Patent Publication Number: US-2022237184-A1

Title: Dynamically assigning queries to secondary query processing resources

Description:
This application is a continuation of U.S. patent application Ser. No. 16/452,385, filed Jun. 25, 2019, which is hereby incorporated by reference herein in its entirety. 
    
    
     BACKGROUND 
     As the technological capacity for organizations to create, track, and retain information continues to grow, a variety of different technologies for managing and storing the rising tide of information have been developed. Database systems, for example, provide clients with many different specialized or customized configurations of hardware and software to manage stored information. However, the increasing amounts of data that organizations must store and manage often correspondingly increases both the size and complexity of data storage and management technologies, like database systems, which in turn escalate the cost of maintaining the information. New technologies more and more seek to reduce both the complexity and storage requirements of maintaining data while simultaneously improving the efficiency of data processing. 
     For example, data processing is often measured by the speed at which requests to access data are performed. Some types of data access requests require intensive computational and storage access workloads, while other types of data access requests may only involve small amounts of work to process. As data stores may have to process both high and low workload access requests, techniques to perform the different types of access requests at processing resources better suited to the performance of the access requests or processing resources that may improve overall access request processing may be implemented so that access request processing is optimally performed. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  illustrates a logical block diagram of dynamically assigning queries to secondary processing resources, according to some embodiments. 
         FIG. 2  is a logical block diagram illustrating a provider network offering data processing services that implement dynamically assigning queries to secondary processing resources, according to some embodiments. 
         FIG. 3  is a logical block diagram of a data warehouse service implementing primary and secondary processing clusters, according to some embodiments. 
         FIG. 4  is a logical block diagram illustrating a data retrieval service, according to some embodiments. 
         FIG. 5  is a logical block diagram illustrating an example primary processing cluster of a data warehouse service that implements dynamically assigning queries to secondary processing clusters, according to some embodiments. 
         FIG. 6  is a logical block diagram illustrating an example secondary processing cluster of a data warehouse service, according to some embodiments. 
         FIG. 7  is a logical block diagram illustrating an example of workload manager that implements dynamically assigning queries to secondary processing resources, according to some embodiments. 
         FIG. 8  is a logical block diagram illustrating example interactions to obtain and release a secondary processing cluster from a pool of secondary processing clusters, according to some embodiments. 
         FIG. 9  is a high-level flowchart illustrating methods and techniques to implement dynamically assigning queries to secondary processing resources, according to some embodiments. 
         FIG. 10  is a high-level flowchart illustrating methods and techniques to implement selecting between primary and secondary processing resources, according to some embodiments. 
         FIG. 11  is a high-level flowchart illustrating methods and techniques to implement reassigning a waiting query to a secondary processing resource, according to some embodiments. 
         FIGS. 12A and 12B  are high-level flowcharts illustrating methods and techniques to implement reassigning performing queries to a different query processing resource, according to some embodiments. 
         FIG. 13  is a high-level flowchart illustrating methods and techniques to implement speculative processing of a query at both primary and secondary query processing resources, according to some embodiments. 
         FIG. 14  illustrates an example system that implements 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 techniques for dynamically assigning queries to secondary processing resources are described herein. Queries for data that satisfies various conditions or criteria, insertions, deletions, modifications, or any other request triggering processing based on a request to access a data store may utilize various processing resources, including various central processing units (CPUs), graphical processing units (GPUs), or other processing components that may execute various tasks to process database queries, in some embodiments. Because database queries may vary in terms of the workload placed upon the processing resources to execute the database query, in some embodiments, the amount of processing resources that any one query engine may provide could be inadequate (or underutilized) to meet the demands of some query workloads, in various embodiments. 
     For example, a query engine could be optimally sized to perform short, quick or other small-sized queries that are performed with a client system expectation that a result of the query will be returned quickly. Long-running, slow, or other large-sized queries may not perform as efficiently at the same query engine (e.g., as it may not be distributed amongst a large number of nodes in a distributed query processing platform like the clusters described below with regard to  FIGS. 2-8 . Therefore, additional or other secondary capacity provided by an additional query engine (or multiple query engines) may be used to handle increases in workload from database queries or other queries that could be more optimally performed using a different query engine, in some embodiments. 
     In various embodiments, dynamically assigning queries to secondary processing resources may be implemented to dynamically choose when to utilize secondary resources to perform a query so that the costs of utilizing the secondary resources are not outweighed by its benefits (e.g., providing faster performance for queries to the database overall without increasing resource costs or wasting additional resources), improving the performance of database queries and utilization of resources to perform database queries for a database system overall. Moreover, as assignments of queries are made dynamically, changing conditions (e.g., changing workloads, secondary resource configuration and availability, and so on) may cause initial query assignments to be modified if a more optimal assignment is available as a result of the changed conditions. 
       FIG. 1  illustrates a logical block diagram of dynamically assigning queries to secondary processing resources, according to some embodiments. Query engine  110  may be a query processing platform, application or system of one or multiple components (e.g., a processing cluster as discussed below that includes one or multiple processing nodes or a single node query engine) that can perform queries, such as query  140  to a database, by accessing database data  130 , in some embodiments. Database data  130  may be stored or co-located with query engine  110  in some embodiments (e.g., in attached storage as described below in  FIG. 5 ) or may be a separate data store (e.g., network attached storage and/or a separate storage service, as illustrated in  FIG. 3 ), in some embodiments. Database data  130  may be stored for various types of databases to which queries may be performed (e.g., relational, non-relational, NoSQL, document, graph, etc.), in some embodiments.  FIGS. 3-8 , for instance, discuss a data warehouse style database that stores database data, as well as other data stores, such as object-based storage service  330  (which may be general data stores which store data other types or formats of data in addition to database data.) 
     In some embodiments, an additional query engine, such as query engine  120 , or multiple additional query engines (not illustrated), may be implemented to perform some queries. Like query engine  110 , query engine  120  may be a query processing platform, application or system of one or multiple components (e.g., a processing cluster as discussed below that includes one or multiple processing nodes or a single node query engine) that can perform queries, such as query  140  to a database, by accessing database data  130 , in some embodiments. Query engine  120  may be different than query engine  110 , in some embodiments (e.g., a different number of nodes, different hardware resources, and/or different engine applications or other query performance components). 
     In some embodiments, query engine  120  may be substantially similar (or the same) as query engine  110 . 
     Query engine  110  may implement techniques for assigning queries to query engine  110  or query engine  120  dynamically. For example, query engine  110  may implement other query engine eligibility analysis  112 , which may determine whether a query is eligible to execute on query engine  120 . Query types for instance, as well as other techniques discussed below with regard to  FIGS. 7 and 9  may be implemented for eligibility analysis. Ineligible queries  174  may be assigned for local query execution  116  at query engine  110 , which may perform the query  162 . Eligible queries  172 , may receive a dynamic assignment by query engine assignment  114 . For example, the predicted execution times or other performance costs may be compared to select a more optimal query engine to perform query. Similarly dynamically assigning queries to secondary processing resources, techniques and comparisons could be performed for multiple secondary query engines, in some embodiments. Whether query engine  110  is available to perform the query (or would queue the query until a later time) may be considered by query engine assignment  114  based on availability information  180  which may indicate a current workload of query engine  110 . Queries may be assigned  152  to query engine  120  (e.g., according to various techniques discussed below with regard to  FIGS. 7 and 9-13 ) or assigned  176  to local query execution  116 . Query engine  120  may perform a query  164  to database data  130 . Likewise, query engine  110  may perform a query  162  to database data  130 . Results  142  of a query may be returned by query engine  110  in response to query  140 . In some embodiments, query engine  120  may return results  144  to query engine  110  which may provide them as results  142 . 
     Please note that the previous description of query engines, dynamic query engine assignment, database data, and performance of queries is a logical description and thus is not to be construed as limiting as to the implementation of these features, or portions thereof. For example, query engine  120  could return query results  144  directly to a client application,. 
     This specification begins with a general description of a provider network that implements multiple different services, including data processing services and storage services, which may perform dynamically assigning queries to secondary processing resources. Then various examples of multiple data processors, such as a data warehouse service and a data retrieval service, including different components/modules, or arrangements of components/module that may be employed as part of implementing the data processors are discussed. A number of different methods and techniques to implement dynamically assigning queries to secondary processing resources 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 offering data processing services that implement dynamically assigning queries to secondary processing resources, 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 . 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., computing system  2000  described below with regard to  FIG. 14 ), 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 data processing service(s)  210 , (e.g., a map reduce service, a data warehouse service, and/or other large scale data processing services or database services), data retrieval service  220 , and data storage services  230  (e.g., object storage services or block-based storage services that may implement a centralized data store for various types of data), and/or any other type of network based services (which may include a virtual compute service and various other types of storage, processing, analysis, communication, event handling, visualization, and security services not illustrated). 
     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. 14  and described below. In various embodiments, the functionality of a given system or service component (e.g., a component of data processing service  210 , data retrieval service  220 , or 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). 
     Data processing services  210  may be various types of data processing services that perform general or specialized data processing functions (e.g., anomaly detection, machine learning, data mining, big data querying, or any other type of data processing operation). For example, in at least some embodiments, data processing services  210  may include a map reduce service that creates clusters of processing nodes that implement map reduce functionality over data stored in the map reduce cluster as well as data stored in one of data storage services  230 . In another example, data processing service(s)  210  may include various types of database 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, such as a database supporting Online Analytics Processing (OLAP) features. Queries may be directed to a database in data processing service(s)  210  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. For instance, data processing service(s)  210  may implement, in some embodiments, a data warehouse service, such as discussed below with regard to  FIG. 3  that utilizes another data processing service, such as data retrieval service  220 , to execute portions of queries or other access requests with respect to data that is stored in a remote data store, such as data storage service(s)  230  (or a data store external to provider network  200 ) to implement distributed data processing for distributed data sets. 
     Data retrieval service  220 , as discussed in more detail below with regard to  FIGS. 3-6 , may provide a service supporting many different data or file formats for data stored in a centralized data store, like one (or more) of data storage service(s)  230  including data stored in the file formats supported by data processing service(s)  210 . 
     Instead of reformatting (if the format of data in remote storage is not supported by the data processing service(s)  210 ) and moving data from data storage service(s)  230  into the data processing service(s)  210 , data retrieval service  220  may efficiently read data from data storage service(s)  230  according to the data format in which the data is already stored in data storage service(s)  230 . Data retrieval service  220  may perform requested operations, such as scan operations that filter or project data results, aggregation operations that aggregate data values and provide partial or complete aggregation results, sorting, grouping, or limiting operations that organize or reduce the determined data results from data in data storage service(s)  230  in order to minimize the amount of data transferred out of data storage service(s)  230 . 
     For example, data retrieval service  220  may execute different operations that are part of a larger query plan generated at a data processing service  210  and provide results to the data processing service  210  by relying upon requests from data processing service(s)  210  to determine the different operations to perform. In this way, data retrieval service  220  may be implemented as a dynamically scalable and stateless data processing service that is fault tolerant without the need to support complex query planning and execution for multiple different data formats. Instead, data retrieval service  230  may offer a set of data processing capabilities to access data stored in a wide variety of data formats (which may not be supported by different data processing service(s)  210 ) that can be programmatically initiated on behalf of another data processing client, such as data processing service  210 . 
     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. Data storage service(s)  230  may also include various kinds of object or file data stores for putting, updating, and getting data objects or files. For example, one data storage service  230  may be an object-based data store that allows for different data objects of different formats or types of data, such as structured data (e.g., database data stored in different database schemas), unstructured data (e.g., different types of documents or media content), or semi-structured data (e.g., different log files, human-readable data in different formats like JavaScript Object Notation (JSON) or Extensible Markup Language (XML)) to be stored and managed according to a key value or other unique identifier that identifies the object. In at least some embodiments, data storage service(s)  230  may be treated as a data lake. For example, an organization may generate many different kinds of data, stored in one or multiple collections of data objects in a data storage service  230 . The data objects in the collection may include related or homogenous data objects, such as database partitions of sales data, as well as unrelated or heterogeneous data objects, such as audio files and web site log files. Data storage service(s)  230  may be accessed via programmatic interfaces (e.g., APIs) or graphical user interfaces. For example, data retrieval service  220  may access data objects stored in data storage services via the programmatic interfaces (as discussed below with regard to  FIGS. 5-6 ). 
     Generally speaking, clients  250  may encompass any type of client that can submit network-based requests to provider network  200  via network  260 , including requests for storage services (e.g., a request to query a data processing service  210 , or a request to create, read, write, obtain, or modify data in data storage service(s)  230 , etc.). 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 can 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 data processing service(s)  210 , data retrieval service  220 , or storage resources in data storage service(s)  230  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 that can 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)  230  (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)  230  may be coordinated by client  250  and the operating system or file system on behalf of applications executing within the operating system environment. Similarly, a client  250  may be an analytics application that relies upon data processing service(s)  210  to execute various queries for data already ingested or stored in the data processing service (e.g., such as data maintained in a data warehouse service, like data warehouse service  300  in  FIG. 3  below) or data stored in a data lake hosted in data storage service(s)  230  by performing federated data processing between the data processing service  210  and data retrieval service  220  (as discussed below with regard to  FIG. 5 ). 
     Clients  250  may convey network-based services requests (e.g., access requests to read or write data may be directed to data in data storage service(s)  230 , or operations, tasks, or jobs, such as queries, being performed as part of data processing service(s)  210 ) 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. In some embodiments, clients of data processing services  210 , data retrieval service  220 , and/or data storage service(s)  230  may be implemented within provider network  200  (e.g., an application hosted on a virtual computing resource that utilizes a data processing service  210  to perform database queries) to implement various application features or functions and thus various features of client(s)  250  discussed above may be applicable to such internal clients as well. 
     In at least some embodiments, one of data processing service(s)  220  may be a data warehouse service.  FIG. 3  is a logical block diagram of a data warehouse service implementing primary and secondary processing clusters, according to some embodiments. A data warehouse service, such as data warehouse service  300 , 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&#39;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). The following discussion describes various embodiments of a relational columnar database system. However, various versions of the components discussed below as may be equally adapted to implement embodiments for various other types of relational database systems, such as row-oriented database systems. Therefore, the following examples are not intended to be limiting as to various other types or formats of database systems. 
     In some embodiments, storing table data in such a columnar fashion may reduce the overall disk I/O requirements for various queries and may improve analytic query performance. For example, storing database table information in a columnar fashion may reduce the number of disk I/O requests performed when retrieving data into memory to perform database operations as part of processing a query (e.g., when retrieving all of the column field values for all of the rows in a table) and may reduce the amount of data that needs to be loaded from disk when processing a query. Conversely, for a given number of disk requests, more column field values for rows may be retrieved than is necessary when processing a query if each data block stored entire table rows. In some embodiments, the disk requirements may be further reduced using compression methods that are matched to the columnar storage data type. For example, since each block contains uniform data (i.e., column field values that are all of the same data type), disk storage and retrieval requirements may be further reduced by applying a compression method that is best suited to the particular column data type. In some embodiments, the savings in space for storing data blocks containing only field values of a single column on disk may translate into savings in space when retrieving and then storing that data in system memory (e.g., when analyzing or otherwise processing the retrieved data). 
     Data warehouse service  300  may be implemented by a large collection of computing devices, such as customized or off-the-shelf computing systems, servers, or any other combination of computing systems or devices, such as the various types of systems  2000  described below with regard to  FIG. 14 . Different subsets of these computing devices may be controlled by control plane  310 . Control plane  310 , for example, may provide a cluster control interface to clients or users who wish to interact with the processing clusters  320  managed by control plane  310 . For example, control plane  310  may generate one or more graphical user interfaces (GUIs) for storage clients, which may then be utilized to select various control functions offered by the control interface for the processing clusters  320  hosted in the data warehouse service  300 . Control plane  310  may provide or implement access to various metrics collected for the performance of different features of data warehouse service  300 , in some embodiments. 
     As discussed above, various clients (or customers, organizations, entities, or users) may wish to store and manage data using a data management service. Processing clusters may respond to various requests, including write/update/store requests (e.g., to write data into storage) or queries for data (e.g., such as a Server Query Language request (SQL) for particular data), as discussed below with regard to  FIG. 5 , along with many other data management or storage services. Multiple users or clients may access a processing cluster to obtain data warehouse services. In at least some embodiments, a data warehouse service  300  may provide network endpoints to the clusters which allow the clients to send requests and other messages directly to a particular cluster. Network endpoints, for example may be a particular network address, such as a URL, which points to a particular cluster. For instance, a client may be given the network endpoint “http://mycluster.com” to send various request messages to. Multiple clients (or users of a particular client) may be given a network endpoint for a particular cluster. Various security features may be implemented to prevent unauthorized users from accessing the clusters. Conversely, a client may be given network endpoints for multiple clusters. 
     Processing clusters, such as processing clusters  320  and  340 , hosted by the data warehouse service  300  may provide an enterprise-class database query and management system that allows users to send data processing requests to be executed by the clusters  320 , such as by sending a query to a cluster control interface implemented by the network-based service. Processing clusters  320  may perform data processing operations with respect to data stored locally in a processing cluster, as well as remotely stored data. For example, object-based storage service  330  may be a data storage service  230  implemented by provider network  200  that stores remote data, such as backups or other data of a database stored in a cluster. In some embodiments, database data may not be stored locally in a processing cluster  320  but instead may be stored in object-based storage service  330  (e.g., with data being partially or temporarily stored in processing cluster  320  to perform queries). Queries sent to a processing cluster  320  (or routed/redirect/assigned/allocated to processing cluster(s)  340  from processing cluster(s)  320 ) may be directed to local data stored in the processing cluster and/or remote data. Therefore, processing clusters may implement local data processing, such as local data processing  322  and  342 , (discussed below with regard to  FIGS. 5 and 6 ) to plan and execute the performance of queries with respect to local data in the processing cluster, as well as a remote data processing client, such as remote data processing clients  324  and  344 , to direct execution of different sub-queries (e.g., operations determined as part of the query plan generated at the processing cluster  320 ) that are assigned to data retrieval service  220  with respect to processing remote database data  332 ). 
     In some embodiments, data warehouse service  300  may implement primary clusters  330  and secondary cluster pool  350 . Primary clusters  330  may be reserved, allocated, permanent, or otherwise dedicated processing resources that store and/or provide access to a database for a client of data warehouse service  300 , in some embodiments. Secondary cluster pool  350  may be a set of warmed, pre-configured, initialized, or otherwise prepared clusters which may be on standby to provide additional query performance capacity for a primary cluster  330 . Control plane  310  may manage secondary cluster pool  350  by managing the size of secondary cluster pool  350  (e.g., by adding or removing processing clusters  340  based on demand). Control plane  310  may determine the capabilities or configuration (which may be different) of processing cluster(s)  340  in secondary cluster pool  350  (e.g., maintaining a number of 10 node clusters, 15 node clusters, 20 node clusters, etc.). Processing clusters  340  in secondary cluster pool  350  may be obtained or provisioned for a primary cluster  330 , as discussed in detail below with regard to  FIG. 8 . 
     As databases are created, updated, and/or otherwise modified, snapshots, copies, or other replicas of the database at different states may be stored separate from data warehouse service  300  in object-based storage service  330 , in some embodiments. For example, a leader node, or other processing cluster component, may implement a backup agent or system that creates and store database backups for a database to be stored as database data  332  in object-based storage service  330 . Database data  332  may include user data (e.g., tables, rows, column values, etc.) and database metadata (e.g., information describing the tables which may be used to perform queries to a database, such as schema information, data distribution, range values or other content descriptors for filtering out portions of a table from a query, etc.). A timestamp or other sequence value indicating the version of database data  332  may be maintained in some embodiments, so that the latest database data  332  may, for instance, be obtained by a processing cluster in order to perform queries sent for secondary query performance. 
       FIG. 4  is a logical block diagram illustrating a data retrieval service, according to some embodiments. As noted above in  FIG. 2 , data retrieval service  220  may receive requests to perform processing operations with respect to data stored  432  stored in a data storage service (e.g., backup data or other database data, such as other database tables or data that is not stored according to a format, schema, or structure like that of data stored in data warehouse service  300 ). Processing requests may be received from a client, such as remote data processing client(s)  402  (which may another data processing service  210 , like data warehouse service  300  or another data processing client, such as a database engine/cluster or map reduce cluster implemented outside of provider network  200  and communicating with data retrieval service  220  in order to process queries with respect to data stored within provider network  200  in a data storage service  230  or to process data stored outside of provider network  200  (when the data is made accessible to data retrieval service  220 ). 
     Data retrieval service  220  may implement a control plane  410  and multiple processing node(s)  420  to execute processing requests received from remote data processing client(s)  402 . Control plane  410  may arbitrate, balance, select, or dispatch requests to different processing node(s)  420  in various embodiments. For example, control plane  410  may implement interface  412  which may be a programmatic interface, such as an application programming interface (API), that allows for requests to be formatted according to the interface  412  to programmatically invoke operations. In some embodiments, the API may be defined to allow operation requests defined as objects of code generated at and sent from remote data processing client(s)  402  (based on a query plan generated at remote data processing client(s)  402 ) to be compiled or executed in order to perform the assigned operations at data retrieval service  220 . 
     In some embodiments, data retrieval service  220  may implement load balancing  418  to distribute remote processing requests across different processing node(s)  420 . For example, a remote processing request received via interface  412  may be directed to a network endpoint for a load-balancing component of load balancing  418  (e.g., a load balancing server or node) which may then dispatch the request to one of processing node(s)  420  according to a load balancing scheme. A round-robin load balancing, for instance, may be used to ensure that remote data processing requests are fairly distributed amongst processing node(s)  420 . However, various other load-balancing schemes may be implemented. As data retrieval service  220  may receive many remote data processing requests from multiple remote data processing client(s)  402 , load balancing  418  may ensure that incoming requests are not directed to busy or overloaded processing node(s)  420 . 
     Data retrieval service  220  may also implement resource scaling  414 . Resource scaling  414  may detect when the current request rate or workload upon a current number of processing node(s)  420  exceeds or falls below over-utilization or under-utilization thresholds for processing nodes. In response to detecting that the request rate or workload exceeds an over-utilized threshold, for example, then resources scaling  414  may provision, spin up, activate, repurpose, reallocate, or otherwise obtain additional processing node(s)  420  to processing received remote data processing requests. Similarly, the number of processing node(s)  420  could be reduced by resource scaling  414  in the event that the request rate or workload of processing node(s) falls below the under-utilization threshold. 
     Data retrieval service  220  may also implement failure management  416  to monitor processing node(s)  420  and other components of data retrieval service  220  for failure or other health or performance states that may need to be repaired or replaced. For example, failure management  416  may detect when a processing node fails or becomes unavailable (e.g., due to a network partition) by polling processing node(s)  420  to obtain health or performance status information. Failure management may initiate shutdown or halting of processing at failing processing node(s)  420  and provision replacement processing node(s)  420 . 
     Processing node(s)  420  may be implemented as separate computing nodes, servers, or devices, such as computing systems  2000  in  FIG. 14 , to perform data processing operations on behalf of remote data processing client(s)  402 . Processing node(s)  420  may implement stateless, in-memory processing to execute processing operations, in some embodiments. In this way, processing node(s)  420  may have fast data processing rates. Processing node(s)  420  may implement client authentication/identification  421  to determine whether a remote data processing client  402  has the right to access data  432  in storage service  430 . For example, client authentication/identification  421  may evaluate access credentials, such as a username and password, token, or other identity indicator by attempting to connect with storage service  430  using the provided access credentials. If the connection attempt is unsuccessful, then the data processing node  402  may send an error indication to remote data processing client  402 . 
     Processing node(s)  420  may implement query processing  422  or other features of a query engine which may perform multiple different sub-queries (e.g., processing operations) and support multiple different data formats. For example, query processing  422  may implement separate tuple scanners for each data format which may be used to perform scan operations that scan data  432  and which may filter or project from the scanned data, search (e.g., using a regular expression) or sort (e.g., using a defined sort order) the scanned data, aggregate values in the scanned data (e.g., count, minimum value, maximum value, and summation), and/or group by or limit results in the scanned data. Remote data processing requests may include an indication of the data format for data  432  so that query processing  422  may use the corresponding tuple scanner for data  432 . Query processing  422  may, in some embodiments, transform results of operations into a different data format or schema according to a specified output data format in the remote data processing request. 
     In some embodiments, data  432  may be stored in encrypted or compressed format. Processing node(s)  420  may implement compression engine(s)  424  to decompress data  432  according to a compression technique identified for data  432 , such as lossless compression techniques like run-length encoding, Lempel-Ziv based encoding, or bzip based encoding. Processing node(s)  420  may implement encryption engine(s)  426  to decrypt data  432  according to an encryption technique and/or encryption credential, such as a key, identified for data  432 , such as symmetric key or public-private key encryption techniques. 
     Processing node(s)  420  may implement storage access  428  to format, generate, send and receive requests to access data  432  in storage service  430 . For example, storage access  428  may generate requests to obtain data according to a programmatic interface for storage service  430 . In some embodiments, other storage access protocols, such as internet small computer interface (iSCSI), may be implemented to access data  432 . 
       FIG. 5  is a logical block diagram illustrating an example primary processing cluster of a data warehouse service that implements dynamically assigning queries to secondary processing clusters, according to some embodiments. Primary processing cluster  500  may be data warehouse service cluster, like processing clusters  320  discussed above with regard to  FIG. 3 , or another processing cluster that distributes execution of a query among multiple processing nodes. As illustrated in this example, a primary processing cluster  500  may include a leader node  510  and compute nodes  520   a  ,  520   b  , and  520   n  , which may communicate with each other over an interconnect (not illustrated). 
     Leader node  510  may implement query planning  512  to generate query plan(s), query execution  514  for executing queries on primary processing cluster  500  that perform data processing that can utilize remote query processing resources for remotely stored data (e.g., by utilizing one or more query execution slot(s)/queue(s)  517 ) and workload manager  515  for selecting, routing, directing, or otherwise causing a received query to be performed using secondary capacity resources, such as a secondary processing cluster  600  in  FIG. 6  discussed below. As described herein, each node in a primary processing cluster  500  may include attached storage, such as attached storage  522   a  ,  522   b  , and  522   n  , on which a database (or portions thereof) may be stored on behalf of clients (e.g., users, client applications, and/or storage service subscribers). 
     Note that in at least some embodiments, query processing capability may be separated from compute nodes, and thus in some embodiments, additional components may be implemented for processing queries. Additionally, it may be that in some embodiments, no one node in processing cluster  500  is a leader node as illustrated in FIG. 
       5 , but rather different nodes of the nodes in processing cluster  500  may act as a leader node or otherwise direct processing of queries to data stored in processing cluster  500 . While nodes of processing cluster may be implemented on separate systems or devices, in at least some embodiments, some or all of processing cluster may be implemented as separate virtual nodes or instance on the same underlying hardware system (e.g., on a same server). 
     In at least some embodiments, primary processing cluster  500  may be implemented as part of a data warehouse service, as discussed above with regard to  FIG. 3 , or another one of data processing service(s)  210 . Leader node  510  may manage communications with clients, such as clients  250  discussed above with regard to  FIG. 2 . 
     For example, leader node  510  may be a server that receives a query  501  from various client programs (e.g., applications) and/or subscribers (users), then parses them and develops an execution plan (e.g., query plan(s)) to carry out the associated database operation(s)). More specifically, leader node  510  may develop the series of steps necessary to obtain results for the query. Query  501  may be directed to data that is stored both locally within processing cluster  500  (e.g., at one or more of compute nodes  520 ) and data stored remotely (which may be accessible by data retrieval service  220 ). Leader node  510  may also manage the communications among compute nodes  520  instructed to carry out database operations for data stored in the processing cluster  500 . For example, node-specific query instructions  504  may be generated or compiled code by query execution  514  that is distributed by leader node  510  to various ones of the compute nodes  520  to carry out the steps needed to perform query  501 , including executing the code to generate intermediate results of query  501  at individual compute nodes may be sent back to the leader node  510 . Leader node  510  may receive data and query responses or results from compute nodes  520  in order to determine a final result  503  for query  501 . A database schema, data format and/or other metadata information for the data stored among the compute nodes, such as the data tables stored in the cluster, may be managed and stored by leader node  510 . Query planning  512  may account for remotely stored data by generating node-specific query instructions that include remote operations to be directed by individual compute node(s). As discussed in more detail below with regard to 
       FIG. 7 , a leader node may implement workload manager  515  to send  506  a query plan generated by query planning  512  to be performed at a secondary processing cluster and return results  508  received from the secondary processing cluster to a client as part of results  503 . In this way, secondary query processing may occur without client application changes to establish a separate connection or communication scheme with secondary processing resources, allowing for seamless scaling between primary and secondary processing capacity. 
     Processing cluster  500  may also include compute nodes, such as compute nodes  520   a  ,  520   b  , and  520   n  . Compute nodes  520 , may for example, be implemented on servers or other computing devices, such as those described below with regard to computer system  2000  in  FIG. 14 , and each may include individual query processing “slices” defined, for example, for each core of a server&#39;s multi-core processor, one or more query processing engine(s), such as query engine(s)  524   a  ,  524   b  , and  524   n  , to execute the instructions  504  or otherwise perform the portions of the query plan assigned to the compute node. Query engine(s)  524  may access a certain memory and disk space in order to process a portion of the workload for a query (or other database operation) that is sent to one or more of the compute nodes  520 . Query engine  524  may access attached storage, such as  522   a  ,  522   b  , and  522   n  , to perform local operation(s), such as local operations  518   a  ,  518   b  , and  518   n  . For example, query engine  524  may scan data in attached storage  522 , access indexes, perform joins, semi joins, aggregations, or any other processing operation assigned to the compute node  520 . 
     Query engine  524   a  may also direct the execution of remote data processing operations, by providing remote operation(s), such as remote operations  516   a  ,  516   b  , and  516   n  , to remote data processing clients, such as remote data processing client  526   a  ,  526   b  , and  526   n  . Remote data processing clients  526  may be implemented by a client library, plugin, driver or other component that sends request sub-queries, such as sub-quer(ies)  532   a  ,  532   b  , and  532   n  to data retrieval service  220 . As noted above, in some embodiments, data retrieval service  220  may implement a common network endpoint to which request sub-quer(ies)  532  are directed, and then may dispatch the requests to respective processing nodes, such as processing nodes  540   a  ,  540   b  , and  540   n  . Remote data processing clients  526  may read, process, or otherwise obtain results from processing nodes, including partial results of different operations (e.g., aggregation operations) and may provide sub-query result(s), including result(s)  534   a  ,  534   b  , and  534   c  , back to query engine(s)  524 , which may further process, combine, and or include them with results of location operations  518 . 
     Compute nodes  520  may send intermediate results from queries back to leader node  510  for final result generation (e.g., combining, aggregating, modifying, joining, etc.). Remote data processing clients  526  may retry remote operation request(s)  532  that do not return within a retry threshold. As data retrieval service  220  may be stateless, processing operation failures at processing node(s)  540  may not be recovered or taken over by other processing nodes  540 , remote data processing clients  526  may track the success or failure of requested operation(s)  532 , and perform retries when needed. 
     Attached storage  522  may be implemented as one or more of any type of storage devices and/or storage system suitable for storing data accessible to the compute nodes, including, but not limited to: redundant array of inexpensive disks (RAID) devices, disk drives (e.g., hard disk drives or solid state drives) or arrays of disk drives such as Just a Bunch Of Disks (JBOD), (used to refer to disks that are not implemented according to RAID), optical storage devices, tape drives, RAM disks, Storage Area Network (SAN), Network Access Storage (NAS), or combinations thereof. In various embodiments, disks may be formatted to store database tables (e.g., in column oriented data formats or other data formats). 
       FIG. 6  is a logical block diagram illustrating an example secondary processing cluster of a data warehouse service, according to some embodiments. Similar to primary processing cluster  500  in  FIG. 5 , secondary processing cluster  600  may include a leader node  610  and compute nodes  620   a  ,  620   b  , and  620   n  , which may communicate with each other over an interconnect (not illustrated). Leader node  610  may implement query execution  612  for executing queries on secondary processing cluster  600 . For example, leader node  610  may receive a query plan  602  to perform a query from a primary processing cluster. Query execution  612  may generate the instructions or compile code to perform the query according to the query plan. Leader node  610  may also manage the communications among compute nodes  620  instructed to carry out database operations for data stored in the secondary processing cluster  600 . For example, node-specific query instructions  604  may be generated or compiled code by query execution  612  that is distributed by leader node  610  to various ones of the compute nodes  620  to carry out the steps needed to perform query plan  602 , including executing the code to generate intermediate results of the query at individual compute nodes may be sent back to the leader node  610 . Leader node  610  may receive data and query responses or results from compute nodes  620  in order to determine a final result  606  for the query to be sent back to the primary processing cluster. 
     In at least some embodiments, secondary processing cluster  600  may not maintain a local copy of the database, but instead may access a backup of the database (or the database directly which may not be maintained locally at primary processing clusters) via data retrieval service  220 . For example, query engine  624   a  may direct the execution of remote data processing operations, by providing remote operation(s), such as remote operations  616   a  ,  616   b  , and  616   n  , to remote data processing clients, such as remote data processing client  626   a  ,  626   b  , and  626   n  , in order to retrieve data from the database data in object storage service  330  to perform the query. As noted earlier, remote data processing clients  626  may be implemented by a client library, plugin, driver or other component that sends request sub-queries, such as sub-quer(ies)  632   a  ,  632   b  , and  632   n  to data retrieval service  220 . As noted above, in some embodiments, data retrieval service  220  may implement a common network endpoint to which request sub-quer(ies)  632  are directed, and then may dispatch the requests to respective processing nodes, such as processing nodes  640   a  ,  640   b  , and  640   n  . Remote data processing clients  626  may read, process, or otherwise obtain results from processing nodes, including partial results of different operations (e.g., aggregation operations) and may provide sub-query result(s), including result(s)  634   a  ,  634   b  , and  634   c  , back to query engine(s)  624 , which may further process, combine, and or include them with results of location operations  618 . In at least some embodiments, processing nodes  640  may filter, aggregate, or otherwise reduce or modify data from the database backups used to perform the query in order to lessen the data transferred and handled by secondary processing cluster  600 , increasing the performance of the query at secondary processing cluster  600 . Although not illustrated in  FIG. 6 , some secondary processing clusters may implement local attached storage and local processing similar to primary processing cluster  500  in  FIG. 5 . For example, a secondary processing cluster that was scheduled for a period of time that exceeds some threshold value (e.g., greater than  1  hour) may read and store in persistent storage database data from the database data (e.g., directly or via data retrieval service  220 ), in some embodiments. 
     Although not illustrated in  FIGS. 5 and 6 , further communications between a primary processing cluster and secondary processing cluster may be implemented. For example, database metadata may be obtained at secondary processing cluster  600  from a database backup and then updated as updates are made at the primary processing cluster, in some embodiments, as discussed below with regard to  FIG. 8 . In some embodiments, compute nodes  620  (or leader node  610 ) may request data directly from compute nodes  520  in primary processing cluster  500 ), such as updated data blocks in a table of a database. In at least one embodiment, all of the data used to perform a query may be obtained by compute nodes  620  from compute nodes  520  instead of utilizing data retrieval service  220  and a backup in a separate data store. 
     In at least some embodiments, secondary processing cluster  600  may be a single tenant resource, only performing secondary queries for one database (or client or user account). In some embodiments, secondary processing cluster  600  may be a multi-tenant environment, handling secondary queries for different databases, different user accounts and/or different clients. In such scenarios, security techniques to prevent data from being visible to unauthorized users may be implemented. 
       FIG. 7  is a logical block diagram illustrating an example of workload management that implements dynamically assigning queries to secondary processing resources, according to some embodiments. Leader node  700  may be similar to leader node  510  in  FIG. 5 . Leader node  700  may implement workload manager  730  to perform dynamic assignments of queries, in some embodiments. 
     As illustrated in  FIG. 5 , workload manager  730  may be implemented by leader node to assign the cluster to perform database query, in some embodiments. For example, workload manager  730  may implement secondary eligibility  732 , which may determine whether a received query is eligible to perform at a secondary query processing resource according to the techniques discussed below with regard to  FIG. 9 . 
     In some embodiments, workload manager  703  may implement query time prediction  738 , which may apply one or more size classifiers to the query plan in order to classify a size that indicates an expected/predicted execution time of the query, (e.g., “small/short,” “medium,” or “large/long” queries). For instance, a rules-based decision engines for classifying the size of a query may be applied (e.g., which may apply different rules to features of the query, such as the size of the table being queried, the type of operations (e.g., joins and/or scans), the source of the query (e.g., which client application or user account, by checking to see if the query has been performed before and how long it performed, number of storage locations accessed, types of queries that cannot by definition be “small” or “medium”, etc.). In some embodiments, query size classifier(s) may be trained using machine learning techniques so that when a size classifier  740  is applied to features of the plan, a probability indicative of a size of the database query may be generated, in some embodiments. For example, a linear classifier may be applied to score the features of the query plan according to a weighted sum of the features (e.g., by applying coefficients to the features determined from training the classifier according to logistic regression). In some embodiments, other features in addition to the query plan may be considered, such as the source of the query (e.g., what user submitted the query), time of day, what table(s) are identified in the query, among others. The size classifiers may be trained to specific query engines, in some embodiments. In this way, the varying configurations of secondary query processing clusters, for instance, may be considered which may alter query performance when classifying a query&#39;s size. 
     The output of the classifiers may be a probability value, in various embodiments. The probability value may be compared to a classification threshold, in some embodiments. For example, if the greater the probability value indicates the longer a query is likely to run and thus a greater size, then ranges of probabilities may correspond to different sizes at different clusters (e.g., “small-secondary,” “small-primary,” “medium-secondary,” “medium-primary,” “large-secondary,” or “large-primary” queries), in some embodiments. In some embodiments, separate size classifiers, such as a classifier for small queries, a classifier for medium queries, and a classifier for large queries may be applied to select as the size the classification with the highest confidence score. 
     Workload manager may implement cluster assignment/reassignment  736 , which may perform various ones of the techniques discussed below with regard to  FIGS. 9-13 , in some embodiments. For example, cluster assignment/reassignment  736  may apply different criteria or other thresholds according to the size classification (e.g., a “medium” query is greater than a threshold that applies rules for “small” queries), in some embodiments. As discussed in detail below with regard to  FIGS. 9-13 , the user, workload, allocation, or otherwise state of the primary processing cluster as indicated by queue/slot state which may be provided and used to make cluster selections. For example, query execution slot(s)/queue(s)  760  may be maintained as part of leader node  700 , in some embodiments. Query execution slot(s)/queue(s)  760  may, in some embodiments, be implemented as part of a queue (not illustrated). A query execution slot, such as query execution slots  766   a  ,  766   b  ,  766   c  ,  768   a  ,  768   b  , and  768   c  , may identify a process that is allocated a certain portion of computing resources at a processing cluster (e.g., processor, memory, I/O bandwidth, network bandwidth, etc.) to perform a query assigned to that slot. As illustrated in  FIG. 7 , some query execution slots may be allocated for secondary query engine ineligible queries  764  for queries. Other slots, such as slots  762 , may be available for secondary eligible queries, in some embodiments. 
     Cluster assignment/reassignment  736  may receive information indicating available secondary cluster(s)  782  for assignment or reassignment of queries. Cluster assignment/reassignment  736  may direct the query for local query execution, in some embodiments. Alternatively, cluster assignment/reassignment  736  may select a secondary processing cluster to perform the query, and provide a request, instruction, or other indication to perform secondary query execution, in some embodiments. In at least some embodiments, further query planning to adapt the query plan to the secondary cluster may be performed (not illustrated). For example, the number of nodes in the secondary processing cluster may be different, which may result in a different division of work in the query plan. Instructions may also be included for accessing data through data retrieval service  220  (e.g., storage object locations, access credentials, including specialized operators or instructions to leverage data retrieval service  220  in the plan), in some embodiments. 
     Workload manager  730  may implement secondary query monitor  734 , which may perform various techniques to manage or observe queries at secondary processing clusters. Remote query node performance  784  may be received to determine whether query is still executing or performing remotely. If not, secondary query monitory  734  may send a request to cluster assignment/reassignment to change the assignment of the query to local execution. For example, as discussed below with regard to  FIG. 12B , in some circumstances a query may be preempted by another query or exceed expected performance criteria . 
     In at least some embodiments, workload manager  730  may be configured via user and/or control plane requests. For example, assignment/reassignment policies  786  may be received, created, deleted, or modified, in some embodiments. In at least some embodiments, assignment/reassignment policies  786  may allow users (or the control) to specify via an interface when secondary performance of queries may be enabled or disabled for a primary processing cluster. For example, secondary can be enabled/disabled automatically in order to optimize for cost or performance, in some embodiments. A maximum queue time or other performance criteria for the primary processing cluster could be specified as part of assignment/reassignment policies  780  for queries, for instance, may determine when secondary processing should occur (e.g., if queries would exceed the queue time then begin using bust capacity). In some embodiments, a secondary budget (e.g., a cost limitation for using secondary processing clusters) or other limitation may be specified as part of assignment/reassignment policies  780  in order to allow a user/client application to indicate when secondary should stop so that the budget or other limitation is not exceeded (e.g., for a given time period, such as a day, week, month, etc.). 
       FIG. 8  is a logical block diagram illustrating example interactions to obtain and release a secondary processing cluster from a pool of secondary processing clusters, according to some embodiments. Workload manager  812  at leader node  810  may detect or determine when to obtain a secondary cluster for performing queries in various scenarios, as discussed above with regard to workload manager  730  in  FIG. 7  and below with regard to  FIGS. 9-13 . Workload manager  812  may then request a secondary cluster  842  from control plane  310 . The request may, in some embodiments, specify a type of secondary cluster. In some embodiments, control plane  310  may evaluate a manifest, index, or other data that describes available processing cluster(s)  822  in secondary cluster pool  820  in order to satisfy the request. For example, control plane  310  may identify a processing cluster that matches (or best matches) the specified configuration of the secondary cluster request, in some embodiments. In some embodiments, control plane  310  may identify a secondary cluster that was previously used for performing queries to the database hosted by the cluster of leader node  810 . 
     Control plane  310  may provision  844  the secondary cluster, in some embodiments, from secondary cluster pool, such as provisioned secondary cluster  824 . Provisioning a secondary cluster may include various operations to configure network connections between provisioned processing cluster for secondary capacity  824  and leader node  810  and other services (e.g., data retrieval service  220 , object storage service  330 , etc.). In some embodiments, access credentials, security tokens, and/or encryption keys may be provided so that provisioned processing cluster for secondary capacity  824  can access and database data to perform queries for the database. In some embodiments, initialization procedures, workflows or other operations may be started by control plane  310  at provisioned processing cluster for secondary capacity  824 . For example, provisioned processing cluster for secondary capacity  824  may get metadata  848  from object-based storage service  330  that is stored as part of database metadata  830  in a database backup in order to perform queries to the database. In some embodiments, provisioned processing cluster for secondary capacity  824  may get metadata updates  850  directly from leader node  810  (or other nodes in a primary processing cluster) in order to catch up the metadata to account for changes that occurred after the backup was stored. 
     Once provisioning is complete, provisioned processing cluster for secondary capacity  824  may be made available for performing queries. Control plane  310  may identify the secondary cluster  846  to leader node  810  (e.g., by providing a network endpoint for provisioned cluster  824 ), in some embodiments. Leader node  810  may then begin directing selected queries  852  to provisioned cluster  824 , which may perform the queries and send back query results  854  to leader node  810 , which may provide the results to a client in turn. In this way, a client application does not have to learn of and receive requests from a second location, provisioned cluster  824  when secondary performance is used, in some embodiments. Workload manager  812  may request different types, sizes, or other configurations of secondary clusters to, for instance, compare performance of queries. 
     When an event that triggering release of the secondary cluster occurs, workload manager  812  may send a request to control plane  310  to release the secondary cluster  856  (e.g., by including the identifier of the provisioned cluster  824 ). Control plane  310  may then delete the secondary cluster  858  (e.g., by removing/deleting data and/or decommissioning/shutting down the host resources for the provisioned cluster  824 ). 
     Although  FIGS. 2-8  have been described and illustrated in the context of a provider network implementing different data processing services, like a data warehousing service, the various components illustrated and described in  FIGS. 2-8  may be easily applied to other data processing systems that can utilize additional query engines to provide for secondary query performance capacity. As such,  FIGS. 2-8  are not intended to be limiting as to other embodiments of dynamically assigning queries to secondary processing resources. 
       FIG. 9  is a high-level flowchart illustrating methods and techniques to implement dynamically assigning queries to secondary processing resources, according to some embodiments. Various different systems and devices may implement the various methods and techniques described below, either singly or working together. Different combinations of services implemented in different provider networks operated by different entities may implement some or all of the methods (e.g., a data warehouse cluster in a service of a first provider network, an intermediate data processing service in a second provider network, and a data set stored in a service of a third provider network). Different types of query engines or non-distributed query performance platforms may implement these techniques. Alternatively, various other combinations of different systems and devices located within or without provider networks may implement the below techniques. 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 devices. 
     As indicated at  910 , a database query may be received at a query engine, in various embodiments. The database query may be received according to various interfaces, formats, and/or protocols. For example, the database query may be formatted according to a query language such as Structured Query Language (SQL), in some embodiments, or may be specified according to an Application Programming Interface (API) for receiving queries. In at least some embodiments, the database query may be one query of many queries that can be submitted by one or many different users to a same database engine, processing platform, or system. For example, the database query may compete for computing resources along with other queries received from other users to be executed with respect to a database in some embodiments. In at least some embodiments, the query may be received at a primary query engine (e.g., received at a primary processing cluster as discussed above with regard to  FIGS. 5-7 ). 
     As indicated at  920 , a determination may be made as to whether the query is eligible to be performed by another query engine. For example, if the query is type of a query that performs certain operations (e.g., Data Manipulation Language (DML) operations, data definition language (DDL) operations, or evaluates system tables), then the query may not be eligible for processing at another query engine. In some embodiments, if a backup of the database (or portion of the database targeted by the query) is not made to be accessible to the secondary query engine, then the query may not be eligible. In such scenarios, a backup of the database may be created in response to the request so that a subsequent query that is the same is received. In some embodiments, eligibility may be determined according to the source of the query (e.g., as some applications may not be allowed to utilize secondary query processing resources). In some embodiments, a queue identified for the query may dictate whether the query is eligible. If not eligible, then as indicated at  922 , the query engine may perform the query. 
     As indicated at  930 , the availability of the query engine to perform the query may be evaluated to determine where to assign the query if the query is eligible, in some embodiments. For example, if the query engine has availability, then the query may still be assigned to the query engine instead of the other query engine. If the query engine does not have availability (e.g., the query has to queue and wait), then the other query engine may be selected.  FIG. 10 , discussed below, provides various selection techniques that may be applied. 
     As indicated by the negative exit from  940 , if the query engine is not assigned to the query engine, then the other query engine may be caused to begin performing the query, in some embodiments, as indicated at  942 . For example the query may be forwarded and sent to an identified other query engine. In some embodiments, the other query engine may be provisioned in response to the query (e.g. as discussed above with regard to  FIG. 8 ). In some embodiments, the query may be sent as part of a batch of queries to be performed by the other query engine. 
     As indicated by the positive exit from  940 , the query engine may be assigned to the query engine. In some embodiments, this assignment may not change. For instance, an available execution slot at the query engine may be ready to begin the query. Thus, as indicated by the negative exit from  950 , the query may be performed at the query, as indicated at  952 . However, in some scenarios, as discussed below with regard to  FIGS. 10 and 11 , a change in the query assignment can happen, such as different availability of secondary query engines. In such a scenario, the other query engine may be caused to begin performing the query, as indicated at  960 . 
     Various exceptions, alternative conditions, or further evaluations may be performed for queries greater than or equal to the size threshold in order to determine to perform it at the second query engine, which may be the secondary performance resource, in some embodiments. For instance,  FIG. 10 , discussed below provides various different scenarios in which a query may still be performed at the first query engine if the size is greater than or equal to the size threshold which are not illustrated. 
     Various techniques for how to make an assignment of a query between different query engines may be implemented, as noted above.  FIG. 10  is a high-level flowchart illustrating methods and techniques to implement selecting between primary and secondary processing resources, according to some embodiments. As indicated at  1010 , a queue identified for a received query at a primary query engine may be evaluated, in various embodiments. For instance, the source of the query (e.g., the client application or user identifier or role associated with the query) may identify which queue the query belongs to and thus may determine query handling and assignment techniques that are to be applied to the query. As indicated at  1020 , a determination as to whether an execution slot for the query is available in the queue. If so, then as indicated at  1022 , the query may be performed at the primary query engine, in some embodiments. 
     If not, then a selection between primary and secondary query engines may be made. For example, as indicated at  1030 , a predicted execution time for the query at the primary query engine may be determined, in some embodiments. Similarly, a predicted execution time for the query at a secondary query engine may be determined, as indicated at  1032 , in some embodiments. As discussed above with regard to  FIG. 7 , different classification schemes or techniques may be applied. Size classifiers predictive of the size or execution time of a query trained on the performance of previous database queries may be used to evaluate features of a received database query and identify a probability that the query is similar in size to a known size of a previous query, in some embodiments. These classification techniques may be dynamically updated so that as the size of queries received changes over time, in some embodiments, so that the determined size for a received query may adjust accordingly (e.g., “small/short” queries may change from less than  5  seconds to less than  10  seconds over time). Other classification techniques, including performance timeouts (e.g., performing a query until it runs longer than a threshold so that it can be determined to have a size longer than a threshold) may be implemented, in some embodiments. In some embodiments, a size of a query may be indicative of resource utilization, amount of data to be read, expected size of results, or other indication of work or cost to perform the query, and thus the previous examples discussing time are not intended to be limiting. 
     As indicated at  1040 , the primary or secondary query engine may be selected based on the predicted execution times, in some embodiments. For example, if the query is classified as short and thus a smaller execution time at the primary query engine, then the primary query engine may be selected (which may ignore or supersede a similar or shorter prediction for the secondary query engine given some performance benefit for performing a query locally). Alternatively, the shortest predicted execution time may determine which query engine is selected. 
     If a secondary query engine is not selected as indicated by the negative exit from  1050 , then the query may be performed at the primary query engine. As discussed below with regard to  FIG. 11 , the query could be queued at the primary query engine and potentially reassigned to the secondary query engine (or another secondary query engine, in some circumstances). 
     If the secondary query engine is selected, then as indicated at  1060 , a determination may be made as to whether the secondary query engine is available to perform the query, in some embodiments. If so, then as indicated at  1062 , a request may be sent to the secondary query engine to perform the query, in some embodiments. If the secondary query engine is not available then the query may be queued for later performance at the secondary query engine, as indicated at  1070 , in some embodiments. 
     As indicated at  1080 , in some scenarios the primary query engine may become available (e.g., an execution slot may become free). If so, then as indicated by the positive exit from  1080 , the query may be reassigned and performed by the primary query engine. However, if the primary query engine does not become available then the query may remain queued until the secondary query engine becomes available, in some embodiments, as indicated by the negative exit from  1080 . 
     Although initial assignments can be made, as discussed in the techniques above, circumstances may change after initial assignments in which reassigning a query to a different query engine may be more optimal for the query and/or the workload of a query engine.  FIG. 11  is a high-level flowchart illustrating methods and techniques to implement reassigning a waiting query to a secondary processing resource, according to some embodiments. 
     As indicated at  1110 , availability of a secondary query engine to perform a query may be detected, in some embodiments. For example, the query results of a prior query may be received at the primary query engine indicating that the query engine is free for another query. In some embodiments, an indication of a newly provisioned secondary query engine, as discussed above with regard to  FIG. 8 , may be received, in some embodiments. 
     As indicated at  1120 , queue(s) of waiting queries at a primary query engine may be evaluated to select one of the waiting queries that is eligible to perform at the secondary query engine to perform at the secondary query engine, in some embodiments. For example, a query that is at the head of a queue (e.g., next to be performed at the primary from that queue) may be selected if eligible (e.g., according to the techniques discussed above with regard to  FIG. 9 ). Or, in another technique a query at the tail of the query (e.g., last to be performed in the queue) may be selected if eligible. Alternatively, selection techniques that can minimize query wait times may be employed. For instance, instead of selecting a query that is eligible to burst after a query that is ineligible in a queue, a query that is eligible to burst and before the ineligible query may be selected instead, reducing the wait time in the queue of the ineligible query. Query selection techniques may be configured or specified according to a request (as discussed above with regard to  FIG. 7 ). 
     As indicated at  1130 , the selected query may be removed from the queue at the primary query engine, in some embodiments. A request may be sent to the secondary query engine to perform the selected query, in some embodiments, as indicated at  1140 . Alternatively, in other embodiments, the query may be kept in the queue and also performed when an execution slot becomes available according to the ordering of queries in the queue, similar to the speculative execution techniques discussed below with regard to  FIG. 13 , so that the first query engine to complete the query may provide the result for the query. 
     In addition to responding to changes in circumstances while a query is waiting to be performed according to its assignment (as discussed above with regard to  FIGS. 10 and 11 ), changes in circumstances while a query is being performed can also provoke reassignment of a query to a different query engine.  FIGS. 12A and 12B  are high-level flowcharts illustrating methods and techniques to implement reassigning performing queries to a different query processing resource, according to some embodiments. 
     In  FIG. 12A , failures, such as the failure of a secondary query engine may be considered. As indicated at  1210 , a primary query engine may monitor the performance of a query at a secondary query engine, in some embodiments. For example, query performance metrics (e.g., progress indicators), current execution time, or other metrics may be received. A heartbeat or other communication received from the secondary query engine to indicate status may be received, in some embodiments. 
     As indicated at  1220 , a failure of the query at the secondary query engine may be detected, in some embodiments. For example, a progress indicator may not progress, execution time may exceed an expected value, or a lack of heartbeat communication may indicate that the secondary query engine is no longer performing the query (or is unable to send results/performance information to the primary query engine due to a network partition or other external failure scenario). In response to detecting the failure of the query at the secondary query engine, the primary query engine may initiate performance of the query, as indicated at  1230 , in some embodiments. For example, the query may be placed in an execution slot (if available) or in a queue at the primary query engine. In some embodiments, performance of the query at the primary query engine may be delayed a period of time to determine if the secondary query engine may become available again. If, while performing the query at the primary query engine a result indication is received from the secondary query engine (earlier determined to be failed) the results may be discarded, or in other embodiments used as the query results (e.g., similar to the speculative query execution technique discussed below with regard to FIG. 
       13 . 
       FIG. 12B  illustrates circumstances where a query can be reassigned by a query engine performing the query to another query engine, in some instances. For example, as indicated at  1240 , performance of a query may begin at a query engine (e.g. that was assigned the query according to the techniques discussed above with regard to  FIGS. 9 and 10  above). A performance threshold or preemption event may be applied for the query, in some embodiments, as indicated at  1250 . For example, an execution time threshold may be evaluated to consider whether a query has exceeded an expected time for performing the query. The performance threshold may be specified in request received at the database (e.g., as discussed above with regard to  FIG. 7 ). A preemption event may occur when a second query received after a first query is classified as shorter or otherwise more optimally performed (e.g., by evaluating a received/queued query with respect to a currently executing query that can be preempted to a second query processing by the first query engine instead of the second query engine. 
     If the performance threshold is exceeded for the query or if a preemption event is detected, then as indicated at  1260 , the query may be sent to another query engine to perform, in some embodiments. If, for instance, the query was classified as short (or more performant on the primary query engine) but is not short according to the current execution time, then the query may be reassigned to an available secondary cluster (or queued and then sent to the next available and/or appropriate secondary cluster), in some embodiments. If, however, the performance threshold is not exceeded the query may proceed at the query engine until complete, as indicated at  1270  (or until the performance threshold is exceeded). If completed, as indicated at  1280 , a result for the query may be sent from the query engine, in some embodiments. 
     In some circumstances, there may be opportunities to take advantage of secondary processing resources to deliver results of a query as fast as possible.  FIG. 13  is a high-level flowchart illustrating methods and techniques to implement speculative processing of a query at both primary and secondary query processing resources, according to some embodiments. As indicated at  1310 , a query may be received at a query engine that is eligible to be performed at a secondary query engine, in some embodiments. For example, as discussed above with regard to  FIG. 9 , features such as the type of query (or the source of the query) may be used to determine eligibility. 
     Availability of both the primary query engine and a secondary query engine may be evaluated. In the event that multiple secondary query engines are provisioned for performing queries to the database, the availability of the multiple secondary query engines may be evaluated. As indicated at  1320 , a determination may be made that both the primary and secondary query engine(s) are available to perform the query, in some embodiments. 
     As indicated at  1330 , the query engine may be performed at the primary query engine. Concurrently, the query may also be performed at the secondary query engine, as indicated at  1340 , in some embodiments. For example, the query may be forwarded to the secondary query engine. In some embodiments, in addition to availability, a feature or setting may be enabled to allow concurrent processing of the query at multiple query engines may be enabled (e.g., by a user or according to a workload/availability/cost evaluation for the query). In some embodiments, other determinations may be performed to determine whether to speculatively perform the query at both query engines, such as a determination that a size classification cannot be performed for the query (or performed with confidence above some threshold value) for one or both query engines. 
     As indicated at  1350 , a result may be returned from the first query engine to complete the query, in some embodiments. In some embodiments, metadata or performance history may be updated to reflect the “winning” query engine so that if the query is received again that same query engine may be selected (instead of concurrently performing the query). 
     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. 14 ) that includes one or more processors executing program instructions stored on a computer-readable storage medium coupled to the processors. The program instructions may 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. 
     Embodiments of dynamically assigning queries to secondary processing resources 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. 14 . 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 node, compute node, computing device, compute device, 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 that execute 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. 14 , memory  2020  may include program instructions  2025 , that 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. 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&#39;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.