EVENT-DRIVEN RESOURCE POOL MANAGEMENT

BACKGROUND

Computing systems for querying of large sets of data can be extremely difficult to implement and maintain. In many scenarios, for example, it is necessary to first create and configure the infrastructure (e.g. server computers, storage devices, networking devices, etc.) to be used for the querying operations. It might then be necessary to perform extract, transform, and load (“ETL”) operations to obtain data from a source system and place the data in data storage. It can also be complex and time consuming to install, configure, and maintain the database management system (“DBMS”) that performs the query operations. Moreover, many DBMS are not suitable for querying extremely large data sets in a performant manner.

Computing clusters can be utilized in some scenarios to query large data sets in a performant manner. For instance, a computing cluster can have many nodes that each execute a distributed query framework for performing distributed querying of a large data set. Such computing clusters and distributed query frameworks are, however, also difficult to implement, configure, and maintain. Moreover, incorrect configuration and/or use of computing clusters such as these can result in the non-optimal utilization of processor, storage, network and, potentially, other types of computing resources.

The disclosure made herein is presented with respect to these and other considerations.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a logical block diagram of event-driven resource pool management, according to some embodiments.

FIG. 3 is a logical block diagram illustrating a managed query service, according to some embodiments.

FIG. 4 is a diagram illustrating interactions between clients and managed query service, according to some embodiments.

FIG. 5 is a sequence diagram for managed execution of queries, according to some embodiments.

FIG. 6 is a logical block diagram illustrating a cluster processing a query as part of managed query execution, according to some embodiments.

FIG. 7 is a logical block diagram illustrating a managed query agent, according to some embodiments.

FIG. 8 is a state diagram illustrating different resource states tracked, detected, or identified by a management agent, according to some embodiments.

FIG. 9 is logical block diagram illustrating interactions between a resource management service and pools of resources, according to some embodiments.

FIG. 10 is a high-level flowchart illustrating various methods and techniques to implement event-driven resource pool management, according to some embodiments.

FIG. 11 is a high-level flowchart illustrating various methods and techniques to monitor a computing resource for pool management events to perform event-driven resource pool management, according to some embodiments.

FIG. 12 is a high-level flowchart illustrating various methods and techniques to perform a pool management event at a computing resource, according to some embodiments.

FIG. 14 is a logical block diagram that shows an illustrative operating environment that includes a service provider network that can be configured to implement aspects of the functionality described herein, according to some embodiments.

FIG. 15 is a logical block diagram illustrating a configuration for a data center that can be utilized to implement aspects of the technologies disclosed herein, according to some embodiments.

FIG. 16 illustrates an example system configured to implement the various methods, techniques, and systems described herein, according to some embodiments.

While embodiments are described herein by way of example for several embodiments and illustrative drawings, those skilled in the art will recognize that embodiments are not limited to the embodiments or drawings described. It should be understood, that the drawings and detailed description thereto are not intended to limit embodiments to the particular form disclosed, but on the contrary, the intention is to cover all modifications, equivalents and alternatives falling within the spirit and scope as defined by the appended claims. The headings used herein are for organizational purposes only and are not meant to be used to limit the scope of the description or the claims. As used throughout this application, the word “may” is used in a permissive sense (i.e., meaning having the potential to), rather than the mandatory sense (i.e., meaning must). Similarly, the words “include,” “including,” and “includes” mean including, but not limited to.

It will also be understood that, although the terms first, second, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first contact could be termed a second contact, and, similarly, a second contact could be termed a first contact, without departing from the scope of the present invention. The first contact and the second contact are both contacts, but they are not the same contact.

DETAILED DESCRIPTION OF EMBODIMENTS

Various embodiments of a stateful resource pool management for job execution are described herein. FIG. 1 illustrates a logical block diagram of event-driven, according to some embodiments. Pool(s) 130 of computing resource(s) 140 may be instantiated, configured, and otherwise prepared for executing different types of job(s) 170 on behalf of network-based service(s) 120, in various embodiments. For example, a query management service, such as discussed below with regard to FIGS. 2-8, may utilize computing resource(s) 140 from different pool(s) 130 in order to execute queries with respect to remotely stored data, in some embodiments. Other types of processing jobs, such as Extract Transform Load (ETL), data validation, log analysis, simulation, numerical analysis, text analysis, machine learning, or other statistical analysis, may be managed, performed, or otherwise executed on behalf of different network-based services, in some embodiments. As the configurations, operations, or requirements of computing resources to execute such job(s) 170 may be costly or time consuming to procure, pool management for job execution resources 110 may provide a dynamically managed set of computing resource(s) 140 in respective pool(s) 130 that are pre-configured and available for executing job(s) 170 without requiring network-based service(s) 120 to directly manage the number of computing resource(s) used by the network-based service(s) 120, in various embodiments.

For instance, pool management for job execution 110 may create pool(s) of computing resources 140, which may be single or multi-node clusters, virtualized servers, instantiated execution platforms, query engines, processing frameworks, or any other set of one or more resource(s) that can execute job(s) 170 selectively routed to computing resource(s) 140, in one embodiment. Computing resources 140 may implement a management agent 142 to provide an interface between computing resource(s) 140 and pool management for job execution 110 and network-based service(s) 120. Computing resource(s) 140 may interact with other services, data stores, or computing resources (not illustrated), such as accessing remotely stored data, or invoking functions, operations, or processes executed by a separate system, in some embodiments. Pool management for job execution resources 110 may then provide the pools of resources 130 to network-based service(s) 120 for job execution. For example, pool management for job execution 110 may implement an interface, such as discussed below with regard to FIG. 9, via which network-based service(s) 120 can programmatically get resource(s) 150 for executing a job 170, in one embodiment. Pool management for job execution resources 110 may identify a pool 130 and computing resource(s) 140 within the pool to execute the job 170 for the network-based service 120 and provide the resource(s) 160 in response to the request, in one embodiment. For example, pool management for job execution resource(s) may identify a pool 130 specially provisioned for the network-based 120 service or a pool 130 provisioned for the type of job to be executed by the network-based service 120, in one embodiment. Pool management for job execution resources 110 may then randomly assign a resource from the pool, or may deterministically select a resource (e.g., based on characteristics of the computing resource, network-based service, or job), in one embodiment, such as a type of computing resource that implements a particular type of query engine for processing a job that is a query. Once the resource(s) 160 are provided to network-based service(s) 120 (e.g., by providing an identifier or access credential for reaching the resource).

As noted above, pool management for job execution resource(s) 110 may dynamically manage computing resource(s) 140 and pool(s) 130. Management agent(s) 142 may proactively monitor the operation of computing resource(s), whether utilized to execute a job 170 or available (not executing a job) to detect pool management events for the computing resource(s) 140. For example, management agent 140 may analyze the state of a resource (e.g., as discussed below with regard to FIG. 8), performance metric(s) (e.g., utilization metrics for processor capacity, network-bandwidth, storage capacity, I/O bandwidth, health metrics for the computing resource(s) itself (e.g., start up time) or the environment of the computing resource(s) (e.g., network events), job execution status or state indications, or other information), in some embodiments. Management agent 142 may evaluate pool management event criteria to identify pool management events to report 180. For example, management agent 142 may detect a pool management event when computing resource 140 completes a job 170 for network-based service 120, a scrub event. Management agent 142 may then perform pool management operation(s), which may be received as indicated at 190 from pool management for job execution resources 110, in one embodiment, or be automatically determined and performed by management agent 142 without input from pool management for job execution resources, in another embodiment. For example, management agent 142 may perform various operations to remove job execution data (e.g., from memory or other storage location at computing resource 140) to scrub the resource and make it ready for executing another job. By detecting pool management events and performing pool management operations at management agent 142, pools(s) 130 may quickly adapt to changing conditions or scenarios that modify the operation of the pool, without requiring that pool management for job execution resources 110 directly monitor each individual computing resource in pool.

Please note that the previous description of event-driven resource pool management is a logical illustration and thus is not to be construed as limiting as to the implementation of a network-based service, pool of computing resources, pool of computing resources, or pool management for job execution resources.

This specification begins with a general description of a provider network that implements a resource management service that provides event-driven management for resource pools that queries received from another network-based service, a managed query service. Then various examples of the managed query service and resource management service (along with other services that may be utilized or implemented) including different components/modules, or arrangements of components/module that may be employed as part of implementing the services are discussed. A number of different methods and techniques to implement event-driven resource pool management are then discussed, some of which are illustrated in accompanying flowcharts. Finally, a description of an example computing system upon which the various components, modules, systems, devices, and/or nodes may be implemented is provided. Various examples are provided throughout the specification.

FIG. 2 is a logical block diagram illustrating a provider network implementing event-driven resource pool management for pools of resources configured to execute jobs on behalf of network-based services in the provider network, according to some embodiments. Provider network 200 may be a private or closed system or may be set up by an entity such as a company or a public sector organization to provide one or more services (such as various types of cloud-based storage) accessible via the Internet and/or other networks to clients 250, in some embodiments. Provider network 200 may be implemented in a single location or may include numerous data centers hosting various resource pools, such as collections of physical and/or virtualized computer servers, storage devices, networking equipment and the like (e.g., FIGS. 15, 16 and computing system 2000 described below with regard to FIG. 16), needed to implement and distribute the infrastructure and storage services offered by the provider network 200. In some embodiments, provider network 200 may implement various computing resources or services, such as a virtual compute service 210, data processing service(s) 220, (e.g., relational or non-relational (NoSQL) database query engines, map reduce processing, data flow processing, and/or other large scale data processing techniques), data storage service(s) 230, (e.g., an object storage service, block-based storage service, or data storage service that may store different types of data for centralized access) other services 240 (any other type of network based services (which may include various other types of storage, processing, analysis, communication, event handling, visualization, and security services not illustrated), managed query service 270, data catalog service 280, and resource management service 290.

In various embodiments, the components illustrated in FIG. 2 may be implemented directly within computer hardware, as instructions directly or indirectly executable by computer hardware (e.g., a microprocessor or computer system), or using a combination of these techniques. For example, the components of FIG. 2 may be implemented by a system that includes a number of computing nodes (or simply, nodes), each of which may be similar to the computer system embodiment illustrated in FIG. 16 and described below. In various embodiments, the functionality of a given system or service component (e.g., a component of data storage service 230) may be implemented by a particular node or may be distributed across several nodes. In some embodiments, a given node may implement the functionality of more than one service system component (e.g., more than one data store component).

Virtual compute service 210 may be implemented by provider network 200, in some embodiments. Virtual computing service 210 may offer instances and according to various configurations for client(s) 250 operation. A virtual compute instance may, for example, comprise one or more servers with a specified computational capacity (which may be specified by indicating the type and number of CPUs, the main memory size, and so on) and a specified software stack (e.g., a particular version of an operating system, which may in turn run on top of a hypervisor). A number of different types of computing devices may be used singly or in combination to implement the compute instances and of provider network 200 in different embodiments, including general purpose or special purpose computer servers, storage devices, network devices and the like. In some embodiments instance client(s) 250 or other any other user may be configured (and/or authorized) to direct network traffic to a compute instance.

Compute instances may operate or implement a variety of different platforms, such as application server instances, Java™ virtual machines (JVMs), general purpose or special-purpose operating systems, platforms that support various interpreted or compiled programming languages such as Ruby, Perl, Python, C, C++ and the like, or high-performance computing platforms) suitable for performing client(s) 202 applications, without for example requiring the client(s) 250 to access an instance. Applications (or other software operated/implemented by a compute instance and may be specified by client(s), such as custom and/or off-the-shelf software.

In some embodiments, compute instances have different types or configurations based on expected uptime ratios. The uptime ratio of a particular compute instance may be defined as the ratio of the amount of time the instance is activated, to the total amount of time for which the instance is reserved. Uptime ratios may also be referred to as utilizations in some implementations. If a client expects to use a compute instance for a relatively small fraction of the time for which the instance is reserved (e.g., 30%-35% of a year-long reservation), the client may decide to reserve the instance as a Low Uptime Ratio instance, and pay a discounted hourly usage fee in accordance with the associated pricing policy. If the client expects to have a steady-state workload that requires an instance to be up most of the time, the client may reserve a High Uptime Ratio instance and potentially pay an even lower hourly usage fee, although in some embodiments the hourly fee may be charged for the entire duration of the reservation, regardless of the actual number of hours of use, in accordance with pricing policy. An option for Medium Uptime Ratio instances, with a corresponding pricing policy, may be supported in some embodiments as well, where the upfront costs and the per-hour costs fall between the corresponding High Uptime Ratio and Low Uptime Ratio costs.

Compute instance configurations may also include compute instances with a general or specific purpose, such as computational workloads for compute intensive applications (e.g., high-traffic web applications, ad serving, batch processing, video encoding, distributed analytics, high-energy physics, genome analysis, and computational fluid dynamics), graphics intensive workloads (e.g., game streaming, 3D application streaming, server-side graphics workloads, rendering, financial modeling, and engineering design), memory intensive workloads (e.g., high performance databases, distributed memory caches, in-memory analytics, genome assembly and analysis), and storage optimized workloads (e.g., data warehousing and cluster file systems). Size of compute instances, such as a particular number of virtual CPU cores, memory, cache, storage, as well as any other performance characteristic. Configurations of compute instances may also include their location, in a particular data center, availability zone, geographic, location, etc. . . . and (in the case of reserved compute instances) reservation term length. Different configurations of compute instances, as discussed below with regard to FIG. 3, may be implemented as computing resources associated in different pools of resources managed by resource management service 290 for executing jobs routed to the resources, such as queries routed to select resources by managed query service 270.

Data processing services 220 may be various types of data processing services to perform different functions (e.g., query or other processing engines to perform functions such as anomaly detection, machine learning, data lookup, or any other type of data processing operation). For example, in at least some embodiments, data processing services 230 may include a map reduce service that creates clusters of processing nodes that implement map reduce functionality over data stored in one of data storage services 240. Various other distributed processing architectures and techniques may be implemented by data processing services 230 (e.g., grid computing, sharding, distributed hashing, etc.). Note that in some embodiments, data processing operations may be implemented as part of data storage service(s) 230 (e.g., query engines processing requests for specified data). Data processing service(s) 230 may be clients of data catalog service 220 in order to obtain structural information for performing various processing operations with respect to data sets stored in data storage service(s) 230, as provisioned resources in a pool for managed query service 270.

Data catalog service 280 may provide a catalog service that ingests, locates, and identifies data and the schema of data stored on behalf of clients in provider network 200 in data storage services 230. For example, a data set stored in a non-relational format may be identified along with a container or group in an object-based data store that stores the data set along with other data objects on behalf of a same customer or client of provider network 200. In at least some embodiments, data catalog service 280 may direct the transformation of data ingested in one data format into another data format. For example, data may be ingested into data storage service 230 as single file or semi-structured set of data (e.g., JavaScript Object Notation (JSON)). Data catalog service 280 may identify the data format, structure, or any other schema information of the single file or semi-structured set of data. In at least some embodiments, the data stored in another data format may be converted to a different data format as part of a background operation (e.g., to discover the data type, column types, names, delimiters of fields, and/or any other information to construct the table of semi-structured data in order to create a structured version of the data set). Data catalog service 280 may then make the schema information for data available to other services, computing devices, or resources, such as computing resources or clusters configured to process queries with respect to the data, as discussed below with regard to FIGS. 3-7.

Data storage service(s) 230 may implement different types of data stores for storing, accessing, and managing data on behalf of clients 250 as a network-based service that enables clients 250 to operate a data storage system in a cloud or network computing environment. For example, data storage service(s) 230 may include various types of database storage services (both relational and non-relational) for storing, querying, and updating data. Such services may be enterprise-class database systems that are highly scalable and extensible. Queries may be directed to a database in data storage service(s) 230 that is distributed across multiple physical resources, and the database system may be scaled up or down on an as needed basis. The database system may work effectively with database schemas of various types and/or organizations, in different embodiments. In some embodiments, clients/subscribers may submit queries in a number of ways, e.g., interactively via an SQL interface to the database system. In other embodiments, external applications and programs may submit queries using Open Database Connectivity (ODBC) and/or Java Database Connectivity (JDBC) driver interfaces to the database system.

One data storage service 230 may be implemented as a centralized data store so that other data storage services may access data stored in the centralized data store for processing and or storing within the other data storage services, in some embodiments. A may provide storage and access to various kinds of object or file data stores for putting, updating, and getting various types, sizes, or collections of data objects or files. Such data storage service(s) 230 may be accessed via programmatic interfaces (e.g., APIs) or graphical user interfaces. A centralized data store may provide virtual block-based storage for maintaining data as part of data volumes that can be mounted or accessed similar to local block-based storage devices (e.g., hard disk drives, solid state drives, etc.) and may be accessed utilizing block-based data storage protocols or interfaces, such as internet small computer interface (iSCSI).

In at least some embodiments, one of data storage service(s) 230 may be a data warehouse service that utilizes a centralized data store implemented as part of another data storage service 230. A data warehouse service as may offer clients a variety of different data management services, according to their various needs. In some cases, clients may wish to store and maintain large of amounts data, such as sales records marketing, management reporting, business process management, budget forecasting, financial reporting, website analytics, or many other types or kinds of data. A client's use for the data may also affect the configuration of the data management system used to store the data. For instance, for certain types of data analysis and other operations, such as those that aggregate large sets of data from small numbers of columns within each row, a columnar database table may provide more efficient performance. In other words, column information from database tables may be stored into data blocks on disk, rather than storing entire rows of columns in each data block (as in traditional database schemes).

Managed query service 270, as discussed below in more detail with regard to FIGS. 3-7, may manage the execution of queries on behalf of clients so that clients may perform queries over data stored in one or multiple locations (e.g., in different data storage services, such as an object store and a database service) without configuring the resources to execute the queries, in various embodiments. Resource management service 290, as discussed in more detail below with regard to FIGS. 8-14, may manage and provide pools of computing resources for different services like managed query service 270 in order to execute jobs on behalf the different services, as discussed above with regard to FIG. 1.

Generally speaking, clients 250 may encompass any type of client configurable to submit network-based requests to provider network 200 via network 260, including requests for storage services (e.g., a request to create, read, write, obtain, or modify data in data storage service(s) 240, etc.) or managed query service 270 (e.g. ,a request to query data in a data set stored in data storage service(s) 230). For example, a given client 250 may include a suitable version of a web browser, or may include a plug-in module or other type of code module that may execute as an extension to or within an execution environment provided by a web browser. Alternatively, a client 250 may encompass an application such as a database application (or user interface thereof), a media application, an office application or any other application that may make use of storage resources in data storage service(s) 240 to store and/or access the data to implement various applications. In some embodiments, such an application may include sufficient protocol support (e.g., for a suitable version of Hypertext Transfer Protocol (HTTP)) for generating and processing network-based services requests without necessarily implementing full browser support for all types of network-based data. That is, client 250 may be an application may interact directly with provider network 200. In some embodiments, client 250 may generate network-based services requests according to a Representational State Transfer (REST)-style network-based services architecture, a document- or message-based network-based services architecture, or another suitable network-based services architecture.

In some embodiments, a client 250 may provide access to provider network 200 to other applications in a manner that is transparent to those applications. For example, client 250 may integrate with an operating system or file system to provide storage on one of data storage service(s) 240 (e.g., a block-based storage service). However, the operating system or file system may present a different storage interface to applications, such as a conventional file system hierarchy of files, directories and/or folders. In such an embodiment, applications may not need to be modified to make use of the storage system service model. Instead, the details of interfacing to the data storage service(s) 240 may be coordinated by client 250 and the operating system or file system on behalf of applications executing within the operating system environment.

Clients 250 may convey network-based services requests (e.g., access requests directed to data in data storage service(s) 240, operations, tasks, or jobs, being performed as part of data processing service(s) 230, or to interact with data catalog service 220) to and receive responses from provider network 200 via network 260. In various embodiments, network 260 may encompass any suitable combination of networking hardware and protocols necessary to establish network-based-based communications between clients 250 and provider network 200. For example, network 260 may generally encompass the various telecommunications networks and service providers that collectively implement the Internet. Network 260 may also include private networks such as local area networks (LANs) or wide area networks (WANs) as well as public or private wireless networks. For example, both a given client 250 and provider network 200 may be respectively provisioned within enterprises having their own internal networks. In such an embodiment, network 260 may include the hardware (e.g., modems, routers, switches, load balancers, proxy servers, etc.) and software (e.g., protocol stacks, accounting software, firewall/security software, etc.) necessary to establish a networking link between given client 250 and the Internet as well as between the Internet and provider network 200. It is noted that in some embodiments, clients 250 may communicate with provider network 200 using a private network rather than the public Internet.

FIG. 3 is a logical block diagram illustrating a managed query service, according to some embodiments. As discussed below with regard to FIGS. 4-9, managed query service 270 may leverage the capabilities of various other services in provider network 200. For example, managed query service 270 may utilize resource management service 290 to provision and manage pools of preconfigured resources to execute queries, provide resources of preconfigured queries, and return utilized resources to availability. For example, resource management service 290 may instantiate, configure, and provide resource pool(s) 350a and 350n that include pool resource(s) 352a and 352n from one or more different resource services, such as computing resource(s) 354 in virtual compute service 210 and computing resource(s) 356 in data processing service(s) 220. Resource management service 290 may send requests to create, configure, tag (or otherwise associate) resources 352 for a particular resource pool, terminate, reboot, otherwise operate resources 352 in order to execute jobs on behalf of other network-based services.

Once a resource from a pool is provided (e.g., by receiving an identifier or other indicator of the resource to utilize), managed query service 270 may interact directly with the resource 354 in virtual compute service 210 or the resource 356 in data processing services 220 to execute queries, in various embodiments. Managed query service 270 may utilize data catalog service 280, in some embodiments to store data set schemas 352, as discussed below with regard to FIGS. 4, for subsequent use when processing queries, as discussed below with regard to FIGS. 5-7, in some embodiments. For example, a data set schema may identify the field or column data types of a table as part of a table definition so that a query engine (executing on a computing resource), may be able to understand the data being queried, in some embodiments. Managed query service 270 may also interact with data storage service(s) 230 to directly source data sets 370 or retrieve query results 380, in some embodiments.

Managed query service 270 may implement a managed query interface 310 to handle requests from different client interfaces, as discussed below with regard to FIG. 4.

For example, different types of requests, such as requests formatted according to an Application Programmer Interface (API), standard query protocol or connection, or requests received via a hosted graphical user interface implemented as part of managed query service may be handled by managed query interface 310.

Managed query service 270 may implement managed query service control plane 320 to manage the operation of service resources (e.g., request dispatchers for managed query interface 310, resource planner workers for resource planner 330, or query tracker monitors for query tracker 340). Managed query service control plane 320 may direct requests to appropriate components as discussed below with regard to FIGS. 5 and 6. Managed query service 270 may implement authentication and authorization controls for handling requests received via managed query interface 310. For example, managed query service control plane 320 may validate the identity or authority of a client to access the data set identified in a query received from a client (e.g., by validating an access credential). In at least some embodiments, managed query service control plane 320 may maintain (in an internal data store or as part of a data set in an external data store, such as in one of data storage service(s) 230), query history, favorite queries, or query execution logs, and other managed query service historical data. Query execution costs may be billed, calculated or reported by managed query service control plane 320 to a billing service (not illustrated) or other system for reporting usage to users of managed query service, in some embodiments.

Managed query service 270 may implement resource planner 330 to intelligently select available computing resources from pools for execution of queries, in some embodiments. For example, resource planner 330 may evaluated collected data statistics associated with query execution (e.g., reported by computing resources) and determine an estimated number or configuration of computing resources for executing a query within some set of parameters (e.g., cost, time, etc.). For example, machine learning techniques may be applied by resource planner 330 to generate a query estimation model that can be applied to the features of a received query to determine the number/configuration of resources, in one embodiment. Resource planner 330 may then provide or identify which ones of the resources available to execute the query from a pool may best fit the estimated number/configuration, in one embodiment.

In various embodiments, managed query service 270 may implement query tracker 340 in order to manage the execution of queries at compute clusters, track the status of queries, and obtain the resources for the execution of queries from resource management service 290. For example, query tracker 340 may maintain a database or other set of tracking information based on updates received from different managed query service agents implemented on provisioned computing resources (e.g., computing clusters as discussed below with regard to FIGS. 5-7). In some embodiments, query tracker may

FIG. 4 is a diagram illustrating interactions between clients and managed query service, according to some embodiments. Client(s) 400 may be client(s) 250 in FIG. 2 above or other clients (e.g., other services systems or components implemented as part of provider network 200 or as part of an external service, system, or component, such as data exploration or visualization tools (e.g., Tableau, Looker, MicroStrategy, Qliktech, or Spotfire). Clients 400 can send various requests to managed query service 270 via managed query interface 310. Managed query interface 310 may offer a management console 440, which may provider a user interface to submit queries 442 (e.g., graphical or command line user interfaces) or register data schemas 444 for executing queries. For example, management console 440 may be implemented as part of a network-based site (e.g., an Internet website for provider network 200) that provides various graphical user interface elements (e.g., text editing windows, drop-down menus, buttons, wizards or workflows) to submit queries or register data schemas. Managed query interface 310 may implement programmatic interfaces 410 (e.g., various Application Programming Interface (API) commands) to perform queries, and various other illustrated requests. In some embodiments, managed query interface 310 may implement custom drivers that support standard communication protocols for querying data, such as JDBC driver 430 or ODBC driver 420.

Clients 400 can submit many different types of request to managed query interface 310. For example, in one embodiment, clients 400 can submit requests 450 to create, read, modify, or delete data schemas. For example, a new table schema can be submitted via a request 450. Request 450 may include a name of the data set (e.g., table), a location of the data set (e.g. an object identifier in an object storage service, such as data storage service 230, file path, uniform resource locator, or other location indicator), number of columns, column names, data types for fields or columns (e.g., string, integer, Boolean, timestamp, array, map, custom data types, or compound data types), data format (e.g., formats including, but not limited to, JSON, CSV, AVRO, ORC, PARQUET, tab delimited, comma separated, as well as custom or standard serializers/desrializers), partitions of a data set (e.g., according to time, geographic location, or other dimensions), or any other schema information for process queries with respect to data sets, in various embodiments. In at least some embodiments, request to create/read/modify/delete data set schemas may be performed using a data definition language (DDL), such as Hive Query Language (HQL). Managed query interface 310 may perform respective API calls or other requests 452 with respect to data catalog service 280, to store the schema for the data set (e.g., as part of table schemas 402). Table schemas 402 may be stored in different formats (e.g., Apache Hive). Note, in other embodiments, managed query service 270 may implement its own metadata store.

Clients 400 may also send queries 460 and query status 470 requests to managed query interface 310 which may direct those requests 460 and 470 to managed query service control plane 320, in various embodiments, as discussed below with regard to FIGS. 5 and 6. Queries 460 may be formatted according to various types of query languages, such as Structured Query Language (SQL) or HQL.

Client(s) 400 may also submit requests for query history 480 or other account related query information (e.g., favorite or common queries) which managed query. In some embodiments, client(s) 400 may programmatically trigger the performance of past queries by sending a request to execute a saved query 490, which managed query service control plane 320 may look-up and execute. For example, execute saved query request may include a pointer or other identifier to a query stored or saved for a particular user account or client. Managed query service control plane 320 may then access that user query store to retrieve and execute the query.

FIG. 5 is a sequence diagram for managed execution of queries, according to some embodiments. Query 530 may be received at managed query service control plane 320 which may submit the query 532 to query tracker 340 indicating the selected cluster 536 for execution. Query tracker 340 may lease a cluster 534 from resource management service 290, which may return a cluster 536. Resource management service 290 and query tracker 340 may maintain lease state information for resources that are leased by query tracker and assigned to execute received queries. Query tracker 340 may then initiate execution of the query 538 at the provisioned cluster 510, sending a query execution instruction to a managed query agent 512.

Managed query agent 512 may get schema 540 for the data sets(s) 520 from data catalog service 280, which may return the appropriate schema 542. Provisioned cluster 510 can then generate a query execution plan and execute the query 544 with respect to data set(s) 520 according to the query plan. Managed query agent 512 may send query status 546 to query tracker 340 which may report query status 548 in response to get query status 546 request, sending a response 550 indicating the query status 550. Provisioned cluster 510 may store the query results 552 in a result store 522 (which may be a data storage service 230). Managed query service control plane 320 may receive q request to get a query results 554 and get query results 556 from results store 522 and provide the query results 558 in response, in some embodiments.

Different types of computing resources may be provisioned and configured in resource pools, in some embodiments. Single-node clusters or multi-node compute clusters may be one example of a type of computing resource provisioned and configured in resource pools by resource management service 290 to service queries for managed query service 270. FIG. 6 is a logical block diagram illustrating a cluster processing a query as part of managed query execution, according to some embodiments. Cluster 610 may implement a computing node 620 that is a leader node (according to the query engine 624 (or multiple query engines, such as Presto and Hive) implemented by cluster 610). In some embodiments, no single node may be a leader node, or the leader node may rotate from processing one query to the next. Managed query agent 622 may be implemented as part of leader node 620 in order to provide an interface between the provisioned resource, cluster 610, and other components of managed query service 270 and resource management service 290. For example, managed query agent 622 may provide further data to managed query service 270, such as the status of the query (e.g. executing, performing I/O, performing aggregation, etc.,) and execution metrics (e.g., health metrics, resource utilization metrics, cost metrics, length of time, etc.). In some embodiments, managed query agent 622 may provide cluster/query status and execution metric(s) to resource management service 290 (in order to make pool management decisions, such as modification events, lease requests, etc.), as discussed below. For example, managed query agent 622 may indicate cluster status to resource management service 290 indicating that a query has completed and that the cluster 610 is ready for reassignment.

Leader node 620 may implement query engine 624 to execute queries, such as query 602 which may be received via managed query agent 622. For instance, managed query agent may implement a programmatic interface for query tracker to submit queries (as discussed above in FIG. 5), and then generate and send the appropriate query execution instruction to query engine 624. Query engine(s) 624 may generate a query execution plan for received queries 603. In at least some embodiments, leader node 620, may obtain schema information for the data set(s) 670 from the data catalog service 280 or metadata stores for data 662 (e.g., data dictionaries, other metadata stores, other data processing services, such as database systems, that maintain schema information) for data 662, in order to incorporate the schema data into the generation of the query plan and the execution of the query. Leader node 620 may generate and send query execution instructions 640 to computing nodes that access and apply the query to data 662 in data store(s) 660. Compute nodes, such as nodes 630a, 630b, and 630n, may respectively implement query engines 632a, 632b, and 632n to execute the query instructions, apply the query to the data 650, and return partial results 640 to leader node 620, which in turn may generate and send query results 604. Query engines 624 and query engines 632 may implement various kinds of distributed query or data processing frameworks, such as the open source Presto distributed query framework or the Apache Spark framework.

FIG. 7 is a logical block diagram illustrating a managed query agent, according to some embodiments. Managed query agent 622 may act as an interface for detecting and performing pool management related events as well as service related events for a computing resource (e.g., cluster) upon which the managed query agent is implement. As illustrated in FIG. 7, managed query agent 622 may implement resource management service interface 710, in some embodiments, to interact with resource management service 290. For example, may provide indications of pool management events 712 detected for the cluster (e.g., changes in resource state, as discussed below in FIG. 8, execution state or status for a query, and other performance metrics which may be related to the management of resources in the resource pool). Operation(s) 714 related to pool management events may be received via resource management service interface 710, in various embodiments, to be performed by event handler 740 at managed query agent 622, according to the various techniques/events discussed below with regard to FIGS. 10 and 11.

Managed query agent 622 may also implement managed query service interface 720, which may determine and send 722 the status of an executing query (e.g., starting, executing, complete, etc.) to managed query service 270. Managed query service interface 720 may also send various metric(s) 724 to managed query service 290, such as resource utilization metrics, job-pending/executing time, or other characteristics of the performance of the query that may be gathered or determined, in one embodiment (e.g., from performance metrics 754 received from execution engine 624). Managed query service interface 720 may also accept and initiate execution of queries 726 received from managed query service. For example, event handler 740 may generate instructions to execute the query and submit the instructions 752 via execution engine interface 750 to execution engine 624 for performance.

Managed query agent may implement cluster monitor 730 to monitor for pool management events for the cluster, whether the cluster is idle or leased for the execution of a query, in some embodiments. Cluster monitor 730 may monitor a resource lifecycle state, execution state for a job, or performance metrics, such as by periodically sampling or monitoring a live stream of metrics or other data to determine if a pool management event is detected. Pool management events may be detected based on pool management criteria (e.g., changes in resource state, execution performance state, or by applying different thresholds or other analysis to performance metrics for a resource), which cluster monitor 730 may maintain in or as part of an event list or other set of configuration information that defines the pool management events to monitor for. In some embodiments, pool management events may be detected in response to external events that are detected at the computing resource (e.g., network partition, power failure, etc.).

Managed query agent 622 may implement event handler 740 to perform operation(s) based on detected pool management events or to execute jobs, like queries 726 that are received from managed query service 270. In some embodiments, query status, performance metric(s), and other information may be provided according to a polling-based model, so that event handler 740 may handle requests to provide information (e.g., by sending query status 722 and query performance metric(s) 724) in response to the requests. Event handler 740 may perform the pool management operations 714 specified by resource management service 290 and/or other operations for handling a pool management event (e.g., such as resource specific operations for a particular execution engine or configuration of cluster 710 to carry out a generally described pool management operation 714, like executing specific operations to scrub certain locations or devices in memory or storage at cluster 722 that are not explicitly identified by pool management operations 714). In some embodiments, pool management operations are performed automatically without receiving the operations from resource management service 290.

FIG. 8 is a state diagram for resources implemented in a resource pool, according to some embodiments. A resource may begin in start state 810 awaiting fulfillment. A pending resource 820 may be a resource that has been launched but is not yet configured for processing jobs (e.g., according to a configured specified for resources in the pool, such as the query image, machine image, software applications, etc.). If an error occurs while provisioning, then the resource may be in failed state 850, which would make the resource unable to be available to process jobs as part of the pool (and may not be counted for idle or overall resource count considerations, in some embodiments. For example, a machine image may crash or fail to load properly at one or more nodes in a cluster, in one embodiment, failing the provisioning of the resource.

For resources that are successful configured to execute jobs, the resource state may transition to ready 830. In ready state 830, a resource may be idle (or leased, but not executing a job). A resource may transition out of ready state to executing state 835. A resource may transition out of ready state in the event of resource failure (to failed state 850) or in the event of the resource being terminated (to terminated state 860). A resource may execute the query in executing state 835, and may transition out of execution state 835 in the event of resource failure (to failed state 850) or in the event of the resource being terminated (to terminated state 860). Termination of a resource may, in some embodiments, occur after a time limit or other usage threshold that limits the amount of work done by a given resource. In this way, a resource that suffers from performance decline (e.g., due to age, software errors that cause memory leaks or other performance problems) or may be vulnerable to security breach can be terminated (and replaced in the pool with another resource). Upon completing execution of job, a resource may move to scrub state 840, in some embodiments. For example, a managed query agent may detect when a cluster has completed execution of the query and report a query completion status to resource management service 290. The managed query agent may then initiate an operation to scrub the resource for reuse in the resource pool. Scrubbed resources may return to resource pool by becoming in ready state 830. In some embodiments, a scrubbed resource that fails to complete a scrub operation may move to failed state 850 or may be terminated (e.g., due to an age/time limit for the resource).

FIG. 9 is logical block diagram illustrating interactions between a resource management service and pools of resources, according to some embodiments. Resource management service 290 may implement a programmatic interface (e.g., API) or other interface that allows other network-based services (or a client or a provider network) to submit requests for preconfigured resources from a resource pool managed by resource management service 290. For example, a request for a cluster 930 may be received (e.g., from query tracker 340) to obtain a cluster to execute a query. Resource management service 290 may determine the appropriate pool for the request 930, a randomly (or selectively according to the techniques discussed below with regard to FIG. 14B) determine a cluster for servicing the request. Resource management service 290 may then provide the identified cluster 940 (e.g., by specifying a location, identifier, or other information for accessing the identified computing resource. Resource management service may update state information for the cluster to indicate that the cluster is leased or otherwise unavailable. Resource management service 290 may also receive requests to release a cluster 950 from a current assignment. Resource management service 290 may then update state information (e.g., the lease) for the cluster and pool to return the cluster to the pool, in some embodiments.

As indicated at 960, resource management service 290 may automatically (or in response to requests (not illustrated)), commission or decommission pool(s) of clusters 910. For example in some embodiments, resource management service 290 may perform techniques that select the number and size of computing clusters 920 for the warm cluster pool 910. The number and size of the computing clusters 920 in the warm cluster pool 910 can be determined based upon a variety of factors including, but not limited to, historical and/or expected volumes of query requests, the price of the computing resources utilized to implement the computing clusters 920, and/or other factors or considerations, in some embodiments.

Once the number and size of computing clusters 920 has been determined, the computing clusters 920 may be instantiated, such as through the use of an on-demand computing service, or virtual compute service or data processing service as discussed above in FIG. 2. The instantiated computing clusters 920 can then be configured to process queries prior to receiving the queries at the managed query service. For example, and without limitation, one or more distributed query frameworks or other query processing engines can be installed on the computing nodes in each of the computing clusters 920. As discussed above, in one particular implementation, the distributed query framework may be the open source PRESTO distributed query framework. Other distributed query frameworks can be utilized in other configurations. Additionally, distributed processing frameworks or other query engines can also be installed on the host computers in each computing cluster 920. As discussed above, the distributed processing frameworks can be utilized in a similar fashion to the distributed query frameworks. For instance, in one particular configuration, the APACHE SPARK distributed processing framework can also, or alternately, be installed on the host computers in the computing clusters 920.

Instantiated and configured computing clusters 920 that are available for use by the managed query service 270 are added to the warm cluster pool 910, in some embodiments. A determination can be made as to whether the number or size of the computing clusters 920 in the warm cluster pool needs is to be adjusted, in various embodiments. The performance of the computing clusters 920 in the warm cluster pool 910 can be monitored based on cluster metric(s) 990 received from the cluster pool. The number of computing clusters 920 assigned to the warm cluster pool 910 and the size of each computing cluster 920 (i.e. the number of host computers in each computing cluster 920) in the warm cluster pool 910 can then be adjusted. Such techniques can be repeatedly performed in order to continually optimize the number and size of the computing clusters 920 in the warm cluster pool 910.

As indicated at 980, in some embodiments, resource management service 270 may scrub clusters(s) 980, (e.g., as a result of the lease state transitioning to expired or terminated) by causing the cluster to perform operations (e.g., a reboot, disk wipe, memory purge/dump, etc.) so that the cluster no longer retains client data and is ready to process another query. For example, resource management service 290 may determine whether a computing cluster 920 is inactive (e.g. the computing cluster 920 has not received a query in a predetermined amount of time). If resource management service 290 determines that the computing cluster 920 is inactive, then the computing cluster 920 may be disassociated from the submitter of the query. The computing cluster 920 may then be “scrubbed,” such as by removing data associated with the submitter of the queries from memory (e.g. main memory or a cache) or mass storage device (e.g. disk or solid state storage device) utilized by the host computers in the computing cluster 920. The computing cluster 920 may then be returned to the warm cluster pool 910 for use in processing other queries. In some embodiments, some clusters that are inactive might not be disassociated from certain users in certain scenarios. In these scenarios, the user may have a dedicated warm pool of clusters 910 available for their use.

Although FIGS. 2-9 have been described and illustrated in the context of a provider network leveraging multiple different services to implement a managed query agent to detect and perform pool management events and operations, the various components illustrated and described in FIGS. 2-9 may be easily applied to other systems, or devices that manage pools of configured resources. As such, FIGS. 2-9 are not intended to be limiting as to other embodiments of a system that may implement event-driven resource pool management. FIG. 10 is a high-level flowchart illustrating various methods and techniques to implement event-driven resource pool management, according to some embodiments. Various different systems and devices may implement the various methods and techniques described below, either singly or working together.

For example, a resource management service as described above with regard to FIGS. 2-9 may implement the various methods. Alternatively, a combination of different systems and devices may implement these methods. Therefore, the above examples and or any other systems or devices referenced as performing the illustrated method, are not intended to be limiting as to other different components, modules, systems, or configurations of systems and devices.

As indicated at 1010, a pool management event may be detected at a first computing resource of a pool of computing resources that are configured to perform jobs associated with a network-based service, in various embodiments. For example, a management agent or other monitor implemented at a computing resource may evaluate the operation of the computing resource, whether the resource is idle or being leased/used to execute a job for the network-based service. Various different pool management event detection criteria may be applied for different types of pool management events. For example, a scrub pool management event may be detected upon determining that the computing resource has completed execution of a current job and is ready to be recycled or reused in the computing cluster to execute a different job (e.g., from a different client of network-based service). In another example, a pool management event criteria may evaluate the age or time since creation of the first computing resource (e.g., according to lifespan time or timestamp) and determine whether the first computing resource has exceeded the age threshold for computing resources in the pool, in one embodiment. If so, then the a termination pool management event may be detected to terminate the first computing resource (e.g., by sending a request to a service implementing the resource to terminate the existence of the resource). In another example of pool management event criteria, an operational metric (e.g., time since last leased) and a state (e.g., “ready” or “pending” state) criteria may be evaluated, and if the first computing resource exceeds the operational metric and matches the state criteria, then a test pool management event may be detected to execute one or more test jobs at the first computing resource, in one embodiment. If one of the test jobs fails to execute in a desired manner, then an indication of the failure may be provided to a pool manager for the resource pool (e.g., resource management service 290), which may provide operations to scrub, reboot, or terminate the first computing resource. In light of these examples, it may be understood that pool management events can be detected based on various combinations of one or more detection criteria evaluated with respect to the operation of the first computing resource.

As indicated at 1020, operations may be performed at the first computing resource based, at least in part, on the pool management event. For example, operations to remove job data (e.g., from memory or non-volatile storage) as part of a scrub event may be performed, in one embodiment. In another example, operations that prepare the first computing resource for termination, such as dumping or storing reports, logs, or other collected information may be performed, along with executing the operation to terminate the first computing resource, in one embodiment. In another example, operations to carry out test jobs on the first computing source, including access a test job, sample data, or other information needed to execute the one or more test jobs, generating instructions to an execution engine at the first computing resource to execute the test jobs and evaluating the results or performance of the first computing resource for the test jobs. Operations for detected pool management events may be instructed (partially or completely) by a pool manager for a resource pool in some embodiments, as discussed below with regard to FIG. 12. In other embodiments, operation(s) may be performed automatically in response to detecting the pool management event (without instruction from a pool manager). In this way, pool management events may be performed quickly so that resources in the pool make the pool respond faster to events that may modify the operation of computing resources in the pool.

FIG. 11 is a high-level flowchart illustrating various methods and techniques to monitor a computing resource in a pool of computing resources for pool management events, according to some embodiments. As discussed above different types of pool management events may be detected at a computing resource. In some embodiments, monitoring for pool management events may be actively performed (e.g., by a management agent like managed query agent 622 in FIG. 6). As indicated at 1110, a first computing resource of a pool of computing resources that are configured to execute jobs associated with a network-based service may be monitored, in some embodiments. For example, a resource lifecycle state, execution state for a job, or performance metrics for may be periodically sampled or checked to determine if a pool management event is detected. Pool management events may be detected based on pool management criteria (e.g., changes in resource state, execution performance state, or by applying different thresholds or other analysis to performance metrics for a resource). In some embodiments, pool management events may be detected in response to external events that are detected at the computing resource (e.g., network partition, power failure, etc.).

As indicated at 1120, if a pool management event is detected, then an indication of the pool management event may be sent to a pool manager for the pool, as indicated at 1130. For example, some pool management events may be maintained in a mapping table or other data structure describing the operation(s) to perform in response to detecting the pool management event, in one embodiment. If the described operation(s) include reporting the pool management event (or no operations are described and the management agent sends an indication of the pool management event in response to the pool management event as a default operation), then the indication for the pool management event may be generated (e.g., according to an interface for the pool manager, such as an API for resource management service 290.

A pool manager may confirm or determine the appropriate responsive actions to perform for the pool management event, in some embodiments. For example, for a scrub operation, the pool manager may determine whether other jobs for the same user are pending or likely to be sent to the computing resource for execution (e.g., by waiting for a period of time before allowing the scrub operation and returning the computing resource to the pool of computing resources for executing other jobs). As indicated at 1140, a request to perform operation(s) based on the pool management event may be received from the pool manager, in some embodiments. For example, the various types of operations described above with regard to element 1020 in FIG. 2 (e.g., scrub operations, termination operations, test operations, etc.) may be identified or included in a request from the pool manager. As indicated at 1150, the requested operations may then be performed at the computing resource.

FIG. 12 is a high-level flowchart illustrating various methods and techniques to execute a job for a network-based service, according to some embodiments. As indicated at 1210, a request to execute a job may be received at a management agent implemented at a computing resource of a pool of computing resources configured to execute jobs associated with a network-based service. The request may be formatted according to a programmatic interface implemented by the management agent, such as managed query service interface 720 discussed above with regard to FIG. 7. The request may include various execution parameters, identifiers, access credentials, tokens, permissions, and other data that may be needed to execute the job. For example, authentication credentials may be needed to execute a job that accesses data, such as query. Other execution parameters, such as execution limitations, timeout values, result destinations may be included, in some embodiments. Execution parameters may describe the behavior of the execution of the job (e.g., returning results in a particular form, such as a paginated stream of query results sent to an interface like management console 440 in FIG. 4).

As indicated at 1220, instruction(s) to execute the job at an execution engine implemented at the first computing resource may be generated by the management agent, in some embodiments. For example, commands corresponding to a programmatic interface for the execution engine, may be generated to execute the job. In other embodiments, a job workflow, script, or executable may be generated (according to the input options or parameters allowed by the execution engine). Once the instruction(s) are generated, then the instruction(s) may be submitted to the execution engine to execute the job, as indicated at 1230, in various embodiments. For example, a function call, procedure, message, or other invocation mechanism may be used to submit the instructions to the execution engine, in one embodiment.

As indicated at 1240, in at least some embodiments, the management agent may send an execution status for the job to the network-based service. For example, the management agent may determine or classify the execution of the job according to a predefined set of execution states (e.g., “initializing,” “start,” “reading,” “writing,” “finalizing,” “error,” etc.). In some embodiments, a progress metric, such as a completion percentage, or indication for the job's execution state within a workflow (e.g., “step 1” or “step 10”) may be determined as the execution status. Once determined, the execution status may be reported to the network-based service according to a programmatic interface (e.g., API call), in one embodiment. The request may be formatted according to the API and sent to a service endpoint for receiving job execution status.

As indicated at 1250, in some embodiments, performance metric(s) for the execution of the job may be sent to the network-based service by the management agent, in various embodiments. For example, resource utilization metrics, job-pending/executing time, or other characteristics of the performance of the job may be gathered or determined, in one embodiment. In some embodiments, performance metric(s) may be stored locally by the management agent while the job is executing and sent as a batch of performance metrics upon completion of the job (or failure of the job). Performance metrics may be formatted according to a metric reporting or storage format for the network-based service, such as a log-based record format, or as a data file including comma delimited metric values, in one embodiment. In other embodiments, performance metrics may be streamed or otherwise reported in real time to the network-based service.

FIG. 13 is a high-level flowchart illustrating various methods and techniques to implement error monitoring at a management agent for a computing resource of a resource pool executing a job for a network-based service, according to some embodiments. As indicated at 1310, a management agent may monitor execution off a job at a computing resource of a pool of computing resources for errors, in various embodiments. For example, the management agent may receive indications of execution engine errors (e.g., due to execution problems, invalid or malformed instructions to execute the job, such as invalid SQL statements), in one embodiment. Management agent may detect errors by observing behavior of the execution engine (e.g., stalling, not-responsive, resource utilization, or other indicators of problematic operation), in one embodiment.

If an error is detected, as indicated by the positive exit from 1320, a determination may be made by the management agent as to the error indication to send to the network-based service, in some embodiments. For example, the error may classified or categorized (e.g., as an internal execution error caused by the operation of internal resources such that the error is not a fault of the client that submitted the job, or as an external error/client error, such as errors in the submission of the job, like incorrect query language statements, invalid operations requested). Some errors, may be categorized based on the error information provided by the execution engine, while other errors may be categorized based on other criteria, such as the state of the resource, status of the execution of the job, or other information collected by the management agent. Mapping information (e.g., in a table mapping detected errors to error indications) may be maintained to translate otherwise provide a template for (or the content of) error indications that are to be provided.

Once determined, the error indication may be sent to the network-based service, as indicated at 1340, in some embodiments. For example, the error indication may be formatted and sent according to an error reporting API or other communication mechanism (e.g., message queue or event stream established between the management agent and the network-based service), in one embodiment. In at least some embodiments, some errors that are detected may be ignored or not reported. For example, errors that do not halt execution of a job may be ignored or not reported. In some embodiments, errors may not be reported until a number of similar or the same error is detected beyond some reporting threshold for the error. Some errors received at the network-based service may be provided to users/clients of the network-based service, while others may remain visible only to the network-based service, in one embodiment.

The methods described herein may in various embodiments be implemented by any combination of hardware and software. For example, in one embodiment, the methods may be implemented by a computer system (e.g., a computer system as in FIG. 16) that includes one or more processors executing program instructions stored on a computer-readable storage medium coupled to the processors. The program instructions may be configured to implement the functionality described herein (e.g., the functionality of various servers and other components that implement the network-based virtual computing resource provider described herein). The various methods as illustrated in the figures and described herein represent example embodiments of methods. The order of any method may be changed, and various elements may be added, reordered, combined, omitted, modified, etc.

FIG. 14 is a logical block diagram that shows an illustrative operating environment that includes a service provider network that can implement aspects of the functionality described herein, according to some embodiments. As discussed above, the service provider network 200 can provide computing resources, like VM instances and storage, on a permanent or an as-needed basis. Among other types of functionality, the computing resources provided by the service provider network 200 can be utilized to implement the various services described above. As also discussed above, the computing resources provided by the service provider network 200 can include various types of computing resources, such as data processing resources like VM instances, data storage resources, networking resources, data communication resources, network services, and the like.

Each type of computing resource provided by the service provider network 200 can be general-purpose or can be available in a number of specific configurations. For example, data processing resources can be available as physical computers or VM instances in a number of different configurations. The VM instances can execute applications, including web servers, application servers, media servers, database servers, some or all of the services described above, and/or other types of programs. The VM instances can also be configured into computing clusters in the manner described above. Data storage resources can include file storage devices, block storage devices, and the like. The service provider network 200 can also provide other types of computing resources not mentioned specifically herein.

The computing resources provided by the service provider network maybe implemented, in some embodiments, by one or more data centers 1404A-1404N (which might be referred to herein singularly as “a data center 1404” or in the plural as “the data centers 1404”). The data centers 1404 are facilities utilized to house and operate computer systems and associated components. The data centers 1404 typically include redundant and backup power, communications, cooling, and security systems. The data centers 1404 can also be located in geographically disparate locations. One illustrative configuration for a data center 1404 that can be utilized to implement the technologies disclosed herein will be described below with regard to FIG. 15.

The customers and other users of the service provider network 200 can access the computing resources provided by the service provider network 200 over a network 1402, which can be a wide area communication network (“WAN”), such as the Internet, an intranet or an Internet service provider (“ISP”) network or a combination of such networks. For example, and without limitation, a computing device 1400 operated by a customer or other user of the service provider network 200 can be utilized to access the service provider network 200 by way of the network 1402. It should be appreciated that a local-area network (“LAN”), the Internet, or any other networking topology known in the art that connects the data centers 1404 to remote customers and other users can be utilized. It should also be appreciated that combinations of such networks can also be utilized.

FIG. 15 is a logical block diagram illustrating a configuration for a data center that can be utilized to implement aspects of the technologies disclosed herein, according to various embodiments. is a computing system diagram that illustrates one configuration for a data center 1404 that implements aspects of the technologies disclosed herein for providing managed query execution, such as managed query execution service 270, in some embodiments. The example data center 1404 shown in FIG. 15 includes several server computers 1502A-1502F (which might be referred to herein singularly as “a server computer 1502” or in the plural as “the server computers 1502”) for providing computing resources 1504A-1504E.

The server computers 1502 can be standard tower, rack-mount, or blade server computers configured appropriately for providing the computing resources described herein (illustrated in FIG. 15 as the computing resources 1504A-1504E). As mentioned above, the computing resources provided by the provider network 200 can be data processing resources such as VM instances or hardware computing systems, computing clusters, data storage resources, database resources, networking resources, and others. Some of the servers 1502 can also execute a resource manager 1506 capable of instantiating and/or managing the computing resources. In the case of VM instances, for example, the resource manager 1506 can be a hypervisor or another type of program may enable the execution of multiple VM instances on a single server computer 1502. Server computers 1502 in the data center 1504 can also provide network services and other types of services, some of which are described in detail above with regard to FIG. 2.

The data center 1504 shown in FIG. 15 also includes a server computer 1502F that can execute some or all of the software components described above. For example, and without limitation, the server computer 1502F can execute various components for providing different services of a provider network 200, such as the managed query service 270, the data catalog service 280, resource management service 290, and other services 1510 (e.g., discussed above) and/or the other software components described above. The server computer 1502F can also execute other components and/or to store data for providing some or all of the functionality described herein. In this regard, it should be appreciated that the services illustrated in FIG. 15 as executing on the server computer 1502F can execute on many other physical or virtual servers in the data centers 1404 in various configurations.

In the example data center 1404 shown in FIG. 15, an appropriate LAN 1506 is also utilized to interconnect the server computers 1502A-1502F. The LAN 1506 is also connected to the network 1402 illustrated in FIG. 14. It should be appreciated that the configuration and network topology described herein has been greatly simplified and that many more computing systems, software components, networks, and networking devices can be utilized to interconnect the various computing systems disclosed herein and to provide the functionality described above. Appropriate load balancing devices or other types of network infrastructure components can also be utilized for balancing a load between each of the data centers 1504A-1504N, between each of the server computers 1502A-1502F in each data center 1404, and, potentially, between computing resources in each of the data centers 1404. It should be appreciated that the configuration of the data center 1404 described with reference to FIG. 15 is merely illustrative and that other implementations can be utilized.

Embodiments of a managed query execution as described herein may be executed on one or more computer systems, which may interact with various other devices. One such computer system is illustrated by FIG. 16. In different embodiments, computer system 2000 may be any of various types of devices, including, but not limited to, a personal computer system, desktop computer, laptop, notebook, or netbook computer, mainframe computer system, handheld computer, workstation, network computer, a camera, a set top box, a mobile device, a consumer device, video game console, handheld video game device, application server, storage device, a peripheral device such as a switch, modem, router, or in general any type of computing device, computing node, compute node, computing system compute system, or electronic device.

In the illustrated embodiment, computer system 2000 includes one or more processors 2010 coupled to a system memory 2020 via an input/output (I/O) interface 2030. Computer system 2000 further includes a network interface 2040 coupled to I/O interface 2030, and one or more input/output devices 2050, such as cursor control device 2060, keyboard 2070, and display(s) 2080. Display(s) 2080 may include standard computer monitor(s) and/or other display systems, technologies or devices. In at least some implementations, the input/output devices 2050 may also include a touch- or multi-touch enabled device such as a pad or tablet via which a user enters input via a stylus-type device and/or one or more digits. In some embodiments, it is contemplated that embodiments may be implemented using a single instance of computer system 2000, while in other embodiments multiple such systems, or multiple nodes making up computer system 2000, may host different portions or instances of embodiments. For example, in one embodiment some elements may be implemented via one or more nodes of computer system 2000 that are distinct from those nodes implementing other elements.

In various embodiments, computer system 2000 may be a uniprocessor system including one processor 2010, or a multiprocessor system including several processors 2010 (e.g., two, four, eight, or another suitable number). Processors 2010 may be any suitable processor capable of executing instructions. For example, in various embodiments, processors 2010 may be general-purpose or embedded processors implementing any of a variety of instruction set architectures (ISAs), such as the x86, PowerPC, SPARC, or MIPS ISAs, or any other suitable ISA. In multiprocessor systems, each of processors 2010 may commonly, but not necessarily, implement the same ISA.

In some embodiments, at least one processor 2010 may be a graphics processing unit. A graphics processing unit or GPU may be considered a dedicated graphics-rendering device for a personal computer, workstation, game console or other computing or electronic device. Modern GPUs may be very efficient at manipulating and displaying computer graphics, and their highly parallel structure may make them more effective than typical CPUs for a range of complex graphical algorithms. For example, a graphics processor may implement a number of graphics primitive operations in a way that makes executing them much faster than drawing directly to the screen with a host central processing unit (CPU). In various embodiments, graphics rendering may, at least in part, be implemented by program instructions configured for execution on one of, or parallel execution on two or more of, such GPUs. The GPU(s) may implement one or more application programmer interfaces (APIs) that permit programmers to invoke the functionality of the GPU(s). Suitable GPUs may be commercially available from vendors such as NVIDIA Corporation, ATI Technologies (AMD), and others.

System memory 2020 may store program instructions and/or data accessible by processor 2010. In various embodiments, system memory 2020 may be implemented using any suitable memory technology, such as static random access memory (SRAM), synchronous dynamic RAM (SDRAM), nonvolatile/Flash-type memory, or any other type of memory. In the illustrated embodiment, program instructions and data implementing desired functions, such as those described above are shown stored within system memory 2020 as program instructions 2025 and data storage 2035, respectively. In other embodiments, program instructions and/or data may be received, sent or stored upon different types of computer-accessible media or on similar media separate from system memory 2020 or computer system 2000. Generally speaking, a non-transitory, computer-readable storage medium may include storage media or memory media such as magnetic or optical media, e.g., disk or CD/DVD-ROM coupled to computer system 2000 via I/O interface 2030. Program instructions and data stored via a computer-readable medium may be transmitted by transmission media or signals such as electrical, electromagnetic, or digital signals, which may be conveyed via a communication medium such as a network and/or a wireless link, such as may be implemented via network interface 2040.

In one embodiment, I/O interface 2030 may coordinate I/O traffic between processor 2010, system memory 2020, and any peripheral devices in the device, including network interface 2040 or other peripheral interfaces, such as input/output devices 2050. In some embodiments, I/O interface 2030 may perform any necessary protocol, timing or other data transformations to convert data signals from one component (e.g., system memory 2020) into a format suitable for use by another component (e.g., processor 2010). In some embodiments, I/O interface 2030 may include support for devices attached through various types of peripheral buses, such as a variant of the Peripheral Component Interconnect (PCI) bus standard or the Universal Serial Bus (USB) standard, for example. In some embodiments, the function of I/O interface 2030 may be split into two or more separate components, such as a north bridge and a south bridge, for example. In addition, in some embodiments some or all of the functionality of I/O interface 2030, such as an interface to system memory 2020, may be incorporated directly into processor 2010.

Network interface 2040 may allow data to be exchanged between computer system 2000 and other devices attached to a network, such as other computer systems, or between nodes of computer system 2000. In various embodiments, network interface 2040 may support communication via wired or wireless general data networks, such as any suitable type of Ethernet network, for example; via telecommunications/telephony networks such as analog voice networks or digital fiber communications networks; via storage area networks such as Fibre Channel SANs, or via any other suitable type of network and/or protocol.

Input/output devices 2050 may, in some embodiments, include one or more display terminals, keyboards, keypads, touchpads, scanning devices, voice or optical recognition devices, or any other devices suitable for entering or retrieving data by one or more computer system 2000. Multiple input/output devices 2050 may be present in computer system 2000 or may be distributed on various nodes of computer system 2000. In some embodiments, similar input/output devices may be separate from computer system 2000 and may interact with one or more nodes of computer system 2000 through a wired or wireless connection, such as over network interface 2040.

As shown in FIG. 16, memory 2020 may include program instructions 2025, may implement the various methods and techniques as described herein, and data storage 2035, comprising various data accessible by program instructions 2025. In one embodiment, program instructions 2025 may include software elements of embodiments as described herein and as illustrated in the Figures. Data storage 2035 may include data that may be used in embodiments. In other embodiments, other or different software elements and data may be included.

Those skilled in the art will appreciate that computer system 2000 is merely illustrative and is not intended to limit the scope of the techniques as described herein. In particular, the computer system and devices may include any combination of hardware or software that can perform the indicated functions, including a computer, personal computer system, desktop computer, laptop, notebook, or netbook computer, mainframe computer system, handheld computer, workstation, network computer, a camera, a set top box, a mobile device, network device, internet appliance, PDA, wireless phones, pagers, a consumer device, video game console, handheld video game device, application server, storage device, a peripheral device such as a switch, modem, router, or in general any type of computing or electronic device. Computer system 2000 may also be connected to other devices that are not illustrated, or instead may operate as a stand-alone system. In addition, the functionality provided by the illustrated components may in some embodiments be combined in fewer components or distributed in additional components. Similarly, in some embodiments, the functionality of some of the illustrated components may not be provided and/or other additional functionality may be available.

Those skilled in the art will also appreciate that, while various items are illustrated as being stored in memory or on storage while being used, these items or portions of them may be transferred between memory and other storage devices for purposes of memory management and data integrity. Alternatively, in other embodiments some or all of the software components may execute in memory on another device and communicate with the illustrated computer system via inter-computer communication. Some or all of the system components or data structures may also be stored (e.g., as instructions or structured data) on a computer-accessible medium or a portable article to be read by an appropriate drive, various examples of which are described above. In some embodiments, instructions stored on a non-transitory, computer-accessible medium separate from computer system 2000 may be transmitted to computer system 2000 via transmission media or signals such as electrical, electromagnetic, or digital signals, conveyed via a communication medium such as a network and/or a wireless link. Various embodiments may further include receiving, sending or storing instructions and/or data implemented in accordance with the foregoing description upon a computer-accessible medium. Accordingly, the present invention may be practiced with other computer system configurations.

It is noted that any of the distributed system embodiments described herein, or any of their components, may be implemented as one or more web services. For example, leader nodes within a data warehouse system may present data storage services and/or database services to clients as network-based services. In some embodiments, a network-based service may be implemented by a software and/or hardware system designed to support interoperable machine-to-machine interaction over a network. A network-based service may have an interface described in a machine-processable format, such as the Web Services Description Language (WSDL). Other systems may interact with the web service in a manner prescribed by the description of the network-based service's interface. For example, the network-based service may define various operations that other systems may invoke, and may define a particular application programming interface (API) to which other systems may be expected to conform when requesting the various operations.

In various embodiments, a network-based service may be requested or invoked through the use of a message that includes parameters and/or data associated with the network-based services request. Such a message may be formatted according to a particular markup language such as Extensible Markup Language (XML), and/or may be encapsulated using a protocol such as Simple Object Access Protocol (SOAP). To perform a web services request, a network-based services client may assemble a message including the request and convey the message to an addressable endpoint (e.g., a Uniform Resource Locator (URL)) corresponding to the web service, using an Internet-based application layer transfer protocol such as Hypertext Transfer Protocol (HTTP).

In some embodiments, web services may be implemented using Representational State Transfer (“RESTful”) techniques rather than message-based techniques. For example, a web service implemented according to a RESTful technique may be invoked through parameters included within an HTTP method such as PUT, GET, or DELETE, rather than encapsulated within a SOAP message.

The various methods as illustrated in the FIGS. and described herein represent example embodiments of methods. The methods may be implemented in software, hardware, or a combination thereof. The order of method may be changed, and various elements may be added, reordered, combined, omitted, modified, etc.

Various modifications and changes may be made as would be obvious to a person skilled in the art having the benefit of this disclosure. It is intended that the invention embrace all such modifications and changes and, accordingly, the above description to be regarded in an illustrative rather than a restrictive sense.

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