Patent Description:
Data platforms are widely used for data storage and data access in computing and communication contexts. With respect to architecture, a data platform could be an on-premises data platform, a network-based data platform (e.g., a cloud-based data platform), a combination of the two, and/or include another type of architecture. With respect to type of data processing, a data platform could implement online transactional processing (OLTP), online analytical processing (OLAP), a combination of the two, and/or another type of data processing. Moreover, a data platform could be or include a relational database management system (RDBMS) and/or one or more other types of database management systems.

In a typical implementation, a data platform includes one or more databases that are maintained on behalf of a customer account. Indeed, the data platform may include one or more databases that are respectively maintained in association with any number of customer accounts, as well as one or more databases associated with a system account (e.g., an administrative account) of the data platform, one or more other databases used for administrative purposes, and/or one or more other databases that are maintained in association with one or more other organizations and/or for any other purposes. A data platform may also store metadata in association with the data platform in general and in association with, as examples, particular databases and/or particular customer accounts as well.

Users and/or executing processes that are associated with a given customer account may, via one or more types of clients, be able to cause data to be ingested into the database, and may also be able to manipulate the data, add additional data, remove data, run queries against the data, generate views of the data, and so forth.

In an example implementation of a data platform, a given database is represented as an account-level object within a customer account, and the customer account may also include one or more other account-level objects such as users, roles, and/or the like. Furthermore, a given account-level database object may itself contain one or more objects such as tables, schemas, views, streams, tasks, and/or the like. A given table may be organized as records (e.g., rows) that each include one or more attributes (e.g., columns). A data platform may physically store database data in multiple storage units, which may be referred to as blocks, micro-partitions, and/or by one or more other names. <CIT> [US]) describes techniques that enable users to replicate information technology (IT) infrastructures located within the users' on-premises computing environments at a computing environment provided by a computing resources service provider. <CIT> [US] ET AL) discloses methods and an apparatus for an account cloning service for cloud computing environments are disclosed. Therein, a system includes a plurality of resources, a plurality of service managers coordinating respective distributed network-accessible services, and a metadata manager, wherein the metadata manager receives an account cloning request specifying a source client account and the metadata manager identifies a representation of an administrative state of the source client account with respect to a plurality of services, including configuration settings of an original set of resources of the plurality of resources providing functionality of the set of services.

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.

In some instances, it may be beneficial to replicate database data across multiple geographic locations, across multiple database vendors or providers, and/or across multiple computing devices that may be located in the same physical location or in two or more different locations. These multiple locations, vendors, providers, and/or computing devices may be referred to herein as "deployments. " This may provide significant benefits to a database client because the data is backed up in more than one location. In the event that one deployment is unavailable due to, for example, a power outage, a system error, a scheduled maintenance downtime, and so forth, a failover process ensures a different deployment takes over the management and operation of the database.

In conventional data platforms, customer accounts can be replicated across multiple deployments. For a typical database with multiple deployments, account administrators must manually manage account security configurations to keep them in-sync across primary and secondary accounts used for the failover. Further, end users must manually re-configure security configurations and re-authenticate all security tokens when an account is replicated.

Aspects of the present disclosure include systems, methods, and devices to address, among other problems, the aforementioned shortcomings of account replication with conventional data platforms by using an approach to account replication that involves automatically replicating security configurations from a primary account to a replicated account (also referred to herein as a "secondary account"). With this approach to account replication, any configuration changes made to the primary account are also automatically replicated, thereby eliminating the need for account administrators to manually manage the security configurations in primary and secondary accounts to keep them in-sync. Also, end users connecting to the data platform can continue to seamlessly work when failover happens from primary to the secondary account.

When replicating from a primary account to a secondary account in accordance with the approach described herein, all existing security configurations are seamlessly replicated. Meanwhile, all long-lived tokens generated by the primary account can be validated by the secondary account. Thus, even if a failover or a recovery happens, end users are not impacted.

In example embodiments, a data platform receives a request to replicate a primary account to a secondary account. Based on the request, the data platform accesses account data of the primary account. The account data can include account-level objects such as users, roles, and the like, as well as one or more security configurations. The security configurations can include: an identity management configuration that defines user and role provisioning features for the primary account such as a System for Cross-Domain Identity Management (SCIM) configuration; an authorization configuration that defines resource access authorizations for the primary account such as an Open Authorization (OAuth) configuration; and an authentication configuration that defines access credential authentication features for the primary account such as a Security Assertion Markup Language (SAML) Single Sign-On (SSO) configuration. The data platform uses the account data to replicate the primary account, which results in the secondary account.

When replicating the primary account, the data platform automatically replicates the security configurations of the primary account to the secondary account. In replicating the security configurations, the data platform replicates the identity management configuration and configures an access token associated with the identity management configuration for validation by the secondary account. The data platform also replicates the authorization configuration and configures a refresh token associated with the authorization configuration for validation by the secondary account. In addition, the data platform automatically replicates the authentication configuration to the secondary account.

This approach to account replication supports complicated replication/failover scenarios. For example, suppose there are multiple accounts in a replication group that form a complicated replication topology such as a chain, a star, or even a loop. All tokens and security configurations generated during the replication/failover process are maintained. A security token generation process may involve multiple objects from an account, such as a user, a security integration, a key, a role, and the like. The account replication approach described herein can make use of the objects replicated from different accounts to generate security tokens, and these tokens are still valid after subsequent replications.

<FIG> illustrates an example computing environment <NUM> that includes a data platform <NUM> in communication with a client device <NUM>, 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 from <FIG>. However, a skilled artisan will readily recognize that various additional functional components may be included as part of the computing environment <NUM> to facilitate additional functionality that is not specifically described herein.

As shown, the data platform <NUM> comprises a database storage <NUM>, a compute service manager <NUM>, an execution platform <NUM>, and a metadata database <NUM>. The database storage <NUM> comprises a plurality of computing machines and provides on-demand computer system resources such as data storage and computing power to the data platform <NUM>. As shown, the database storage <NUM> comprises multiple data storage devices <NUM>-<NUM> to <NUM>-N. In some embodiments, the data storage devices <NUM>-<NUM> to <NUM>-N are cloud-based storage devices located in one or more geographic locations. For example, the data storage devices <NUM>-<NUM> to <NUM>-N may be part of a public cloud infrastructure or a private cloud infrastructure. The data storage devices <NUM>-<NUM> to <NUM>-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 database storage <NUM> may include distributed file systems (e.g., Hadoop Distributed File Systems (HDFS)), object storage systems, and the like.

The data platform <NUM> is used for reporting and analysis of integrated data from one or more disparate sources including the storage devices <NUM>-<NUM> to <NUM>-N within the database storage <NUM>. The data platform <NUM> hosts and provides data reporting and analysis services to multiple customer 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. Generally, the data platform <NUM> maintains numerous customer accounts for numerous respective customers. The data platform <NUM> maintains each customer account in one or more storage devices of the database storage <NUM>. Moreover, the data platform <NUM> may maintain metadata associated with the customer accounts in the metadata database <NUM>. Each customer account includes multiple data objects with examples including users, roles, permissions, stages, and the like.

The compute service manager <NUM> coordinates and manages operations of the data platform <NUM>. The compute service manager <NUM> also performs query optimization and compilation as well as managing clusters of compute services that provide compute resources (also referred to as "virtual warehouses"). The compute service manager <NUM> can support any number and type of clients 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 manager <NUM>. As an example, the compute service manager <NUM> is in communication with the client device <NUM>. The client device <NUM> can be used by a user of one of the multiple customer accounts supported by the data platform <NUM> to interact with and utilize the functionality of the data platform <NUM>. In some embodiments, the compute service manager <NUM> does not receive any direct communications from the client device <NUM> and only receives communications concerning jobs from a queue within the data platform <NUM>.

The compute service manager <NUM> is also coupled to metadata database <NUM>. The metadata database <NUM> stores data pertaining to various functions and aspects associated with the data platform <NUM> and its users. In some embodiments, the metadata database <NUM> includes a summary of data stored in remote data storage systems as well as data available from a local cache. Additionally, the metadata database <NUM> may include information regarding how data is organized in remote data storage systems (e.g., the database storage <NUM>) and the local caches. The metadata database <NUM> allows 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 manager <NUM> is further coupled to the execution platform <NUM>, which provides multiple computing resources that execute various data storage and data retrieval tasks. The execution platform <NUM> is coupled to the database storage <NUM>. The execution platform <NUM> comprises a plurality of compute nodes. A set of processes on a compute node executes a query plan compiled by the compute service manager <NUM>. The set of processes can include: a first process to execute the query plan; a second process to monitor and delete micro-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 manager <NUM>; a fourth process to establish communication with the compute service manager <NUM> after a system boot; and a fifth process to handle all communication with a compute cluster for a given job provided by the compute service manager <NUM> and to communicate information back to the compute service manager <NUM> and other compute nodes of the execution platform <NUM>.

In some embodiments, communication links between elements of the computing environment <NUM> are 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 in <FIG>, the data storage devices <NUM>-<NUM> to <NUM>-N are decoupled from the computing resources associated with the execution platform <NUM>. This architecture supports dynamic changes to the data platform <NUM> based 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 data platform <NUM> to scale quickly in response to changing demands on the systems and components within the data platform <NUM>. 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 manager <NUM>, metadata database <NUM>, execution platform <NUM>, and database storage <NUM> are shown in <FIG> as individual discrete components. However, each of the compute service manager <NUM>, metadata database <NUM>, execution platform <NUM>, and database storage <NUM> may be implemented as a distributed system (e.g., distributed across multiple systems/platforms at multiple geographic locations). Additionally, each of the compute service manager <NUM>, metadata database <NUM>, execution platform <NUM>, and database storage <NUM> can be scaled up or down (independently of one another) depending on changes to the requests received and the changing needs of the data platform <NUM>. Thus, in the described embodiments, the data platform <NUM> is dynamic and supports regular changes to meet the current data processing needs.

During typical operation, the data platform <NUM> processes multiple jobs determined by the compute service manager <NUM>. These jobs are scheduled and managed by the compute service manager <NUM> to determine when and how to execute the job. For example, the compute service manager <NUM> may 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 manager <NUM> may assign each of the multiple discrete tasks to one or more nodes of the execution platform <NUM> to process the task. The compute service manager <NUM> may determine what data is needed to process a task and further determine which nodes within the execution platform <NUM> are 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 metadata database <NUM> assists the compute service manager <NUM> in determining which nodes in the execution platform <NUM> have already cached at least a portion of the data needed to process the task. One or more nodes in the execution platform <NUM> process the task using data cached by the nodes and, if necessary, data retrieved from the database storage <NUM>. It is desirable to retrieve as much data as possible from caches within the execution platform <NUM> because the retrieval speed is typically much faster than retrieving data from the database storage <NUM>.

As shown in <FIG>, the computing environment <NUM> separates the execution platform <NUM> from the database storage <NUM>. In this arrangement, the processing resources and cache resources in the execution platform <NUM> operate independently of the data storage devices <NUM>-<NUM> to <NUM>-N in the database storage <NUM>. Thus, the computing resources and cache resources are not restricted to specific data storage devices <NUM>-<NUM> to <NUM>-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 database storage <NUM>.

<FIG> is a block diagram illustrating components of the compute service manager <NUM>, in accordance with some embodiments of the present disclosure. As shown in <FIG>, the compute service manager <NUM> includes an access manager <NUM> and a key manager <NUM> coupled to a data storage device <NUM>. Access manager <NUM> handles authentication and authorization tasks for the systems described herein. Key manager <NUM> manages storage and authentication of keys used during authentication and authorization tasks. For example, access manager <NUM> and key manager <NUM> manage the keys used to access data stored in remote storage devices (e.g., data storage devices in database storage <NUM>). As used herein, the remote storage devices may also be referred to as "persistent storage devices" or "shared storage devices.

A request processing service <NUM> manages received data storage requests and data retrieval requests (e.g., jobs to be performed on database data). For example, the request processing service <NUM> may 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 platform <NUM> or in a data storage device in database storage <NUM>.

A management console service <NUM> supports access to various systems and processes by administrators and other system managers. Additionally, the management console service <NUM> may receive a request to execute a job and monitor the workload on the system.

The compute service manager <NUM> also includes a job compiler <NUM>, a job optimizer <NUM>, and a job executor <NUM>. The job compiler <NUM> parses a job into multiple discrete tasks and generates the execution code for each of the multiple discrete tasks. The job optimizer <NUM> determines the best method to execute the multiple discrete tasks based on the data that needs to be processed. The job optimizer <NUM> also handles various data pruning operations and other data optimization techniques to improve the speed and efficiency of executing the job. The job executor <NUM> executes the execution code for jobs received from a queue or determined by the compute service manager <NUM>.

A job scheduler and coordinator <NUM> sends received jobs to the appropriate services or systems for compilation, optimization, and dispatch to the execution platform <NUM>. For example, jobs may be prioritized and processed in that prioritized order. In an embodiment, the job scheduler and coordinator <NUM> determines a priority for internal jobs that are scheduled by the compute service manager <NUM> with 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 platform <NUM>. In some embodiments, the job scheduler and coordinator <NUM> identifies or assigns particular nodes in the execution platform <NUM> to process particular tasks. A virtual warehouse manager <NUM> manages the operation of multiple virtual warehouses implemented in the execution platform <NUM>. As discussed below, each virtual warehouse includes multiple execution nodes that each include a cache and a processor.

Additionally, the compute service manager <NUM> includes a configuration and metadata manager <NUM>, 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 platform <NUM>). The configuration and metadata manager <NUM> uses the metadata to determine which data micro-partitions need to be accessed to retrieve data for processing a particular task or job. A monitor and workload analyzer <NUM> oversees processes performed by the compute service manager <NUM> and manages the distribution of tasks (e.g., workload) across the virtual warehouses and execution nodes in the execution platform <NUM>. The monitor and workload analyzer <NUM> also redistributes tasks, as needed, based on changing workloads throughout the data platform <NUM> and may further redistribute tasks based on a user (e.g., "external") query workload that may also be processed by the execution platform <NUM>. The configuration and metadata manager <NUM> and the monitor and workload analyzer <NUM> are coupled to a data storage device <NUM>. Data storage device <NUM> in <FIG> represents any data storage device within the data platform <NUM>. For example, data storage device <NUM> may represent caches in execution platform <NUM>, storage devices in database storage <NUM>, or any other storage device.

As shown, the compute service manager <NUM> further includes an account replication manager <NUM>. The account replication manager <NUM> is responsible for handling account replication including automatic replication of security features. Further details regarding the generation of pruning indexes are discussed below.

<FIG> is a block diagram illustrating components of the execution platform <NUM>, in accordance with some embodiments of the present disclosure. As shown in <FIG>, the execution platform <NUM> includes multiple virtual warehouses, including virtual warehouse <NUM>, virtual warehouse <NUM>, 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 platform <NUM> can 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 platform <NUM> to 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 database storage <NUM>).

Although each virtual warehouse shown in <FIG> includes 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 devices <NUM>-<NUM> to <NUM>-N shown in <FIG>. Thus, the virtual warehouses are not necessarily assigned to a specific data storage device <NUM>-<NUM> to <NUM>-N and, instead, can access data from any of the data storage devices <NUM>-<NUM> to <NUM>-N within the database storage <NUM>. Similarly, each of the execution nodes shown in <FIG> can access data from any of the data storage devices <NUM>-<NUM> to <NUM>-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 of <FIG>, virtual warehouse <NUM> includes three execution nodes <NUM>-<NUM>, <NUM>-<NUM>, and <NUM>-N. Execution node <NUM>-<NUM> includes a cache <NUM>-<NUM> and a processor <NUM>-<NUM>. Execution node <NUM>-<NUM> includes a cache <NUM>-<NUM> and a processor <NUM>-<NUM>. Execution node <NUM>-N includes a cache <NUM>-N and a processor <NUM>-N. Each execution node <NUM>-<NUM>, <NUM>-<NUM>, and <NUM>-N is 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 warehouse <NUM> discussed above, virtual warehouse <NUM> includes three execution nodes <NUM>-<NUM>, <NUM>-<NUM>, and <NUM>-N. Execution node <NUM>-<NUM> includes a cache <NUM>-<NUM> and a processor <NUM>-<NUM>. Execution node <NUM>-<NUM> includes a cache <NUM>-<NUM> and a processor <NUM>-<NUM>. Execution node <NUM>-N includes a cache <NUM>-N and a processor <NUM>-N. Additionally, virtual warehouse N includes three execution nodes <NUM>-<NUM>, <NUM>-<NUM>, and <NUM>-N. Execution node <NUM>-<NUM> includes a cache <NUM>-<NUM> and a processor <NUM>-<NUM>. Execution node <NUM>-<NUM> includes a cache <NUM>-<NUM> and a processor <NUM>-<NUM>. Execution node <NUM>-N includes a cache <NUM>-N and a processor <NUM>-N.

In some embodiments, the execution nodes shown in <FIG> are 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 in <FIG> each 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 in <FIG> store, in the local execution node, data that was retrieved from one or more data storage devices in database storage <NUM>. 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 database storage <NUM>.

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 warehouses <NUM>, <NUM>, and n are associated with the same execution platform <NUM>, the virtual warehouses may be implemented using multiple computing systems at multiple geographic locations. For example, virtual warehouse <NUM> can be implemented by a computing system at a first geographic location, while virtual warehouses <NUM> and 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 in <FIG> as 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 warehouse <NUM> implements execution nodes <NUM>-<NUM> and <NUM>-<NUM> on one computing platform at a geographic location and implements execution node <NUM>-N at 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.

A particular execution platform <NUM> may 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 database storage <NUM>, 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.

<FIG> is a conceptual diagram illustrating various customer account replication groups, in accordance with some embodiments of the present disclosure. A replication group refers to a group of customer accounts that includes a primary account and one or more secondary accounts that are produced by replicating the primary account. The data platform <NUM> can use a replication group identifier to identify accounts that are part of the same replication group.

As noted above, each customer account includes multiple data objects with examples including users, roles, permissions, stages, and the like. Additionally, a customer account may include one or more security configurations. The one or more security configurations can include an identity management (e.g., SCIM) configuration, an authorization (e.g., OAuth) configuration, and an authentication (e.g., SAML SSO) configuration, among others. Each security configuration can include one or more integrations, which are objects that provide an interface between the data platform <NUM> and a third-party service. For example, a customer account may include: a first integration object that provides an interface with a third-party identity management service (e.g., SCIM); a second integration object that provides an interface with a third-party authorization service (e.g., OAuth); and a third integration that provides an interface with a third-party authentication service (e.g., SAML SSO).

An account can be replicated from one deployment to another deployment or within the same deployment. In addition, as shown in <FIG>, multiple accounts in the same replication group can be replicated to/from each other and therefore create various topologies such as a star <NUM>, a chain <NUM>, or a loop <NUM>. Regardless of the topology, the data platform <NUM> replicates accounts such that all security configurations are inherited from its ancestor accounts and all long-lived security tokens generated by ancestor accounts can be validated by the replicated account.

<FIG> are flow diagrams illustrating operations of the network-based data platform <NUM> in performing a method <NUM> for customer account replication, in accordance with some embodiments of the present disclosure. The method <NUM> may 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 method <NUM> may be performed by components of data platform <NUM>. Accordingly, the method <NUM> is described below, by way of example with reference thereto. However, it shall be appreciated that method <NUM> may be deployed on various other hardware configurations and is not intended to be limited to deployment within the data platform <NUM>.

Depending on the embodiment, an operation of the method <NUM> may be repeated in different ways or involve intervening operations not shown. Though the operations of the method <NUM> may 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.

At operation <NUM>, the compute service manager <NUM> receives a request to replicate an account maintained by the data platform <NUM> (hereinafter referred to as a "primary account"). The request can be received from client device <NUM> or from a programmatic client of the data platform <NUM>.

At operation <NUM>, the compute service manager <NUM> accesses account data associated with the primary account from a database (e.g., metadata database <NUM> and/or a database maintained in the database storage <NUM>). The account data describes various aspects of the primary account. The account data can include account-level objects such as users, roles, and the like, as well as one or more security configurations. The security configurations for the primary account can include an identity management configuration (e.g., a SCIM configuration), an authorization configuration (e.g., an OAuth configuration), and an authentication configuration (e.g., a SAML SSO configuration). Each security configuration can include one or more integration objects to provide an interface with a corresponding third-party service, as noted above.

In response to the request, the compute service manager <NUM> replicates the primary account using the account data, at operation <NUM>. In replicating the primary account, the compute service manager <NUM> generates a secondary account (also referred to herein as a "replicated account"). In replicating the primary account to the secondary account, the compute service manager <NUM> automatically replicates the security configurations of the primary account to the secondary account. As shown, the operations <NUM>, <NUM>, <NUM>, <NUM>, and <NUM> can be performed as part of replicating the account and specifically as part of replicating the security configurations of the primary account.

At operation <NUM>, the compute service manager <NUM> replicates an identity management configuration (e.g., a SCIM configuration) of the primary account. At operation <NUM>, the compute service manager <NUM> configures an access token associated with the identity management configuration so that the access token can be validated by the secondary account. Further details regarding operations <NUM> and <NUM> are discussed below in reference to <FIG>.

At operation <NUM>, the compute service manager <NUM> replicates an authorization configuration (e.g., an OAuth configuration) of the account. At operation <NUM>, the compute service manager <NUM> configures a refresh token associated with the authorization configuration so that the access token can be validated by the secondary account. Further details regarding operations <NUM> and <NUM> are discussed below in reference to <FIG>.

At operation <NUM>, the compute service manager <NUM> replicates an authentication configuration of the account (e.g., a SAML SSO configuration). Further details regarding operation <NUM> are discussed below in reference to <FIG>.

As shown in <FIG>, the method <NUM> can include operations <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, and <NUM>. The operations <NUM>, <NUM>, and <NUM> can be performed as part of operation <NUM> where the compute service manager <NUM> replicates the identity management configuration of the account. At operation <NUM>, the compute service manager <NUM> replicates a provisioner role of the identity management configuration along with its permissions. As a result, a replicated provisioner role is produced. The provisioner role for the primary account has associated permissions to grant new users and roles in the primary account. The replicated provisional role for the secondary account has associated permissions to grant new users and roles in the secondary account. The replicating of the provisioner role from the primary account results in creation of a replicated provisioner role.

At operation <NUM>, the compute service manager <NUM> replicates an integration object associated with the identity management configuration (hereinafter referred to also as a "primary integration object"). The primary integration object associated with the identity management configuration provides an interface between the data platform <NUM> and a third-party identity management service that corresponds to the identity management configuration of the primary account. In replicating the primary integration object, the compute service manager <NUM> generates a secondary integration object (also referred to as a "replicated integration object").

The primary integration object includes a field that identifies a role in the primary account used to execute the integration with the third-party identity management service. When the secondary integration object is initially created through replication of the primary integration object, the field is empty. Accordingly, at operation <NUM>, the compute service manager <NUM> connects the replicated provisioner role to the replicated integration object. In doing so, the compute service manager <NUM> may remap an identifier of the replicated provisioner role to the replicated integration object.

As shown, the operations <NUM>, <NUM>, <NUM>, and <NUM> can be performed as part of operation <NUM> where the compute service manager <NUM> configures the access token associated with the identity management configuration for use by the secondary account. At operation <NUM>, the compute service manager <NUM> modifies a string structure of the access token. In modifying the string structure, the compute service manager <NUM> performs a number of operations including: changing a schema version number; adding a new deployment identifier that is outside of an encryption string portion of the string; adding integration issuing information that includes the deployment identifier and an integration source identifier; and adding a replication group identifier in the encryption string portion.

At operation <NUM>, the compute service manager <NUM> modifies a data structure of the access token. The compute service manager <NUM> modifies the data structure to include a global identifier. The global identifier comprises a combination of a deployment identifier and an entity identifier. The deployment identifier identifies the deployment for the primary account, and the entity identifier identifies a customer entity that corresponds to the primary account. In addition, the compute service manager <NUM> may further modify the data structure by adding an organization name into the attributes of the data structure and add an identifier of an issuing deployment and version.

At operation <NUM>, the compute service manager <NUM> replicates one or more token encryption keys used to encrypt the access token. The one or more replicated token encryption keys may be stored in a data object that includes metadata associated with the secondary account. In some instances, the compute service manager <NUM> can perform key cleaning or removal so as not to store keys in the secondary account indefinitely. In performing a cleaning operation on a key, the compute service manager <NUM> may remove a key from the data object associated with the account (primary or secondary) and add the key to a list of expired keys that is used by a key expiration service to delete expired keys. The compute service manager <NUM> may perform a key cleaning, for example, when: a customer suspends a replication of a first account to a second account; when a customer disables replication of a replication group from a first account to a second account by removing the second account from an allowed list; or when a customer disables a failover group from a first account to a second account by removing the second account from the allowed list.

At operation <NUM>, the compute service manager <NUM> modifies a format of a user identifier associated with the access token. Initially, a user identifier is generated when a new user is provisioned. The user identifier comprises an account identifier, a local entity identifier, and a <NUM>-bit flag. In modifying the identifier format, the compute service manager <NUM> changes the local entity identifier to the global identifier discussed above.

As shown in <FIG>, the method <NUM> can include operations <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, and <NUM>. The operations <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, and <NUM> can be performed as part of the operation <NUM> where the compute service manager <NUM> replicates the authentication configuration of the account. At operation <NUM>, the compute service manager <NUM> replicates one or more integration objects associated with the authorization configuration. Each such integration object provides an interface between the data platform <NUM> and a third-party authorization service (e.g., OAuth) that corresponds to the authorization configuration. The compute service manager <NUM> can replicate a system (internal) integration object, a client (external) integration object, or both depending on the circumstances. In instances in which both the system and client integration object are replicated, the compute service manager <NUM> replicates both integration objects to include the same client identifier and client secret.

The compute service manager <NUM> further replicates a user associated with the authorization configuration (operation <NUM>), a role associated with the authorization configuration (operation <NUM>), and an authorization consent of the authorization configuration (operation <NUM>). The authorization consent corresponds to a stored indication that the user consents to use of the role in a session. At operation <NUM>, the compute service manager <NUM> links the authorization consent to the integration objects, the user, and the roll.

The operations <NUM>, <NUM>, and <NUM> can be performed as part of operation <NUM> where the compute service manager <NUM> configures the refresh token associated with the authorization configuration for use with the secondary account. At operation <NUM>, the compute service manager <NUM> modifies a token string structure of the refresh token. In modifying the token string structure, the compute service manager <NUM> may perform one or more of: changing a version number, adding a deployment identifier corresponding to the deployment from where the fresh token is generated; adding the global identifier into an encrypted string portion; and adding a replication group identifier into the encrypted string portion.

At operation <NUM>, the compute service manager <NUM> modifies a data structure of the refresh token. The compute service manager <NUM> modifies the data structure to include the global identifier mentioned above. At operation <NUM>, the compute service manager <NUM> replicates one or more token encryption keys used to encrypt the refresh token.

As shown in <FIG>, the method <NUM> can, in some embodiments, include operations <NUM> and <NUM>. Consistent with these embodiments, the operations <NUM> and <NUM> can be performed as part of the operation <NUM> where the compute service manager <NUM> replicates the authorization configuration.

At operation <NUM>, the compute service manager <NUM> replicates an integration object associated with the authorization configuration. The integration object provides an interface between the data platform <NUM> and a third-party authorization service corresponding to the authorization configuration. In replicating the security integration object, the compute service manager <NUM> generates a replicated security integration object. At operation <NUM>, the compute service manager <NUM> configures the new security integration object to include a global account URL.

<FIG> illustrates a diagrammatic representation of a machine <NUM> in the form of a computer system within which a set of instructions may be executed for causing the machine <NUM> to perform any one or more of the methodologies discussed herein, according to an example embodiment. Specifically, <FIG> shows a diagrammatic representation of the machine <NUM> in the example form of a computer system, within which instructions <NUM> (e.g., software, a program, an application, an applet, an app, or other executable code) for causing the machine <NUM> to perform any one or more of the methodologies discussed herein may be executed. For example, the instructions <NUM> may cause the machine <NUM> to execute any one or more operations of any one or more of the method <NUM>. In this way, the instructions <NUM> transform a general, non-programmed machine into a particular machine <NUM> (e.g., the compute service manager <NUM>, the execution platform <NUM>, and the data storage devices <NUM>) that is specially configured to carry out any one of the described and illustrated functions in the manner described herein.

In alternative embodiments, the machine <NUM> operates as a standalone device or may be coupled (e.g., networked) to other machines. In a networked deployment, the machine <NUM> may 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 machine <NUM> may 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 instructions <NUM>, sequentially or otherwise, that specify actions to be taken by the machine <NUM>. Further, while only a single machine <NUM> is illustrated, the term "machine" shall also be taken to include a collection of machines <NUM> that individually or jointly execute the instructions <NUM> to perform any one or more of the methodologies discussed herein.

The machine <NUM> includes processors <NUM>, memory <NUM>, and input/output (I/O) components <NUM> configured to communicate with each other such as via a bus <NUM>. In an example embodiment, the processors <NUM> (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 processor <NUM> and a processor <NUM> that may execute the instructions <NUM>. The term "processor" is intended to include multi-core processors <NUM> that may comprise two or more independent processors (sometimes referred to as "cores") that may execute instructions <NUM> contemporaneously. Although <FIG> shows multiple processors <NUM>, the machine <NUM> may 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 memory <NUM> may include a main memory <NUM>, a static memory <NUM>, and a storage unit <NUM>, all accessible to the processors <NUM> such as via the bus <NUM>. The main memory <NUM>, the static memory <NUM>, and the storage unit <NUM> store the instructions <NUM> embodying any one or more of the methodologies or functions described herein. The instructions <NUM> may also reside, completely or partially, within the main memory <NUM>, within the static memory <NUM>, within the storage unit <NUM>, within at least one of the processors <NUM> (e.g., within the processor's cache memory), or any suitable combination thereof, during execution thereof by the machine <NUM>.

The I/O components <NUM> include components to receive input, provide output, produce output, transmit information, exchange information, capture measurements, and so on. The specific I/O components <NUM> that are included in a particular machine <NUM> will 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 components <NUM> may include many other components that are not shown in <FIG>. The I/O components <NUM> are 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 components <NUM> may include output components <NUM> and input components <NUM>. The output components <NUM> may 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 components <NUM> may 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 components <NUM> may include communication components <NUM> operable to couple the machine <NUM> to a network <NUM> or devices <NUM> via a coupling <NUM> and a coupling <NUM>, respectively. For example, the communication components <NUM> may include a network interface component or another suitable device to interface with the network <NUM>. In further examples, the communication components <NUM> may include wired communication components, wireless communication components, cellular communication components, and other communication components to provide communication via other modalities. The devices <NUM> may 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 machine <NUM> may correspond to any one of the compute service manager <NUM>, the execution platform <NUM>, and the devices <NUM> may include the data storage device <NUM> or any other computing device described herein as being in communication with the data platform <NUM> or the database storage <NUM>.

The various memories (e.g., <NUM>, <NUM>, <NUM>, and/or memory of the processor(s) <NUM> and/or the storage unit <NUM>) may store one or more sets of instructions <NUM> and data structures (e.g., software) embodying or utilized by any one or more of the methodologies or functions described herein. These instructions <NUM>, when executed by the processor(s) <NUM>, 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.

In various example embodiments, one or more portions of the network <NUM> may 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 network <NUM> or a portion of the network <NUM> may include a wireless or cellular network, and the coupling <NUM> may 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 coupling <NUM> may implement any of a variety of types of data transfer technology, such as Single Carrier Radio Transmission Technology (IxRTT), 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 <NUM>, fourth generation wireless (<NUM>) 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 instructions <NUM> may be transmitted or received over the network <NUM> using a transmission medium via a network interface device (e.g., a network interface component included in the communication components <NUM>) and utilizing any one of a number of well-known transfer protocols (e.g., hypertext transfer protocol (HTTP)). Similarly, the instructions <NUM> may be transmitted or received using a transmission medium via the coupling <NUM> (e.g., a peer-to-peer coupling) to the devices <NUM>. 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 instructions <NUM> for execution by the machine <NUM>, 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 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.

Similarly, the methods described herein may be at least partially processor-implemented. For example, at least some of the operations of the method <NUM> may 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.

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.

Claim 1:
A data platform comprising:
at least one hardware processor; and
at least one memory storing instructions that cause the at least one hardware processor to perform operations comprising:
receiving (<NUM>) a request to replicate a first account maintained by the data platform;
accessing (<NUM>), based on the request, account data associated with the first account, the account data comprising one or more security configurations for the first account; and
in response to the request, replicating (<NUM>) the first account using the account data, the replicating of the first account resulting in a second account, the replicating of the first account comprising automatically replicating the one or more security configurations for the first account to the second account, characterized in that the replicating the one or more security configurations for the first account comprises configuring (<NUM>) a refresh token associated with an authorization configuration of the first account, wherein the configuring (<NUM>) of the refresh token comprises
modifying (<NUM>) a data structure of the refresh token to include a global identifier; and
modifying (<NUM>) a string structure of the refresh token to include the global identifier and a replication group identifier, the replication group identifier identifying a group of accounts, the group of accounts including the first account and the second account.