U.S. patent application number 15/470837 was filed with the patent office on 2018-03-01 for event-driven resource pool management.
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 | 20180060133 15/470837 |
Document ID | / |
Family ID | 61242674 |
Filed Date | 2018-03-01 |
United States Patent
Application |
20180060133 |
Kind Code |
A1 |
Fang; Jian ; et al. |
March 1, 2018 |
EVENT-DRIVEN RESOURCE POOL MANAGEMENT
Abstract
Event-driven management may be implemented for resource pools.
Pool management events may be detected at computing resources in a
resource pool. Operations based on the pool management events may
then be performed at the computing resources. In some embodiments,
pool management events may trigger operations to a recycle a
computing resource for reuse in a resource pool or perform other
resource lifecycle operations.
Inventors: |
Fang; Jian; (Sammamish,
WA) ; Wu; Xing; (Redmond, WA) ; Kalathuru;
Bhargava Ram; (Seattle, 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) ; Maru; Sumeetkumar
Veniklal; (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.: |
15/470837 |
Filed: |
March 27, 2017 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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62382477 |
Sep 1, 2016 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06F 16/24549 20190101;
G06F 16/282 20190101; G06F 16/248 20190101; G06F 2209/501 20130101;
H04L 67/1008 20130101; G06F 9/505 20130101; G06F 16/20 20190101;
G06F 16/90335 20190101; G06F 9/50 20130101; G06F 9/5044 20130101;
G06F 9/5027 20130101; G06F 9/5088 20130101; H04L 29/08171 20130101;
G06F 2209/5011 20130101; G06F 2209/503 20130101; H04L 29/08135
20130101; G06F 16/25 20190101; H04L 29/0827 20130101; H04L 67/1031
20130101; G06F 16/24545 20190101; G06F 16/2455 20190101; G06F
9/5055 20130101; G06F 16/27 20190101; H04L 29/08261 20130101; G06F
9/5022 20130101; H04L 67/10 20130101; H04L 67/1029 20130101; G06F
16/245 20190101; G06F 9/5061 20130101; G06F 16/211 20190101; G06F
2209/508 20130101; G06F 16/24553 20190101; G06F 16/2471
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, at a first
computing resource of a pool comprising a plurality of computing
resources, the first computing resource for pool management events,
wherein the plurality of computing resources in the pool are
configured to perform jobs associated with a network-based service;
based on the monitoring, detect a pool management event at the
first computing resource; and perform one or more operations at the
first computing resource based at least in part on the pool
management event.
2. The system of claim 1, wherein the pool management event is a
scrub event for the first computing resource, and wherein to
perform the one or more operations at the first computing resource,
the method comprises remove data associated with a job executed at
the first computing resource from the first computing resource.
3. The system of claim 1, wherein the monitoring the first
computing resource, the detecting the pool management event, and
the performing the one or more operations are performed by a
management agent for the network-based service implemented at the
first computing resource, and wherein the method further comprises:
receiving, at the management agent, a request to execute a job at
the first computing resource from the network-based service;
generating, by the management agent, one or more instructions to
execute the job at an execution engine implemented at the first
computing resource; and submitting, by the management agent, the
one or more instructions to the execution engine to execute the
job.
4. The system of claim 1, wherein the method further comprises
sending an indication of the pool management event to a resource
management service, wherein the resource management service is
implemented as part of a same provider network as the network-based
service, wherein the network-based service is a managed query
service that executes queries on behalf of clients of the managed
query service, and wherein the one or more operations are performed
in response to a request to perform the one or more operations from
the resource management service.
5. A method, comprising: detecting a pool management event at a
first computing resource of a pool comprising a plurality of
computing resources, wherein the plurality of computing resources
in the pool are configured to perform jobs associated with a
network-based service; and performing one or more operations at the
first computing resource based at least in part on the pool
management event.
6. The method of claim 5, further comprising: sending an indication
of the pool management event to a pool manager for the pool; and
performing the operations in response to a request to perform the
operations received from the pool manager.
7. The method of claim 5, wherein the pool management event is a
scrub event for the first resource, and wherein performing the one
or more operations at the first computing resource, comprises
removing data associated with a job executed at the first computing
resource from the first computing resource.
8. The method of claim 5, wherein the pool management event is an
execution engine test event, and wherein performing the operations
comprises executing one or more test jobs on an execution engine
implemented at the first computing resource.
9. The method of claim 8, wherein performing the operations further
comprises: determining a failure event for at least one of the test
jobs; and sending an indication that the first computing resource
is in a failed state to a pool manager for the pool.
10. The method of claim 5, wherein the detecting the pool
management event and the performing the one or more operations are
performed by a management agent for the network-based service
implemented at the first computing resource, and wherein the method
further comprises: receiving, at the management agent, a request to
execute a job at the first computing resource from the
network-based service; generating, by the management agent, one or
more instructions to execute the job at an execution engine
implemented at the first computing resource; and submitting, by the
management agent, the one or more instructions to the execution
engine to execute the job.
11. The method of claim 10, further comprising: detecting, by the
management agent, an error for the execution of the job at the
execution engine; determining, by the management agent, an error
indication for the error to send to the network-based service; and
sending, by the management agent, the error indication to the
network-based service.
12. The method of claim 10, further comprising sending, by the
management agent, an execution status for the job to the
network-based service.
13. The method of claim 5, wherein the network-based service is a
managed query service that executes queries on behalf of clients of
the managed query service.
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:
detecting a pool management event at a first computing resource of
a pool comprising a plurality of computing resources, wherein the
plurality of computing resources in the pool are configured to
perform jobs associated with a network-based service; and
performing one or more operations at the first computing resource
based at least in part on the pool management event.
15. The non-transitory, computer-readable storage medium of claim
14, wherein the pool management event is a termination event for
the first computing resource, and wherein, in performing the one or
more operations at the first computing resource, the program
instructions cause the one or more computing devices to implement
terminating the first computing resource.
16. The non-transitory, computer-readable storage medium of claim
14, wherein the pool management event is a scrub event for the
first computing resource, and wherein, in performing the one or
more operations at the first computing resource, the program
instructions cause the one or more computing devices to implement
removing data associated with a job executed at the first computing
resource from the first computing resource.
17. The non-transitory, computer-readable storage medium of claim
14, wherein the detecting the pool management event and the
performing the one or more operations are performed by a management
agent for the network-based service implemented at the first
computing resource, and wherein the program instructions cause the
one or more computing devices to further implement: receiving, at
the management agent, a request to execute a job at the first
computing resource from the network-based service; generating, by
the management agent, one or more instructions to execute the job
at an execution engine implemented at the first computing resource;
and submitting, by the management agent, the one or more
instructions to the execution engine to execute the job.
18. The non-transitory, computer-readable storage medium of claim
17, wherein the program instructions cause the one or more
computing devices to implement sending, by the management agent, an
execution status for the job to the network-based service.
19. The non-transitory, computer-readable storage medium of claim
17, wherein the program instructions cause the one or more
computing devices to implement sending, by the management agent,
one or more performance metrics for the execution of the job to the
network-based service.
20. The non-transitory, computer-readable storage medium of claim
14, wherein the program instructions cause the one or more
computing devices to further implement sending an indication of the
pool management event to a resource management service, wherein the
resource management service is implemented as part of a same
provider network as the network-based service, and wherein the one
or more operations are performed in response to a request to
perform the one or more operations from the resource management
service.
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 event-driven
resource pool management, according to some embodiments.
[0006] FIG. 2 is a logical block diagram illustrating a provider
network implementing event-driven resource pool management for
pools of resources configured to execute jobs on behalf of
network-based services in the provider network, according to some
embodiments.
[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 logical block diagram illustrating a cluster
processing a query as part of managed query execution, according to
some embodiments.
[0011] FIG. 7 is a logical block diagram illustrating a managed
query agent, according to some embodiments.
[0012] FIG. 8 is a state diagram illustrating different resource
states tracked, detected, or identified by a management agent,
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 high-level flowchart illustrating various
methods and techniques to implement event-driven resource pool
management, according to some embodiments.
[0015] FIG. 11 is a high-level flowchart illustrating various
methods and techniques to monitor a computing resource for pool
management events to perform event-driven resource pool management,
according to some embodiments.
[0016] FIG. 12 is a high-level flowchart illustrating various
methods and techniques to perform a pool management event at a
computing resource, according to some embodiments.
[0017] FIG. 13 is a high-level flowchart illustrating various
methods and techniques to implement error monitoring at a
management agent for a computing resource of a resource pool
executing a job for a network-based service, according to some
embodiments.
[0018] FIG. 14 is a logical block diagram that shows an
illustrative operating environment that includes a service provider
network that can be configured to implement aspects of the
functionality described herein, according to some embodiments.
[0019] FIG. 15 is a logical block diagram illustrating a
configuration for a data center that can be utilized to implement
aspects of the technologies disclosed herein, according to some
embodiments.
[0020] FIG. 16 illustrates an example system configured to
implement the various methods, techniques, and systems described
herein, according to some embodiments.
[0021] 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.
[0022] 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
[0023] Various embodiments of a stateful resource pool management
for job execution are described herein. FIG. 1 illustrates a
logical block diagram of event-driven, according to some
embodiments. Pool(s) 130 of computing resource(s) 140 may be
instantiated, configured, and otherwise prepared for executing
different types of job(s) 170 on behalf of network-based service(s)
120, in various embodiments. For example, a query management
service, such as discussed below with regard to FIGS. 2-8, may
utilize computing resource(s) 140 from different pool(s) 130 in
order to execute queries with respect to remotely stored data, in
some embodiments. Other types of processing jobs, such as Extract
Transform Load (ETL), data validation, log analysis, simulation,
numerical analysis, text analysis, machine learning, or other
statistical analysis, may be managed, performed, or otherwise
executed on behalf of different network-based services, in some
embodiments. As the configurations, operations, or requirements of
computing resources to execute such job(s) 170 may be costly or
time consuming to procure, pool management for job execution
resources 110 may provide a dynamically managed set of computing
resource(s) 140 in respective pool(s) 130 that are pre-configured
and available for executing job(s) 170 without requiring
network-based service(s) 120 to directly manage the number of
computing resource(s) used by the network-based service(s) 120, in
various embodiments.
[0024] For instance, pool management for job execution 110 may
create pool(s) of computing resources 140, which may be single or
multi-node clusters, virtualized servers, instantiated execution
platforms, query engines, processing frameworks, or any other set
of one or more resource(s) that can execute job(s) 170 selectively
routed to computing resource(s) 140, in one embodiment. Computing
resources 140 may implement a management agent 142 to provide an
interface between computing resource(s) 140 and pool management for
job execution 110 and network-based service(s) 120. Computing
resource(s) 140 may interact with other services, data stores, or
computing resources (not illustrated), such as accessing remotely
stored data, or invoking functions, operations, or processes
executed by a separate system, in some embodiments. Pool management
for job execution resources 110 may then provide the pools of
resources 130 to network-based service(s) 120 for job execution.
For example, pool management for job execution 110 may implement an
interface, such as discussed below with regard to FIG. 9, via which
network-based service(s) 120 can programmatically get resource(s)
150 for executing a job 170, in one embodiment. Pool management for
job execution resources 110 may identify a pool 130 and computing
resource(s) 140 within the pool to execute the job 170 for the
network-based service 120 and provide the resource(s) 160 in
response to the request, in one embodiment. For example, pool
management for job execution resource(s) may identify a pool 130
specially provisioned for the network-based 120 service or a pool
130 provisioned for the type of job to be executed by the
network-based service 120, in one embodiment. Pool management for
job execution resources 110 may then randomly assign a resource
from the pool, or may deterministically select a resource (e.g.,
based on characteristics of the computing resource, network-based
service, or job), in one embodiment, such as a type of computing
resource that implements a particular type of query engine for
processing a job that is a query. Once the resource(s) 160 are
provided to network-based service(s) 120 (e.g., by providing an
identifier or access credential for reaching the resource).
[0025] As noted above, pool management for job execution
resource(s) 110 may dynamically manage computing resource(s) 140
and pool(s) 130. Management agent(s) 142 may proactively monitor
the operation of computing resource(s), whether utilized to execute
a job 170 or available (not executing a job) to detect pool
management events for the computing resource(s) 140. For example,
management agent 140 may analyze the state of a resource (e.g., as
discussed below with regard to FIG. 8), performance metric(s)
(e.g., utilization metrics for processor capacity,
network-bandwidth, storage capacity, I/O bandwidth, health metrics
for the computing resource(s) itself (e.g., start up time) or the
environment of the computing resource(s) (e.g., network events),
job execution status or state indications, or other information),
in some embodiments. Management agent 142 may evaluate pool
management event criteria to identify pool management events to
report 180. For example, management agent 142 may detect a pool
management event when computing resource 140 completes a job 170
for network-based service 120, a scrub event. Management agent 142
may then perform pool management operation(s), which may be
received as indicated at 190 from pool management for job execution
resources 110, in one embodiment, or be automatically determined
and performed by management agent 142 without input from pool
management for job execution resources, in another embodiment. For
example, management agent 142 may perform various operations to
remove job execution data (e.g., from memory or other storage
location at computing resource 140) to scrub the resource and make
it ready for executing another job. By detecting pool management
events and performing pool management operations at management
agent 142, pools(s) 130 may quickly adapt to changing conditions or
scenarios that modify the operation of the pool, without requiring
that pool management for job execution resources 110 directly
monitor each individual computing resource in pool.
[0026] Please note that the previous description of event-driven
resource pool management is a logical illustration and thus is not
to be construed as limiting as to the implementation of a
network-based service, pool of computing resources, pool of
computing resources, or pool management for job execution
resources.
[0027] This specification begins with a general description of a
provider network that implements a resource management service that
provides event-driven management for resource pools that queries
received from another network-based service, a managed query
service. Then various examples of the managed query service and
resource management service (along with other services that may be
utilized or implemented) including different components/modules, or
arrangements of components/module that may be employed as part of
implementing the services are discussed. A number of different
methods and techniques to implement event-driven resource pool
management are then discussed, some of which are illustrated in
accompanying flowcharts. Finally, a description of an example
computing system upon which the various components, modules,
systems, devices, and/or nodes may be implemented is provided.
Various examples are provided throughout the specification.
[0028] FIG. 2 is a logical block diagram illustrating a provider
network implementing event-driven resource pool management for
pools of resources configured to execute jobs on behalf of
network-based services in the provider network, according to some
embodiments. Provider network 200 may be a private or closed system
or may be set up by an entity such as a company or a public sector
organization to provide one or more services (such as various types
of cloud-based storage) accessible via the Internet and/or other
networks to clients 250, in some embodiments. Provider network 200
may be implemented in a single location or may include numerous
data centers hosting various resource pools, such as collections of
physical and/or virtualized computer servers, storage devices,
networking equipment and the like (e.g., FIGS. 15, 16 and computing
system 2000 described below with regard to FIG. 16), needed to
implement and distribute the infrastructure and storage services
offered by the provider network 200. In some embodiments, provider
network 200 may implement various computing resources or services,
such as a virtual compute service 210, data processing service(s)
220, (e.g., relational or non-relational (NoSQL) database query
engines, map reduce processing, data flow processing, and/or other
large scale data processing techniques), data storage service(s)
230, (e.g., an object storage service, block-based storage service,
or data storage service that may store different types of data for
centralized access) other services 240 (any other type of network
based services (which may include various other types of storage,
processing, analysis, communication, event handling, visualization,
and security services not illustrated), managed query service 270,
data catalog service 280, and resource management service 290.
[0029] In various embodiments, the components illustrated in FIG. 2
may be implemented directly within computer hardware, as
instructions directly or indirectly executable by computer hardware
(e.g., a microprocessor or computer system), or using a combination
of these techniques. For example, the components of FIG. 2 may be
implemented by a system that includes a number of computing nodes
(or simply, nodes), each of which may be similar to the computer
system embodiment illustrated in FIG. 16 and described below. In
various embodiments, the functionality of a given system or service
component (e.g., a component of data storage service 230) may be
implemented by a particular node or may be distributed across
several nodes. In some embodiments, a given node may implement the
functionality of more than one service system component (e.g., more
than one data store component).
[0030] 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.
[0031] 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.
[0032] 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.
[0033] 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.
[0034] 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.
[0035] 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.
[0036] 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.
[0037] 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).
[0038] 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).
[0039] 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.
[0040] 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.
[0041] 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.
[0042] 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.
[0043] 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.
[0044] 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.
[0045] 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.
[0046] 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.
[0047] 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.
[0048] 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.
[0049] 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
[0050] FIG. 4 is a diagram illustrating interactions between
clients and managed query service, according to some embodiments.
Client(s) 400 may be client(s) 250 in FIG. 2 above or other clients
(e.g., other services systems or components implemented as part of
provider network 200 or as part of an external service, system, or
component, such as data exploration or visualization tools (e.g.,
Tableau, Looker, MicroStrategy, Qliktech, or Spotfire). Clients 400
can send various requests to managed query service 270 via managed
query interface 310. Managed query interface 310 may offer a
management console 440, which may provider a user interface to
submit queries 442 (e.g., graphical or command line user
interfaces) or register data schemas 444 for executing queries. For
example, management console 440 may be implemented as part of a
network-based site (e.g., an Internet website for provider network
200) that provides various graphical user interface elements (e.g.,
text editing windows, drop-down menus, buttons, wizards or
workflows) to submit queries or register data schemas. Managed
query interface 310 may implement programmatic interfaces 410
(e.g., various Application Programming Interface (API) commands) to
perform queries, and various other illustrated requests. In some
embodiments, managed query interface 310 may implement custom
drivers that support standard communication protocols for querying
data, such as JDBC driver 430 or ODBC driver 420.
[0051] 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.
[0052] 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.
[0053] 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.
[0054] 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.
[0055] Managed query agent 512 may get schema 540 for the data
sets(s) 520 from data catalog service 280, which may return the
appropriate schema 542. Provisioned cluster 510 can then generate a
query execution plan and execute the query 544 with respect to data
set(s) 520 according to the query plan. Managed query agent 512 may
send query status 546 to query tracker 340 which may report query
status 548 in response to get query status 546 request, sending a
response 550 indicating the query status 550. Provisioned cluster
510 may store the query results 552 in a result store 522 (which
may be a data storage service 230). Managed query service control
plane 320 may receive q request to get a query results 554 and get
query results 556 from results store 522 and provide the query
results 558 in response, in some embodiments.
[0056] Different types of computing resources may be provisioned
and configured in resource pools, in some embodiments. Single-node
clusters or multi-node compute clusters may be one example of a
type of computing resource provisioned and configured in resource
pools by resource management service 290 to service queries for
managed query service 270. FIG. 6 is a logical block diagram
illustrating a cluster processing a query as part of managed query
execution, according to some embodiments. Cluster 610 may implement
a computing node 620 that is a leader node (according to the query
engine 624 (or multiple query engines, such as Presto and Hive)
implemented by cluster 610). In some embodiments, no single node
may be a leader node, or the leader node may rotate from processing
one query to the next. Managed query agent 622 may be implemented
as part of leader node 620 in order to provide an interface between
the provisioned resource, cluster 610, and other components of
managed query service 270 and resource management service 290. For
example, managed query agent 622 may provide further data to
managed query service 270, such as the status of the query (e.g.
executing, performing I/O, performing aggregation, etc.,) and
execution metrics (e.g., health metrics, resource utilization
metrics, cost metrics, length of time, etc.). In some embodiments,
managed query agent 622 may provide cluster/query status and
execution metric(s) to resource management service 290 (in order to
make pool management decisions, such as modification events, lease
requests, etc.), as discussed below. For example, managed query
agent 622 may indicate cluster status to resource management
service 290 indicating that a query has completed and that the
cluster 610 is ready for reassignment.
[0057] Leader node 620 may implement query engine 624 to execute
queries, such as query 602 which may be received via managed query
agent 622. For instance, managed query agent may implement a
programmatic interface for query tracker to submit queries (as
discussed above in FIG. 5), and then generate and send the
appropriate query execution instruction to query engine 624. Query
engine(s) 624 may generate a query execution plan for received
queries 603. In at least some embodiments, leader node 620, may
obtain schema information for the data set(s) 670 from the data
catalog service 280 or metadata stores for data 662 (e.g., data
dictionaries, other metadata stores, other data processing
services, such as database systems, that maintain schema
information) for data 662, in order to incorporate the schema data
into the generation of the query plan and the execution of the
query. Leader node 620 may generate and send query execution
instructions 640 to computing nodes that access and apply the query
to data 662 in data store(s) 660. Compute nodes, such as nodes
630a, 630b, and 630n, may respectively implement query engines
632a, 632b, and 632n to execute the query instructions, apply the
query to the data 650, and return partial results 640 to leader
node 620, which in turn may generate and send query results 604.
Query engines 624 and query engines 632 may implement various kinds
of distributed query or data processing frameworks, such as the
open source Presto distributed query framework or the Apache Spark
framework.
[0058] FIG. 7 is a logical block diagram illustrating a managed
query agent, according to some embodiments. Managed query agent 622
may act as an interface for detecting and performing pool
management related events as well as service related events for a
computing resource (e.g., cluster) upon which the managed query
agent is implement. As illustrated in FIG. 7, managed query agent
622 may implement resource management service interface 710, in
some embodiments, to interact with resource management service 290.
For example, may provide indications of pool management events 712
detected for the cluster (e.g., changes in resource state, as
discussed below in FIG. 8, execution state or status for a query,
and other performance metrics which may be related to the
management of resources in the resource pool). Operation(s) 714
related to pool management events may be received via resource
management service interface 710, in various embodiments, to be
performed by event handler 740 at managed query agent 622,
according to the various techniques/events discussed below with
regard to FIGS. 10 and 11.
[0059] Managed query agent 622 may also implement managed query
service interface 720, which may determine and send 722 the status
of an executing query (e.g., starting, executing, complete, etc.)
to managed query service 270. Managed query service interface 720
may also send various metric(s) 724 to managed query service 290,
such as resource utilization metrics, job-pending/executing time,
or other characteristics of the performance of the query that may
be gathered or determined, in one embodiment (e.g., from
performance metrics 754 received from execution engine 624).
Managed query service interface 720 may also accept and initiate
execution of queries 726 received from managed query service. For
example, event handler 740 may generate instructions to execute the
query and submit the instructions 752 via execution engine
interface 750 to execution engine 624 for performance.
[0060] Managed query agent may implement cluster monitor 730 to
monitor for pool management events for the cluster, whether the
cluster is idle or leased for the execution of a query, in some
embodiments. Cluster monitor 730 may monitor a resource lifecycle
state, execution state for a job, or performance metrics, such as
by periodically sampling or monitoring a live stream of metrics or
other data to determine if a pool management event is detected.
Pool management events may be detected based on pool management
criteria (e.g., changes in resource state, execution performance
state, or by applying different thresholds or other analysis to
performance metrics for a resource), which cluster monitor 730 may
maintain in or as part of an event list or other set of
configuration information that defines the pool management events
to monitor for. In some embodiments, pool management events may be
detected in response to external events that are detected at the
computing resource (e.g., network partition, power failure,
etc.).
[0061] Managed query agent 622 may implement event handler 740 to
perform operation(s) based on detected pool management events or to
execute jobs, like queries 726 that are received from managed query
service 270. In some embodiments, query status, performance
metric(s), and other information may be provided according to a
polling-based model, so that event handler 740 may handle requests
to provide information (e.g., by sending query status 722 and query
performance metric(s) 724) in response to the requests. Event
handler 740 may perform the pool management operations 714
specified by resource management service 290 and/or other
operations for handling a pool management event (e.g., such as
resource specific operations for a particular execution engine or
configuration of cluster 710 to carry out a generally described
pool management operation 714, like executing specific operations
to scrub certain locations or devices in memory or storage at
cluster 722 that are not explicitly identified by pool management
operations 714). In some embodiments, pool management operations
are performed automatically without receiving the operations from
resource management service 290.
[0062] FIG. 8 is a state diagram for resources implemented in a
resource pool, according to some embodiments. A resource may begin
in start state 810 awaiting fulfillment. A pending resource 820 may
be a resource that has been launched but is not yet configured for
processing jobs (e.g., according to a configured specified for
resources in the pool, such as the query image, machine image,
software applications, etc.). If an error occurs while
provisioning, then the resource may be in failed state 850, which
would make the resource unable to be available to process jobs as
part of the pool (and may not be counted for idle or overall
resource count considerations, in some embodiments. For example, a
machine image may crash or fail to load properly at one or more
nodes in a cluster, in one embodiment, failing the provisioning of
the resource.
[0063] For resources that are successful configured to execute
jobs, the resource state may transition to ready 830. In ready
state 830, a resource may be idle (or leased, but not executing a
job). A resource may transition out of ready state to executing
state 835. A resource may transition out of ready state in the
event of resource failure (to failed state 850) or in the event of
the resource being terminated (to terminated state 860). A resource
may execute the query in executing state 835, and may transition
out of execution state 835 in the event of resource failure (to
failed state 850) or in the event of the resource being terminated
(to terminated state 860). Termination of a resource may, in some
embodiments, occur after a time limit or other usage threshold that
limits the amount of work done by a given resource. In this way, a
resource that suffers from performance decline (e.g., due to age,
software errors that cause memory leaks or other performance
problems) or may be vulnerable to security breach can be terminated
(and replaced in the pool with another resource). Upon completing
execution of job, a resource may move to scrub state 840, in some
embodiments. For example, a managed query agent may detect when a
cluster has completed execution of the query and report a query
completion status to resource management service 290. The managed
query agent may then initiate an operation to scrub the resource
for reuse in the resource pool. Scrubbed resources may return to
resource pool by becoming in ready state 830. In some embodiments,
a scrubbed resource that fails to complete a scrub operation may
move to failed state 850 or may be terminated (e.g., due to an
age/time limit for the resource).
[0064] 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.
[0065] 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.
[0066] 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.
[0067] Instantiated and configured computing clusters 920 that are
available for use by the managed query service 270 are added to the
warm cluster pool 910, in some embodiments. A determination can be
made as to whether the number or size of the computing clusters 920
in the warm cluster pool needs is to be adjusted, in various
embodiments. The performance of the computing clusters 920 in the
warm cluster pool 910 can be monitored based on cluster metric(s)
990 received from the cluster pool. The number of computing
clusters 920 assigned to the warm cluster pool 910 and the size of
each computing cluster 920 (i.e. the number of host computers in
each computing cluster 920) in the warm cluster pool 910 can then
be adjusted. Such techniques can be repeatedly performed in order
to continually optimize the number and size of the computing
clusters 920 in the warm cluster pool 910.
[0068] 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.
[0069] Although FIGS. 2-9 have been described and illustrated in
the context of a provider network leveraging multiple different
services to implement a managed query agent to detect and perform
pool management events and operations, the various components
illustrated and described in FIGS. 2-9 may be easily applied to
other systems, or devices that manage pools of configured
resources. As such, FIGS. 2-9 are not intended to be limiting as to
other embodiments of a system that may implement event-driven
resource pool management. FIG. 10 is a high-level flowchart
illustrating various methods and techniques to implement
event-driven resource pool management, according to some
embodiments. Various different systems and devices may implement
the various methods and techniques described below, either singly
or working together.
[0070] For example, a resource management service as described
above with regard to FIGS. 2-9 may implement the various methods.
Alternatively, a combination of different systems and devices may
implement these methods. Therefore, the above examples and or any
other systems or devices referenced as performing the illustrated
method, are not intended to be limiting as to other different
components, modules, systems, or configurations of systems and
devices.
[0071] As indicated at 1010, a pool management event may be
detected at a first computing resource of a pool of computing
resources that are configured to perform jobs associated with a
network-based service, in various embodiments. For example, a
management agent or other monitor implemented at a computing
resource may evaluate the operation of the computing resource,
whether the resource is idle or being leased/used to execute a job
for the network-based service. Various different pool management
event detection criteria may be applied for different types of pool
management events. For example, a scrub pool management event may
be detected upon determining that the computing resource has
completed execution of a current job and is ready to be recycled or
reused in the computing cluster to execute a different job (e.g.,
from a different client of network-based service). In another
example, a pool management event criteria may evaluate the age or
time since creation of the first computing resource (e.g.,
according to lifespan time or timestamp) and determine whether the
first computing resource has exceeded the age threshold for
computing resources in the pool, in one embodiment. If so, then the
a termination pool management event may be detected to terminate
the first computing resource (e.g., by sending a request to a
service implementing the resource to terminate the existence of the
resource). In another example of pool management event criteria, an
operational metric (e.g., time since last leased) and a state
(e.g., "ready" or "pending" state) criteria may be evaluated, and
if the first computing resource exceeds the operational metric and
matches the state criteria, then a test pool management event may
be detected to execute one or more test jobs at the first computing
resource, in one embodiment. If one of the test jobs fails to
execute in a desired manner, then an indication of the failure may
be provided to a pool manager for the resource pool (e.g., resource
management service 290), which may provide operations to scrub,
reboot, or terminate the first computing resource. In light of
these examples, it may be understood that pool management events
can be detected based on various combinations of one or more
detection criteria evaluated with respect to the operation of the
first computing resource.
[0072] As indicated at 1020, operations may be performed at the
first computing resource based, at least in part, on the pool
management event. For example, operations to remove job data (e.g.,
from memory or non-volatile storage) as part of a scrub event may
be performed, in one embodiment. In another example, operations
that prepare the first computing resource for termination, such as
dumping or storing reports, logs, or other collected information
may be performed, along with executing the operation to terminate
the first computing resource, in one embodiment. In another
example, operations to carry out test jobs on the first computing
source, including access a test job, sample data, or other
information needed to execute the one or more test jobs, generating
instructions to an execution engine at the first computing resource
to execute the test jobs and evaluating the results or performance
of the first computing resource for the test jobs. Operations for
detected pool management events may be instructed (partially or
completely) by a pool manager for a resource pool in some
embodiments, as discussed below with regard to FIG. 12. In other
embodiments, operation(s) may be performed automatically in
response to detecting the pool management event (without
instruction from a pool manager). In this way, pool management
events may be performed quickly so that resources in the pool make
the pool respond faster to events that may modify the operation of
computing resources in the pool.
[0073] FIG. 11 is a high-level flowchart illustrating various
methods and techniques to monitor a computing resource in a pool of
computing resources for pool management events, according to some
embodiments. As discussed above different types of pool management
events may be detected at a computing resource. In some
embodiments, monitoring for pool management events may be actively
performed (e.g., by a management agent like managed query agent 622
in FIG. 6). As indicated at 1110, a first computing resource of a
pool of computing resources that are configured to execute jobs
associated with a network-based service may be monitored, in some
embodiments. For example, a resource lifecycle state, execution
state for a job, or performance metrics for may be periodically
sampled or checked to determine if a pool management event is
detected. Pool management events may be detected based on pool
management criteria (e.g., changes in resource state, execution
performance state, or by applying different thresholds or other
analysis to performance metrics for a resource). In some
embodiments, pool management events may be detected in response to
external events that are detected at the computing resource (e.g.,
network partition, power failure, etc.).
[0074] As indicated at 1120, if a pool management event is
detected, then an indication of the pool management event may be
sent to a pool manager for the pool, as indicated at 1130. For
example, some pool management events may be maintained in a mapping
table or other data structure describing the operation(s) to
perform in response to detecting the pool management event, in one
embodiment. If the described operation(s) include reporting the
pool management event (or no operations are described and the
management agent sends an indication of the pool management event
in response to the pool management event as a default operation),
then the indication for the pool management event may be generated
(e.g., according to an interface for the pool manager, such as an
API for resource management service 290.
[0075] A pool manager may confirm or determine the appropriate
responsive actions to perform for the pool management event, in
some embodiments. For example, for a scrub operation, the pool
manager may determine whether other jobs for the same user are
pending or likely to be sent to the computing resource for
execution (e.g., by waiting for a period of time before allowing
the scrub operation and returning the computing resource to the
pool of computing resources for executing other jobs). As indicated
at 1140, a request to perform operation(s) based on the pool
management event may be received from the pool manager, in some
embodiments. For example, the various types of operations described
above with regard to element 1020 in FIG. 2 (e.g., scrub
operations, termination operations, test operations, etc.) may be
identified or included in a request from the pool manager. As
indicated at 1150, the requested operations may then be performed
at the computing resource.
[0076] FIG. 12 is a high-level flowchart illustrating various
methods and techniques to execute a job for a network-based
service, according to some embodiments. As indicated at 1210, a
request to execute a job may be received at a management agent
implemented at a computing resource of a pool of computing
resources configured to execute jobs associated with a
network-based service. The request may be formatted according to a
programmatic interface implemented by the management agent, such as
managed query service interface 720 discussed above with regard to
FIG. 7. The request may include various execution parameters,
identifiers, access credentials, tokens, permissions, and other
data that may be needed to execute the job. For example,
authentication credentials may be needed to execute a job that
accesses data, such as query. Other execution parameters, such as
execution limitations, timeout values, result destinations may be
included, in some embodiments. Execution parameters may describe
the behavior of the execution of the job (e.g., returning results
in a particular form, such as a paginated stream of query results
sent to an interface like management console 440 in FIG. 4).
[0077] As indicated at 1220, instruction(s) to execute the job at
an execution engine implemented at the first computing resource may
be generated by the management agent, in some embodiments. For
example, commands corresponding to a programmatic interface for the
execution engine, may be generated to execute the job. In other
embodiments, a job workflow, script, or executable may be generated
(according to the input options or parameters allowed by the
execution engine). Once the instruction(s) are generated, then the
instruction(s) may be submitted to the execution engine to execute
the job, as indicated at 1230, in various embodiments. For example,
a function call, procedure, message, or other invocation mechanism
may be used to submit the instructions to the execution engine, in
one embodiment.
[0078] As indicated at 1240, in at least some embodiments, the
management agent may send an execution status for the job to the
network-based service. For example, the management agent may
determine or classify the execution of the job according to a
predefined set of execution states (e.g., "initializing," "start,"
"reading," "writing," "finalizing," "error," etc.). In some
embodiments, a progress metric, such as a completion percentage, or
indication for the job's execution state within a workflow (e.g.,
"step 1" or "step 10") may be determined as the execution status.
Once determined, the execution status may be reported to the
network-based service according to a programmatic interface (e.g.,
API call), in one embodiment. The request may be formatted
according to the API and sent to a service endpoint for receiving
job execution status.
[0079] As indicated at 1250, in some embodiments, performance
metric(s) for the execution of the job may be sent to the
network-based service by the management agent, in various
embodiments. For example, resource utilization metrics,
job-pending/executing time, or other characteristics of the
performance of the job may be gathered or determined, in one
embodiment. In some embodiments, performance metric(s) may be
stored locally by the management agent while the job is executing
and sent as a batch of performance metrics upon completion of the
job (or failure of the job). Performance metrics may be formatted
according to a metric reporting or storage format for the
network-based service, such as a log-based record format, or as a
data file including comma delimited metric values, in one
embodiment. In other embodiments, performance metrics may be
streamed or otherwise reported in real time to the network-based
service.
[0080] FIG. 13 is a high-level flowchart illustrating various
methods and techniques to implement error monitoring at a
management agent for a computing resource of a resource pool
executing a job for a network-based service, according to some
embodiments. As indicated at 1310, a management agent may monitor
execution off a job at a computing resource of a pool of computing
resources for errors, in various embodiments. For example, the
management agent may receive indications of execution engine errors
(e.g., due to execution problems, invalid or malformed instructions
to execute the job, such as invalid SQL statements), in one
embodiment. Management agent may detect errors by observing
behavior of the execution engine (e.g., stalling, not-responsive,
resource utilization, or other indicators of problematic
operation), in one embodiment.
[0081] If an error is detected, as indicated by the positive exit
from 1320, a determination may be made by the management agent as
to the error indication to send to the network-based service, in
some embodiments. For example, the error may classified or
categorized (e.g., as an internal execution error caused by the
operation of internal resources such that the error is not a fault
of the client that submitted the job, or as an external
error/client error, such as errors in the submission of the job,
like incorrect query language statements, invalid operations
requested). Some errors, may be categorized based on the error
information provided by the execution engine, while other errors
may be categorized based on other criteria, such as the state of
the resource, status of the execution of the job, or other
information collected by the management agent. Mapping information
(e.g., in a table mapping detected errors to error indications) may
be maintained to translate otherwise provide a template for (or the
content of) error indications that are to be provided.
[0082] Once determined, the error indication may be sent to the
network-based service, as indicated at 1340, in some embodiments.
For example, the error indication may be formatted and sent
according to an error reporting API or other communication
mechanism (e.g., message queue or event stream established between
the management agent and the network-based service), in one
embodiment. In at least some embodiments, some errors that are
detected may be ignored or not reported. For example, errors that
do not halt execution of a job may be ignored or not reported. In
some embodiments, errors may not be reported until a number of
similar or the same error is detected beyond some reporting
threshold for the error. Some errors received at the network-based
service may be provided to users/clients of the network-based
service, while others may remain visible only to the network-based
service, in one embodiment.
[0083] The methods described herein may in various embodiments be
implemented by any combination of hardware and software. For
example, in one embodiment, the methods may be implemented by a
computer system (e.g., a computer system as in FIG. 16) that
includes one or more processors executing program instructions
stored on a computer-readable storage medium coupled to the
processors. The program instructions may be configured to implement
the functionality described herein (e.g., the functionality of
various servers and other components that implement the
network-based virtual computing resource provider described
herein). The various methods as illustrated in the figures and
described herein represent example embodiments of methods. The
order of any method may be changed, and various elements may be
added, reordered, combined, omitted, modified, etc.
[0084] FIG. 14 is a logical block diagram that shows an
illustrative operating environment that includes a service provider
network that can implement aspects of the functionality described
herein, according to some embodiments. As discussed above, the
service provider network 200 can provide computing resources, like
VM instances and storage, on a permanent or an as-needed basis.
Among other types of functionality, the computing resources
provided by the service provider network 200 can be utilized to
implement the various services described above. As also discussed
above, the computing resources provided by the service provider
network 200 can include various types of computing resources, such
as data processing resources like VM instances, data storage
resources, networking resources, data communication resources,
network services, and the like.
[0085] 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.
[0086] The computing resources provided by the service provider
network maybe implemented, in some embodiments, by one or more data
centers 1404A-1404N (which might be referred to herein singularly
as "a data center 1404" or in the plural as "the data centers
1404"). The data centers 1404 are facilities utilized to house and
operate computer systems and associated components. The data
centers 1404 typically include redundant and backup power,
communications, cooling, and security systems. The data centers
1404 can also be located in geographically disparate locations. One
illustrative configuration for a data center 1404 that can be
utilized to implement the technologies disclosed herein will be
described below with regard to FIG. 15.
[0087] The customers and other users of the service provider
network 200 can access the computing resources provided by the
service provider network 200 over a network 1402, which can be a
wide area communication network ("WAN"), such as the Internet, an
intranet or an Internet service provider ("ISP") network or a
combination of such networks. For example, and without limitation,
a computing device 1400 operated by a customer or other user of the
service provider network 200 can be utilized to access the service
provider network 200 by way of the network 1402. It should be
appreciated that a local-area network ("LAN"), the Internet, or any
other networking topology known in the art that connects the data
centers 1404 to remote customers and other users can be utilized.
It should also be appreciated that combinations of such networks
can also be utilized.
[0088] FIG. 15 is a logical block diagram illustrating a
configuration for a data center that can be utilized to implement
aspects of the technologies disclosed herein, according to various
embodiments. is a computing system diagram that illustrates one
configuration for a data center 1404 that implements aspects of the
technologies disclosed herein for providing managed query
execution, such as managed query execution service 270, in some
embodiments. The example data center 1404 shown in FIG. 15 includes
several server computers 1502A-1502F (which might be referred to
herein singularly as "a server computer 1502" or in the plural as
"the server computers 1502") for providing computing resources
1504A-1504E.
[0089] The server computers 1502 can be standard tower, rack-mount,
or blade server computers configured appropriately for providing
the computing resources described herein (illustrated in FIG. 15 as
the computing resources 1504A-1504E). As mentioned above, the
computing resources provided by the provider network 200 can be
data processing resources such as VM instances or hardware
computing systems, computing clusters, data storage resources,
database resources, networking resources, and others. Some of the
servers 1502 can also execute a resource manager 1506 capable of
instantiating and/or managing the computing resources. In the case
of VM instances, for example, the resource manager 1506 can be a
hypervisor or another type of program may enable the execution of
multiple VM instances on a single server computer 1502. Server
computers 1502 in the data center 1504 can also provide network
services and other types of services, some of which are described
in detail above with regard to FIG. 2.
[0090] The data center 1504 shown in FIG. 15 also includes a server
computer 1502F that can execute some or all of the software
components described above. For example, and without limitation,
the server computer 1502F can execute various components for
providing different services of a provider network 200, such as the
managed query service 270, the data catalog service 280, resource
management service 290, and other services 1510 (e.g., discussed
above) and/or the other software components described above. The
server computer 1502F can also execute other components and/or to
store data for providing some or all of the functionality described
herein. In this regard, it should be appreciated that the services
illustrated in FIG. 15 as executing on the server computer 1502F
can execute on many other physical or virtual servers in the data
centers 1404 in various configurations.
[0091] In the example data center 1404 shown in FIG. 15, an
appropriate LAN 1506 is also utilized to interconnect the server
computers 1502A-1502F. The LAN 1506 is also connected to the
network 1402 illustrated in FIG. 14. It should be appreciated that
the configuration and network topology described herein has been
greatly simplified and that many more computing systems, software
components, networks, and networking devices can be utilized to
interconnect the various computing systems disclosed herein and to
provide the functionality described above. Appropriate load
balancing devices or other types of network infrastructure
components can also be utilized for balancing a load between each
of the data centers 1504A-1504N, between each of the server
computers 1502A-1502F in each data center 1404, and, potentially,
between computing resources in each of the data centers 1404. It
should be appreciated that the configuration of the data center
1404 described with reference to FIG. 15 is merely illustrative and
that other implementations can be utilized.
[0092] Embodiments of a managed query execution as described herein
may be executed on one or more computer systems, which may interact
with various other devices. One such computer system is illustrated
by FIG. 16. In different embodiments, computer system 2000 may be
any of various types of devices, including, but not limited to, a
personal computer system, desktop computer, laptop, notebook, or
netbook computer, mainframe computer system, handheld computer,
workstation, network computer, a camera, a set top box, a mobile
device, a consumer device, video game console, handheld video game
device, application server, storage device, a peripheral device
such as a switch, modem, router, or in general any type of
computing device, computing node, compute node, computing system
compute system, or electronic device.
[0093] 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.
[0094] 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.
[0095] 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.
[0096] 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.
[0097] 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.
[0098] 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.
[0099] 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.
[0100] As shown in FIG. 16, memory 2020 may include program
instructions 2025, may implement the various methods and techniques
as described herein, and data storage 2035, comprising various data
accessible by program instructions 2025. In one embodiment, program
instructions 2025 may include software elements of embodiments as
described herein and as illustrated in the Figures. Data storage
2035 may include data that may be used in embodiments. In other
embodiments, other or different software elements and data may be
included.
[0101] 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.
[0102] 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.
[0103] 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.
[0104] 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).
[0105] 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.
[0106] 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.
[0107] Various modifications and changes may be made as would be
obvious to a person skilled in the art having the benefit of this
disclosure. It is intended that the invention embrace all such
modifications and changes and, accordingly, the above description
to be regarded in an illustrative rather than a restrictive
sense.
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