CN110990444A - Data query method and device - Google Patents
Data query method and device Download PDFInfo
- Publication number
- CN110990444A CN110990444A CN201911185127.4A CN201911185127A CN110990444A CN 110990444 A CN110990444 A CN 110990444A CN 201911185127 A CN201911185127 A CN 201911185127A CN 110990444 A CN110990444 A CN 110990444A
- Authority
- CN
- China
- Prior art keywords
- data
- data source
- query
- query request
- source
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2457—Query processing with adaptation to user needs
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/24569—Query processing with adaptation to specific hardware, e.g. adapted for using GPUs or SSDs
Abstract
The embodiment of the invention provides a data query method and a data query device, wherein the method comprises the following steps: calculating data quality parameters of each data source in real time by using a pre-constructed data analysis system, wherein the data quality parameters comprise average return time consumption and data error rate; adjusting the priority weights of the respective data sources based on the calculated data quality parameters; wherein the priority weight is positively correlated with the data quality parameter; preferentially sending a data query request to a data source with high priority weight; and receiving structured data which are fed back by the data source and contain target data, and extracting the target data from the structured data based on data source configuration information. The influence of data source faults on the system is reduced, and the stability of the system is improved.
Description
Technical Field
The invention relates to the technical field of computer application, in particular to a data query method and a data query device.
Background
For credit investigation enterprises, the credit investigation business has very high dependence on the data source, and specifically, the credit investigation enterprises acquire the query request from the user side, send the query request to an external data source, and then receive and feed back the data queried by the data source to the user. When the data source fails, the service thread will be blocked, and a large number of user query requests cannot be processed in real time at the moment, so that service paralysis is caused.
Therefore, the existing data query mode has high dependency on the data source, and a series of problems are caused by the failure of the data source, so that the system is unstable.
Disclosure of Invention
The embodiment of the invention aims to provide a data query method and a data query device so as to reduce the influence of data source faults on a system and improve the stability of the system. The specific technical scheme is as follows:
in order to achieve the above object, an embodiment of the present invention provides a data query method, where the method includes:
calculating data quality parameters of each data source in real time by using a pre-constructed data analysis system, wherein the data quality parameters comprise average return time consumption and data error rate;
adjusting the priority weights of the respective data sources based on the calculated data quality parameters; wherein the priority weight is positively correlated with the data quality parameter;
preferentially sending a data query request to a data source with high priority weight;
and receiving structured data which are fed back by the data source and contain target data, and extracting the target data from the structured data based on data source configuration information.
Optionally, before sending the data query request to the data source, the method further includes:
acquiring a query field input by a user;
checking the validity of the query field;
when the query fields are legal input, combining the query fields into a data query request conforming to a data source input format;
after the extracting the target data from the structured data, the method further comprises:
feeding back the target data to the user.
Optionally, after the query fields are combined into a data query request conforming to the data source input format, the method further includes:
storing the data query requests in a pre-constructed cache queue according to a time sequence;
the step of preferentially sending the data query request to the data source with high priority weight comprises the following steps:
taking out a first data query request from the cache queue, and determining a target data source aiming at the first data query request, wherein the target data source is the data source with the highest priority weight in the data sources containing the target data of the first data query request; and sending the first data query request to the target data source, and returning to the step of taking out the first data query request from the cache queue.
Optionally, the control platform comprises a plurality of distributed service nodes,
before the combining the query fields into a data query request conforming to a data source input format, further comprising:
and sending the query field to the distributed service node.
In order to achieve the above object, an embodiment of the present invention further provides a data query apparatus, where the apparatus includes:
the analysis module is used for calculating the data quality parameters of each data source in real time by using a pre-constructed data analysis system, wherein the data quality parameters comprise average return time consumption and data error rate;
an adjustment module for adjusting the priority weight of each data source based on the calculated data quality parameter; wherein the priority weight is positively correlated with the data quality parameter;
the request module is used for preferentially sending a data query request to a data source with high priority weight;
and the receiving module is used for receiving the structured data which are fed back by the data source and contain the target data, and extracting the target data from the structured data based on the data source configuration information.
Optionally, the apparatus further comprises:
the acquisition module is used for acquiring a query field input by a user;
the checking module is used for checking the legality of the query field;
the combination module is used for combining the query fields into a data query request conforming to a data source input format when the query fields are legally input;
and the feedback module is used for feeding the target data back to the user.
The device further comprises:
the storage module is used for storing the data query requests in a pre-constructed cache queue according to a time sequence;
the request module is specifically configured to take out a first data query request from the cache queue, and determine, for the first data query request, a target data source, where the target data source is a data source with a highest priority weight among data sources including target data of the first data query request; and sending the first data query request to the target data source, and returning to the step of taking out the first data query request from the cache queue.
Optionally, the control platform includes a plurality of distributed service nodes, and the apparatus further includes: a sending module for sending the data to the receiving module,
and the sending module is used for sending the query field to the distributed service node.
In order to achieve the above object, an embodiment of the present invention further provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, where the processor, the communication interface, and the memory complete communication with each other through the communication bus;
a memory for storing a computer program;
and the processor is used for realizing any method step when executing the program stored in the memory.
To achieve the above object, an embodiment of the present invention further provides a computer-readable storage medium, in which a computer program is stored, and the computer program, when executed by a processor, implements any of the above method steps.
Therefore, by applying the data query method and the data query device provided by the embodiment of the invention, the data quality parameters of each data source are calculated in real time by utilizing a pre-constructed data analysis system, wherein the data quality parameters comprise average return time and data error rate; adjusting the priority weights of the respective data sources based on the calculated data quality parameters; wherein the priority weight is positively correlated with the data quality parameter; preferentially sending a data query request to a data source with high priority weight; and receiving structured data which are fed back by the data source and contain target data, and extracting the target data from the structured data based on data source configuration information. The method can count the quality of the data source, preferentially sends the data request to the data source with higher quality, thereby avoiding the problem of unstable system caused by the fault of part of the data source.
Of course, it is not necessary for any product or method of practicing the invention to achieve all of the above-described advantages at the same time.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a data query method according to an embodiment of the present invention;
FIG. 2 is a diagram of a data query system according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a data query apparatus according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In order to solve the technical problem that the existing data query mode is high in dependence on a data source and causes system instability, the embodiment of the invention provides a data query method and a data query device.
The method can be applied to a control platform of a credit investigation enterprise, wherein the control platform can be built on the basis of third-party technologies such as kubernets, Spring clouds and spark.
The present invention will be described below with reference to specific examples.
Referring to fig. 1, fig. 1 is a schematic flow chart of a data query method according to an embodiment of the present invention, which may include the following steps:
s101: and calculating data quality parameters of each data source in real time by using a pre-constructed data analysis system, wherein the data quality parameters comprise average return time consumption and data error rate.
For credit-assessing enterprises, the data source is usually an external database, such as a central bank credit database, a database of a payment company, etc.
In the embodiment of the invention, a big data analysis frame can be set up in advance, data fed back by each data source is collected and analyzed, and data quality parameters of the data source can be obtained, wherein the data quality parameters can include average return time and data error rate.
In the embodiment of the invention, the weighting operation can be carried out based on the average return time and the data error rate to obtain the parameter value representing the data quality. The less the average return time is, the smaller the data error rate, and the higher the data quality of the data source.
As one example, a distributed computing system spark may be constructed to collect the average return time consumption and data error rate of the data source. Among them, spark is a fast, general-purpose computing engine designed specifically for large-scale data processing.
Other computing systems may also be used in embodiments of the invention, which are not limited in this regard.
S102: adjusting a priority weight of each data source based on the calculated data quality parameter, wherein the priority weight is positively correlated with the data quality parameter.
In the embodiment of the present invention, the time interval for adjusting the priority weight may be preset, that is, the data quality parameters of each data source are calculated at intervals, and the priority weight is adjusted based on the data quality parameters.
The priority weight and the data quality parameter are positively correlated, that is, the better the data quality of the data source, the higher the priority weight of the data source.
S103: and preferentially sending the data query request to the data source with high priority weight.
In the embodiment of the invention, the priority weight is set for the data source according to the data quality parameter of the data source, and the higher the priority weight is, the higher the data quality of the data source is, the shorter the average duration of the data fed back by the data source is, and the lower the error rate of the fed back data is. Therefore, the data query request can be preferentially sent to the data source with high priority weight.
In addition, if the data quality parameters of the data source show that the average duration and the error rate of the feedback data of the data source both exceed the preset threshold values, the connection with the data source can be closed, so that the influence of the fault of the data source on the system is reduced as much as possible.
S104: and receiving structured data which are fed back by the data source and contain target data, and extracting the target data from the structured data based on data source configuration information.
In the credit investigation field, data stored in a data source is usually structured data with a high redundancy degree, and after the structured data is fed back to a credit investigation platform, the credit investigation platform is required to extract target data required by a user from the structured data.
For example, for enterprise credit investigation, the data source stores various information related to the enterprise, including the name of the enterprise, the registration information of the enterprise, the address of the enterprise, the credit investigation situation of the enterprise, and so on, and a large part of the data is not needed by the user. For example, a user typically only desires to query a business's credit assessment report and is not interested in other information about the business.
In the embodiment of the invention, in order to extract the target data required by the user from the data fed back by the data source, the data source configuration information may be preset, for example, only the relevant data concerned by the user is configured, and further, after receiving the structured data with higher redundancy degree fed back by the data source, the configured relevant data may be automatically extracted therefrom, and the data is fed back to the user as the target data. Compared with a method without data source configuration in the related technology, the method can reduce the writing of repeated codes and further reduce the development cost.
Therefore, by applying the data query method provided by the embodiment of the invention, the data quality parameters of each data source are calculated in real time by using the pre-constructed data analysis system, wherein the data quality parameters comprise average return time consumption and data error rate; adjusting the priority weights of the respective data sources based on the calculated data quality parameters; wherein the priority weight is positively correlated with the data quality parameter; preferentially sending a data query request to a data source with high priority weight; and receiving structured data which are fed back by the data source and contain target data, and extracting the target data from the structured data based on data source configuration information. The method can count the quality of the data source, preferentially sends the data request to the data source with higher quality, thereby avoiding the problem of unstable system caused by the fault of part of the data source.
In an embodiment of the present invention, before preferentially sending the data query request to the data source with the high priority weight, the method may further include:
acquiring a query field input by a user;
checking the validity of the query field;
and when the query fields are legal input, combining the query fields into a data query request conforming to a data source input format.
Specifically, the user can log in an official website of the credit investigation platform, and input a query field, such as identity information of an individual to be queried, registration information of an enterprise, and the like, in an interface provided by the credit investigation platform.
The credit investigation platform can check the user account and the validity of the input inquiry field, for example, verify whether the user account has corresponding authority, whether the input field of the user meets the specification, and the like.
If the data source input rule is legal, combining the query fields input by the user into a data query request according to a preset rule, wherein the combined data query request conforms to the data source input rule. Further, the above step S103 may be performed: and preferentially sending the data query request to the data source with high priority weight.
Accordingly, after the target data is extracted from the structured data returned from the data source, the target data can be fed back to the user.
In an embodiment of the invention, since a large number of user requests may be received in a short time, in order to avoid that a large number of requests are simultaneously flooded to cause an excessive pressure on a server, a cache system may be set up in advance. As one example, cache systems may be built using ehcache and redis technology.
Specifically, after the query fields are combined into data query requests conforming to the data source input format, the data query requests may be stored in a pre-constructed cache queue according to a time sequence.
In the embodiment of the present invention, the step of preferentially sending the data query request to the data source with high priority weight may specifically include:
a first data query request is fetched from the cache queue,
determining a target data source aiming at the first data query request, wherein the target data source is the data source with the highest priority weight in the data sources containing the target data of the first data query request;
and sending the first data query request to the target data source, and returning to the step of taking out the first data query request from the cache queue.
Specifically, in the process of sending the data query request to the data source, the data query request may be sequentially read from the cache queue, and the currently read data query request is defined as the first data query request. For the first data query request, a target data source may be determined, where the target data source includes data requested to be queried by the first data query request, and the target data source is a data source with the highest priority weight among data sources including target data of the first data query request.
For example, if the first data query request is a credit query request for an enterprise, and the data sources of the enterprise credit query request include the data source a, the data source b and the data source c, and the priority weights of the three data sources are a > b > c, the first data query request may be sent to the data source a by using the data source a as the target data source.
Of course, for the first data query request, a plurality of target data sources may also be determined, and each of the target data sources includes corresponding target data and has a higher priority weight. The embodiment of the present invention is not limited thereto.
In the embodiment of the present invention, after the current first data query request is sent to the target data source, the step of taking out the first data query request from the cache queue may be returned, that is, the next data query request is taken out, and the query process is repeated.
Therefore, in the embodiment of the invention, the cache queue is preset, and the data query requests are read from the cache queue in sequence, so that the system pressure caused by the flooding of a large number of query requests in a short time is relieved, and the system stability is improved.
In an embodiment of the present invention, in order to further improve scalability of the system, a containerization technology may be adopted, a plurality of distributed service nodes are set up in advance, and each service node may independently implement the data query process. As one example, distributed service nodes may be built using SpringCloud, kubernets technology.
In an embodiment of the present invention, after verifying that the query field input by the user is legal, the query field may be sent to the distributed service node, and the distributed service node may complete the subsequent query process.
In an embodiment of the present invention, the number of distributed service nodes can be increased or decreased according to actual requirements, and thus, the scalability of the system is improved by using a distributed node manner, and the stability of the system is further improved.
For ease of understanding, the data query method provided by the embodiment of the present invention is further described below with reference to the schematic diagram of the data query system shown in fig. 2.
As shown in fig. 2, after a query field input by a user is checked, a control center selects a service node, and sends the query field to the service node, where each service node is used as a pod in the kubernets container technology, and fig. 2 takes three service nodes as an example, each of the three service nodes includes a combination module, a storage module, a request module, and a feedback module, and is used to execute relevant steps of the data query method provided by the embodiment of the present invention. On the data source side, the analysis module analyzes data quality parameters of the data source in real time, the adjustment module adjusts the priority weight of the data source in real time, and then each service node can preferentially send a query request to the data source with high priority weight.
Based on the same inventive concept, according to the above data query method, an embodiment of the present invention further provides a data query apparatus, referring to fig. 3, which may include the following modules:
the analysis module 301 is configured to calculate, in real time, data quality parameters of each data source by using a pre-constructed data analysis system, where the data quality parameters include average return time and data error rate;
an adjusting module 302 for adjusting the priority weight of each data source based on the calculated data quality parameter; wherein the priority weight is positively correlated with the data quality parameter;
a request module 303, configured to preferentially send a data query request to a data source with a high priority weight;
a receiving module 304, configured to receive structured data including target data fed back by the data source, and extract the target data from the structured data based on data source configuration information.
In an embodiment of the present invention, on the basis of the apparatus shown in fig. 3, the apparatus may further include:
the acquisition module is used for acquiring a query field input by a user;
the checking module is used for checking the legality of the query field;
the combination module is used for combining the query fields into a data query request conforming to a data source input format when the query fields are legally input;
and the feedback module is used for feeding the target data back to the user.
In an embodiment of the present invention, on the basis of the apparatus shown in fig. 3, the apparatus may further include:
the storage module is used for storing the data query requests in a pre-constructed cache queue according to a time sequence;
the request module is specifically configured to take out a first data query request from the cache queue, and determine, for the first data query request, a target data source, where the target data source is a data source with a highest priority weight among data sources including target data of the first data query request; and sending the first data query request to the target data source, and returning to the step of taking out the first data query request from the cache queue.
In an embodiment of the present invention, the control platform includes a plurality of distributed service nodes, and on the basis of the apparatus shown in fig. 3, the apparatus may further include: a sending module, configured to send the query field to the distributed service node.
Therefore, by applying the data query device provided by the embodiment of the invention, the data quality parameters of each data source are calculated in real time by using the pre-constructed data analysis system, wherein the data quality parameters comprise average return time consumption and data error rate; adjusting the priority weights of the respective data sources based on the calculated data quality parameters; wherein the priority weight is positively correlated with the data quality parameter; preferentially sending a data query request to a data source with high priority weight; and receiving structured data which are fed back by the data source and contain target data, and extracting the target data from the structured data based on data source configuration information. The method can count the quality of the data source, preferentially sends the data request to the data source with higher quality, thereby avoiding the problem of unstable system caused by the fault of part of the data source.
Based on the same inventive concept, according to the above data query method embodiment, an electronic device is further provided in the embodiments of the present invention, as shown in fig. 4, and includes a processor 401, a communication interface 402, a memory 403, and a communication bus 404, where the processor 401, the communication interface 402, and the memory 403 complete mutual communication through the communication bus 404,
a memory 403 for storing a computer program;
the processor 401, when executing the program stored in the memory 403, implements the following steps:
calculating data quality parameters of each data source in real time by using a pre-constructed data analysis system, wherein the data quality parameters comprise average return time consumption and data error rate;
adjusting the priority weights of the respective data sources based on the calculated data quality parameters; wherein the priority weight is positively correlated with the data quality parameter;
preferentially sending a data query request to a data source with high priority weight;
and receiving structured data which are fed back by the data source and contain target data, and extracting the target data from the structured data based on data source configuration information.
The communication bus mentioned in the electronic device may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The communication bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown, but this does not mean that there is only one bus or one type of bus.
The communication interface is used for communication between the electronic equipment and other equipment.
The Memory may include a Random Access Memory (RAM) or a Non-Volatile Memory (NVM), such as at least one disk Memory. Optionally, the memory may also be at least one memory device located remotely from the processor.
The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component.
Therefore, by applying the electronic equipment provided by the embodiment of the invention, the data quality parameters of each data source are calculated in real time by using the pre-constructed data analysis system, wherein the data quality parameters comprise average return time consumption and data error rate; adjusting the priority weights of the respective data sources based on the calculated data quality parameters; wherein the priority weight is positively correlated with the data quality parameter; preferentially sending a data query request to a data source with high priority weight; and receiving structured data which are fed back by the data source and contain target data, and extracting the target data from the structured data based on data source configuration information. The method can count the quality of the data source, preferentially sends the data request to the data source with higher quality, thereby avoiding the problem of unstable system caused by the fault of part of the data source.
Based on the same inventive concept, according to the above-mentioned data query method embodiment, in a further embodiment provided by the present invention, there is further provided a computer readable storage medium, in which a computer program is stored, and the computer program realizes any of the above-mentioned data query method steps when being executed by a processor.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
All the embodiments in the present specification are described in a related manner, and the same and similar parts among the embodiments may be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, as for the data query apparatus embodiment, the electronic device embodiment and the computer storage medium embodiment, since they are substantially similar to the data query method embodiment, the description is relatively simple, and the relevant points can be referred to the partial description of the data query method embodiment.
The above description is only for the preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention shall fall within the protection scope of the present invention.
Claims (10)
1. A data query method is applied to a control platform, and comprises the following steps:
calculating data quality parameters of each data source in real time by using a pre-constructed data analysis system, wherein the data quality parameters comprise average return time consumption and data error rate;
adjusting the priority weights of the respective data sources based on the calculated data quality parameters; wherein the priority weight is positively correlated with the data quality parameter;
preferentially sending a data query request to a data source with high priority weight;
and receiving structured data which are fed back by the data source and contain target data, and extracting the target data from the structured data based on data source configuration information.
2. The method of claim 1, prior to sending a data query request to the data source, further comprising:
acquiring a query field input by a user;
checking the validity of the query field;
when the query fields are legal input, combining the query fields into a data query request conforming to a data source input format;
after the extracting the target data from the structured data, the method further comprises:
feeding back the target data to the user.
3. The method of claim 2, after assembling the query fields into a data query request conforming to a data source input format, further comprising:
storing the data query requests in a pre-constructed cache queue according to a time sequence;
the step of preferentially sending the data query request to the data source with high priority weight comprises the following steps:
taking out a first data query request from the cache queue, and determining a target data source aiming at the first data query request, wherein the target data source is the data source with the highest priority weight in the data sources containing the target data of the first data query request; and sending the first data query request to the target data source, and returning to the step of taking out the first data query request from the cache queue.
4. The method of claim 2, wherein the control platform comprises a plurality of distributed service nodes,
before the combining the query fields into a data query request conforming to a data source input format, further comprising:
and sending the query field to the distributed service node.
5. A data query apparatus, characterized in that the apparatus comprises:
the analysis module is used for calculating the data quality parameters of each data source in real time by using a pre-constructed data analysis system, wherein the data quality parameters comprise average return time consumption and data error rate;
an adjustment module for adjusting the priority weight of each data source based on the calculated data quality parameter; wherein the priority weight is positively correlated with the data quality parameter;
the request module is used for preferentially sending a data query request to a data source with high priority weight;
and the receiving module is used for receiving the structured data which are fed back by the data source and contain the target data, and extracting the target data from the structured data based on the data source configuration information.
6. The apparatus of claim 5, further comprising:
the acquisition module is used for acquiring a query field input by a user;
the checking module is used for checking the legality of the query field;
the combination module is used for combining the query fields into a data query request conforming to a data source input format when the query fields are legally input;
and the feedback module is used for feeding the target data back to the user.
7. The method of claim 6, wherein the apparatus further comprises:
the storage module is used for storing the data query requests in a pre-constructed cache queue according to a time sequence;
the request module is specifically configured to take out a first data query request from the cache queue, and determine, for the first data query request, a target data source, where the target data source is a data source with a highest priority weight among data sources including target data of the first data query request; and sending the first data query request to the target data source, and returning to the step of taking out the first data query request from the cache queue.
8. The apparatus of claim 6, wherein the control platform comprises a plurality of distributed service nodes, the apparatus further comprising: a sending module for sending the data to the receiving module,
and the sending module is used for sending the query field to the distributed service node.
9. An electronic device is characterized by comprising a processor, a communication interface, a memory and a communication bus, wherein the processor and the communication interface are used for realizing mutual communication by the memory through the communication bus;
a memory for storing a computer program;
a processor for implementing the method steps of any of claims 1 to 4 when executing a program stored in the memory.
10. A computer-readable storage medium, characterized in that a computer program is stored in the computer-readable storage medium, which computer program, when being executed by a processor, carries out the method steps of any one of claims 1 to 4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911185127.4A CN110990444A (en) | 2019-11-27 | 2019-11-27 | Data query method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911185127.4A CN110990444A (en) | 2019-11-27 | 2019-11-27 | Data query method and device |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110990444A true CN110990444A (en) | 2020-04-10 |
Family
ID=70087515
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911185127.4A Pending CN110990444A (en) | 2019-11-27 | 2019-11-27 | Data query method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110990444A (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105190532A (en) * | 2013-03-13 | 2015-12-23 | 高通股份有限公司 | Hierarchical orchestration of data providers for the retrieval of point of interest metadata |
| CN107391739A (en) * | 2017-08-07 | 2017-11-24 | 北京奇艺世纪科技有限公司 | A kind of query statement generation method, device and electronic equipment |
| CN108900434A (en) * | 2018-07-18 | 2018-11-27 | 北京木瓜移动科技股份有限公司 | Data collection distribution method and device |
| CN110290217A (en) * | 2019-07-01 | 2019-09-27 | 腾讯科技（深圳）有限公司 | Processing method and processing device, storage medium and the electronic device of request of data |
| CN110377421A (en) * | 2019-06-26 | 2019-10-25 | 联动优势科技有限公司 | Data source access method and device based on single data items label |
-
2019
- 2019-11-27 CN CN201911185127.4A patent/CN110990444A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105190532A (en) * | 2013-03-13 | 2015-12-23 | 高通股份有限公司 | Hierarchical orchestration of data providers for the retrieval of point of interest metadata |
| CN107391739A (en) * | 2017-08-07 | 2017-11-24 | 北京奇艺世纪科技有限公司 | A kind of query statement generation method, device and electronic equipment |
| CN108900434A (en) * | 2018-07-18 | 2018-11-27 | 北京木瓜移动科技股份有限公司 | Data collection distribution method and device |
| CN110377421A (en) * | 2019-06-26 | 2019-10-25 | 联动优势科技有限公司 | Data source access method and device based on single data items label |
| CN110290217A (en) * | 2019-07-01 | 2019-09-27 | 腾讯科技（深圳）有限公司 | Processing method and processing device, storage medium and the electronic device of request of data |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111177005B (en) | Service application testing method, device, server and storage medium | |
| US20190394023A1 (en) | Systems and methods for controlled random endorsement in a blockchain network | |
| AU2010314292C1 (en) | Method and system for adapting a session timeout period | |
| CN110362473B (en) | Test environment optimization method and device, storage medium and terminal | |
| CN107784205B (en) | User product auditing method, device, server and storage medium | |
| CN108416665B (en) | Data interaction method and device, computer equipment and storage medium | |
| CN110909013B (en) | Service list generation method, device, equipment and computer readable storage medium | |
| US20200394448A1 (en) | Methods for more effectively moderating one or more images and devices thereof | |
| CN110471842A (en) | A kind of test method, device and computer readable storage medium | |
| CN110750443A (en) | Webpage testing method and device, computer equipment and storage medium | |
| CN113886590A (en) | Data summarizing method and device, computer equipment and storage medium | |
| US11030024B2 (en) | Assigning a severity level to a computing service using tenant telemetry data | |
| CN110990444A (en) | Data query method and device | |
| CN106294090A (en) | A kind of data statistical approach and device | |
| CN110543509A (en) | Monitoring system, method and device for user access data and electronic equipment | |
| CN116167860A (en) | Account checking processing method and device, electronic equipment and storage medium | |
| CN116185797A (en) | Method, device and storage medium for predicting server resource saturation | |
| CN112070470B (en) | Annual report reporting method and device, electronic equipment and storage medium | |
| CN115204733A (en) | Data auditing method and device, electronic equipment and storage medium | |
| US20170124611A1 (en) | Methods for Monitoring and Valuating Transactions for Document Processes | |
| CN101308457B (en) | User feedback reliability guaranteeing method | |
| CN108306859B (en) | Method, apparatus and computer-readable storage medium for limiting server access volume | |
| CN111582832A (en) | Fair competition examination method and system based on block chain | |
| CN114428723A (en) | Test system, system test method, related device and storage medium | |
| CN112435000B (en) | Pending order notification method and device, computer equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |