CN113791961A - Data acquisition method, device and equipment based on microservice and storage medium - Google Patents
Data acquisition method, device and equipment based on microservice and storage medium Download PDFInfo
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Abstract
The invention relates to the field of artificial intelligence, and discloses a data acquisition method, a device, equipment and a storage medium based on microservice, wherein the method comprises the following steps: calculating performance index values by acquiring a data acquisition request and performance parameters of an application server, and sequencing 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 data acquisition strategies according to the first sequence and the second sequence, and selecting an optimal acquisition strategy from the data acquisition strategies according to the acquisition capacity corresponding to each application server to acquire data of the microservice. According to the invention, the data acquisition is carried out 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 also relates to the field of block chains, in which performance parameters can be stored.
Description
Technical Field
The invention relates to the field of artificial intelligence, in particular to a data acquisition method, a data acquisition device, data acquisition equipment and a storage medium based on micro-services.
Background
With the progress of information-based process, data is the focus of people. How to intelligently, efficiently and accurately collect massive data is a serious problem faced by many enterprises nowadays. With the continuous development and growth of enterprises, the related business range is also continuous and deep, and the generated data volume is increased.
The electronic device can collect various data such as environmental parameter values and operation 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 intelligence degree of the electronic device for data acquisition is low, which results in low data acquisition efficiency based on micro-service.
Disclosure of Invention
The invention mainly aims to solve the technical problem of low data acquisition efficiency based on microservice in the prior art.
The invention provides a data acquisition method based on micro-service in a first aspect, which comprises the following steps: 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 value 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; carrying out priority ordering on the 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 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 a performance index value corresponding to each of the application servers according to each of the performance parameters includes: extracting 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 reference in the performance parameters of each application server, and calculating the CPU occupancy rate of each application server according to the CPU processing capacity reference; extracting disk rotation delay time and 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 taking the throughput, the CPU occupation ratio 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 value, and generating a first sequence includes: performing weighted operation on the performance index values according to preset weights of the performance indexes to obtain index weights 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 microservice according to the first sequence and the second sequence includes: extracting the selected number of the 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 number to generate a data acquisition strategy of various micro services.
Optionally, in a fourth implementation manner of the first aspect of the present invention, the filtering, performed on each of the application servers according to the first sequence and the second sequence, to obtain a first server set includes: performing weighting operation on the first sequence and the second sequence based on the preset index weight and the preset weight of the acquisition priority 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 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 filtering, performed on each of the application servers 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 the sequence position of each application server 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 so, extracting the corresponding application server to generate a first server set.
The second aspect of the present invention provides a data acquisition device based on micro-services, including: 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 a performance index value corresponding to each application server according to each performance parameter; the first sequencing module is used for sequencing the application servers according to the performance index value 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 carrying out priority sequencing on the application servers according to the corresponding acquisition capacity to obtain an acquisition capacity sequence; and the selection module is used for selecting an optimal acquisition strategy from the data acquisition strategies according to the acquisition capacity sequence and acquiring data based on micro-service according to the optimal acquisition strategy.
Optionally, in a first implementation manner of the second aspect of the present invention, the calculation module includes: the throughput calculation unit is used for extracting the concurrency number and the 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; an occupancy calculation unit, configured to extract a CPU processing capability standard from the performance parameters of each of the application servers, and calculate a CPU occupancy of each of the application servers according to the CPU processing capability standard; the storage capacity calculation 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 ratio 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 ordering module includes: the weighted operation unit is used for carrying out weighted operation on the performance index value according to the preset weight of each performance index to obtain the index weight of each application server; 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 the application servers 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 number to generate a data acquisition strategy of multiple micro services.
Optionally, in a fourth implementation manner of the second aspect of the present invention, the screening unit is specifically configured to: performing weighting operation on the first sequence and the second sequence based on the preset index weight and the preset weight of the acquisition priority 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 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 the sequence position of each application server 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 microservice-based data acquisition device, comprising: a memory having a computer program stored therein and at least one processor, the memory and the at least one processor interconnected by a line; the at least one processor invokes the computer program in the memory to cause the microservice-based data acquisition device to perform the steps of the microservice-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 above-mentioned microservice-based data acquisition method.
In the technical scheme provided by the invention, the performance parameters of each application server are obtained by receiving a data acquisition request; calculating performance index values corresponding to the application servers according to the performance parameters; sequencing all the application servers according to the performance index value 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 microservice according to the first sequence and the second sequence; the application servers are subjected to priority ordering 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 data based on the micro-service according to the optimal acquisition strategy. According to the invention, the data acquisition strategy is determined according to the performance parameter values and the acquisition priorities of the application servers, and the optimal acquisition strategy is selected for data acquisition according to the acquisition capacity of each application server, so that the data acquisition efficiency is improved, and the time cost of data acquisition is saved.
Drawings
FIG. 1 is a diagram of a first embodiment of a microservice-based data collection method in accordance with an embodiment of the present invention;
FIG. 2 is a diagram of a data collection method based on microservice in a second embodiment of the present invention;
FIG. 3 is a diagram of a data collection method based on microservice in a third embodiment of the present invention;
FIG. 4 is a diagram of a fourth embodiment of a microservice-based data collection method in an embodiment of the present invention;
FIG. 5 is a schematic diagram of an embodiment of a microservice-based data collection apparatus in an embodiment of the present invention;
FIG. 6 is a schematic diagram of another embodiment of a microservice-based data collection apparatus in an embodiment of the present invention;
FIG. 7 is a diagram of an embodiment of a microservice-based data collection device in 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 performance index values corresponding to the application servers according to the performance parameters; sequencing all the application servers according to the performance index value 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 microservice according to the first sequence and the second sequence; the application servers are subjected to priority ordering 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 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 according to the performance parameter values and the acquisition priorities of the application servers, and the optimal acquisition strategy is selected for data acquisition according to the acquisition capacity of each application server, 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, as well as in the drawings, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It will be appreciated that the data so used may be interchanged under appropriate circumstances such that the embodiments described herein may be practiced otherwise than as specifically illustrated or described herein. Furthermore, the terms "comprises," "comprising," or "having," and any variations thereof, are intended to cover non-exclusive inclusions, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
For the sake of understanding, the following describes specific contents of an embodiment of the present invention, and referring to fig. 1, a first embodiment of a data collection method based on microservice 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 method comprises the steps that a server receives a data acquisition request and obtains performance parameters of each application server capable of running a data acquisition process of the micro-service, wherein the data acquisition request carries acquisition information, and performance index values corresponding to the application servers are calculated according to the performance parameters of the application servers.
In this embodiment, a heartbeat packet is embedded in an acquisition program of a microservice acquisition center by a server, and the heartbeat packet is used for performing preview calculation on performance indexes of each application server, where the performance indexes of the application servers include throughput (F) (the number of transactions processed and completed within a fixed time), CPU occupancy Rate (RCL), disk storage capacity (IOPS), the number of cores, and the like, and the microservice acquisition center is responsible for data acquisition of microservices; the performance parameters include query rate per second (QPS/TPS), number of concurrencies, response time, CPU throughput benchmarks, 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 them, Artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result. The artificial intelligence infrastructure generally includes 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 the like.
103, sequencing the application servers according to the performance index value to generate a first sequence;
104, extracting the acquisition priority in the acquisition information carried by the data acquisition request, and sequencing the application servers according to the acquisition priority to generate a second sequence;
and sequencing the application servers from large to small according to the performance index values corresponding to the application servers 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 parameter of each application server, and compares and sorts the application servers according to the performance index value 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 customized by the user when the client sends 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 between the service tables of the application servers, and the priority a1 of the prev _ rcpt temporary collection table is greater than the priority a2 of the adv _ prev pre-collection 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 sequencing 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 ranking conditions of the application servers of the first sequence and the second sequence are matched, the application servers which are ranked first in the first sequence and are ranked first in the second sequence are selected from the first sequence, the selected application servers are combined arbitrarily 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. In this embodiment, the number of the application servers selected according to the first sequence and the second sequence is not limited, and may be limited according to an actual situation.
106, extracting the acquisition capacity in the performance parameters of each application server, and performing priority ordering on each application server according to the corresponding acquisition capacity to obtain an acquisition capacity sequence;
and 107, 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.
And extracting the acquisition capacity in the performance parameters of each application server, and performing priority ordering on each application server 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. For example, according to the collection capability sequence, the W1 server has the best collection capability, then the data collection strategy (W1W2, W1W3, W1W4) comprising the W1 server combination is selected, and then the server W2 comprising 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 data collection task, the application servers W1 and W2 trigger the data collection command, and the data collection work of the micro service is executed.
In the embodiment of the invention, performance index values are calculated and sequenced 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 data acquisition strategies according to the first sequence and the second sequence, and selecting an optimal acquisition strategy from the data acquisition strategies according to the acquisition capacity corresponding to each application server to acquire data of the microservice. According to the embodiment of the invention, the data acquisition is carried out 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 the data collection method based on microservice according to the embodiment of the present invention includes:
201, receiving a data acquisition request and acquiring performance parameters of each application server;
202, extracting 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 method comprises the steps that a server receives a data acquisition request and obtains performance parameters of each application server capable of running a data acquisition process of the micro-service, wherein the data acquisition request carries acquisition information, and performance index values corresponding to the application servers are calculated according to the performance parameters of the application servers.
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 these, throughput (F) involves several important parameters: query rate per second (QPS/TPS), number of concurrencies, response time.
In the present embodiment, throughput refers to the number of requests processed by an application server per unit time. For application servers without concurrency, throughput and response time are in strict inverse proportion, and in fact, the throughput is the reciprocal of the response time. As already mentioned above, for a single-user application server, the response time (or the application server response time and the application delay time) can well measure the performance of the application server, but for a concurrent application server, throughput is generally required as a performance indicator. For a multi-user application server, if the average response time of the application server is t when only one user is using the application server, when there are n users, the response time seen by each user is usually not n × t, but often much smaller than n × t (of course, in some special cases, it may be larger than n × t, or even much larger). This is because many resources are needed to process each request, and since many requests are difficult to be executed concurrently in the processing process of each request, the occupied resources are often not much at a specific time point. That is, when processing a single request, there may be many resources left idle at each point in time, and when processing multiple requests, the average response time seen by each user does not increase linearly with the number of users if the resources are reasonably configured. In fact, the average response time of different application servers will not increase at the same rate as the number of users increases, which is also the main reason for using throughput to measure the performance of concurrent application servers. Generally speaking, throughput is a relatively general index, and if the maximum throughputs of two application servers with different numbers of users and user usage patterns are substantially consistent, it can be determined that the processing capacities of the two application servers are substantially consistent. Where peak throughput (MF) is the maximum amount of processed data per second.
The response time refers to the time the application server responds to the request. Intuitively, this metric is very consistent with a human subjective perception of software performance, as it completely records the time for the entire application server to process a request. Since one application server usually provides many functions and the processing logic of different functions varies, the response time of different functions is different, even the same function has different response time under different input data. Therefore, when discussing the response time of an application server, one typically 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 necessary to discuss the average response time and the maximum response time for each function or group of functions.
The number of concurrent users refers to the number of users who normally use the functions of the application server and can be simultaneously borne by the application server. Compared with throughput, the number of concurrent users is a more intuitive but also more general performance index. In practice, the number of concurrent users is a very inaccurate indicator, because different usage patterns of users will result in different numbers of requests per unit time being made by different users. Taking a website application server as an example, suppose that a user can only use the website after registering, but the registered user does not use the website at all times, so that only part of registered users are online at a specific moment, and the online user spends a lot of time reading information on the website when browsing the website, so that only part of online users send requests to the application server at a specific moment. Thus, for the 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 simultaneous request users. Since the registered user may not log in the website for a long time, a large error may be caused by using the number of registered users as a performance index. The number of online users and the number of co-workers sending requests can be used as performance indexes. In contrast, the performance index is more intuitive when the online users are used, and the performance index is more accurate when the number of the users who send requests at the same time is used.
Query Per Second (QPS) is a measure of how much traffic a particular query server processes within a specified time, and on the internet, the performance of a machine acting as a domain name application server is often measured by query per second.
In this embodiment, the query per second rate (QPS/TPS) is the quotient of the request (R) received by the application server per second and the number of transactions (N); the throughput is the quotient of the concurrency number and the response time; wherein, the concurrency number (current) is the quotient of the request (R) processed by the application server and the transaction number (N); taking average response time from response time (Times); when the network transmission time is N1+ N2+ N3+ N4, the processing time of the application server is a1+ A3, and the processing time of the database server is: and A2, the response time T is (N1+ N2+ N3+ N4)/3+ (A1+ A3)/2+ A2.
203, extracting CPU processing capacity reference in the performance parameters of each application server, and calculating the CPU occupancy of each application server according to the CPU processing capacity reference;
CPU processing capacity criteria among the performance parameters of each application server are extracted, and a CPU occupancy (RCL) of each application server is calculated based on the CPU processing capacity criteria. Before the application server starts, testing the processing capacity reference (CPUPERForformanceBase) of the CPU by using an interrupt; and after the application server runs, executing an algorithm which is completely the same as the standard of the processing capacity of the test CPU by using an idle task to obtain the RTCPP performance. The CPU occupancy is calculated periodically and the value of rtcpupexecution is cleared, 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 head seek time in the performance parameters of each application server, and calculating the corresponding disk storage capacity (IOPS) of each application server according to the disk rotation delay time and the head seek time. In this embodiment, the disk storage capacity IOPS is 1000 ms/(disk rotation delay time (ms) + head seek time (ms)).
205, taking throughput, CPU occupancy and disk storage capacity as performance index values corresponding to each application server;
and taking the throughput, the CPU occupation ratio and the disk storage capacity corresponding to each application server as the performance index value corresponding to each application server.
206, performing weighted operation on the performance index values according to the preset weights of the performance indexes to obtain the index weights of the application servers;
207, sorting the application servers according to the index weight to obtain a first sequence;
the server performs weighted operation on the performance index values according to the preset weights of the performance indexes of the application servers, namely, the product of each performance index value and the weight corresponding to each performance index is calculated, so that the index weight of each corresponding application server is obtained. And sequencing the application servers according to the sequence of the corresponding index weights from large to small to obtain a first sequence.
In this embodiment, the weight of each performance index of the application server is set according to a preset ranking priority of each performance index, where the ranking priority of each performance index is: CPU occupancy (RCL) > concurrency (current) > peak throughput (MF) > response time (Times). In addition, 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 the 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 customized by the user when the client sends 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 between the service tables of the application servers, and the priority a1 of the prev _ rcpt temporary collection table is greater than the priority a2 of the adv _ prev pre-collection table.
209, extracting the selected number of the 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 data acquisition strategies of various micro services;
the server extracts the selected number of the application servers selected in the data acquisition process specified in the acquisition information, and screens all the application servers according to the sorting condition of each application server in the first sequence and the second sequence, namely screens the application servers with the front sorting according to the first sequence and the second sequence to form a first server set.
The application servers in the first server set are randomly combined according to the selected number, that is, the possible combination modes of the application servers according to the selected number are calculated through a sequencing combination formula, and one combination mode is a data acquisition strategy: c (n, m) is P (n, m)/P (m, m) is n! M! (n-m)! Wherein n is the number of the application servers in the first server set, and m is the selected number of the application servers, for example, when the number n of the application servers in the first server set is 5 and the selected number of the application servers is 2, the combination manner is: c (5, 2) ═ 5! I.e.,/(2 | × 3 |) -5 × 4/(2 × 1) ═ 10, i.e., there are 10 combinations, which can generate 10 data acquisition strategies.
212, extracting the acquisition capacity in the performance parameters of each application server, and performing priority ordering on each application server according to the corresponding acquisition capacity to obtain an acquisition capacity sequence;
and 213, 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.
In the embodiment of the present invention, step 212-213 is the same as step 106-107 in the first embodiment of the data collection method based on microservices, and will not be described herein again.
In the embodiment of the invention, the screened application servers are randomly combined according to the selected number of the application servers to obtain a plurality of data acquisition strategies, so that data acquisition can be carried out on the micro-service 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 the data collection method based on microservice in the 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, sequencing the application servers according to the performance index value to generate a first sequence;
304, extracting the acquisition priority in the acquisition information, and sequencing the application servers according to the acquisition priority to generate a second sequence;
305, extracting the selected number of the application servers in the acquisition information carried by the data acquisition request;
306, performing weighted operation on the first sequence and the second sequence based on a preset index weight and the weight of the acquisition priority to obtain a calculation result;
307, sequencing the application servers according to the calculation result to obtain a third sequence;
the server extracts the selected number of the application servers selected in the data acquisition process specified in the acquisition information, and screens all the application servers according to the sorting condition of each application server in the first sequence and the second sequence, namely screens the application servers with the front sorting according to the first sequence and the second sequence to form a first server set. Specifically, the server performs weighting operation on the first sequence and the second sequence according to the obtained index weight and the weight of the acquisition priority, that is, performs weighting operation on each application server in the second sequence according to the weight of the corresponding acquisition priority to obtain the weight of the acquisition priority, and then performs arithmetic addition operation on the weight of the acquisition priority corresponding to each server and the index weight 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 of the corresponding calculation results from large to small to generate a third sequence.
308, screening the application servers according to the third sequence, determining the sequence 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 to generate a first server set;
and screening all the application servers according to the third sequence, determining the sequence position of each application server in the third sequence, namely searching the corresponding sequencing position of each application server 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 a plurality of micro services;
the application servers in the first server set are randomly combined according to the selected number, that is, the possible combination modes of the application servers according to the selected number are calculated through a sequencing combination formula, and one combination mode is a data acquisition strategy: c (n, m) is P (n, m)/P (m, m) is n! M! (n-m)! Wherein n is the number of the application servers in the first server set, and m is the selected number of the application servers, for example, when the number n of the application servers in the first server set is 5 and the selected number of the application servers is 2, the combination manner is: c (5, 2) ═ 5! I.e.,/(2 | × 3 |) -5 × 4/(2 × 1) ═ 10, i.e., there are 10 combinations, which can generate 10 data acquisition strategies.
310, extracting the acquisition capacity in the performance parameters of each application server, and performing priority ordering on each application server according to the corresponding acquisition capacity to obtain an acquisition capacity sequence;
and 311, selecting an optimal acquisition strategy from the data acquisition strategies according to the acquisition capacity sequence, and acquiring the data of the microservice according to the optimal acquisition strategy.
In the embodiment of the present invention, the steps 301-304 and 310-311 are the same as the steps 101-104 and 106-107 in the first embodiment of the data collection method based on micro service, and will not be described herein again.
In the embodiment of the invention, each application server is subjected to weighted 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 subjected to random combination to obtain a plurality of data acquisition strategies; and screening the application servers before generating the data acquisition strategy to obtain the application server with stronger performance for data acquisition, thereby improving the efficiency of subsequent data acquisition.
Referring to fig. 4, a fourth embodiment of the data collection method based on microservice in the embodiment of the present invention includes:
401, receiving a data acquisition request and acquiring performance parameters of each application server;
402, calculating performance index values corresponding to the application servers according to the performance parameters;
403, sorting the application servers according to the performance index value to generate a first sequence;
404, extracting the acquisition priority in the acquisition information, and sequencing the application servers according to the acquisition priority to generate a second sequence;
405, extracting the selected number of application servers in the acquisition information carried by the data acquisition request;
406, determining sequence positions of the application servers in the first sequence, and selecting the application servers located at the first M sequence positions from the first sequence to obtain a second server set;
determining the sequence position of each application server in the first sequence, namely searching the corresponding sequencing position of each application server in the first sequence, and selecting the application servers positioned at the first M sequence positions from the first sequence, namely extracting the first M sequencing application servers in the first sequence to form a second server set, wherein M is a positive integer.
407, determining sequence positions of the application servers in the second sequence, and selecting the application servers located at the first N sequence positions from the second sequence to obtain a third server set;
and determining the sequence position of each application server in the second sequence, namely searching the corresponding sequencing position of each application server in the second sequence, and selecting the application servers positioned at the first N sequence positions from the second sequence, namely extracting the application servers sequenced at the first N positions in the second sequence to form a third server set, wherein N is a positive integer.
408, judging whether the second server set and the third server set 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;
and 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 set, 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, the application servers are all independently formed into the first server set, that is, 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 performing priority ordering on each application server according to the corresponding acquisition capacity to obtain an acquisition capacity sequence;
and 412, selecting an optimal acquisition strategy from the data acquisition strategies according to the acquisition capacity sequence, and acquiring the data of the microservice according to the optimal acquisition strategy.
In the embodiment of the present invention, the steps 401-.
In the embodiment of the invention, the application servers with the front ranking positions in the two sequences are selected according to the ranking of the performance index values and the acquisition priorities, namely, the application servers with stronger performance and higher acquisition priorities are selected to obtain a first server set, and the first server set is randomly combined to obtain a plurality of data acquisition strategies; before generating a data acquisition strategy, screening each application server according to the performance index value and the sorting condition of the acquisition priority to obtain the application server with stronger performance and higher acquisition priority for data acquisition, thereby improving the efficiency of subsequent data acquisition.
With reference to fig. 5, the data acquisition method based on micro-services in the embodiment of the present invention is described above, and the data acquisition device based on micro-services in the embodiment of the present invention is described below, where an embodiment of the data acquisition device based on micro-services in the embodiment of the present invention includes:
a receiving module 501, configured to receive a data acquisition request and obtain performance parameters of each application server, where the data acquisition request includes acquisition information;
a calculating module 502, configured to calculate a performance index value corresponding to each application server according to each performance parameter;
a first sorting module 503, configured to sort the application servers according to the performance index value, and generate a first sequence;
an extracting module 504, configured to extract an acquisition priority in the acquisition information, sort the application servers according to the acquisition priority, and 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 perform priority sorting on the application servers according to the corresponding acquisition capabilities to obtain an acquisition capability sequence;
and the selection module 507 is configured to select an optimal acquisition strategy from the data acquisition strategies according to the acquisition capability sequence, and perform data acquisition based on a microservice according to the optimal acquisition strategy.
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, calculates performance index values and sorts 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 data acquisition strategies according to the first sequence and the second sequence, and selecting an optimal acquisition strategy from the data acquisition strategies according to the acquisition capacity corresponding to each application server to acquire data of the microservice. According to the embodiment of the invention, the data acquisition is carried out 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 the data collecting apparatus based on microservice in the embodiment of the present invention includes:
a receiving module 501, configured to receive a data acquisition request and obtain performance parameters of each application server, where the data acquisition request includes acquisition information;
a calculating module 502, configured to calculate a performance index value corresponding to each application server according to each performance parameter;
a first sorting module 503, configured to sort the application servers according to the performance index value, and generate a first sequence;
an extracting module 504, configured to extract an acquisition priority in the acquisition information, sort the application servers according to the acquisition priority, and 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 perform priority sorting on the application servers according to the corresponding acquisition capabilities to obtain an acquisition capability sequence;
and the selection module 507 is configured to select an optimal acquisition strategy from the data acquisition strategies according to the acquisition capability sequence, and perform data acquisition based on a microservice according to the optimal acquisition strategy.
Wherein the calculating module 502 comprises:
a throughput calculation unit 5021, configured to extract a concurrency number and response time in the performance parameters of each application server, and calculate the throughput of each application server according to the concurrency number and the response time;
an occupancy calculation unit 5022, configured to extract a CPU processing capability standard from the performance parameters of each application server, and calculate a CPU occupancy of each application server according to the CPU processing capability standard;
a storage capacity calculation unit 5023, configured to extract disk rotation delay time and head seek time in the performance parameters of each application server, and calculate the disk storage capacity of each application server according to the disk rotation delay time and the head seek time;
and the comprehensive calculation unit 5024 is used for taking the throughput, the CPU occupancy and the disk storage capacity as performance index values corresponding to the application servers.
Wherein the first ordering module 503 comprises:
a weighting calculation unit 5031, configured to perform weighting calculation on the performance index value according to a preset weight of each performance index to obtain an index weight of each application server;
a first sorting unit 5032, configured to sort the application servers according to the index weight to obtain a first sequence.
Wherein the determining module 505 comprises:
an extracting unit 5051, configured to extract the selected number of application servers in the acquired 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;
a combining unit 5053, configured to randomly combine the application servers in the first server set according to the selected number, so as to generate a data acquisition policy for multiple microservices.
The screening unit 5052 is specifically configured to:
performing weighting operation on the first sequence and the second sequence based on the preset index weight and the preset weight of the acquisition priority 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 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.
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 the sequence position of each application server 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.
In the embodiment of the invention, the application servers are sorted according to the performance index value and the collection priority by the data collection device based on the micro service, the application servers with the front sorting positions in the two sequences are selected, namely, the application servers with stronger performance and higher collection priority are screened out to obtain a first server set, and the first server set is randomly combined to obtain a plurality of data collection strategies; before generating a data acquisition strategy, screening each application server according to the performance index value and the sorting condition of the acquisition priority to obtain the application server with stronger performance and higher acquisition priority for data acquisition, thereby improving the efficiency of subsequent data acquisition.
Referring to fig. 7, an embodiment of the microservice-based data acquisition device in the embodiment of the present invention will be described in detail from the perspective of hardware processing.
Fig. 7 is a schematic structural diagram of a microservice-based data collecting apparatus 700 according to an embodiment of the present invention, where the microservice-based data collecting apparatus 700 may have a relatively large difference due to different configurations or performances, and may include one or more processors (CPUs) 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) for storing an application 733 or data 732. Memory 720 and storage medium 730 may be, among other things, transient storage or persistent storage. The program stored on the storage medium 730 may include one or more modules (not shown), each of which may include a sequence of instructions operating on the microservice-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 microservice-based data collection device 700.
The microservice-based data collection apparatus 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 the like. Those skilled in the art will appreciate that the configuration of the microservice-based data collection facility shown in fig. 7 does not constitute a limitation of the microservice-based data collection facility and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components.
The server referred by the invention can be an independent server, and can also be a cloud server for providing basic cloud computing services such as cloud service, a cloud database, cloud computing, cloud functions, cloud storage, Network service, cloud communication, middleware service, domain name service, security service, Content Delivery Network (CDN), big data and artificial intelligence platform and the like.
The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service 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 which may also be a volatile computer-readable storage medium, having stored therein instructions, which, when run on a computer, cause the computer to perform the steps of the microservice-based data collection method.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present 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 solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A data acquisition method based on micro-services is characterized in that the data acquisition method based on micro-services comprises the following steps:
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 value 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;
carrying out priority ordering on the 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 data of the micro-service according to the optimal acquisition strategy.
2. The microservice-based data collection method of claim 1, wherein the calculating the performance index value for each of the application servers based on each of the performance parameters comprises:
extracting 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 reference in the performance parameters of each application server, and calculating the CPU occupancy rate of each application server according to the CPU processing capacity reference;
extracting disk rotation delay time and 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 taking the throughput, the CPU occupation ratio 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 ranking the application servers according to the performance metric value 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 weights 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 any of claims 1-3, wherein determining the microservice data collection policy based on the first sequence and the second sequence comprises:
extracting the selected number of the 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 number to generate a data acquisition strategy of various micro services.
5. The microservice-based data collection method of claim 4, wherein the filtering each of the application servers according to the first sequence and the second sequence to obtain a first server set comprises:
performing weighting operation on the first sequence and the second sequence based on the preset index weight and the preset weight of the acquisition priority 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 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.
6. The microservice-based data collection method of claim 4, 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 the sequence position of each application server 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 so, extracting the corresponding application server to generate a first server set.
7. A microservice-based data collection system, the microservice-based data collection system 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 a performance index value corresponding to each application server according to each performance parameter;
the first sequencing module is used for sequencing the application servers according to the performance index value 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 carrying out priority sequencing on the application servers according to the corresponding acquisition capacity to obtain an acquisition capacity sequence;
and the selection module is used for selecting an optimal acquisition strategy from the data acquisition strategies according to the acquisition capacity sequence and acquiring data based on micro-service according to the optimal acquisition strategy.
8. The microservice-based data collection apparatus of claim 7, wherein the computing module comprises:
the throughput calculation unit is used for extracting the concurrency number and the 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;
an occupancy calculation unit, configured to extract a CPU processing capability standard from the performance parameters of each of the application servers, and calculate a CPU occupancy of each of the application servers according to the CPU processing capability standard;
the storage capacity calculation 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 ratio and the disk storage capacity as performance index values corresponding to the application servers.
9. A microservice-based data collection device, the microservice-based data collection device comprising:
a memory having a computer program stored therein and at least one processor, the memory and the at least one processor interconnected by a line;
the at least one processor invokes the computer program in the memory to cause the microservice-based data acquisition apparatus to perform the steps of the microservice-based data acquisition method of any of claims 1-6.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the microservice-based data acquisition method according to any one of claims 1 to 6.
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