hold traditional sports activities such as horse racing, polo, camel racing, boules, archery, horse swinging, Mongolian chess, etc. The Mongolian people are characterized by their robust and bold character. They are stout and robust. Equestrianism and Mongolian wrestling are the national sports competitions of the ethnic minorities.

1)

Strong closure

Traditional sports of ethnic minorities in Inner Mongolia Autonomous Region, despite a long period of development, is still in the primary stage of development, traditional sports of ethnic minorities also exist only in minority settlements, closed, people less local traditional sports development is slow, therefore, in the follow-up development process should focus on vigorously publicizing and promoting the traditional sports of ethnic minorities in the folk.

The fundamental purpose of establishing the database of traditional national sports resources in Inner Mongolia is to organize and store the traditional national sports resources in Inner Mongolia, the current development status and the related characteristic information, update and manage them, and apply them to the whole process of designing, constructing, operating and using the venues by integrating and analyzing the data. The database mainly targets the designers, operators, and users of the venues. Considering that the users of the database have less knowledge about the database system, the database system adopts a simple and easy-to-understand interface, which can provide users with concise and clear instructions for use, and is easy for users to query and use.

Clear data structure can help the database achieve efficient use by allowing for rapid query and positioning, which reduces the time required for data queries. This can help achieve efficient use of the database. Therefore, in the data logic design of the database, it is crucial to have clear data logic relationships and reduce redundant data structures.

Standardized data types: the basic data of the database and the related analytical data should meet the requirements of the relevant design specifications and standard units to facilitate the setting of uniform data coding, simplify the amount of data established by the database, and at the same time facilitate the data in different data platforms to achieve multiple platforms for data sharing, and to better connect the deep data analysis platform, expand the scope of use of the database, and improve the efficiency of the use of the database. The use of the database to expand the scope of use and improve the efficiency of the database.

Independence and security: Independence and security is the security guarantee for the smooth operation of the database system, the independence of the data can ensure the flexibility of the database management system, when the database is maintained and upgraded, the use of the operational database as well as the data structure of the database is not much affected.

Data backup and recovery: database data backup and data recovery is a guarantee of database security, to deal with the database in the case of unexpected data loss, so that the database can resume operation faster and improve the reliability of the database.

In the cloud computing environment, Inner Mongolia national traditional sports resources database design needs to consider and follow the following basic principles of database design, which can establish a more secure, stable and reliable database system [26-27]. 1)

Unified and systematic analysis and design of data sources, coordination of various data sources, to ensure the consistency and validity of the data, to meet the needs of the relevant basic information and statistical data.

The traditional sports resources data of Inner Mongolia nationalities are extracted and processed to form the basic data of the database, and the basic data are the prerequisite for implementing the functions of the database. The basic data is the prerequisite for realizing the functions of the database. It is the key technology of the database to convert the traditional sports resources data of Inner Mongolia ethnic groups into the basic data of the database, and the data conversion is divided into two parts, namely, data coding and data entry. 1)

Data encoding

Through the Inner Mongolia national traditional sports resources data for data coding to ensure the completeness and accuracy of the data information, the main role of data coding is to realize the data of the Inner Mongolia national traditional sports resources. The data coding dictionary table of Mongolian ethnic traditional sports resources is divided into two categories: data table dictionary and statistical information dictionary. And this paper uses the data table dictionary. The data dictionary table is mainly designed for the research data table, and the format of the research data table is unified and data management, on the one hand, it improves the convenience of the data research of Mongolian national traditional sports resources, on the other hand, it also improves the accuracy of the data entry of the database of Mongolian national traditional sports resources, and it provides the basis for the data entry, analysis and operation of the database in the next step.

Database guardianship is divided into four main dimensions, including corrective guardianship, adaptive guardianship, perfective guardianship, and preventive guardianship. The following is a specific analysis of the four dimensions of this guardianship: 1)

Error Correction Guardianship

Although the software testing process to try to find the database errors and corrected, but no matter what way of testing can not be all the errors in the database are exposed, so in the database after a certain period of time, the database hidden errors will gradually be exposed, this time the need to correct, this guardianship is the corrective guardianship.

The database guardianship operation is shown in Figure 1. When the user starts to guard the database, they need to use the terminal to access the network database quality of service guardianship system and guard the database through the start button. The browser or client will send the start command to the server, the server listens to the start command and then accesses the local database to get the database information that needs to be monitored, the server sends a request to the network database retrieval platform according to the database information obtained, the network database retrieval platform returns the corresponding results according to the request command, the server analyzes the return results and the response time to generate logs, then stores the log information to the system database, the system database will store the log information to the system database, and the system database will store the log information to the system database. The server analyzes the returned results and response time to generate logs, and then stores the log information in the system database. The system database returns the execution results to the server. When the user wants to close the monitoring function, the user needs to click the stop monitoring button on the browser or client, the browser or client will send the stop command to the server, the server will stop the monitoring after receiving the information, the system server will query the monitoring log information of the monitoring system, and the server will send the monitoring results to the browser or client for display.

Figure 1.

Database monitoring operation

The TPC (Transaction Processing Performance Council) series of benchmarks are commonly used in the industry, including TPC-A, App, B, C, D, DS, E, Energy, H, Pricing, R, VMS, and W benchmarks. Among them, the benchmarks on transaction processing (OLTP) include TPC-A, C, and E, those on decision support include TPC-D, DS, H, and R, those on stress testing include TPC-H, and the others are benchmarks based on other purposes. In this paper, TPC-B and TPC-H are chosen as benchmarks for the three major scenarios of stress testing, OLTP, and decision support to analyze the performance of database systems. In the following, this paper measures the impact of I/O operations on TPC-B and TPC-H benchmarks respectively. In this section, the database performance tests deployed in memory will be conducted in a server with large memory, and the servers and desktops used for the TPC benchmarks are shown in Table 1.

The TPC-B benchmark was designed as a stress test. It simulates a scenario in which there are 200 users and 5000 traditional sports resources for each ethnic group in a region of Inner Mongolia, and then the users keep creating and deleting the resources. The reason why it is a stress test is that each simulated user is issuing queries without interruption, and the final statistic to be counted is how many queries can be processed per minute (i.e., the evaluation metric TPS).

In order to examine the characteristics of the I/O operations generated by the TPC-B benchmark, this paper uses strace to capture the system calls generated by the PG database management system while running the TPC-B benchmark, and then analyzes the lseek(), read(), and write() calls for all the disk files that were opened with open(), so that we can be informed of the location of each opened file. Was read or written to. Relating these read and write locations to the time when the operation occurred, Figure 2 illustrates the I/O characteristics of the TPC-B benchmark. Green represents read operations and orange represents write operations. Unfortunately, it is not possible to make out the read operations in this figure: the range of write operations completely covers the range of read operations. The figure includes large swaths of random operations (the large orange squares in the lower half of the figure), a single sequential operation (the orange slash in the figure), and an operation that repeats reads and writes somewhere (the horizontal line in the upper half of the figure). The single feature in the figure is associated with the single operation type of the TPC-B benchmark.

Figure 2.

I/O characteristics of the TPC-B benchmark

The performance of TPC-B on the server is shown in Figure 3.In this paper, the TPC-B benchmark was run on a server with a scale of 15,000.Putting all of the database data on the server was up to 22 times faster than putting it on disk. This shows the superiority of storing data as a server, but it also suggests that shorter I/O latency can lead to greater performance improvements.

Figure 3.

TPC-B performance on the server

The performance of TPC-B on a desktop machine is shown in Figure 4. To further confirm this bottleneck, the performance of TPC-B was also tested on a desktop machine, with the data deployed in this paper’s database and on disk, respectively. The data size is still set to 15,000. the amount of data (including the data itself and the indexes) for this size is about 18GB, and there is only 6GB of RAM on the desktop machine, which allows the data reads and writes to occur on the system server or disk, avoiding excessive memory caching that affects the effectiveness of the experiment. It can be noted that deploying the data on the server is still about 8 times faster than deploying it on disk.The increase in I/O performance does contribute to the overall performance, which further suggests that the bottleneck in the TPC-B benchmark is in I/O.

Figure 4.

Performance of the TPC-B on the desktop

TPC-H is a decision support benchmark. It simulates an offline database where one or more analysts issue queries of various statistical types, and the results of the requests are used to help advance the digitization of sports resources in Inner Mongolia. Since, most of the request types are statistical and are able to contribute to the digitization of sports resources in Inner Mongolia, the query itself is quite complex, so the query plan generated by the database system is particularly important.The evaluation metric of TPC-H is the total execution time, which is as small as possible.

The TPC-H benchmark will generate a total of 30 queries per run. These queries have a fixed format, query the same type of data, but contain values (usually appearing in WHERE clauses and filtered as conditions) that can vary. In this paper, the DBGEN tool is used to generate three sets of TPC-H queries, and these three sets are run for each test, with the times averaged.

In this paper, we still created about 32GB of raw TPC-H data with a size of 8. The I/O characteristics of the TPC-H benchmark are shown in Fig. 5.The characteristics of TPC-H are quite different from those of both previous benchmarks.TPC-H is a write-less benchmark, and most of the reads are either table scans, index scans, or index lookups. As can be seen in the figure, the orange slashes and blocks indicate sequential reads resulting from scan operations, and random reads resulting from lookup operations, respectively. However, green write operations are still visible in the figure. According to a more in-depth analysis, these write operations write logs and temporary files, such as the history and origin of the development of traditional sports of ethnic minorities in the Inner Mongolia Autonomous Region, inheritors and other types of data. Combined with the analysis of 3.1.2 and 3.1.3, it can be seen that the database constructed in this paper has excellent performance and can meet the requirements of the digital development of traditional sports resources of ethnic minorities in the Inner Mongolia Autonomous Region and the needs of users.

Figure 5.

I/O characteristics of the TPC-H benchmark

As can be seen from subsection 2.4.2, the browser or client will send the start command to the server, the server listens to the start command and then accesses the local database to obtain the database information that needs to be monitored, the server sends a request to the network database retrieval platform based on the database information obtained, the network database retrieval platform returns the corresponding results based on the request command, and when the server receives the information it will stop monitoring. When the server receives the information, it will stop monitoring, and the server will send the monitoring results to the browser or client for display. After understanding the principle of database guardianship, 60 users (professionals in the field of database to make the evaluation results persuasive) are invited to evaluate and score the effect of database guardianship (error correction, adaptability, perfection, and preventive), with 0-2.5 as poor, 2.5-5 as passing, 5-7.5 as good, and 7.5-10 as excellent, and the results of database guardianship evaluation are shown in Table 2. The results of the evaluation of the database monitoring effect are shown in Table 2, where N indicates the Nth user, and A1~A4 are error correction, adaptability, perfection, and precautionary, respectively. It can be seen that the evaluation results of error correction, adaptability, perfection and precaution are all in the interval of 6~8, and the average values of the four dimensions are further calculated as 6.999, 6.776, 6.930, 6.943, which indicates that the database guardianship designed in this paper is good, and there is still a lot of improvement to be made from the excellent.

From the above analysis, it can be seen that the database designed in this paper is good, in order to further improve the database monitoring effect, which puts forward targeted strategies and recommendations, so that the database to better serve the digital development and inheritance of traditional sports resources of the Inner Mongolian nationalities.

To ensure the adaptability and perfection of the database system, we must strengthen the management and maintenance of hardware and software systems. Hardware performance for regular, regular power supply, server equipment and network lines and other adjustments, maintenance, overhaul and update to ensure that the hardware equipment to run in the best state, reduce the possibility of hardware problems caused by the collapse of the database system, so that the database adaptability and perfection has been improved. Specific measures for corrective and preventive measures include timely debugging and version updating of the operating system, timely repair of software loopholes and hidden presets, and regular and periodic checking and killing of viruses and Trojan horses, so as to effectively prevent the invasion and attack of unlawful elements, and at the same time, further improve the corrective and preventive measures for system operation, so as to prevent the operation of the software system from being negatively affected by frequent operations or improper operations.