CN106506605B - SaaS application construction method based on micro-service architecture - Google Patents
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- H04L67/10—Protocols in which an application is distributed across nodes in the network
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Abstract
The invention provides a method for constructing SaaS application based on micro-service architecture, aiming at the defects of a plurality of construction methods based on traditional SOA architecture, the method comprises the following steps: formulating a design principle and an attribute model for developing the micro-service, and realizing the deployment of the micro-service instance through a Docker container; a distributed micro-service metadata cluster is set up, and metadata of micro-services, micro-service providers and micro-service users are uniformly managed; high-availability storage schemes are respectively designed for the real-time updated state data and the relatively fixed attribute data; aiming at the real-time high-concurrency reading and writing requirements of metadata, a set of scheme combining a Redis master-slave server model and a reading and writing separation architecture is designed; aiming at the situation of dynamic addition and deletion of the micro-service instances, a load balancing strategy based on a consistent hash algorithm is designed. The invention solves the development, deployment and management scheme of the micro-service and improves the efficiency of developing the SaaS application.
Description
Technical Field
The invention belongs to the field of SaaS application development, and particularly relates to a SaaS application construction method based on a micro-service architecture.
Background
The cloud computing technology has strong computing power and high-efficiency IT resource integration capability, and brings a revolution in the development and use modes of software in the Internet era. The SaaS application development mode is a brand-new mode for providing software service through the Internet generated under the background of wide development of cloud computing technology, and users can flexibly use software in a renting mode according to actual requirements of the users.
With the development of the SaaS application mode, the increasingly changing requirements of people lead to frequent changes of software function requirements, so that the delivery and iteration periods of software are gradually shortened, and the traditional long-period development mode cannot meet the software development requirements in the internet era. The microservice architecture model that has emerged with the evolution of the Docker container technology can better address the issue of frequent deliveries.
The microservice architecture calls for splitting an application into multiple independent servlets, each dedicated to its own independent function and running in separate processes. There is a clear boundary between services, and the services communicate with each other through some relatively lightweight communication mechanisms.
The concept of the micro-service is similar to that of the SOA, and the method advocates dividing the complex application program into different services according to different and reusable functions, but the micro-service has finer service granularity, is deployed in an independent process to operate, communicates through a lightweight mechanism, and is more suitable for the Internet application with frequent change of the current demand, high iteration speed and short delivery cycle. Therefore, it is very important to research the design principle, attribute model and deployment method of the key micro-service constituting the micro-service architecture, and the principles to be followed in the process of designing and developing the micro-service need to be made clear, and the advantages brought by the architecture can be better brought into play by developing and deploying under the condition of conforming to the principles as much as possible.
When a developer develops the SaaS application by using the micro-service architecture in a cloud environment, the situations of rapid increase of the number of micro-services, dynamic update of a real-time state, dynamic addition and deletion of nodes and the like occur, and a management scheme capable of effectively aiming at the characteristics of the micro-services is needed so as to improve the efficiency of the developer developing the SaaS application by using the micro-service architecture.
Disclosure of Invention
The technical problem to be solved by the invention is to develop micro-services according to a micro-service design principle and an attribute model, and deploy a plurality of micro-service instances by using a Docker container; updating the metadata of the micro-service instance to a distributed metadata cluster in real time; triggering load balancing service while updating metadata, calculating the change of the instance distribution condition through a consistency hash algorithm, and informing a caller of the instance; and the SaaS application developer selects an optimal micro-service instance through the load balancing service and calls the micro-service instance through the REST interface.
In order to solve the above technical problem, the present invention provides a high availability management method for a microservice architecture, which includes:
(1) designing an attribute model of the microservice: according to micro-service design principles such as single function, independent deployment, stateless and lightweight communication, the attributes which can be used for describing micro-services are formulated, wherein the attributes comprise names, positions, versions, real-time states, identity authentication information, support concurrency, provider information and the like;
(2) micro-service deployment: generating an executable JAR package through a Spring Boot framework, and deploying and operating a micro-service instance by using a Docker container based on a Linux container virtualization technology;
(3) updating the metadata of the microservice: updating real-time updated state data into a Redis memory database, updating relatively fixed attribute data into a MySQL relational database, and realizing the updating function of different types of metadata by adopting different storage schemes;
(4) micro-service instance allocation: a micro-service can have a plurality of running instances, and the micro-service instances can be dynamically added and deleted, and the instances are distributed through a load balancing service, so that the instances are uniformly distributed to different callers.
(5) Micro-service calling: after the SaaS application developer obtains the optimal instance through the load balancing service, the SaaS application developer can call the micro-service through the REST interface and realize efficient development of the SaaS application by combining different optimal micro-services.
Compared with the prior art, the SaaS application construction method based on the micro-service architecture achieves the following effects:
(1) the invention adopts a deployment method that the Spring Boot framework generates executable JAR package and Docker container, thus effectively realizing independent deployment of micro-service instances.
(2) The method and the system have the advantages that the unified management of massive micro-service metadata is taken as a core, a series of schemes including high-availability micro-service metadata storage, read-write separation supporting real-time updating, load balancing supporting dynamic change and the like are included, and the problem that the types and the number of micro-services are increased sharply when a developer develops SaaS application by using a micro-service framework in a cloud environment is solved effectively.
Drawings
FIG. 1 is a logical diagram of a microservice metadata unified management scheme architecture.
FIG. 2 is a schematic diagram of a high availability metadata storage scheme architecture.
Fig. 3 is a flow diagram of a load balancing scheme supporting dynamic changes.
Detailed Description
The practice of the present invention will be further illustrated, but is not limited, by the accompanying drawings and examples.
Microservice design principles and attribute model formulation
The microservice architecture as a software development mode has many points of identity and difference with the traditional SOA service-oriented architecture, and the reason for the difference is the difference of the objects researched by the microservice architecture. Therefore, it is very important to research the design principles and attribute models of the key microservices constituting the microservice architecture, and the principles to be followed in the microservice process need to be clearly designed and developed, and the advantages brought by the architecture can be better brought out by developing and deploying under the condition of conforming to the principles as much as possible.
The microservice design rules formulated in this example are as follows:
(1) single functional principle: the business logic handled by a single microservice is dedicated to a specific function and has well-designed external interfaces that explicitly define the boundaries between functions, specifying which factors change to cause a change in the function.
(2) Independent deployment principle: the whole process of development, test, construction and deployment of each micro-service is defined in a physically independent environment from the perspective of physical attributes, and when a single micro-service is modified, upgraded and redeployed, the operation of other micro-services is not influenced.
(3) Stateless principles: the micro-service does not need to record communication information related to external users when receiving and processing external requests, so that stateless instances can be added or reduced very swiftly when a certain micro-service instance fails or is dynamically expanded or reduced.
(4) Lightweight communication principle: some mechanism unrelated to the development language and the development platform is adopted for interaction. Common lightweight communication protocols include XML and JSON, and the protocols have the expression capability on complex information and can realize cross-platform information transmission.
According to the above design principle, we formulate an attribute model of the microservice needs, including: name attribute, location attribute, version attribute, real-time status attribute, authentication attribute, capacity support attribute, service provider attribute.
Micro-service metadata unified management scheme
As shown in fig. 1, it is demonstrated that unified high-availability and high-performance management is performed on massive, dynamic, real-time updated micro-service instance states, and the key features are as follows:
(1) a plurality of micro-service instances are independently deployed, and massive state metadata and attribute metadata need to be updated to a metadata cluster in real time;
(2) the micro service instances can be dynamically expanded or reduced, and the addition and deletion changes of the instances are updated to the metadata cluster in real time;
(3) the load balancing service is deployed in a cluster form and provides services, and the position information and the state information of the micro-service instance are obtained by reading a high-availability metadata cluster;
(4) and when the instance caller subscribes the micro service for the first time, accessing the load balancing service, optimally selecting the micro service instance by combining the state information of the micro service and a preset load balancing strategy, and recording the selection condition to the local caller.
(5) The load balancing service records the relation between the caller and the used micro-service instance, and if the instance is dynamically increased or decreased, the caller is informed in time.
(6) And finally, the caller of the instance communicates with the specific micro-service instance in a direct connection mode through the REST interface provided by the instance.
High availability metadata storage scheme
Firstly, aiming at the state data updated in real time, the data has the characteristics that the data volume of each update is not large, a large storage space is not needed for processing the single updated data, but the update frequency is high, and the total update times are large, so that a storage mechanism which is relatively simple and can provide high data reading performance is needed, and a Redis memory data storage scheme is selected.
The rich data types provided by Redis are utilized to design the data structure of the state data to facilitate sorted storage and quick retrieval. For example, the key of the state attribute of the micro service instance may be set to a string "micro service: instance: state: instance: instanceId", and the location information of the micro service instance in real time may be set to a string "service _ id: instance _ id: url". The mode of forming keys by a plurality of fields and symbol intervals has good structurality, and is very convenient for system users to classify and search data in Redis.
Data persistence is performed by using an AOF (application Only File) mode of Redis to avoid data loss caused by the volatility of a memory. And the disaster recovery backup of multiple Redis servers is realized by utilizing the Redis synchronous copying function. Redis provides simple SLAVEOF commands and SLAVEOF configuration attributes that allow the present instance to set one Redis server as a slave to another Redis server, so that operations on the master server can be synchronized to updates on the slave in a timely manner.
Aiming at high concurrent read-write requirements, a plurality of servers are adopted to store data and are divided into a main server and a slave server, the servers and the servers copy the data through a synchronization mechanism, wherein the main server is mainly responsible for providing operations which can bring changes to the data such as addition, deletion and modification of the data to the outside, and one or more slave servers are responsible for providing operations of inquiring and reading the data to the outside.
Secondly, for some relatively fixed and structured data, the data can be described, stored and processed in a data form of a two-dimensional table, which is convenient for query and is relatively suitable for being used for describing, storing and processing, so that the traditional relational database MySQL is used for storing. Fig. 2 shows the overall architecture of the metadata storage scheme.
Load balancing scheme supporting dynamic changes
The stateless principle of the micro-service enables a single micro-service to be freely expanded into a plurality of executable specific instances, each instance is independently deployed, and operations inside the instances are not affected by each other. In the face of such a situation where multiple homogeneous instances are running simultaneously, a load balancing scheme is needed that can distribute requests of multiple callers to different instances for processing.
The case adopts a consistent hash algorithm to combine with the real-time load condition of the case to solve the dynamic adding and deleting condition of the case. Fig. 3 is a flow chart of a load balancing scheme, wherein the steps are as follows:
(1) firstly, the micro-service instance monitors the real-time load state of the micro-service instance, such as the processing time of each call, the real-time concurrent call quantity and other information.
(2) And the micro-service instance updates self state information to the metadata cluster at regular time, such as real-time load state and instance addition or active deletion. Furthermore, the metadata cluster detects whether the instance crashes through a certain heartbeat mechanism.
(3) The load balancing service detects whether the state change of each micro-service instance is the addition or deletion of the instance at regular time, if so, the request is redistributed by adopting a consistent hash algorithm, and relevant affected callers are informed.
(4) If the load state of the running of the instance is not the addition or deletion of the instance, the load state of each micro-service instance is regularly judged whether to reach a preset load threshold value, and if not, the requested adjustment is not made.
(5) If the load of a micro-service instance is detected to exceed the preset load threshold value, part of the requests of the instance are evenly distributed to other instances which do not exceed the load threshold value.
(6) And finally, notifying the affected caller according to the result after load balancing, and modifying the micro-service address information of the local cache of the caller by the caller.
In the built micro-service management framework data model, each micro-service instance has a load _ threshold attribute representing the load threshold value of the instance and a load _ factor attribute representing the real-time load rate of the instance, the load threshold value represents early warning information, and if the real-time load condition exceeds the threshold value, the instance is prone to being down.
In order to reduce the frequency of instance downtime, the present embodiment implements a "virtual downtime" policy through a load _ threshold attribute, that is, when the real-time load condition exceeds a load threshold value, a load balancing service is triggered in advance, so as to implement that partial requests on the load-overloaded instance are distributed to other instances before the real downtime occurs, and the caller is timely notified of the condition of instance distribution change through a real-time notification component. Such a prevention strategy may reduce the frequency of real instance outages, thereby improving the availability of the system as a whole.
Aiming at the conditions of rapid increase of the number of micro services, dynamic change of real-time states, dynamic addition and deletion of nodes and the like when the micro service architecture is applied to develop SaaS application, the invention designs a set of highly available micro service metadata management schemes, and respectively designs a metadata storage scheme, a state updating scheme and a load balancing scheme aiming at micro service management. Therefore, a developer can better manage and schedule the micro-service when the micro-service is used for developing the SaaS application, and the development efficiency is improved.
Therefore, the deployment method of the combination of the executable JAR package and the Docker container generated by the Spring Boot framework adopted by the invention effectively realizes the independent deployment of the micro-service instances. The method and the system have the advantages that the unified management of massive micro-service metadata is taken as a core, a series of schemes including high-availability micro-service metadata storage, read-write separation supporting real-time updating, load balancing supporting dynamic change and the like are included, and the problem that the types and the number of micro-services are increased sharply when a developer develops SaaS application by using a micro-service framework in a cloud environment is solved effectively.
The foregoing description shows and describes several preferred embodiments of the invention, but as aforementioned, it is to be understood that the invention is not limited to the forms disclosed herein, but is not to be construed as excluding other embodiments and is capable of use in various other combinations, modifications, and environments and is capable of changes within the scope of the inventive concept as expressed herein, commensurate with the above teachings, or the skill or knowledge of the relevant art.
Claims (1)
1. A SaaS application construction method based on a micro-service architecture is characterized by comprising the following steps:
the method comprises the following steps that a SaaS application developer develops micro-services according to micro-service design principles and an attribute model, and deploys a plurality of micro-service instances by using a Docker container; then updating the metadata of the micro service instance to a distributed metadata cluster in real time; triggering load balancing service while updating metadata, calculating the change of the instance distribution condition through a consistency hash algorithm, and informing a caller of the instance; the SaaS application developer selects an optimal micro-service instance through the load balancing service and calls the micro-service instance through an REST interface; the method specifically comprises the following steps:
(1) designing a micro-service attribute model: according to a micro-service design principle of single function, independent deployment, stateless and lightweight communication, establishing attributes which can be used for describing micro-services, including names, positions, versions, real-time states, identity authentication information, support concurrency and provider information;
(2) micro-service independent deployment: generating an executable JAR package through a Spring Boot framework, and deploying and operating a micro-service instance by using a Docker container based on a Linux container virtualization technology;
(3) updating the microservice metadata in real time: updating real-time updated state data into a Redis memory database, updating relatively fixed attribute data into a MySQL relational database, realizing the updating function of different types of metadata by adopting different storage schemes, and realizing read-write separation by combining a Redis master-slave server model;
(4) and (3) micro service instance load balancing: a single microservice can have a plurality of running instances, and the instances can be dynamically added and deleted, and requests for access can be uniformly distributed by combining a consistent hashing algorithm with the real-time load states of the instances;
the microservice design principle is as follows:
(1) single functional principle: the business logic which is handled by a single microservice is dedicated to a specific function and has a good external interface which explicitly defines the boundaries between functions and which factors change to cause a change in the function;
(2) independent deployment principle: defining each micro-service from the perspective of physical attributes to be carried out in a physically independent environment in the whole process of development, testing, construction and deployment;
(3) stateless principles: the micro-service does not need to record communication information related to an external user when receiving and processing an external request, so that stateless instances can be added or reduced very swiftly when a certain micro-service instance fails or is dynamically expanded or reduced;
(4) lightweight communication principle: adopting lightweight communication protocols XML and JSON irrelevant to the development language and the development platform to carry out interaction, and realizing cross-platform information transmission;
the SaaS application construction method also comprises a unified management method of micro-service metadata, a high-availability metadata storage method and a load balancing method supporting dynamic change;
the unified management method of the microservice metadata comprises the following steps:
(1) a plurality of micro-service instances are independently deployed, and massive state metadata and attribute metadata need to be updated to a metadata cluster in real time;
(2) the micro service instances can be dynamically expanded or reduced, and the addition and deletion changes of the instances are updated to the metadata cluster in real time;
(3) the load balancing service is deployed in a cluster form and provides services, and the position information and the state information of the micro-service instance are obtained by reading a high-availability metadata cluster;
(4) when an instance caller subscribes to the micro service for the first time, accessing the load balancing service, optimally selecting the micro service instance by combining the state information of the micro service and a preset load balancing strategy, and recording the selection condition to the local caller;
(5) the load balancing service records the relation between the caller and the used micro-service instance, and if the instances are dynamically increased or decreased, the caller is informed in time;
(6) the final instance caller communicates with the concrete micro-service instance by adopting a direct connection mode through an REST interface provided by the instance;
the high-availability metadata storage method comprises the following steps:
firstly, selecting a Redis memory data storage scheme aiming at real-time updated state data;
designing a data structure of the state data by utilizing Redis to facilitate classified storage and quick retrieval; the key form of the state attribute of the micro service instance is set as a character string 'micro service: instance: state: instance id', and the real-time position information of the micro service instance is set as a character string 'service _ id: instance _ id: url'; a key mode is formed by a plurality of fields and symbol intervals, so that a system user can classify and search data in Redis conveniently;
data persistence is carried out by utilizing AOF (application Only File) of Redis so as to avoid data loss caused by volatility of a memory; the disaster recovery backup of multiple Redis servers is realized by utilizing the Redis synchronous copying function; redis sets one Redis server as a slave server of another Redis server by utilizing SLAVEOF command and SLAVEOF configuration attribute, so that the operation of the master server can be synchronously updated to the slave server in time;
aiming at high concurrent read-write requirements, a plurality of servers are adopted to store data and are divided into a main server and a slave server, the servers and the servers realize data copying through a synchronization mechanism, wherein the main server is mainly responsible for providing data adding, deleting and modifying operations which bring changes to the data for the outside, and one or more slave servers are responsible for providing data inquiring and reading operations for the outside;
secondly, aiming at some relatively fixed and structured data, storing the data by adopting a traditional relational database MySQL;
the load balancing method supporting dynamic change comprises the following steps:
the stateless principle of the micro-service enables a single micro-service to be freely expanded into a plurality of executable specific instances, each instance is independently deployed, and operations in the instances are not mutually influenced; in the case of simultaneously operating a plurality of isomorphic instances, a load balancing scheme is needed, which can distribute requests of a plurality of callers to different instances for processing; the load balancing scheme comprises the following steps:
(1) firstly, the micro-service instance monitors the real-time load state of the micro-service instance, and monitors the processing time of each call and the real-time information of the number of concurrent calls;
(2) the method comprises the following steps that a micro-service instance updates self-state information to a metadata cluster at regular time, wherein the self-state comprises a real-time load state and instance addition or active deletion; in addition, the metadata cluster detects whether the instance crashes through a certain heartbeat mechanism;
(3) the load balancing service regularly detects whether the state change of each micro-service instance is the instance addition or deletion, if so, the consistent Hash algorithm is adopted to redistribute the request, and relevant affected callers are informed;
(4) if the load state of the micro service instances is not increased or deleted, only the running load state of the instances is reported, whether the load state of each micro service instance reaches a preset load threshold value is judged regularly, and if not, the requested adjustment is not carried out;
(5) if the load of a micro service instance is detected to exceed the preset load threshold value, distributing part of requests of the instance to other instances which do not exceed the load threshold value uniformly;
(6) finally, the affected caller is notified according to the result after load balancing, and the caller modifies the micro-service address information of the local cache;
a strategy of 'virtual downtime' is realized through a load _ threshold attribute, namely, when the real-time load condition exceeds a load threshold value, a load balancing service is triggered in advance to realize that partial requests on the load overload example are distributed to other examples before the real downtime occurs, and a caller is timely notified of the condition of example distribution change through a real-time notification component; the load _ threshold attribute represents a load threshold for the instance.
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