Abstract: Techniques and systems are provided for more efficient and reliable caching of responses that are based on API calls to a backend system. The disclosed embodiments use a containerized pipeline that can be linked to each application instance regardless of the specific cloud environment(s) on which the instance is hosted and executed. Instances of the application operating on different cloud environments can access the cached responses from the other environments, thereby reducing or eliminating latency that typically results from the deployment of caching and database solutions across different availability zones in one or more cloud environments.

Abstract: Configuration management e.g., configuration validation and remediation (when necessary) of entities in a collective of databases and/or other machines or devices can be burdensome when vendor/cloud provider tools are used to manage the entities due to lack of control over the management. Rather than rely on vendor/cloud provider tools, instead configuration management is offloaded to, e.g., a local API and/or local machine, where configuration deviation detection from an expected configuration is locally determined and remediation needs may be prioritized so higher-priority collective entities are remediated first and other entities deferred. Local processing reduces burdens associated with entity remediation, such as in a cloud-hosted environment having many burdens associated with accessing cloud data and/or databases.

Abstract: A self-healing build pipeline architecture for a software application build job across a distributed computer platform comprises a public API that receives configuration data describing the build job, stores the configuration data in a decentralized database, serves requests to/from a pipeline tracker API, and stores states of build pipelines during the build job. The decentralized database stores the configuration data and a project identifier for the build, and metadata regarding states of the build pipelines collected across the distributed computer platform. The pipeline tracker API runs local to the build environment in the distributed computer platform and sends a build status to public API for updating the decentralized database. For any failures in any of the build pipelines, the state is retrieved from the decentralize database and a new build pipeline is triggered locally that resumes from the failed state to provide a self-healing build pipeline architecture.

Abstract: A self-healing build pipeline architecture for a software application build job across a distributed computer platform comprises a public API that receives configuration data describing the build job, stores the configuration data in a decentralized database, serves requests to/from a pipeline tracker API, and stores states of build pipelines during the build job. The decentralized database stores the configuration data and a project identifier for the build, and metadata regarding states of the build pipelines collected across the distributed computer platform. The pipeline tracker API runs local to the build environment in the distributed computer platform and sends a build status to public API for updating the decentralized database. For any failures in any of the build pipelines, the state is retrieved from the decentralize database and a new build pipeline is triggered locally that resumes from the failed state to provide a self-healing build pipeline architecture.

Abstract: Systems and methods are described for receiving a user request from a user computing system located in a first geographic region, generating a user request context for the user request, selecting a cloud computing system assigned to a second geographic region having more computing resources currently available to process the user request than a cloud computing system assigned to the first geographic region based at least in part on the user request context and a machine learning model including current utilizations of computing resources of cloud computing systems assigned to the first and second geographic regions, and sending the user request to the selected cloud computing system in the second geographic region.

Abstract: Optimizing transaction times for distributed databases is described. A system receives a request which specifies an identifier of a database record associated with a distributed database. If the request is associated with a primary database of the distributed database, any user-defined function is associated with the database record in the primary database, and any associated user-defined function does not require any call that is external to a subnet and/or a firewall associated with the primary database, then the system executes, within the subnet and/or the firewall associated with the primary database, each associated user-defined function for the database record in the primary database. The system outputs a result which is based on executing any associated user-defined functions.

Abstract: Optimizing transaction times for distributed databases is described. A system receives a request which specifies an identifier of a database record associated with a distributed database. If the request is associated with a primary database of the distributed database, any user-defined function is associated with the database record in the primary database, and any associated user-defined function does not require any call that is external to a subnet and/or a firewall associated with the primary database, then the system executes, within the subnet and/or the firewall associated with the primary database, each associated user-defined function for the database record in the primary database. The system outputs a result which is based on executing any associated user-defined functions.

Abstract: Systems and methods are described for receiving a user request from a user computing system located in a first geographic region, generating a user request context for the user request, selecting a cloud computing system assigned to a second geographic region having more computing resources currently available to process the user request than a cloud computing system assigned to the first geographic region based at least in part on the user request context and a machine learning model including current utilizations of computing resources of cloud computing systems assigned to the first and second geographic regions, and sending the user request to the selected cloud computing system in the second geographic region.