CN113296825A

CN113296825A - Application gray level publishing method and device and application publishing system

Info

Publication number: CN113296825A
Application number: CN202010749191.7A
Authority: CN
Inventors: 吴凤元
Original assignee: Alibaba Group Holding Ltd
Current assignee: Alibaba Group Holding Ltd
Priority date: 2020-07-30
Filing date: 2020-07-30
Publication date: 2021-08-24

Abstract

The application provides a gray level issuing method and device of an application and an application issuing system, wherein the method comprises the following steps: collecting gray level flow data in the gray level publishing process; performing data analysis on the gray level flow data; and if the gray scale flow data does not accord with the preset conditions, generating gray scale release alarm information, wherein the alarm information is used for triggering gray scale rule adjustment. By means of the scheme, the problem that the existing gray scale issuing cannot be intelligently analyzed and adjusted based on real-time flow conditions is solved, faults can be timely found in the issuing process, and therefore the purpose of intelligent flow adjustment can be achieved.

Description

Application gray level publishing method and device and application publishing system

Technical Field

The application belongs to the technical field of internet, and particularly relates to a gray level issuing method and device and an application issuing system for an application.

Background

For the upgrade release of the application, a blue-green deployment mode is generally adopted at present, and the so-called blue-green deployment mode is a deployment mode without shutdown and is a technology for releasing the application in a predictable mode. The blue-green deployment mode can reduce the time for service stop in the release process. The blue-green deployment is implemented in principle to solve the problem by redundancy.

Typically, a production environment requires two sets of configurations: one set of configurations for active production environments (called green configurations) and one set of configurations for inactive environments (called blue configurations). When the user accesses, the user only accesses the active server cluster, and the application in the current production environment is operated in the green environment (active), namely the old version application version 1. When an upgrade to version2 is needed, the operation is performed in the blue environment (inactive), i.e. a new version application is deployed and tested. If the test is not problematic, the load balancer/reverse proxy/route, etc. can be directed to the blue environment. It is then necessary to monitor whether the new version application (i.e., version2) is faulty and anomalous. If it works well, the resources used by version1 may be removed, and if there is a problem with the operation, a quick rollback to the green environment may be pointed to by the load balancer.

However, the gray scale publishing method based on the blue-green deployment is complex in configuration and many in configuration items, and cannot be intelligently analyzed and adjusted based on the real-time flow condition.

In view of the above problems, no effective solution has been proposed.

Disclosure of Invention

The application aims to provide an application gray level issuing method, an application gray level issuing device and an application issuing system, which can perform real-time analysis and adjustment in the gray level issuing process.

The application provides an application gray level publishing method, an application gray level publishing device and an application publishing system, which are realized as follows:

a method of gray scale publishing for an application, the method comprising:

collecting gray level flow data in the gray level publishing process;

performing data analysis on the gray level flow data;

and if the gray scale flow data does not accord with the preset conditions, generating gray scale release alarm information, wherein the alarm information is used for triggering gray scale rule adjustment.

An applied gradation issuance apparatus comprising:

the acquisition module is used for acquiring gray level flow data in the gray level publishing process;

the analysis module is used for carrying out data analysis on the gray level flow data;

and the generating module is used for generating gray release alarm information under the condition that the gray flow data does not accord with the preset conditions, wherein the alarm information is used for triggering gray rule adjustment.

An application publishing method, comprising:

receiving an input gray level release rule;

constructing a gray rule according to an input gray release rule;

receiving the uploaded application to be subjected to gray release;

carrying out gray scale release on the application to be subjected to gray scale release, acquiring gray scale flow data in the gray scale release process, and carrying out data analysis on the gray scale flow data;

An application publishing method, comprising:

receiving an input gray level release rule of the retail cloud application;

constructing a gray rule according to an input gray release rule;

receiving an uploaded retail cloud application update package;

carrying out gray scale release on the retail cloud application, acquiring gray scale flow data in the gray scale release process, and carrying out data analysis on the gray scale flow data;

An application issuing system comprising a processor and a memory for storing processor-executable instructions, the instructions when executed by the processor implementing the steps of the method of:

collecting gray level flow data in the gray level publishing process;

performing data analysis on the gray level flow data;

A computer readable storage medium having stored thereon computer instructions which, when executed, implement the steps of a method comprising:

collecting gray level flow data in the gray level publishing process;

performing data analysis on the gray level flow data;

According to the gray scale issuing method and device, gray scale flow data are collected in the gray scale issuing process, then data analysis is carried out on the gray scale flow data to determine whether the gray scale flow data are abnormal or not, if the gray scale flow data are determined to be abnormal, gray scale issuing alarm information is generated to carry out manual intervention on the gray scale issuing process, and if the gray scale flow data are not abnormal, automatic gray scale issuing can be continuously carried out according to a preset gray scale rule. Through the method, the gray scale can be issued according to the preset rule, and the problem in the issuing process can be found in real time for manual intervention, so that the problem that the existing gray scale issuing cannot be intelligently analyzed and adjusted based on the real-time flow condition can be solved, and the fault can be timely found in the issuing process, so that the purpose of intelligent flow adjustment can be realized.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only some embodiments described in the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without any creative effort.

FIG. 1 is a flowchart of a method of one embodiment of a gray scale publishing method for an application provided by the present application;

FIG. 2 is a gray scale service designation provided by the present application;

FIG. 3 is a schematic diagram of a model architecture for one embodiment of an application product system provided herein;

FIG. 4 is a schematic diagram of a gray-centric technology logic architecture provided herein;

FIG. 5 is a system architecture diagram of a gray center provided herein;

fig. 6 is a flowchart of the timing of the gray scale distribution provided by the present application;

FIG. 7 is an architecture diagram of a server provided herein;

fig. 8 is a block diagram of a structure of a gray scale distribution apparatus applied to the present application.

Detailed Description

In order to make those skilled in the art better understand the technical solutions in the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

The existing gray scale release is generally realized by adopting the following modes: deploying applications to be issued to container cloud platforms A and B, and providing services to the outside through load balancing; when the version of the application needs to be upgraded, shielding external flow of the application on the container cloud platform B, and upgrading the version on the container cloud platform B to upgrade the version from X to Y; analyzing a service request source address through an intelligent DNS, and sending an application request of a WEB region container cloud platform to a container cloud platform with the same version application; switching external traffic through load balancing, and shielding the external traffic of the application X version on the container cloud platform A; and after the application X version on the container cloud platform A is successfully upgraded to the application Y version through test confirmation, the flow is opened through load balancing. However, the gray scale publishing method based on blue-green deployment has the problems that the configuration is complex, the configuration items are more, the gray scale rule cannot be customized individually, and the intelligent analysis and adjustment cannot be performed based on the real-time flow condition.

Fig. 1 is a flowchart of a method of an embodiment of a gray scale publishing method applied in the present application. Although the present application provides method operational steps or apparatus configurations as illustrated in the following examples or figures, more or fewer operational steps or modular units may be included in the methods or apparatus based on conventional or non-inventive efforts. In the case of steps or structures which do not logically have the necessary cause and effect relationship, the execution sequence of the steps or the module structure of the apparatus is not limited to the execution sequence or the module structure described in the embodiments and shown in the drawings of the present application. When the described method or module structure is applied in an actual device or end product, the method or module structure according to the embodiments or shown in the drawings can be executed sequentially or executed in parallel (for example, in a parallel processor or multi-thread processing environment, or even in a distributed processing environment).

Specifically, as shown in fig. 1, an applied gray scale publishing method according to an embodiment of the present application may include:

step 101: collecting gray level flow data in the gray level publishing process;

the gray level distribution refers to gradually expanding the range of a use group for the distribution of a certain function, and if no abnormity exists, all the flow is migrated. The stability of the whole system can be ensured through gray scale release, and problems can be found and adjusted in the initial gray scale so as to ensure the influence degree of the gray scale. Graying is to upgrade the old version according to the proportion, for example, 80% of users access the old version and 20% of users access the new version.

In the process of issuing the gray scale, gray scale flow data may be collected, and specifically, the gray scale flow data may be obtained by collecting a gray scale flow log. In the software upgrading system, a log storage module can be specially arranged, and all the gray-scale flow logs are recorded and stored in the log storage module. A data processing and analyzing module in the system can extract the gray scale flow log from the log storage module, so that gray scale flow data in the gray scale releasing process are obtained and serve as the basis of subsequent analysis.

Step 102: performing data analysis on the gray level flow data;

after obtaining the gray scale flow log, or, the gray scale flow data, the data may be analyzed. Specifically, several analysis items may be set, for example: current system stability, proportion of failures in new version traffic, proportion of new and old versions each divided into traffic, and the like. These can be used as analysis items to analyze the gray scale flow data, and if the preset conditions are met, that is, the current gray scale release state is normal, the execution can be continued according to the current state until the preset observation period is reached.

The above-mentioned predetermined observation period can be understood as: assume that the set gray scale distribution rule is: and dividing the traffic of 5% of the new version and 95% of the old version, executing a first time length, and switching to the following steps if no fault exists in the first time length: and dividing the traffic of 20% of the new version and 80% of the old version, executing for a second time, and switching to the next state for continuing executing if no fault exists in the second time. The first time length and the second time length are preset observation periods.

Step 103: and if the gray scale flow data does not accord with the preset conditions, generating gray scale release alarm information, wherein the alarm information is used for triggering gray scale rule adjustment.

In consideration of the fact that the existing method cannot intelligently adjust the gray scale flow in the gray scale release process, a fault alarm mechanism is provided in the embodiment, and alarm information is generated under the condition that the data abnormity is determined (namely the gray scale flow data is determined not to meet the preset condition), so that a user can know that the data abnormity exists, and the gray scale rule adjustment can be triggered.

Specifically, for example, the current flow distribution ratio may be adjusted (the ratio of the new version is decreased), or the previous distribution ratio may be returned, or the subsequent flow distribution ratio may be adjusted, or the subsequent or current observation period may be adjusted, and so on. The user can carry out adaptive adjustment according to the alarm information in combination with the current condition, the application attribute and the like, so that the requirements of intelligent flow scheduling and elastic gray scale are met.

In practical implementation, after gray scale release alarm information is generated, the alarm information can be sent to a preset communication application; displaying the alarm information through the communication application; then, receiving a flow rule set by a user; switching the gray level flow to a flow value corresponding to a flow rule set by a user; and performing gray scale distribution according to the switched gray scale flow.

For example, after the alarm information is generated, the alarm information may be sent to the user by nailing, that is, the user is informed of the gray release in real time by nailing for manual intervention. However, it should be noted that the above-mentioned nail is only an exemplary description as a communication application, and other applications with communication function may be adopted, as long as the means can notify the user of the gray release, and the present application does not limit the application.

Specifically, during implementation, if it is determined that the gray scale flow data meets the preset condition, the gray scale distribution may be continuously performed according to a preset gray scale rule. That is, if the gray scale flow data meets the preset condition, the gray scale flow may be adjusted according to the preset gray scale rule after the preset observation period is reached. That is, if it is determined that the gray-scale flow data meets the preset condition, the data is not abnormal, and the data is normal, the current flow distribution can be executed to continue to operate until the preset issuing observation period is reached, and then the next flow distribution state is switched to. For example: the current traffic distribution is the new version: 10%, old version 90%, observation period is 1 hour, then in case of 1 hour duration. The preset gray rule can be called, and the gray rule can be preset by a user or intelligently set according to the application condition. In the preset gray scale rule, the next flow distribution is set, and the assumption is that the new version: 20%, old version: 80%, the observation period was 2 hours. Then the gray-scale flow is adjusted according to the next flow distribution setting state, that is, the current gray-scale flow is adjusted to: and (4) new version: 20%, old version: 80%, and observed for two hours.

The gradation rules described above may be initially set, that is, may be configured before gradation distribution. For example, before collecting the gray-scale flow data, the gray-scale rule configured for the target application may be acquired in response to the gray-scale rule configuration request; and storing the gray rule.

Considering that after the existing application is released in a gray scale, the flow is random according to the machine proportion, accurate drainage cannot be performed for a certain function, and verification cannot be performed from a link dimension because multiple services exist in the application. For this purpose, in this example, a gray scale service object is provided, which is configured for different versions of the respective service to generate the drainage rules. As shown in FIG. 2, assume that an application X has five services A-B-C-D-E, and a gray service label, tag1 and tag2, is set for the application, wherein the service path corresponding to tag1 is: A-B1-C1-D-E1, the service path corresponding to tag2 is A2-B-C2-D2-E, and the conventional service path is A-B-C-D-E. Wherein 1 corresponds to the upgrade version1 and 2 corresponds to the upgrade version 2. In an application domain, flow values can be respectively set for a conventional service path, a service path corresponding to tag1 and a service path corresponding to tag2 according to requirements when gray scale release is carried out, so that the purpose of drainage is achieved. And the multi-demand and multi-level drainage demand can be realized by setting the form of the gray scale service target.

For example, 10% traffic may be set for a regular service path, 40% traffic may be set for a service path corresponding to tag1, 50% traffic may be set for a service path corresponding to tag2, and so on.

However, it should be noted that the above is only described in the form of two gray service targets, and in practical implementation, more service targets may be set, for example, the service path corresponding to tag3 is a2-B2-C2-D2-E2, the service path corresponding to tag4 is a1-B1-C1-D1-E1, and so on, so as to implement the application of fully upgraded version1 and the application of fully upgraded version 2. In this way, an accurate drainage of one or more services in the target application can be achieved.

That is, the gray scale distribution method of the above application may further include: acquiring gray service targets configured for different versions of each service in the target application; generating a drainage rule according to gray level service targets configured for different versions of each service; storing the drainage rule, wherein the drainage rule is used for drainage in an application domain.

In consideration of the problem that the machine has a fault and the like to cause insufficient resources in the gray scale release process, the resources can be intelligently monitored in order to solve the problem, and the automatic capacity expansion of the resources is carried out when needed. After the gray scale release is completed, the expanded resources can be automatically recycled. Specifically, after the data analysis is performed on the gray level flow data, whether a fault exists in a machine in the gray level publishing cluster or whether machine resources are sufficient can be determined; under the condition that the machine is determined to have a fault or the machine resources are insufficient, carrying out capacity expansion on the gray scale publishing cluster; and after the gray scale release is finished, recycling the expanded resources.

For example, an intelligent monitoring driver may be provided, and when the intelligent monitoring driver finds that the grayscale distribution cluster machine has a fault or verifies that the required machine resources are insufficient, the resources are automatically expanded, and when the grayscale distribution is completed, the expanded resources are automatically recovered.

In order to implement the gray scale distribution method of the application, an application product system is provided in this example, as shown in fig. 3, and may include: the system comprises an environment infrastructure part, a flow control capacity part and an environment building capacity part.

Specifically, the method comprises the following steps:

1) the environmental infrastructure portion may include: the system comprises an environment view and an automated intelligent operation and maintenance, wherein the environment view can comprise: the grayscale view, the machine view and the isolation configuration are used for knowing the grayscale configuration of the current application access grayscale system; the automatic intelligent operation and maintenance is used for carrying out automatic operation and maintenance detection and alarm on various indexes of the gray level environment, and the gray level is driven to be released in a datamation mode to avoid risks;

2) the traffic management capability section may include: drainage capacity (including operation platform and on-line) for meeting different flow requirements; the system comprises traffic accurate configuration capability (comprising HTTP and HSF), which is used for identifying HTTP and HSF inlet traffic so as to meet the traffic requirement of HSF service characteristics at the back end of retail cloud film and support the introduction of test traffic; and the flow safety guarantee capability is realized, the flow is enabled/disabled, and the flow safety is guaranteed.

3) And the environment building capacity part is used for realizing the closed loop capacity of automatically and quickly building and quickly releasing the application dimension gray level environment by means of the environment capacity of the research and development operation platform. The method can comprise the following steps: an application delineation for applying pre-inspection and isolation presets; application access auditing for message notification; the application access is used for uniformly accessing dpath and nacos, newly adding access application, automatically expanding the capacity of a machine and automatically deploying the application; machine capacity expansion is used for automatic application deployment and secondary capacity expansion control; the machine is offline and used for automatically reducing the volume; and (4) releasing the environment.

Specifically, the method can further include a bottom layer isolation capability, and the implementation is, for example: middleware traffic isolation for HSF, Spring Cloud, Dubbo, MQ, Diamond, etc. And the service discovery and configuration capability is used for realizing service discovery, dynamic configuration service and dynamic dns service by means of the nacos capability.

A logical architecture for the gray center technique can be shown in fig. 4, including: the office network flow is hijacked to the safe production environment, 1% of the on-line flow enters the safe production environment through the cloud resolution DNS, and 99% of the on-line flow enters the on-line production environment.

Wherein the secure production environment may include: the system comprises an access layer, a middleware and a service layer, wherein the access layer can comprise an API gateway, an SLB and a TEengine, and the middleware can comprise dpath and nacos. The on-line production environment may include: access stratum, middleware, service stratum and cache database. Wherein, the access layer in the safe production environment can interact with the access layer in the on-line production environment through a single return line, the service layer in the safe production environment can interact with the middleware in the on-line production environment by using the on-line middleware, and the service layer in the safe production environment can interact with the on-line production environment by using the on-line cache, the database and the on-line production

The access layer can establish an independent cluster and is isolated from production, 1% of flow is defaulted based on GSLB shunting, and a rear-end route in the API gateway flow control plug-in is suitable for tenant routing and blue-green distribution. The access stratum marks the flow to facilitate later problem investigation. The gray level environment is applied by using an independent resource pool, the gray level environment is automatically created by application access, and the server is provided with a small flow identifier. The flow isolation uses dpath to realize middleware flow isolation, service flow rule configuration based on the dpath has gray scale identification and routing capability, and a gray scale center depends on an EDAS POP interface.

For the center of gray scale, as shown in fig. 5, it may include: a regulatory domain and an application domain. Wherein, the regulatory domain may include: and researching and developing an operation platform, a unified flow gateway, ARMS, Diamond, Tengine and APP. The application domain performs gray scale distribution based on rules. In the gray scale distribution process, the method may include: grey scale publication submission, grey scale space configuration items, cut-flow, deployment/rollback, drainage, and grey scale confirmation.

That is, the problem that service grayscales cannot be customized individually is solved by introducing a service drainage component, a traffic scheduling architecture and a flow, and a grayscale system is realized by a middleware traffic isolation component, for example: the flow of the middleware of HSF, Spring Cloud, Dubbo, MQ, Diamond and the like is isolated, intelligent analysis is carried out through flow data acquisition to realize intelligent flow allocation, and elastic gray scale is realized through the native elastic technology of Serverless Cloud, so that service simulation, gray scale release and the like are achieved to effectively guarantee the iterative upgrade of a service system.

Specifically, the gray scale publishing process may be as shown in fig. 6, and includes:

step 1: a gray level releasing user creates a gray level rule through a research and development operation platform, and the rule is configured on Diamond;

step 2: the user configures a service gray scale, and the service gray scale is stored in Diamond through the unified flow gateway;

and step 3: the user executes the gray level release process, and the intelligent monitoring driver ARMS monitors the change of the rule in real time;

and 4, step 4: collecting a gray level flow log from Tengine;

and 5: collecting a gray level flow log from an APP;

step 6: carrying out flow data analysis on gray level flow logs collected from Tengine and APP;

and 7: if the data is abnormal, the user can adjust the gray level flow through manual intervention of a research and development operation platform to switch the flow;

and 8: if the data is normal, automatically calling the unified flow gateway to initiate gray level flow adjustment (for example, adjusting the gray level flow from 5% to 20%) after the preset issuing observation period time is reached, then continuing new flow issuing observation period time, and if the data is normal, continuing gray level flow adjustment until the flow is switched to the gray level environment by 100%, namely, completing switching from the blue environment to the green environment, thereby completing gray level issuing/blue-green issuing;

and step 9: if the data is abnormal, the nailing robot informs the gray level issuing user to perform manual intervention in real time;

step 10: analyzing insufficient data machines or machine faults, and expanding the capacity of resources if the capacity of the resources needs to be expanded;

step 11: and after the gray scale release is finished, automatically recovering the capacity expansion resources.

Namely, the accuracy and reliability of gray scale release are ensured through mechanisms such as intelligent gray scale flow scheduling, manual intervention in special scenes, intelligent monitoring and alarming and the like; when the intelligent monitoring driver finds that the gray scale publishing cluster machine fails or the machine resources required by verification are insufficient, the intelligent monitoring driver can automatically expand the resources; and when the gray release is finished, the expanded resources can be automatically recovered.

Specifically, the application issuing method may be applied to an application issuing side of a developer, for example, the developer may input a gray level issuing rule to construct a gray level rule, and then upload an application to be subjected to gray level issuing, so as to trigger gray level issuing on the application to be subjected to gray level issuing, and in a gray level issuing process, gray level flow data is acquired and data analysis is performed on the gray level flow data; and if the gray scale flow data does not accord with the preset conditions, generating gray scale release alarm information, wherein the alarm information is used for triggering gray scale rule adjustment.

The application to be grayed-out may be, but is not limited to, an e-commerce platform application.

The application release method is applied to the retail cloud scene, and can comprise the following steps:

step 1: receiving an input gray level release rule of the retail cloud application;

step 2: constructing a gray rule according to an input gray release rule;

and step 3: receiving an uploaded retail cloud application update package;

and 4, step 4: carrying out gray scale release on the retail cloud application, acquiring gray scale flow data in the gray scale release process, and carrying out data analysis on the gray scale flow data;

and 5: if the gray level flow data does not accord with the preset conditions, generating gray level release alarm information, wherein the alarm information is used for triggering gray level rule adjustment;

step 6: and if the gray scale flow data meet the preset conditions, adjusting the gray scale flow according to a preset gray scale rule after reaching a preset observation period.

The method embodiments provided in the above embodiments of the present application may be executed in a server, a computer terminal, or a similar computing device. Taking the operation on the server side as an example, fig. 7 is a hardware structure block diagram of a server of the grayscale publishing method applied in the embodiment of the present invention. As shown in fig. 7, the server 10 may include one or more (only one shown) processors 102 (the processors 102 may include, but are not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA, etc.), a memory 104 for storing data, and a transmission module 106 for communication functions. It will be understood by those skilled in the art that the structure shown in fig. 7 is only an illustration and is not intended to limit the structure of the electronic device. For example, the server 10 may also include more or fewer components than shown in FIG. 7, or have a different configuration than shown in FIG. 7.

The memory 104 may be used to store software programs and modules of application software, such as program instructions/modules corresponding to the gray scale issuing method of the application in the embodiment of the present invention, and the processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, the gray scale issuing method of the application implementing the application program. The memory 104 may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory located remotely from the processor 102, which may be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The transmission module 106 is used to receive or transmit data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of the computer terminal 10. In one example, the transmission module 106 includes a Network adapter (NIC) that can be connected to other Network devices through a base station to communicate with the internet. In one example, the transmission module 106 may be a Radio Frequency (RF) module, which is used for communicating with the internet in a wireless manner.

In terms of software, the gray scale issuing apparatus applied as described above may be as shown in fig. 8, and includes:

the acquisition module 801 is used for acquiring gray level flow data in the gray level publishing process;

an analysis module 802, configured to perform data analysis on the grayscale flow data;

a generating module 803, configured to generate a grayscale release alarm message when the grayscale flow data does not meet a preset condition, where the alarm message is used to trigger a grayscale rule adjustment.

In an embodiment, the above-mentioned applied gray scale publishing device may further include an adjusting module, configured to adjust the gray scale flow according to a preset gray scale rule after a preset observation period is reached when the gray scale flow data meets a preset condition;

in one embodiment, the grayscale issuing device of the above application may further include: the sending module is used for sending the alarm information to a preset communication application after the gray scale release alarm information is generated; the display module is used for displaying the alarm information through the communication application; the receiving module is used for receiving a flow rule set by a user; the switching module is used for switching the gray level flow to a flow value corresponding to a flow rule set by a user; and the issuing module is used for issuing the gray level according to the switched gray level flow.

In one embodiment, the grayscale issuing device of the above application may further include: the first acquisition module is used for responding to a gray rule configuration request before acquiring gray flow data in the gray release process and acquiring a gray rule configured for target application; and the storage module is used for storing the gray rule.

In one embodiment, the grayscale issuing device of the above application may further include: the second acquisition module is used for acquiring gray service targets configured for different versions of each service in the target application; the generation module is used for generating a drainage rule according to the gray level service targets configured for different versions of each service; and the storage module is used for storing the drainage rule, wherein the drainage rule is used for conducting drainage in an application domain.

In one embodiment, the grayscale issuing device of the above application may further include: the determining module is used for determining whether the machine in the gray level issuing cluster has a fault or whether the machine resources are sufficient after the data analysis is carried out on the gray level flow data; the capacity expansion module is used for expanding the gray level distribution cluster under the condition that the fault exists in the machine or the machine resources are insufficient; and the recovery module is used for recovering the expanded resources after the gray release is finished.

In one embodiment, the above-mentioned acquisition module 801 may include: the first acquisition unit is used for acquiring a gray level flow log; and the second acquisition unit is used for acquiring the gray scale flow data according to the gray scale flow log.

An embodiment of the present application further provides a specific implementation manner of an electronic device, which is capable of implementing all steps in the grayscale issuing method of the application in the foregoing embodiment, where the electronic device specifically includes the following contents: a processor (processor), a memory (memory), a communication Interface (Communications Interface), and a bus; the processor, the memory and the communication interface complete mutual communication through the bus; the processor is configured to call a computer program in the memory, and the processor implements all the steps in the grayscale release method applied in the above embodiment when executing the computer program, for example, the processor implements the following steps when executing the computer program:

step 1: collecting gray level flow data in the gray level publishing process;

step 2: performing data analysis on the gray level flow data;

and step 3: if the gray scale flow data meet the preset conditions, adjusting the gray scale flow according to a preset gray scale rule after a preset observation period is reached;

and 4, step 4: and if the gray scale flow data does not accord with the preset conditions, generating gray scale release alarm information, wherein the alarm information is used for triggering gray scale rule adjustment.

As can be seen from the above description, in the embodiment of the present application, in the process of gray scale publishing, gray scale flow data is collected, and then data analysis is performed on the gray scale flow data to determine whether there is an abnormality, if it is determined that there is an abnormality in the gray scale flow data, a gray scale publishing alarm message is generated to perform manual intervention on the gray scale publishing process, and if there is no abnormality, automatic gray scale publishing is performed continuously according to a preset gray scale rule. Through the method, the gray scale can be issued according to the preset rule, and the problem in the issuing process can be found in real time for manual intervention, so that the problem that the existing gray scale issuing cannot be intelligently analyzed and adjusted based on the real-time flow condition can be solved, and the purpose of intelligent flow adjustment is achieved.

Embodiments of the present application also provide a computer-readable storage medium capable of implementing all steps in the grayscale distribution method of the application in the above embodiments, where the computer-readable storage medium stores thereon a computer program, and when the computer program is executed by a processor, the computer program implements all steps of the grayscale distribution method of the application in the above embodiments, for example, the processor implements the following steps when executing the computer program:

step 1: collecting gray level flow data in the gray level publishing process;

step 2: performing data analysis on the gray level flow data;

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the hardware + program class embodiment, since it is substantially similar to the method embodiment, the description is simple, and the relevant points can be referred to the partial description of the method embodiment.

The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.

Although the present application provides method steps as described in an embodiment or flowchart, additional or fewer steps may be included based on conventional or non-inventive efforts. The order of steps recited in the embodiments is merely one manner of performing the steps in a multitude of orders and does not represent the only order of execution. When an actual apparatus or client product executes, it may execute sequentially or in parallel (e.g., in the context of parallel processors or multi-threaded processing) according to the embodiments or methods shown in the figures.

The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a vehicle-mounted human-computer interaction device, a cellular telephone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

Although embodiments of the present description provide method steps as described in embodiments or flowcharts, more or fewer steps may be included based on conventional or non-inventive means. The order of steps recited in the embodiments is merely one manner of performing the steps in a multitude of orders and does not represent the only order of execution. When an actual apparatus or end product executes, it may execute sequentially or in parallel (e.g., parallel processors or multi-threaded environments, or even distributed data processing environments) according to the method shown in the embodiment or the figures. The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, the presence of additional identical or equivalent elements in a process, method, article, or apparatus that comprises the recited elements is not excluded.

For convenience of description, the above devices are described as being divided into various modules by functions, and are described separately. Of course, in implementing the embodiments of the present description, the functions of each module may be implemented in one or more software and/or hardware, or a module implementing the same function may be implemented by a combination of multiple sub-modules or sub-units, and the like. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.

Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may therefore be considered as a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.

The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.

Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.

As will be appreciated by one skilled in the art, embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, embodiments of the present description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present description may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and so forth) having computer-usable program code embodied therein.

The embodiments of this specification may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The described embodiments may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment. In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of an embodiment of the specification. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.

The above description is only an example of the embodiments of the present disclosure, and is not intended to limit the embodiments of the present disclosure. Various modifications and variations to the embodiments described herein will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the embodiments of the present specification should be included in the scope of the claims of the embodiments of the present specification.

Claims

1. A method for gray scale publishing for an application, the method comprising:

collecting gray level flow data in the gray level publishing process;

performing data analysis on the gray level flow data;

2. The method of claim 1, further comprising, after data analyzing the gray scale flow data:

and if the gray scale flow data meet the preset conditions, adjusting the gray scale flow according to a preset gray scale rule after reaching a preset observation period.

3. The method of claim 1, after generating the grayscale release alarm message, further comprising:

sending the alarm information to a preset communication application;

displaying the alarm information through the communication application;

receiving a flow rule set by a user;

switching the gray level flow to a flow value corresponding to a flow rule set by a user;

and performing gray scale distribution according to the switched gray scale flow.

4. The method of claim 1, wherein before collecting the gray-scale flow data during the gray-scale distribution, the method further comprises:

responding to a gray rule configuration request, and acquiring a gray rule configured for a target application;

and storing the gray rule.

5. The method of claim 1, further comprising:

acquiring gray service targets configured for different versions of each service in a target application;

generating a drainage rule according to gray level service targets configured for different versions of each service;

storing the drainage rule, wherein the drainage rule is used for drainage in an application domain.

6. The method of claim 1, wherein after performing data analysis on the gray scale flow data, further comprising:

determining whether a machine in the gray scale publishing cluster has a fault or whether machine resources are sufficient;

under the condition that the machine is determined to have a fault or the machine resources are insufficient, carrying out capacity expansion on the gray scale publishing cluster;

and after the gray scale release is finished, recycling the expanded resources.

7. The method of claim 1, wherein collecting gray scale flow data comprises:

acquiring a gray level flow log;

and acquiring gray scale flow data according to the gray scale flow log.

8. An application publishing method, comprising:

receiving an input gray level release rule;

constructing a gray rule according to an input gray release rule;

receiving the uploaded application to be subjected to gray release;

9. The method of claim 8, wherein the application to be grayed out is an e-commerce platform application.

10. An application publishing method, comprising:

receiving an input gray level release rule of the retail cloud application;

constructing a gray rule according to an input gray release rule;

receiving an uploaded retail cloud application update package;

11. A gradation issuing apparatus for an application, characterized by comprising:

12. The apparatus of claim 11, further comprising:

and the adjusting module is used for adjusting the gray scale flow according to a preset gray scale rule after the gray scale flow data meets a preset condition after the gray scale flow data is subjected to data analysis and the gray scale flow data reaches a preset observation period.

13. The apparatus of claim 11, further comprising:

the sending module is used for sending the alarm information to a preset communication application after the gray scale release alarm information is generated;

the display module is used for displaying the alarm information through the communication application;

the receiving module is used for receiving a flow rule set by a user;

the switching module is used for switching the gray level flow to a flow value corresponding to a flow rule set by a user;

and the issuing module is used for issuing the gray level according to the switched gray level flow.

14. The apparatus of claim 11, further comprising:

the first acquisition module is used for responding to a gray rule configuration request before acquiring gray flow data in the gray release process and acquiring a gray rule configured for target application;

and the storage module is used for storing the gray rule.

15. The apparatus of claim 11, further comprising:

the second acquisition module is used for acquiring gray service targets configured for different versions of each service in the target application;

the generation module is used for generating a drainage rule according to the gray level service targets configured for different versions of each service;

and the storage module is used for storing the drainage rule, wherein the drainage rule is used for conducting drainage in an application domain.

16. The apparatus of claim 11, further comprising:

the determining module is used for determining whether the machine in the gray level issuing cluster has a fault or whether the machine resources are sufficient after the data analysis is carried out on the gray level flow data;

the capacity expansion module is used for expanding the gray level distribution cluster under the condition that the fault exists in the machine or the machine resources are insufficient;

and the recovery module is used for recovering the expanded resources after the gray release is finished.

17. The apparatus of claim 11, wherein the acquisition module comprises:

the first acquisition unit is used for acquiring a gray level flow log;

and the second acquisition unit is used for acquiring the gray scale flow data according to the gray scale flow log.

18. An application issuing system comprising a processor and a memory for storing processor-executable instructions which, when executed by the processor, implement the steps of the method of any one of claims 1 to 7.

19. A computer readable storage medium having stored thereon computer instructions which, when executed, implement the steps of the method of any one of claims 1 to 7.