Patent Description:
Application lifecycle management is a technique to control an application from the idea phase through development, deployment, upgrade, and removal from service. Tools for managing the lifecycle of applications are able to deploy, troubleshoot, manage governance, and test applications in a structured way.

Terabytes, petabytes, and exabytes of data are generated daily by digital devices and services. Big data is a field that analyzes vast data sets too large for traditional software. Data may include archived documents, documents and websites, media files (images, videos, audio files), data storage systems (repositories, databases, file systems), business applications, public and governmental data sources, social media activity, machine log data, and sensor data (from media devices, intemet-of-things devices, appliances, farm equipment, vehicle sensors). This data may be analyzed or mined to perform prediction, analysis, modeling, and training for artificial intelligence. Monitoring and analyzing the data, however, may be a challenge due to the volume, quality, veracity, speed of data received as well as the use of numerous systems and applications to complete.

Because of the hardware and software requirements placed upon systems running and storing big data applications tools for managing the lifecycle of non-big-data applications cannot work for big-data applications. Thus, there is a need for tools to manage the lifecycle of these applications including creating, building, testing, deploying, and maintaining big- data applications.

<CIT> discloses systems for accelerating the development and distribution of data science workloads, including a consistent, portable and preconfigured data science workspace for development of data science containers. The containers may be submitted to a build and deployment process that ensures consistency across multiple environments in terms of the application code and the operating system environment. <CIT> discloses a system for testing a network-based application has a continuous integration (CI) service that performs CI testing of a server application that is being developed to support a client application. The CI service detects server source code changes and in response rebuilds the server application and deploys it to a test server. An article entitled "<NPL> describes that container-based virtualization techniques. Containerization techniques such as Docker are used to speed-up big data applications. VM provides distributed resource management for different virtual machines running with their own allocated resources, while Docker relies on shared pool of resources among all containers.

The invention is defined in the independent claim. Optional features are defined in the dependent claims. It should be appreciated that like reference numerals are used to identify like elements illustrated in one or more of the figures, whereas showings therein are for purposes of illustrating embodiments of the present disclosure and not for purposes of limiting the same.

In the following description, specific details are set forth describing some embodiments consistent with the present disclosure. It will be apparent, however, to one skilled in the art that some embodiments may be practiced without some or all of these specific details. The specific embodiments disclosed herein are meant to be illustrative but not limiting. One skilled in the art may realize other elements that, although not specifically described here, are within the scope of this disclosure. In addition, to avoid unnecessary repetition, one or more features shown and described in association with one embodiment may be incorporated into other embodiments unless specifically described otherwise or if the one or more features would make an embodiment non-functional.

Aspects of the present disclosure involve systems, methods, devices, and the like for creating an application lifecycle management platform for big data applications. A big- data application includes an application where the data cannot fit on a single computer/server or when a single computer cannot process a single transaction. The process to manage lifecycles of big-data applications, which includes creating, building, testing, deploying, analyzing, and maintaining big-data applications. In certain systems, lifecycle management has many steps, many of which are manual, and needs direct contact and communication with many teams/platforms and services.

Features to manage application life cycles are built on top of multiple development and management platforms. These platforms may include, for example, an orchestration platform for code development, building, deployment, and monitoring. Platforms may also include a container management platform, APACHE MESOS, GITHUB, JENKINS, TWISTLOCK, Assembler that creates lower-level code from higher-level languages, the HADOOP Ecosystem, the Kerberos security-focused network authentication protocol, LIVY and other related RESTful interfaces to submit and manage APACHE SPARK jobs on server clusters, a credential and secret management platform used to store encrypted keys and passwords such that they do not need to be stored on the disk, and the GIMEL unified API to access data stored on any data store and platform to write SQL to access data on any data store.

According to the present embodiments, these services may be modified and improved to accommodate big-data applications. In one embodiment, a multiple-layer container file (e.g. a DOCKER image) may be created that integrates with the above tools/platforms. Each layer may add capabilities to work with one or more of the described platforms. The multiple-layer container may allow multiple platform teams to create generic components that each platform team can build for their developers which may then be layered on top of base code. This may allow for increased code reusability and standardization between development teams in a single organization. In one embodiment, a four-layer container is described. The first layer describes a base layer containing tools, platforms, and configurations for a runtime environment, networking and security. The first layer may also include other core software and core libraries such as python, curl, clib, among many other libraries and software. In some examples, the first layer includes an operating system. This operating system may be a customized operating system to be used by programmers and to run applications. For example, the operating system may include software and libraries for creating and building programs. The second layer describes a big-data layer which contains features to access a big-data framework (e.g., the APACHE HADOOP Ecosystem including APACHE PIG, APACHE HIVE, APACHE HBASE, APACHE PHOENIX, APACHE SPARK, APACHE ZOOKEEPER, CLOUDERA IMPALA, APACHE FLUME, APACHE SQOOP, APACHE OOZIE, and APACHE STORM) and upload the application dependencies to a distributed file-system (e.g., the HADOOP Distributed File System (HDFS)). The third layer describes a platform/team layer including features required by each specific platform or team, such as templates for runtime parameters and dependencies. The fourth layer describes an application layer which contains features used or required by the application such as application specific dependencies.

In embodiments of the present disclosure, a user instructs the system to create a new big-data application for development. A user may be a single developer, or a team of developers, or an organization or sub-organization of developers. The system creates a generic template application (in some embodiments the generic application is developer-group or team specific), creates a build environment for the generic template application, creates a test environment for the generic template application and runs the built generic template application in the test environment prior to the user writing any new code in the generic template application. An advantage of the present embodiments may enable developers/development teams to focus on their specific application's code and not have to worry about environments, infrastructure complexity, connectivity, etc. This problem is more difficult in the big data world because of the number of components, infrastructure complexity and connectivity compared to a traditional relational database management system (RDBMS)-based application world. In one embodiment, the test environment includes a virtual machine/container application that launches the big data application (which may be the generic template application before a developer edits the file) on a separate big-data server cluster. In certain environments, it may be difficult to create a test or stage environment for data applications, especially big-data applications, because of the various dependencies which need to be part of that environment and because the APIs and interactions may be finicky when even minor versions differ. Embodiments of the present disclosure alleviate this by testing the generic components in build and test environments before testing application code providing a simplified debugging process. In some examples, the virtual machine/ container application is launched on a cluster management system and the big-data application runs on a big-data cluster. After the generic template application is built and tested, the user or group of users may update the generic template application with application specific code in a code repository. Updating/saving the application in the code repository or updating a master branch or version of the big-data application may trigger the build environment to rebuild the big-data application. The test environment may then automatically run the new version of the big-data application.

An automated tool for big-data application lifecycle management may improve the experience of developers creating and managing applications and improve operations to view and control running applications. The automated big-data application lifecycle management system may minimize the amount of manual data entry in setting up and building big-data applications, create a standardized environment with which to create and test big-data applications, and allow for multiple big-data applications to be scheduled, tested, and managed that allows for integration with third party (open source and proprietary) tools and platforms. The big-data application lifecycle management platform, among other things, may also scan the application for vulnerabilities, enforced coding standards, and provide monitoring and alerting.

Therefore, in one embodiment, a system <NUM> for performing application lifecycle management configured to manage the lifecycle of big-data applications is introduced. <FIG> presents a system <NUM> for performing lifecycle management of big-data applications. A lifecycle management platform <NUM> may include an interface for a user device <NUM> to connect to directly or via a network <NUM>. System <NUM> may also include a build environment <NUM>, a code repository <NUM>, and a configuration and settings server <NUM>. System <NUM> may include a test environment <NUM> and a production environment <NUM>. Each of test environment <NUM> and production environment <NUM> may include one or more scheduling and management servers <NUM> and big-data server clusters <NUM>.

User device <NUM> may be implemented using any appropriate combination of hardware and/or software configured for wired and/or wireless communication over network <NUM>. For example, in one embodiment, user device <NUM> may include a personal computer device. The personal computer may include desktop computing systems, laptop/notebook computing systems, tablet computing systems, mobile phones, Application Specific Integrated Circuit (ASIC) computing systems, an internet of things (IoT) device, and/or other computing device known in the art. The personal computer may include software (e.g., applications or a browser interface) to perform or connect to devices that may be configured to perform big-data application lifecycle management e.g., the lifecycle management platform <NUM>.

User device <NUM> may include one or more applications, e.g., browser applications which may be used, for example, to provide an interface to permit the user to browse information available over the network <NUM>. For example, in one embodiment, the application may be a web browser configured to view information available over the Internet.

Lifecycle management platform <NUM> is configured to manage the lifecycle of big-data applications. Lifecycle management platform <NUM> may be configured to interact with user device <NUM>. For example, lifecycle management platform <NUM> may present an interface to setup a new big-data application, to check the current status of a big-data application including whether the application is new, has been built, has been tested in a test environment (e.g. test environment <NUM>), or is running in a production environment (e.g., production environment <NUM>). Further, lifecycle management platform <NUM> may provide an interface to view logs and monitor settings and resource use of server clusters, governance, access control, and any errors or alerts. Lifecycle management platform <NUM> may connect with code repository <NUM> to store and manage code.

Further, lifecycle management platform <NUM> may provide that data analytics and integration that enable users to register their big data applications and deploy on production. Lifecycle management platform <NUM> enables the ability to view available datasets, view schema, and view system and object attributes. Lifecycle management platform <NUM> may auto discover datasets across all data stores. Lifecycle management platform <NUM> may provide a dashboard and alerts which may include operational metrics including statistics, refresh times and trends, visibility on approvals and audits as well as administrative alerts (e.g., capacity issues, data access violations, and data classification violations) and user alerts (e.g., refresh delays, and profile anomalies). Further, the lifecycle management platform <NUM> can include a query and integration feature designed for the integration of notebooks and tools. To provide such features, lifecycle management platform <NUM> may provide categorization, classification, analysis, and mechanisms for running and storing the big data.

Lifecycle management platform <NUM> can be a platform designed to provide a unified user experience for any computer engine. That is to say, lifecycle management platform <NUM> can be an analytics platform or framework which enables a user to run one or more big data applications. For example, lifecycle management platform <NUM> can enable a user to run big data applications/data processing frameworks including but not limited to SPARK, HIVE, PRESTO, etc. The big data applications can be run through a software architectural style or other interface protocol-based application programming interface (API). For example, the API can include but is not limited to representational state transfer (REST) based API, Thrift based API, and simple object access protocol (SOAP) based API. Additionally, lifecycle management platform <NUM> may be designed to run without installation, setup, and/or configuration. Lifecycle management platform <NUM> may therefore be used to provide a complete set of tools and technologies for application development, execution, logging, monitoring, alerting, security, workload management, performance tuning, etc. Further, lifecycle management platform <NUM> may be used to support general compute engines (e.g., SPARK) for large-scale data processing and for running interactive code(s), scheduling jobs, and for machine learning analysis. For example, interactive SPARK may be used with interactive shells, JUPYTER Notebooks, APACHE ZEPPELIN, and Squirrel/DBVISUALIZER SQL clients. As an example of scheduled jobs, lifecycle management platform <NUM> may be used to schedule jobs with low latency applications, batch heavy applications, and streaming applications in coordination with one or more scheduling and management servers <NUM>. Benefits of lifecycle management platform <NUM> include and not limited to improvements in administration (e.g., less maintenance, deployment of software stack, and ability to administer system configurations at one place), operations/Security (e.g., through single job execution, coding standards, logging, monitoring and alerting, auditing, and complete statement level history and metrics), development (e.g., through application modularity, ease of restorability, decreased latency, cache sharing, etc.), and analytics (e.g., for direct SQL execution, multi-user support notebooks ability, user friendly interactive applications, and authentication integration).

Lifecycle management platform <NUM> that can provide a unified access API for any data storage. In particular, lifecycle management platform <NUM> can provide scalable platform services. Such scalable data services can include data integration. For example, lifecycle management platform <NUM> can facilitate the orchestration of the acquisition and transformation of data and provide a reliable and secure delivery means of the data to various destinations via streaming or batch. The core data platform can also be used as an analytics data processing platform for accessing the data located in big-data applications (e.g., HADOOP) and data and analytics platform. In addition, lifecycle management platform <NUM> can also provide access to data storage for self-service lifecycle management of a singular and clustered data stores and management of commodity-based storage. Additionally, lifecycle management platform <NUM> can be used for learning, optimizing, building, deploying and running various applications and changes. For example, the data application lifecycle on lifecycle management platform <NUM> can include onboarding big-data applications and managing compute engine changes, compute version changes, storage API changes, storage connector upgrades, storage host migrations, and storages changes.

Build environment <NUM> may be configured to convert source code files into standalone software programs (artifacts, binaries, executables) or files ready to be interpreted that can be run on a computer/server cluster. Build environment <NUM> may be configured to collect dependent software code and included libraries for use in building the big-data application. Build environment <NUM> may be a standalone server or use shared resources. Build environment <NUM> may use automated tools to trigger builds and scheduling regular builds, or building an application based on a trigger, e.g., a commit in code repository <NUM>. In one example, build environment <NUM> may use continuous delivery development tools such as JENKINS.

Code repository <NUM> may be configured to provide storage and development tools for a big-data application. Code repository <NUM> may provide collaboration tools, version control, issue tracking, and documentation, for a developer. Code repository <NUM> may be integrated directly into lifecycle management platform <NUM> or may be a third-party server/application (e.g., GITHUB using a version control system such as GIT) that can connect to lifecycle management platform <NUM> via a network.

Configuration and settings server <NUM> may contain and manage configurations and settings for various server environments such as test environment <NUM> and production environment <NUM>. Lifecycle management platform <NUM> may connect to configuration and settings server <NUM> and retrieve settings and configuration files and templates for development of a big-data application. Settings may include the address of a resource manager and addresses of where data is stored.

Test environment <NUM> and production environment <NUM> are two separated server environments. Test environment <NUM> and production environment <NUM> may have the same or similar settings, however, the data used in test environment <NUM> uses test data and is not configured to be accessed by end users while data used in production environment <NUM> is a live data and production environment <NUM> will run the application for an end user processing operational data. Test environment <NUM> and production environment <NUM> may include one or more scheduling and management servers <NUM>. Each of scheduling and management servers <NUM> may be associated with a big-data server cluster <NUM>. In another embodiment, scheduling and management servers <NUM> may be associated with multiple big-data server clusters <NUM>.

Scheduling and management servers <NUM> may be configured to manage the big-data application on a big-data server cluster <NUM>. In some examples, a virtual machine or container application is created on scheduling and management servers <NUM> to launch a big-data application on big-data server cluster <NUM>. Scheduling and management servers <NUM> may provide scheduling of server resources between multiple applications (or multiple versions of the same application) running on a big-data server cluster <NUM>. Scheduling and management servers <NUM> may trigger a big-data application to be run after another application completes or fails or when there is new data available for processing. Scheduling and management servers <NUM> may also trigger a big-data application under other circumstances. As an example, the scheduling and management servers <NUM> may trigger a second application as soon as a first application starts regardless of the first application's ultimate success or failure. As another example, the scheduling and management servers <NUM> may trigger the second application <NUM> minutes after application B starts. As another example, the second application may get triggered if the first application fails a number of times consecutively (e.g., the first application fails three times). Scheduling and management servers <NUM> may trigger a big-data application to run after a particular period of time (daily, hourly, etc.). Big-data applications may be throttled by the scheduling and management servers <NUM> to spread out when applications begin. Resource usage metrics and statistics may be collected by scheduling and management servers <NUM>. Recommendations may be provided to lifecycle management platform <NUM> on when to run the big-data application. Other analysis performed by scheduling and management servers including timing of running the big-data application (and running the application in relation to other applications), predictions, risk assessments, resource utilization, and more.

Big-data server clusters <NUM> may include a group of servers working together to run one or more applications. Big-data server clusters <NUM> may include a scalable big-data storage (e.g. HDFS) storage pool on a plurality of commodity computer systems. In some embodiments, specialized hardware for big-data applications is used.

<FIG> illustrates an exemplary block diagram of a template container used by lifecycle management platform <NUM>. Build environment <NUM> and/or lifecycle management platform <NUM> generates and uses a template file or a template <NUM> to organize and automate the big-data lifecycle management. Template <NUM> is a multi-layer container file that contains all or the majority of settings, tools, and utilities, to test, build, run, manage, and analyze a big-data application. Template <NUM> includes a four-level container file with a base layer <NUM>, a big-data layer <NUM>, a group layer <NUM>, and an application layer <NUM>.

An organization may have multiple development groups/teams. In some examples, different groups may contribute to and develop different layers of template <NUM>. For example, base layer <NUM> may be developed by a base platform team and a security team. Bigdata platform team and big-data teams may develop big-data layer <NUM>. The developer may develop application layer <NUM>. The developer may develop application layer <NUM> with a source code management team and a build and test team. The group that the developer is a member of may develop group layer <NUM>. Overall template function may be overseen by an application lifecycle team, an application repository team, a container management team, a hardware provisioning team, and/or a support team.

In some examples, template <NUM> is a DOCKER image/container file. A DOCKER image is a file, which may include multiple layers, used to execute code in a DOCKER container. A DOCKER image is essentially built from the instructions for a complete and executable version of an application, which may rely on a host operating system (OS) kernel. In some examples, template <NUM> is a DOCKER image with a readable/writeable layer (for the big-data application) on top of one or more read-only layers. These layers (also called intermediate images) may be generated when the commands in the container/DOCKER file are executed during the container/DOCKER image build.

Base layer <NUM> includes settings, utilities, and tools used in the runtime environment, networking, and security for the application. Settings in base layer <NUM> may include settings applicable for big-data and non-big-data applications including the operating system <NUM> (e.g., a LINUX distribution, WINDOWS, APPLE OS etc.), platform tools and configurations <NUM> including low level network topological settings and sever cluster configurations and settings, and platform security tools and configurations <NUM>. Security tools may include connections to secure key chain applications to provide access permissions to resources and to protect users and application credentials, passwords, and other secrets.

Big-data layer <NUM> includes features to access a big-data ecosystem (e.g., a HADOOP ecosystem) and upload dependencies to a big-data file system (e.g., HDFS). Bigdata layer <NUM> includes big-data configuration and libraries <NUM> which may include information regarding how to connect to one or more big-data clusters <NUM> in either test environment <NUM> or production environment <NUM>. An orchestration, monitoring, and alerting client <NUM> may be an interface configured to retrieve information from scheduling and management servers <NUM> to retrieve information regarding logging, monitoring, governance, access control, and alerting information. A launch, kill, update, and recover client <NUM> may be an interface configured to launch and manage a big-data application that is running on one or more big-data cluster <NUM>. Big-data file system client <NUM> may include a HADOOP HDFS client. Big-data file system client <NUM> may include an interface to communicate with the big-data file system and perform file related tasks like reading and writing block data. A job server client <NUM> (e.g., an APACHE Livy client) may be used to interact with one or more scheduling and management server <NUM> to instruct or receive information regarding scheduling of jobs. Job server client <NUM> may include an API or REST interface.

Group layer <NUM> includes user specific features, where a user includes platform/development team/group specific features, that are specifically required or useful for a particular team. These features include templates for runtime parameters and dependencies. A team/group includes a group of developers that create similar applications having the same end points or library dependencies. Group layer <NUM> may include shared libraries <NUM>. Shared libraries <NUM> may include shared libraries that will be included in each application developed by the specific team. Group layer <NUM> may include shared test environment settings <NUM> and shared production environment settings <NUM> to interact with a big-data ecosystem (e.g., the HADOOP ecosystem) on a big-data server cluster <NUM> as a group may have one or more dedicated big-data server clusters <NUM> for testing or production.

Application layer <NUM> includes features required by the application such as application specific dependencies, tools, or utilities. This includes dependencies that are not typically found or used by the specific development team (which may be placed in group layer <NUM>). Application layer <NUM> includes user libraries and configuration <NUM> which includes all code that the user application <NUM> depends on (to be built). A user application <NUM> initially comprises a stub or template application. In some examples, user application <NUM> may be a team/group specific stub or template file. The template file may be buildable and able to be run in test environment <NUM> (or production environment <NUM>).

Referring now to <FIG> is a flow diagram <NUM> illustrating the use of system <NUM> to manage the lifecycle of a big-data application according to one embodiment. User device <NUM> may connect to lifecycle management platform <NUM> via network <NUM>. Lifecycle management platform <NUM> may present a user interface to user device <NUM> to create a new big-data application. The user interface may request certain information in order to generate a new big-data application. In one example, lifecycle management platform <NUM> requests a team or development group of the big-data application to create the big-data application.

Lifecycle management platform <NUM> creates a source code template, at block <NUM>. The source code template may be specific to a particular development or user group. In certain examples, the group may be specified by user input. In other embodiments, the group may be implied by a user's credentials connecting to the lifecycle management platform <NUM>. Lifecycle management platform <NUM> creates a container instance, at block <NUM>. The container instance stores the source code template.

Lifecycle management platform <NUM> sets up build environment <NUM> for building the lifecycle management application and test environment <NUM> to run the big-data application on big-data server cluster <NUM>, at block <NUM>. Scheduling and management server <NUM> may be configured by lifecycle management platform <NUM> to test the big-data application.

Lifecycle management platform <NUM> may instruct build environment <NUM> to build and test a stub application, at block <NUM>. The stub application may include only the automatically generated files (based on, e.g., the source code template) and not any user modified or created files. In some examples, the build creates a self-contained container and/or a set of container compose files and may contain all the library dependencies to run the stub applications.

If the building and testing succeeds, the user may modify the big-data application via the source code template. Modification of the source code may occur using a code repository <NUM>. When complete (or prior to completion to test the code), a user may commit or finalize the code. This procedure may trigger the source code and dependencies to be transferred to the lifecycle management platform <NUM>, at block <NUM>. In other examples, a user may manually provide the updated source code to the lifecycle management platform <NUM>. The updated source code may be placed in the container instance by lifecycle management platform <NUM>. Lifecycle management platform <NUM> may re-build and re-test the newly updated code/application, at block <NUM>.

Prior to using lifecycle management platform <NUM>, users and groups may generate a template or stub application source code file and configuration files. The lifecycle management platform <NUM> may use these pre-generated application source code files and configuration files to generate a self-contained container file.

User device <NUM> may submit and lifecycle management platform <NUM> and receive an instruction to create a new application specifying a user group, at block <NUM>. Lifecycle management platform <NUM> may create a source code template specific to the user group submitted, at block <NUM>. Lifecycle management platform <NUM> may create a container instance (e.g., a DOCKERFile), at block <NUM>. The container instance may store the source code template specific to the user group created at block <NUM>.

Lifecycle management platform <NUM> may setup build environment <NUM> for building the lifecycle management application, at block <NUM>. Lifecycle management platform <NUM> may setup a test environment at block <NUM> with the scheduling and management server <NUM> of test environment <NUM> to run the big-data application on big-data server cluster <NUM>.

Lifecycle management platform <NUM> may instruct build environment <NUM> to build a stub application. In some examples, the build creates a self-contained container (e.g., a DOCKER image) and a set of container (e.g. DOCKER) compose files. In an example, self-contained means: the DOCKER image contains all or substantially all of the binary files required to run the application. In some examples, the binary files are java archive (. jar) files. Further, the DOCKER image may contain all the library dependencies to run the applications. In some cases, library dependencies are managed by a dependency manager like MAVEN, SBT, and/or GRADEL. The DOCKER image may contain multiple sets of configurations to run the applications on four different environments. These environments may include a base/development environment, a quality assurance(QA) environment which may include user stages of a cloud platform such as the GOOGLE CLOUD platform (GCP), a sandbox environment (for third-party use), and production environments. Multiple combinations of environments and configurations may be used for the same application. For example, a single application may have one configuration set for the development environment, three configuration sets for QA environments, one configuration set for a sandbox environment, and five configuration sets for production environments.

The DOCKER image may contain scripts to connect to a keychain application to get the secrets/passwords required to run the application securely from the keychain application. The DOCKER image may contain scripts to put all the files required to run the application on the big-data file system (e.g. HDFS). The DOCKER image may contain scripts to submit a new job to a job assignment/scheduling (e.g. a Livy) server. The DOCKER image may contain scripts configured to check the status of a submitted big-data application/job (e.g. APACHE SPARK job) on a big-data cluster (e.g. APACHE HADOOP 'Yet Another Resource Negotiator' (YARN)) periodically to perform pull based scheduling and resource management. The DOCKER image may contain scripts to clean up the uploaded files into the big-data file system (e.g. HDFS) after the application ends (or fails).

The container (e.g., DOCKER) compose files may contain all the parameters that are needed to run the application in an environment and may make up the command that is executed when submitting a big-data application on a compute engine, e.g., the APACHE SPARK compute engine. Some of the parameters may be provided by the big-data lifecycle management platform and others may be defined by the application developer. In some examples the application developer may define and add their own parameters to their big-data application. Some of the following parameters may have the same value across all environments, but others may vary for each environment. These parameters may include: SPARK_USER, HDFS_SERVER_ADDRESS, WEBHDFS_SERVER_PORT, CLUSTER_ENV, AVAILABILITY_ZONE, APP_MAIN_CLASS, APP_MAIN_JAR, EXTRA_JARS, EXTRA_FILES, APP_NAME, VERSION, LIVY_SERVER, AUTHENTICATION_TYPE, and KEYCHAIN _ENDPOINT.

Depending on the specific environment, one or more types of container-compose files, e.g. docker-compose files, may be generated for each environment or group of environments. For example, for base/development environments, a docker-compose-base. yml file may be generated; for QA/GCP user stages, a docker-compose-qa. yml file may be generated; for sandbox environments, a docker-compose-sandbox. yml file may be generated; and for production, a docker-compose-production. yml file may be generated. In one example, the big-data application lifecycle management platform may support as many environments as needed at any granularity level. These environments can be as fine-grained or as coarse-grained as needed. For example, a parameter can be configured for all test environments. Yet another parameter can be configured only for the production environment located in one city, e.g., Salt Lake City, or one region, e.g., Southwest United States, or one country, e.g., the United States. Naming of the compose files may be based on the environment that being configured.

The newly created container can be run in test environment <NUM> (which may include one of the test environments previously described), at block <NUM>. Test environment <NUM> may be a separate management of the application and running the application on specific server clusters. The application may be run in a virtual machine or in a container in the test environment <NUM>. Once the application has been fully built and tested, a user may update source code of the new application with new code written for this application to supplement or replace the application that was built and tested. New code may be sent to code repository <NUM>. When a user commits the code in code repository <NUM>, code repository <NUM> may send a trigger to lifecycle management platform <NUM>. At block <NUM>, the lifecycle management platform <NUM> may receive the trigger alerting that the source code has been updated. Lifecycle management platform <NUM> may rebuild and retest the application, at block <NUM>.

When an application is complete, and has been tested, a user may instruct lifecycle management platform <NUM> that the application is ready to be placed in production, at block <NUM>. A user may provide confirmation to indicate the application is ready for placement into production or the process may be automated. Lifecycle management platform <NUM> may perform additional testing (e.g., a security scan, resource usage testing) to determine whether the application is ready to be run in production environment <NUM>, at block <NUM>. Test environment <NUM> may be a separate management of the application and running the application on specific server clusters during the testing and/or during the full lifecycle of the application. Upon completion of the testing, lifecycle management platform <NUM> may setup a production environment (e.g., production environment <NUM>) to run the application, at block <NUM>. At block <NUM>, metrics may be collected by lifecycle management platform <NUM> about the application (logs, monitoring) and then analyzed. Lifecycle management platform <NUM> may provide an automated approval of the application for use in production. The user may provide an instruction confirming the application is ready for production, at block <NUM>. Following receipt of the instruction, lifecycle management platform <NUM> may run the application in production environment <NUM> at block <NUM>. In certain embodiments, launching the application in the production environment includes staggering deployment of the big-data application into a plurality of big-data server clusters <NUM>. For example, the application may be deployed in a first big-data server cluster at a first time, and if it is stable, deploying the application in a second big-data server cluster at a second later time.

While in the production environment, the application may be run. Data about the application, resource usage statistics, error logs, etc. may be collected and stored, at block <NUM>. The application may be scheduled (by scheduling and management server <NUM>) to run at a specific time, to be coordinated with one or more other application (e.g., if there are data dependencies between the applications), or to be run more or less frequently based on the data collected, stored, and analyzed, at block <NUM>.

During the lifecycle of the application, the software may be revised, managed, tested, and updated, at block <NUM>. Once the application is no longer useful, has been replaced by another application, or completed its set function, the application may be wound down, at block <NUM>. This may include removal from the system, movement of the production data to a different location, or deletion. The present system may manage the lifecycle of a single application or may be used to manage the lifecycle of multiple applications in parallel. Therefore, multiple applications may be being built, in testing, or in production environments or a combination of the three.

Referring now to <FIG>, a system <NUM> for performing lifecycle management of big-data applications is illustrated. Lifecycle management platform <NUM> may include an interface for a user device. An application developer may interact with lifecycle management platform <NUM> and code repository <NUM>. In certain embodiments, code repository <NUM> may include user configurations <NUM> and an application source code <NUM> that are editable by the user.

A configuration preparer server <NUM> may receive user configurations <NUM> and application source code <NUM> from code repository <NUM> as well as environmental configurations <NUM> and environmental templates <NUM>. Each of the configurations <NUM> and templates <NUM> may be designed to work with a particular big-data server cluster <NUM>.

When a big-data application is built, library dependencies may be collected from a library repository <NUM>. Application libraries <NUM> in library repository <NUM> may be combined and compiled with application source code <NUM> to build the big-data application. Application source code <NUM> and application libraries <NUM> are linked and/or compiled together to form an application binary <NUM> by an automated build environment server <NUM>. Automated build environment server <NUM> may run an automated build application (e.g., JENKINS). Application binary <NUM> is placed into a template container image for the application <NUM>. Other library dependencies and application configurations may be placed into the template container image for the application <NUM>. Once the application binaries (as well as libraries and configurations) are placed into the template container image, the result is the container image of the application. The container image for the application <NUM> may then be moved to an artifact repository <NUM>, which may include a DOCKERHUB or ARTIFACTORY server.

Application secrets <NUM> may include passwords, encryption keys, etc. and may be stored in a secure keychain <NUM>. Secure keychain <NUM> may be used to launch the big-data application on server clusters <NUM>. Container image <NUM> is sent to a job server <NUM> running a scheduler application (e.g., a LIVY server) which is configured to schedule the big-data application to run on the big-data server clusters <NUM>. Job server <NUM> may be configured to schedule multiple applications and can be configured to schedule applications based on data dependencies between multiple applications. Job server <NUM> is also configured to track statistics and health information about the big-data server clusters <NUM> including logging <NUM>, monitoring <NUM>, governance <NUM>, access control <NUM>, and alerting <NUM>. This information <NUM> - <NUM> is provided to lifecycle management platform <NUM>.

Application binary <NUM> from the automated build environment server <NUM> and configuration and parameters to the application <NUM> (from configuration preparer server <NUM>) may be transferred to cluster management system <NUM> running a big-data management application configured to manage computer clusters (e.g., APACHE MESOS). Cluster management system <NUM> may include an application launcher <NUM> configured to launch the template container image for the application <NUM>.

Referring now to <FIG>, an embodiment of a computer system <NUM> suitable for implementing, for example, the user devices, platforms, and servers (including server clusters when combined), is illustrated. It should be appreciated that other devices utilized in the application lifecycle management system may be implemented as the computer system <NUM> in a manner as follows.

In accordance with various embodiments of the present disclosure, computer system <NUM>, such as a computer and/or a network server, includes a bus <NUM> or other communication mechanism for communicating information, which interconnects subsystems and components, such as a processor <NUM> (e.g., processor, micro-controller, digital signal processor (DSP), etc.), a system memory component <NUM> (e.g., RAM), a static storage component <NUM> (e.g., ROM), a disk drive component <NUM> (e.g., magnetic, optical, flash memory, or solid state), a network interface component <NUM> (e.g., modem or Ethernet card), a display component <NUM> (e.g., CRT or LCD), an input component <NUM> (e.g., keyboard, keypad, or virtual keyboard, microphone), a cursor control component <NUM> (e.g., mouse, pointer, or trackball), and/or a location determination component <NUM> (e.g., a Global Positioning System (GPS) device as illustrated, a cell tower triangulation device, and/or a variety of other location determination devices known in the art). In one implementation, the disk drive component <NUM> may comprise a database having one or more disk drive components.

In accordance with embodiments of the present disclosure, the computer system <NUM> performs specific operations by the processor <NUM> executing one or more sequences of instructions contained in the memory component <NUM>, such as described herein with respect to the user devices, server devices (including the payment provider server, merchant server, and authentication server), data stores, and nodes. Such instructions may be read into the system memory component <NUM> from another computer readable medium, such as the static storage component <NUM> or the disk drive component <NUM>. In other embodiments, hard-wired circuitry may be used in place of or in combination with software instructions to implement the present disclosure.

Logic may be encoded in a computer readable medium, which may refer to any medium that participates in providing instructions to the processor <NUM> for execution. Such a medium may take many forms, including but not limited to, non-volatile media, volatile media, and transmission media. In one embodiment, the computer readable medium is non-transitory. In various implementations, non-volatile media includes optical or magnetic disks, such as the disk drive component <NUM>, volatile media includes dynamic memory, such as the system memory component <NUM>, and transmission media includes coaxial cables, copper wire, and fiber optics, including wires that comprise the bus <NUM>. In one example, transmission media may take the form of acoustic or light waves, such as those generated during radio wave and infrared data communications.

Some common forms of computer readable media includes, for example, floppy disk, flexible disk, hard disk, solid state drives (SSD), magnetic tape, any other magnetic medium, CD-ROM, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, flash storage, RAM, PROM, EPROM, FLASH-EPROM, any other memory chip or cartridge, carrier wave, or any other medium from which a computer is adapted to read. In one embodiment, the computer readable media is non-transitory.

In various embodiments of the present disclosure, execution of instruction sequences to practice the present disclosure may be performed by the computer system <NUM>. In various other embodiments of the present disclosure, a plurality of the computer systems <NUM> coupled by a communication link <NUM> to the network <NUM> (e.g., such as a LAN, WLAN, PTSN, and/or various other wired or wireless networks, including telecommunications, mobile, and cellular phone networks) may perform instruction sequences to practice the present disclosure in coordination with one another.

Network interface <NUM> of computer system <NUM> may also include a short-range communications interface. Thus, network interface <NUM>, in various embodiments, may include transceiver circuitry, an antenna, and/or waveguide. Network interface <NUM> may use one or more short-range wireless communication technologies, protocols, and/or standards (e.g., Wi-Fi, Bluetooth®, Bluetooth Low Energy (BLE), infrared, NFC, etc.).

Network interface <NUM>, in various embodiments, may be configured to detect other systems, devices, peripherals, and data stores with short range communications technology near computer system <NUM>. Network interface <NUM> may create a communication area for detecting other devices with short range communication capabilities. When other devices with short range communications capabilities are placed in the communication area of network interface <NUM>, network interface <NUM> may detect the other devices and exchange data with the other devices. Network interface <NUM> may receive identifier data packets from the other devices when in sufficiently close proximity. The identifier data packets may include one or more identifiers, which may be operating system registry entries, cookies associated with an application, identifiers associated with hardware of the other device, and/or various other appropriate identifiers.

In some embodiments, network interface <NUM> may identify a local area network using a short-range communications protocol, such as Wi-Fi, and join the local area network. In some examples, computer system <NUM> may discover and/or communicate with other devices that are a part of the local area network using network interface <NUM>.

The computer system <NUM> may transmit and receive messages, data, information and instructions, including one or more programs (i.e., application code) through the communication link <NUM> and the network interface component <NUM>. The network interface component <NUM> may include an antenna, either separate or integrated, to enable transmission and reception via the communication link <NUM>. Received program code may be executed by processor <NUM> as received and/or stored in disk drive component <NUM> or some other non-volatile storage component for execution.

Where applicable, various embodiments provided by the present disclosure may be implemented using hardware, software, or combinations of hardware and software. Also, where applicable, the various hardware components and/or software components set forth herein may be combined into composite components comprising software, hardware, and/or both without departing from the scope of the present disclosure. Where applicable, the various hardware components and/or software components set forth herein may be separated into sub-components comprising software, hardware, or both without departing from the scope of the present disclosure. In addition, where applicable, it is contemplated that software components may be implemented as hardware components and vice-versa.

Software, in accordance with the present disclosure, such as program code and/or data, may be stored on one or more computer readable mediums. It is also contemplated that software identified herein may be implemented using one or more general purpose or specific purpose computers and/or computer systems, networked and/or otherwise. Where applicable, the ordering of various steps described herein may be changed, combined into composite steps, and/or separated into sub-steps to provide features described herein.

Claim 1:
A method of managing a lifecycle of a big-data application by a computer system (<NUM>), comprising:
receiving (<NUM>) an indication to create a big-data application;
generating (<NUM>) a big-data application instance using a big-data container template, the big-data container template comprising a multi-layer container configured to manage a lifecycle of the big-data application, the multi-layer container comprising:
a first container layer comprising operating system settings and security settings;
a second container layer comprising big-data settings comprising settings and libraries common to a plurality of big-data applications;
a third container layer comprising settings associated with a user and templates for runtime parameters and dependencies; and
a fourth container layer comprising a big-data application source code template and big-data application specific settings; and
configuring (<NUM>, <NUM>) a build environment and a test environment for the big-data application instance.