Patent Description:
As the world becomes more data driven, database systems and other data systems are storing more and more data. For a business to use this data, different operations or queries are typically run on this large amount of data. Some operations, for example those including large table scans, can take a substantial amount of time to execute on a large amount of data. The time to execute such operations can be proportional to the number of computing resources used for execution, so time can be shortened using more computing resources.

To this end, some data systems can provide a pool of computing resources, and those resources can be assigned to execute different operations. However, in such systems, the assigned computing resources typically work in conjunction, for example in a process group. Hence, their assignments are fixed and static. That is, a computing resource can remain assigned to an operation, which no longer needs that computing resource. The assignments of those computing resources cannot be easily modified in response to demand changes. Hence, the computing resources are not utilized to their full capacity. American patent application <CIT> describes a system for monitoring incoming code execution requests and scheduling the corresponding code executions. It is determined whether some of the incoming code execution requests exhibit periodicity, and cause a reduced amount of idle compute capacity to be maintained on the virtual compute system. <CIT> describes a system where requests are received to allocate instances to be shared by a virtual network function component. An indication of a maximum number of instances to be allocated is provided to one of the hosts. <CIT> describes a system for facilitating tiered service model-based fair allocation of resources for application servers in multi-tenant environments.

Various ones of the appended drawings merely illustrate example embodiments of the present disclosure and should not be considered as limiting its scope.

Embodiments of the present disclosure may provide dynamic assignment techniques for allocating resources on a demand basis. Assignment control may be separated into at least two components: a local component and a global component. Each component may have an active dialog with each other; the dialog may include two aspects: <NUM>) a demand for computing resources, and <NUM>) a total allowed number of computing resources. The local component may set the first aspect, the current demand for computing resources; the global component may set the second aspect, the total allowed number of computing resources. This division of control provides benefits such as allocating resources proportionally to competing requests using fair distribution algorithms. The global component may allocate resources from a pool of resources to different local components, and the local components in turn may assign their allocated resources to local competing requests. Both the global and local components may utilize fair distribution algorithms for their respective allocations and assignments. Hence, the assignments of the resources may be dynamically modified as demand changes, leading to a more optimized use of computing resources.

<FIG> illustrates an example shared data processing platform <NUM> implementing secure messaging between deployments, 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 the figures. However, a skilled artisan will readily recognize that various additional functional components may be included as part of the shared data processing platform <NUM> to facilitate additional functionality that is not specifically described herein.

As shown, the shared data processing platform <NUM> comprises the network-based data warehouse system <NUM>, a cloud computing storage platform <NUM> (e.g., a storage platform, an AWS® service, Microsoft Azure®, or Google Cloud Services®), and a remote computing device <NUM>. The network-based data warehouse system <NUM> is a network-based system used for storing and accessing data (e.g., internally storing data, accessing external remotely located data) in an integrated manner, and reporting and analysis of the integrated data from the one or more disparate sources (e.g., the cloud computing storage platform <NUM>). The cloud computing storage platform <NUM> comprises a plurality of computing machines and provides on-demand computer system resources such as data storage and computing power to the network-based data warehouse system <NUM>. While in the embodiment illustrated in <FIG>, a data warehouse is depicted, other embodiments may include other types of databases or other data processing systems.

The remote computing device <NUM> (e.g., a user device such as a laptop computer) comprises one or more computing machines (e.g., a user device such as a laptop computer) that execute a remote software component <NUM> (e.g., browser accessed cloud service) to provide additional functionality to users of the network-based data warehouse system <NUM>. The remote software component <NUM> comprises a set of machine-readable instructions (e.g., code) that, when executed by the remote computing device <NUM>, cause the remote computing device <NUM> to provide certain functionality. The remote software component <NUM> may operate on input data and generates result data based on processing, analyzing, or otherwise transforming the input data. As an example, the remote software component <NUM> can be a data provider or data consumer that enables database tracking procedures, such as streams on shared tables and views, as discussed in further detail below.

The network-based data warehouse system <NUM> comprises an access management system <NUM>, a compute service manager <NUM>, an execution platform <NUM>, and a database <NUM>. The access management system <NUM> enables administrative users to manage access to resources and services provided by the network-based data warehouse system <NUM>. Administrative users can create and manage users, roles, and groups, and use permissions to allow or deny access to resources and services. The access management system <NUM> can store share data that securely manages shared access to the storage resources of the cloud computing storage platform <NUM> amongst different users of the network-based data warehouse system <NUM>, as discussed in further detail below.

The compute service manager <NUM> coordinates and manages operations of the network-based data warehouse system <NUM>. The compute service manager <NUM> also performs query optimization and compilation as well as managing clusters of computing services that provide compute resources (e.g., virtual warehouses, virtual machines, EC2 clusters). The compute service manager <NUM> can 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 manager <NUM>.

The compute service manager <NUM> is also coupled to database <NUM>, which is associated with the entirety of data stored on the shared data processing platform <NUM>. The database <NUM> stores data pertaining to various functions and aspects associated with the network-based data warehouse system <NUM> and its users.

In some embodiments, database <NUM> includes a summary of data stored in remote data storage systems as well as data available from one or more local caches. Additionally, database <NUM> may include information regarding how data is organized in the remote data storage systems and the local caches. 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 an execution platform <NUM>, which provides multiple computing resources (e.g., virtual warehouses) that execute various data storage and data retrieval tasks, as discussed in greater detail below.

Execution platform <NUM> is coupled to multiple data storage devices <NUM>-<NUM> to <NUM>-n that are part of a cloud computing storage platform <NUM>. In some embodiments, data storage devices <NUM>-<NUM> to <NUM>-n are cloud-based storage devices located in one or more geographic locations. For example, data storage devices <NUM>-<NUM> to <NUM>-n may be part of a public cloud infrastructure or a private cloud infrastructure. Data storage devices <NUM>-<NUM> to <NUM>-n may be hard disk drives (HDDs), solid state drives (SSDs), storage clusters, Amazon S3 storage systems or any other data storage technology. Additionally, cloud computing storage platform <NUM> may include distributed file systems (such as Hadoop Distributed File Systems (HDFS)), object storage systems, and the like.

The execution platform <NUM> comprises a plurality of compute nodes (e.g., virtual warehouses). 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 information 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>.

The cloud computing storage platform <NUM> also comprises an access management system <NUM> and a web proxy <NUM>. As with the access management system <NUM>, the access management system <NUM> allows users to create and manage users, roles, and groups, and use permissions to allow or deny access to cloud services and resources. The access management system <NUM> of the network-based data warehouse system <NUM> and the access management system <NUM> of the cloud computing storage platform <NUM> can communicate and share information so as to enable access and management of resources and services shared by users of both the network-based data warehouse system <NUM> and the cloud computing storage platform <NUM>. The web proxy <NUM> handles tasks involved in accepting and processing concurrent API calls, including traffic management, authorization and access control, monitoring, and API version management. The web proxy <NUM> provides HTTP proxy service for creating, publishing, maintaining, securing, and monitoring APIs (e.g., REST APIs).

In some embodiments, communication links between elements of the shared data processing platform <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>, data storage devices <NUM>-<NUM> to <NUM>-N are decoupled from the computing resources associated with the execution platform <NUM>. That is, new virtual warehouses can be created and terminated in the execution platform <NUM> and additional data storage devices can be created and terminated on the cloud computing storage platform <NUM> in an independent manner. This architecture supports dynamic changes to the network-based data warehouse system <NUM> based on the changing data storage/retrieval needs as well as the changing needs of the users and systems accessing the shared data processing platform <NUM>. The support of dynamic changes allows network-based data warehouse system <NUM> to scale quickly in response to changing demands on the systems and components within network-based data warehouse system <NUM>. The decoupling of the computing resources from the data storage devices <NUM>-<NUM> to <NUM>-n 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. Additionally, the decoupling of resources enables different accounts to handle creating additional compute resources to process data shared by other users without affecting the other users' systems. For instance, a data provider may have three compute resources and share data with a data consumer, and the data consumer may generate new compute resources to execute queries against the shared data, where the new compute resources are managed by the data consumer and do not affect or interact with the compute resources of the data provider.

Compute service manager <NUM>, database <NUM>, execution platform <NUM>, cloud computing storage platform <NUM>, and remote computing device <NUM> are shown in <FIG> as individual components. However, each of compute service manager <NUM>, database <NUM>, execution platform <NUM>, cloud computing storage platform <NUM>, and remote computing environment may be implemented as a distributed system (e.g., distributed across multiple systems/platforms at multiple geographic locations) connected by APIs and access information (e.g., tokens, login data). Additionally, each of compute service manager <NUM>, database <NUM>, execution platform <NUM>, and cloud computing storage platform <NUM> can be scaled up or down (independently of one another) depending on changes to the requests received and the changing needs of shared data processing platform <NUM>. Thus, in the described embodiments, the network-based data warehouse system <NUM> is dynamic and supports regular changes to meet the current data processing needs.

During typical operation, the network-based data warehouse system <NUM> processes multiple jobs (e.g., queries) 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 (due to the nodes having recently downloaded the data from the cloud computing storage platform <NUM> for a previous job) and, therefore, be a good candidate for processing the task. Metadata stored in the 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 cloud computing storage platform <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 cloud computing storage platform <NUM>.

As shown in <FIG>, the shared data processing platform <NUM> separates the execution platform <NUM> from the cloud computing storage platform <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 cloud computing storage platform <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 cloud computing storage platform <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>, 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 cloud computing storage platform <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 stream share engine <NUM> manages change tracking on database objects, such as a data share (e.g., shared table) or shared view, according to some example embodiments, and as discussed in further detail below.

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 (e.g., a virtual machine, a operating system level container execution environment).

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 (i.e., 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 network-based data warehouse system <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> represent any data storage device within the network-based data warehouse system <NUM>. For example, data storage device <NUM> may represent caches in execution platform <NUM>, storage devices in cloud computing storage platform <NUM>, or any other storage device.

<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>, execution platform <NUM> includes multiple virtual warehouses, which are elastic clusters of compute instances, such as virtual machines. In the example illustrated, the virtual warehouses include virtual warehouse <NUM>, virtual warehouse <NUM>, and virtual warehouse n. Each virtual warehouse (e.g., EC2 cluster) includes multiple execution nodes (e.g., virtual machines) that each include a data cache and a processor. The virtual warehouses can execute multiple tasks in parallel by using the multiple execution nodes. As discussed herein, 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 cloud computing storage platform <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 (e.g., upon a query or job completion).

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 cloud computing storage platform <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. For instance, the storage device <NUM>-<NUM> of a first user (e.g., provider account user) may be shared with a worker node in a virtual warehouse of another user (e.g., consumer account user), such that the other user can create a database (e.g., read-only database) and use the data in storage device <NUM>-<NUM> directly without needing to copy the data (e.g., copy it to a new disk managed by the consumer account user). 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 <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.

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 include one data cache and one processor, alternative 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 (e.g., local disk), data that was retrieved from one or more data storage devices in cloud computing storage platform <NUM> (e.g., S3 objects recently accessed by the given node). In some example embodiments, the cache stores file headers and individual columns of files as a query downloads only columns necessary for that query.

To improve cache hits and avoid overlapping redundant data stored in the node caches, the job optimizer <NUM> assigns input file sets to the nodes using a consistent hashing scheme to hash over table file names of the data accessed (e.g., data in database <NUM> or database <NUM>). Subsequent or concurrent queries accessing the same table file will therefore be performed on the same node, according to some example embodiments.

As discussed, the nodes and virtual warehouses may change dynamically in response to environmental conditions (e.g., disaster scenarios), hardware/software issues (e.g., malfunctions), or administrative changes (e.g., changing from a large cluster to smaller cluster to lower costs). In some example embodiments, when the set of nodes changes, no data is reshuffled immediately. Instead, the least recently used replacement policy is implemented to eventually replace the lost cache contents over multiple jobs. 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 cloud computing storage platform <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 execution platform <NUM> implements skew handling to distribute work amongst the cache resources and computing resources associated with a particular execution, where the distribution may be further based on the expected tasks to be performed by the execution nodes. 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. Further, some nodes may be executing much slower than others due to various issues (e.g., virtualization issues, network overhead). In some example embodiments, the imbalances are addressed at the scan level using a file stealing scheme. In particular, whenever a node process completes scanning its set of input files, it requests additional files from other nodes. If the one of the other nodes receives such a request, the node analyzes its own set (e.g., how many files are left in the input file set when the request is received), and then transfers ownership of one or more of the remaining files for the duration of the current job (e.g., query). The requesting node (e.g., the file stealing node) then receives the data (e.g., header data) and downloads the files from the cloud computing storage platform <NUM> (e.g., from data storage device <NUM>-<NUM>), and does not download the files from the transferring node. In this way, lagging nodes can transfer files via file stealing in a way that does not worsen the load on the lagging nodes.

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.

Execution platform <NUM> is 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 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 cloud computing storage platform <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> shows an example foreground global service (GS) <NUM>, according to some example embodiments. GS may also be referred to as a compute service manager. The foreground GS <NUM> may receive query requests and develop query plans to execute the query requests. The foreground GS <NUM> may broker requests to computing nodes or resources that execute a query plan, as explained in further detail herein. The foreground GS <NUM> may include query coordinators (QCs) <NUM>-<NUM>, which are coupled to a local background service (BG) <NUM>. In an embodiment, the foreground GS <NUM> may be defined for a particular type of service, such as copy (replicate), ingest (a type of large table scan), compute, large table scan type queries, etc. The QCs <NUM>-<NUM> may receive query requests from different sources, which may have different account IDs. For certain operations, such as those involving multiple computing resources working together to execute different portions of an operation (e.g., large table scans), the source may be defined at a data warehouse level granularity. The QCs <NUM>-<NUM> may communicate information regarding the query requests and their sources to the local BG <NUM>.

As explained in further detail below, the local BG <NUM> may assign computing resources (also sometimes referred to as execution platforms) to the QC <NUM>-<NUM>. The computing resources may be computing nodes allocated to the foreground GS <NUM> from a pool of computing nodes. In an embodiment, the computing resources may be machines, servers, and/or processors. In an embodiment, the computing resources may be processing cores of a machine. Upon receiving its assignment of computing resources, the QCs <NUM>-<NUM> may communicate directly with the assigned computing resources to execute respective query plans.

<FIG> shows an example of a network system <NUM> for allocating computing resources, according to some example embodiments. The network system <NUM> may include a plurality of foreground services <NUM>, <NUM>, <NUM>. Each foreground service may include a plurality of query coordinators and local BGs as described above. For example, foreground service <NUM> may include query coordinators <NUM>-<NUM> coupled to a local BG <NUM>; foreground service <NUM> may include query coordinators <NUM>-<NUM> coupled to a local BG <NUM>; and foreground service <NUM> may include query coordinators <NUM>-<NUM> coupled to a local BG <NUM>.

The local BGs <NUM>, <NUM>, <NUM> may communicate with a global BG <NUM> over a network. In an embodiment, communications between the local BGs <NUM>, <NUM>, <NUM> and the global BG <NUM> may be performed via a metadata database <NUM>. That is, the local BG <NUM>, <NUM>, <NUM> may transmit messages, for example relating to their current computing demands, to the metadata database <NUM>, where the information from those messages may be stored. And the global BG <NUM> may read the information sent by the local BG <NUM>, <NUM>, <NUM> from the metadata database <NUM>. In another embodiment, communications between the local BGs <NUM>, <NUM>, <NUM> and the global BG <NUM> may be performed directly via, for example, remote procedure calls such as gRPCs. Moreover, communications between the local BGs <NUM>, <NUM>, <NUM> and the global BG <NUM> may be performed using a combination of direct communication (e.g., remote procedure calls) and indirect communications (e.g., via metadata database).

The global BG <NUM> may be coupled to a cloud resource provider <NUM>. The cloud resource provider <NUM> may maintain a pool of computing resources. In an embodiment, the global BG <NUM> may communicate with a communication layer over the cloud resource provider <NUM>.

As explained in further detail below, the network system <NUM> may implement dynamic computing resource allocation techniques by dividing allocation or assignment controls between the local BGs <NUM>, <NUM>, <NUM> and the global BG <NUM>. Each of the allocation-control components, the local BGs and the global BG, may allocate or assign computing resources based on fair distribution algorithms, such as a Max-min fairness algorithm. And each of the allocation-control components may modify their allocations or assignments on a demand basis.

<FIG> shows a flow diagram of a method <NUM> for calculating a local demand for computing resources by a foreground GS, according to some example embodiments. As shown, portions of the method <NUM> may be executed by QCs (e.g., QC <NUM>) and a local BG (e.g., local BG <NUM>).

At operation <NUM>, the QC may receive a query request from a source. For example, the query request may be a copy command or large scan command or the like. The source may be identified by an account ID or by the data warehouse or the like. At operation <NUM>, the QC may communicate information regarding the query request to the local BG; the communicated information may include the source of the query request. Other QCs in the foreground GS may also communicate information regarding their respective query requests to the local BG, including the sources of the query requests. In an embodiment, the foreground GS may be service-specific; for example, while it may handle queries from different sources, the foreground GS may handle one service type, e.g., copy, ingest, large table scan, etc..

At operation <NUM>, the local BG may receive and collect query information from its QCs. At operation <NUM>, the local BG may consolidate the query information from the QCs, translate the demand by the QCs to a per source (or account) demand, and may calculate a current local demand for computing resources for the foreground GS. The local BG may store and maintain the current local demand for computing resources with the information regarding demand per source in a local memory.

Communicating the current local demand and receiving a current allowed number of resources from the global BG is described next with reference to <FIG> shows a flow diagram of a method <NUM> for allocation of computing resources by a global BG, according to some example embodiments. As shown, portions of the method <NUM> may be executed by local BGs (e.g., local BG <NUM>), a global BG (e.g., global BG <NUM>), and cloud resource provider (e.g., cloud resource provider <NUM>). Also, as discussed herein, communication between the local BGs and the global BGs may be performed at least in part via a metadata DB (e.g., <NUM>). For the sake of clarity and brevity, the metadata DB is not shown in <FIG>, but it should be understood that communications between the local BG and the global BG may be performed in whole or in part via a metadata DB. It should also be understood that communications between global BG and the cloud resource provider may be performed via a communication layer over the cloud resource provider.

At operation <NUM>, the local BG may communicate its current local demand for computing resources and the information regarding demand per source. In an embodiment, the local BG may be collecting, updating, and storing this information (current local demand and source information) in its local memory and may be polled by the global BG to communicate this information periodically (say, every two minutes). At that time, the local BG may communicate the latest information stored in its memory. In an embodiment, the frequency of communicating the current local demand may be adjusted based on system conditions. For example, if the demand is more volatile, the frequency of reporting may be increased, and if the demand is less volatile, the frequency of reporting may be decreased.

At operation <NUM>, the global BG may receive and collect the current local demands and source information from the local BGs. At operation <NUM>, based on the collected information, the global BG may calculate a current demand for the system, N number of resources. In an embodiment, calculated current demand for the system, N, may be the sum of the current local demands of the local BGs. In an embodiment, N may be less than the sum of the current local demands of the local BGs. For example, the global BG may smooth the data and/or factor historical data in its calculation of the current demand for the system. This may prevent requesting too many resources based on a shortlasting spike in demand where the resources may be not fully utilized once the spike in demand dissipates. Additionally, the global BG may review the source information from the different query requests and determine that one or more of the sources may not be eligible to receive all demanded resources related to their requests, based on a quota or an account cap of resources for a source. The quota or account cap may be set by a source or an administrator.

At operation <NUM>, the global BG may request the cloud resource provider for the N number of resources. At operation <NUM>, the cloud resource provider may receive the request for the N number of resources. The cloud resource provider may also receive requests from other sources. At operation <NUM>, the cloud provider may transmit a distribution of M number of computing resources to the global BG and identification of the M computing resources, and at operation <NUM>, the global BG may receive this information. In an embodiment, M may be equal to or less than N. In other words, the global BG may receive all the computing resources requested or may receive fewer computing resources than requested.

At operation <NUM>, the global BG may allocate the M computing resources, or at least a portion thereof, to the different local BGs using fair distribution algorithms, such as a Max-min fairness algorithm. The global BG may set the allowed number of computing resources for each local BG. In an embodiment, the global BG may employ tiered hierarchical classification for allocation. For example, a top tier may be service type, where the global BG may distribute the M computing resources based on service types. Some services may be prioritized over other services. Under the service tier, the next tier may be source. Here, the allocated computing resources under each service type may then be distributed based on the sources of the query requests. Under the source tier, the next tier may be GSes. That is, the allocated resources under each source type may then be distributed based on the GSes that are submitting the requests.

In an embodiment, source classification may be used for implementing limiting and throttling, if needed, based on set quotas or cap limits for sources. For example, a source may set a quota or cap limit of how many resources it can use for a time period, for example a month. Therefore, the global BG may limit or throttle the computing resources allocated to a source based on the quota for that source.

At operation <NUM>, the global BG may communicate its allocation decision to the local BGs. The global BG may communicate to each local BG its respective allowed number of computing resources. In an embodiment, the global BG may communicate the allocation of M computing resources. At operation <NUM>, the local BG may receive notification of its allocation, e.g., its allowed number of computing resources.

The global BG may also maintain a list of the M computing resources, or at least a portion thereof that are to be allocated to the local BGs, for example as a persistent list. In an embodiment, the global BG may assign the computing resources to the local BGs based on each local BG's allowed number of computing resources. For example, the global BG may communicate identification information of the assigned computing resources to each of the local BGs. In another embodiment, the global BG may maintain the list of M computing resources and may allow each local BG to claim computing resources up to their allowed number of computing resources. The global BG may maintain a list of the computing resources assigned to each of the local BGs. In an embodiment, the list of the computing resources and their assignments may be maintained in the metadata DB. For example, the list may be maintained in the metadata DB by the global BG, and different identifiers may be used to identify whether a computing resource is assigned or free, and if assigned, to which local BG (or foreground GS) it is assigned. As explained in further detail below, each local BG may be capable of releasing computing resources and may communicate notifications of that release to update the list.

<FIG> show a flow diagram of a method <NUM> for dynamic allocation of computing resources by local and global BGs, according to some example embodiments. As shown, portions of the method <NUM> may be executed by QCs (e.g., QC <NUM>), local BG (e.g., local BG <NUM>), a global BG (e.g., global BG <NUM>). Also, as discussed herein, communication between the local BGs and the global BGs may be performed at least in part via a metadata DB. For the sake of clarity and brevity, the metadata DB is not shown in <FIG>, but it should be understood that communications between the local BG and the global BG may be performed in whole or in part via a metadata DB.

At operation <NUM>, in response to receiving its allowed number of computing resources from the global BG (e.g., operation <NUM>), the local BG may assign computing resources to its QCs. This assignment may be implemented using fair distribution algorithms, such as a Max-min fairness algorithm. In an embodiment, local BG may consider the source of the query requests in its assignment decision making. For example, the local BG may assign the computing resources to each source based on the current allowed number of resources and request ordering.

At operation <NUM>, the local BG may communicate the computing resource assignments to the QCs. The assignment may include identification information for the computing resources. At operation <NUM>, the QC may receive its assignment of computing resources from the local BG. At operation <NUM>, the QC may coordinate with its assigned computing resources to execute respective query results. The QC may communicate directly with its assigned computing resources for scheduling jobs. The QC, for example, may distribute files or batches of files to the computing resource to process. The QC may track the performance of its assigned computing resources.

At operation <NUM>, the local BG may monitor conditions at the QCs and may reassign the computing resources to the QCs, as needed. For example, the QCs may be polled to send information to the local BG. At operation <NUM>, in response to monitoring performance by the QCs, the local BG may dynamically reassign the computing resources to its various QCs in the time between being polled by the global BG. For example, this reassignment may be performed by the local BG periodically, say every <NUM> or <NUM> seconds (as compared to every two minutes by the global BG). Hence, the local BG may assign and reassign computing resources at a faster rate and with more flexibility, as compared to the global BG, thus optimizing the use of the assigned computing resources. Moreover, because the local BG may store its list of allocated computing resources and their assignments to QC in local memory (as compared to a persistent list in a metadata DB, for example), the speed of changing assignments to QCs may be increased. The local BG and the QCs may repeat operations <NUM>-<NUM> until the time to generate the next current local demand at the local BG.

As discussed above, the local BGs may be polled periodically (say, every two minutes) regarding their current demand for computing resources. At operation <NUM>, the local BG, based on the monitored conditions from the QCs (e.g., operations <NUM>-<NUM>, <NUM>), may calculate its current local demand for computing resources for the foreground service (e.g., operation <NUM>). At operation <NUM>, the local BG may communicate its current local demand to the global BG, which may receive the current local demands from the local BGs at operation <NUM>. At operation <NUM>, the global BG may interact with the cloud resource provider and generate the current allocation (e.g., current allowed number of computing resources for each local BG) as discussed above with reference to <FIG>, e.g., execute method <NUM> or portions thereof.

At operation <NUM>, the global BG may communicate its current (revised) allocation decision to the local BGs. For example, the global BG may communicate to each local BG its respective allowed number of computing resources. At operation <NUM>, the local BG may receive notification of its allocation, e.g., its allowed number of computing resources. The local BG may synchronize its stored information regarding current allowed number of computing resources accordingly. If the current allowed number of resources has been increased, the local BG may claim additional computing resources from the list maintained by the global BG and use those additional computing resources to schedule its jobs on. At operation <NUM>, the local BG may assign computing resources to its QC (or modify previous assignments).

Next, consider an example where the allowed number of computing resources for the local BG has been reduced. For example, the local BG was previously allocated ten computing resources, but it has now been allocated eight computing resources by the global BG - a reduction of two. In this example, the local BG must release two computing resources to the global BG. At operation <NUM>, the local BG may transmit instructions to the QC to release one or more computing resources, and the QC may receive the instructions at operation <NUM>. At operation <NUM>, the QC may determine which computing resource(s) to release. The QC may allow the selected computing resource(s) to complete its current operation, for example operating on a file or on a batch of files, and may cease to distribute any more files to the selected computing resource(s), thus concluding its use the selected computing resource(s). At operations <NUM>, the QC may notify the local BG which selected computing resource(s) (e.g., identification information of released computing resource(s)) it is no longer using, and the local BG may receive that notification at operation <NUM> and may pass that information to the global BG at operation <NUM>.

Next, at operation <NUM>, in response to receiving notification of released resources(s), the global BG may further implement its current allocation (e.g., operation <NUM>). For example, if the released computing resource(s) are to be allocated to other local BGs, the global BG may communicate a notification of that allocation to those other local BGs as discussed herein. If the released computing resources are to be released back to the cloud resource provider, the global BG may communicate a notification of that release to the cloud resource provider. The list of the computing resource assignments may be revised accordingly.

Moreover, the network system for allocation control may implement recovery procedures in the event one or more components experience a failure. As discussed herein, the network system for allocation control may include at least three primary components: <NUM>) the QCs, <NUM>) the local BGs (or foreground GSes), and <NUM>) the global BG. If a QC experiences a failure, the local BG may detect the failure and modify the query plans accordingly. This may involve reassigning queries and computing resources. If a local BG (or a foreground GS) experiences a failure, the global BG may purge that local BG's information until the local BG is recovered. And if the global BG experiences a failure, the state of the allocations (e.g., current demand per local BG, current allowed number per local BG) may be recovered from prior dialogs or communications until the global BG recovers.

<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 methods <NUM>, <NUM>, and <NUM>. As another example, the instructions <NUM> may cause the machine <NUM> to implemented portions of the data flows illustrated in any one or more of <FIG>. In this way, the instructions <NUM> transform a general, non-programmed machine into a particular machine <NUM> (e.g., the remote computing device <NUM>, the access management system <NUM>, the compute service manager <NUM>, the execution platform <NUM>, the access management system <NUM>, the Web proxy <NUM>, remote computing device <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 remote computing device <NUM>, the access management system <NUM>, the compute service manager <NUM>, the execution platform <NUM>, the access management system <NUM>, the Web proxy <NUM>, and the devices <NUM> may include any other of these systems and devices.

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 nonvolatile 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 (1xRTT), 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 methods <NUM>, <NUM>, and <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.

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 openended; 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 method comprising:
receiving, by one or more processors of a local background service (<NUM>), information relating to query requests from a plurality of query coordinators (<NUM>..<NUM>, <NUM>..<NUM>, <NUM>..<NUM>);
based on the information relating to query requests and historical data, generating a current local demand for computing resources;
communicating the current local demand for computing resources to a global background service (<NUM>) over a network;
receiving a current allowed number of computing resources from the global background service;
assigning the current allowed number of computing resources to the plurality of query coordinators such that the query coordinators communicate directly with assigned computing resources to execute the query requests;
communicating an updated current local demand to the global background service;
receiving an updated current allowed number of computing resources from the global background service;
assigning the updated current allowed number of computing resources to the plurality of query coordinators;
in response to the updated current allowed number of computing resources being fewer than the current allowed number of computing resources, transmitting an instruction to release an assigned computing resource to a first query coordinator of the plurality of query coordinators;
receiving a notification from the first query coordinator regarding the release of the assigned computing resource, the notification being read from a metadata database; and
allocating the released computing resource to a second query coordinator of the plurality of query coordinators.