Patent Publication Number: US-2022237192-A1

Title: Predictive resource allocation for distributed query execution

Description:
TECHNICAL FIELD 
     Embodiments of the disclosure relate generally to databases and, more specifically, to allocation of computing resource(s) to improve database query processing in a cloud data system. 
     BACKGROUND 
     Databases are widely used for data storage and access in computing applications. A goal of database storage is to provide enormous sums of information in an organized manner so that it can be accessed, managed, and updated. In a database, data may be organized into rows, columns, and tables. Databases are used by various entities and companies for storing information that may need to be accessed or analyzed. 
     A cloud data warehouse (also referred to as a “network-based data warehouse” or simply as a “data warehouse”) is a network-based system used for data analysis and reporting that comprises a central repository of integrated data from one or more disparate sources. A cloud data warehouse can store current and historical data that can be used for creating analytical reports for an enterprise based on data stored within databases of the enterprise. To this end, data warehouses typically provide business intelligence tools, tools to extract, transform, and load data into the repository, and tools to manage and retrieve metadata. 
     When certain information is to be extracted from a database, a query statement may be executed against the database data. A cloud data warehouse system processes the query and returns certain data according to one or more query predicates that indicate what information should be returned by the query. The data warehouse system extracts specific data from the database and formats that data into a readable form. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The present disclosure will be understood more fully from the detailed description given below and from the accompanying drawings of various embodiments of the disclosure. 
         FIG. 1  illustrates an example computing environment that includes a network-based data warehouse system in communication with a cloud storage platform, in accordance with some embodiments of the present disclosure. 
         FIG. 2  is a block diagram illustrating components of a compute service manager, in accordance with some embodiments of the present disclosure. 
         FIG. 3  is a block diagram illustrating components of an execution platform, in accordance with some embodiments of the present disclosure. 
         FIG. 4  is a computing environment conceptually illustrating an example software architecture for providing a prediction for computing resource(s) allocation for query execution, in accordance with some embodiments of the present disclosure. 
         FIG. 5  is a flow diagram illustrating operations of a database system in performing a method, in accordance with some embodiments of the present disclosure. 
         FIG. 6  illustrates a diagrammatic representation of a machine in the form of a computer system within which a set of instructions may be executed for causing the machine to perform any one or more of the methodologies discussed herein, in accordance with some embodiments of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     Reference will now be made in detail to specific example embodiments for carrying out the inventive subject matter. Examples of these specific embodiments are illustrated in the accompanying drawings, and specific details are set forth in the following description in order to provide a thorough understanding of the subject matter. It will be understood that these examples are not intended to limit the scope of the claims to the illustrated embodiments. On the contrary, they are intended to cover such alternatives, modifications, and equivalents as may be included within the scope of the disclosure. 
     The subject technology provides implementations to improve allocation of computing resource to execute queries in a cloud database system. In some existing cloud database systems, a customer (e.g., user) may have to specify an amount of computing resources (e.g., a number of servers) to utilize to execute a query. The customer may be charged based on the amount of computing resource, along with an amount of time that the query takes to execute using those computing resources. Thus, the total cost (e.g., in terms of money) for executing the query is directly related to the amount of computing resources used and the execution time of the query. If the customer specifies too many computing resources or too little computing resources to utilize for executing the query, this could increase the total cost for the user in unexpected and unwanted ways. Moreover, inaccurate allocation of computing resources introduces inefficiencies (e.g., wasting such resources) as these computing resources could remain idle, or become saturated thereby increasing execution time which causes increased utilization of such resources for a longer than expected period of time. To address at least the aforementioned issues, the subject system advantageously enables provisioning the appropriate amount of computing resources for executing queries, including individual SQL queries. 
     As described herein, implementations of the subject technology utilize several categories of information: local (query-specific) and global data to more accurately predict the amount of required compute resources. As mentioned herein, local historical data or information refers to knowledge extracted from the execution of the same query in the past. As also mentioned herein, global historical data or information has been curated from a plethora of queries as opposed to previous executions of only this specific query. Given the characteristics of a newly seen query, the subject system leverages knowledge of queries with similar characteristics to make an informed decision(s) regarding allocation of computing resources. 
       FIG. 1  illustrates an example computing environment  100  that includes a database system in the example form of a network-based data warehouse system  102 , 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. 1 . However, a skilled artisan will readily recognize that various additional functional components may be included as part of the computing environment  100  to facilitate additional functionality that is not specifically described herein. In other embodiments, the computing environment may comprise another type of network-based database system or a cloud data platform. 
     As shown, the computing environment  100  comprises the network-based data warehouse system  102  in communication with a cloud storage platform  104  (e.g., AWS®, Microsoft Azure Blob Storage®, or Google Cloud Storage), and a cloud credential store provider  106 . The network-based data warehouse system  102  is a network-based system used for reporting and analysis of integrated data from one or more disparate sources including one or more storage locations within the cloud storage platform  104 . The cloud storage platform  104  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  102 . 
     The network-based data warehouse system  102  comprises a compute service manager  108 , an execution platform  110 , and one or more metadata databases  112 . The network-based data warehouse system  102  hosts and provides data reporting and analysis services to multiple client accounts. 
     The compute service manager  108  coordinates and manages operations of the network-based data warehouse system  102 . The compute service manager  108  also performs query optimization and compilation as well as managing clusters of computing services that provide compute resources (also referred to as “virtual warehouses”). The compute service manager  108  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  108 . 
     The compute service manager  108  is also in communication with a client device  114 . The client device  114  corresponds to a user of one of the multiple client accounts supported by the network-based data warehouse system  102 . A user may utilize the client device  114  to submit data storage, retrieval, and analysis requests to the compute service manager  108 . 
     The compute service manager  108  is also coupled to one or more metadata databases  112  that store metadata pertaining to various functions and aspects associated with the network-based data warehouse system  102  and its users. For example, a metadata database  112  may include a summary of data stored in remote data storage systems as well as data available from a local cache. Additionally, a metadata database  112  may include information regarding how data is organized in remote data storage systems (e.g., the cloud storage platform  104 ) and the local caches. Information stored by a metadata database  112  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. 
     As another example, a metadata database  112  can store one or more credential objects  115 . In general, a credential object  115  indicates one or more security credentials to be retrieved from a remote credential store. For example, the credential store provider  106  maintains multiple remote credential stores  118 - 1  to  118 -N. Each of the remote credential stores  118 - 1  to  118 -N may be associated with a user account and may be used to store security credentials associated with the user account. A credential object  115  can indicate one of more security credentials to be retrieved by the compute service manager  108  from one of the remote credential stores  118 - 1  to  118 -N (e.g., for use in accessing data stored by the storage platform  104 ). 
     In an embodiment, a data structure can be utilized for storage of database metadata in the metadata database  112 . For example, such a data structure may be generated from metadata micro-partitions and may be stored in a metadata cache memory. The data structure includes table metadata pertaining to database data stored across a table of the database. The table may include multiple micro-partitions serving as immutable storage devices that cannot be updated in-place. Each of the multiple micro-partitions can include numerous rows and columns making up cells of database data. The table metadata may include a table identification and versioning information indicating, for example, how many versions of the table have been generated over a time period, which version of the table includes the most up-to-date information, how the table was changed over time, and so forth. A new table version may be generated each time a transaction is executed on the table, where the transaction may include a DML statement such as an insert, delete, merge, and/or update command. Each time a DML statement is executed on the table, and a new table version is generated, one or more new micro-partitions may be generated that reflect the DML statement. 
     In an embodiment, the aforementioned table metadata includes global information about the table of a specific version. The aforementioned data structure further includes file metadata that includes metadata about a micro-partition of the table. The terms “file” and “micro-partition” may each refer to a subset of database data and may be used interchangeably in some embodiments. The file metadata includes information about a micro-partition of the table. Further, metadata may be stored for each column of each micro-partition of the table. The metadata pertaining to a column of a micro-partition may be referred to as an expression property (EP) and may include any suitable information about the column, including for example, a minimum and maximum for the data stored in the column, a type of data stored in the column, a subject of the data stored in the column, versioning information for the data stored in the column, file statistics for all micro-partitions in the table, global cumulative expressions for columns of the table, and so forth. Each column of each micro-partition of the table may include one or more expression properties. 
     As mentioned above, a table of a database may include many rows and columns of data. One table may include millions of rows of data and may be very large and difficult to store or read. A very large table may be divided into multiple smaller files corresponding to micro-partitions. For example, one table may be divided into six distinct micro-partitions, and each of the six micro-partitions may include a portion of the data in the table. Dividing the table data into multiple micro-partitions helps to organize the data and to find where certain data is located within the table. 
     In an embodiment, all data in tables is automatically divided into an immutable storage device referred to as a micro-partition. The micro-partition may be considered a batch unit where each micro-partition has contiguous units of storage. By way of example, each micro-partition may contain between 50 MB and 500 MB of uncompressed data (note that the actual size in storage may be smaller because data may be stored compressed). 
     Groups of rows in tables may be mapped into individual micro-partitions organized in a columnar fashion. This size and structure allow for extremely granular selection of the micro-partitions to be scanned, which can be comprised of millions, or even hundreds of millions, of micro-partitions. This granular selection process may be referred to herein as “pruning” based on metadata as described further herein. 
     In an example, pruning involves using metadata to determine which portions of a table, including which micro-partitions or micro-partition groupings in the table, are not pertinent to a query, and then avoiding those non-pertinent micro-partitions when responding to the query and scanning only the pertinent micro-partitions to respond to the query. Metadata may be automatically gathered about all rows stored in a micro-partition, including: the range of values for each of the columns in the micro-partition; the number of distinct values; and/or additional properties used for both optimization and efficient query processing. In one embodiment, micro-partitioning may be automatically performed on all tables. For example, tables may be transparently partitioned using the ordering that occurs when the data is inserted/loaded. 
     The micro-partitions as described herein can provide considerable benefits for managing database data, finding database data, and organizing database data. Each micro-partition organizes database data into rows and columns and stores a portion of the data associated with a table. One table may have many micro-partitions. The partitioning of the database data among the many micro-partitions may be done in any manner that makes sense for that type of data. 
     A query may be executed on a database table to find certain information within the table. To respond to the query, a compute service manager  108  scans the table to find the information requested by the query. The table may include millions and millions of rows, and it would be very time consuming and it would require significant computing resources for the compute service manager  108  to scan the entire table. The micro-partition organization along with the systems, methods, and devices for database metadata storage of the subject technology provide significant benefits by at least shortening the query response time and reducing the amount of computing resources that are required for responding to the query. 
     The compute service manager  108  may find the cells of database data by scanning database metadata. The multiple level database metadata of the subject technology enable the compute service manager  108  to quickly and efficiently find the correct data to respond to the query. The compute service manager  108  may find the correct table by scanning table metadata across all the multiple tables in a given database. The compute service manager  108  may find a correct grouping of micro-partitions by scanning multiple grouping expression properties across the identified table. Such grouping expression properties include information about database data stored in each of the micro-partitions within the grouping. 
     The compute service manager  108  may find a correct micro-partition by scanning multiple micro-partition expression properties within the identified grouping of micro-partitions. The compute service manager  108  may find a correct column by scanning one or more column expression properties within the identified micro-partition. The compute service manager  108  may find the correct row(s) by scanning the identified column within the identified micro-partition. The compute service manager  108  may scan the grouping expression properties to find groupings that have data based on the query. The compute service manager  108  reads the micro-partition expression properties for that grouping to find one or more individual micro-partitions based on the query. The compute service manager  108  reads column expression properties within each of the identified individual micro-partitions. The compute service manager  108  scans the identified columns to find the applicable rows based on the query. 
     In an embodiment, an expression property is information about the one or more columns stored within one or more micro-partitions. For example, multiple expression properties are stored that each pertain to a single column of a single micro-partition. In an alternative embodiment, one or more expression properties are stored that pertain to multiple columns and/or multiple micro-partitions and/or multiple tables. The expression property is any suitable information about the database data and/or the database itself. In an embodiment, the expression property includes one or more of: a summary of database data stored in a column, a type of database data stored in a column, a minimum and maximum for database data stored in a column, a null count for database data stored in a column, a distinct count for database data stored in a column, a structural or architectural indication of how data is stored, and the like. 
     In an embodiment, the metadata organization structures of the subject technology may be applied to database “pruning” based on the metadata as described further herein. The metadata organization may lead to extremely granular selection of pertinent micro-partitions of a table. Pruning based on metadata is executed to determine which portions of a table of a database include data that is relevant to a query. Pruning is used to determine which micro-partitions or groupings of micro-partitions are relevant to the query, and then scanning only those relevant micro-partitions and avoiding all other non-relevant micro-partitions. By pruning the table based on the metadata, the subject system can save significant time and resources by avoiding all non-relevant micro-partitions when responding to the query. After pruning, the system scans the relevant micro-partitions based on the query. 
     In an embodiment, the metadata database  112  includes EP files (expression property files), where each of the EP files store a collection of expression properties about corresponding data. Metadata may be stored for each column of each micro-partition of a given table. In an embodiment, the aforementioned EP files can be stored in a cache provided by the subject system for such EP files (e.g., “EP cache”). 
     The compute service manager  108  is further coupled to the execution platform  110 , which provides multiple computing resources that execute various data storage and data retrieval tasks. The execution platform  110  is coupled to storage platform  104  of the cloud storage platform  104 . The storage platform  104  comprises multiple data storage devices  120 - 1  to  120 -N. In some embodiments, the data storage devices  120 - 1  to  120 -N are cloud-based storage devices located in one or more geographic locations. For example, the data storage devices  120 - 1  to  120 -N may be part of a public cloud infrastructure or a private cloud infrastructure. The data storage devices  120 - 1  to  120 -N may be hard disk drives (HDDs), solid state drives (SSDs), storage clusters, Amazon S3™ storage systems, or any other data storage technology. Additionally, the cloud storage platform  104  may include distributed file systems (such as Hadoop Distributed File Systems (HDFS)), object storage systems, and the like. 
     The execution platform  110  comprises a plurality of compute nodes. A set of processes on a compute node executes a query plan compiled by the compute service manager  108 . The set of processes can include: a first process to execute the query plan; a second process to monitor and delete cache 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  108 ; a fourth process to establish communication with the compute service manager  108  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  108  and to communicate information back to the compute service manager  108  and other compute nodes of the execution platform  110 . 
     In some embodiments, communication links between elements of the computing environment  100  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. 
     The compute service manager  108 , metadata database(s)  112 , execution platform  110 , and storage platform  104 , are shown in  FIG. 1  as individual discrete components. However, each of the compute service manager  108 , metadata database(s)  112 , execution platform  110 , and storage platform  104  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  108 , metadata database(s)  112 , execution platform  110 , and storage platform  104  can be scaled up or down (independently of one another) depending on changes to the requests received and the changing needs of the network-based data warehouse system  102 . Thus, in the described embodiments, the network-based data warehouse system  102  is dynamic and supports regular changes to meet the current data processing needs. 
     During typical operation, the network-based data warehouse system  102  processes multiple jobs determined by the compute service manager  108 . These jobs are scheduled and managed by the compute service manager  108  to determine when and how to execute the job. For example, the compute service manager  108  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  108  may assign each of the multiple discrete tasks to one or more nodes of the execution platform  110  to process the task. The compute service manager  108  may determine what data is needed to process a task and further determine which nodes within the execution platform  110  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 a metadata database  112  assists the compute service manager  108  in determining which nodes in the execution platform  110  have already cached at least a portion of the data needed to process the task. One or more nodes in the execution platform  110  process the task using data cached by the nodes and, if necessary, data retrieved from the cloud storage platform  104 . It is desirable to retrieve as much data as possible from caches within the execution platform  110  because the retrieval speed is typically much faster than retrieving data from the cloud storage platform  104 . 
     As shown in  FIG. 1 , the computing environment  100  separates the execution platform  110  from the storage platform  104 . In this arrangement, the processing resources and cache resources in the execution platform  110  operate independently of the data storage devices  120 - 1  to  120 -N in the cloud storage platform  104 . Thus, the computing resources and cache resources are not restricted to specific data storage devices  120 - 1  to  120 -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 storage platform  104 . 
       FIG. 2  is a block diagram illustrating components of the compute service manager  108 , in accordance with some embodiments of the present disclosure. As shown in  FIG. 2 , the compute service manager  108  includes an access manager  202  and a credential management system  204  coupled to an access metadata database  206 , which is an example of the metadata database(s)  112 . Access manager  202  handles authentication and authorization tasks for the systems described herein. The credential management system  204  facilitates use of remote stored credentials (e.g., credentials stored in one of the remote credential stores  118 - 1  to  118 -N) to access external resources such as data resources in a remote storage device. As used herein, the remote storage devices may also be referred to as “persistent storage devices” or “shared storage devices.” For example, the credential management system  204  may create and maintain remote credential store definitions and credential objects (e.g., in the access metadata database  206 ). A remote credential store definition identifies a remote credential store (e.g., one or more of the remote credential stores  118 - 1  to  118 -N) and includes access information to access security credentials from the remote credential store. A credential object identifies one or more security credentials using non-sensitive information (e.g., text strings) that are to be retrieved from a remote credential store for use in accessing an external resource. When a request invoking an external resource is received at run time, the credential management system  204  and access manager  202  use information stored in the access metadata database  206  (e.g., a credential object and a credential store definition) to retrieve security credentials used to access the external resource from a remote credential store. 
     A request processing service  208  manages received data storage requests and data retrieval requests (e.g., jobs to be performed on database data). For example, the request processing service  208  may determine the data 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  110  or in a data storage device in storage platform  104 . 
     A management console service  210  supports access to various systems and processes by administrators and other system managers. Additionally, the management console service  210  may receive a request to execute a job and monitor the workload on the system. 
     The compute service manager  108  also includes a job compiler  212 , a job optimizer  214  and a job executor  216 . The job compiler  212  parses a job into multiple discrete tasks and generates the execution code for each of the multiple discrete tasks. The job optimizer  214  determines the best method to execute the multiple discrete tasks based on the data that needs to be processed. The job executor  216  executes the execution code for jobs received from a queue or determined by the compute service manager  108 . 
     As further illustrated, the compute service manager  108  includes a resource allocation predictor  228 , which is described in further detail in  FIG. 4  below. In an example, the resource allocation predictor  228  can communicate with the job compiler  212 . In an embodiment, the resource allocation predictor  228  generates a prediction for allocation of computing resources for execution of a given query and may return information regarding the same to the job compiler  212  so that a query plan can be generated utilizing the prediction. 
     As described further herein, the resource allocation predictor  228  can perform operations (e.g., using one or more of a machine learning model, heuristics, rules-based system, and the like) to generate a prediction of computing resources for allocation based at least in part on analyzing various metadata (e.g., query metadata, table metadata, query history, etc.) received from one or more metadata databases (e.g., metadata database(s)  112 ). 
     A job scheduler and coordinator  218  sends received jobs to the appropriate services or systems for compilation, optimization, and dispatch to the execution platform  110 . For example, jobs may be prioritized and then processed in that prioritized order. In an embodiment, the job scheduler and coordinator  218  determines a priority for internal jobs that are scheduled by the compute service manager  108  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  110 . In some embodiments, the job scheduler and coordinator  218  identifies or assigns particular nodes in the execution platform  110  to process particular tasks. A virtual warehouse manager  220  manages the operation of multiple virtual warehouses implemented in the execution platform  110 . For example, the virtual warehouse manager  220  may generate query plans for executing received queries. Alternatively or conjunctively, the job compiler  212  can generate query plans for executing received queries as discussed further herein. 
     Additionally, the compute service manager  108  includes a configuration and metadata manager  222 , which manages the information related to the data stored in the remote data storage devices and in the local buffers (e.g., the buffers in execution platform  110 ). The configuration and metadata manager  222  uses metadata to determine which data files need to be accessed to retrieve data for processing a particular task or job. A monitor and workload analyzer  224  oversee processes performed by the compute service manager  108  and manages the distribution of tasks (e.g., workload) across the virtual warehouses and execution nodes in the execution platform  110 . The monitor and workload analyzer  224  also redistributes tasks, as needed, based on changing workloads throughout the network-based data warehouse system  102  and may further redistribute tasks based on a user (e.g., “external”) query workload that may also be processed by the execution platform  110 . The configuration and metadata manager  222  and the monitor and workload analyzer  224  are coupled to a data storage device  226 . Data storage device  226  in  FIG. 2  represents any data storage device within the network-based data warehouse system  102 . For example, data storage device  226  may represent buffers in execution platform  110 , storage devices in storage platform  104 , or any other storage device. 
     As described in embodiments herein, the compute service manager  108  validates all communication from an execution platform (e.g., the execution platform  110 ) to validate that the content and context of that communication are consistent with the task(s) known to be assigned to the execution platform. For example, an instance of the execution platform executing a query A should not be allowed to request access to data-source D (e.g., data storage device  226 ) that is not relevant to query A. Similarly, a given execution node (e.g., execution node  302 - 1  may need to communicate with another execution node (e.g., execution node  302 - 2 ), and should be disallowed from communicating with a third execution node (e.g., execution node  312 - 1 ) and any such illicit communication can be recorded (e.g., in a log or other location). Also, the information stored on a given execution node is restricted to data relevant to the current query and any other data is unusable, rendered so by destruction or encryption where the key is unavailable. 
       FIG. 3  is a block diagram illustrating components of the execution platform  110 , in accordance with some embodiments of the present disclosure. As shown in  FIG. 3 , the execution platform  110  includes multiple virtual warehouses, including virtual warehouse  1 , virtual warehouse  2 , and virtual warehouse n. Each virtual warehouse includes multiple execution nodes 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, the execution platform  110  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  110  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 storage platform  104 ). 
     Although each virtual warehouse shown in  FIG. 3  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  120 - 1  to  120 -N shown in  FIG. 1 . Thus, the virtual warehouses are not necessarily assigned to a specific data storage device  120 - 1  to  120 -N and, instead, can access data from any of the data storage devices  120 - 1  to  120 -N within the cloud storage platform  104 . Similarly, each of the execution nodes shown in  FIG. 3  can access data from any of the data storage devices  120 - 1  to  120 -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. 3 , virtual warehouse  1  includes three execution nodes  302 - 1 ,  302 - 2 , and  302 - n . Execution node  302 - 1  includes a cache  304 - 1  and a processor  306 - 1 . Execution node  302 - 2  includes a cache  304 - 2  and a processor  306 - 2 . Execution node  302 - n  includes a cache  304 - n  and a processor  306 - n . Each execution node  302 - 1 ,  302 - 2 , and  302 - 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  1  discussed above, virtual warehouse  2  includes three execution nodes  312 - 1 ,  312 - 2 , and  312 - n . Execution node  312 - 1  includes a cache  314 - 1  and a processor  316 - 1 . Execution node  312 - 2  includes a cache  314 - 2  and a processor  316 - 2 . Execution node  312 - n  includes a cache  314 - n  and a processor  316 - n . Additionally, virtual warehouse  3  includes three execution nodes  322 - 1 ,  322 - 2 , and  322 - n . Execution node  322 - 1  includes a cache  324 - 1  and a processor  326 - 1 . Execution node  322 - 2  includes a cache  324 - 2  and a processor  326 - 2 . Execution node  322 - n  includes a cache  324 - n  and a processor  326 - n.    
     In some embodiments, the execution nodes shown in  FIG. 3  are stateless with respect to the data being cached by the execution nodes. 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. 3  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. 3  store, in the local execution node, data that was retrieved from one or more data storage devices in cloud storage platform  104 . 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 storage platform  104 . 
     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  1 ,  2 , and n are associated with the same execution platform  110 , the virtual warehouses may be implemented using multiple computing systems at multiple geographic locations. For example, virtual warehouse  1  can be implemented by a computing system at a first geographic location, while virtual warehouses  2  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. 3  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  1  implements execution nodes  302 - 1  and  302 - 2  on one computing platform at a geographic location and implements execution node  302 - 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  110  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  110  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 storage platform  104 , 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. 
     Embodiments of the subject technology provide approaches to provisioning an appropriate amount of computing resources for executing individual queries (e.g., an SQL query, and the like). In the context of the subject system, such computing resources can be either individual servers or virtual warehouses as discussed before. 
     In an example where computing resources are under allocated, then it is possible that queries will take longer to execute, and in some instance can be monetarily more expensive, too, for the user(s). On the other hand, if computing resources are over allocated, a higher cost may be incurred since the additional resources will remain idle, or to make use of all resources the job compiler  212  and job scheduler and coordinator  218  might determine a sub-optimal query plan for executing the query. As discussed further herein, embodiments of the subject technology enable determining and allocating computing resources in cases where 1) prior knowledge of executing a specific query is unavailable, and 2) knowledge of executing a specific query in the past is available. 
       FIG. 4  is a computing environment conceptually illustrating an example software architecture for providing a prediction for computing resource(s) allocation for query execution, which can be performed by the resource allocation predictor  228  of the compute service manager  108 , in accordance with some embodiments of the present disclosure. 
     As illustrated, a query  410  can be received by the compute service manager  108 . The compute service manager  108  forwards the query to the job compiler  212 . As mentioned before, the job compiler  212  initiates operations to generate (e.g., compilation process) execution code for executing the query. In an embodiment, the job compiler  212  retrieves metadata  420  from one or more metadata database(s)  112  and halts the compilation process. As illustrated, the metadata  420  includes query metadata, table metadata, and query history information. In an embodiment, the job compiler  212  retrieves (e.g., by performing a lookup) query metadata and query history information from query metadata database(s)  450 , which includes query history metadata  460  which at least some of the aforementioned query metadata, table metadata, and query history information are stored. Based at least in part on the retrieved metadata, the job compiler  212  determines whether query  410  has been executed previously (e.g., the same SQL query as the current query). 
     In an embodiment, the job compiler  212  generates relevant metadata based at least in part on the query metadata, table metadata, and query history information. The job compiler  212  forwards the relevant metadata to the resource allocation predictor  228  for analysis as part of generating a prediction of computing resources for allocation to execute query  410 . 
     In an example, the relevant table-metadata generated by the job compiler  212  includes, but is not limited to, a number and total size of pages/micro-partition files, a number of rows, a number of referenced tables or table-aliases (e.g., for the case of join and self-join operations, respectively). 
     The aforementioned query metadata includes, but is not limited to, counts of different query-execution operations (e.g. join, filter, sample) and the query topology (query graph). In an example, a maximum degree of parallelism can be dependent upon a shape of the query graph. For example, a join operation of two tables after filtering can be parallelized, as compared to performing multiple consecutive operations on the same row-set. 
     The query history information (e.g., local query historical metadata) includes, but is not limited to, table metadata summaries of previous executions of queries, execution time, an amount of allocated resources, resource utilization values, and the like. 
     In an example, the resource allocation predictor  228  retrieves global historical information from global metadata database(s)  455 , which may be stored in global history metadata  465 . Based at least in part on the retrieved global historical information, the resource allocation predictor  228  generates output information corresponding to a prediction of computing resources to allocate for executing query  410 . 
     Based at least in part on the global historical information of queries executed in parallel in a virtual warehouse(s) of various sizes, the resource allocation predictor  228  can generate an appropriate prediction for allocation of computing resources (e.g., a number of virtual warehouses and/or nodes) to execute query  410 . In a first example, the resource allocation predictor  228  generates a prediction that parallelization (e.g., allocation of more or additional virtual warehouse or computing resources) is not needed since the query  410  runs in a given period of time (e.g., ˜1 second) irrespective of other conditions (e.g., substantially the same execution time whether one virtual warehouse is utilized to execute query  410 , or X+1 number of virtual warehouses is utilized to execute query  410 ), and therefore allocation of “smaller” virtual warehouses (e.g., with fewer execution node(s)) is cheaper and more optimal. In a second example, as a number of computing resources is allocated (e.g., number of virtual warehouses or nodes), execution time of query  410  decreases until reaching a parallelization limit. Thus, the resource allocation predictor  228  generates a prediction that allocation of a greater number of computing resources, but preferably below the parallelization limit, is recommended for executing query  410 . In a third example, execution of query  410  becomes faster when adding more computing resources (e.g., nodes), but the execution time does not reduce as much proportionally to the amount of added servers. Thus, the total cost increases, and the resource allocation predictor  228  can generate a prediction indicating allocation of an amount of computing resources somewhat in the middle of the amount of computing resources that were analyzed for query execution. In yet another example, due to memory limits, the performance of executing query  410  increases (e.g., execution time decreases) in a super-linear manner when adding compute resources (e.g., servers or virtual warehouses) when the existing resources are few, but when adding more computing resources to already moderately-sized allocated compute resources, the execution time of query  410  does not reduce as quickly. Thus, the cost increases, and the resource allocation predictor  228  can generate a prediction to allocate computing resources that is past a number of virtual warehouses beyond the super-linear portion but less than a second number of virtual warehouses where execution time does not reduce as drastically. 
     The aforementioned global historical information can be either built into the resource allocation predictor  228  (e.g., where ML models are pre-trained), or be used as an additional input source. 
     In an example, global historical information can be something other than a fixed dataset. Ongoing and future query executions can be included (e.g., added) in the global metadata database(s)  455  upon query-execution termination, thereby creating a “richer” dataset over time. 
     In the context of ML, this has the implication that new and improved ML models can be trained and deployed over time to replace the previous ML models. In an example, replacing such ML models does not have to be instantaneous, instead it can follow “flighting” strategies (e.g., the new model(s) is originally introduced to a small subset of all queries/customers and gradually receives increased workloads until completely replacing the previous model). 
     In an embodiment, the resource allocation predictor  228  can be an implementation of a decision-based system, with rules created by human domain experts (e.g., expert system), or automatically created by computer methods (e.g., one or more machine learning (ML) models). The resource allocation predictor  228  module may therefore implement multiple underlying mechanisms for making the decisions (e.g., generating a prediction for allocating computing resources to execute query  410 ). In an example, these mechanisms are applied/utilized in several modes:
         a) independently: for specific query types (based on information from the compute service manager  108  and/or the job compiler  212 ), resource allocation predictor  228  utilizes a specific mechanism (e.g., ML model(s)) that has deployed taking into consideration possible specific intricacies of such queries. Further, instead of or in addition to the different query-types, particular mechanisms can be employed based on different users. As a result, this can lead to more personalized services for customers or customer groups (e.g., users or groups of users) with extraordinary requests.   b) ensemble/in parallel: multiple mechanisms are performed in parallel, and their decision(s) (e.g., output) is utilized by the resource allocation predictor  228  for voting-based outcomes, or as signals to aggregator methods such as ensemble-based ML models (e.g., where multiple ML algorithms or model may be utilized to determine a prediction).       

     In an embodiment, the output information is forwarded to the job compiler  212 . After receiving the output information, the job compiler  212  generates a (finalized) query plan (e.g., final query execution plan) based on the indicated amount of available resources in the output information. In an embodiment, the job scheduler and coordinator  218  sends the finalized query plan to the appropriate node(s) (e.g., cluster of servers or nodes) in the execution platform  110  for executing query  410 . 
     Depending on the resource allocation process, execution of the query  410  can be routed to the “most” appropriate batch of existing pre-allocated resources, or alternatively, the compute service manager  108  (or the job scheduler and coordinator  218 ) can request the allocation of new resources to the appropriate actor (e.g., execution node(s), virtual warehouse(s), and the like). 
       FIG. 5  is a flow diagram illustrating operations of a database system in performing a method, in accordance with some embodiments of the present disclosure. The method  500  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  500  may be performed by components of network-based data warehouse system  102 , such as components of the compute service manager  108  or a node in the execution platform  110 . Accordingly, the method  500  is described below, by way of example with reference thereto. However, it shall be appreciated that the method  500  may be deployed on various other hardware configurations and is not intended to be limited to deployment within the network-based data warehouse system  102 . 
     At operation  502 , the job compiler  212  receives a query directed to a set of source tables, each source table organized into a set of micro-partitions. 
     At operation  504 , the job compiler  212  determines a set of metadata, the set of metadata comprising table metadata, query metadata, and historical data related to the query. 
     In an embodiment, the job compiler  212  analyzes the query against the historical data (e.g., query history) related to the query to determine whether a previous query, being a same query as the query, has been executed at a previous time prior to receiving the query. In response to the query not being executed at the previous time, in an embodiment, the job compiler  212  can include information (e.g., additional metadata) that indicates that the query has yet to be executed prior to forwarding the metadata. The job compiler  212  then forwards the set of metadata to the resource allocation predictor  228  for processing. In an embodiment, the job compiler  212  provides the set of metadata as input data to a machine learning model (as discussed below), for example, by forwarding the set of metadata to the resource allocation predictor  228 . 
     At operation  506 , the resource allocation predictor  228  predicts, using a machine learning model, an indicator of an amount of computing resources for executing the query based at least in part on the set of metadata. 
     In an embodiment, the resource allocation predictor  228  analyzes global history information of previous queries in which the global history information comprises query execution times of the previous queries and corresponding computing resources utilized to execute the previous queries, and can provide information related to the global history information as an (additional) input data to the machine learning model. It is appreciated that the resource allocation predictor  228  can analyze global history information irrespective of whether the query has been executed before, and therefore the resource allocation predictor  228  can perform the analysis of the global history information when the query has yet to be executed, while in other embodiments, the resource allocation predictor  228  analyzes the global history information even when the query has been executed before. 
     The resource allocation predictor  228  runs the machine learning model to generate a value indicating the amount of computing resources for executing the query, the value corresponding to a prediction of the amount of computing resources to utilize for executing the query in an execution platform. The machine learning model provides output data corresponding to the amount of computing resources to allocate (which is then used by a query compiler when generating a query plan), and the resource allocation predictor  228  provides the value indicating the amount of computing resources to the job compiler  212 . 
     In an embodiment, the machine learning model receives the input data at an input layer of the machine learning model. The machine learning model forwards, from the input layer, at least the received input data to a hidden layer of the machine learning model. The machine learning model applies, by the hidden layer, an activation function to the received input data to generate first output data, the first output data being received by an output layer of the machine learning model. The machine learning model then applies, by the output layer, a second activation function to the first output data. The machine learning model provides second output data of the second activation function as the prediction of the amount of computing resources to utilize for executing the query in an execution platform. The resource allocation predictor  228  can receive the prediction and forward information related to the prediction to the job compiler  212  (e.g., for generating a query plan for the query). In this discussion above, it is also appreciated that in some embodiments, the machine learning model may not include a hidden layer(s) and instead include an input layer and an output layer. In yet other embodiments, the machine learning model can include multiple hidden layers instead of one as discussed in the example above. Additionally, although the above discussed relates to a discussion of one type of model (e.g., neural network, or deep neural network with many hidden layers), it is appreciated that other types of machine learning models may be deployed and utilized and still be within the scope of the subject technology. For example, the machine learning model may provide a regression model, ensemble model(s), or support vector machine(s). 
     In an embodiment, the resource allocation predictor  228 , in predicting the indicator of the amount of computing resources, further (or in lieu of using the machine learning model) utilizes an expert system including a set of rules, the set of rules emulating a decision making of a human, the set of rules utilizing information stored in a knowledge base. 
     In an embodiment, the resource allocation predictor  228  determines that the stored global query metadata includes a threshold amount of new data since a previous time that the machine learning model was trained using a previous set of global query metadata. The resource allocation predictor  228  trains the machine learning model based at least in part on the retrieved global query metadata. The resource allocation predictor  228  then deploys the trained machine learning model as a new machine learning model to predict the indicator of the amount of computing resources for executing the query. 
     At operation  508 , the job compiler  212  generates a query plan for executing the query based at least in part on the predicted indicator of the amount of computing resources. The job compiler  212  then forwards the generated query plan to the execution platform  110  to execute the query. Alternatively, the job compiler  212  can forward the generated query plan to the job scheduler and coordinate  218 , which in turn can schedule and forward the query plan to the execution platform  110  for execution. 
     At operation  510 , the execution platform  110  executes the query based at least in part on the query plan. 
       FIG. 6  illustrates a diagrammatic representation of a machine  600  in the form of a computer system within which a set of instructions may be executed for causing the machine  600  to perform any one or more of the methodologies discussed herein, according to an example embodiment. Specifically,  FIG. 6  shows a diagrammatic representation of the machine  600  in the example form of a computer system, within which instructions  616  (e.g., software, a program, an application, an applet, an app, or other executable code) for causing the machine  600  to perform any one or more of the methodologies discussed herein may be executed. For example, the instructions  616  may cause the machine  600  to execute any one or more operations of the method  500 . As another example, the instructions  616  may cause the machine  600  to implement portions of the data flows illustrated in at least  FIG. 4 . In this way, the instructions  616  transform a general, non-programmed machine into a particular machine  600  (e.g., the compute service manager  108  or a node in the execution platform  110 ) 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  600  operates as a standalone device or may be coupled (e.g., networked) to other machines. In a networked deployment, the machine  600  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  600  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  616 , sequentially or otherwise, that specify actions to be taken by the machine  600 . Further, while only a single machine  600  is illustrated, the term “machine” shall also be taken to include a collection of machines  600  that individually or jointly execute the instructions  616  to perform any one or more of the methodologies discussed herein. 
     The machine  600  includes processors  610 , memory  630 , and input/output (I/O) components  650  configured to communicate with each other such as via a bus  602 . In an example embodiment, the processors  610  (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  612  and a processor  614  that may execute the instructions  616 . The term “processor” is intended to include multi-core processors  610  that may comprise two or more independent processors (sometimes referred to as “cores”) that may execute instructions  616  contemporaneously. Although  FIG. 6  shows multiple processors  610 , the machine  600  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  630  may include a main memory  632 , a static memory  634 , and a storage unit  636 , all accessible to the processors  610  such as via the bus  602 . The main memory  632 , the static memory  634 , and the storage unit  636  store the instructions  616  embodying any one or more of the methodologies or functions described herein. The instructions  616  may also reside, completely or partially, within the main memory  632 , within the static memory  634 , within machine storage medium  638  of the storage unit  636 , within at least one of the processors  610  (e.g., within the processor&#39;s cache memory), or any suitable combination thereof, during execution thereof by the machine  600 . 
     The I/O components  650  include components to receive input, provide output, produce output, transmit information, exchange information, capture measurements, and so on. The specific I/O components  650  that are included in a particular machine  600  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  650  may include many other components that are not shown in  FIG. 6 . The I/O components  650  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  650  may include output components  652  and input components  654 . The output components  652  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  654  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  650  may include communication components  664  operable to couple the machine  600  to a network  680  or devices  670  via a coupling  682  and a coupling  672 , respectively. For example, the communication components  664  may include a network interface component or another suitable device to interface with the network  680 . In further examples, the communication components  664  may include wired communication components, wireless communication components, cellular communication components, and other communication components to provide communication via other modalities. The devices  670  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  600  may correspond to any one of the compute service manager  108  or the execution platform  110 , and the devices  670  may include the client device  114  or any other computing device described herein as being in communication with the network-based data warehouse system  102  or the cloud storage platform  104 . 
     Executable Instructions and Machine Storage Medium 
     The various memories (e.g.,  630 ,  632 ,  634 , and/or memory of the processor(s)  610  and/or the storage unit  636 ) may store one or more sets of instructions  616  and data structures (e.g., software) embodying or utilized by any one or more of the methodologies or functions described herein. These instructions  616 , when executed by the processor(s)  610 , cause various operations to implement the disclosed embodiments. 
     As used herein, the terms “machine-storage medium,” “device-storage medium,” and “computer-storage medium” mean the same thing and may be used interchangeably in this disclosure. The terms refer to a single or multiple storage devices and/or media (e.g., a centralized or distributed database, and/or associated caches and servers) that store executable instructions and/or data. The terms shall accordingly be taken to include, but not be limited to, solid-state memories, and optical and magnetic media, including memory internal or external to processors. Specific examples of machine-storage media, computer-storage media, and/or device-storage media include non-volatile memory, including by way of example semiconductor memory devices, e.g., erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), field-programmable gate arrays (FPGAs), and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. The terms “machine-storage media,” “computer-storage media,” and “device-storage media” specifically exclude carrier waves, modulated data signals, and other such media, at least some of which are covered under the term “signal medium” discussed below. 
     Transmission Medium 
     In various example embodiments, one or more portions of the network  680  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  680  or a portion of the network  680  may include a wireless or cellular network, and the coupling  682  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  682  may implement any of a variety of types of data transfer technology, such as Single Carrier Radio Transmission Technology (1×RTT), Evolution-Data Optimized (EVDO) technology, General Packet Radio Service (GPRS) technology, Enhanced Data rates for GSM Evolution (EDGE) technology, third Generation Partnership Project (3GPP) including 3G, fourth generation wireless (4G) networks, Universal Mobile Telecommunications System (UMTS), High-Speed Packet Access (HSPA), Worldwide Interoperability for Microwave Access (WiMAX), Long Term Evolution (LTE) standard, others defined by various standard-setting organizations, other long-range protocols, or other data transfer technology. 
     The instructions  616  may be transmitted or received over the network  680  using a transmission medium via a network interface device (e.g., a network interface component included in the communication components  664 ) and utilizing any one of a number of well-known transfer protocols (e.g., hypertext transfer protocol (HTTP)). Similarly, the instructions  616  may be transmitted or received using a transmission medium via the coupling  672  (e.g., a peer-to-peer coupling) to the devices  670 . 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  616  for execution by the machine  600 , and include digital or analog communications signals or other intangible media to facilitate communication of such software. Hence, the terms “transmission medium” and “signal medium” shall be taken to include any form of modulated data signal, carrier wave, and so forth. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. 
     Computer-Readable Medium 
     The terms “machine-readable medium,” “computer-readable medium,” and “device-readable medium” mean the same thing and may be used interchangeably in this disclosure. The terms are defined to include both machine-storage media and transmission media. Thus, the terms include both storage devices/media and carrier waves/modulated data signals. 
     The various operations of example methods described herein may be performed, at least partially, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Similarly, the methods described herein may be at least partially processor-implemented. For example, at least some of the operations of the method  500  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. 
     CONCLUSION 
     Although the embodiments of the present disclosure have been described with reference to specific example embodiments, it will be evident that various modifications and changes may be made to these embodiments without departing from the broader scope of the inventive subject matter. Accordingly, the specification and drawings are to be regarded in an illustrative rather than a restrictive sense. The accompanying drawings that form a part hereof show, by way of illustration, and not of limitation, specific embodiments in which the subject matter may be practiced. The embodiments illustrated are described in sufficient detail to enable those skilled in the art to practice the teachings disclosed herein. Other embodiments may be used and derived therefrom, such that structural and logical substitutions and changes may be made without departing from the scope of this disclosure. This Detailed Description, therefore, is not to be taken in a limiting sense, and the scope of various embodiments is defined only by the appended claims, along with the full range of equivalents to which such claims are entitled. 
     Such embodiments of the inventive subject matter may be referred to herein, individually and/or collectively, by the term “invention” merely for convenience and without intending to voluntarily limit the scope of this application to any single invention or inventive concept if more than one is in fact disclosed. Thus, although specific embodiments have been illustrated and described herein, it should be appreciated that any arrangement calculated to achieve the same purpose may be substituted for the specific embodiments shown. This disclosure is intended to cover any and all adaptations or variations of various embodiments. Combinations of the above embodiments, and other embodiments not specifically described herein, will be apparent, to those of skill in the art, upon reviewing the above description. 
     In this document, the terms “a” or “an” are used, as is common in patent documents, to include one or more than one, independent of any other instances or usages of “at least one” or “one or more.” In this document, the term “or” is used to refer to a nonexclusive or, such that “A or B” includes “A but not B,” “B but not A,” and “A and B,” unless otherwise indicated. In the appended claims, the terms “including” and “in which” are used as the plain-English equivalents of the respective terms “comprising” and “wherein.” Also, in the following claims, the terms “including” and “comprising” are open-ended; that is, a system, device, article, or process that includes elements in addition to those listed after such a term in a claim is still deemed to fall within the scope of that claim.