CN116108089A

CN116108089A - Big data testing method, computer equipment and storage medium

Info

Publication number: CN116108089A
Application number: CN202211400707.2A
Authority: CN
Inventors: 潘力; 林川
Original assignee: Guangzhou Junbo Network Technology Co ltd
Current assignee: Guangzhou Junbo Network Technology Co ltd
Priority date: 2022-11-09
Filing date: 2022-11-09
Publication date: 2023-05-12

Abstract

The invention is suitable for the technical field of big data testing, and provides a big data testing method, computer equipment and a storage medium, wherein the method is used for acquiring testing types and information of a to-be-tested table; acquiring test configuration information aiming at the to-be-tested table, wherein the test configuration information comprises a test field input by a user; testing according to the test type and the test configuration information and generating an intermediate test table; taking the intermediate test table as a to-be-tested table; and repeatedly executing the steps until a task submitting instruction is received, and generating and executing a task test script. By determining the information of the to-be-tested meter, the test configuration information, the test type and the like, a test script is automatically generated, and the convenience and the efficiency of the test are improved; and the original complex data processing is simplified step by introducing an intermediate test table, so that the complexity of the logic customization of the test script is greatly simplified.

Description

Big data testing method, computer equipment and storage medium

Technical Field

The present invention relates to the field of big data testing, and in particular, to a big data testing method, a computer device, and a storage medium.

Background

Big data or huge amount of data refers to information which is huge in size and cannot be retrieved, managed, processed and tidied through a mainstream software tool in a reasonable time, and becomes a positive purpose for helping business operation decision.

ETL is an abbreviation for Extract-Transform-Load, and is used to describe the process of extracting (Extract), converting (Transform), and loading (Load) data from a source to a destination. The term ETL is more commonly used in data warehouses, but its objects are not limited to data warehouses.

Based on big data test, ETL test is its main test content, and the purpose is to ensure data quality, and various fields of inspection accuracy, repeatability, uniformity, validity and data integrity etc. in this process, the test personnel tests through writing test script, require the test personnel to master ETL test thinking, test script through writing to test data.

In the prior art, the manual writing of the test script is easy to make mistakes, and the requirement on the script writing capability of a tester is higher.

Disclosure of Invention

Based on this, it is necessary to provide a big data test method, a computer device, and a storage medium in order to solve the above-described problems.

In one aspect, a big data testing method is provided, the testing method comprising:

acquiring a test type and information of a to-be-tested meter;

acquiring test configuration information aiming at the to-be-tested table, wherein the test configuration information comprises a test field input by a user;

testing according to the test type and the test configuration information and generating an intermediate test table;

taking the intermediate test table as a to-be-tested table;

and repeatedly executing the steps until a task submitting instruction is received, and generating and executing a task test script.

A computer device comprising a memory and a processor, the memory having stored therein a computer program which, when executed by the processor, causes the processor to perform the steps of the big data testing method described above.

A computer readable storage medium having stored thereon a computer program which, when executed by a processor, causes the processor to perform the steps of the big data testing method described above.

According to the big data testing method, the computer equipment and the storage medium, the testing script is automatically generated by determining the information of the to-be-tested table, the testing configuration information, the testing type and the like, so that the convenience and the efficiency of testing are improved; and the original complex data processing is simplified step by introducing an intermediate test table, so that the complexity of the logic customization of the test script is greatly simplified.

Drawings

FIG. 1 is a flow chart of a big data testing method in one embodiment;

FIG. 2 is a flow chart of a big data testing method in another embodiment;

FIG. 3 is a flow chart of a big data testing method in one embodiment;

FIG. 4 is a flow chart of a big data testing method in one embodiment;

FIG. 5 is a flow chart of a big data testing method in one embodiment;

FIG. 6 is a flow chart of a big data testing method in one embodiment;

FIG. 7 is a flow chart of a big data testing method in one embodiment;

FIG. 8 is a block diagram of a big data testing device in one embodiment;

FIG. 9 is a block diagram of the internal architecture of a computer device in one embodiment.

Detailed Description

The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.

It will be understood that the terms "first," "second," and the like, as used herein, may be used to describe various elements, but these elements are not limited by these terms unless otherwise specified. These terms are only used to distinguish one element from another element. For example, a first xx script may be referred to as a second xx script, and similarly, a second xx script may be referred to as a first xx script, without departing from the scope of the present application.

The big data testing method provided by the embodiment of the application can be applied to a terminal or computer equipment.

The computer device may be an independent physical server or terminal, or may be a server cluster formed by a plurality of physical servers, and may be a cloud server for providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDNs, basic cloud computing services such as big data and artificial intelligence platforms, and the like.

The terminal may be, but is not limited to, a smart phone, a tablet computer, a notebook computer, a desktop computer, a smart speaker, a smart watch, etc.

The subject application is operational with numerous general purpose or special purpose computer system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like. The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.

In the embodiments of the present application, when related processing is required according to user information, user behavior data, user history data, user location information, and other data related to user identity or characteristics, permission or consent of the user is obtained first, and the collection, use, processing, and the like of the data comply with related laws and regulations and standards of related countries and regions. In addition, when the embodiment of the application needs to acquire the sensitive personal information of the user, the independent permission or independent consent of the user is acquired through a popup window or a jump to a confirmation page or the like, and after the independent permission or independent consent of the user is explicitly acquired, necessary user related data for enabling the embodiment of the application to normally operate is acquired.

As shown in fig. 1, in one embodiment, a big data testing method is provided, and this embodiment is mainly used for illustrating the method applied to a computer device. The method specifically comprises the following steps:

step S102, obtaining test types and information of a to-be-tested table;

step S104, test configuration information aiming at the to-be-tested table is obtained, wherein the test configuration information comprises a test field input by a user;

step S106, testing is carried out according to the test type and the test configuration information, and an intermediate test table is generated;

step S108, taking the intermediate test table as a table to be tested;

step S110, repeatedly executing the steps until a task submitting instruction is received, and generating and executing a task test script.

In this embodiment, big data is difficult to process by a single computer, and a distributed architecture is necessary. The method is characterized by carrying out distributed data mining on mass data. It must rely on distributed processing of cloud computing, distributed databases, and cloud storage and virtualization technologies. Big data includes structured, semi-structured and unstructured data, unstructured data becoming an increasingly important part of data. For the test of the ETL part, the test thought is basically similar, the test scripts are also different in size, a great amount of repeated work exists, labor is consumed, and finally the comparison of the test results is complicated. Therefore, by templating the test script, different templates can be built for different test types, and different data such as a table to be tested, a test field and the like are input as test configuration information, so that the test script is automatically built, and big data is tested. For some test types, information of a plurality of tables to be tested, such as data association test types, needs to be obtained, and information of two tables to be tested needs to be obtained; for data checking test types, it is also necessary to acquire information of two tables to be tested.

In this embodiment, the test types include query, deduplication, data statistics, data conversion, data association, data core, etc., each test type having different processing logic; the table names of the tables to be tested are input by a user, the corresponding tables are inquired according to the table names input by the user, the information of the corresponding tables is obtained, and fields in the tables can be identified.

In this embodiment, the test configuration information required for different test types is different, and the test configuration information is input or selected by the user. The field information in the test table can be obtained after the test table to be tested is obtained, different field names are displayed to the user, and the user can select the field name of a specific test to configure the test. The test configuration information also includes other configuration conditions necessary for testing. For example, for a data association test type, an association field needs to be configured, and the field needing to be associated is selected and determined by a user after being displayed to the user; for the data checking test type, a checking field needs to be configured, and the field needing to be checked is selected and determined by a user after being displayed to the user.

In this embodiment, after determining the test configuration information input by the user, the necessary condition for automatically creating the test script is obtained, based on the test type, the big data script execution interface is called, the test table is calculated, and then an intermediate test table is obtained, and the intermediate test table is a table after a certain degree of test is performed, and can also be used for continuing the test of other test types, and the test can be directly performed on the basis of the intermediate test table during the test.

In this embodiment, when a user performs a test of a certain test type, the user may directly input an intermediate test table name when inputting the table name, and the computer performs a test by using the intermediate test table name as a test table to be tested, and the obtained information is also an intermediate table subjected to tests of other test types; the user may then configure the intermediate test table.

In this embodiment, steps S102 to S108 may be repeatedly performed, which may, of course, be performed for different tables to be tested, test configuration information, test types, etc., and when a final test result needs to be obtained, the user may choose to submit the test task after configuring the test configuration information, and the computer device generates and executes a task test script of the test task to finally obtain the test result.

In the embodiment, the information such as the table information to be tested, the test configuration information, the test type and the like is determined by inputting the table to be tested and the test template by a user, and a test script is automatically generated, so that the convenience and the efficiency of the test are improved; and the original complex data processing is simplified step by introducing an intermediate test table, so that the complexity of the logic customization of the test script is greatly simplified.

As shown in fig. 2, as a preferred embodiment of the present invention, the steps before step S102 of acquiring the test type and the table information to be tested include:

step S202, a test template is obtained, wherein the test template is a script template preset for different test types, and different test templates are used for providing different test configuration information input options for users.

In one embodiment, for obtaining the test template, after the user selects the operation template, the computer loads the corresponding test template, and the test type can be determined according to the test template. The test templates comprise a data query template, a data deduplication template, a data verification template, a data conversion template, a data association template and a data statistics template. The test template can templated the test flow and the test script, and can provide a table name input box and a test configuration information input option box of the to-be-tested table for obtaining the test configuration information configured by the user and the like. For example, it has the same input box for different test types, specifically: the table name input box, the "where" data screening box, the "group by" data grouping box, the "having" result filtering box, the "order by" result ordering box, the "where" selection box can add multiple groups, or the combination operation symbols "=, in, like, >, <" selection box, the input condition content and the like. Different test templates also have differentiated test configuration information, such as adding a deduplication field selection box for a deduplication template; for the data statistics template, a statistics function frame, a statistics field selection frame, an alias naming frame and an expected value input frame are added, and it is understood that only the data statistics template has the expected value input frame, and other field content checks need to select a data checking template to check the data; for the data conversion template, adding a 'when, then, other' field selection frame and a corresponding content filling frame; for the data association template, two table name input boxes are provided, and an association field selection box is provided; as for the data checking template, the data checking template also has two table name input boxes, an associated field selection box and a checking field selection box, it is understood that the associated field represents the condition that two tables are associated together, and the checking field is the field of the two tables which needs to be checked for consistency, for example, the "ID" and the "COST" fields of the A table and the B table are all the same, and whether the "COST" of the two identical "IDs" are consistent or not needs to be checked, namely, the "ID" is the associated field and the "COST" is the checking field.

In one embodiment, the test logic and the corresponding test script are fixed through the test template, the user can set the table name input box of the test template and the option box for inputting the test configuration information, for example, the number of the table name input box and the option box for inputting the test configuration information can be set, and the set number of the boxes is bound with the test template so as to obtain the to-be-tested table, the test configuration information and the like later.

As shown in fig. 3, as a preferred embodiment of the present invention, step S104, the step of obtaining test configuration information for the table to be tested includes:

s302, loading field names in the to-be-tested table;

s304, displaying field names in the to-be-tested table;

s306, receiving the field name selected by the user and taking the field name as a test field.

In this embodiment, after the information of the to-be-tested table is obtained, the computer reads the field name of the to-be-tested table and displays the field name to the user, specifically, the field name may be assigned to an option box of the test template, the user selects the field name through a drop-down option box, and after the computer confirms the configured field name, the computer tests the corresponding field according to the rule of the test template.

As shown in fig. 4, as a preferred embodiment of the present invention, step S106, the step of testing according to the test type and the test configuration information and generating an intermediate test table includes:

s402, generating an intermediate test script according to the test type information and the test configuration information aiming at the to-be-tested table;

s404, calling a big data execution interface to run the intermediate test script;

and S406, generating and analyzing an intermediate result set to obtain an intermediate test table.

In one embodiment, for a to-be-tested table, after a user selects a test template and configures test configuration information, an intermediate test script is automatically generated, the intermediate test script is stored, the intermediate test script is operated by calling a big data execution interface, an intermediate result set generated after testing is obtained, the intermediate test table is obtained after analysis, and then the intermediate test table is stored. The test of a certain test type can be carried out on the to-be-tested meter, and other tests can be continued on the intermediate result set obtained correspondingly after the test, so that the complicated test steps originally aiming at the to-be-tested meter are distributed, and the test steps are simplified.

As shown in fig. 5, as a preferred embodiment of the present invention, step S110, the steps after repeatedly executing the above steps until receiving the task submitting instruction and generating and executing the task test script include:

s502, displaying the task test script to a user, and confirming the task test script modified by the user;

s504, executing the task test script immediately or executing the task test script according to the set time.

In one embodiment, the task test script is modifiable, the user can view the automatically generated task test script, when the task test script does not meet the test of the requirement or the requirement is changed, the user can edit and modify the task test script directly manually, and the computer equipment modifies and saves the task test script in the database. When executing the task test script, the task test script may be executed immediately or at regular time.

As shown in fig. 6, as a preferred embodiment of the present invention, step S110, the steps after repeatedly executing the above steps until receiving the task submitting instruction and generating and executing the task test script further includes:

s602, acquiring return data, and outputting analysis results according to the return data.

In this embodiment, after the task test script is executed, the computer obtains the returned data, parses the returned data, and stores the parsed data details in an Excel table, and stores the Excel table in the static resource manager for subsequent downloading and use; and monitoring the data is realized.

As shown in fig. 7, as a preferred embodiment of the present invention, S602, acquiring return data, and outputting an analysis result according to the return data includes the steps of:

s702, acquiring and analyzing return data;

s704, confirming the test type of a to-be-tested table before receiving a task submitting instruction, and if the test type is data check or data statistics, judging whether returned data are empty and determining whether the returned data pass the test;

s706, storing and outputting the analysis result.

In one embodiment, the return data of the big data script execution interface is obtained, the return data is analyzed, and then the test type of one test step before the task instruction is submitted is judged, wherein the test type can be determined by the test type of the test template, and the output result of data statistics and data verification is that: if the test data are consistent, the data are not returned, and if the test data are inconsistent, the inconsistent data are returned; therefore, if the test type is data check or data statistics, judging whether the returned data is empty, if the returned data is empty, the test state is passed, if the returned data is not empty, the test state is not passed, and then storing the analysis result in an Excel table, for example, storing the first 100 inconsistent data; if the test type is not data check or data statistics, namely, only data are processed and a result is output at the moment, the data are not checked, the test state is a default value of no, then the analysis result is directly stored, if 5000 pieces of data are stored in Excel, and finally the result Excel is stored in a static resource server. And finally, outputting the test result to a task corresponding to the current test, and simultaneously sending a test report to a corresponding mailbox or nailing message by the task with the failed test state, so as to realize timely monitoring of data.

To facilitate an understanding of the invention, a specific embodiment of a personal computer interaction is illustratively provided:

for example: there are two tables named "to_product" and "to_order", and developing and cleaning the data of these two tables to obtain a wide table named "td_bigable" requires verifying the accuracy and integrity of data processing. At this time, a user can click on a task management page of the established task platform, input a task name of 'task 1' on the newly-added task page, select a script storage space, input a step name of 'step 1', select a duplicate removal template, load a duplicate related field input frame and a drop-down frame on a corresponding test template according to the type of the test template selected by the user, input a table name of 'to_order', load a field of the table of 'to_order' by the computer, assign the fields to each drop-down frame, select the duplicate removal field by the drop-down frame, input a data time range to be processed, click for storage, automatically generate an intermediate test script by the computer according to test configuration information output by the user, and automatically generate an intermediate temporary table with the name of 'step 1' after executing the intermediate test script; the user clicks the adding step, the step name step2 is input, the data association template is selected, the computer loads a field input frame and a drop-down frame of the data association template, the table name input frame inputs a middle temporary table step1 and a table name to product respectively, the system loads fields of the step1 and the to product tables, the drop-down frame on the left side of each field input frame is assigned with the field of the middle temporary table step1, the drop-down frame on the right side is assigned with the field of the to product table, the association mode is selected for left association, the association field is selected for the to product, the test template and the test configuration information selected by the user are clicked for storage, and the computer automatically generates a middle test script according to the test template and the test configuration information selected by the user and automatically generates the middle temporary table with the table name step 2; the user clicks the adding step, the step name 'step 3' is input, the data checking template is selected, the computer loads the field input frame and the drop-down frame of the data checking template, the table name input frame is input in one table name input frame, the table name 'step 2' is input in the other table name input frame, the computer loads the fields of the two tables of 'step 2' and 'td_biggest', the drop-down frame of each field input frame is assigned with the field of the temporary table in the middle of 'step 2', the drop-down frame of each field input frame is assigned with the field of 'td_biggest', the association condition is selected for left association, the drop-down frame of the association field is selected for 'order_id' and 'st_tm', the drop-down frame on the left side and the drop-down frame of the check field are respectively selected for the two tables to be checked, the computer clicks the submitting task, and then all scripts of the whole data processing process, namely the task test script is automatically generated, and the task is generated; the user finds out the task to click and execute on the task management page, the computer calls the cluster big data script execution interface to execute the task test script, and returns the checking result and details, which can be downloaded or configured for the task to execute at fixed time and alarm setting, if the data check is inconsistent, the spike message or mail can be received.

As shown in fig. 8, in one embodiment, a big data testing apparatus is provided, where the big data testing apparatus may be integrated into the above-mentioned computer device, and may specifically include:

the first acquisition module is used for acquiring the test type and the information of the to-be-tested table;

the second acquisition module is used for acquiring test configuration information aiming at the to-be-tested table, wherein the test configuration information comprises a test field input by a user;

the intermediate execution module is used for testing according to the test type and the test configuration information and generating an intermediate test table;

the repeated execution module is used for taking the intermediate test table as a table to be tested;

and the task test script execution module is used for repeatedly executing the steps until receiving a task submitting instruction, and generating and executing a task test script.

FIG. 9 illustrates an internal block diagram of a computer device in one embodiment. The computer device may in particular be a terminal or a server. As shown in fig. 9, the computer device includes a processor, a memory, a network interface, an input device, and a display screen connected by a system bus. The memory includes a nonvolatile storage medium and an internal memory. The non-volatile storage medium of the computer device stores an operating system, and may also store a computer program that, when executed by a processor, causes the processor to implement a big data testing method. The internal memory may also store a computer program that, when executed by the processor, causes the processor to perform the big data testing method. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, the input device of the computer equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.

It will be appreciated by those skilled in the art that the structure shown in fig. 9 is merely a block diagram of a portion of the structure associated with the present application and is not limiting of the computer device to which the present application applies, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.

In one embodiment, the big data testing apparatus provided herein may be implemented in the form of a computer program that is executable on a computer device as shown in fig. 9. The memory of the computer device may store therein various program modules constituting the big data testing apparatus, such as the a module, the B module, and the C module shown in fig. 8. The computer program constituted by the respective program modules causes the processor to execute the steps in the big data testing method of the respective embodiments of the present application described in the present specification.

For example, the computer apparatus shown in fig. 9 may perform step S102 through the first acquisition module in the big data testing device shown in fig. 8. The computer device may perform step S104 through the second acquisition module. The computer device may perform step S106 through the intermediate execution module. The computer device may execute step S108 by repeating the execution module. The computer device may execute step S110 through the task test script execution module.

In one embodiment, a computer device is presented, the computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the computer program:

step S102, obtaining test types and information of a to-be-tested table;

step S108, taking the intermediate test table as a table to be tested;

In one embodiment, a computer readable storage medium is provided, having a computer program stored thereon, which when executed by a processor causes the processor to perform the steps of:

step S102, obtaining test types and information of a to-be-tested table;

step S108, taking the intermediate test table as a table to be tested;

It should be understood that, although the steps in the flowcharts of the embodiments of the present invention are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in various embodiments may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor do the order in which the sub-steps or stages are performed necessarily performed in sequence, but may be performed alternately or alternately with at least a portion of the sub-steps or stages of other steps or other steps.

Those skilled in the art will appreciate that all or part of the processes in the methods of the above embodiments may be implemented by a computer program for instructing relevant hardware, where the program may be stored in a non-volatile computer readable storage medium, and where the program, when executed, may include processes in the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the various embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.

The technical features of the above-described embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above-described embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.

The foregoing examples illustrate only a few embodiments of the invention and are described in detail herein without thereby limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.

Claims

1. A big data testing method, the testing method comprising:

acquiring a test type and information of a to-be-tested meter;

taking the intermediate test table as a to-be-tested table;

2. The big data testing method of claim 1, wherein the test types include data query, data deduplication, data collation, data conversion, data correlation, and data statistics.

3. The big data testing method according to claim 1, wherein the step before obtaining the information of the table to be tested comprises:

and acquiring test templates, wherein the test templates are script templates preset for different test types, and the different test templates are used for providing different test configuration information input options for users.

4. A big data testing method according to claim 3, wherein the step of obtaining test configuration information for the table to be tested comprises:

loading field names in the to-be-tested table;

displaying field names in the to-be-tested table;

and receiving the field name selected by the user and taking the field name as a test field.

5. A big data testing method according to claim 1 or 3, wherein said step of testing according to said test type and said test configuration information and generating an intermediate test table comprises:

generating an intermediate test script according to the test type information and the test configuration information aiming at the to-be-tested table;

calling a big data execution interface to run the intermediate test script;

and generating and analyzing an intermediate result set to obtain an intermediate test table.

6. The big data testing method of claim 1, wherein the step after generating the task test script further comprises:

displaying the task test script to a user, and confirming the task test script modified by the user;

and executing the task test script immediately or according to the set time.

7. The big data testing method of claim 1, wherein the step after executing the task test script comprises:

and acquiring return data, and outputting an analysis result according to the return data.

8. The big data testing method of claim 7, wherein the step of obtaining the returned data and outputting the analysis result according to the returned data comprises:

acquiring and analyzing return data;

confirming the test type of a to-be-tested table before receiving a task submitting instruction, and judging whether data is output or not and determining whether the test is passed or not if the test type is data check or data statistics;

and storing and outputting the analysis result.

9. A computer device comprising a memory and a processor, the memory having stored therein a computer program which, when executed by the processor, causes the processor to perform the steps of the big data testing method of any of claims 1 to 8.

10. A computer readable storage medium, characterized in that the computer readable storage medium has stored thereon a computer program which, when executed by a processor, causes the processor to perform the steps of the big data testing method according to any of claims 1 to 8.