CN115617899A

CN115617899A - Data visualization processing method, device, equipment and storage medium

Info

Publication number: CN115617899A
Application number: CN202211313581.5A
Authority: CN
Inventors: 何炜
Original assignee: Ping An Technology Shenzhen Co Ltd
Current assignee: Ping An Technology Shenzhen Co Ltd
Priority date: 2022-10-25
Filing date: 2022-10-25
Publication date: 2023-01-17

Abstract

The application discloses a data visualization processing method, a device, equipment and a storage medium, wherein a data query statement based on an SQL statement is generated and displayed in a linkage mode through a determined target Elasticissearch cluster, an Elasticissearch table and a data query rule, and a format of a returned result is controlled by using a format parameter of the SQL statement, so that a query result is displayed in a table form, the data visualization processing method is clearer and simpler, the problem that the current operations of increasing, deleting, modifying, searching and the like of data in the Elasticissearch table need to be based on a Kibana platform command line mode, the query mode and the display result of the data are in a json format, and when the query conditions are more, the condition of wrong input of the query conditions easily occurs; when the data volume of the query result is large, the cost for comparing the query result is high, and the comparison is difficult to be intuitively and clearly carried out.

Description

Data visualization processing method, device and equipment and storage medium

Technical Field

The application relates to the technical field of financial technology, in particular to a data visualization processing method, device, equipment and storage medium.

Background

In a huge business system, each business team is usually connected with a real-time data application system through respective interfaces, so that the business team obtains corresponding business data in a real-time data Elasticissearch table according to a data statistical rule, and the corresponding business data are displayed to a front-end page by using Kibana.

For example, if the app front-end needs to show the installment amount of the current day, a data interface queryVariable connected with the real-time data application system will be called, and the statistical data rule of the interface depends on the data in the Elasticsearch table dc _ board 10_ dwd _ txn _ aibma _ detail _ helper. If data of the current-day installment transaction amount needs to be prepared, whether data meeting the statistical data rule exist or not needs to be inquired in the elastic search table, and if the data meet the statistical data rule, the corresponding field data can be directly used and checked to determine whether the corresponding field data is matched with the front-end display data or not; if not, the data needs to be reconstructed.

At present, operations such as adding, deleting, modifying and searching data in an elastic search table need to be based on a Kibana platform command line mode, the query mode and the display result of the data are in a json format, and when the query conditions are more, the condition of wrong query condition input is easy to occur; when the data volume of the query result is large, the cost for comparing the query result is high, and the comparison is difficult to be carried out intuitively and clearly.

Disclosure of Invention

The application provides a data visualization processing method, a data visualization processing device, data visualization processing equipment and a storage medium, and solves the problems that at present, operations such as addition, deletion, modification, check and the like of data in an elastic search table need to be based on a Kibana platform command line mode, the query mode and the display result of the data are in a json format, and when query conditions are more, the condition of wrong query condition input is easy to occur; when the data volume of the query result is large, the cost for comparing the query result is high, and the comparison is difficult to be carried out intuitively and clearly.

In view of this, a first aspect of the present application provides a data visualization processing method, where the method includes:

s1, acquiring a target Elasticissearch cluster and an Elasticissearch table;

s2, acquiring a data query rule;

s3, displaying data query statements based on SQL statements in a linkage mode according to the Elasticissearch cluster, the Elasticissearch table and the data query rules;

and S4, executing the data query statement, and displaying the query result in a query result display area in a table form.

Optionally, after the step S4, the method further includes:

s5, acquiring a data modification instruction, wherein the data modification instruction comprises a field to be modified of the query result and an updated field value of the field to be modified;

and S6, synchronously updating the field value corresponding to the field to be modified in the query result display area and the field value corresponding to the field to be modified in the Elasticissearch table according to the data modification instruction.

Optionally, after the step S1, the method further includes:

s7, acquiring a data operation instruction, wherein the data operation instruction comprises addition, deletion or modification operation;

s8, displaying a data operation statement based on an SQL statement in a linkage mode according to the Elasticissearch cluster, the Elasticissearch table and the data operation instruction;

s9, executing the data operation statement to correspondingly operate the target data in the Elasticissearch table according to the data operation instruction.

Optionally, the method further comprises:

s10, receiving a table reference operator query instruction, wherein the table reference operator query instruction carries a target Elasticissearch table;

s11, displaying all data query rules referring to the target Elasticissearch table in the query result display area according to the table reference operator query instruction.

Optionally, the step S11 further includes:

and displaying the data source of the target Elasticissearch table in the query result display area.

Optionally, the data source includes mq message queue writes and offline data job writes.

A second aspect of the present application provides a data visualization processing apparatus, including:

a first obtaining unit, configured to obtain a target Elasticsearch cluster and an Elasticsearch table;

the second acquisition unit is used for acquiring the data query rule;

the statement generating unit is used for displaying the data query statement based on the SQL statement in a linkage manner according to the Elasticissearch cluster, the Elasticissearch table and the data query rule;

and the first processing unit is used for executing the data query statement and displaying the query result in a query result display area in a tabular form.

Optionally, the method further comprises:

a third obtaining unit, configured to obtain a data modification instruction, where the data modification instruction includes a field to be modified of the query result and an updated field value of the field to be modified;

and the second processing unit is used for synchronously updating the field value corresponding to the field to be modified in the query result display area and the field value corresponding to the field to be modified in the Elasticissearch table according to the data modification instruction.

A third aspect of the present application provides a data visualization processing apparatus, the apparatus comprising a processor and a memory:

the memory is used for storing program codes and transmitting the program codes to the processor;

the processor is configured to execute the steps of the data visualization processing method according to the first aspect, according to instructions in the program code.

A fourth aspect of the present application provides a computer-readable storage medium for storing program code for executing the data visualization processing method according to the first aspect.

Causing said computer to perform the method of the first aspect.

According to the technical scheme, the embodiment of the application has the following advantages:

the method comprises the steps of generating and displaying a data query statement based on an SQL statement in a linkage manner through a determined target Elasticissearch cluster, an Elasticissearch table and a data query rule, and simultaneously controlling the format of a returned result by using a format parameter of the SQL statement, so that the query result is displayed in a table form, the method is clearer and simpler, the problems that the existing operations of increasing, deleting, modifying, checking and the like of data in the Elasticissearch table need to be carried out based on a Kibana platform command line mode, the query mode and the displayed result of the data are in a json format, and when the query conditions are multiple, the condition of query condition input errors easily occurs; when the data volume of the query result is large, the cost for comparing the query result is high, and the comparison is difficult to be carried out intuitively and clearly.

Drawings

Fig. 1 is a flowchart of a first method of a data visualization processing method according to an embodiment of the present application;

FIG. 2 is a flowchart illustrating a second method of a data visualization processing method according to an embodiment of the present application;

fig. 3 is a flowchart of a third method of a data visualization processing method in an embodiment of the present application;

FIG. 4 is a flowchart illustrating a fourth method of a data visualization processing method according to an embodiment of the present application;

fig. 5 is a schematic structural diagram of a data visualization processing apparatus in an embodiment of the present application;

fig. 6 is a schematic structural diagram of a data visualization processing device in an embodiment of the present application.

Detailed Description

In order to make the technical solutions of the present application better understood, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present application without making any creative effort belong to the protection scope of the present application.

The application designs a data visualization processing method, a data visualization processing device, data visualization processing equipment and a data visualization processing storage medium, and solves the problems that at present, operations such as adding, deleting, modifying, searching and the like of data in an elastic search table need to be based on a Kibana platform command line mode, the query mode and the display result of the data are in a json format, and when query conditions are more, the condition of wrong query condition input is easy to occur; when the data volume of the query result is large, the cost for comparing the query result is high, and the comparison is difficult to be intuitively and clearly carried out.

For convenience of understanding, please refer to fig. 1, fig. 1 is a flowchart illustrating a first method of a data visualization processing method according to an embodiment of the present application, and as shown in fig. 1, the first method specifically includes:

s1, acquiring a target Elasticissearch cluster and an Elasticissearch table;

it should be noted that, for the created Elasticsearch cluster and the Elasticsearch table under each Elasticsearch cluster, the user may autonomously determine the Elasticsearch cluster and the Elasticsearch table that need to perform data query in a form of drop-down box selection.

S2, acquiring a data query rule;

it should be noted that, the data query rule will include the fields of the query and the query conditions, and the fields required by different business teams are different. It can be understood that, for the same business team, multiple data query rules can be acquired simultaneously and executed simultaneously.

S3, displaying the data query statement based on the SQL statement in a linkage manner according to the Elasticissearch cluster, the Elasticissearch table and the data query rule;

it should be noted that, according to the determined Elasticsearch cluster, elasticsearch table and data query rule for performing query, a data query statement based on an SQL statement is generated and displayed, for example:

POST/_sqlformat＝txt

{

“query”:“SELECT account_number,address,age,balance FROM account LIMIT 10”

}

in the data query statement, account is a determined Elasticsearch table, account _ number, address, age, and balance are fields to be queried included in the data query rule, and an output format is a table form in txt format.

It can be understood that the syntax of using SQL query in Elasticsearch is basically consistent with the use in database, and the specific format may include but is not limited to:

SELECT select_exper[,…]

[FROM table_name]

[WHERE condition]

[GROUP BY grouping_element[,…]]

[HAVING condition]

[ORDER BY expression[ASC|DESC][,…]]

[LIMIT[COUNT]]

[PIVOT(aggregation_expr FOR column IN(value[[AS]alias][,…]))]

it is understood that we can use WHERE statement to set query condition, such as querying the record with state field as VA;

grouping data BY using a GROUP BY statement, and counting information such as the number of grouped records, maximum age, average balance and the like;

the packet data can be secondarily screened by using the HAVING statement, for example, the information with the quantity of the screening packet records larger than 15;

the data may be sorted using the ORDER BY statement, such as from high to low in the balance field;

the DESCRIBE statement can be used to look up which fields are in the table (index in ES), such as looking up the fields of the account table;

all TABLES (indexes in ES) can be looked up using SHOW TABLES;

the SQL queries the data in the ES, and not only some functions in the SQL but also some functions specific to the ES can be used.

Query supported functions

We can use the SHOW FUNCTION statement to look at all supported FUNCTIONS, such as searching all FUNCTIONS with DATE fields.

In summary, according to the fields and query conditions included in the obtained data query rule, the preset SQL query function is automatically called to be combined, and a data query statement based on the SQL statement is formed.

And S4, executing a data query statement, and displaying the query result in a query result display area in a table form.

It should be noted that, the query result can be displayed in the query result display area in a table format with the output format of txt, and compared with the original query result display method which is still in a code format, the query result provided by the method is displayed more clearly and visually, and data comparison is facilitated.

Referring to fig. 2, fig. 2 is a flowchart illustrating a second method of a data visualization processing method according to an embodiment of the present application, as shown in fig. 2, specifically:

s5, acquiring a data modification instruction, wherein the data modification instruction comprises a field to be modified of the query result and a field value after the field to be modified is updated;

It should be noted that, based on the embodiment shown in fig. 1 of the present application, further, on the basis of the query result displayed in the query result display area, the field value in the query result display area may be directly updated according to the data modification instruction, so that the field value corresponding to the field to be modified in the Elasticsearch table is synchronously updated, which is convenient for data processing and improves the efficiency of data modification.

Referring to fig. 3, fig. 3 is a flowchart illustrating a third method of a data visualization processing method according to an embodiment of the present application, as shown in fig. 3, specifically:

s1, acquiring a target Elasticissearch cluster and an Elasticissearch table;

s7, acquiring a data operation instruction, wherein the data operation instruction comprises adding, deleting or modifying operation;

s8, displaying the data operation statement based on the SQL statement in a linkage mode according to the elastic search cluster, the elastic search table and the data operation instruction;

and S9, executing a data operation statement to perform corresponding operation on the target data in the Elasticissearch table according to the data operation instruction.

It should be noted that, based on the embodiment shown in fig. 1 of the present application, in addition to performing data query, operations of adding, deleting, or modifying data may also be performed directly through a data operation instruction, and a data operation statement based on an SQL statement is displayed in a linkage manner according to the Elasticsearch cluster, the Elasticsearch table, and the data operation instruction, so that the target data in the Elasticsearch table performs corresponding operations.

Referring to fig. 4, fig. 4 is a fourth method flowchart of a data visualization processing method according to an embodiment of the present application, as shown in fig. 4, specifically:

s10, receiving a table reference operator query instruction, wherein the table reference operator query instruction carries a target elastic search table;

s11, displaying all data query rules and data sources for referring to the target Elasticissearch table in a query result display area according to the table reference operator query instruction.

It should be noted that, based on the embodiment shown in fig. 1 of the present application, further, all data query rules and data sources in the target Elasticsearch table that refer to the target Elasticsearch table may be queried according to the table reference operator query instruction. Data sources include mq message queue writes and offline data job writes.

Referring to fig. 5, fig. 5 is a schematic structural diagram of a data visualization processing apparatus in an embodiment of the present application, as shown in fig. 5, specifically including:

a first obtaining unit 501, configured to obtain a target Elasticsearch cluster and an Elasticsearch table;

a second obtaining unit 502, configured to obtain a data query rule;

a statement generating unit 503, configured to display the SQL statement-based data query statement in a linkage manner according to the Elasticsearch cluster, the Elasticsearch table, and the data query rule;

the first processing unit 504 is configured to execute a data query statement, and display a query result in a query result display area in a tabular form.

Further, still include:

a third obtaining unit 505, configured to obtain a data modification instruction, where the data modification instruction includes a field to be modified of the query result and a field value after the field to be modified is updated;

a second processing unit 506, configured to update, according to the data modification instruction, a field value corresponding to the field to be modified in the query result display area and a field value corresponding to the field to be modified in the Elasticsearch table synchronously.

Further, the method also comprises the following steps:

a fourth obtaining unit 507, configured to obtain a data operation instruction, where the data operation instruction includes an add, delete, or modify operation;

an operation statement generating unit 508, configured to display the data operation statement based on the SQL statement in a linkage manner according to the Elasticsearch cluster, the Elasticsearch table, and the data operation instruction;

the third processing unit 509 is configured to execute a data operation statement, so that a corresponding operation is performed on target data in the Elasticsearch table according to the data operation instruction.

Further, the method also comprises the following steps:

a receiving unit 510, configured to receive a table reference operator query instruction, where the table reference operator query instruction carries a target Elasticsearch table;

and the fourth processing unit 511 is configured to display all data query rules referencing the target Elasticsearch table in the query result display area according to the table referencing operator query instruction.

The embodiment of the present application further provides another data visualization processing apparatus, as shown in fig. 6, for convenience of description, only the portions related to the embodiment of the present application are shown, and specific technical details are not disclosed, please refer to the method portion in the embodiment of the present application. The terminal may be any terminal device including a mobile phone, a tablet computer, a Personal Digital Assistant (Personal Digital Assistant, PDA for short), a Point of sale terminal (POS for short), a vehicle-mounted computer, and the like, taking the terminal as the mobile phone:

fig. 6 is a block diagram illustrating a partial structure of a mobile phone related to a terminal provided in an embodiment of the present application. Referring to fig. 6, the handset includes: radio Frequency (RF) circuit 1010, memory 1020, input unit 1030, display unit 1040, sensor 1050, audio circuit 1060, wireless fidelity (WiFi) module 1070, processor 1080, and power source 1090. Those skilled in the art will appreciate that the handset configuration shown in fig. 6 is not intended to be limiting and may include more or fewer components than shown, or some components may be combined, or a different arrangement of components.

The following specifically describes each constituent component of the mobile phone with reference to fig. 6:

RF circuit 1010 may be used for receiving and transmitting signals during a message transmission or a call, and in particular, for receiving downlink information from a base station and processing the received downlink information to processor 1080; in addition, data for designing uplink is transmitted to the base station. In general, RF circuit 1010 includes, but is not limited to, an antenna, at least one Amplifier, a transceiver, a coupler, a Low Noise Amplifier (Low Noise Amplifier, LNA), a duplexer, and the like. In addition, the RF circuitry 1010 may communicate with networks and other devices via wireless communications. The wireless communication may use any communication standard or protocol, including but not limited to Global System for Mobile communications (GSM), general Packet Radio Service (GPRS), code Division Multiple Access (CDMA), wideband Code Division Multiple Access (WCDMA), long Term Evolution (LTE), e-mail, short message Service (Short SMS), and so on.

The memory 1020 can be used for storing software programs and modules, and the processor 1080 executes various functional applications and data processing of the mobile phone by operating the software programs and modules stored in the memory 1020. The memory 1020 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, etc. Further, the memory 1020 may include high speed random access memory and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device.

The input unit 1030 may be used to receive input numeric or character information and generate key signal inputs related to user settings and function control of the cellular phone. Specifically, the input unit 1030 may include a touch panel 1031 and other input devices 1032. The touch panel 1031, also referred to as a touch screen, may collect touch operations by a user (e.g., operations by a user on or near the touch panel 1031 using any suitable object or accessory such as a finger, a stylus, etc.) and drive corresponding connection devices according to a preset program. Alternatively, the touch panel 1031 may include two parts, a touch detection device and a touch controller. The touch detection device detects the touch direction of a user, detects a signal brought by touch operation and transmits the signal to the touch controller; the touch controller receives touch information from the touch sensing device, converts the touch information into touch point coordinates, sends the touch point coordinates to the processor 1080, and can receive and execute commands sent by the processor 1080. In addition, the touch panel 1031 may be implemented by various types such as a resistive type, a capacitive type, an infrared ray, and a surface acoustic wave. The input unit 1030 may include other input devices 1032 in addition to the touch panel 1031. In particular, other input devices 1032 may include, but are not limited to, one or more of a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a track ball, a mouse, a joystick, or the like.

The display unit 1040 may be used to display information input by a user or information provided to the user and various menus of the cellular phone. The Display unit 1040 may include a Display panel 1041, and optionally, the Display panel 1041 may be configured by a Liquid Crystal Display (LCD), an Organic Light-Emitting Diode (OLED), or the like. Further, the touch panel 1031 can cover the display panel 1041, and when the touch panel 1031 detects a touch operation on or near the touch panel 1031, the touch operation is transferred to the processor 1080 to determine the type of the touch event, and then the processor 1080 provides a corresponding visual output on the display panel 1041 according to the type of the touch event. Although in fig. 6, the touch panel 1031 and the display panel 1041 are two independent components to implement the input and output functions of the mobile phone, in some embodiments, the touch panel 1031 and the display panel 1041 may be integrated to implement the input and output functions of the mobile phone.

The handset may also include at least one sensor 1050, such as a light sensor, motion sensor, and other sensors. Specifically, the light sensor may include an ambient light sensor and a proximity sensor, wherein the ambient light sensor may adjust the brightness of the display panel 1041 according to the brightness of ambient light, and the proximity sensor may turn off the display panel 1041 and/or the backlight when the mobile phone moves to the ear. As one of the motion sensors, the accelerometer sensor can detect the magnitude of acceleration in each direction (generally, three axes), can detect the magnitude and direction of gravity when stationary, and can be used for applications of recognizing the gesture of the mobile phone (such as horizontal and vertical screen switching, related games, magnetometer gesture calibration), vibration recognition related functions (such as pedometer and tapping), and the like; as for other sensors such as a gyroscope, a barometer, a hygrometer, a thermometer, and an infrared sensor, which can be configured on the mobile phone, further description is omitted here.

Audio circuitry 1060, speaker 1061, microphone 1062 may provide an audio interface between the user and the handset. The audio circuit 1060 can transmit the electrical signal converted from the received audio data to the speaker 1061, and convert the electrical signal into a sound signal for output by the speaker 1061; on the other hand, the microphone 1062 converts the collected sound signal into an electrical signal, which is received by the audio circuit 1060 and converted into audio data, which is then processed by the audio data output processor 1080 and then sent to, for example, another cellular phone via the RF circuit 1010, or output to the memory 1020 for further processing.

WiFi belongs to short-distance wireless transmission technology, and the mobile phone can help the user to receive and send e-mail, browse web page and access streaming media etc. through WiFi module 1070, it provides wireless broadband internet access for the user. Although fig. 6 shows the WiFi module 1070, it is understood that it does not belong to the essential constitution of the handset, and can be omitted entirely as needed within the scope not changing the essence of the invention.

The processor 1080 is a control center of the mobile phone, connects various parts of the whole mobile phone by using various interfaces and lines, and executes various functions of the mobile phone and processes data by operating or executing software programs and/or modules stored in the memory 1020 and calling data stored in the memory 1020, thereby integrally monitoring the mobile phone. Optionally, processor 1080 may include one or more processing units; preferably, the processor 1080 may integrate an application processor, which handles primarily the operating system, user interfaces, applications, etc., and a modem processor, which handles primarily the wireless communications. It is to be appreciated that the modem processor described above may not be integrated into processor 1080.

The handset also includes a power source 1090 (e.g., a battery) for powering the various components, which may preferably be logically coupled to the processor 1080 via a power management system to manage charging, discharging, and power consumption via the power management system.

Although not shown, the mobile phone may further include a camera, a bluetooth module, etc., which will not be described herein.

In the embodiment of the present application, the processor 1080 included in the terminal further has the following functions:

acquiring a target Elasticissearch cluster and an Elasticissearch table;

acquiring a data query rule;

displaying the data query statement based on the SQL statement in a linkage manner according to the Elasticissearch cluster, the Elasticissearch table and the data query rule;

and executing the data query statement, and displaying the query result in a query result display area in a table form.

The embodiment of the present application further provides a computer-readable storage medium for storing a program code, where the program code is configured to execute any one implementation of the data visualization processing method described in the foregoing embodiments.

In the embodiment of the application, a data visualization processing method, a device, equipment and a storage medium are provided, a data query statement based on an SQL statement is generated and displayed in a linkage mode through a determined target Elasticissearch cluster, an Elasticissearch table and a data query rule, and a format of a returned result is controlled by using a format parameter of the SQL statement, so that the query result is displayed in a table form, the method is clearer and simpler, the problem that the existing operations such as data addition, deletion, modification and search in the Elasticissearch table need to be performed based on a Kibana platform command line mode, the query mode and the display result of the data are both in a json format, and when the query conditions are more, the condition of query condition input error is easy to occur; when the data volume of the query result is large, the cost for comparing the query result is high, and the comparison is difficult to be carried out intuitively and clearly.

It can be clearly understood by those skilled in the art that, for convenience and simplicity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.

The terms "first," "second," "third," "fourth," and the like in the description of the application and the above-described figures, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are, for example, capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

It should be understood that in the present application, "at least one" means one or more, "a plurality" means two or more. "and/or" for describing an association relationship of associated objects, indicating that there may be three relationships, e.g., "a and/or B" may indicate: only A, only B and both A and B are present, wherein A and B may be singular or plural. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "at least one of the following" or similar expressions refer to any combination of these items, including any combination of single item(s) or plural items. For example, at least one (one) of a, b, or c, may represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", wherein a, b and c may be single or plural.

In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.

The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application, which are essential or part of the technical solutions contributing to the prior art, or all or part of the technical solutions, may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a U disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.

The above embodiments are only used to illustrate the technical solutions of the present application, and not to limit the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present application.

Claims

1. A data visualization processing method is characterized by comprising the following steps:

s1, acquiring a target Elasticissearch cluster and an Elasticissearch table;

s2, acquiring a data query rule;

2. The data visualization processing method according to claim 1, wherein the step S4 is followed by further comprising:

3. The data visualization processing method according to claim 1, wherein the step S1 is followed by further comprising:

s8, displaying data operation statements based on SQL statements in a linkage mode according to the Elasticissearch cluster, the Elasticissearch table and the data operation instruction;

and S9, executing the data operation statement to correspondingly operate the target data in the Elasticissearch table according to the data operation instruction.

4. The data visualization processing method according to claim 1, further comprising:

5. The data visualization processing method according to claim 4, wherein the step S11 further comprises:

6. The data visualization processing method according to claim 5, wherein the data sources include mq message queue writes and offline data job writes.

7. A data visualization processing apparatus, comprising:

the second acquisition unit is used for acquiring the data query rule;

8. The data visualization processing device of claim 7, further comprising:

9. A data visualization processing apparatus, the apparatus comprising a processor and a memory:

the processor is configured to execute the data visualization processing method according to any one of claims 1 to 6 according to instructions in the program code.

10. A computer-readable storage medium for storing program code for performing the data visualization processing method of any one of claims 1 to 6.