Method for realizing statistical analysis of database in large data volume scene

Abstract

The invention relates to the technical field of information technology, in particular to a method for realizing statistical analysis of a database in a large data volume scene, which comprises the following steps of: 1, synchronizing data sources; 2, index granularity and dimensions are determined; 3, deleting historical index data; 4, processing increment indexes; and 5, assembling index data. According to the method, statistical analysis can be well realized.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and in particular to a method for implementing statistical analysis of a database in a large data volume scenario. Background Art

[0002] With the development of information technology, enterprises and organizations have accumulated a large amount of business data. This data is crucial for the operation management and decision support of enterprises. However, traditional big data analysis methods often face the problems of inefficiency and serious resource consumption when processing large-scale data sets. Especially in data-intensive industries such as banking and insurance, each business operation generates a large amount of data records, which need to be efficiently processed and analyzed to support business decisions. Summary of the invention

[0003] The present invention provides a method for implementing statistical analysis of a database in a large data volume scenario, which can solve the problems of low efficiency and high resource consumption in large data processing in the prior art.

[0004] A method for implementing statistical analysis of a database in a large data volume scenario according to the present invention comprises the following steps:

[0005] 1. Data source synchronization;

[0006] 2. Determine the granularity and dimension of indicators;

[0007] 3. Delete historical indicator data;

[0008] 4. Processing increment indicators;

[0009] 5. Assemble indicator data.

[0010] Preferably, in step one, specifically:

[0011] Prepare data sources for multiple business systems, perform indexed data processing through isomorphic tables, perform incremental synchronization based on the actual data volume of the business tables, first delete data after a fixed time node, then insert incremental data, and complete basic data preparation.

[0012] Preferably, in step 2, specifically:

[0013] In combination with specific business scenarios, distinguish the granularity and dimension of processing indicators; create new indicator tables, and establish daily, weekly, monthly, quarterly, and annual indicator tables based on actual conditions, determine the indicator granularity, and divide it into two categories: detailed and summarized. The finer the indicator granularity, the higher the reusability, but the larger the data volume, the lower the efficiency. Determine the optimal granularity based on actual conditions to meet reusability; to meet business needs, it is necessary to determine the indicator dimension. If it is the visit data of the customer management system in the customer area, it is divided into the smallest dimensions of salesperson, workplace, county branch, and city company to determine the statistical dimension.

[0014] Preferably, the indicator table includes an indicator name, an indicator value, a time stamp and an extended field.

[0015] Preferably, in step three, specifically:

[0016] Delete historical indicator values ​​according to business scenario requirements to ensure that indicator data is the latest and complete data.

[0017] As a preference, in step 4, specifically:

[0018] After determining the processing granularity and processing dimension of the indicator value, based on business needs, insert all the indicators needed into the relevant indicator tables for standby use.

[0019] As a preference, in step five, specifically:

[0020] According to the actual needs of the business department, SQL statistical analysis is performed through the summation, deduplication, and counting methods of the indicator table to reduce the amount of data and improve the efficiency of statistical analysis.

[0021] The beneficial effects of the present invention are as follows:

[0022] The present invention significantly improves the data processing efficiency in large data volume scenarios through the index processing method, reduces the occupancy of server resources, avoids the problem of repeated data processing, improves the reusability and consistency of data, and supports the efficient operation of the business. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a flowchart of a method for implementing statistical analysis of a database in a large data volume scenario according to an embodiment;

[0024] Figure 2 This is a schematic diagram of data source synchronization in the embodiment;

[0025] Figure 3 A schematic diagram for determining the granularity and dimension of indicators in the embodiment;

[0026] Figure 4 It is a schematic diagram of the processing increment index in the embodiment;

[0027] Figure 5It is a schematic diagram of assembling indicator data in the embodiment. DETAILED DESCRIPTION

[0028] In order to further understand the content of the present invention, the present invention is described in detail in conjunction with the accompanying drawings and embodiments. It should be understood that the embodiments are only for explaining the present invention and are not intended to limit it.

[0029] Example

[0030] like Figure 1 As shown, this embodiment provides a method for implementing statistical analysis of a database in a large data volume scenario, which includes the following steps:

[0031] 1. Data source synchronization;

[0032] Prepare data sources for multiple business systems, perform indexed data processing through isomorphic tables, perform incremental synchronization based on the actual data volume of the business tables, first delete data after a fixed time node, then insert incremental data, and complete basic data preparation.

[0033] This embodiment adopts a more advanced incremental synchronization strategy. Through the incremental synchronization mechanism, only the newly added data is synchronized, avoiding repeated transmission of the full amount of data, greatly improving the efficiency of data synchronization, and reducing the consumption of network bandwidth and storage resources. Figure 2 As shown, taking a single table as an example, first determine the backtracking data time based on the data volume and business, then delete the historical data, and finally insert the data before the backtracking time to the present.

[0034] 2. Determine the granularity and dimension of indicators;

[0035] like Figure 3 As shown, in combination with specific business scenarios, the granularity and dimension of processing indicators are distinguished; new indicator tables are created, and indicator tables such as daily, weekly, monthly, quarterly, and annual indicators are established according to actual conditions. The indicator tables include indicator names, indicator values, time stamps, and extended fields; in order to improve the high availability of indicator values, it is necessary to take efficiency into consideration and determine the indicator granularity, which is divided into two categories: details and summaries. The finer the indicator granularity, the higher the reusability, but the larger the data volume, the lower the efficiency. The optimal granularity is determined based on actual conditions to meet reusability; in order to meet business needs, it is necessary to determine the indicator dimension. If it is the visit data of the customer management system in the guest area, it is divided into the smallest salesperson (based on the person dimension), workplace, county branch company, and city company dimensions to determine the statistical dimension.

[0036] This embodiment supports multi-dimensional and multi-granular indicator definition and management methods, which can more flexibly respond to complex data analysis needs. This multi-dimensional and multi-granular processing method makes the indicators more flexible and can meet the needs of different business scenarios.

[0037] 3. Delete historical indicator data;

[0038] Delete historical indicator values ​​according to business scenario requirements to ensure that indicator data is the latest and complete data.

[0039] This embodiment regularly cleans up expired historical data, reduces storage pressure, and improves system performance. Through efficient management of historical data, the timeliness and accuracy of the data are ensured.

[0040] 4. Processing increment indicators;

[0041] After determining the processing granularity and processing dimension of the indicator value, based on business needs, insert all the indicators that need to be used into the relevant indicator table for standby use, such as Figure 4 shown.

[0042] This embodiment performs timely index processing on the newly added data to maintain the timeliness of the index. Through incremental index processing, the complexity of data processing is reduced and the efficiency of data processing is improved.

[0043] 5. Assemble indicator data;

[0044] According to the actual needs of the business department, SQL statistical analysis is performed through the summation, deduplication, and counting methods of the indicator table to reduce the amount of data and improve the efficiency of statistical analysis. For example Figure 5 shown.

[0045] This embodiment can quickly generate various statistical results through combined query of the indicator table without modifying the underlying data processing logic, thereby improving the flexibility and adaptability of the system. This flexible combined query capability enables the system to quickly respond to various business needs.

[0046] This embodiment has the following advantages:

[0047] 1) Efficient incremental synchronization mechanism: This embodiment uses an incremental synchronization mechanism to synchronize only newly added data, avoiding repeated transmission of the full amount of data, greatly improving the efficiency of data synchronization, and reducing the consumption of network bandwidth and storage resources.

[0048] 2) Multi-dimensional and multi-granular index processing: This embodiment not only supports multi-time dimension indicators of day, week, month, quarter, and year, but also supports indicator processing of detail and summary granularity. This multi-dimensional and multi-granular processing method makes the indicators more flexible and can meet the needs of different business scenarios.

[0049] 3) Efficient data processing and statistical analysis: Through index processing, large-scale data is converted into small-scale index values, which greatly reduces the computing resources and storage space requirements for data processing. Even when faced with billions of data, it can be reduced to tens of thousands of index values, achieving results in seconds.

[0050] 4) High reusability and consistency: This embodiment avoids repeated data processing and improves data reusability through index processing. At the same time, through a unified index library, data consistency is ensured, avoiding data inconsistency problems caused by different business scenarios.

[0051] 5) Flexible combination query capability: This embodiment can quickly generate various statistical results through combination queries of indicator tables without modifying the underlying data processing logic, thereby improving the flexibility and adaptability of the system.

[0052] 6) Cross-system data sharing and integration: This embodiment achieves high data reusability and rapid supply to other systems by synchronizing indicators to different data warehouses. This cross-system data sharing and integration capability enables data to be reused in multiple business scenarios, improving the overall efficiency of the system. Compared with the prior art, the cross-system data sharing and integration mechanism of this embodiment is more efficient and can better support data sharing and integration requirements among multiple systems.

[0053] Based on the above method, this embodiment can complete the statistics project of the entire database construction process and the customer platform statistics project.

[0054] Database construction full process statistics project

[0055] Background: The full-process report on database construction is the core evaluation system for the provincial company's personal insurance channel in customer management, covering the entire process of database construction, visits, invitations, platform participation, platform issuance of orders, referrals, etc.

[0056] Features: large amount of data, rich data sources, and wide statistical dimensions.

[0057] Implementation: Through the index processing mode based on the domestic OceanBase database (the method of this embodiment), the basic data of tens of millions of levels are processed into monthly index values ​​by statistical indicators with a single marketing agent as the smallest granularity and stored in the index library. The statistical presentation layer directly summarizes and sums the index values ​​in the index library to complete the statistical analysis of the entire database construction process.

[0058] Customer Platform Statistics Project

[0059] According to the indicators of the company's historical insurance policy premium related data, the platform's policy statistics business support is realized.

[0060] Platform statistics refer to a series of process links, including creating a new platform through the customer area system, sales staff inviting customers to attend the meeting, and customers scanning the code to attend the meeting. After the platform, customers will purchase insurance and generate insurance premiums. The association between the platform and the premium is the effectiveness of the platform. It is an important measurement indicator for evaluating the efficiency of various customer platforms and plays a decisive role in the company's platform resource allocation.

[0061] In the past, the traditional production database implemented statistics to obtain results, which took a long time to present and was easy to affect the function of the production system. Especially during the peak period of customer platform meetings, the platform usage in the province was high, resulting in system function abnormalities, and low query efficiency. Staff often needed to wait for a long time to obtain results. Through the use of indicator processing in this embodiment, staff at all levels can quickly and easily query the platform performance data without waiting, which provides an important handle for improving work efficiency and supporting customer business management decisions.

[0062] The present invention and its embodiments are described schematically above, and the description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. Therefore, if a person skilled in the art is inspired by it and designs a structural method and an embodiment similar to the technical solution without creativity without departing from the purpose of the invention, they shall all fall within the protection scope of the present invention.

Claims

1. A method for implementing statistical analysis of a database in a large data volume scenario, characterized in that: The following steps are involved:

1. Data source synchronization; 2. Determine the granularity and dimension of indicators; 3. Delete historical indicator data; 4. Processing increment indicators; 5. Assemble indicator data.

2. The method for implementing statistical analysis of a database in a large data volume scenario according to claim 1, characterized in that: In step one, specifically: Prepare data sources for multiple business systems, perform indexed data processing through isomorphic tables, perform incremental synchronization based on the actual data volume of the business tables, first delete data after a fixed time node, then insert incremental data, and complete basic data preparation.

3. The method for implementing statistical analysis of a database in a large data volume scenario according to claim 2, characterized in that: In step 2, specifically: In combination with specific business scenarios, distinguish the granularity and dimension of processing indicators; create new indicator tables, and establish daily, weekly, monthly, quarterly, and annual indicator tables based on actual conditions, determine the indicator granularity, and divide it into two categories: detailed and summarized. The finer the indicator granularity, the higher the reusability, but the larger the data volume, the lower the efficiency. Determine the optimal granularity based on actual conditions to meet reusability; to meet business needs, it is necessary to determine the indicator dimension. If it is the visit data of the customer management system in the customer area, it is divided into the smallest dimensions of salesperson, workplace, county branch, and city company to determine the statistical dimension.

4. The method for implementing statistical analysis of a database in a large data volume scenario according to claim 3, characterized in that: The indicator table includes indicator name, indicator value, time stamp and extended fields.

5. The method for implementing statistical analysis of a database in a large data volume scenario according to claim 4, characterized in that: In step three, specifically: Delete historical indicator values ​​according to business scenario requirements to ensure that indicator data is the latest and complete data.

6. The method for implementing statistical analysis of a database in a large data volume scenario according to claim 5, characterized in that: In step 4, specifically: After determining the processing granularity and processing dimension of the indicator value, based on business needs, insert all the indicators needed into the relevant indicator tables for standby use.

7. The method for implementing statistical analysis of a database in a large data volume scenario according to claim 6, characterized in that: In step five, specifically: According to the actual needs of the business department, SQL statistical analysis is performed through the summation, deduplication, and counting methods of the indicator table to reduce the amount of data and improve the efficiency of statistical analysis.

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