Patent ID: 12229424

DETAILED DESCRIPTION

Reference will now be made in detail to specific example embodiments for carrying out the inventive subject matter. Examples of these specific embodiments are illustrated in the accompanying drawings, and specific details are set forth in the following description in order to provide a thorough understanding of the subject matter. It will be understood that these examples are not intended to limit the scope of the claims to the illustrated embodiments. On the contrary, they are intended to cover such alternatives, modifications, and equivalents as may be included within the scope of the disclosure.

Aspects of the present disclosure address a multi-tenant database system that supports sharing of user data with a multi-tenant application. The application includes a user interface that exposes an element that allows a user of the application to incorporate their data cloud assets (including first, second, third-party assets) into the application context to enrich the experience. Once the connection is established, the application can use specified data for a specified context and can also add more data to the user's data graph.

FIG.1illustrates an example computing environment100that includes a database system102in communication with a storage platform104, in accordance with some embodiments of the present disclosure. To avoid obscuring the inventive subject matter with unnecessary detail, various functional components that are not germane to conveying an understanding of the inventive subject matter have been omitted fromFIG.1. However, a skilled artisan will readily recognize that various additional functional components may be included as part of the computing environment100to facilitate additional functionality that is not specifically described herein.

As shown, the computing environment100comprises the database system102and a storage platform104(e.g., AWS®, Microsoft Azure Blob Storage®, or Google Cloud Storage®). The database system102is used for reporting and analysis of integrated data from one or more disparate sources including storage devices106-1to106-N within the storage platform104. The storage platform104comprises a plurality of computing machines and provides on-demand computer system resources such as data storage and computing power to the database system102.

The database system102comprises a compute service manager108, an execution platform110, and a database114. The database system102hosts and provides data reporting and analysis services to multiple client accounts. Administrative users can create and manage identities (e.g., users, roles, and groups) and use permissions to allow or deny access to the identities to resources and services.

The compute service manager108coordinates and manages operations of the database system102. The compute service manager108also performs query optimization and compilation as well as managing clusters of computing services that provide compute resources (also referred to as “virtual warehouses”). The compute service manager108can support any number of client accounts, such as end users providing data storage and retrieval requests, system administrators managing the systems and methods described herein, and other components/devices that interact with compute service manager108.

The compute service manager108is also in communication with a computing device112. The computing device112corresponds to a user of one of the multiple client accounts supported by the database system102. In some embodiments, the compute service manager108does not receive any direct communications from the computing device112and only receives communications concerning jobs from a queue within the database system102.

The compute service manager108is also coupled to database114, which is associated with the data stored in the computing environment100. The database114stores data pertaining to various functions and aspects associated with the database system102and its users. In some embodiments, the database114includes a summary of data stored in remote data storage systems as well as data available from a local cache. Additionally, the database114may include information regarding how data is organized in remote data storage systems (e.g., the storage platform104) and the local caches. The database114allows systems and services to determine whether a piece of data needs to be accessed without loading or accessing the actual data from a storage device.

The compute service manager108is further coupled to the execution platform110, which provides multiple computing resources that execute various data storage and data retrieval tasks. The execution platform110is coupled to storage platform104. The storage platform104comprises multiple data storage devices106-1to106-N. In some embodiments, the data storage devices106-1to106-N are cloud-based storage devices located in one or more geographic locations. For example, the data storage devices106-1to106-N may be part of a public cloud infrastructure or a private cloud infrastructure. The data storage devices106-1to106-N may be hard disk drives (HDDs), solid state drives (SSDs), storage clusters, Amazon S3TM storage systems, or any other data storage technology. Additionally, the storage platform104may include distributed file systems (e.g., Hadoop Distributed File Systems (HDFS)), object storage systems, and the like.

The execution platform110comprises a plurality of compute nodes. A set of processes on a compute node executes a query plan compiled by the compute service manager108. The set of processes can include: a first process to execute the query plan; a second process to monitor and delete database partition files using a least recently used (LRU) policy and implement an out of memory (OOM) error mitigation process; a third process that extracts health information from process logs and status to send back to the compute service manager108; a fourth process to establish communication with the compute service manager108after a system boot; and a fifth process to handle all communication with a compute cluster for a given job provided by the compute service manager108and to communicate information back to the compute service manager108and other compute nodes of the execution platform110.

In some embodiments, communication links between elements of the computing environment100are implemented via one or more data communication networks. These data communication networks may utilize any communication protocol and any type of communication medium. In some embodiments, the data communication networks are a combination of two or more data communication networks (or sub-networks) coupled to one another. In alternate embodiments, these communication links are implemented using any type of communication medium and any communication protocol.

As shown inFIG.1, the data storage devices106-1to106-N are decoupled from the computing resources associated with the execution platform110. This architecture supports dynamic changes to the database system102based on the changing data storage/retrieval needs as well as the changing needs of the users and systems. The support of dynamic changes allows the database system102to scale quickly in response to changing demands on the systems and components within the database system102. The decoupling of the computing resources from the data storage devices supports the storage of large amounts of data without requiring a corresponding large amount of computing resources. Similarly, this decoupling of resources supports a significant increase in the computing resources utilized at a particular time without requiring a corresponding increase in the available data storage resources.

The compute service manager108, database114, execution platform110, and storage platform104are shown inFIG.1as individual discrete components. However, each of the compute service manager108, database114, execution platform110, and storage platform104may be implemented as a distributed system (e.g., distributed across multiple systems/platforms at multiple geographic locations). Additionally, each of the compute service manager108, database114, execution platform110, and storage platform104can be scaled up or down (independently of one another) depending on changes to the requests received and the changing needs of the database system102. Thus, in the described embodiments, the database system102is dynamic and supports regular changes to meet the current data processing needs.

During typical operation, the database system102processes multiple jobs determined by the compute service manager108. These jobs are scheduled and managed by the compute service manager108to determine when and how to execute the job. For example, the compute service manager108may divide the job into multiple discrete tasks and may determine what data is needed to execute each of the multiple discrete tasks. The compute service manager108may assign each of the multiple discrete tasks to one or more nodes of the execution platform110to process the task. The compute service manager108may determine what data is needed to process a task and further determine which nodes within the execution platform110are best suited to process the task. Some nodes may have already cached the data needed to process the task and, therefore, be a good candidate for processing the task. Metadata stored in the database114assists the compute service manager108in determining which nodes in the execution platform110have already cached at least a portion of the data needed to process the task. One or more nodes in the execution platform110process the task using data cached by the nodes and, if necessary, data retrieved from the storage platform104. It is desirable to retrieve as much data as possible from caches within the execution platform110because the retrieval speed is typically much faster than retrieving data from the storage platform104.

As shown inFIG.1, the computing environment100separates the execution platform110from the storage platform104. In this arrangement, the processing resources and cache resources in the execution platform110operate independently of the data storage devices106-1to106-N in the storage platform104. Thus, the computing resources and cache resources are not restricted to specific data storage devices106-1to106-N. Instead, all computing resources and all cache resources may retrieve data from, and store data to, any of the data storage resources in the storage platform104.

FIG.2is a block diagram illustrating components of the compute service manager108, in accordance with some embodiments of the present disclosure. As shown inFIG.2, the compute service manager108includes an access manager202and a key manager204coupled to a data storage device206. Access manager202handles authentication and authorization tasks for the systems described herein. Key manager204manages storage and authentication of keys used during authentication and authorization tasks. For example, access manager202and key manager204manage the keys used to access data stored in remote storage devices (e.g., data storage devices in storage platform104). As used herein, the remote storage devices may also be referred to as “persistent storage devices” or “shared storage devices.”

A request processing service208manages received data storage requests and data retrieval requests (e.g., jobs to be performed on database data). For example, the request processing service208may determine the data necessary to process a received query (e.g., a data storage request or data retrieval request). The data may be stored in a cache within the execution platform110or in a data storage device in storage platform104.

A management console service210supports access to various systems and processes by administrators and other system managers. Additionally, the management console service210may receive a request to execute a job and monitor the workload on the system.

The compute service manager108also includes a job compiler212, a job optimizer214and a job executor216. The job compiler212parses a job into multiple discrete tasks and generates the execution code for each of the multiple discrete tasks. The job optimizer214determines the best method to execute the multiple discrete tasks based on the data that needs to be processed. The job optimizer214also handles various data pruning operations and other data optimization techniques to improve the speed and efficiency of executing the job. The job executor216executes the execution code for jobs received from a queue or determined by the compute service manager108.

A job scheduler and coordinator218sends received jobs to the appropriate services or systems for compilation, optimization, and dispatch to the execution platform110. For example, jobs may be prioritized and processed in that prioritized order. In an embodiment, the job scheduler and coordinator218determines a priority for internal jobs that are scheduled by the compute service manager108with other “outside” jobs such as user queries that may be scheduled by other systems in the database but may utilize the same processing resources in the execution platform110. In some embodiments, the job scheduler and coordinator218identifies or assigns particular nodes in the execution platform110to process particular tasks. A virtual warehouse manager220manages the operation of multiple virtual warehouses implemented in the execution platform110. As discussed below, each virtual warehouse includes multiple execution nodes that each include a cache and a processor.

Additionally, the compute service manager108includes a configuration and metadata manager222, which manages the information related to the data stored in the remote data storage devices and in the local caches (e.g., the caches in execution platform110). The configuration and metadata manager222uses the metadata to determine which partitions need to be accessed to retrieve data for processing a particular task or job. A monitor and workload analyzer224oversees processes performed by the compute service manager108and manages the distribution of tasks (e.g., workload) across the virtual warehouses and execution nodes in the execution platform110. The monitor and workload analyzer224also redistributes tasks, as needed, based on changing workloads throughout the database system102and may further redistribute tasks based on a user (e.g., “external”) query workload. The execution platform110may also redistribute tasks based on a user (e.g., “external”) query workload. The configuration and metadata manager222and the monitor and workload analyzer224are coupled to a data storage device226. Data storage device226inFIG.2represents any data storage device within the database system102. For example, data storage device226may represent caches in execution platform110, storage devices in storage platform104, or any other storage device.

FIG.3is a block diagram illustrating components of the execution platform110, in accordance with some embodiments of the present disclosure. As shown inFIG.3, the execution platform110includes multiple virtual warehouses, including virtual warehouse1, virtual warehouse2, and virtual warehouse n. Each virtual warehouse includes multiple execution nodes that each includes a data cache and a processor. The virtual warehouses can execute multiple tasks in parallel by using the multiple execution nodes. As discussed herein, the execution platform110can add new virtual warehouses and drop existing virtual warehouses in real-time based on the current processing needs of the systems and users. This flexibility allows the execution platform110to quickly deploy large amounts of computing resources when needed without being forced to continue paying for those computing resources when they are no longer needed. All virtual warehouses can access data from any data storage device (e.g., any storage device in storage platform104).

Although each virtual warehouse shown inFIG.3includes three execution nodes, a particular virtual warehouse may include any number of execution nodes. Further, the number of execution nodes in a virtual warehouse is dynamic, such that new execution nodes are created when additional demand is present, and existing execution nodes are deleted when they are no longer necessary.

Each virtual warehouse is capable of accessing any of the data storage devices106-1to106-N shown inFIG.1. Thus, the virtual warehouses are not necessarily assigned to a specific data storage device106-1to106-nand, instead, can access data from any of the data storage devices106-1to106-N within the storage platform104. Similarly, each of the execution nodes shown inFIG.3can access data from any of the data storage devices106-1to106-N. In some embodiments, a particular virtual warehouse or a particular execution node may be temporarily assigned to a specific data storage device, but the virtual warehouse or execution node may later access data from any other data storage device.

In the example ofFIG.3, virtual warehouse1includes three execution nodes302-1,302-2, and302-n. Execution node302-1includes a cache304-1and a processor306-1. Execution node302-2includes a cache304-2and a processor306-2. Execution node302-nincludes a cache304-nand a processor306-n. Each execution node302-1,302-2, and302-nis associated with processing one or more data storage and/or data retrieval tasks. For example, a virtual warehouse may handle data storage and data retrieval tasks associated with an internal service, such as a clustering service, a materialized view refresh service, a file compaction service, a storage procedure service, or a file upgrade service. In other implementations, a particular virtual warehouse may handle data storage and data retrieval tasks associated with a particular data storage system or a particular category of data.

Similar to virtual warehouse1discussed above, virtual warehouse2includes three execution nodes312-1,312-2, and312-n. Execution node312-1includes a cache314-1and a processor316-1. Execution node312-2includes a cache314-2and a processor316-2. Execution node312-nincludes a cache314-nand a processor316-n. Additionally, virtual warehouse3includes three execution nodes322-1,322-2, and322-n. Execution node322-1includes a cache324-1and a processor326-1. Execution node322-2includes a cache324-2and a processor326-2. Execution node322-nincludes a cache324-nand a processor326-n.

In some embodiments, the execution nodes shown inFIG.3are stateless with respect to the data the execution nodes are caching. For example, these execution nodes do not store or otherwise maintain state information about the execution node or the data being cached by a particular execution node. Thus, in the event of an execution node failure, the failed node can be transparently replaced by another node. Since there is no state information associated with the failed execution node, the new (replacement) execution node can easily replace the failed node without concern for recreating a particular state.

Although the execution nodes shown inFIG.3each includes one data cache and one processor, alternate embodiments may include execution nodes containing any number of processors and any number of caches. Additionally, the caches may vary in size among the different execution nodes. The caches shown inFIG.3store, in the local execution node, data that was retrieved from one or more data storage devices in storage platform104. Thus, the caches reduce or eliminate the bottleneck problems occurring in platforms that consistently retrieve data from remote storage systems. Instead of repeatedly accessing data from the remote storage devices, the systems and methods described herein access data from the caches in the execution nodes, which is significantly faster and avoids the bottleneck problem discussed above. In some embodiments, the caches are implemented using high-speed memory devices that provide fast access to the cached data. Each cache can store data from any of the storage devices in the storage platform104.

Further, the cache resources and computing resources may vary between different execution nodes. For example, one execution node may contain significant computing resources and minimal cache resources, making the execution node useful for tasks that require significant computing resources. Another execution node may contain significant cache resources and minimal computing resources, making this execution node useful for tasks that require caching of large amounts of data. Yet another execution node may contain cache resources providing faster input-output operations, useful for tasks that require fast scanning of large amounts of data. In some embodiments, the cache resources and computing resources associated with a particular execution node are determined when the execution node is created, based on the expected tasks to be performed by the execution node.

Additionally, the cache resources and computing resources associated with a particular execution node may change over time based on changing tasks performed by the execution node. For example, an execution node may be assigned more processing resources if the tasks performed by the execution node become more processor-intensive. Similarly, an execution node may be assigned more cache resources if the tasks performed by the execution node require a larger cache capacity.

Although virtual warehouses1,2, and n are associated with the same execution platform110, the virtual warehouses may be implemented using multiple computing systems at multiple geographic locations. For example, virtual warehouse1can be implemented by a computing system at a first geographic location, while virtual warehouses2and n are implemented by another computing system at a second geographic location. In some embodiments, these different computing systems are cloud-based computing systems maintained by one or more different entities.

Additionally, each virtual warehouse is shown inFIG.3as having multiple execution nodes. The multiple execution nodes associated with each virtual warehouse may be implemented using multiple computing systems at multiple geographic locations. For example, an instance of virtual warehouse1implements execution nodes302-1and302-2on one computing platform at a geographic location and implements execution node302-nat a different computing platform at another geographic location. Selecting particular computing systems to implement an execution node may depend on various factors, such as the level of resources needed for a particular execution node (e.g., processing resource requirements and cache requirements), the resources available at particular computing systems, communication capabilities of networks within a geographic location or between geographic locations, and which computing systems are already implementing other execution nodes in the virtual warehouse.

Execution platform110is also fault tolerant. For example, if one virtual warehouse fails, that virtual warehouse is quickly replaced with a different virtual warehouse at a different geographic location.

A particular execution platform110may include any number of virtual warehouses. Additionally, the number of virtual warehouses in a particular execution platform is dynamic, such that new virtual warehouses are created when additional processing and/or caching resources are needed. Similarly, existing virtual warehouses may be deleted when the resources associated with the virtual warehouse are no longer necessary.

In some embodiments, the virtual warehouses may operate on the same data in storage platform104, but each virtual warehouse has its own execution nodes with independent processing and caching resources. This configuration allows requests on different virtual warehouses to be processed independently and with no interference between the requests. This independent processing, combined with the ability to dynamically add and remove virtual warehouses, supports the addition of new processing capacity for new users without impacting the performance observed by the existing users.

FIGS.4A and4Billustrates an example computing environment400in which an application402(a software application) is connected to the database system102, in accordance with some embodiments of the present disclosure. As shown inFIG.4A, the application402executes on an application provider platform. Initially, the application402only has access to application data owned by the application provider (e.g., application data created and managed by the application402). The application data is stored and maintained by the database system102.

A user of the application402may be associated with a customer account404provided by the database system102. Customer data406associated with the customer account404is maintained by the database system102. The customer account404within the database system102is granted access to second-party application data408and third-party data410. The second-party application data408can include application data corresponding to one or more other applications. For example, the second-party application data408can include application data created, managed, and/or consumed by a second application. The second-party application data408is associated with one or more second-party application provider accounts maintained by the database system102. Each second-party application provider account corresponds to a second-party application provider. The third-party data410can include data from one or more third-party data providers corresponding to one or more accounts maintained by the database system102. For example, the third-party data410can include customer data associated with a second customer account provider by the database system102.

With reference toFIG.4B, to enable the user of the application402to provide the application402access to the customer data406as well as the second-party application data408and third-party data410, the application402presents a user interface (UI) element412to receive security credentials associated with the customer account404to authenticate the user as an authorized user of the customer account404. The UI element412can include one or more input fields along with a prompt for the user to input security credentials associated with the customer account404(e.g., a username and password). The security credentials are provided by the application402to the database system102. The database system102authenticates the security credentials, and in response to the database system102successfully authenticating the security credentials, the application402presents UI element414that allows the user to specify data to be shared with the application402. The application402can display the UI element414in response to receiving an indication from the database system102of successful authentication or the database system102can cause the application402to display the UI element414(e.g., by providing instructions to the computing device executing the application402that cause the computing device to present the UI element414) in response to successful authentication. From the UI element414, the user can specify one or more databases or one or more portions of a database to be shared with the application402.

As an example, the user can use the UI element414to establish a connection that allows the application402to use any one or more of the customer data406, the second-party application data408, and the third-party data410. That is, the user can use the UI element414to share data from a database that is within the customer account404, an account of a second-party application data provider, or an account of a third-party data provider, or to share data from a database that includes a combination of any one or more of customer data, second-party application data, and third-party data.

The user may further specify a context in which data is to be used by the application402. That is, the user can specify one or more limitations on use of the data by the application402. For example, the user may limit access to portions of the database or restrict access to one or more portions of the second-party application data408or third-party data410. As another example, the user can specify read and write permissions for the application402associated with any one or more of the customer data406, the second-party application data408, or the third-party data410.

A request to share cloud data with the application402is generated based on user input provided to the UI element414and the request is submitted to the database system102. The database system102provides access to the cloud data specified in the request to the application402. In the example illustrated inFIG.4B, the cloud data includes the customer data406, the second-party application data408, and the third-party data410. That is, the database system102enables the application402to access the customer data406, the second-party application data408, and the third-party data410in accordance with any restrictions on use specified in the request.

Consistent with some embodiments, prior to providing access to the data to the application402, the database system102can perform one or more validations to validate a schema or format of the data to ensure compatibility with the application402. The database system102may further perform one or more normalization operations on the data prior to providing access to the application402.

FIG.5illustrates an example architecture500to support cloud data sharing between applications (e.g., application402) and the database system, in accordance with some embodiments of the present disclosure. As shown, the architecture500includes an application platform502that supports a multi-tenant application. The application platform502utilizes data504that includes application data (e.g., customer data406) as well as data obtained from a data marketplace506(e.g., second-party application data408and third-party data410). Access to the data504can be provisioned to customers A, B, and C of the application platform502. As shown, customer data from a customer account within the database system102is shared with and used by the application platform502. Second-party application data and third-party data that the customer account is permitted to access can also be shared with the application platform502.

FIGS.6and7are flow diagrams illustrating operations of the database system102in performing a method600for connecting an application to a database system102, in accordance with some embodiments of the present disclosure. The method600may be embodied in computer-readable instructions for execution by one or more hardware components (e.g., one or more processors) such that the operations of the method600may be performed by components of database system102. Accordingly, the method600is described below, by way of example with reference thereto. However, it shall be appreciated that the method600may be deployed on various other hardware configurations and is not intended to be limited to deployment within the database system102.

Depending on the embodiment, an operation of the method600may be repeated in different ways or involve intervening operations not shown. Though the operations of the method600may be depicted and described in a certain order, the order in which the operations are performed may vary among embodiments, including performing certain operations in parallel or performing sets of operations in separate processes. For example, although the use and generation of the pruning index are described and illustrated together as part of the method600, it shall be appreciated that the use and generation of the restricted view may be performed as separate processes, consistent with some embodiments.

At operation605, the database system102receives an authentication request associated with a request to share customer data with an application. The authentication request includes security credentials (e.g., a username and password) associated with a first account provided by the database system102(e.g., a customer account). In some embodiments, the authentication request can be received from a computing device executing the application and based on input provided by a user via a first user interface element presented by the application. In some embodiments, the authentication request can be received by a computing device in communication with the database system102and based on user input provided to a UI provided by the database system102.

At operation610, the database system102authenticates the security credentials and, based on successful authentication of the security credentials, the database system102provides a UI element to share customer data with the application, at operation615. In embodiments in which the authentication request is provided based on user interaction with the application, the database system102may cause a computing device executing the application to display a second UI element for the customer to share customer data with the application.

At operation620, the database system102receives a request to share customer data in the first account with an application. In some embodiments, the request can be received, for example, from the computing device executing the application. The request can be generated at the computing device executing the application based on input received from the user associated with the first account via the second interface element. In some embodiments, the request is generated based on user input received via a user interface provided by the database system102to a computing device without the computing device executing the application. That is, a user can submit a request to share data with an application from a first computing device without interacting directly with the application or a second computing device on which the application is executable.

The request can specify a particular context in which the application can use the customer data. For example, the request can indicate one or more limits on the manner in which the customer data is used by the application (e.g., limits on the application read or write permissions with respect to one or more portions of the data). As another example, the request can specify whether the application is able to access second-party application data and/or third-party data that is accessible by the customer account.

At operation625, the database system102identifies second-party application data that is accessible by the first account, and at operation625, the database system102identifies third-party data that is accessible by the first account. The second-party application data comprises application data corresponding to a second application (e.g., data created, managed, and/or consumed by a second application) associated with a second account (e.g., a second-party application provider account) maintained by the database system102. The second account corresponds to a second-party application provider. The third-party data can include data from a third-party data provider corresponding to a third account (e.g., a third-party data provider account) maintained by the database system102. For example, the third-party data can correspond to customer data associated with a second customer account provider by the database system102. In instances in which the request restricts access of the application to the second-party application data or the third-party data, the database system102may not perform operation625or630.

At operation635, the database system102enables the application to access cloud data associated with the customer account. The cloud data includes the customer data, the second-party application data, and third-party data associated with the customer account. In instances in which the request to share the customer data includes one or more limits on use of the cloud data, the enabling of the application to access the cloud data further includes restricting use of the cloud data by the application according to the one or more limits.

As shown inFIG.7, the method600can further include operations705and710, in some embodiments. Consistent with these embodiments, the operations705and710can be performed prior to operation635where the database system102enables the application to access the cloud data associated with the customer account.

At operation705, the database system102performs one or more validations of the cloud data to ensure it is compatible with the application. As an example, the one or more validations can include any one or more of validation a format of the cloud data and validation a schema of the cloud data. At operation710, the database system102performs one or more normalizations on the cloud data to normalize the cloud data.

FIG.8illustrates a diagrammatic representation of a machine800in the form of a computer system within which a set of instructions may be executed for causing the machine800to perform any one or more of the methodologies discussed herein, according to an example embodiment. Specifically,FIG.8shows a diagrammatic representation of the machine800in the example form of a computer system, within which instructions816(e.g., software, a program, an application, an applet, an app, or other executable code) for causing the machine800to perform any one or more of the methodologies discussed herein may be executed. For example, the instructions816may cause the machine800to execute any one or more operations of the method600. As another example, the instructions816may cause the machine800to implement portions of the functionality illustrated in any one or more ofFIGS.4A,4B, and5. In this way, the instructions816transform a general, non-programmed machine into a particular machine800(e.g., the compute service manager108, the execution platform110, and the computing device112) that is specially configured to carry out any one of the described and illustrated functions in the manner described herein.

In alternative embodiments, the machine800operates as a standalone device or may be coupled (e.g., networked) to other machines. In a networked deployment, the machine800may operate in the capacity of a server machine or a client machine in a server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine800may comprise, but not be limited to, a server computer, a client computer, a personal computer (PC), a tablet computer, a laptop computer, a netbook, a smart phone, a mobile device, a network router, a network switch, a network bridge, or any machine capable of executing the instructions816, sequentially or otherwise, that specify actions to be taken by the machine800. Further, while only a single machine800is illustrated, the term “machine” shall also be taken to include a collection of machines800that individually or jointly execute the instructions816to perform any one or more of the methodologies discussed herein.

The machine800includes processors810, memory830, and input/output (I/O) components850configured to communicate with each other such as via a bus802. In an example embodiment, the processors810(e.g., a central processing unit (CPU), a reduced instruction set computing (RISC) processor, a complex instruction set computing (CISC) processor, a graphics processing unit (GPU), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a radio-frequency integrated circuit (RFIC), another processor, or any suitable combination thereof) may include, for example, a processor812and a processor814that may execute the instructions816. The term “processor” is intended to include multi-core processors810that may comprise two or more independent processors (sometimes referred to as “cores”) that may execute instructions816contemporaneously. AlthoughFIG.8shows multiple processors810, the machine800may include a single processor with a single core, a single processor with multiple cores (e.g., a multi-core processor), multiple processors with a single core, multiple processors with multiple cores, or any combination thereof.

The memory830may include a main memory832, a static memory834, and a storage unit836, all accessible to the processors810such as via the bus802. The main memory832, the static memory834, and the storage unit836store the instructions816embodying any one or more of the methodologies or functions described herein. The instructions816may also reside, completely or partially, within the main memory832, within the static memory834, within the storage unit836, within at least one of the processors810(e.g., within the processor's cache memory), or any suitable combination thereof, during execution thereof by the machine800.

The I/O components850include components to receive input, provide output, produce output, transmit information, exchange information, capture measurements, and so on. The specific I/O components850that are included in a particular machine800will depend on the type of machine. For example, portable machines such as mobile phones will likely include a touch input device or other such input mechanisms, while a headless server machine will likely not include such a touch input device. It will be appreciated that the I/O components850may include many other components that are not shown inFIG.8. The I/O components850are grouped according to functionality merely for simplifying the following discussion and the grouping is in no way limiting. In various example embodiments, the I/O components850may include output components852and input components854. The output components852may include visual components (e.g., a display such as a plasma display panel (PDP), a light emitting diode (LED) display, a liquid crystal display (LCD), a projector, or a cathode ray tube (CRT)), acoustic components (e.g., speakers), other signal generators, and so forth. The input components854may include alphanumeric input components (e.g., a keyboard, a touch screen configured to receive alphanumeric input, a photo-optical keyboard, or other alphanumeric input components), point-based input components (e.g., a mouse, a touchpad, a trackball, a joystick, a motion sensor, or another pointing instrument), tactile input components (e.g., a physical button, a touch screen that provides location and/or force of touches or touch gestures, or other tactile input components), audio input components (e.g., a microphone), and the like.

Communication may be implemented using a wide variety of technologies. The I/O components850may include communication components864operable to couple the machine800to a network870or devices880via a coupling872and a coupling882, respectively. For example, the communication components864may include a network interface component or another suitable device to interface with the network870. In further examples, the communication components864may include wired communication components, wireless communication components, cellular communication components, and other communication components to provide communication via other modalities. The devices880may be another machine or any of a wide variety of peripheral devices (e.g., a peripheral device coupled via a universal serial bus (USB)). For example, as noted above, the machine800may correspond to any one of the compute service manager108, the execution platform110, and the devices880may include the data storage device206or any other computing device described herein as being in communication with the database system102or the storage platform104.

Executable Instructions and Machine Storage Medium

The various memories (e.g.,830,832,834, and/or memory of the processor(s)810and/or the storage unit836) may store one or more sets of instructions816and data structures (e.g., software) embodying or utilized by any one or more of the methodologies or functions described herein. These instructions816, when executed by the processor(s)810, cause various operations to implement the disclosed embodiments.

As used herein, the terms “machine-storage medium,” “device-storage medium,” and “computer-storage medium” mean the same thing and may be used interchangeably in this disclosure. The terms refer to a single or multiple storage devices and/or media (e.g., a centralized or distributed database, and/or associated caches and servers) that store executable instructions and/or data. The terms shall accordingly be taken to include, but not be limited to, solid-state memories, and optical and magnetic media, including memory internal or external to processors. Specific examples of machine-storage media, computer-storage media, and/or device-storage media include non-volatile memory, including by way of example semiconductor memory devices, e.g., erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), field-programmable gate arrays (FPGAs), and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. The terms “machine-storage media,” “computer-storage media,” and “device-storage media” specifically exclude carrier waves, modulated data signals, and other such media, at least some of which are covered under the term “signal medium” discussed below.

Transmission Medium

In various example embodiments, one or more portions of the network870may be an ad hoc network, an intranet, an extranet, a virtual private network (VPN), a local-area network (LAN), a wireless LAN (WLAN), a wide-area network (WAN), a wireless WAN (WWAN), a metropolitan-area network (MAN), the Internet, a portion of the Internet, a portion of the public switched telephone network (PSTN), a plain old telephone service (POTS) network, a cellular telephone network, a wireless network, a Wi-Fi® network, another type of network, or a combination of two or more such networks. For example, the network870or a portion of the network870may include a wireless or cellular network, and the coupling872may be a Code Division Multiple Access (CDMA) connection, a Global System for Mobile communications (GSM) connection, or another type of cellular or wireless coupling. In this example, the coupling872may implement any of a variety of types of data transfer technology, such as Single Carrier Radio Transmission Technology (1×RTT), Evolution-Data Optimized (EVDO) technology, General Packet Radio Service (GPRS) technology, Enhanced Data rates for GSM Evolution (EDGE) technology, third Generation Partnership Project (3GPP) including 3G, fourth generation wireless (4G) networks, Universal Mobile Telecommunications System (UMTS), High-Speed Packet Access (HSPA), Worldwide Interoperability for Microwave Access (WiMAX), Long Term Evolution (LTE) standard, others defined by various standard-setting organizations, other long-range protocols, or other data transfer technology.

The instructions816may be transmitted or received over the network870using a transmission medium via a network interface device (e.g., a network interface component included in the communication components864) and utilizing any one of a number of well-known transfer protocols (e.g., hypertext transfer protocol (HTTP)). Similarly, the instructions816may be transmitted or received using a transmission medium via the coupling882(e.g., a peer-to-peer coupling) to the devices880. The terms “transmission medium” and “signal medium” mean the same thing and may be used interchangeably in this disclosure. The terms “transmission medium” and “signal medium” shall be taken to include any intangible medium that is capable of storing, encoding, or carrying the instructions816for execution by the machine800, and include digital or analog communications signals or other intangible media to facilitate communication of such software. Hence, the terms “transmission medium” and “signal medium” shall be taken to include any form of modulated data signal, carrier wave, and so forth. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal.

Computer-Readable Medium

The terms “machine-readable medium,” “computer-readable medium,” and “device-readable medium” mean the same thing and may be used interchangeably in this disclosure. The terms are defined to include both machine-storage media and transmission media. Thus, the terms include both storage devices/media and carrier waves/modulated data signals.

The various operations of example methods described herein may be performed, at least partially, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Similarly, the methods described herein may be at least partially processor-implemented. For example, at least some of the operations of the method600may be performed by one or more processors. The performance of certain of the operations may be distributed among the one or more processors, not only residing within a single machine, but also deployed across a number of machines. In some example embodiments, the processor or processors may be located in a single location (e.g., within a home environment, an office environment, or a server farm), while in other embodiments the processors may be distributed across a number of locations.

Although the embodiments of the present disclosure have been described with reference to specific example embodiments, it will be evident that various modifications and changes may be made to these embodiments without departing from the broader scope of the inventive subject matter. Accordingly, the specification and drawings are to be regarded in an illustrative rather than a restrictive sense. The accompanying drawings that form a part hereof show, by way of illustration, and not of limitation, specific embodiments in which the subject matter may be practiced. The embodiments illustrated are described in sufficient detail to enable those skilled in the art to practice the teachings disclosed herein. Other embodiments may be used and derived therefrom, such that structural and logical substitutions and changes may be made without departing from the scope of this disclosure. This Detailed Description, therefore, is not to be taken in a limiting sense, and the scope of various embodiments is defined only by the appended claims, along with the full range of equivalents to which such claims are entitled.

Such embodiments of the inventive subject matter may be referred to herein, individually and/or collectively, by the term “invention” merely for convenience and without intending to voluntarily limit the scope of this application to any single invention or inventive concept if more than one is in fact disclosed. Thus, although specific embodiments have been illustrated and described herein, it should be appreciated that any arrangement calculated to achieve the same purpose may be substituted for the specific embodiments shown. This disclosure is intended to cover any and all adaptations or variations of various embodiments. Combinations of the above embodiments, and other embodiments not specifically described herein, will be apparent, to those of skill in the art, upon reviewing the above description.

In this document, the terms “a” or “an” are used, as is common in patent documents, to include one or more than one, independent of any other instances or usages of “at least one” or “one or more.” In this document, the term “or” is used to refer to a nonexclusive or, such that “A or B” includes “A but not B,” “B but not A,” and “A and B,” unless otherwise indicated. In the appended claims, the terms “including” and “in which” are used as the plain-English equivalents of the respective terms “comprising” and “wherein.” Also, in the following claims, the terms “including” and “comprising” are open-ended; that is, a system, device, article, or process that includes elements in addition to those listed after such a term in a claim is still deemed to fall within the scope of that claim.