U.S. patent application number 15/470834 was filed with the patent office on 2018-03-01 for stateful resource pool management for job execution.
This patent application is currently assigned to Amazon Technologies, Inc.. The applicant listed for this patent is Amazon Technologies, Inc.. Invention is credited to Jason Douglas Denton, Jian Fang, Pratik Bhagwat Gawande, Turkay Mert Hocanin, Yufeng Jiang, Bhargava Ram Kalathuru, Sumeetkumar Veniklal Maru, Luca Natali, Rahul Sharma Pathak, Abhishek Rajnikant Sinha, Armen Tangamyan, Xing Wu, Yuanyuan Yue.
Application Number | 20180060132 15/470834 |
Document ID | / |
Family ID | 61242674 |
Filed Date | 2018-03-01 |
United States Patent
Application |
20180060132 |
Kind Code |
A1 |
Maru; Sumeetkumar Veniklal ;
et al. |
March 1, 2018 |
STATEFUL RESOURCE POOL MANAGEMENT FOR JOB EXECUTION
Abstract
Stateful resource pool management may be implemented for
executing jobs. Metrics for pools of computing resources that are
configured to execute jobs on behalf of network-based services may
be collected. The metrics may be evaluated to detect a modification
event for a pool of computing resources. The pool of computing
resources may then be modified according to the detected
modification event for the pool. Evaluation of metrics may be
performed automatically as part of monitoring a resource pool, in
some embodiments.
Inventors: |
Maru; Sumeetkumar Veniklal;
(Redmond, WA) ; Kalathuru; Bhargava Ram; (Seattle,
WA) ; Fang; Jian; (Sammamish, WA) ; Wu;
Xing; (Redmond, WA) ; Yue; Yuanyuan;
(Bellevue, WA) ; Gawande; Pratik Bhagwat;
(Seattle, WA) ; Hocanin; Turkay Mert; (New York,
NY) ; Denton; Jason Douglas; (Seattle, WA) ;
Natali; Luca; (Kenmore, WA) ; Pathak; Rahul
Sharma; (Seattle, WA) ; Sinha; Abhishek
Rajnikant; (Redmond, WA) ; Tangamyan; Armen;
(Bellevue, WA) ; Jiang; Yufeng; (Sammamish,
WA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Amazon Technologies, Inc. |
Seattle |
WA |
US |
|
|
Assignee: |
Amazon Technologies, Inc.
Seattle
WA
|
Family ID: |
61242674 |
Appl. No.: |
15/470834 |
Filed: |
March 27, 2017 |
Related U.S. Patent Documents
|
|
|
|
|
|
Application
Number |
Filing Date |
Patent Number |
|
|
62382477 |
Sep 1, 2016 |
|
|
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06F 2209/503 20130101;
H04L 29/08135 20130101; G06F 16/2455 20190101; G06F 9/5022
20130101; G06F 9/5061 20130101; G06F 16/2471 20190101; G06F 16/25
20190101; H04L 29/08171 20130101; G06F 9/5088 20130101; G06F
2209/5011 20130101; H04L 67/10 20130101; G06F 9/5044 20130101; G06F
16/24553 20190101; G06F 16/248 20190101; G06F 16/282 20190101; H04L
67/1008 20130101; H04L 67/1029 20130101; G06F 9/50 20130101; G06F
16/24545 20190101; G06F 16/27 20190101; G06F 2209/508 20130101;
G06F 16/245 20190101; G06F 2209/501 20130101; H04L 29/08261
20130101; G06F 9/5027 20130101; H04L 67/1031 20130101; G06F 16/211
20190101; G06F 9/505 20130101; G06F 9/5055 20130101; H04L 29/0827
20130101; G06F 16/90335 20190101; G06F 16/24549 20190101; G06F
16/20 20190101 |
International
Class: |
G06F 9/50 20060101
G06F009/50; H04L 29/08 20060101 H04L029/08; G06F 17/30 20060101
G06F017/30 |
Claims
1. A system, comprising: a memory to store program instructions
which, if performed by at least one processor, cause the at least
one processor to perform a method to at least: monitor metrics for
one or more pools of computing resources for a network-based
service, wherein the pools of computing resources execute jobs
selectively routed by the network-based service to different ones
of the computing resources; based on the monitoring, detect a
modification event for one of the pools; and in response to the
detection of the modification event, add or remove one or more
computing resources for the one pool.
2. The system of claim 1, wherein the method further comprises:
prior to executing jobs routed from the network-based service,
instantiate and configure computing resources for the one pool of
computing resources.
3. The system of claim 1, wherein the method further comprises
receive a request that specifies one or more criteria for detecting
the modification event for the one pool.
4. The system of claim 1, wherein the at least one processor is a
resource management service, wherein the network-based service is a
managed query service, and wherein the jobs are queries executed
with respect to data sets stored in a data storage service, wherein
the resource management service, managed query service, and the
data storage service are implemented as part of a same provider
network.
5. A method, comprising: performing, by one or more computing
devices: obtaining a plurality of metrics for one or more pools of
computing resources for a network-based service, wherein the pools
of computing resources execute jobs selectively routed by the
network-based service to different ones of the computing resources;
evaluating the metrics of the pools of computing resources to
detect a modification event for at least one of the pools of
computing resources; and modifying the at least one pool of
computing resources according to the detected modification
event.
6. The method of claim 5, wherein the modification event is a
decommissioning event, and wherein the modifying the at least one
pool of computing resources according to the detected modification
event comprises decommissioning the computing resources of the
pool.
7. The method of claim 5, wherein the modifying the at least one
pool of computing resources according to the detected modification
event comprises adding a computing resource to the at least one
pool of computing resources.
8. The method of claim 5, further comprising receiving a request
that specifies one or more criteria for detecting the modification
event for the one pool.
9. The method of claim 5, further comprising: causing a computing
resource from the at least one pool that completed execution of a
job assigned to the computing resource to be scrubbed; and
assigning the computing resource back to the at least one pool to
be available to execute a different job.
10. The method of claim 5, further comprising receiving a request
that identifies a configuration for the computing resources of the
at least one pool of computing resources, wherein the computing
resources are instantiated and configured for the at least one pool
according to the identified configuration.
11. The method of claim 5, wherein evaluating the metrics of the
pools of computing resources to detect the modification event
determining an aggregate metric for the at least one pool based on
individual metrics for the computing resources of the at least one
pool.
12. The method of claim 5, wherein different ones of the pools of
computing resources comprise computing resources configured to
execute different types of jobs.
13. The method of claim 5, wherein the pools of computing resources
comprise a plurality of clusters for distributed execution of
queries with respect to remotely stored data, and wherein the jobs
selectively routed to the pools of computing resources are
queries.
14. A non-transitory, computer-readable storage medium, storing
program instructions that when executed by one or more computing
devices cause the one or more computing devices to implement:
obtaining a plurality of metrics for one or more pools of computing
resources for a network-based service, wherein the pools of
computing resources execute jobs selectively routed by the
network-based service to different ones of the computing resources;
evaluating the metrics of the pools of computing resources to
detect a modification event for at least one of the pools of
computing resources; and modifying the at least one pool of
computing resources according to the detected modification
event.
15. The non-transitory, computer-readable storage medium of claim
14, wherein the program instructions cause the one or more
computing devices to implement: prior to executing jobs routed from
the network-based service, instantiate and configure computing
resources for the at least one pool of computing resources.
16. The non-transitory, computer-readable storage medium of claim
14, wherein, in the modifying the at least one pool of computing
resources according to the detected modification event, the program
instructions cause the one or more computing devices to implement
removing a computing resource from the at least one pool of
computing resources.
17. The non-transitory, computer-readable storage medium of claim
14, wherein the program instructions cause the one or more
computing devices to further implement: causing a computing
resource from the at least one pool that completed execution of a
job assigned to the computing resource to be scrubbed; and
assigning the computing resource back to the at least one pool to
be available to execute a different job.
18. The non-transitory, computer-readable storage medium of claim
14, wherein the program instructions cause the one or more
computing devices to implement receiving a request that identifies
a configuration for the computing resources of the at least one
pool of computing resources, wherein the computing resources are
instantiated and configured for the at least one pool according to
the identified configuration.
19. The non-transitory, computer-readable storage medium of claim
14, wherein the pools of computing resources comprise a plurality
of clusters for distributed execution of queries with respect to
remotely stored data, wherein the jobs selectively routed to the
pools of computing resources are queries, and wherein different
ones of the pools comprise clusters of different sizes.
20. The non-transitory, computer-readable storage medium of claim
14, wherein the one or more computing devices are implemented as
part of a resource management service, wherein the network-based
service is a managed query service, wherein the jobs are queries
executed with respect to data sets stored in a data storage
service, wherein the resource management service, managed query
service, and the data storage service are implemented as part of a
same provider network.
Description
RELATED APPLICATIONS
[0001] This application claims benefit of priority to U.S.
Provisional Application Ser. No. 62/382,477, entitled "Managed
Query Service," filed Sep. 1, 2016, and which is incorporated
herein by reference in its entirety.
BACKGROUND
[0002] Computing systems for querying of large sets of data can be
extremely difficult to implement and maintain. In many scenarios,
for example, it is necessary to first create and configure the
infrastructure (e.g. server computers, storage devices, networking
devices, etc.) to be used for the querying operations. It might
then be necessary to perform extract, transform, and load ("ETL")
operations to obtain data from a source system and place the data
in data storage. It can also be complex and time consuming to
install, configure, and maintain the database management system
("DBMS") that performs the query operations. Moreover, many DBMS
are not suitable for querying extremely large data sets in a
performant manner.
[0003] Computing clusters can be utilized in some scenarios to
query large data sets in a performant manner. For instance, a
computing cluster can have many nodes that each execute a
distributed query framework for performing distributed querying of
a large data set. Such computing clusters and distributed query
frameworks are, however, also difficult to implement, configure,
and maintain. Moreover, incorrect configuration and/or use of
computing clusters such as these can result in the non-optimal
utilization of processor, storage, network and, potentially, other
types of computing resources.
[0004] The disclosure made herein is presented with respect to
these and other considerations.
BRIEF DESCRIPTION OF THE DRAWINGS
[0005] FIG. 1 illustrates a logical block diagram of stateful
resource pool management for job execution, according to some
embodiments.
[0006] FIG. 2 is a logical block diagram illustrating a provider
network offering a resource management service for performing
stateful pool management for jobs executed on behalf of other
network-based services in the provider network, according to some
embodiments.
[0007] FIG. 3 is a logical block diagram illustrating a managed
query service, according to some embodiments.
[0008] FIG. 4 is a diagram illustrating interactions between
clients and managed query service, according to some
embodiments.
[0009] FIG. 5 is a sequence diagram for managed execution of
queries, according to some embodiments.
[0010] FIG. 6 is a sequence diagram for managed execution of
queries utilizing a resource planner, according to some
embodiments.
[0011] FIG. 7 is a logical block diagram illustrating a cluster
processing a query as part of managed query execution, according to
some embodiments.
[0012] FIG. 8 is a logical block diagram illustrating a resource
management service, according to some embodiments.
[0013] FIG. 9 is logical block diagram illustrating interactions
between a resource management service and pools of resources,
according to some embodiments.
[0014] FIG. 10 is a state diagram illustrating different resource
pool states tracked by a resource manager service, according to
some embodiments.
[0015] FIG. 11 is a state diagram illustrating different computing
resource states, according to some embodiments according to some
embodiments.
[0016] FIG. 12 is a high-level flowchart illustrating various
methods and techniques to implement stateful management of
resources pools executing jobs, according to some embodiments.
[0017] FIG. 13 is a high-level flowchart illustrating techniques to
monitor a resource pool for modification events according to some
embodiments.
[0018] FIGS. 14A-4C describe various techniques for managing a pool
of computing resources for executing queries, according to some
embodiments.
[0019] FIG. 15 is a logical block diagram that shows an
illustrative operating environment that includes a service provider
network that can be configured to implement aspects of the
functionality described herein, according to some embodiments.
[0020] FIG. 16 is a logical block diagram illustrating a
configuration for a data center that can be utilized to implement
aspects of the technologies disclosed herein, according to some
embodiments.
[0021] FIG. 17 illustrates an example system configured to
implement the various methods, techniques, and systems described
herein, according to some embodiments.
[0022] 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.
[0023] 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
[0024] Various embodiments of a stateful resource pool management
for job execution are described herein. FIG. 1 illustrates a
logical block diagram of stateful resource pool management for job
execution, according to some embodiments. Pool(s) 130 of computing
resource(s) 140 may be instantiated, configured, and otherwise
prepared for executing different types of job(s) 170 on behalf of
network-based service(s) 120, in various embodiments. For example,
a query management service, such as discussed below with regard to
FIGS. 2-8, may utilize computing resource(s) 140 from different
pool(s) 130 in order to execute queries with respect to remotely
stored data, in some embodiments. Other types of processing jobs,
such as Extract Transform Load (ETL), data validation, log
analysis, simulation, numerical analysis, text analysis, machine
learning, or other statistical analysis, may be managed, performed,
or otherwise executed on behalf of different network-based
services, in some embodiments. As the configurations, operations,
or requirements of computing resources to execute such job(s) 170
may be costly or time consuming to procure, pool management for job
execution resources 110 may provide a dynamically managed set of
computing resource(s) 140 in respective pool(s) 130 that are
pre-configured and available for executing job(s) 170 without
requiring network-based service(s) 120 to directly manage the
number of computing resource(s) used by the network-based
service(s) 120, in various embodiments.
[0025] For instance, as illustrated in FIG. 1, pool management for
job execution 110 may create 190 pool(s) of computing resources
140, which may be single or multi-node clusters, virtualized
servers, instantiated execution platforms, query engines,
processing frameworks, or any other set of one or more resource(s)
that can execute job(s) 170 selectively routed to computing
resource(s) 140, in one embodiment. Computing resource(s) 140 may
interact with other services, data stores, or computing resources
(not illustrated), such as accessing remotely stored data, or
invoking functions, operations, or processes executed by a separate
system, in some embodiments. Pool management for job execution
resources 110 may then provide the pools of resources 130 to
network-based service(s) 120 for job execution. For example, pool
management for job execution 110 may implement an interface, such
as discussed below with regard to FIG. 9, via which network-based
service(s) 120 can programmatically get resource(s) 150 for
executing a job 170, in one embodiment. Pool management for job
execution resources 110 may identify a pool 130 and computing
resource(s) 140 within the pool to execute the job 170 for the
network-based service 120 and provide the resource(s) 160 in
response to the request, in one embodiment. For example, pool
management for job execution resource(s) may identify a pool 130
specially provisioned for the network-based 120 service or a pool
130 provisioned for the type of job to be executed by the
network-based service 120, in one embodiment. Pool management for
job execution resources 110 may then randomly assign a resource
from the pool, or may deterministically select a resource (e.g.,
based on characteristics of the computing resource, network-based
service, or job), in one embodiment, such as a type of computing
resource that implements a particular type of query engine for
processing a job that is a query. Once the resource(s) 160 are
provided to network-based service(s) 120 (e.g., by providing an
identifier or access credential for reaching the resource).
[0026] As noted above, pool management for job execution
resource(s) 110 may dynamically manage computing resource(s) 140
and pool(s) 130. For example, pool management for job execution
resource 110 may collect metric(s) 180 for a pool 130 of computing
resources 140. For instance, various kinds of data events for
individual computing resource(s) 140, like performance utilization
metrics for processor capacity, network-bandwidth, storage
capacity, I/O bandwidth, health metrics for the computing
resource(s) itself (e.g., start up time) or the environment of the
computing resource(s) (e.g., network events), job execution status
or state indications, or other information may be provided as part
of metric(s) 180, in some embodiments. Pool management for job
execution resource(s) 110 may collect, aggregate, and analyze
metric(s) 180 for different pools 130, in one embodiment. For
example, pool management for job execution resource(s) 110 may
determine the average time it takes for a computing resource to
clean up, scrub, or otherwise prepare to accept a new job for
execution, in one embodiment. Based on such an average time, pool
management for job execution resource(s) may increase or decrease
the rate at which computing resource(s) 140 are added to pool(s)
130. As discussed below with regard to FIGS. 10-11, pool management
for job execution resource(s) 110 may track the state of the pool
(e.g., based on event data or other metrics) and the state of
resources in the pool (e.g., based on event data or other
metric(s), in one embodiment.
[0027] Pool management for job execution resources 110 may modify
pool(s) 190 based on modification events detected for a pool based
on metric(s) 180. For example, based on health or other liveness
metrics for computing resource(s) 140 of a given pool, pool
management for job execution resource(s) 110 may add or remove
computing resource(s) 140 from a pool 130 so that the pool 130
maintains an efficient number of computing resource(s) based on job
execution demand for that pool. Modification events may trigger
different changes to or affect the operation of a pool 130. For
example, modification events may be triggered or detected based on
life cycle events or states of a pool 130 (e.g., a pool in warm up
state triggers the addition of computing resources, while a pool in
a decommission state, triggers the scrubbing or releasing of
resources from the pool to discontinue the pool). In some
embodiments, modification events may be detected or triggered based
on or more modification event criteria (e.g., aggregate metric
values for a pool 130 compared to a threshold, a state of the pool
or aggregation of state for individual resources in the pool
compared to state conditions). Modification event criteria may be
determined based on machine learning or other statistical analyses
of historical pool metrics or may be specified according to an
interface that allows a user of a pool (e.g., an administrator or
develop of a network-based service 120) to specify the event
criteria for different types of modification events (e.g., values
for thresholds that trigger the adding or removing of resources
from a pool), in some embodiments.
[0028] Please note that the previous description of stateful
resource pool management for executing jobs is a logical
illustration and thus is not to be construed as limiting as to the
implementation of a network-based service, pool of computing
resources, pool of computing resources, or pool management for job
execution resources.
[0029] This specification begins with a general description of a
provider network that implements a resource management service that
provides stateful pool management for the execution of jobs that
are queries received from another network-based service, a managed
query service. Then various examples of the managed query service
and resource management service (along with other services that may
be utilized or implemented) including different components/modules,
or arrangements of components/module that may be employed as part
of implementing the services are discussed. A number of different
methods and techniques to implement stateful pool management for
the execution of jobs 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.
[0030] FIG. 2 is a logical block diagram illustrating a provider
network offering a resource management service for performing
stateful pool management for jobs executed on behalf of other
network-based services in the provider network, according to some
embodiments. Provider network 200 may be a private or closed system
or may be set up by an entity such as a company or a public sector
organization to provide one or more services (such as various types
of cloud-based storage) accessible via the Internet and/or other
networks to clients 250, in some embodiments. Provider network 200
may be implemented in a single location or may include numerous
data centers hosting various resource pools, such as collections of
physical and/or virtualized computer servers, storage devices,
networking equipment and the like (e.g., FIGS. 15, 16 and computing
system 2000 described below with regard to FIG. 17), needed to
implement and distribute the infrastructure and storage services
offered by the provider network 200. In some embodiments, provider
network 200 may implement various computing resources or services,
such as a virtual compute service 210, data processing service(s)
220, (e.g., relational or non-relational (NoSQL) database query
engines, map reduce processing, data flow processing, and/or other
large scale data processing techniques), data storage service(s)
230, (e.g., an object storage service, block-based storage service,
or data storage service that may store different types of data for
centralized access) other services 240 (any other type of network
based services (which may include various other types of storage,
processing, analysis, communication, event handling, visualization,
and security services not illustrated), managed query service 270,
data catalog service 280, and resource management service 290.
[0031] 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. 17 and described below. In
various embodiments, the functionality of a given system or service
component (e.g., a component of data storage service 230) may be
implemented by a particular node or may be distributed across
several nodes. In some embodiments, a given node may implement the
functionality of more than one service system component (e.g., more
than one data store component).
[0032] Virtual compute service 210 may be implemented by provider
network 200, in some embodiments. Virtual computing service 210 may
offer instances and according to various configurations for
client(s) 250 operation. A virtual compute instance may, for
example, comprise one or more servers with a specified
computational capacity (which may be specified by indicating the
type and number of CPUs, the main memory size, and so on) and a
specified software stack (e.g., a particular version of an
operating system, which may in turn run on top of a hypervisor). A
number of different types of computing devices may be used singly
or in combination to implement the compute instances and of
provider network 200 in different embodiments, including general
purpose or special purpose computer servers, storage devices,
network devices and the like. In some embodiments instance
client(s) 250 or other any other user may be configured (and/or
authorized) to direct network traffic to a compute instance.
[0033] Compute instances may operate or implement a variety of
different platforms, such as application server instances, Java.TM.
virtual machines (JVMs), general purpose or special-purpose
operating systems, platforms that support various interpreted or
compiled programming languages such as Ruby, Perl, Python, C, C++
and the like, or high-performance computing platforms) suitable for
performing client(s) 202 applications, without for example
requiring the client(s) 250 to access an instance. Applications (or
other software operated/implemented by a compute instance and may
be specified by client(s), such as custom and/or off-the-shelf
software.
[0034] In some embodiments, compute instances have different types
or configurations based on expected uptime ratios. The uptime ratio
of a particular compute instance may be defined as the ratio of the
amount of time the instance is activated, to the total amount of
time for which the instance is reserved. Uptime ratios may also be
referred to as utilizations in some implementations. If a client
expects to use a compute instance for a relatively small fraction
of the time for which the instance is reserved (e.g., 30%-35% of a
year-long reservation), the client may decide to reserve the
instance as a Low Uptime Ratio instance, and pay a discounted
hourly usage fee in accordance with the associated pricing policy.
If the client expects to have a steady-state workload that requires
an instance to be up most of the time, the client may reserve a
High Uptime Ratio instance and potentially pay an even lower hourly
usage fee, although in some embodiments the hourly fee may be
charged for the entire duration of the reservation, regardless of
the actual number of hours of use, in accordance with pricing
policy. An option for Medium Uptime Ratio instances, with a
corresponding pricing policy, may be supported in some embodiments
as well, where the upfront costs and the per-hour costs fall
between the corresponding High Uptime Ratio and Low Uptime Ratio
costs.
[0035] Compute instance configurations may also include compute
instances with a general or specific purpose, such as computational
workloads for compute intensive applications (e.g., high-traffic
web applications, ad serving, batch processing, video encoding,
distributed analytics, high-energy physics, genome analysis, and
computational fluid dynamics), graphics intensive workloads (e.g.,
game streaming, 3D application streaming, server-side graphics
workloads, rendering, financial modeling, and engineering design),
memory intensive workloads (e.g., high performance databases,
distributed memory caches, in-memory analytics, genome assembly and
analysis), and storage optimized workloads (e.g., data warehousing
and cluster file systems). Size of compute instances, such as a
particular number of virtual CPU cores, memory, cache, storage, as
well as any other performance characteristic. Configurations of
compute instances may also include their location, in a particular
data center, availability zone, geographic, location, etc. . . .
and (in the case of reserved compute instances) reservation term
length. Different configurations of compute instances, as discussed
below with regard to FIG. 3, may be implemented as computing
resources associated in different pools of resources managed by
resource management service 290 for executing jobs routed to the
resources, such as queries routed to select resources by managed
query service 270.
[0036] Data processing services 220 may be various types of data
processing services to perform different functions (e.g., query or
other processing engines to perform functions such as anomaly
detection, machine learning, data lookup, or any other type of data
processing operation). For example, in at least some embodiments,
data processing services 230 may include a map reduce service that
creates clusters of processing nodes that implement map reduce
functionality over data stored in one of data storage services 240.
Various other distributed processing architectures and techniques
may be implemented by data processing services 230 (e.g., grid
computing, sharding, distributed hashing, etc.). Note that in some
embodiments, data processing operations may be implemented as part
of data storage service(s) 230 (e.g., query engines processing
requests for specified data). Data processing service(s) 230 may be
clients of data catalog service 220 in order to obtain structural
information for performing various processing operations with
respect to data sets stored in data storage service(s) 230, as
provisioned resources in a pool for managed query service 270.
[0037] Data catalog service 280 may provide a catalog service that
ingests, locates, and identifies data and the schema of data stored
on behalf of clients in provider network 200 in data storage
services 230. For example, a data set stored in a non-relational
format may be identified along with a container or group in an
object-based data store that stores the data set along with other
data objects on behalf of a same customer or client of provider
network 200. In at least some embodiments, data catalog service 280
may direct the transformation of data ingested in one data format
into another data format. For example, data may be ingested into
data storage service 230 as single file or semi-structured set of
data (e.g., JavaScript Object Notation (JSON)). Data catalog
service 280 may identify the data format, structure, or any other
schema information of the single file or semi-structured set of
data. In at least some embodiments, the data stored in another data
format may be converted to a different data format as part of a
background operation (e.g., to discover the data type, column
types, names, delimiters of fields, and/or any other information to
construct the table of semi-structured data in order to create a
structured version of the data set). Data catalog service 280 may
then make the schema information for data available to other
services, computing devices, or resources, such as computing
resources or clusters configured to process queries with respect to
the data, as discussed below with regard to FIGS. 3-7.
[0038] Data storage service(s) 230 may implement different types of
data stores for storing, accessing, and managing data on behalf of
clients 250 as a network-based service that enables clients 250 to
operate a data storage system in a cloud or network computing
environment. For example, data storage service(s) 230 may include
various types of database storage services (both relational and
non-relational) for storing, querying, and updating data. Such
services may be enterprise-class database systems that are highly
scalable and extensible. Queries may be directed to a database in
data storage service(s) 230 that is distributed across multiple
physical resources, and the database system may be scaled up or
down on an as needed basis. The database system may work
effectively with database schemas of various types and/or
organizations, in different embodiments. In some embodiments,
clients/subscribers may submit queries in a number of ways, e.g.,
interactively via an SQL interface to the database system. In other
embodiments, external applications and programs may submit queries
using Open Database Connectivity (ODBC) and/or Java Database
Connectivity (JDBC) driver interfaces to the database system.
[0039] One data storage service 230 may be implemented as a
centralized data store so that other data storage services may
access data stored in the centralized data store for processing and
or storing within the other data storage services, in some
embodiments. A may provide storage and access to various kinds of
object or file data stores for putting, updating, and getting
various types, sizes, or collections of data objects or files. Such
data storage service(s) 230 may be accessed via programmatic
interfaces (e.g., APIs) or graphical user interfaces. A centralized
data store may provide virtual block-based storage for maintaining
data as part of data volumes that can be mounted or accessed
similar to local block-based storage devices (e.g., hard disk
drives, solid state drives, etc.) and may be accessed utilizing
block-based data storage protocols or interfaces, such as internet
small computer interface (iSCSI).
[0040] In at least some embodiments, one of data storage service(s)
230 may be a data warehouse service that utilizes a centralized
data store implemented as part of another data storage service 230.
A data warehouse service as may offer clients a variety of
different data management services, according to their various
needs. In some cases, clients may wish to store and maintain large
of amounts data, such as sales records marketing, management
reporting, business process management, budget forecasting,
financial reporting, website analytics, or many other types or
kinds of data. A client's use for the data may also affect the
configuration of the data management system used to store the data.
For instance, for certain types of data analysis and other
operations, such as those that aggregate large sets of data from
small numbers of columns within each row, a columnar database table
may provide more efficient performance. In other words, column
information from database tables may be stored into data blocks on
disk, rather than storing entire rows of columns in each data block
(as in traditional database schemes).
[0041] Managed query service 270, as discussed below in more detail
with regard to FIGS. 3-7, may manage the execution of queries on
behalf of clients so that clients may perform queries over data
stored in one or multiple locations (e.g., in different data
storage services, such as an object store and a database service)
without configuring the resources to execute the queries, in
various embodiments. Resource management service 290, as discussed
in more detail below with regard to FIGS. 8-14, may manage and
provide pools of computing resources for different services like
managed query service 270 in order to execute jobs on behalf the
different services, as discussed above with regard to FIG. 1.
[0042] Generally speaking, clients 250 may encompass any type of
client configurable to submit network-based requests to provider
network 200 via network 260, including requests for storage
services (e.g., a request to create, read, write, obtain, or modify
data in data storage service(s) 240, etc.) or managed query service
270 (e.g., a request to query data in a data set stored in data
storage service(s) 230). For example, a given client 250 may
include a suitable version of a web browser, or may include a
plug-in module or other type of code module that may execute as an
extension to or within an execution environment provided by a web
browser. Alternatively, a client 250 may encompass an application
such as a database application (or user interface thereof), a media
application, an office application or any other application that
may make use of storage resources in data storage service(s) 240 to
store and/or access the data to implement various applications. In
some embodiments, such an application may include sufficient
protocol support (e.g., for a suitable version of Hypertext
Transfer Protocol (HTTP)) for generating and processing
network-based services requests without necessarily implementing
full browser support for all types of network-based data. That is,
client 250 may be an application may interact directly with
provider network 200. In some embodiments, client 250 may generate
network-based services requests according to a Representational
State Transfer (REST)-style network-based services architecture, a
document- or message-based network-based services architecture, or
another suitable network-based services architecture.
[0043] In some embodiments, a client 250 may provide access to
provider network 200 to other applications in a manner that is
transparent to those applications. For example, client 250 may
integrate with an operating system or file system to provide
storage on one of data storage service(s) 240 (e.g., a block-based
storage service). However, the operating system or file system may
present a different storage interface to applications, such as a
conventional file system hierarchy of files, directories and/or
folders. In such an embodiment, applications may not need to be
modified to make use of the storage system service model. Instead,
the details of interfacing to the data storage service(s) 240 may
be coordinated by client 250 and the operating system or file
system on behalf of applications executing within the operating
system environment.
[0044] Clients 250 may convey network-based services requests
(e.g., access requests directed to data in data storage service(s)
240, operations, tasks, or jobs, being performed as part of data
processing service(s) 230, or to interact with data catalog service
220) to and receive responses from provider network 200 via network
260. In various embodiments, network 260 may encompass any suitable
combination of networking hardware and protocols necessary to
establish network-based-based communications between clients 250
and provider network 200. For example, network 260 may generally
encompass the various telecommunications networks and service
providers that collectively implement the Internet. Network 260 may
also include private networks such as local area networks (LANs) or
wide area networks (WANs) as well as public or private wireless
networks. For example, both a given client 250 and provider network
200 may be respectively provisioned within enterprises having their
own internal networks. In such an embodiment, network 260 may
include the hardware (e.g., modems, routers, switches, load
balancers, proxy servers, etc.) and software (e.g., protocol
stacks, accounting software, firewall/security software, etc.)
necessary to establish a networking link between given client 250
and the Internet as well as between the Internet and provider
network 200. It is noted that in some embodiments, clients 250 may
communicate with provider network 200 using a private network
rather than the public Internet.
[0045] FIG. 3 is a logical block diagram illustrating a managed
query service, according to some embodiments. As discussed below
with regard to FIGS. 4-9, managed query service 270 may leverage
the capabilities of various other services in provider network 200.
For example, managed query service 270 may utilize resource
management service 290 to provision and manage pools of
preconfigured resources to execute queries, provide resources of
preconfigured queries, and return utilized resources to
availability. For example, resource management service 290 may
instantiate, configure, and provide resource pool(s) 350a and 350n
that include pool resource(s) 352a and 352n from one or more
different resource services, such as computing resource(s) 354 in
virtual compute service 210 and computing resource(s) 356 in data
processing service(s) 220. Resource management service 290 may send
requests to create, configure, tag (or otherwise associate)
resources 352 for a particular resource pool, terminate, reboot,
otherwise operate resources 352 in order to execute jobs on behalf
of other network-based services.
[0046] Once a resource from a pool is provided (e.g., by receiving
an identifier or other indicator of the resource to utilize),
managed query service 270 may interact directly with the resource
354 in virtual compute service 210 or the resource 356 in data
processing services 220 to execute queries, in various embodiments.
Managed query service 270 may utilize data catalog service 280, in
some embodiments to store data set schemas 352, as discussed below
with regard to FIGS. 4, for subsequent use when processing queries,
as discussed below with regard to FIGS. 5-7, in some embodiments.
For example, a data set schema may identify the field or column
data types of a table as part of a table definition so that a query
engine (executing on a computing resource), may be able to
understand the data being queried, in some embodiments. Managed
query service 270 may also interact with data storage service(s)
230 to directly source data sets 370 or retrieve query results 380,
in some embodiments.
[0047] Managed query service 270 may implement a managed query
interface 310 to handle requests from different client interfaces,
as discussed below with regard to FIG. 4. For example, different
types of requests, such as requests formatted according to an
Application Programmer Interface (API), standard query protocol or
connection, or requests received via a hosted graphical user
interface implemented as part of managed query service may be
handled by managed query interface 310.
[0048] Managed query service 270 may implement managed query
service control plane 320 to manage the operation of service
resources (e.g., request dispatchers for managed query interface
310, resource planner workers for resource planner 330, or query
tracker monitors for query tracker 340). Managed query service
control plane 320 may direct requests to appropriate components as
discussed below with regard to FIGS. 5 and 6. Managed query service
270 may implement authentication and authorization controls for
handling requests received via managed query interface 310. For
example, managed query service control plane 320 may validate the
identity or authority of a client to access the data set identified
in a query received from a client (e.g., by validating an access
credential). In at least some embodiments, managed query service
control plane 320 may maintain (in an internal data store or as
part of a data set in an external data store, such as in one of
data storage service(s) 230), query history, favorite queries, or
query execution logs, and other managed query service historical
data. Query execution costs may be billed, calculated or reported
by managed query service control plane 320 to a billing service
(not illustrated) or other system for reporting usage to users of
managed query service, in some embodiments.
[0049] Managed query service 270 may implement resource planner 330
to intelligently select available computing resources from pools
for execution of queries, in some embodiments. For example,
resource planner 330 may evaluated collected data statistics
associated with query execution (e.g., reported by computing
resources) and determine an estimated number or configuration of
computing resources for executing a query within some set of
parameters (e.g., cost, time, etc.). For example, machine learning
techniques may be applied by resource planner 330 to generate a
query estimation model that can be applied to the features of a
received query to determine the number/configuration of resources,
in one embodiment. Resource planner 330 may then provide or
identify which ones of the resources available to execute the query
from a pool may best fit the estimated number/configuration, in one
embodiment.
[0050] In various embodiments, managed query service 270 may
implement query tracker 340 in order to manage the execution of
queries at compute clusters, track the status of queries, and
obtain the resources for the execution of queries from resource
management service 290. For example, query tracker 340 may maintain
a database or other set of tracking information based on updates
received from different managed query service agents implemented on
provisioned computing resources (e.g., computing clusters as
discussed below with regard to FIGS. 5-7). In some embodiments,
query tracker may
[0051] FIG. 4 is a diagram illustrating interactions between
clients and managed query service, according to some embodiments.
Client(s) 400 may be client(s) 250 in FIG. 2 above or other clients
(e.g., other services systems or components implemented as part of
provider network 200 or as part of an external service, system, or
component, such as data exploration or visualization tools (e.g.,
Tableau, Looker, MicroStrategy, Qliktech, or Spotfire). Clients 400
can send various requests to managed query service 270 via managed
query interface 310. Managed query interface 310 may offer a
management console 440, which may provider a user interface to
submit queries 442 (e.g., graphical or command line user
interfaces) or register data schemas 444 for executing queries. For
example, management console 440 may be implemented as part of a
network-based site (e.g., an Internet website for provider network
200) that provides various graphical user interface elements (e.g.,
text editing windows, drop-down menus, buttons, wizards or
workflows) to submit queries or register data schemas. Managed
query interface 310 may implement programmatic interfaces 410
(e.g., various Application Programming
[0052] Interface (API) commands) to perform queries, and various
other illustrated requests. In some embodiments, managed query
interface 310 may implement custom drivers that support standard
communication protocols for querying data, such as JDBC driver 430
or ODBC driver 420.
[0053] Clients 400 can submit many different types of request to
managed query interface 310. For example, in one embodiment,
clients 400 can submit requests 450 to create, read, modify, or
delete data schemas. For example, a new table schema can be
submitted via a request 450. Request 450 may include a name of the
data set (e.g., table), a location of the data set (e.g. an object
identifier in an object storage service, such as data storage
service 230, file path, uniform resource locator, or other location
indicator), number of columns, column names, data types for fields
or columns (e.g., string, integer, Boolean, timestamp, array, map,
custom data types, or compound data types), data format (e.g.,
formats including, but not limited to, JSON, CSV, AVRO, ORC,
PARQUET, tab delimited, comma separated, as well as custom or
standard serializers/desrializers), partitions of a data set (e.g.,
according to time, geographic location, or other dimensions), or
any other schema information for process queries with respect to
data sets, in various embodiments. In at least some embodiments,
request to create/read/modify/delete data set schemas may be
performed using a data definition language (DDL), such as Hive
Query Language (HQL). Managed query interface 310 may perform
respective API calls or other requests 452 with respect to data
catalog service 280, to store the schema for the data set (e.g., as
part of table schemas 402). Table schemas 402 may be stored in
different formats (e.g., Apache Hive). Note, in other embodiments,
managed query service 270 may implement its own metadata store.
[0054] Clients 400 may also send queries 460 and query status 470
requests to managed query interface 310 which may direct those
requests 460 and 470 to managed query service control plane 320, in
various embodiments, as discussed below with regard to FIGS. 5 and
6. Queries 460 may be formatted according to various types of query
languages, such as Structured Query Language (SQL) or HQL.
[0055] Client(s) 400 may also submit requests for query history 480
or other account related query information (e.g., favorite or
common queries) which managed query. In some embodiments, client(s)
400 may programmatically trigger the performance of past queries by
sending a request to execute a saved query 490, which managed query
service control plane 320 may look-up and execute. For example,
execute saved query request may include a pointer or other
identifier to a query stored or saved for a particular user account
or client. Managed query service control plane 320 may then access
that user query store to retrieve and execute the query (according
to techniques discussed below with regard to FIGS. 5-7).
[0056] FIG. 5 is a sequence diagram for managed execution of
queries, according to some embodiments. Query 530 may be received
at managed query service control plane 320 which may submit the
query 532 to query tracker 340 indicating the selected cluster 536
for execution. Query tracker 340 may lease a cluster 534 from
resource management service 290, which may return a cluster 536.
Resource management service 290 and query tracker 340 may maintain
lease state information for resources that are leased by query
tracker and assigned to execute received queries. Query tracker 340
may then initiate execution of the query 538 at the provisioned
cluster 510, sending a query execution instruction to a managed
query agent 512.
[0057] Managed query agent 512 may get schema 540 for the data
sets(s) 520 from data catalog service 280, which may return the
appropriate schema 542 (e.g., implementing a query processing
technique that applies schema on-read of data from data set(s)).
Provisioned cluster 510 can then generate a query execution plan
and execute the query 544 with respect to data set(s) 520 according
to the query plan. Managed query agent 512 may send query status
546 to query tracker 340 which may report query status 548 in
response to get query status 546 request, sending a response 550
indicating the query status 550. Provisioned cluster 510 may store
the query results 552 in a result store 522 (which may be a data
storage service 230). Managed query service control plane 320 may
receive q request to get a query results 554 and get query results
556 from results store 522 and provide the query results 558 in
response, in some embodiments.
[0058] FIG. 6 is a sequence diagram for managed execution of
queries utilizing a resource planner, according to some
embodiments. Query 630 may be received at managed query service
control plane 320 which may submit the query 632 to resource
planner 340. Resource planner 340 may analyze the query to
determine the optimal cluster to process the query based on
historical data for processing queries and available cluster(s) 634
received from resource management service 290. Resource planner 340
may then select a query and submit the query to query tracker 340
indicating the selected cluster 636 for execution. Query tracker
340 may then initiate execution of the query 638 at the provisioned
cluster 610, sending a query execution instruction to a managed
query agent 612.
[0059] Managed query agent 612 may get schema 640 for the data
sets(s) 620 from data catalog service 280, which may return the
appropriate schema 642. Provisioned cluster 610 can then generate a
query execution plan and execute the query 644 with respect to data
set(s) 620 according to the query plan. Managed query agent 612 may
send query status 646 to query tracker 340 which may report query
status 648 in response to get query status 646 request, sending a
response 650 indicating the query status 650. Provisioned cluster
610 may store the query results 652 in a result store 622 (which
may be a data storage service 230). Managed query service control
plane 320 may receive q request to get a query results 654 and get
query results 656 from results store 622 and provide the query
results 658 in response, in some embodiments.
[0060] Different types of computing resources may be provisioned
and configured in resource pools, in some embodiments. Single-node
clusters or multi-node compute clusters may be one example of a
type of computing resource provisioned and configured in resource
pools by resource management service 290 to service queries for
managed query service 270. FIG. 7 is a logical block diagram
illustrating a cluster processing a query as part of managed query
execution, according to some embodiments. Cluster 710 may implement
a computing node 720 that is a leader node (according to the query
engine 724 implemented by cluster 710). In some embodiments, no
single node may be a leader node, or the leader node may rotate
from processing one query to the next. Managed query agent 722 may
be implemented as part of leader node 720 in order to provide an
interface between the provisioned resource, cluster 710, and other
components of managed query service 270 and resource management
service 290. For example, managed query agent 722 may provide
further data to managed query service 270, such as the status 708
of the query (e.g. executing, performing I/O, performing
aggregation, etc.,) and metrics 706 (e.g., health metrics, resource
utilization metrics, cost metrics, length of time, etc.). In some
embodiments, managed query agent 722 may provide cluster/query
status 708 and execution metric(s) 706 to resource management
service 290 (in order to make pool management decisions, such as
modification events, lease requests, etc.). For example, managed
query agent 722 may indicate cluster status 708 to resource
management service 290 indicating that a query has completed and
that the cluster 710 is ready for reassignment (or other resource
lifecycle operations, as discussed below with regard to FIG.
10).
[0061] Leader node 720 may implement query engine 724 to execute
queries, such as query 702 which may be received via managed query
agent 722 as query 703. For instance, managed query agent may
implement a programmatic interface for query tracker to submit
queries (as discussed above in FIGS. 5 and 6), and then generate
and send the appropriate query execution instruction to query
engine 724. Query engine 724 may generate a query execution plan
for received queries 703. In at least some embodiments, leader node
720, may obtain schema information for the data set(s) 770 from the
data catalog service 280 or metadata stores for data 762 (e.g.,
data dictionaries, other metadata stores, other data processing
services, such as database systems, that maintain schema
information) for data 762, in order to incorporate the schema data
into the generation of the query plan and the execution of the
query. Leader node 720 may generate and send query execution
instructions 740 to computing nodes that access and apply the query
to data 762 in data store(s) 760. Compute nodes, such as nodes
730a, 730b, and 730n, may respectively implement query engines
732a, 732b, and 732n to execute the query instructions, apply the
query to the data 750, and return partial results 740 to leader
node 720, which in turn may generate and send query results 704.
Query engine 724 and query engines 732 may implement various kinds
of distributed query or data processing frameworks, such as the
open source Presto distributed query framework or the Apache Spark
framework.
[0062] FIG. 8 is a logical block diagram illustrating a resource
management service, according to some embodiments. Resource
management service 290 may be responsible for managing resource
pools, such as pools of resources that can execute queries on
behalf of managed query service 270, or pools of resources
configured to execute other types of jobs for other services (e.g.,
other services in provider network 200). For example, resource
management service 290 may implement a lease framework that
assigns, allocates, or otherwise leases a computing resource, such
as cluster 710 in FIG. 7, to a requesting network-based service
(e.g., when query tracker 340 submits a request for a lease to a
cluster to execute a query). Resource management service 290 may
track the leases of the different resources in the pools as part of
resource state 860. Resource management service 290 may
instantiate, configure and/or provision resource pools and monitor
their health.
[0063] Resource management service 290 may track the state of a
pool 850 in order to detect modification events for a pool. For
example, a resource pool may have a maximum pool size, a minimum
number of idle resources in the pool, and a maximum number of idle
resources in the pool, which may trigger modification events, as
discussed below with regard to FIG. 10. Resource management service
290 may monitor the state of resources 860 according to metrics and
other information collected from managed query agents. For example,
managed query agent 722 on clusters 710 in FIG. 7 may detect life
cycle or other pool management events and send them to the resource
management service 290. Once a leased resource is returned,
resource management service 290 may check or ensure that the
returned resource does not have any state related to previous job
executions and can be safely used for another job. Resource
management service 290 may direct reuse operations, such as a
scrubbing operation, as discussed below with regard to FIG.
14C.
[0064] Resource management service 290 may implement metric
collection 810, in various embodiments, to handle metrics and other
data events received for computing resources in pools. For example,
cluster and query execution state reported from clusters can be
processed by a group of metrics collection workers that categorize
and store metrics according to resource pool. In some embodiments,
metric collection 810 may perform initial metrics processing,
generating aggregated data values for a pool and storing them for
evaluation by pool monitoring 820. In some embodiments, performance
metric collection may poll or ping resources (e.g., by sending a
status request to a managed query agent) to check for liveness if
no metrics have been recorded for the resource within some period
of time. Performance metrics for resources may be recorded in
metrics store 870 (which may be internally implemented or
externally implemented data store (e.g., in storage service 230).
State information for resources, including reported state/lifecycle
changes discussed below with regard to FIG. 10, may be stored in
resource state 860.
[0065] Resource management service 290 may monitor resource pools
for changes, as discussed below with regard to FIGS. 11-14C. For
example, one or more modification events may be defined for a
resource pool based on one or more modification event criteria, in
some embodiments. Modification event criteria may include numbers
of resources in a particular state (e.g., pending, ready,
scrubbing, terminated, failed, etc.) compared to thresholds,
whether a resource is in a particular state, whether a pool is in a
particular state (e.g., warm up, available, cool down,
decommission). Pool monitoring 820 may, in some embodiments,
generate pool-focused statistics, such as average operation times,
median operation times, etc., which may be evaluated as part of
event criteria (e.g., average pool startup time exceed X
threshold). In at least some embodiments, pool monitoring 820 may
access resource state 860 or performance metric(s) 870 to detect a
modification event (e.g., checking whether network bandwidth
utilization has increased across a group of resources in a
pool).
[0066] Resource management service 290 may implement pool
management 840 to create and decommission pools, instantiate, tag,
and configure resources within pools, and remove, migrate, or
otherwise modify resources within pools, in various embodiments.
For example, pool management 840 may generate and send various
requests to other network-based services (e.g., virtual compute
service 210 and data processing service 220) to launch, configure,
or otherwise provision computing resources, in one embodiment. The
launched computing resources may be treated and managed as a pool
by resource management service 270 so that virtual compute service
210 and data processing service 220 are unaware of which resource
belongs to which pool. Pool management may access resource
configuration(s) 880 to determine how resources should be
configured. For example, a machine image, resource launch template,
configuration script, or other information to configure computing
resources for a pool may be maintained in resource configuration(s)
880, which pool management 840 may use to instantiate and configure
the computing resources (e.g., in the other services), in one
embodiment.
[0067] Pool management 840 may perform modification events to
resource pools based on the modification events detected by pool
monitoring 820. For example, modification events to add or remove
computing resources in a resource pool, reconfigure existing
resources, migrate resources from one pool to another or any other
modification event, as discussed below with regard to FIGS. 11-14C
may be performed by pool management 840 performing corresponding
requests or operations at other services (e.g., API requests to
launch more resources, terminate existing resources, pause
resources, reconfigure resources, etc.).
[0068] Resource management service 290 may implement resource
assignment 830 to handle requests for resources from resource
pools, in some embodiments. For example, resource assignment 830
may identify a pool for a resource request (e.g., based on an
identifier included in the request, such as a pool identifier or
user identifier). Resource assignment 830 may then select a
resource from the pool (e.g., randomly selecting an available
resource) or according to a deterministic selection technique
(e.g., a queue of resources). In at least some embodiments,
resource assignment 830 may utilize a leasing scheme, granting
resources to leases in a pool for a period of time. For example,
resource assignment may track lease states for resources. Lease
states may include ongoing (e.g., a job is executing), expired
(e.g., the time is up and the resource may be forcibly returned),
or terminated (e.g., job completed prior to lease expiration), in
some embodiments. Some modification events for a cluster, such as
returning a cluster to the pool, may be triggered based on a change
in lease state (e.g., a change to expired or terminated). Lease
states may be maintained as part of resource state store 860, in
some embodiments.
[0069] FIG. 9 is logical block diagram illustrating interactions
between a resource management service and pools of resources,
according to some embodiments. Resource management service 290 may
implement a programmatic interface (e.g., API) or other interface
that allows other network-based services (or a client or a provider
network) to submit requests for preconfigured resources from a
resource pool managed by resource management service 290. For
example, a request for a cluster 930 may be received (e.g., from
query tracker 340) to obtain a cluster to execute a query. Resource
management service 290 may determine the appropriate pool for the
request 930, a randomly (or selectively according to the techniques
discussed below with regard to FIG. 14B) determine a cluster for
servicing the request. Resource management service 290 may then
provide the identified cluster 940 (e.g., by specifying a location,
identifier, or other information for accessing the identified
computing resource. Resource management service may update state
information for the cluster to indicate that the cluster is leased
or otherwise unavailable. Resource management service 290 may also
receive requests to release a cluster 950 from a current
assignment. Resource management service 290 may then update state
information (e.g., the lease) for the cluster and pool to return
the cluster to the pool, in some embodiments.
[0070] As indicated at 960, resource management service 290 may
automatically (or in response to requests (not illustrated)),
commission or decommission pool(s) of clusters 910. For example in
some embodiments, resource management service 290 may perform
techniques that select the number and size of computing clusters
920 for the warm cluster pool 910. The number and size of the
computing clusters 920 in the warm cluster pool 910 can be
determined based upon a variety of factors including, but not
limited to, historical and/or expected volumes of query requests,
the price of the computing resources utilized to implement the
computing clusters 920, and/or other factors or considerations, in
some embodiments.
[0071] Once the number and size of computing clusters 920 has been
determined, the computing clusters 920 may be instantiated, such as
through the use of an on-demand computing service, or virtual
compute service or data processing service as discussed above in
FIG. 2. The instantiated computing clusters 920 can then be
configured to process queries prior to receiving the queries at the
managed query service. For example, and without limitation, one or
more distributed query frameworks or other query processing engines
can be installed on the computing nodes in each of the computing
clusters 920. As discussed above, in one particular implementation,
the distributed query framework may be the open source PRESTO
distributed query framework. Other distributed query frameworks can
be utilized in other configurations. Additionally, distributed
processing frameworks or other query engines can also be installed
on the host computers in each computing cluster 920. As discussed
above, the distributed processing frameworks can be utilized in a
similar fashion to the distributed query frameworks. For instance,
in one particular configuration, the APACHE SPARK distributed
processing framework can also, or alternately, be installed on the
host computers in the computing clusters 920.
[0072] Instantiated and configured computing clusters 920 that are
available for use by the managed query service 270 are added to the
warm cluster pool 910, in some embodiments. A determination can be
made as to whether the number or size of the computing clusters 920
in the warm cluster pool needs is to be adjusted, in various
embodiments. The performance of the computing clusters 920 in the
warm cluster pool 910 can be monitored based on metric(s) 990
received from the cluster pool. The number of computing clusters
920 assigned to the warm cluster pool 910 and the size of each
computing cluster 920 (i.e. the number of host computers in each
computing cluster 920) in the warm cluster pool 910 can then be
adjusted. Such techniques can be repeatedly performed in order to
continually optimize the number and size of the computing clusters
920 in the warm cluster pool 910.
[0073] As indicated at 980, in some embodiments, resource
management service 270 may scrub clusters(s) 980, (e.g., as a
result of the lease state transitioning to expired or terminated)
by causing the cluster to perform operations (e.g., a reboot, disk
wipe, memory purge/dump, etc.) so that the cluster no longer
retains client data and is ready to process another query. For
example, resource management service 290 may determine whether a
computing cluster 920 is inactive (e.g. the computing cluster 920
has not received a query in a predetermined amount of time). If
resource management service 290 determines that the computing
cluster 920 is inactive, then the computing cluster 920 may be
disassociated from the submitter of the query. The computing
cluster 920 may then be "scrubbed," such as by removing data
associated with the submitter of the queries from memory (e.g. main
memory or a cache) or mass storage device (e.g. disk or solid state
storage device) utilized by the host computers in the computing
cluster 920. The computing cluster 920 may then be returned to the
warm cluster pool 910 for use in processing other queries. In some
embodiments, some clusters that are inactive might not be
disassociated from certain users in certain scenarios. In these
scenarios, the user may have a dedicated warm pool of clusters 910
available for their use.
[0074] As indicated at 960, in some embodiments, resource
management service 290 may receive requests to configure resources
or a pool of resources. For example, a request to configure a pool
of resources may identify a type or size of cluster, a processing
engine, machine image, or software to execute for individual
clusters in the pool. In some embodiments, the request may indicate
a maximum number of resources in the pool, a minimum number of idle
resources in the pool, and a maximum number of idle resources in
the pool. As indicated at 970, resource management service may
receive a request to configure or specify a pool modification event
for a pool, in some embodiments. For example, the pool modification
event may be defined according to one or more criteria, such as the
minimum number of idle resources, maximum number of idle resources,
average job execution time thresholds, pool or resource
lifecycle/state conditions, or any other set of one or more
criteria that may be evaluated to detect a pool modification
event.
[0075] As noted above, one or more pool modification events may be
detected or otherwise triggered based on the state of a resource
pool. FIG. 10 is a state diagram illustrating different resource
pool states tracked by a resource manager service, according to
some embodiments. Start state 1010 may be an initial state for a
resource pool that has been identified or determined for creation
(e.g., in response to a request to create a pool). Various
different characteristics may be implemented to determine state
transitions for a resource pool. In one embodiment, such
characteristics may include minimum idle resource count (e.g., a
minimum number of resources to keep available for clients), a
maximum idle resource count (e.g., a maximum number of resources to
keep available for clients), and an overall maximum resource count
(e.g., a maximum number of resources that can be a member of a
resource pool, whether available or leased to a client).
[0076] From start state 1010 a resource pool may either be
decommissioned (e.g., in response to a request to halt or block
creation of the resource pool), or transition to warmup state 1020.
Warmup state 1020 may be a state where the resource pool is under
provisioned, because the available or idle resource count for the
pool may be less than the minimum idle resource count. Modification
events to add resources to the resource pool may be triggered while
the resource pool is in warm up state 1020 (e.g., either triggered
only by the state being "warm up" or along with other criteria,
such as criteria to throttle or limit the rate at which resources
are added to a resource pool so as not to flood a resource service
when creating a new pool). When the resource count for the pool
equals the minimum resource count, then the resource pool may be in
available state 1030, in one embodiment. Available state 1030 may
be a stable state for the resource pool, neither over or under
provisioned. The resource pool may remain in available state 1030
while the resource count remains between the minimum and maximum
idle count. In some embodiments, if the resource count falls below
minimum idle count, then the resource pool may return to warm up
state 1020 (e.g., as a result of a large number of resources being
leased to clients). In some embodiments, a resource pool may remain
in warmup state unable to add new resources to a resource pool as
adding resources may be subject to the overall count of resources
(idle or leased) being less than the overall maximum count. In such
a scenario, when resources are released and returned to the pool,
the resource count may increase to return the resource pool to
available state 1030. In scenarios where a number of resources are
terminated, and removed from the pool, modification events to add
resources can commence (as the overall number of resources may be
less than the overall maximum).
[0077] A resource pool can transition to cool down state 1040, when
the resource pool becomes over provisioned (e.g., having a current
number of idle resources greater than the maximum idle count.
Modification events to remove resources from the pool may be
triggered (e.g., either triggered only by the state being "cool
down" or along with other criteria, such as criteria to throttle or
limit the rate at which resources are removed from a resource pool
so as not to over compensate in the event a number of resources
terminate in a short time span). A resource pool may be moved to
decommission state 1050 in order to stop leasing or providing
resources from the pool to clients. Leased resources in
decommissioned state, in some embodiments, may be allowed to
complete execution of the jobs running on the resource.
Decommission state 1050 may trigger a modification events to
perform decommission operations to halt, release, de-provision, or
otherwise remove resources from service for a resource pool until
the resource pool is empty, in one embodiment. The decommissioned
pool may then transition to decommissioned state 1060, which may be
logged or recorded in a resource pool history store to maintain a
record of the resource pool's existence.
[0078] In addition to the life cycle of a pool triggering
modification events, the lifecycle of resources may trigger
modification events. Such modification events may be detected
and/or performed at the resource (e.g., by an agent like managed
query agent 722 in FIG. 7). FIG. 11 is a state diagram for
resources implemented in a resource pool, according to some
embodiments. A resource may begin in start state 1110 awaiting
fulfillment. A pending resource 1120 may be a resource that has
been launched but is not yet configured for processing jobs (e.g.,
according to a configured specified for resources in the pool, such
as the query image, machine image, software applications, etc.). If
an error occurs while provisioning, then the resource may be in
failed state 1150, which would make the resource unable to be
available to process jobs as part of the pool (and may not be
counted for idle or overall resource count considerations, in some
embodiments. For example, a machine image may crash or fail to load
properly at one or more nodes in a cluster, in one embodiment,
failing the provisioning of the resource.
[0079] For resources that are successful configured to execute
jobs, the resource state may transition to ready 1130. In ready
state 1130, a resource may be idle and ready to execute a job. A
resource may transition out of ready state in the event of resource
failure (to failed state 1150) or in the event of the resource
being terminated (to terminated state 1160). Once leased or
assigned to the execution of a job, resource may move to executing
state 1135. Similar to ready state 1130, resource may transition
out of executing state 1135 in the event of resource failure (to
failed state 1150) or in the event of the resource being terminated
(to terminated state 1160). Termination of a resource may, in some
embodiments, occur after a time limit or other usage threshold that
limits the amount of work done by a given resource. In this way, a
resource that suffers from performance decline (e.g., due to age,
software errors that cause memory leaks or other performance
problems) or may be vulnerable to security breach can be terminated
(and replaced in the pool with another resource). Upon completing
execution of job, a resource may move to scrub state 1140, in some
embodiments. For example, a managed query agent may detect when a
cluster has completed execution of the query and report a query
completion status to resource management service 290. The managed
query agent may then initiate an operation to scrub the resource
for reuse in the resource pool (as discussed above and below with
regard to FIG. 14C). Scrubbed resources may return to resource pool
by becoming in ready state 1130. In some embodiments, a scrubbed
resource that fails to complete a scrub operation may move to
failed state 1150 or may be terminated (e.g., due to an age/time
limit for the resource).
[0080] Although FIGS. 2-11 have been described and illustrated in
the context of a provider network leveraging multiple different
services to implement resource management service to provide
stateful management of resource pools, the various components
illustrated and described in FIGS. 2-11 may be easily applied to
other systems, or devices that manage pools of configured
resources. As such, FIGS. 2-11 are not intended to be limiting as
to other embodiments of a system that may implement stateful
management of resource pools for executing jobs. FIG. 12 is a
high-level flowchart illustrating various methods and techniques to
implement stateful management of resources pools executing jobs,
according to some embodiments. Various different systems and
devices may implement the various methods and techniques described
below, either singly or working together. For example, a resource
management service as described above with regard to FIGS. 2-11 may
implement the various methods. Alternatively, a combination of
different systems and devices may implement these methods.
Therefore, the above examples and or any other systems or devices
referenced as performing the illustrated method, are not intended
to be limiting as to other different components, modules, systems,
or configurations of systems and devices.
[0081] As indicated at 1210, metrics may be obtained for pool(s) of
computing resources for a network-based service that are pools of
computing resources that execute jobs selectively routed by the
network-based service to different ones of the computing resources,
in various embodiments. Metrics may include various kinds of events
or data associated with a resource pool. For example, metrics may
include lifecycle events or state changes for a resource pool, as
discussed above with regard to FIG. 10 or lifecycle events or state
changes for a resource, as discussed above with regard to FIG. 11.
Metrics may include, in some embodiments, utilization, cost, speed,
or other performance or operational information for a resource
(e.g., processor utilization, network bandwidth utilization, etc.).
Metrics may include, in some embodiments, health metrics (e.g.,
failure information or states from individual resources, service or
provider network infrastructure) or information based on external
events, such as weather, power failures, or network events (e.g.,
network partitions).
[0082] As indicated at 1220, the metrics of the pools of computing
resources may be evaluated to detect a modification event for at
least one of the pools of the computing resources, in various
embodiments. For example, aggregate values (based on individual
resource metrics) may be generated (e.g., statistical values, such
as averaged values, median values, etc.) and compared with one or
more modification event criteria (e.g., threshold values, exact
values, etc.). Modification events may be detected according to one
or modification event criteria for the metrics. For example, an add
resource modification event may be detected if the idle resource
count is below a minimum idle threshold and if a resource addition
throttle time threshold has expired.
[0083] As indicated at 1230, the at least one resource pool of
computing resources may be modified according to the modification
event, in various embodiments. Modification events may include
events to perform any modification to the resources of a pool (e.g.
cluster resize, migration, addition, removal, reconfigure, etc.) or
the operation the resource pool (e.g., change the rate at which
resources are added, removed, change the selection of resources to
be leased, decommissioning of the resource pool, shrink the maximum
size of the resource pool, increase the maximum size of the
resource pool, block lease requests to the pool, etc.).
[0084] The techniques described above with regard to FIG. 12, may
be implemented as part of a manually triggered pool evaluation
process (e.g., by provider network or service administrator) or as
part of an automated pool management process that dynamically
modifies the pool according to detected modification events. FIG.
13 is a high-level flowchart illustrating techniques to monitor a
resource pool for modification events according to some
embodiments. As indicated at 1310, metrics for pool(s) of computing
resources may be monitored for a network-based service, in some
embodiments. For example, a pool monitor, such as pool monitor 820
in FIG. 8 above, may proactively evaluate the operation of a pool
according to the various types of metrics discussed above. In some
embodiments, metrics may be collected by polling, pinging, or
otherwise requesting the metrics from different resources in the
pool to generate a sample of pool performance. Different messaging
or communication protocols, such as data log or event streams may
be implemented so that monitoring may be performed on a live stream
of metrics or on various snapshots of metrics for pool(s).
Monitoring may be performed by a single worker dedicated to a
single resource pool so that individual pools may not have a long
lag time between the occurrence of a modification events and its
detection. Monitoring of the resource pool(s) may continue as long
as no modification event is detected, as indicated by the negative
exit from 1320.
[0085] If a modification event is detected, then a modification may
be performed corresponding to the event, as discussed above with
regard to element 1230 in FIG. 12. As indicated at 1330, computing
resource(s) may be added or removed for the one pool, in some
embodiments.
[0086] As discussed above, resource pools can be configured to
execute different types of jobs, such as queries on behalf of a
managed query service. FIGS. 14A-14C describe various techniques
for managing a pool of computing resources for executing queries,
pools of clusters like cluster 710 in FIG. 7 discussed above. As
indicated at 1402 and 1404, a number of clusters (e.g., maximum
overall count) and size of computing clusters (e.g., number of
hosts or nodes per cluster) for a cluster pool may be selected. As
discussed above, the number and size of the computing clusters in
the cluster pool can be determined based upon a variety of factors
including, but not limited to, historical and/or expected volumes
of query requests, the price of the computing resources utilized to
implement the computing clusters, and/or other factors or
considerations. As indicated at 1406, the computing clusters may be
instantiated, such as through the use of an on-demand computing
service, like virtual compute service 210 or processing service 220
in FIG. 2 above. The instantiated computing clusters can then be
configured to process queries (e.g., by installing machine images
with query engine prior to receiving queries from managed query
service 270. For example, and without limitation, one or more
distributed query or processing frameworks can be installed on the
nodes or hosts in each of the computing clusters. As discussed
above, in one embodiment, the distributed query framework may be
the open source Presto distributed query framework. Other
distributed query frameworks can be utilized in other embodiments.
Distributed processing frameworks can also be installed on the
nodes or hosts in each computing cluster, in some embodiments. Such
distributed processing frameworks can be utilized in a similar
fashion to distributed query frameworks. For instance, in one
embodiment, the Apache Spark distributed processing framework can
be installed on the host(s) or node(s) in the computing
clusters.
[0087] As indicated at 1408, the instantiated and configured
computing clusters that are available for use by the managed query
service may be added to the cluster pool. A determination may be
made, in some embodiments, as to whether the number or size of the
computing clusters in the warm cluster pool should be adjusted
(e.g., according to the detection of a modification event), as
indicated at 1410. For example, a resource management service can
monitor the performance of the computing clusters in the cluster
pool to determine whether the number of size of computing clusters
should be adjusted (e.g., according to the pool lifecycle discussed
above with regard to FIG. 10). The number of computing clusters
assigned to the cluster pool and the size of each computing cluster
(i.e. the number of nodes or hosts in each computing cluster) in
the cluster pool can then be adjusted. Such a technique can be
repeated in order to continually optimize the number and size of
the computing clusters in the cluster pool.
[0088] FIG. 14B illustrates a high-level flowchart for a technique
to select a resource from a resource pool, according to some
embodiments. For example, as indicated at 1452 the arrival of a
query may occur (e.g., at managed query service). A request may be
made to provision a cluster from a cluster pool for processing the
query (e.g., to a resource management service 290 or other
component that manages the cluster pool). As indicated at 1454, a
computing cluster from the computing clusters in the cluster for
executing the received query may be made, in various embodiments.
The selection of the computing cluster for executing the query 108
can be based upon a number of factors including, but not limited
to, previous queries submitted by the same requestor, desired query
performance, user preferences, the amount of data to be queried,
column statistics, empirical data, the price of the computing
resources utilized to perform the query, other types of statistics
relating to the performance of the clusters, and others, in some
embodiments.
[0089] As indicated at 1456, the selected computing cluster may be
removed from the cluster pool. For example, the state of the
resource may be marked as leased, in one embodiment. The selected
computing cluster may be associated with the submitter of the query
(e.g., according to a username, identifier, or other credential
linked to a user or account), as indicated at 1458. For instance,
the cluster can be associated with the user when the user logs into
a management console. As indicated at 1460, the query may be routed
to the selected selected computing cluster 106A for execution of
the query with respect to data identified in the query. Subsequent
queries received from the same submitter may be routed to the
selected computing cluster, in some embodiments.
[0090] FIG. 14C illustrates a technique for scrubbing a computing
resource in a resource pool, according to some embodiments. As
indicated at 1472, a determination may be made as to whether a
computing cluster is inactive (e.g. the computing cluster has not
received a query in a predetermined amount of time). If the
computing cluster is determined to be inactive, then the computing
cluster may be disassociated from the submitter of the query, as
indicated at 1474. As indicated at 1476, the computing cluster may
be "scrubbed," in various embodiments (e.g., by removing data
associated with the submitter of the queries from memory, such as a
main memory or a cache, or mass storage device, disk or solid state
storage device, utilized by the nodes or host computers in the
computing cluster. As indicated at 1478, the computing cluster may
be returned to the cluster pool for use in processing other
queries. In some embodiments, it may be that clusters may not be
disassociated from certain users in certain scenarios. In these
scenarios, the user may have a dedicated pool of clusters available
for their use.
[0091] 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. 17) that
includes one or more processors executing program instructions
stored on a computer-readable storage medium coupled to the
processors. The program instructions may be configured to implement
the functionality described herein (e.g., the functionality of
various servers and other components that implement the
network-based virtual computing resource provider described
herein). The various methods as illustrated in the figures and
described herein represent example embodiments of methods. The
order of any method may be changed, and various elements may be
added, reordered, combined, omitted, modified, etc.
[0092] FIG. 15 is a logical block diagram that shows an
illustrative operating environment that includes a service provider
network that can implement aspects of the functionality described
herein, according to some embodiments. As discussed above, the
service provider network 200 can provide computing resources, like
VM instances and storage, on a permanent or an as-needed basis.
Among other types of functionality, the computing resources
provided by the service provider network 200 can be utilized to
implement the various services described above. As also discussed
above, the computing resources provided by the service provider
network 200 can include various types of computing resources, such
as data processing resources like VM instances, data storage
resources, networking resources, data communication resources,
network services, and the like.
[0093] Each type of computing resource provided by the service
provider network 200 can be general-purpose or can be available in
a number of specific configurations. For example, data processing
resources can be available as physical computers or VM instances in
a number of different configurations. The VM instances can execute
applications, including web servers, application servers, media
servers, database servers, some or all of the services described
above, and/or other types of programs. The VM instances can also be
configured into computing clusters in the manner described above.
Data storage resources can include file storage devices, block
storage devices, and the like. The service provider network 200 can
also provide other types of computing resources not mentioned
specifically herein.
[0094] The computing resources provided by the service provider
network maybe implemented, in some embodiments, by one or more data
centers 1304A-1304N (which might be referred to herein singularly
as "a data center 1304" or in the plural as "the data centers
1304"). The data centers 1304 are facilities utilized to house and
operate computer systems and associated components. The data
centers 1304 typically include redundant and backup power,
communications, cooling, and security systems. The data centers
1304 can also be located in geographically disparate locations. One
illustrative configuration for a data center 1304 that can be
utilized to implement the technologies disclosed herein will be
described below with regard to FIG. 16.
[0095] The customers and other users of the service provider
network 200 can access the computing resources provided by the
service provider network 200 over a network 1302, which can be a
wide area communication network ("WAN"), such as the Internet, an
intranet or an Internet service provider ("ISP") network or a
combination of such networks. For example, and without limitation,
a computing device 1300 operated by a customer or other user of the
service provider network 200 can be utilized to access the service
provider network 200 by way of the network 1302. It should be
appreciated that a local-area network ("LAN"), the Internet, or any
other networking topology known in the art that connects the data
centers 1304 to remote customers and other users can be utilized.
It should also be appreciated that combinations of such networks
can also be utilized.
[0096] FIG. 16 is a logical block diagram illustrating a
configuration for a data center that can be utilized to implement
aspects of the technologies disclosed herein, according to various
embodiments. is a computing system diagram that illustrates one
configuration for a data center 1304 that implements aspects of the
technologies disclosed herein for providing managed query
execution, such as managed query execution service 270, in some
embodiments. The example data center 1304 shown in FIG. 16 includes
several server computers 1402A-1402F (which might be referred to
herein singularly as "a server computer 1402" or in the plural as
"the server computers 1402") for providing computing resources
1404A-1404E.
[0097] The server computers 1402 can be standard tower, rack-mount,
or blade server computers configured appropriately for providing
the computing resources described herein (illustrated in FIG. 16 as
the computing resources 1404A-1404E). As mentioned above, the
computing resources provided by the provider network 200 can be
data processing resources such as VM instances or hardware
computing systems, computing clusters, data storage resources,
database resources, networking resources, and others. Some of the
servers 1402 can also execute a resource manager 1406 capable of
instantiating and/or managing the computing resources. In the case
of VM instances, for example, the resource manager 1406 can be a
hypervisor or another type of program may enable the execution of
multiple VM instances on a single server computer 1402. Server
computers 1402 in the data center 1304 can also provide network
services and other types of services, some of which are described
in detail above with regard to FIG. 2.
[0098] The data center 1304 shown in FIG. 16 also includes a server
computer 1402F that can execute some or all of the software
components described above. For example, and without limitation,
the server computer 1402F can execute various components for
providing different services of a provider network 200, such as the
managed query service 270, the data catalog service 280, resource
management service 290, and other services 1410 (e.g., discussed
above) and/or the other software components described above. The
server computer 1402F can also execute other components and/or to
store data for providing some or all of the functionality described
herein. In this regard, it should be appreciated that the services
illustrated in FIG. 16 as executing on the server computer 1402F
can execute on many other physical or virtual servers in the data
centers 1304 in various configurations.
[0099] In the example data center 1304 shown in FIG. 16, an
appropriate LAN 1406 is also utilized to interconnect the server
computers 1402A-1402F. The LAN 1406 is also connected to the
network 1302 illustrated in FIG. 15. It should be appreciated that
the configuration and network topology described herein has been
greatly simplified and that many more computing systems, software
components, networks, and networking devices can be utilized to
interconnect the various computing systems disclosed herein and to
provide the functionality described above. Appropriate load
balancing devices or other types of network infrastructure
components can also be utilized for balancing a load between each
of the data centers 1304A-1304N, between each of the server
computers 1402A-1402F in each data center 1304, and, potentially,
between computing resources in each of the data centers 1304. It
should be appreciated that the configuration of the data center
1304 described with reference to FIG. 16 is merely illustrative and
that other implementations can be utilized.
[0100] Embodiments of a managed query execution as described herein
may be executed on one or more computer systems, which may interact
with various other devices. One such computer system is illustrated
by FIG. 17. In different embodiments, computer system 2000 may be
any of various types of devices, including, but not limited to, a
personal computer system, desktop computer, laptop, notebook, or
netbook computer, mainframe computer system, handheld computer,
workstation, network computer, a camera, a set top box, a mobile
device, a consumer device, video game console, handheld video game
device, application server, storage device, a peripheral device
such as a switch, modem, router, or in general any type of
computing device, computing node, compute node, computing system
compute system, or electronic device.
[0101] 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.
[0102] 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.
[0103] In some embodiments, at least one processor 2010 may be a
graphics processing unit. A graphics processing unit or GPU may be
considered a dedicated graphics-rendering device for a personal
computer, workstation, game console or other computing or
electronic device. Modern GPUs may be very efficient at
manipulating and displaying computer graphics, and their highly
parallel structure may make them more effective than typical CPUs
for a range of complex graphical algorithms. For example, a
graphics processor may implement a number of graphics primitive
operations in a way that makes executing them much faster than
drawing directly to the screen with a host central processing unit
(CPU). In various embodiments, graphics rendering may, at least in
part, be implemented by program instructions configured for
execution on one of, or parallel execution on two or more of, such
GPUs. The GPU(s) may implement one or more application programmer
interfaces (APIs) that permit programmers to invoke the
functionality of the GPU(s). Suitable GPUs may be commercially
available from vendors such as NVIDIA Corporation, ATI Technologies
(AMD), and others.
[0104] 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.
[0105] 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.
[0106] 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.
[0107] 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.
[0108] As shown in FIG. 17, memory 2020 may include program
instructions 2025, may implement the various methods and techniques
as described herein, and data storage 2035, comprising various data
accessible by program instructions 2025. In one embodiment, program
instructions 2025 may include software elements of embodiments as
described herein and as illustrated in the Figures. Data storage
2035 may include data that may be used in embodiments. In other
embodiments, other or different software elements and data may be
included.
[0109] 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.
[0110] 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.
[0111] It is noted that any of the distributed system embodiments
described herein, or any of their components, may be implemented as
one or more web services. For example, leader nodes within a data
warehouse system may present data storage services and/or database
services to clients as network-based services. In some embodiments,
a network-based service may be implemented by a software and/or
hardware system designed to support interoperable
machine-to-machine interaction over a network. A network-based
service may have an interface described in a machine-processable
format, such as the Web Services Description Language (WSDL). Other
systems may interact with the web service in a manner prescribed by
the description of the network-based service's interface. For
example, the network-based service may define various operations
that other systems may invoke, and may define a particular
application programming interface (API) to which other systems may
be expected to conform when requesting the various operations.
[0112] 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).
[0113] 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.
[0114] 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.
[0115] 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.
* * * * *