CN113791961B - Data acquisition method, device, equipment and storage medium based on micro-service - Google Patents
Data acquisition method, device, equipment and storage medium based on micro-service Download PDFInfo
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Abstract
The invention relates to the field of artificial intelligence, and discloses a data acquisition method, device, equipment and storage medium based on micro-service, wherein the method comprises the following steps: calculating and sequencing performance index values by acquiring a data acquisition request and performance parameters of an application server to generate a first sequence; sequencing the application servers according to the acquisition priority in the data acquisition request to generate a second sequence; and determining a data acquisition strategy according to the first sequence and the second sequence, and selecting an optimal acquisition strategy from the data acquisition strategies according to the corresponding acquisition capacity of each application server to acquire the micro-service data. According to the invention, the data acquisition is performed by selecting the optimal acquisition strategy, so that the data acquisition efficiency is improved, and the time cost of data acquisition is saved. In addition, the invention relates to the field of blockchains, and performance parameters can be stored in the blockchain.
Description
Technical Field
The present invention relates to the field of artificial intelligence, and in particular, to a method, an apparatus, a device, and a storage medium for acquiring data based on micro services.
Background
With the advancement of the informatization process today, data is becoming an overwhelming focus of people. How to collect massive data intelligently, efficiently and accurately is a serious problem faced by many enterprises today. As enterprises continue to develop and grow, the business scope involved continues to go deep, and the amount of data generated increases.
The electronic device may collect various data such as environmental parameter values and operational behavior data of a user through hardware and software technologies, and perform certain operations according to the data. However, in the existing data acquisition mode, the intelligent degree of data acquisition by the electronic equipment is low, so that the data acquisition efficiency based on micro-service is low.
Disclosure of Invention
The invention mainly aims to solve the technical problem of low data acquisition efficiency based on micro-service in the prior art.
The first aspect of the present invention provides a data acquisition method based on micro-services, the data acquisition method based on micro-services comprising: receiving a data acquisition request and acquiring performance parameters of each application server, wherein the data acquisition request carries acquisition information; calculating a performance index value corresponding to each application server according to each performance parameter; sequencing the application servers according to the performance index values to generate a first sequence; extracting the acquisition priority in the acquisition information, and sequencing the application servers according to the acquisition priority to generate a second sequence; determining a data acquisition strategy of the micro-service according to the first sequence and the second sequence; the application servers are subjected to priority ranking according to the corresponding acquisition capacity to obtain an acquisition capacity sequence; and selecting an optimal acquisition strategy from the data acquisition strategies according to the acquisition capacity sequence, and acquiring the data of the micro-service according to the optimal acquisition strategy.
Optionally, in a first implementation manner of the first aspect of the present invention, the calculating, according to each performance parameter, a performance index value corresponding to each application server includes: extracting the concurrency number and response time in the performance parameters of each application server, and calculating the throughput of each application server according to the concurrency number and the response time; extracting CPU processing capacity references in the performance parameters of each application server, and calculating the CPU occupation rate of each application server according to the CPU processing capacity references; extracting disk rotation delay time and magnetic head seek time in performance parameters of each application server, and calculating disk storage capacity of each application server according to the disk rotation delay time and the magnetic head seek time; and taking the throughput, the CPU occupation rate and the disk storage capacity as performance index values corresponding to the application servers.
Optionally, in a second implementation manner of the first aspect of the present invention, the sorting the application servers according to the performance index values, generating a first sequence includes: performing weighted operation on the performance index values according to preset weights of the performance indexes to obtain index weight values of the application servers; and sequencing the application servers according to the index weight to obtain a first sequence.
Optionally, in a third implementation manner of the first aspect of the present invention, the determining a data acquisition policy of a micro service according to the first sequence and the second sequence includes: extracting the selected number of application servers in the acquired information; screening each application server according to the first sequence and the second sequence to obtain a first server set; and randomly combining the application servers in the first server set according to the selected quantity to generate a data acquisition strategy of a plurality of micro services.
Optionally, in a fourth implementation manner of the first aspect of the present invention, the screening each application server according to the first sequence and the second sequence to obtain a first server set includes: based on the preset index weight and the weight of the acquisition priority, carrying out weighted operation on the first sequence and the second sequence to obtain a calculation result; sequencing the application servers according to the calculation result to obtain a third sequence; and screening the application servers according to the third sequence, determining the sequence positions of the application servers in the third sequence, selecting the application servers positioned at the first K sequence positions from the third sequence, and generating a first server set, wherein K is a positive integer.
Optionally, in a fifth implementation manner of the first aspect of the present invention, the screening each application server according to the first sequence and the second sequence to obtain a first server set includes: determining sequence positions of the application servers in the first sequence, and selecting the application servers positioned at the first M sequence positions from the first sequence to obtain a second server set; determining sequence positions of the application servers in the second sequence, and selecting the application servers positioned at the first N sequence positions from the second sequence to obtain a third server set; judging whether the second server set and the third server set contain the same application server or not; if yes, extracting the corresponding application server, and generating a first server set.
A second aspect of the present invention proposes a micro-service based data acquisition device, the micro-service based data acquisition device comprising: the system comprises a receiving module, a processing module and a processing module, wherein the receiving module is used for receiving a data acquisition request and acquiring performance parameters of each application server, and the data acquisition request comprises acquisition information; the calculation module is used for calculating the performance index value corresponding to each application server according to each performance parameter; the first ordering module is used for ordering the application servers according to the performance index values to generate a first sequence; the extraction module is used for extracting the acquisition priority in the acquisition information, sequencing all the application servers according to the acquisition priority and generating a second sequence; the determining module is used for determining a data acquisition strategy of the micro-service according to the first sequence and the second sequence; the second sequencing module is used for sequencing the priority of each application server according to the corresponding acquisition capacity to obtain an acquisition capacity sequence; and the selecting module is used for selecting an optimal acquisition strategy from the data acquisition strategies according to the acquisition capacity sequence and carrying out microservice-based data acquisition according to the optimal acquisition strategy.
Optionally, in a first implementation manner of the second aspect of the present invention, the calculating module includes: the throughput computing unit is used for extracting the concurrency number and the response time in the performance parameters of each application server and computing the throughput of each application server according to the concurrency number and the response time; the occupancy rate calculation unit is used for extracting CPU processing capacity references in the performance parameters of each application server and calculating the CPU occupancy rate of each application server according to the CPU processing capacity references; the storage capacity calculating unit is used for extracting the disk rotation delay time and the magnetic head seek time in the performance parameters of each application server, and calculating the disk storage capacity of each application server according to the disk rotation delay time and the magnetic head seek time; and the comprehensive calculation unit is used for taking the throughput, the CPU occupation rate and the disk storage capacity as performance index values corresponding to the application servers.
Optionally, in a second implementation manner of the second aspect of the present invention, the first sorting module includes: the weighting operation unit is used for carrying out weighting operation on the performance index values according to the preset weights of the performance indexes to obtain index weight values of the application servers; and the first sequencing unit is used for sequencing the application servers according to the index weight to obtain a first sequence.
Optionally, in a third implementation manner of the second aspect of the present invention, the determining module includes: the extraction unit is used for extracting the selected number of the application servers in the acquired information; the screening unit is used for screening each application server according to the first sequence and the second sequence to obtain a first server set; and the combination unit is used for randomly combining the application servers in the first server set according to the selected quantity to generate a data acquisition strategy of various micro services.
Optionally, in a fourth implementation manner of the second aspect of the present invention, the screening unit is specifically configured to: based on the preset index weight and the weight of the acquisition priority, carrying out weighted operation on the first sequence and the second sequence to obtain a calculation result; sequencing the application servers according to the calculation result to obtain a third sequence; and screening the application servers according to the third sequence, determining the sequence positions of the application servers in the third sequence, selecting the application servers positioned at the first K sequence positions from the third sequence, and generating a first server set, wherein K is a positive integer.
Optionally, in a fifth implementation manner of the second aspect of the present invention, the screening unit is further specifically configured to: determining sequence positions of the application servers in the first sequence, and selecting the application servers positioned at the first M sequence positions from the first sequence to obtain a second server set; determining sequence positions of the application servers in the second sequence, and selecting the application servers positioned at the first N sequence positions from the second sequence to obtain a third server set; judging whether the second server set and the third server set contain the same application server or not; and if the second server set and the third server set contain the same application server, extracting the corresponding application server to generate a first server set.
A third aspect of the present invention provides a micro-service based data acquisition device comprising: a memory and at least one processor, the memory having a computer program stored therein, the memory and the at least one processor being interconnected by a wire; the at least one processor invokes the computer program in the memory to cause the micro-service based data acquisition device to perform the steps of the micro-service based data acquisition method described above.
A fourth aspect of the present invention provides a computer readable storage medium having stored thereon a computer program which, when run on a computer, causes the computer to perform the steps of the microservice-based data acquisition method described above.
In the technical scheme provided by the invention, the performance parameters of each application server are obtained by receiving a data acquisition request; calculating a performance index value corresponding to each application server according to each performance parameter; sequencing all application servers according to the performance index values to generate a first sequence; extracting acquisition priorities in the acquisition information, and sequencing all application servers according to the acquisition priorities to generate a second sequence; determining a data acquisition strategy of the micro-service according to the first sequence and the second sequence; sequencing all application servers according to the corresponding acquisition capacity to obtain an acquisition capacity sequence; and selecting an optimal acquisition strategy from the data acquisition strategies according to the acquisition capacity sequence, and acquiring the data based on the micro-service according to the optimal acquisition strategy. According to the invention, the data acquisition strategy is determined through the performance parameter values and the acquisition priorities of the application servers, and the optimal acquisition strategy is selected from the data acquisition strategies according to the acquisition capacity of the application servers, so that the data acquisition efficiency is improved, and the time cost of data acquisition is saved.
Drawings
FIG. 1 is a schematic diagram of a first embodiment of a microservice-based data acquisition method according to an embodiment of the present invention;
FIG. 2 is a diagram of a second embodiment of a microservice-based data acquisition method according to an embodiment of the present invention;
FIG. 3 is a diagram of a third embodiment of a microservice-based data acquisition method according to an embodiment of the present invention;
FIG. 4 is a diagram of a fourth embodiment of a microservice-based data acquisition method according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of an embodiment of a microservice-based data acquisition device according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of another embodiment of a microservice-based data acquisition device according to an embodiment of the present invention;
FIG. 7 is a schematic diagram of an embodiment of a microservice-based data acquisition device according to an embodiment of the present invention.
Detailed Description
The embodiment of the invention provides a data acquisition method, a device, equipment and a storage medium based on micro-service, which are used for receiving a data acquisition request and acquiring performance parameters of each application server; calculating a performance index value corresponding to each application server according to each performance parameter; sequencing all application servers according to the performance index values to generate a first sequence; extracting acquisition priorities in the acquisition information, and sequencing all application servers according to the acquisition priorities to generate a second sequence; determining a data acquisition strategy of the micro-service according to the first sequence and the second sequence; sequencing all application servers according to the corresponding acquisition capacity to obtain an acquisition capacity sequence; and selecting an optimal acquisition strategy from the data acquisition strategies according to the acquisition capacity sequence, and acquiring the data based on the micro-service according to the optimal acquisition strategy. According to the embodiment of the invention, the data acquisition strategy is determined through the performance parameter values and the acquisition priorities of the application servers, and the optimal acquisition strategy is selected from the data acquisition strategies according to the acquisition capacity of the application servers, so that the data acquisition efficiency is improved, and the time cost of data acquisition is saved.
The terms "first," "second," "third," "fourth" and the like in the description and in the claims and in the above drawings, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments described herein may be implemented in other sequences than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," or any other variation 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 or inherent to such process, method, article, or apparatus.
For ease of understanding, the following describes embodiments of the present invention, and referring to fig. 1, a first embodiment of a microservice-based data acquisition method in an embodiment of the present invention includes:
101, receiving a data acquisition request and acquiring performance parameters of each application server;
102, calculating a performance index value corresponding to each application server according to each performance parameter;
The server receives a data acquisition request and acquires performance parameters of each application server in a data acquisition process of the executable micro-service, wherein the data acquisition request carries acquisition information, and the performance index value corresponding to each application server is calculated according to the performance parameters of each application server.
In this embodiment, a heartbeat packet is embedded in an acquisition program of a micro-service acquisition center by a server, where the heartbeat packet is used to perform a previewing calculation on performance indexes of each application server, where the performance indexes of the application server include throughput (F) (the number of transactions processed in a fixed time), CPU occupancy Rate (RCL), disk storage capability (IOPS), kernel number, and the like, and the micro-service acquisition center is responsible for data acquisition of the micro-service; the performance parameters include query rate per second (QPS/TPS), concurrency, response time, CPU processing power reference, disk rotation delay time, head seek time, and acquisition capability.
In addition, the embodiment of the invention can acquire and process the data acquisition request and the performance parameters of the application server based on the artificial intelligence technology. Among these, artificial intelligence (Artificial Intelligence, AI) is the theory, method, technique and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend and extend human intelligence, sense the environment, acquire knowledge and use knowledge to obtain optimal results. Artificial intelligence infrastructure technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and other directions.
103, sequencing each application server according to the performance index values to generate a first sequence;
104, extracting the acquisition priority in the acquisition information carried by the data acquisition request, and sequencing all application servers according to the acquisition priority to generate a second sequence;
and sequencing the application servers according to the size of the performance index value corresponding to each application server from large to small to obtain a first sequence. Specifically, the server feeds back the performance index value calculated by each application server to the micro-service acquisition center every 5 minutes, the micro-service acquisition center calculates the performance index value of the received performance parameters of each application server, and the application servers are compared and sequenced according to the performance index values to obtain a first sequence.
And extracting the acquisition priority in the acquisition information carried by the data acquisition request, wherein the acquisition priority is the acquisition priority of the service table corresponding to each application server user-defined by a user when the client transmits the data acquisition request, and sequencing each application server according to the acquisition priority to obtain a second sequence. For example, there is a priority among the service tables of each application server, and the priority A1 of the pro_rcpt temporary table is greater than the priority A2 of the adv_prem pre-table.
105, determining a data acquisition strategy of the micro-service according to the first sequence and the second sequence;
and screening and combining the application servers according to the ordering conditions of the application servers in the first sequence and the second sequence to form a data acquisition strategy of the micro-service. In this embodiment, the sorting conditions of the application servers of the first sequence and the second sequence are matched, an application server which is not only ranked in front of the first sequence but also ranked in front of the second sequence is selected, the selected application servers are arbitrarily combined to form a plurality of application server combination modes, and a data acquisition strategy is generated according to the combination modes of the application servers. The combination mode of the application servers is a data acquisition strategy, and the number of the application servers selected according to the first sequence and the second sequence in the embodiment is not limited and can be limited according to practical situations.
106, extracting the acquisition capacity in the performance parameters of each application server, and sequencing each application server according to the priority of the corresponding acquisition capacity to obtain an acquisition capacity sequence;
107, selecting an optimal acquisition strategy from the data acquisition strategies according to the acquisition capacity sequence, and carrying out data acquisition of the micro-service according to the optimal acquisition strategy.
Extracting the acquisition capacity in the performance parameters of each application server, and sequencing each application server according to the priority of the corresponding acquisition capacity to obtain an acquisition capacity sequence. And selecting an optimal acquisition strategy from the data acquisition strategies according to the acquisition capacity sequence. For example, according to the collection capability sequence, the W1 server has the best collection capability, then the data collection strategy (W1W 2, W1W3, W1W 4) including the combination of W1 servers is selected, then the server W2 including the second best collection capability is selected from the data collection strategies, such combination W1W2 server is the optimal data collection strategy, according to the data collection strategy, the data collection task of the micro service is distributed to the application servers W1 and W2 corresponding to the data collection strategy matched with the application servers W1 and W2, the application servers W1 and W2 trigger the data collection instruction, and the data collection work of the micro service is executed.
In the embodiment of the invention, a first sequence is generated by acquiring a data acquisition request and calculating and sequencing performance index values of performance parameters of an application server; sequencing the application servers according to the acquisition priority in the data acquisition request to generate a second sequence; and determining a data acquisition strategy according to the first sequence and the second sequence, and selecting an optimal acquisition strategy from the data acquisition strategies according to the corresponding acquisition capacity of each application server to acquire the micro-service data. According to the embodiment of the invention, the data acquisition is performed by selecting the optimal acquisition strategy, so that the data acquisition efficiency is improved, and the time cost of data acquisition is saved.
Referring to fig. 2, a second embodiment of a microservice-based data acquisition method according to an embodiment of the present invention includes:
201, receiving a data acquisition request and acquiring performance parameters of each application server;
202, extracting the concurrency number and response time in the performance parameters of each application server, and calculating the throughput of each application server according to the concurrency number and the response time;
the server receives a data acquisition request and acquires performance parameters of each application server in a data acquisition process of the executable micro-service, wherein the data acquisition request carries acquisition information, and the performance index value corresponding to each application server is calculated according to the performance parameters of each application server.
And extracting the concurrency number and the response time in the performance parameters of each application server, and calculating the throughput of the corresponding application server according to the concurrency number and the response time. Among them, the throughput (F) involves several important parameters: query rate per second (QPS/TPS), number of concurrency, response time.
In the present embodiment, throughput refers to the number of requests processed by an application server per unit time. For non-concurrent application servers, throughput is in a strict inverse relationship with response time, where throughput is in fact the inverse of response time. It has been stated previously that for a single user application server, the response time (or application server response time and application delay time) can measure the performance of the application server well, but for concurrent application servers, throughput is often required as a performance indicator. For a multi-user application server, if only one user is using the application server, the average response time of the application server is t, and when n users are using the application server, the response time seen by each user is typically not nxt, but is often much smaller than nxt (of course, may be much larger than nxt, or even much larger, in some special cases). This is because many resources are needed to process each request, and because there are many ways to process each request that are difficult to execute concurrently, this results in that at a particular point in time, the resources are often not too much. That is, there may be many resources that are idle at each point in time when a single request is processed, and when multiple requests are processed, if the resources are reasonably configured, the average response time seen by each user does not increase linearly with increasing number of users. In fact, the average response time of different application servers does not grow much as the number of users increases, which is also a major reason for measuring the performance of concurrent application servers with throughput. In general, throughput is a relatively universal indicator, and if two application servers with different numbers of users and usage patterns of users have substantially identical maximum throughput, it may be determined that the processing capacities of the two application servers are substantially identical. Where peak throughput (MF) is the maximum amount of data processed per second.
The response time refers to the time the application server responds to the request. Intuitively, this index is very consistent with human subjective perception of software performance, as it completely records the time the entire application server processes the request. Since an application server typically provides many functions, the processing logic of different functions varies widely, so the response times of different functions are different, even if the same function has different input data. Therefore, when discussing the response time of an application server, one generally refers to the average time of all functions of the application server or the maximum response time of all functions. Of course, it is often also necessary to discuss the average response time and the maximum response time for each or each group of functions.
The number of concurrent users refers to the number of users normally using the application server function that the application server can simultaneously carry. The number of concurrent users is a more intuitive but also more general performance index than throughput. In fact, the number of concurrent users is a very inaccurate indicator, as different usage patterns of users may result in different users issuing different numbers of requests per unit time. Taking a website application server as an example, it is assumed that a user can only use the website after registration, but the registered user is not using the website at all times, so that only part of the registered users are online at a specific moment, the online user can take a lot of time to read information on the website when browsing the website, and only part of the online users send requests to the application server at the specific moment. Thus, for a web application server we have three statistics about the number of users: the number of registered users, the number of online users and the number of simultaneously requested users. Because the registered user may not log on to the website for a long time, using the registered user number as the performance index may cause a large error. And the number of online users and the number of co-workers requesting users can be used as performance indexes. Compared with the prior art, the method has the advantages that the online user is more visual as the performance index, and the simultaneous request user is more accurate as the performance index.
The query rate per second (QPS) is a measure of how much traffic a particular query server handles in a specified time, and the performance of a machine as a domain name application server on the internet is often measured as a query rate per second.
In this embodiment, the query rate per second (QPS/TPS) is the quotient of the requests (R) received by the application server and the number of transactions (N) per second; throughput is the quotient of the number of concurrency and response time; wherein the concurrency number (current) is the quotient of the request (R) and the number of transactions (N) processed by the application server at the same time; response Times (Times) average response Times; when the network transmission time is n1+n2+n3+n4, the application server processing time is a1+a3, and the database server processing time is: a2, then response time t= (n1+n2+n3+n4)/3+ (a1+a3)/2+a2.
203, extracting a CPU processing capacity reference in the performance parameters of each application server, and calculating the CPU occupation rate of each application server according to the CPU processing capacity reference;
CPU processing capacity reference in the performance parameters of each application server is extracted, and CPU occupancy (RCL) of each application server is calculated according to the CPU processing capacity reference. Before the application server starts, using interrupt to test the processing capability benchmark (CPUPerformanceBase) of the CPU; after the application server enters into operation, an idle task is used for executing an algorithm which is identical to a processing capacity standard of the test CPU, and RTCPUPerperformance is obtained. Periodically calculate the CPU occupancy and clear the value of rtcp UPerperformance, typically once per second:
RCL＝1-(RTCPUPerformance/CPUPerformanceBase)*100％。
204, extracting the disk rotation delay time and the magnetic head seek time in the performance parameters of each application server, and calculating the disk storage capacity of each application server according to the disk rotation delay time and the magnetic head seek time;
and extracting the disk rotation delay time and the magnetic head seek time in the performance parameters of each application server, and calculating the disk storage capacity (IOPS) corresponding to each application server according to the disk rotation delay time and the magnetic head seek time. In the present embodiment, the disk storage capability iops=1000 ms/(disk rotation delay time (ms) +head seek time (ms)).
205, taking throughput, CPU occupation rate and disk storage capacity as performance index values corresponding to the application servers;
and taking the throughput, the CPU occupation rate and the disk storage capacity corresponding to each application server as performance index values corresponding to each application server.
206, carrying out weighting operation on the performance index values according to the preset weights of the performance indexes to obtain index weight values of the application servers;
207, sorting the application servers according to the index weight to obtain a first sequence;
and the server performs weighted operation on the performance index values according to the preset weights of the performance indexes of the application servers, namely, calculates the product of the performance index values and the weights corresponding to the performance indexes, so as to obtain the index weight values of the corresponding application servers. And sequencing the application servers according to the sequence from the large index weight to the small index weight, so as to obtain a first sequence.
In this embodiment, the weight of each performance index of the application server is set according to a preset sorting priority of each performance index, where the sorting priority of each performance index is: CPU occupancy (RCL) > number of concurrency (current) > peak throughput (MF) > response time (Times). The setting of the weight value corresponding to each performance index is not limited in this embodiment.
208, extracting the acquisition priority in the acquisition information carried by the data acquisition request, and sequencing all application servers according to the acquisition priority to generate a second sequence;
and extracting the acquisition priority in the acquisition information carried by the data acquisition request, wherein the acquisition priority is the acquisition priority of the service table corresponding to each application server user-defined by a user when the client transmits the data acquisition request, and sequencing each application server according to the acquisition priority to obtain a second sequence. For example, there is a priority among the service tables of each application server, and the priority A1 of the pro_rcpt temporary table is greater than the priority A2 of the adv_prem pre-table.
209, extracting the selection number of application servers in the acquisition information carried by the data acquisition request;
210, screening each application server according to the first sequence and the second sequence to obtain a first server set;
211, randomly combining the application servers in the first server set according to the selected number to generate a data acquisition strategy of a plurality of micro services;
the servers extract the selection number of application servers selected in the data acquisition process, and screen all application servers according to the sorting condition of each application server in the first sequence and the second sequence, namely screen the application servers with the front sorting according to the first sequence and the second sequence to form a first server set.
Randomly combining the application servers in the first server set according to the selected number, namely calculating possible combination modes of the application servers according to the selected number through a sequencing combination formula, wherein one combination mode is a data acquisition strategy: c (n, m) =p (n, m)/P (m, m) =n-! /m-! (n-m) ++! Wherein n is the number of application servers in the first server set, m is the selected number of application servers, for example, when the number n of application servers in the first server set is 5 and the selected number of application servers is 2, the combination mode is as follows: c (5, 2) =5-! (2 | 3 |) = 5*4/(2*1) =10, i.e. 10 combinations total, 10 data acquisition strategies can be generated.
212, extracting the acquisition capacity in the performance parameters of each application server, and sequencing each application server according to the priority of the corresponding acquisition capacity to obtain an acquisition capacity sequence;
213, selecting an optimal acquisition strategy from the data acquisition strategies according to the acquisition capacity sequence, and carrying out data acquisition of the micro-service according to the optimal acquisition strategy.
In the embodiment of the present invention, the steps 212-213 are identical to the steps 106-107 in the first embodiment of the microservice-based data acquisition method described above, and will not be described herein.
In the embodiment of the invention, the screened application servers are randomly combined according to the selected number of the application servers to obtain various data acquisition strategies, so that the data acquisition can be performed on the micro-services according to the data acquisition strategies, the data acquisition efficiency is improved, the generated data acquisition strategies meet the actual acquisition requirements, the resource waste is avoided, and the cost is saved.
Referring to fig. 3, a third embodiment of a microservice-based data acquisition method according to an embodiment of the present invention includes:
301, receiving a data acquisition request and acquiring performance parameters of each application server;
302, calculating a performance index value corresponding to each application server according to each performance parameter;
303, sorting the application servers according to the performance index values to generate a first sequence;
304, extracting the acquisition priority in the acquisition information, and sequencing all application servers according to the acquisition priority to generate a second sequence;
305, extracting the selection number of application servers in the acquisition information carried by the data acquisition request;
306, carrying out weighted operation on the first sequence and the second sequence based on the preset index weight and the weight of the acquisition priority to obtain a calculation result;
307, sorting the application servers according to the calculation result to obtain a third sequence;
the servers extract the selection number of application servers selected in the data acquisition process, and screen all application servers according to the sorting condition of each application server in the first sequence and the second sequence, namely screen the application servers with the front sorting according to the first sequence and the second sequence to form a first server set. Specifically, the servers perform weighting operation on the first sequence and the second sequence according to the obtained index weight and the weight of the acquisition priority, namely, each application server in the second sequence performs weighting operation according to the weight of the corresponding acquisition priority to obtain the weight of the acquisition priority, and then the weight of the acquisition priority corresponding to each server and the index weight are subjected to arithmetic addition operation to obtain a calculation result. And sequencing all the application servers according to the calculation results, namely sequencing all the application servers according to the sequence from the large to the small of the corresponding calculation results, and generating a third sequence.
308, screening application servers according to the third sequence, determining sequence positions of the application servers in the third sequence, selecting application servers positioned at the first K sequence positions from the third sequence, and generating a first server set;
and screening all the application servers according to the third sequence, determining the sequence positions of the application servers in the third sequence, namely searching the corresponding sequencing positions of the application servers in the third sequence, and selecting the application servers positioned at the first K sequence positions from the third sequence, namely extracting the application servers sequenced at the first K sequence positions in the third sequence to form a first server set, wherein K is a positive integer.
309, randomly combining the application servers in the first server set according to the selected number to generate a data acquisition strategy of multiple micro-services;
randomly combining the application servers in the first server set according to the selected number, namely calculating possible combination modes of the application servers according to the selected number through a sequencing combination formula, wherein one combination mode is a data acquisition strategy: c (n, m) =p (n, m)/P (m, m) =n-! /m-! (n-m) ++! Wherein n is the number of application servers in the first server set, m is the selected number of application servers, for example, when the number n of application servers in the first server set is 5 and the selected number of application servers is 2, the combination mode is as follows: c (5, 2) =5-! (2 | 3 |) = 5*4/(2*1) =10, i.e. 10 combinations total, 10 data acquisition strategies can be generated.
310, extracting the acquisition capacity in the performance parameters of each application server, and sequencing each application server according to the priority of the corresponding acquisition capacity to obtain an acquisition capacity sequence;
311, selecting an optimal acquisition strategy from the data acquisition strategies according to the acquisition capacity sequence, and carrying out data acquisition of the micro-service according to the optimal acquisition strategy.
In the embodiment of the present invention, steps 301 to 304 and steps 310 to 311 are identical to steps 101 to 104 and steps 106 to 107 in the first embodiment of the foregoing microservice-based data acquisition method, and are not described herein.
In the embodiment of the invention, each application server carries out weighting operation and screening according to the corresponding index weight and the weight of the acquisition priority to obtain a first server set, and the first server set is randomly combined to obtain a plurality of data acquisition strategies; before the data acquisition strategy is generated, each application server is screened to obtain the application server with stronger performance for data acquisition, so that the efficiency of subsequent data acquisition is improved.
Referring to fig. 4, a fourth embodiment of a microservice-based data acquisition method according to an embodiment of the present invention includes:
401, receiving a data acquisition request and acquiring performance parameters of each application server;
402, calculating a performance index value corresponding to each application server according to each performance parameter;
403, sorting the application servers according to the performance index values to generate a first sequence;
404, extracting the acquisition priority in the acquisition information, and sequencing all application servers according to the acquisition priority to generate a second sequence;
405, extracting the selection number of application servers in the acquisition information carried by the data acquisition request;
406, determining the sequence positions of the application servers in the first sequence, and selecting application servers positioned at the first M sequence positions from the first sequence to obtain a second server set;
determining sequence positions of application servers in a first sequence, namely searching corresponding sequencing positions of the application servers in the first sequence, selecting application servers positioned at the first M sequence positions from the first sequence, namely extracting application servers sequenced at the first M sequence positions in the first sequence, and forming a second server set, wherein M is a positive integer.
407, determining the sequence positions of the application servers in the second sequence, and selecting the application servers positioned at the first N sequence positions from the second sequence to obtain a third server set;
Determining sequence positions of the application servers in the second sequence, namely searching corresponding sequencing positions of the application servers in the second sequence, selecting application servers positioned at the first N sequence positions from the second sequence, namely extracting the application servers sequenced at the first N sequence positions in the second sequence, and forming a third server set, wherein N is a positive integer.
408, determining whether the second set of servers and the third set of servers contain the same application server;
409, if the second server set and the third server set contain the same application server, extracting the corresponding application server to generate a first server set;
judging whether the second server set and the third server set contain the same application server or not, namely whether at least one application server belongs to the second server set and the third server or not, and if the second server set and the third server set contain the same application server, extracting the application server to form a first server set.
In addition, if the second server set and the third server set do not contain the same application server, each application server is independently formed into the first server set, i.e. one server set corresponds to one application server.
410, randomly combining the application servers in the first server set according to the selected number to generate a data acquisition strategy of a plurality of micro services;
411, extracting the acquisition capacity in the performance parameters of each application server, and sequencing each application server according to the priority of the corresponding acquisition capacity to obtain an acquisition capacity sequence;
412, selecting an optimal acquisition strategy from the data acquisition strategies according to the acquisition capability sequence, and performing micro-service data acquisition according to the optimal acquisition strategy.
In the embodiment of the present invention, steps 401-405 and steps 410-412 are identical to steps 301-305 and steps 309-311 in the first embodiment of the foregoing microservice-based data acquisition method, which are not described herein.
In the embodiment of the invention, according to the sorting of the performance index value and the acquisition priority, selecting an application server with a front sorting position in the two sequences, namely, selecting an application server with stronger performance and higher acquisition priority to obtain a first server set, and carrying out random combination on the first server set to obtain various data acquisition strategies; before the data acquisition strategy is generated, each application server is screened according to the sorting condition of the performance index value and the acquisition priority, so that the application server with higher performance and higher acquisition priority is used for data acquisition, and the efficiency of subsequent data acquisition is improved.
The foregoing describes a micro-service based data acquisition method in the embodiment of the present invention, and the following describes a micro-service based data acquisition device in the embodiment of the present invention, referring to fig. 5, an embodiment of the micro-service based data acquisition device in the embodiment of the present invention includes:
a receiving module 501, configured to receive a data acquisition request, and acquire performance parameters of each application server, where the data acquisition request includes acquisition information;
a calculating module 502, configured to calculate, according to each performance parameter, a performance index value corresponding to each application server;
a first ordering module 503, configured to order each of the application servers according to the performance index values, to generate a first sequence;
the extracting module 504 is configured to extract an acquisition priority in the acquisition information, and sort each application server according to the acquisition priority, so as to generate a second sequence;
a determining module 505, configured to determine a data acquisition policy of the micro service according to the first sequence and the second sequence;
a second sorting module 506, configured to sort the application servers according to the priorities of the corresponding acquisition capabilities, so as to obtain an acquisition capability sequence;
And a selecting module 507, configured to select an optimal acquisition policy from the data acquisition policies according to the acquisition capability sequence, and perform microservice-based data acquisition according to the optimal acquisition policy.
In the embodiment of the invention, a data acquisition device based on micro-service acquires a data acquisition request and performance parameters of an application server to calculate performance index values and sort the performance index values to generate a first sequence; sequencing the application servers according to the acquisition priority in the data acquisition request to generate a second sequence; and determining a data acquisition strategy according to the first sequence and the second sequence, and selecting an optimal acquisition strategy from the data acquisition strategies according to the corresponding acquisition capacity of each application server to acquire the micro-service data. According to the embodiment of the invention, the data acquisition is performed by selecting the optimal acquisition strategy, so that the data acquisition efficiency is improved, and the time cost of data acquisition is saved.
Referring to fig. 6, another embodiment of a micro-service based data acquisition device according to an embodiment of the present invention includes:
a receiving module 501, configured to receive a data acquisition request, and acquire performance parameters of each application server, where the data acquisition request includes acquisition information;
A calculating module 502, configured to calculate, according to each performance parameter, a performance index value corresponding to each application server;
a first ordering module 503, configured to order each of the application servers according to the performance index values, to generate a first sequence;
the extracting module 504 is configured to extract an acquisition priority in the acquisition information, and sort each application server according to the acquisition priority, so as to generate a second sequence;
a determining module 505, configured to determine a data acquisition policy of the micro service according to the first sequence and the second sequence;
a second sorting module 506, configured to sort the application servers according to the priorities of the corresponding acquisition capabilities, so as to obtain an acquisition capability sequence;
and a selecting module 507, configured to select an optimal acquisition policy from the data acquisition policies according to the acquisition capability sequence, and perform microservice-based data acquisition according to the optimal acquisition policy.
Wherein the computing module 502 includes:
a throughput computing unit 5021, configured to extract a concurrency number and a response time in performance parameters of each application server, and compute throughput of each application server according to the concurrency number and the response time;
An occupancy rate calculating unit 5022, configured to extract a CPU processing capability reference in the performance parameters of each application server, and calculate a CPU occupancy rate of each application server according to the CPU processing capability reference;
a storage capacity calculating unit 5023, configured to extract a disk rotation delay time and a head seek time in performance parameters of each application server, and calculate a disk storage capacity of each application server according to the disk rotation delay time and the head seek time;
and a comprehensive calculation unit 5024, configured to use the throughput, the CPU occupancy, and the disk storage capacity as performance index values corresponding to the application servers.
Wherein the first sorting module 503 includes:
a weighting operation unit 5031, configured to perform a weighting operation on the performance index values according to weights preset by the performance indexes, so as to obtain index weights of the application servers;
a first sorting unit 5032, configured to sort each of the application servers according to the index weight, to obtain a first sequence.
Wherein the determining module 505 includes:
an extracting unit 5051, configured to extract a selected number of application servers in the collected information;
A screening unit 5052, configured to screen each application server according to the first sequence and the second sequence, to obtain a first server set;
and a combining unit 5053, configured to randomly combine the application servers in the first server set according to the selected number, to generate a data collection policy of multiple micro services.
Wherein, the screening unit 5052 is specifically configured to:
based on the preset index weight and the weight of the acquisition priority, carrying out weighted operation on the first sequence and the second sequence to obtain a calculation result;
sequencing the application servers according to the calculation result to obtain a third sequence;
and screening the application servers according to the third sequence, determining the sequence positions of the application servers in the third sequence, selecting the application servers positioned at the first K sequence positions from the third sequence, and generating a first server set, wherein K is a positive integer.
Wherein, the screening unit 5052 is further specifically configured to:
determining sequence positions of the application servers in the first sequence, and selecting the application servers positioned at the first M sequence positions from the first sequence to obtain a second server set;
Determining sequence positions of the application servers in the second sequence, and selecting the application servers positioned at the first N sequence positions from the second sequence to obtain a third server set;
judging whether the second server set and the third server set contain the same application server or not;
and if the second server set and the third server set contain the same application server, extracting the corresponding application server to generate a first server set.
According to the embodiment of the invention, each application server is ordered according to the performance index value and the acquisition priority through the micro-service-based data acquisition device, and the application server with the front ordering position in the two sequences is selected, namely, the application server with stronger performance and higher acquisition priority is screened out, a first server set is obtained, and the first server set is randomly combined to obtain various data acquisition strategies; before the data acquisition strategy is generated, each application server is screened according to the sorting condition of the performance index value and the acquisition priority, so that the application server with higher performance and higher acquisition priority is used for data acquisition, and the efficiency of subsequent data acquisition is improved.
Referring to fig. 7, one embodiment of the micro-service based data acquisition device in the embodiment of the present invention is described in detail from the viewpoint of hardware processing.
Fig. 7 is a schematic structural diagram of a micro-service based data acquisition device according to an embodiment of the present invention, where the micro-service based data acquisition device 700 may have a relatively large difference due to different configurations or performances, and may include one or more processors (central processing units, CPU) 710 (e.g., one or more processors) and a memory 720, and one or more storage media 730 (e.g., one or more mass storage devices) storing application programs 733 or data 732. Wherein memory 720 and storage medium 730 may be transitory or persistent. The program stored on the storage medium 730 may include one or more modules (not shown), each of which may include a series of instruction operations on the micro-service based data acquisition device 700. Still further, the processor 710 may be configured to communicate with the storage medium 730 to execute a series of instruction operations in the storage medium 730 on the micro-service based data acquisition device 700.
The microservice-based data acquisition device 700 may also include one or more power supplies 740, one or more wired or wireless network interfaces 750, one or more input/output interfaces 760, and/or one or more operating systems 731, such as Windows Server, mac OS X, unix, linux, freeBSD, and so forth. It will be appreciated by those skilled in the art that the micro-service based data acquisition device structure shown in fig. 7 does not constitute a limitation of the micro-service based data acquisition device, and may include more or fewer components than shown, or may combine certain components, or a different arrangement of components.
The server can be an independent server, and can also be a cloud server for providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDNs), basic cloud computing services such as big data and artificial intelligence platforms, and the like.
The blockchain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and the like. The Blockchain (Blockchain), which is essentially a decentralised database, is a string of data blocks that are generated by cryptographic means in association, each data block containing a batch of information of network transactions for verifying the validity of the information (anti-counterfeiting) and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, an application services layer, and the like.
The present invention also provides a computer readable storage medium, which may be a non-volatile computer readable storage medium, and may also be a volatile computer readable storage medium, in which instructions are stored which, when executed on a computer, cause the computer to perform the steps of the microservice-based data acquisition method.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the apparatus and units described above may refer to corresponding procedures in the foregoing method embodiments, which are not described herein again.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a random access memory (random access memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (9)
1. The data acquisition method based on the micro-service is characterized by comprising the following steps of:
receiving a data acquisition request and acquiring performance parameters of each application server, wherein the data acquisition request carries acquisition information;
calculating a performance index value corresponding to each application server according to each performance parameter;
sequencing the application servers according to the performance index values to generate a first sequence;
extracting the acquisition priority in the acquisition information, sequencing the application servers according to the acquisition priority to generate a second sequence, wherein the acquisition priority is the acquisition priority of a service table corresponding to each application server defined by a user;
Determining a data acquisition strategy of the micro-service according to the first sequence and the second sequence;
the application servers are subjected to priority ranking according to the corresponding acquisition capacity to obtain an acquisition capacity sequence;
selecting an optimal acquisition strategy from the data acquisition strategies according to the acquisition capacity sequence, and acquiring the data of the micro-service according to the optimal acquisition strategy;
the determining a data acquisition strategy of the micro-service according to the first sequence and the second sequence comprises:
extracting the selected number of application servers in the acquired information;
screening each application server according to the first sequence and the second sequence to obtain a first server set;
and randomly combining the application servers in the first server set according to the selected quantity to generate a data acquisition strategy of a plurality of micro services.
2. The microservice-based data collection method of claim 1 wherein the calculating a performance index value corresponding to each of the application servers according to each of the performance parameters comprises:
extracting the concurrency number and response time in the performance parameters of each application server, and calculating the throughput of each application server according to the concurrency number and the response time;
Extracting CPU processing capacity references in the performance parameters of each application server, and calculating the CPU occupation rate of each application server according to the CPU processing capacity references;
extracting disk rotation delay time and magnetic head seek time in performance parameters of each application server, and calculating disk storage capacity of each application server according to the disk rotation delay time and the magnetic head seek time;
and taking the throughput, the CPU occupation rate and the disk storage capacity as performance index values corresponding to the application servers.
3. The microservice-based data collection method of claim 2 wherein the ordering the application servers according to the performance indicator values to generate a first sequence comprises:
performing weighted operation on the performance index values according to preset weights of the performance indexes to obtain index weight values of the application servers;
and sequencing the application servers according to the index weight to obtain a first sequence.
4. The microservice-based data collection method of claim 3 wherein the filtering each of the application servers according to the first sequence and the second sequence to obtain a first server set comprises:
Based on the preset index weight and the weight of the acquisition priority, carrying out weighted operation on the first sequence and the second sequence to obtain a calculation result;
sequencing the application servers according to the calculation result to obtain a third sequence;
and screening the application servers according to the third sequence, determining the sequence positions of the application servers in the third sequence, selecting the application servers positioned at the first K sequence positions from the third sequence, and generating a first server set, wherein K is a positive integer.
5. The microservice-based data collection method of claim 1 wherein the filtering each of the application servers according to the first sequence and the second sequence to obtain a first server set comprises:
determining sequence positions of the application servers in the first sequence, and selecting the application servers positioned at the first M sequence positions from the first sequence to obtain a second server set;
determining sequence positions of the application servers in the second sequence, and selecting the application servers positioned at the first N sequence positions from the second sequence to obtain a third server set;
Judging whether the second server set and the third server set contain the same application server or not;
if yes, extracting the corresponding application server, and generating a first server set.
6. A micro-service based data acquisition device, the micro-service based data acquisition device comprising:
the system comprises a receiving module, a processing module and a processing module, wherein the receiving module is used for receiving a data acquisition request and acquiring performance parameters of each application server, and the data acquisition request comprises acquisition information;
the calculation module is used for calculating the performance index value corresponding to each application server according to each performance parameter;
the first ordering module is used for ordering the application servers according to the performance index values to generate a first sequence;
the extraction module is used for extracting the acquisition priority in the acquisition information, sequencing all application servers according to the acquisition priority, and generating a second sequence, wherein the acquisition priority is the acquisition priority of a service table corresponding to each application server defined by a user;
the determining module is used for determining a data acquisition strategy of the micro-service according to the first sequence and the second sequence;
the second sequencing module is used for sequencing the priority of each application server according to the corresponding acquisition capacity to obtain an acquisition capacity sequence;
The selecting module is used for selecting an optimal acquisition strategy from the data acquisition strategies according to the acquisition capacity sequence and carrying out microservice-based data acquisition according to the optimal acquisition strategy;
the determining module includes: the extraction unit is used for extracting the selected number of the application servers in the acquired information; the screening unit is used for screening each application server according to the first sequence and the second sequence to obtain a first server set; and the combination unit is used for randomly combining the application servers in the first server set according to the selected quantity to generate a data acquisition strategy of various micro services.
7. The microservice-based data acquisition device of claim 6 wherein the computing module comprises:
the throughput computing unit is used for extracting the concurrency number and the response time in the performance parameters of each application server and computing the throughput of each application server according to the concurrency number and the response time;
the occupancy rate calculation unit is used for extracting CPU processing capacity references in the performance parameters of each application server and calculating the CPU occupancy rate of each application server according to the CPU processing capacity references;
A storage capacity calculation unit, configured to extract a disk rotation delay time and a head seek time in performance parameters of each application server, and calculate a disk storage capacity of each application server according to the disk rotation delay time and the head seek time;
and the comprehensive calculation unit is used for taking the throughput, the CPU occupation rate and the disk storage capacity as performance index values corresponding to the application servers.
8. A micro-service based data acquisition device, the micro-service based data acquisition device comprising:
a memory and at least one processor, the memory having a computer program stored therein, the memory and the at least one processor being interconnected by a wire;
the at least one processor invoking the computer program in the memory to cause the micro-service based data acquisition device to perform the steps of the micro-service based data acquisition method of any of claims 1-5.
9. A computer readable storage medium having stored thereon a computer program, characterized in that the computer program when executed by a processor implements the steps of the microservice based data acquisition method of any of claims 1-5.
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