CN113495845B

CN113495845B - Data testing method and device, electronic equipment and storage medium

Info

Publication number: CN113495845B
Application number: CN202110852801.0A
Authority: CN
Inventors: 侯立凯; 刘京伟
Original assignee: Lazas Network Technology Shanghai Co Ltd
Current assignee: Lazas Network Technology Shanghai Co Ltd
Priority date: 2021-07-27
Filing date: 2021-07-27
Publication date: 2022-09-13
Anticipated expiration: 2041-07-27
Also published as: CN113495845A

Abstract

The application relates to the technical field of databases, and discloses a data testing method, a data testing device, electronic equipment and a storage medium, wherein the method comprises the following steps: acquiring target data to be detected and first sequencing information; the first ordering information is ordering information of a target data group to be detected corresponding to the target data to be detected in the data group to be detected; each to-be-detected data group comprises a section of to-be-detected data in a continuous numerical range; determining a target historical data group corresponding to the first sequencing information; the target historical data set is a data set of which the second sorting information in the historical data set corresponds to the first sorting information; and extracting target historical data from the target historical data group, and performing data test according to the historical data. The embodiment of the application solves the problem that in the prior art, when pressure testing is carried out on data, the pressure testing distortion is easy to occur.

Description

Data testing method and device, electronic equipment and storage medium

Technical Field

The present application relates to the field of database technologies, and in particular, to a data testing method and apparatus, an electronic device, and a storage medium.

Background

With the rapid development of the internet industry, mass data are usually stored in a database of the internet service, and when the mass data are subjected to system pressure test, pressure test distortion easily occurs. Specifically, taking a real-time operating data system as an example, in order to make the data system bear larger data traffic, a full data link pressure test at regular intervals is required, and due to the instantaneity of data, the pressure test once lasting for a longer time will cause distortion of the pressure test.

Disclosure of Invention

The embodiment of the application provides a data testing method, which aims to solve the problem that in the prior art, when pressure testing is carried out on data, the pressure testing distortion is easy to occur.

Correspondingly, the embodiment of the application also provides a data testing device, electronic equipment and a storage medium, which are used for ensuring the realization and application of the method.

In order to solve the above problem, an embodiment of the present application discloses a data testing method, including:

acquiring target data to be detected and first sequencing information; the first ordering information is ordering information of a target data group to be detected corresponding to the target data to be detected in the data group to be detected; each to-be-detected data group comprises a section of to-be-detected data in a continuous numerical range;

determining a target historical data group corresponding to the first sequencing information; the target historical data set is a data set of which the second sorting information in the historical data set corresponds to the first sorting information;

and extracting target historical data from the target historical data group, and performing data test according to the historical data.

The embodiment of the application also discloses a data testing device, the device includes:

the information acquisition module is used for acquiring target data to be detected and first sequencing information; the first ordering information is ordering information of a target data group to be detected corresponding to the target data to be detected in the data group to be detected; each to-be-detected data group comprises a section of to-be-detected data in a continuous numerical range;

the data determining module is used for determining a target historical data group corresponding to the first sequencing information; the target historical data set is a data set of which second ordering information in the historical data set corresponds to the first ordering information;

and the data testing module is used for extracting target historical data from the target historical data group and carrying out data testing according to the historical data.

The embodiment of the present application further discloses an electronic device, which includes a memory, a processor, and a computer program that is stored in the memory and can be run on the processor, and when the processor executes the program, the data testing method shown in the first aspect of the present application is implemented.

The embodiment of the application also discloses a computer readable storage medium, wherein a computer program is stored on the computer readable storage medium, and the computer program is used for realizing the method according to one or more of the embodiments of the application when being executed by a processor.

The technical scheme provided by the embodiment of the application has the following beneficial effects:

in the embodiment of the application, target data to be detected and first sequencing information are obtained; determining a target historical data group corresponding to the first sequencing information; extracting target historical data from the target historical data group, performing data test according to the historical data, replacing target to-be-tested data with the target historical data, performing data test, and improving the authenticity of a test result; target historical data can be dynamically selected, and hot spot data is avoided; in the test process, the test of a plurality of wave crests can be supported, and the accuracy of the test result is improved.

Additional aspects and advantages of embodiments of the present application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the present application.

Drawings

The foregoing and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:

FIG. 1 is a flow chart of a data testing method provided in an embodiment of the present application;

fig. 2 is a flowchart of a third example provided by an embodiment of the present application;

fig. 3 is a second flowchart of a third example provided by the embodiment of the present application;

fig. 4 is a schematic structural diagram of a data testing apparatus according to an embodiment of the present disclosure;

fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Detailed Description

Reference will now be made in detail to the embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary only for the purpose of explaining the present application and are not to be construed as limiting the present application.

As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or wirelessly coupled. As used herein, the term "and/or" includes all or any element and all combinations of one or more of the associated listed items.

It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.

The scheme provided by the embodiment of the application can be executed by any electronic device, such as a terminal device, or a server, wherein the server can be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server providing cloud computing service. The terminal may be, but is not limited to, a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, and the like. The terminal and the server may be directly or indirectly connected through wired or wireless communication, and the application is not limited herein. For technical problems in the prior art, the data testing method, the data testing device, the electronic device and the storage medium provided by the application aim to solve at least one of the technical problems in the prior art.

The following describes the technical solutions of the present application and how to solve the above technical problems in detail with specific embodiments. These several specific embodiments may be combined with each other below, and details of the same or similar concepts or processes may not be repeated in some embodiments. Embodiments of the present application will be described below with reference to the accompanying drawings.

The embodiment of the present application provides a possible implementation manner, and as shown in fig. 1, provides a flowchart of a data testing method, where the scheme may be executed by any electronic device, and optionally may be executed at a server or a terminal device.

The method and the device can be applied to the field of databases, and for a system to be tested with large data volume, real-time data to be tested usually has instantaneity; target historical data can be dynamically selected, and hot spot data is avoided; in the testing process, if the target historical data comprises a plurality of data wave peaks, the simultaneous testing of the plurality of wave peaks can be supported, and the accuracy of the testing result is improved.

As shown in fig. 1, an embodiment of the present application provides a data testing method, which may include the following steps:

step 101, acquiring target data to be detected and first sequencing information; the first ordering information is ordering information of a target data group to be detected corresponding to the target data to be detected in the data group to be detected; each data group to be detected comprises a section of data to be detected in a continuous numerical value range.

The data testing in the embodiment of the present application is, for example, data pressure testing, specifically, for a data system with a large data volume, for example, in the field of instant distribution, the field of e-commerce platforms, and the like, mass data is usually stored in a database thereof, and when the system runs, the limit of the system, including the contents of using limit load, defining an upper limit, defining capacity, and the like, needs to be measured through the data pressure testing, so as to test whether the system will report an error under a high concurrency condition, and whether a process will not run normally; and testing the compression resistance of the system, estimating the bearing capacity of the system and the like, and providing a basis for capacity expansion for operation and maintenance personnel.

The target data to be detected is data in the data to be detected, the data to be detected can be real-time data of a system to be detected, taking an instant distribution system as an example, the data to be detected can comprise data of a buyer user, a seller user and a rider user; taking the logistics scheduling system as an example, the data to be tested can comprise the data of the freight note to be dispatched and the data of the dispatcher; because the data volume is usually large, in the data testing process, the target data to be tested is selected from the data to be tested for testing. Optionally, a data test period can be set, and in each pressure test period, real-time data of the period is collected, and a data test is executed; the test period can be determined according to the attribute information of the data to be tested; taking the instant distribution system as an example, the order data usually has a time attribute, for example, in the take-out industry, the lunch time and the dinner time are usually hot periods, so each hot period or non-hot period can be respectively used as a test cycle, and since the data difference between the hot period and the non-hot period is large, if the test cycle is the same, the accuracy of the test result is reduced.

Optionally, in the process of acquiring the target data to be detected from the data to be detected, one or at least two pieces of data may be randomly extracted from the large data to be detected as the target data to be detected, or the target data to be detected may be selected according to a preset rule, for example, a median in the data to be detected is selected as the target data to be detected.

After the target data to be detected is obtained, first sequencing information of the target data to be detected is also required to be obtained; specifically, the first ordering information is ordering information of a target data group to be detected corresponding to the target data to be detected in the data group to be detected; each to-be-detected data group comprises a section of to-be-detected data in a continuous numerical range; that is, for data to be measured, for example, data to be measured of one cycle is sorted according to the magnitude and then divided into a plurality of data groups.

Optionally, the grouping may be performed by using a numerical value as a boundary, or may be performed by presetting a grouping number; specifically, when the numerical value is used as a boundary division, the sorting information indicates the ratio of the numerical value of a group of data groups to be tested to the numerical value of all data to be tested; for example, the data size range of the data to be measured is 0 to 50, the data size range may be divided into one group from 1 to 10, one group from 11 to 30, and one group from 41 to 50, and the data limit is 1 to 10, 10 to 30, and 40 to 50, respectively. When the predetermined number of packets is divided, for example, the predetermined number of packets is N, all the data to be tested are randomly allocated to each data group.

Or under the condition of determining the grouped data, determining the data proportion of each data group to be tested, namely the ratio obtained by dividing the number of data included in the group by the total amount of the data to be tested; specifically, if the total amount of the data to be tested is M, the number of the groups is N, and the data volume ratio of each group is y 1%, y 2%, … … and yn; determining the data volume and the data range of each data group to be tested according to the product of the data volume ratio and the total data volume, for example, the data volume of a first group is n1, sorting all the data to be tested by small and large, determining the data volume to be n1 corresponding to the value range of x1 to xk, and then distributing the value of the value range from x1 to x2 to the first group; the data amount of the first group is n2, and according to the sorting, the data amount of n2 is determined to correspond to the value range from x3 to x4, and the values with the value size from x3 to x4 are distributed to the second group. Wherein x1 to x4 represent any numerical value. The data amount ratio of each packet may be randomly selected or may be set in advance based on a historical empirical value. For example, for the immediate delivery domain, 24 hours per day are divided into 12 groups, and the data amount of the data group corresponding to the hot time period (e.g., lunch or dinner time) is higher than that of the data group at non-hot time.

After grouping the data to be detected, determining a target data group to be detected of a target data to be detected, and further determining the sequencing information of the target data group to be detected; for example, the data to be measured is grouped into small and large groups according to the size of the numerical value, and then the sorting information of the data group is determined according to the data group, so as to be used for determining the target historical group subsequently.

As can be seen, the sorting information indicates the data volume ratio or the numerical value ratio of the data of one group of data sets to be tested in all the data to be tested.

As a first example, taking a tested system as an even distribution system as an example, the system distributes orders in a diffusion manner; specifically, diffusion is the dispatching of orders to multiple riders (i.e., dispatchers); the system is operated, and 1 hour is taken as a data test period; the data to be detected is the single amount of each rider; the order quantity is the order quantity received by each rider within a preset time limit; taking the current period as a hot period as an example, acquiring the personal quantity of all riders in the region A in the period, sequentially grouping the personal quantity according to the size sequence of the personal quantity to obtain a test data set, acquiring one (or more) target data to be tested in the data to be tested during testing, and determining the sequencing information of the data set to be tested to which the target data to be tested belongs.

Step 102, determining a target historical data group corresponding to the first sequencing information; the target historical data set is a data set of which the second sorting information in the historical data set corresponds to the first sorting information.

The historical data may be historical data associated with the data to be measured, for example, by time attribute association, such as historical data of the first few same-period periods of the current period, so that the selected historical data has the same time attribute information as the data to be measured; or, through region attribute association, for example, regions such as popular business circles, for example, historical data of previous cycles of the current cycle of the same region, the selected historical data has the same region attribute information as the data to be measured.

After determining the historical data, further determining historical data group information of the historical data; optionally, the historical data can be grouped in the same grouping mode as the data to be tested; as a second example, when the history data is grouped, the numerical range of the person-to-person data in the history data is K1 to Kp, the maximum person-to-person length is Kp, the total amount of data is a, the number of groups is B, and the data amount ratio of each group is y 1%, y 2%, … …%, yn;

the grouping steps are as follows:

step 1, firstly, sorting all historical data by size and size (or by size and size);

step 2, determining a data range corresponding to each group;

for example, for the first historical group, whose total amount of data is a × y 1%, starting from K1, a × y 1% is selected to be assigned to the first historical data group, such as just to K2; then, starting from K3 (one bit after K2), a × y 2% is sequentially selected and allocated to the second historical data group, for example, just to K4, … …, and until the last grouping is completed, the data range of the first historical data group is K1 to K2, and the data range of the second historical data group is K3 to K4.

In this way, after grouping of each historical data group is completed, the historical data groups are sorted according to the numerical value ranges of the historical data groups, and second sorting information of each historical data group is obtained.

If the total number of the groups of the data group to be tested is the same as the total number of the groups of the historical data group, and the sorting modes of the data group to be tested and the historical data group are the same (the sorting modes comprise big and small according to the size of the data, or small and big), the sorting digit number indicated by the second sorting information is the same as the sorting digit number indicated by the first sorting information; if the total number of the to-be-detected data group and the historical data group is different and the sorting modes of the to-be-detected data group and the historical data group are the same, the corresponding relation between the second sorting information and the first sorting information can be preset, and the target historical data group and the target to-be-detected data group are ensured to have an attribute association relation; for example, the total number of packets of the history data group is twice the total number of packets of the data group to be measured, and the number of sorting bits indicated by the second sorting information is twice the number of sorting bits indicated by the first sorting information.

Thus, by the sorting information, the target history data group corresponding to the first sorting information is determined.

With reference to the first example, before determining the second ranking information corresponding to the first ranking information, historical data of N cycles before the current cycle is also obtained; for example, if N is 1, the data to be measured is the person order quantity at the lunch time in the hot period, and the historical data is the person order quantity at the lunch time before the current cycle; grouping the historical data to obtain a historical data group and second sequencing information of the historical data group; grouping the data to be tested, wherein the grouped data is the same as the historical data group; it can be understood that the steps of grouping the historical data and grouping the data to be tested may be performed separately (without limitation on the execution sequence), or may be performed simultaneously; and then searching a corresponding target historical data set according to the bit order indicated by the first sequencing information.

And 103, extracting target historical data from the target historical data group, and performing data test according to the historical data.

After a target historical data set is determined, extracting target historical data from the target historical data set, wherein the number of the extracted data can be preset; and then replacing the target data to be tested with the target historical data to perform data testing.

Specifically, based on the selection mode of the target historical data, the target historical data selected in the embodiment of the application has attribute relevance with the target data to be tested, and the target historical data is used for simulating the target data to be tested to perform data testing, so that a test result of performing data testing with the target data to be tested can be obtained; specifically, taking the associated data as the time attribute as an example, for example, if the target data to be tested is data of a current hot time period and the historical data to be tested is data of a hot time period of a previous test cycle, the target data to be tested is very close to or even the same as the target historical data; more specifically, with reference to the first example, the target data to be tested is data of a lunch period of a current cycle (cycle is 1 day), the target history data is data of a lunch period of a day before the current cycle, and a difference between the target data to be tested and the target history data is usually very small for an operating instant delivery system, so that the target data to be tested can be replaced by the target history data for testing.

However, for a system under test with a large data volume, the real-time data under test is usually transient, and relatively, the data test (e.g., data pressure test) lasts for a long time, which results in distortion of the data test; and only instantaneous wave peaks can be tested in the test process, so that the accuracy of the test result is reduced. In the embodiment of the application, data testing is performed through the target historical data, so that the influence on the testing result due to data instantaneity is avoided; target historical data can be dynamically selected, and hot spot data are prevented from being generated; in the testing process, if the target historical data comprises a plurality of data wave crests, the simultaneous testing of the plurality of wave crests can be supported, and the accuracy of the testing result is improved.

Still referring to the first example, after the target historical data set is determined, the N-entry target historical data is selected from the target historical data set for pressure testing, and distortion of the pressure testing is reduced by acquiring real historical data and simulating data to be tested on the line.

In the embodiment of the application, target data to be detected and first sequencing information are obtained; determining a target historical data group corresponding to the first sequencing information; extracting target historical data from the target historical data group, performing data test according to the historical data, replacing target to-be-tested data with the target historical data, performing data test, and improving the authenticity of a test result; target historical data can be dynamically selected, and hot spot data is avoided; in the test process, the test of a plurality of wave crests can be supported, and the accuracy of the test result is improved. The embodiment of the application solves the problem that in the prior art, when pressure testing is carried out on data, the pressure testing distortion is easy to occur.

In an optional embodiment, before the acquiring the target data to be measured, the method further includes:

acquiring historical data;

dividing the historical data into at least two historical data groups according to the numerical value of the historical data; wherein, each historical data group comprises a segment of historical data of continuous value range.

The historical data can be historical data associated with the data to be tested, for example, by time attribute, such as historical data of the previous several same-period cycles of the current cycle, so that the selected historical data has the same time attribute information as the data to be tested; or, the selected historical data has the same region attribute information as the data to be measured by associating region attributes, for example, regions such as popular business circles, for example, historical data of previous periods of the current period of the same region.

After obtaining the historical data, dividing the historical data into at least two historical data groups according to the numerical value of the historical data; optionally, the grouping may be performed by using a value of the historical data as a boundary, or may be performed by presetting a grouping number; specifically, when the value is divided by taking the value as a boundary, the second sorting information indicates the value size of a group of historical data groups in the ratio of the value size of all historical data; for example, if the data amount of the historical data ranges from 0 to 100, the historical data can be grouped into one group from 1 to 20, one group from 21 to 60, and one group from 61 to 100, the data limits are respectively 1 to 20, 21 to 60, and 61 to 10. When the data is divided by the preset grouping number, for example, the preset grouping number is N, all the historical data are randomly distributed to each data group.

In an optional embodiment, the dividing the historical data into at least two historical data groups according to the numerical size of the historical data includes:

determining a first number of the historical data sets; wherein the first number is predetermined or randomly determined; a first number, i.e., the number of sets of historical data sets; under the condition that the number of grouped groups is determined, determining the number included by each historical data group according to the total amount of the historical data;

then, according to the numerical value of the historical data, sequentially dividing the historical data into the first number of historical data groups; for example, all historical data are sorted by size or by size, and are sequentially divided into corresponding historical data groups according to the data amount included in each historical data group.

In an optional embodiment, said sequentially dividing the historical data into the first number of the historical data groups according to the numerical size of the historical data comprises:

acquiring the data volume ratio of each historical data group;

determining the data range of each historical data group according to the data volume ratio;

and determining the historical data group corresponding to each historical data according to the numerical value of the historical data and the data range of the historical data group, and dividing the historical data into the corresponding historical data groups.

In the case that the first number is determined, determining the data proportion of each historical data group, namely the proportion of the number of data included in the group divided by the total amount of the historical data; specifically, if the total amount of the historical data is M, the first number is N, and the data amount of each group is x 1%, x 2%, … …, xn%; determining the data volume and the data range of each historical data group according to the product of the data volume proportion and the total data volume, for example, the data volume of a first group is m1, sorting all historical data by small and large, determining the data volume to be m1 corresponding to the value range from W1 to Wk, and then distributing the value with the value size from W1 to W2 to the first group; the data quantity of the first group is m2, and according to the sorting, the data quantity of m2 is determined to correspond to the value range from W3 to W4, and the values with the value size from W3 to W4 are distributed to the second group. Wherein W1 to W4 represent any numerical value. The data amount ratio of each packet may be randomly selected or may be set in advance based on a historical empirical value. For example, for the immediate distribution field, 24 hours of each day are divided into 12 groups, and the data amount of the data group corresponding to the hot time period (for example, lunch or dinner time) is higher than that of the data group at non-hot time.

acquiring the data to be detected; for example, the data to be tested may include data of buyer users, seller users and rider users; taking the logistics dispatching system as an example, the data to be tested can comprise data of the freight note to be dispatched and data of the dispatcher;

dividing the data to be detected into a second number of historical data groups according to the numerical value of the data to be detected; the second number is the same as the first number or has an error within a first preset range.

When the preset grouping number is used for dividing, for example, the preset grouping number is N, all the data to be detected are randomly distributed to each data group; optionally, the grouping is performed by presetting the grouping number to be divided, namely, presetting the grouping number to be a second number; for example, when the second number is 3, the data amount range of the data to be measured is 0 to 50, the data amount range may be divided into one group from 1 to 10, one group from 11 to 30, and one group from 41 to 50, and the data limit is 1 to 10, 10 to 30, and 40 to 50, respectively.

The second number is the same as the first number or the error is in a first preset range, namely the difference between the total number of the historical data group and the total number of the data group to be tested is controlled to be smaller, so that the reference of the historical data group is improved

In an alternative embodiment, the first ordering information is the same as the second ordering information or has an error within a second preset range.

For example, if the total number of the packets of the data group to be tested is the same as the total number of the packets of the historical data group, and the sorting modes of the data group to be tested and the historical data group are the same (the sorting mode includes that the sorting mode is large and small or small and large according to the size of the data), the sorting digit number indicated by the second sorting information is the same as the sorting digit number indicated by the first sorting information; if the total number of the to-be-detected data group and the historical data group is different, the sorting modes of the to-be-detected data group and the historical data group are the same, the first sorting information and the second sorting information are the same or the error is within a second preset range, namely the difference between the first sorting information and the second sorting information is small, the corresponding relation between the second sorting information and the first sorting information can be preset, and the target historical data group and the target to-be-detected data group are ensured to have an attribute association relation.

In an optional embodiment, the historical data is the same as the attribute information of the data to be tested;

the attribute information includes at least one of a time attribute, a region attribute, and a use attribute.

The time attribute is time information of the data to be detected and/or the historical data, and the time information can be a visual time value or time parameter information, such as a hot time period, a non-hot time period and the like; for example, for the field of instant distribution, the field of public transportation, etc., the data usually has a strong time characteristic and changes obviously with time.

Geographic attributes such as visual geographic location, or geographic parameter information such as hot area, non-hot area, etc.; still take the fields of instant distribution, public transportation, etc. as examples, the data also has strong regional characteristics and changes obviously with the region.

The use attribute, i.e. an attribute indicating the use of the data, such as the person order quantity, is used in the instant delivery system as a reference for dispatching an order for a rider.

In the embodiment of the application, the selected target historical data and the target data to be tested have the same attribute information, and the target historical data is used for simulating the target data to be tested to perform data testing, so that a test result of performing the data testing on the target data to be tested can be obtained; specifically, taking the associated data as the time attribute as an example, for example, if the target data to be tested is data of a current hot time period and the historical data to be tested is data of a hot time period of a previous test cycle, the target data to be tested is very close to or even the same as the target historical data.

In an optional embodiment, in a case that the attribute information includes the time attribute, the method further includes:

determining a data test period according to the time attribute; wherein each of said data test periods includes one of said time attributes;

and executing the data test once in each data test period.

Taking the time attribute comprising the hot time period and the non-hot time period as an example, dividing each hot time period in a day into a single data test period; then the time between two adjacent hot periods can be used as a data test period; and executing the data test once in each data test period.

As a third example, taking a data pressure test on an instant distribution system as an example, the data test method provided in the embodiment of the present application mainly includes the following data preloading and dynamic balancing processes.

Referring to fig. 2, the data preloading mainly includes the following steps:

step 201, starting preloading and loading historical data;

step 202, determining whether the data is loaded:

if yes, the flow is ended, otherwise, step 203 is executed;

step 203, the history data is divided into 3 history groups.

When the historical data are grouped, for example, the numerical range of the personal list data in the historical data is K1-Kp, the maximum personal list length is Kp, the total data amount is A, and the data amount ratio of each group is y 1%, y 2% and y 3%;

the grouping steps are as follows:

1, firstly, sorting all historical data by small and large (or by large and small);

2, determining a data range corresponding to each group;

for example, for the first historical group, whose total amount of data is a × y 1%, starting from K1, a × y 1% is selected to be assigned to the first historical data group, such as just to K2; then starting from K3 (one bit after K2), a x y 2% is selected in sequence to be assigned to the second historical data set, such as just to K4; then, starting from K5 (one bit after K4), a × y 3% is sequentially selected to be allocated to the third history data set, for example, just to Kp, until the last grouping is completed, the data range of the first history data set is K1 to K2, the data range of the second history data set is K3 to K4, and the data range of the third history data set is K5 to Kp.

Step 204, reading the history data into a cache.

Referring to fig. 3, the dynamic equalization process mainly includes the following steps:

step 301, starting a pressure test;

step 302, grouping the data to be tested;

the same grouping strategy as the historical data is adopted, and the details are not repeated here.

Step 303, randomly selecting a target data to be measured.

Step 304, if the target data to be tested belongs to the first data group to be tested, reading m1 pieces of historical data from the first historical data group to perform a pressure test;

step 305, if the target data to be tested belongs to a second data group to be tested, reading m2 pieces of historical data from the second historical data group to perform pressure testing;

step 306, if the target data to be tested belongs to a third data group to be tested, reading m3 pieces of historical data from the third historical data group for pressure testing;

therefore, the distortion degree of the pressure test is reduced by acquiring real historical data and simulating the data to be tested on the line.

In the embodiment of the application, target data to be detected and first sequencing information are obtained; determining a target historical data group corresponding to the first sequencing information; extracting target historical data from the target historical data group, performing data test according to the historical data, replacing target to-be-tested data with the target historical data, performing data test, and improving the authenticity of a test result; target historical data can be dynamically selected, and hot spot data are prevented from being generated; in the test process, the test of a plurality of wave crests can be supported, and the accuracy of the test result is improved.

Based on the same principle as the method provided by the embodiment of the present application, the embodiment of the present application further provides a data testing apparatus, as shown in fig. 4, the apparatus includes:

an information obtaining module 401, configured to obtain target data to be detected and first sequencing information; the first ordering information is ordering information of a target data group to be detected corresponding to the target data to be detected in the data group to be detected; each data group to be detected comprises a segment of data to be detected in a continuous numerical range.

The data testing in the embodiment of the present application includes, for example, data pressure testing, specifically, for a data system with a large data volume, such as an instant distribution field, an e-commerce platform field, and the like, mass data is usually stored in a database of the data system, and when the system runs, the limit of the system, including the use of the contents of limit load, upper limit definition, capacity definition and the like, needs to be measured through the data pressure testing, so as to test whether the system will report an error under a high concurrency condition, and whether a process cannot run normally; and the compression resistance of the system is tested, the bearing capacity of the system is estimated, and the like, so that a basis for capacity expansion is provided for operation and maintenance personnel.

Optionally, the grouping may be performed by using a numerical value as a boundary, or may be performed by presetting a grouping number; specifically, when the numerical value is divided as a boundary, the sorting information indicates the ratio of the numerical value of a group of data groups to be tested to the numerical value of all data to be tested; for example, the data size range of the data to be measured is 0 to 50, the data size range may be divided into one group from 1 to 10, one group from 11 to 30, and one group from 41 to 50, and the data limit is 1 to 10, 10 to 30, and 40 to 50, respectively. When the predetermined number of packets is used for division, for example, the predetermined number of packets is N, all the data to be tested are randomly allocated to each data group.

Or under the condition of determining the grouped data, determining the data proportion of each data group to be tested, namely the ratio obtained by dividing the number of data included in the group by the total amount of the data to be tested; specifically, if the total amount of the data to be detected is M, the number of the groups is N, and the data volume proportion of each group is y 1%, y 2%, … … and yn%; determining the data volume and the data range of each data group to be tested according to the product of the data volume ratio and the total data volume, for example, the data volume of a first group is n1, sorting all the data to be tested by small and large, determining the data volume to be n1 corresponding to the value range of x1 to xk, and then distributing the value of the value range from x1 to x2 to the first group; the data amount of the first group is n2, and according to the sorting, the numerical value range corresponding to the data amount of n2 is determined to be x3 to x4, and then the numerical values with the numerical values from x3 to x4 are distributed to the second group. Wherein x1 to x4 represent any numerical value. The data amount ratio of each packet may be randomly selected or may be set in advance based on a historical empirical value. For example, for the immediate delivery domain, 24 hours per day are divided into 12 groups, and the data amount of the data group corresponding to the hot time period (e.g., lunch or dinner time) is higher than that of the data group at non-hot time.

A data determining module 402, configured to determine a target historical data set corresponding to the first sorting information; the target historical data set is a data set of which the second sorting information in the historical data set corresponds to the first sorting information.

The historical data may be historical data associated with the data to be measured, for example, by a time attribute, such as historical data of previous same-period cycles of a current cycle, so that the selected historical data has the same time attribute information as the data to be measured; or, the selected historical data has the same region attribute information as the data to be measured by associating region attributes, for example, regions such as popular business circles, for example, historical data of previous periods of the current period of the same region.

the grouping steps are as follows:

step 2, determining a data range corresponding to each group;

Thus, after the grouping of each historical data group is completed, the historical data groups are sorted according to the numerical range of the historical data groups, and second sorting information of each historical data group is obtained.

And a data testing module 403, configured to extract target historical data from the target historical data set, and perform data testing according to the historical data.

Specifically, based on the selection mode of the target historical data, the target historical data selected in the embodiment of the application has attribute relevance with the target data to be tested, and the target historical data is used for simulating the target data to be tested to perform data testing, so that a test result of performing data testing with the target data to be tested can be obtained; specifically, taking the associated data as the time attribute as an example, for example, if the target data to be tested is data of a current hot time period and the historical data to be tested is data of a hot time period of a previous test cycle, the target data to be tested is very close to or even the same as the target historical data; more specifically, with reference to the first example, the target data to be tested is data of a lunch period of a current cycle (cycle is 1 day), the target history data is data of a lunch period of a day before the current cycle, and a difference between the target data to be tested and the target history data is usually very small for an operating instant distribution system, so that the target data to be tested can be replaced by the target history data for testing.

However, for a system under test with a large data volume, the real-time data under test is usually transient, and relatively, the data test (e.g., data pressure test) lasts for a long time, which results in distortion of the data test; and only instantaneous wave peaks can be tested usually during testing, so that the accuracy of the test result is reduced. In the embodiment of the application, data testing is performed through the target historical data, so that the influence on the testing result due to data instantaneity is avoided; target historical data can be dynamically selected, and hot spot data are prevented from being generated; in the testing process, if the target historical data comprises a plurality of data wave crests, the simultaneous testing of the plurality of wave crests can be supported, and the accuracy of the testing result is improved.

Still in combination with the first example, after the target historical data set is determined, N item historical data are selected from the target historical data set for pressure testing, and the distortion degree of the pressure testing is reduced by acquiring real historical data and simulating data to be tested on the line.

In an optional embodiment, before the acquiring the target data to be measured, the apparatus further includes:

the data acquisition module is used for acquiring historical data;

the data grouping module is used for dividing the historical data into at least two historical data groups according to the numerical value of the historical data; wherein each historical data set comprises a segment of historical data of a continuous numerical range.

In an optional embodiment, the data packet module comprises:

a first determining sub-module for determining a first number of the historical data sets; wherein the first number is predetermined or randomly determined;

and the grouping submodule is used for sequentially dividing the historical data into the first number of historical data groups according to the numerical value of the historical data.

In an alternative embodiment, the grouping sub-module comprises:

a ratio acquisition unit for acquiring a data amount ratio of each of the historical data groups;

a first determining unit, configured to determine a data range of each of the historical data sets according to the data amount ratio;

and the second determining unit is used for determining the historical data group corresponding to each historical data according to the numerical value of the historical data and the data range of the historical data group, and dividing the historical data into the corresponding historical data groups.

In an optional embodiment, the apparatus further comprises:

the to-be-detected data acquisition module is used for acquiring the to-be-detected data;

the data grouping module to be tested is used for dividing the data to be tested into a second number of historical data groups according to the numerical value of the data to be tested; the second number is the same as the first number or has an error within a first preset range.

In an alternative embodiment, the first ordering information is the same as the second ordering information or has an error within a second predetermined range.

In an optional embodiment, the apparatus further comprises:

the period determining module is used for determining a data testing period according to the time attribute; wherein each of said data test periods includes one of said time attributes;

and the test execution module is used for executing the data test once in each data test period.

The data testing device provided in the embodiment of the present application can implement each process implemented in the method embodiments of fig. 1 to fig. 3, and is not described here again to avoid repetition.

In the data testing apparatus provided by the application, the information obtaining module 401 obtains target data to be tested and first sequencing information; the data determination module 402 determines a target historical data group corresponding to the first sorting information; the data testing module 403 extracts target historical data from the target historical data group, performs data testing according to the historical data, replaces target to-be-tested data with the target historical data, performs data testing, and improves the authenticity of a testing result; target historical data can be dynamically selected, and hot spot data is avoided; in the test process, the test of a plurality of wave crests can be supported, and the accuracy of the test result is improved. The embodiment of the application solves the problem that in the prior art, when pressure testing is carried out on data, the pressure testing distortion is easy to occur.

The data testing apparatus of the embodiment of the present application can execute the data testing method provided by the embodiment of the present application, and the implementation principle is similar, the actions executed by each module and unit in the data testing apparatus in each embodiment of the present application correspond to the steps in the data testing method in each embodiment of the present application, and for the detailed functional description of each module of the data testing apparatus, reference may be specifically made to the description in the corresponding data testing method shown in the foregoing, and details are not repeated here.

Based on the same principle as the method shown in the embodiments of the present application, the embodiments of the present application also provide an electronic device, which may include but is not limited to: a processor and a memory; a memory for storing a computer program; and the processor is used for executing the data testing method shown in any optional embodiment of the application by calling the computer program. Compared with the prior art, the data testing method provided by the application obtains target data to be tested and first sequencing information; determining a target historical data group corresponding to the first sequencing information; extracting target historical data from the target historical data group, performing data test according to the historical data, replacing target to-be-tested data with the target historical data, performing data test, and improving the authenticity of a test result; target historical data can be dynamically selected, and hot spot data is avoided; in the test process, the test of a plurality of wave crests can be supported, and the accuracy of the test result is improved.

In an alternative embodiment, there is also provided an electronic device, as shown in fig. 5, where the electronic device 5000 shown in fig. 5 may be a server, including: a processor 5001 and a memory 5003. The processor 5001 and the memory 5003 are coupled, such as via a bus 5002. Optionally, the electronic device 5000 may also include a transceiver 5004. It should be noted that the transceiver 5004 is not limited to one in practical application, and the structure of the electronic device 5000 does not limit the embodiments of the present application.

The Processor 5001 may be a CPU (Central Processing Unit), a general-purpose Processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other Programmable logic device, a transistor logic device, a hardware component, or any combination thereof. Which may implement or execute the various illustrative logical blocks, modules, and circuits described in connection with the disclosure herein. The processor 5001 may also be a combination of computing functions, e.g., comprising one or more microprocessors, a combination of a DSP and a microprocessor, or the like.

Bus 5002 can include a path that conveys information between the aforementioned components. The bus 5002 may be a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The bus 5002 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown in FIG. 5, but this is not intended to represent only one bus or type of bus.

The Memory 5003 may be a ROM (Read Only Memory) or other type of static storage device that can store static information and instructions, a RAM (Random Access Memory) or other type of dynamic storage device that can store information and instructions, an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical Disc storage, optical Disc storage (including Compact Disc, laser Disc, optical Disc, digital versatile Disc, blu-ray Disc, etc.), a magnetic Disc storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, but is not limited to these.

The memory 5003 is used for storing application program codes for executing the present solution, and the execution is controlled by the processor 5001. The processor 5001 is configured to execute application program code stored in the memory 5003 to implement the illustrated aspects of the foregoing method embodiments.

Among them, electronic devices include but are not limited to: mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and the like, and fixed terminals such as digital TVs, desktop computers, and the like. The electronic device shown in fig. 5 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present application.

The server provided by the application can be an independent physical server, can also be a server cluster or distributed system formed by a plurality of physical servers, and can also be a cloud server for providing basic cloud computing services such as cloud service, a cloud database, cloud computing, cloud functions, cloud storage, network service, cloud communication, middleware service, domain name service, security service, CDN (content delivery network) and big data and artificial intelligence platforms. The terminal may be, but is not limited to, a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, and the like. The terminal and the server may be directly or indirectly connected through wired or wireless communication, and the application is not limited herein.

The present application provides a computer-readable storage medium, on which a computer program is stored, which, when running on a computer, enables the computer to execute the corresponding content in the foregoing method embodiments.

It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and may be performed in other orders unless explicitly stated herein. Moreover, at least a portion of the steps in the flow chart of the figure may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed alternately or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

It should be noted that the computer readable medium mentioned above in the present application may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In this application, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.

The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device.

The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to perform the methods shown in the above embodiments.

According to an aspect of the application, there is provided a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and executes the computer instructions, so that the computer device executes the data testing method provided in the above-mentioned various alternative implementation modes.

Computer program code for carrying out operations for aspects of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The modules described in the embodiments of the present application may be implemented by software or hardware. Where the name of a module does not constitute a limitation on the module itself in some cases, for example, the search intention category determination module may also be described as a "search intention category module that determines a search request.

The above description is only a preferred embodiment of the application and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the disclosure herein is not limited to the particular combination of features described above, but also encompasses other arrangements formed by any combination of the above features or their equivalents without departing from the spirit of the disclosure. For example, the above features may be replaced with (but not limited to) features having similar functions disclosed in the present application.

Claims

1. A method of data testing, comprising:

determining a target historical data group corresponding to the first sequencing information; the target historical data set is a data set of which the second sorting information in the historical data set corresponds to the first sorting information; the grouping mode of the historical data group is the same as that of the data group to be detected;

2. The data testing method of claim 1, wherein before the obtaining the target data to be tested, the method further comprises:

acquiring historical data;

dividing the historical data into at least two historical data groups according to the numerical value of the historical data; wherein each historical data set comprises a segment of historical data of a continuous numerical range.

3. The data testing method of claim 2, wherein the dividing the historical data into at least two historical data groups according to the numerical size of the historical data comprises:

determining a first number of the historical data sets; wherein the first number is predetermined or randomly determined;

and sequentially dividing the historical data into the first number of historical data groups according to the numerical value of the historical data.

4. The data testing method of claim 3, wherein said sequentially dividing the historical data into the first number of the historical data sets according to the numerical size of the historical data comprises:

acquiring the data volume ratio of each historical data group;

5. The data testing method of claim 3, wherein before the obtaining the target data to be tested, the method further comprises:

acquiring the data to be detected;

6. The data testing method of claim 1, wherein the first ordering information is the same as the second ordering information or has an error within a second preset range.

7. The data testing method of claim 1, wherein the historical data is the same as the attribute information of the data to be tested;

8. The data testing method of claim 7, wherein in the case that the attribute information includes the time attribute, the method further comprises:

and executing the data test once in each data test period.

9. A data testing apparatus, comprising:

the data determining module is used for determining a target historical data group corresponding to the first sequencing information; the target historical data set is a data set of which second ordering information in the historical data set corresponds to the first ordering information; the grouping mode of the historical data group is the same as that of the data group to be detected;

10. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method of any one of claims 1 to 8 when executing the program.

11. A computer-readable storage medium, characterized in that a computer program is stored on the computer-readable storage medium, which computer program, when being executed by a processor, carries out the method of any one of claims 1 to 8.