Embodiments relate to techniques for providing application-aware database functionality. More particularly, embodiments relate to techniques for utilizing information about an application, user, context and/or other information to provide a more efficient database environment.
Existing database management systems (DBMSs) are generic and not application aware. This means that the database has no knowledge of applications, modules, or queries. Without such knowledge, at startup time, the database is not in a position to pre-parse and/or cache queries and query execution plans. As a result, when the application servers first connect to the database, it takes a relatively long time for the application servers to be ready to serve users. This database time is on top of the time that application servers themselves take to start the connection pools and send queries to the database. The total time for the database and application(s) to be ready is sequentially cumulative. The result is a less than optimal operational environment.
Embodiments of the invention are illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings in which like reference numerals refer to similar elements.
In the following description, numerous specific details are set forth. However, embodiments of the invention may be practiced without these specific details. In other instances, well-known structures and techniques have not been shown in detail in order not to obscure the understanding of this description.
There currently exists no comprehensive solution to solve the problem discussed above. However, some attempts have been made to reduce the hard parse time by methods such as structured query language (SQL) stored outlines by storing the SQL execution plans in the database. This is generally a manual process and thus is limited to a few problematic queries that are known in advance. That is, using current systems, an administrator must manually create the stored outline and register it with the database with exactly matching semantics. But it does not eliminate hard parsing of the SQL queries coming from the application servers during startup time as well as on an ongoing basis. Thus, current techniques are not efficient and do not scale well.
In current RDBMS architectures, there is no concept of storing metadata for applications, modules, and their related queries. For example, in Salesforce, there may be Sales, Service, or Marketing applications. Further, the modules within Sales application could be Opportunity Management, Lead Management, etc. Each of these modules may have different sets of queries for rendering their respective home pages on UI. Within the database, according to various embodiments, this metadata (e.g., application, module, queries) can be registered and stored like other database objects such as users, tables, indexes, packages, etc.
The techniques and mechanisms discussed herein can be utilized to create structures in a manner that provide the ability for the RDBMS to establish the context in terms the application for the optimizer to use this information to preload, parse and/or store execution plans in memory for queries expected to be received from the application server. Such established context can minimize the number of queries to be evaluated during startup as well as on an ongoing basis with the aim of eliminating hard parse time.
In one embodiment, the database can pre-parse the queries that are expected from application server, independent of the application server availability. By pre-parsing such queries, the time needed to serve the queries from the application server can be reduced, which provides a more efficient system.
In one embodiment, this concept can be extended to persist the current state of queries and their execution plans during the database shutdown, and bring them back to the same state on database restart. This mechanism can be utilized to propagate state of queries and execution plans to all the nodes in a multi-node database environment.
The embodiments described herein can provide significantly improved startup performance, hence reducing the response time for the customers. Further, it aids in significantly reducing disaster recovery time. The embodiments described herein provide a capability in the RDBMS to potentially pre-register applications, modules and queries for optimization. This can appreciably shorten the application response time. It further adds a layer of security by providing privileges to a defined set of users for each combination of application and modules.
By building the repository of queries and their related execution plans over time, the database will be able to identify an existing query and execution plans to be reused. This is especially useful where the queries are run in multi-tenancy applications where execution plans can be different for different tenants/organizations.
A tenant includes a group of users who share a common access with specific privileges to a software instance. A multi-tenant architecture provides a tenant with a dedicated share of the software instance typically including one or more of tenant specific data, user management, tenant-specific functionality, configuration, customizations, non-functional properties, associated applications, etc. Multi-tenancy contrasts with multi-instance architectures, where separate software instances operate on behalf of different tenants.
For various embodiments herein, the following metadata, including object privileges, are utilized. In one embodiment, to implement an application-aware database, applications and modules are declared as database objects on which the database can take action as and when required. This can be implemented similar to currently used objects like users, tables, indexes, packages, etc. In one embodiment, SQL query and execution plans are also declared as database objects. These are registered with the database along with an application, its modules and related set(s) of queries.
Using the techniques described herein, it is possible to declare the set of queries that are essential for each of the registered modules. Hence, the scheme of specific combinations of application, modules, and queries will be stored as metadata. In one embodiment there is provided creation and maintenance of metadata related to application, modules, and queries by the RDBMS. Additionally, the query set priority can be registered so that the database can parse and load in the prioritization order. In one embodiment, application architects/owners can then register/create application, modules, and queries with/in the database in the same manner as other objects are currently created. Once created and registered with the database, the application, modules, and queries could be used for pre-parsing, loading and storing execution plans.
The registration with the database could be done using commands such as:
The above example would register an application App1 along with Module 1 and queries query1, query2, accord priority 1 to query1 and priority 2 to query2, and allow execute privilege on App1 to User1. Providing such a granular, declarative way for the implementation of application-aware database to application architects, the RDBMS can use this metadata to pre-parse and cache queries and query execution plans, including those of connection pools coming from the appserver, and persist the query execution plans. This stored metadata can be used by the database at all startup times. As a result, the optimizer will do less work, save on time and database resources and arguably produce efficient, stable execution plans, especially for multi-tenancy SQL queries. Application architects, database administrators and SQL tuning professionals will be able to make adjustments to further fine-tune the matrix for optimal performance.
Thus, the techniques described herein optimize processing of “query flows” rather than individual SQL queries and also utilize relatively detailed application information to optimize query plans quickly and more efficiently.
In contrast to the techniques described herein, when reviewing current techniques two concepts stand out that point to limitations in existing architecture. One, the focus of the database optimizer is to optimize one query at a time. Two, the input parameters fed into the optimizer's decisions only include the structure of the query, statistics of objects referenced in the query and optimizer's execution features. There is no mechanism for optimizing query-flows or using application and user metadata as inputs to the query optimization process.
For example, in the case of Microsoft's SQL Server databases, the SQL Server Query Optimizer's purpose is to find an efficient physical execution plan that fulfills a query request. It attempts this by assigning estimated costs to various query execution plan alternatives and then choosing the plan alternative with the lowest estimated cost. The focus is on optimizing a single query at a time and the query optimizer relies on distribution statistics when it estimates the resource costs of different methods for extracting information from a table or index.
Similarly, IBM's DB2 UDB database optimizer documentation provides mechanisms for using a query graph model as input, the optimizer portion of the compiler generates many alternative execution plans for satisfying the query. To estimate the execution cost of each of these plans, the optimizer uses statistics for tables, indexes, columns and functions. It then chooses the plan with the smallest estimated execution cost. The optimizer uses the query graph model to analyze the query semantics and to obtain information about a wide variety of factors, including indexes, base tables, derived tables, subqueries, correlations, and recursion. Thus, in current database systems, each SQL query is optimized as a single query based only on statistics for database objects (e.g., tables, indexes) and not statistics or metadata related to the applications or users. Additionally, there is no provision to persist ongoing optimized plans and reuse those based on application and user information.
Thus, as described in greater detail, the embodiments described herein not only addresses the three major limitations of current RDBMS architecture as discussed above but also can provide one or more of the following significant benefits: 1) improved start-up performance, hence reducing the response time for customers; 2) reduced disaster recovery time; 3) the capability in the RDBMS to pre-register applications, modules and queries for optimization, which can shorten application response time; 4) adding a layer of security by providing privileges to a defined set of users for each combination of application and modules; and 5) by building the repository of queries and their related execution plans over time, the database will be able to identify an existing query and execution plans to be reused, which is especially useful where the queries are run in multi-tenancy applications where execution plans can be different for different tenants/orgs.
Applications 110 and 115 can be any type of applications that generate database queries. In one embodiment, the database queries are built of structured query language (SQL) statements; however, other languages can also be supported. In the example of
Query parser/translator 130 operates to parse, translate and/or otherwise process the queries to prepare the queries for execution in the database. In one embodiment, query parser/translator 130 is configured to have access to application and query catalog 170. As described in greater detail below, application and query catalog 170 stores information about queries and applications that generate queries to provide the benefits discussed above.
Query optimizer 140 is configured to have access to query parser/translator 130, application and query catalog 170, query flow optimizer engine 180 and database/catalog 150. Query optimizer 140 operates to optimize queries using information from these sources. In one embodiment, query optimizer 140 can copy a previous optimization for an incoming query from application and query catalog 170. If a query has not been previously optimized, query optimizer 140 can write query optimization information to application and query catalog 170. In one embodiment, query flow optimizer engine 180 can provide predictive information so that optimized queries can be preloaded.
Query optimizer 140 provides optimized queries to query execution engine 150. Query execution engine 150 executes the optimized queries against one or more database nodes (not illustrated in
In one embodiment, query pre-processor 220 functions to establish whether, for a given query arriving for execution (225), there exists a corresponding entry in the application and query catalog (AQC) 235 with a valid and current execution plan. If so, query pre-processor 220 sends the query along with the retrieved execution plan to query-execution engine 270 for execution. If not (225), the query is passed to query parser 230 for further processing. In one embodiment, once an execution plan is generated for the query, the query and execution plan are dispatched to AQC 235 for persistence.
Conceptually, AQC 235 lays out the ground work for application-aware databases. In one embodiment, AQC 235 provides a persistence layer for storing and caching application metadata (e.g., modules, actions, query-flows, tenant and user information and statistics) as well as storing historical execution plans for queries, which can be retrieved for use without the need for a hard parse, given that the plan is not invalidated due to any of the factors that can render it obsolete based on detailed factors already established in existing architectures.
In one embodiment, application metadata not only includes the capability to register applications, modules and/or actions but also includes the capability to register business entities and their relationships rather than simply the standard tables and foreign keys. This is a distinction from existing architectures because the database optimizer can make better decisions during query optimization if it knows the structure of application business entities comprising of individual tables and how business entities are related to each other.
AQC 235 provides the database (or other components) with full knowledge of applications and their components from which queries are expected to arrive for execution. In one embodiment, based on the knowledge of applications, business entities, modules, actions and/or other metadata, individual queries can be optimized and also provide inputs to the query flow optimizer (QFO) engine 280 that for optimization of query-flows rather than individual queries. QFO engine 280 is described in greater detail below.
In one embodiment, each query sent from the query generation engine(s) 210 can carry with it application metadata as discussed above. In one embodiment, the query metadata includes the application, module, and action from where it originated as well, metadata of business entities involved as well as the user's environment and context attributes. Other query metadata configurations can be supported as well. This metadata will be used as inputs to the query and query-flow optimization processes. Once the query has been executed, the query execution statistics (e.g., number of executions, elapsed time, processor utilization, number of rows processed, logical and physical reads, and memory utilization) can be captured and stored in AQC 235.
In one embodiment, in addition to the application and query metadata, AQC 235 can also store tenant metadata along with usage metadata that could include attributes that can be used as inputs for query and query-flow optimization. In one embodiment, this includes capturing and storing metadata and attributes such as which user from which tenant organization typically uses which applications, modules and actions as well as the usage frequency. The application usage patterns of users thus captured and interpreted can provide information for QFO 280 to apply predictive intelligence techniques to prepare query-flows for execution as early as when a user logs in to the application.
As discussed above, QFO 280 optimizes query-flows based on metadata stored in AQC 235. In one embodiment, as applications are used, AQC 235 builds the necessary data about queries and their execution statistics. In one embodiment, QFO 280 utilizes machine learning techniques utilizing the data in AQC 235 to build predictive intelligence in order to form query-flows and store them back in AQC 235 and improve such query-flows based on actual execution statistics in the future.
In one embodiment, supervised learning algorithms can be used for this purpose which make predictions on given set of samples. In these algorithms, models are developed through a training process to make predictions and adjust for correct outcome when mistakes are detected. This is an iterative process that helps the model reach the desired prediction accuracy. As more training data is added, the models become more robust. Such a closed-loop of persisting all the required metadata and statistics and then utilizing machine learning and data science to form and predict query-flows provides the backbone of QFO 280.
In one embodiment, QFO 280 can utilize the intelligence about intermediate result sets of queries and sub-queries that can become useful for re-use as well as optimization of multiple queries and entire query-flows. For example, once all the metadata about applications, modules, actions, business entities, their relationships, users and their query execution patterns are available to the optimizer, the optimizer can make intelligent decisions when optimizing queries and query-flows.
Once query parser 230 has the optimized query, the optimized query is passed (240) to optimizer query transformer 245 that can provide any necessary transformations and pass the query to optimizer estimator 250. In one embodiment, optimizer estimator 250 can utilize query statistics from DBMS catalog 290 to estimate costs for the query that can be used by optimizer plan generator 260 to generate an execution plan for the query. The execution plan can be stored in AQC 235.
The execution plan can be provided to query execution engine 270 for execution against one or more database nodes (not illustrated in
Environment 310 is an environment in which an on-demand database service exists. User system 312 may be any machine or system that is used by a user to access a database user system. For example, any of user systems 312 can be a handheld computing device, a mobile phone, a laptop computer, a work station, and/or a network of computing devices. As illustrated in herein
An on-demand database service, such as application-aware database environment 316, is a database system that is made available to outside users that do not need to necessarily be concerned with building and/or maintaining the database system, but instead may be available for their use when the users need the database system (e.g., on the demand of the users). Some on-demand database services may store information from one or more tenants stored into tables of a common database image to form a multi-tenant database system (MTS). A database image may include one or more database objects. A relational database management system (RDMS) or the equivalent may execute storage and retrieval of information against the database object(s). Application platform 318 may be a framework that allows the applications of application-aware database environment 316 to run, such as the hardware and/or software, e.g., the operating system. In an embodiment, application-aware database environment 316 may include an application platform 318 that enables creation, managing and executing one or more applications developed by the provider of the on-demand database service, users accessing the on-demand database service via user systems 312, or third party application developers accessing the on-demand database service via user systems 312.
The users of user systems 312 may differ in their respective capacities, and the capacity of a particular user system 312 might be entirely determined by permissions (permission levels) for the current user. For example, where a salesperson is using a particular user system 312 to interact with application-aware database environment 316, that user system has the capacities allotted to that salesperson. However, while an administrator is using that user system to interact with application-aware database environment 316, that user system has the capacities allotted to that administrator. In systems with a hierarchical role model, users at one permission level may have access to applications, data, and database information accessible by a lower permission level user, but may not have access to certain applications, database information, and data accessible by a user at a higher permission level. Thus, different users will have different capabilities with regard to accessing and modifying application and database information, depending on a user's security or permission level.
Network 314 is any network or combination of networks of devices that communicate with one another. For example, network 314 can be any one or any combination of a LAN (local area network), WAN (wide area network), telephone network, wireless network, point-to-point network, star network, token ring network, hub network, or other appropriate configuration. As the most common type of computer network in current use is a TCP/IP (Transfer Control Protocol and Internet Protocol) network, such as the global internetwork of networks often referred to as the “Internet” with a capital “I,” that network will be used in many of the examples herein. However, it should be understood that the networks that one or more implementations might use are not so limited, although TCP/IP is a frequently implemented protocol.
User systems 312 might communicate with application-aware database environment 316 using TCP/IP and, at a higher network level, use other common Internet protocols to communicate, such as HTTP, FTP, AFS, WAP, etc. In an example where HTTP is used, user system 312 might include an HTTP client commonly referred to as a “browser” for sending and receiving HTTP messages to and from an HTTP server at application-aware database environment 316. Such an HTTP server might be implemented as the sole network interface between application-aware database environment 316 and network 314, but other techniques might be used as well or instead. In some implementations, the interface between application-aware database environment 316 and network 314 includes load sharing functionality, such as round-robin HTTP request distributors to balance loads and distribute incoming HTTP requests evenly over a plurality of servers. At least as for the users that are accessing that server, each of the plurality of servers has access to the MTS' data; however, other alternative configurations may be used instead.
In one embodiment, application-aware database environment 316, shown in
One arrangement for elements of application-aware database environment 316 is shown in
Several elements in the system shown in
According to one embodiment, each user system 312 and all of its components are operator configurable using applications, such as a browser, including computer code run using a central processing unit such as an Intel Core series processor or the like. Similarly, application-aware database environment 316 (and additional instances of an MTS, where more than one is present) and all of their components might be operator configurable using application(s) including computer code to run using a central processing unit such as processor system 317, which may include an Intel Core series processor or the like, and/or multiple processor units. A computer program product embodiment includes a machine-readable storage medium (media) having instructions stored thereon/in which can be used to program a computer to perform any of the processes of the embodiments described herein. Computer code for operating and configuring application-aware database environment 316 to intercommunicate and to process webpages, applications and other data and media content as described herein are preferably downloaded and stored on a hard disk, but the entire program code, or portions thereof, may also be stored in any other volatile or non-volatile memory medium or device as is well known, such as a ROM or RAM, or provided on any media capable of storing program code, such as any type of rotating media including floppy disks, optical discs, digital versatile disk (DVD), compact disk (CD), microdrive, and magneto-optical disks, and magnetic or optical cards, nanosystems (including molecular memory ICs), or any type of media or device suitable for storing instructions and/or data. Additionally, the entire program code, or portions thereof, may be transmitted and downloaded from a software source over a transmission medium, e.g., over the Internet, or from another server, as is well known, or transmitted over any other conventional network connection as is well known (e.g., extranet, VPN, LAN, etc.) using any communication medium and protocols (e.g., TCP/IP, HTTP, HTTPS, Ethernet, etc.) as are well known. It will also be appreciated that computer code for implementing embodiments can be implemented in any programming language that can be executed on a client system and/or server or server system such as, for example, C, C++, HTML, any other markup language, Java™ JavaScript, ActiveX, any other scripting language, such as VBScript, and many other programming languages as are well known may be used. (Java™ is a trademark of Sun Microsystems, Inc.).
According to one embodiment, each application-aware database environment 316 is configured to provide webpages, forms, applications, data and media content to user (client) systems 312 to support the access by user systems 312 as tenants of application-aware database environment 316. As such, application-aware database environment 316 provides security mechanisms to keep each tenant's data separate unless the data is shared. If more than one MTS is used, they may be located in close proximity to one another (e.g., in a server farm located in a single building or campus), or they may be distributed at locations remote from one another (e.g., one or more servers located in city A and one or more servers located in city B). As used herein, each MTS could include one or more logically and/or physically connected servers distributed locally or across one or more geographic locations. Additionally, the term “server” is meant to include a computer system, including processing hardware and process space(s), and an associated storage system and database application (e.g., OODBMS or RDBMS) as is well known in the art. It should also be understood that “server system” and “server” are often used interchangeably herein. Similarly, the database object described herein can be implemented as single databases, a distributed database, a collection of distributed databases, a database with redundant online or offline backups or other redundancies, etc., and might include a distributed database or storage network and associated processing intelligence.
User system 312, network 314, application-aware database environment 316, tenant data storage 322, and system data storage 324 were discussed above in
Application platform 318 includes an application setup mechanism 438 that supports application developers' creation and management of applications, which may be saved as metadata into tenant data storage 322 by save routines 436 for execution by subscribers as one or more tenant process spaces 404 managed by tenant management process 410 for example. Invocations to such applications may be coded using PL/SOQL 434 that provides a programming language style interface extension to API 432. A detailed description of some PL/SOQL language embodiments is discussed in commonly owned U.S. Pat. No. 7,730,478 entitled, “Method and System for Allowing Access to Developed Applicants via a Multi-Tenant Database On-Demand Database Service”, issued Jun. 1, 2010 to Craig Weissman, which is incorporated in its entirety herein for all purposes. Invocations to applications may be detected by one or more system processes, which manage retrieving application metadata 416 for the subscriber making the invocation and executing the metadata as an application in a virtual machine.
Each application server 400 may be communicably coupled to database systems, e.g., having access to system data 325 and tenant data 323, via a different network connection. For example, one application server 4001 might be coupled via the network 314 (e.g., the Internet), another application server 400N-1 might be coupled via a direct network link, and another application server 400N might be coupled by yet a different network connection. Transfer Control Protocol and Internet Protocol (TCP/IP) are typical protocols for communicating between application servers 400 and the database system. However, it will be apparent to one skilled in the art that other transport protocols may be used to optimize the system depending on the network interconnect used.
In certain embodiments, each application server 400 is configured to handle requests for any user associated with any organization that is a tenant. Because it is desirable to be able to add and remove application servers from the server pool at any time for any reason, there is preferably no server affinity for a user and/or organization to a specific application server 400. In one embodiment, therefore, an interface system implementing a load balancing function (e.g., an F5 BIG-IP load balancer) is communicably coupled between the application servers 400 and the user systems 312 to distribute requests to the application servers 400. In one embodiment, the load balancer uses a least connections algorithm to route user requests to the application servers 400. Other examples of load balancing algorithms, such as round robin and observed response time, also can be used. For example, in certain embodiments, three consecutive requests from the same user could hit three different application servers 400, and three requests from different users could hit the same application server 400. In this manner, application-aware database environment 316 is multi-tenant, wherein application-aware database environment 316 handles storage of, and access to, different objects, data and applications across disparate users and organizations.
As an example of storage, one tenant might be a company that employs a sales force where each salesperson uses application-aware database environment 316 to manage their sales process. Thus, a user might maintain contact data, leads data, customer follow-up data, performance data, goals and progress data, etc., all applicable to that user's personal sales process (e.g., in tenant data storage 322). In an example of a MTS arrangement, since all of the data and the applications to access, view, modify, report, transmit, calculate, etc., can be maintained and accessed by a user system having nothing more than network access, the user can manage his or her sales efforts and cycles from any of many different user systems. For example, if a salesperson is visiting a customer and the customer has Internet access in their lobby, the salesperson can obtain critical updates as to that customer while waiting for the customer to arrive in the lobby.
While each user's data might be separate from other users' data regardless of the employers of each user, some data might be organization-wide data shared or accessible by a plurality of users or all of the users for a given organization that is a tenant. Thus, there might be some data structures managed by application-aware database environment 316 that are allocated at the tenant level while other data structures might be managed at the user level. Because an MTS might support multiple tenants including possible competitors, the MTS should have security protocols that keep data, applications, and application use separate. Also, because many tenants may opt for access to an MTS rather than maintain their own system, redundancy, up-time, and backup are additional functions that may be implemented in the MTS. In addition to user-specific data and tenant specific data, application-aware database environment 316 might also maintain system level data usable by multiple tenants or other data. Such system level data might include industry reports, news, postings, and the like that are sharable among tenants.
In certain embodiments, user systems 312 (which may be client systems) communicate with application servers 400 to request and update system-level and tenant-level data from application-aware database environment 316 that may require sending one or more queries to tenant data storage 322 and/or system data storage 324. Application-aware database environment 316 (e.g., an application server 400 in application-aware database environment 316) automatically generates one or more SQL statements (e.g., one or more SQL queries) that are designed to access the desired information. System data storage 324 may generate query plans to access the requested data from the database.
Each database can generally be viewed as a collection of objects, such as a set of logical tables, containing data fitted into predefined categories. A “table” is one representation of a data object, and may be used herein to simplify the conceptual description of objects and custom objects. It should be understood that “table” and “object” may be used interchangeably herein. Each table generally contains one or more data categories logically arranged as columns or fields in a viewable schema. Each row or record of a table contains an instance of data for each category defined by the fields. For example, a CRM database may include a table that describes a customer with fields for basic contact information such as name, address, phone number, fax number, etc. Another table might describe a purchase order, including fields for information such as customer, product, sale price, date, etc. In some multi-tenant database systems, standard entity tables might be provided for use by all tenants. For CRM database applications, such standard entities might include tables for Account, Contact, Lead, and Opportunity data, each containing pre-defined fields. It should be understood that the word “entity” may also be used interchangeably herein with “object” and “table”.
In some multi-tenant database systems, tenants may be allowed to create and store custom objects, or they may be allowed to customize standard entities or objects, for example by creating custom fields for standard objects, including custom index fields. U.S. patent application Ser. No. 10/817,161, filed Apr. 2, 2004, entitled “Custom Entities and Fields in a Multi-Tenant Database System”, and which is hereby incorporated herein by reference, teaches systems and methods for creating custom objects as well as customizing standard objects in a multi-tenant database system. In certain embodiments, for example, all custom entity data rows are stored in a single multi-tenant physical table, which may contain multiple logical tables per organization. It is transparent to customers that their multiple “tables” are in fact stored in one large table or that their data may be stored in the same table as the data of other customers.
Reference in the specification to “one embodiment” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the invention. The appearances of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment.
While the invention has been described in terms of several embodiments, those skilled in the art will recognize that the invention is not limited to the embodiments described, but can be practiced with modification and alteration within the spirit and scope of the appended claims. The description is thus to be regarded as illustrative instead of limiting.
Number | Name | Date | Kind |
---|---|---|---|
5577188 | Zhu | Nov 1996 | A |
5608872 | Schwartz et al. | Mar 1997 | A |
5649104 | Carleton et al. | Jul 1997 | A |
5715450 | Ambrose et al. | Feb 1998 | A |
5761419 | Schwartz et al. | Jun 1998 | A |
5819038 | Carleton et al. | Oct 1998 | A |
5821937 | Tonelli et al. | Oct 1998 | A |
5831610 | Tonelli et al. | Nov 1998 | A |
5873096 | Lim et al. | Feb 1999 | A |
5918159 | Fomukong et al. | Jun 1999 | A |
5963953 | Cram et al. | Oct 1999 | A |
6092083 | Brodersen et al. | Jul 2000 | A |
6169534 | Raffel et al. | Jan 2001 | B1 |
6178425 | Brodersen et al. | Jan 2001 | B1 |
6189011 | Lim et al. | Feb 2001 | B1 |
6216135 | Brodersen et al. | Apr 2001 | B1 |
6233617 | Rothwein et al. | May 2001 | B1 |
6266669 | Brodersen et al. | Jul 2001 | B1 |
6295530 | Ritchie et al. | Sep 2001 | B1 |
6324568 | Diec | Nov 2001 | B1 |
6324693 | Brodersen et al. | Nov 2001 | B1 |
6336137 | Lee et al. | Jan 2002 | B1 |
D454139 | Feldcamp | Mar 2002 | S |
6367077 | Brodersen et al. | Apr 2002 | B1 |
6393605 | Loomans | May 2002 | B1 |
6405220 | Brodersen et al. | Jun 2002 | B1 |
6434550 | Warner et al. | Aug 2002 | B1 |
6446089 | Brodersen et al. | Sep 2002 | B1 |
6466931 | Attaluri | Oct 2002 | B1 |
6535909 | Rust | Mar 2003 | B1 |
6549908 | Loomans | Apr 2003 | B1 |
6553563 | Ambrose et al. | Apr 2003 | B2 |
6560461 | Fomukong et al. | May 2003 | B1 |
6574635 | Stauber et al. | Jun 2003 | B2 |
6577726 | Huang et al. | Jun 2003 | B1 |
6601087 | Zhu et al. | Jul 2003 | B1 |
6604117 | Lim et al. | Aug 2003 | B2 |
6604128 | Diec | Aug 2003 | B2 |
6609150 | Lee et al. | Aug 2003 | B2 |
6621834 | Scherpbier et al. | Sep 2003 | B1 |
6654032 | Zhu et al. | Nov 2003 | B1 |
6665648 | Brodersen et al. | Dec 2003 | B2 |
6665655 | Warner et al. | Dec 2003 | B1 |
6684438 | Brodersen et al. | Feb 2004 | B2 |
6711565 | Subramaniam et al. | Mar 2004 | B1 |
6724399 | Katchour et al. | Apr 2004 | B1 |
6728702 | Subramaniam et al. | Apr 2004 | B1 |
6728960 | Loomans | Apr 2004 | B1 |
6732095 | Warshavsky et al. | May 2004 | B1 |
6732100 | Brodersen et al. | May 2004 | B1 |
6732111 | Brodersen et al. | May 2004 | B2 |
6754681 | Brodersen et al. | Jun 2004 | B2 |
6763351 | Subramaniam et al. | Jul 2004 | B1 |
6763501 | Zhu et al. | Jul 2004 | B1 |
6768904 | Kim | Jul 2004 | B2 |
6782383 | Subramaniam et al. | Aug 2004 | B2 |
6804330 | Jones et al. | Oct 2004 | B1 |
6826565 | Ritchie et al. | Nov 2004 | B2 |
6826582 | Chatterjee et al. | Nov 2004 | B1 |
6826745 | Coker et al. | Nov 2004 | B2 |
6829655 | Huang et al. | Dec 2004 | B1 |
6842748 | Warner et al. | Jan 2005 | B1 |
6850895 | Brodersen et al. | Feb 2005 | B2 |
6850949 | Warner et al. | Feb 2005 | B2 |
7289976 | Kihneman et al. | Oct 2007 | B2 |
7340411 | Cook | Mar 2008 | B2 |
7548905 | Burger | Jun 2009 | B2 |
7620655 | Larsson et al. | Nov 2009 | B2 |
20010044791 | Richter et al. | Nov 2001 | A1 |
20020022986 | Coker et al. | Feb 2002 | A1 |
20020029161 | Brodersen et al. | Mar 2002 | A1 |
20020029376 | Ambrose et al. | Mar 2002 | A1 |
20020035577 | Brodersen et al. | Mar 2002 | A1 |
20020042264 | Kim | Apr 2002 | A1 |
20020042843 | Diec | Apr 2002 | A1 |
20020072951 | Lee et al. | Jun 2002 | A1 |
20020082892 | Raffel et al. | Jun 2002 | A1 |
20020129352 | Brodersen et al. | Sep 2002 | A1 |
20020140731 | Subramaniam et al. | Oct 2002 | A1 |
20020143997 | Huang et al. | Oct 2002 | A1 |
20020152102 | Brodersen et al. | Oct 2002 | A1 |
20020161734 | Stauber et al. | Oct 2002 | A1 |
20020162090 | Parnell et al. | Oct 2002 | A1 |
20020165742 | Robins | Nov 2002 | A1 |
20030004971 | Gong et al. | Jan 2003 | A1 |
20030018705 | Chen et al. | Jan 2003 | A1 |
20030018830 | Chen et al. | Jan 2003 | A1 |
20030066031 | Laane | Apr 2003 | A1 |
20030066032 | Ramachadran et al. | Apr 2003 | A1 |
20030069936 | Warner et al. | Apr 2003 | A1 |
20030070000 | Coker et al. | Apr 2003 | A1 |
20030070004 | Mukundan et al. | Apr 2003 | A1 |
20030070005 | Mukundan et al. | Apr 2003 | A1 |
20030074418 | Coker | Apr 2003 | A1 |
20030088545 | Subramaniam et al. | May 2003 | A1 |
20030120675 | Stauber et al. | Jun 2003 | A1 |
20030151633 | George et al. | Aug 2003 | A1 |
20030159136 | Huang et al. | Aug 2003 | A1 |
20030187921 | Diec | Oct 2003 | A1 |
20030189600 | Gune et al. | Oct 2003 | A1 |
20030191743 | Brodersen et al. | Oct 2003 | A1 |
20030204427 | Gune et al. | Oct 2003 | A1 |
20030206192 | Chen et al. | Nov 2003 | A1 |
20030225730 | Warner et al. | Dec 2003 | A1 |
20040001092 | Rothwein et al. | Jan 2004 | A1 |
20040010489 | Rio | Jan 2004 | A1 |
20040015981 | Coker et al. | Jan 2004 | A1 |
20040027388 | Berg et al. | Feb 2004 | A1 |
20040128001 | Levin et al. | Jul 2004 | A1 |
20040186860 | Lee et al. | Sep 2004 | A1 |
20040193510 | Catahan, Jr. et al. | Sep 2004 | A1 |
20040199489 | Barnes-Leon et al. | Oct 2004 | A1 |
20040199536 | Barnes-Leon et al. | Oct 2004 | A1 |
20040199543 | Braud et al. | Oct 2004 | A1 |
20040249854 | Barnes-Leon et al. | Dec 2004 | A1 |
20040260534 | Pak et al. | Dec 2004 | A1 |
20040260659 | Chan et al. | Dec 2004 | A1 |
20040268299 | Lei et al. | Dec 2004 | A1 |
20050050555 | Exley et al. | Mar 2005 | A1 |
20050091098 | Brodersen et al. | Apr 2005 | A1 |
20050171945 | Colrain | Aug 2005 | A1 |
20090177744 | Marlow et al. | Jul 2009 | A1 |
20110246449 | Collins | Oct 2011 | A1 |
20120330924 | Rajan | Dec 2012 | A1 |
20140304824 | Sirohi | Oct 2014 | A1 |
20150112965 | Tokuda | Apr 2015 | A1 |
20150149501 | Prakash | May 2015 | A1 |
20150347506 | Annapragada | Dec 2015 | A1 |
20170024433 | Neelakanthappa | Jan 2017 | A1 |
Entry |
---|
“Database PL/SQL Packages and Types Reference”, Oracle Corporation. Database PL/SQL Packages and Types Reference, accessed on World Wide Web at http://docs.oracle.com/database/121/ARPLS/d_appinf.htm#ARPLS65229, Oct. 29, 2016. |
“Database SQL Tuning Guide”, Oracle Corporation. Oracle Database 12c Query Optimizer Concepts accessed on the World Wide Web at https://docs.oracle.com/database/121/TGSQL/tgsql_optcncpt.htm#TGSQL192, Oct. 25, 2016. |
“Force.com Query Optimizer Secrets You Can Use Today”, Salesforce.com. “Force.com Query Optimizer Secrets You Can Use Today” accessed on World Wide Web at https://developer.salesforce.com/blogs/engineering/2013/12/force-com-query-optimizer-secrets-you-can-use-today.html, Oct. 25, 2016. |
“Query Optimization”, Oracle Corporation. Oracle Database 12c Query Optimization overview accessed on the World Wide Web at http://www.oracle.com/technetwork/database/database-technologies/query-optimization/overview/index.html, Oct. 25, 2016. |
“SQL Statement Processing”, Microsoft Corporation. SQL Statement Processing, Optimizing SELECT Statements, accessed on World Wide Web at https://technet.microsoft.com/en-us/library/ms190623(v=sql.105).aspx, Oct. 25, 2016. |
“The SQL and XQuery Compiler Process”, IBM Corporation. The SQL and XQuery compiler process for DB2 UDB Version 11.1 accessed on World Wide Web at http://www.ibm.com/support/knowledgecenter/SSEPGG_9.7.0/com.ibm.db2.luw.admin.perf.doc/doc/c0005292.html, Oct. 25, 2016. |
Bursztyn, Damian, et al., “Teaching an RDBMS About Ontological Constraints”, Proc. VLDB Endow. 9, 12 (Aug. 2016), 1161-1172. DOI: http://dx.DOI.org/10.14778/2994509.2994532. |
Chinneck, John, et al., “Real-Time Multi-Cloud Management Needs Application Awareness”, In Proceedings of the 5th ACM/SPEC international conference on Performance engineering (ICPE '14). ACM, New York, NY, USA, 293-296. DOI=http://dx.DOI.org/10.1145/2568088.2576763. |
Dalvi, Nilesh N., et al., “Pipelining in Multi-Query Optimization”, In Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems (PODS '01). ACM, New York, NY, USA, 59-70. DOI=http://dx.DOI.org/10.1145/375551.375561. |
Du, Linna “Pricing and Resource Allocation in a Cloud Computing Market”, In Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012) (CCGRID '12). IEEE Computer Society, Washington, DC, USA, 817-822. DOI=http://dx.DOI.org/10.1109/CCGrid.2012.148. |
Epstein, Robert, et al., “Design Decisions for the Intelligent Database Machine”, In Proceedings of the May 19-22, 1980, National Computer Conference (AFIPS '80). ACM, New York, NY, USA, 237-241. DOI=http://dx.DOI.org/10.1145/1500518.1500553. |
Haijat, Mohammad, et al., “Dealer: Application-Aware Request Splitting for Interactive CLoud Applications”, In Proceedings of the 8th international conference on Emerging networking experiments and technologies (CoNEXT '12). ACM, New York, NY, USA, 157-168. DOI=http://dx.DOI.org/10.1145/2413176.2413195. |
Kozuka, Nobuaki, et al., “Building a Product Line Architecture for Variant-Rich Enterprise Applications Using a Data-Oriented Approach”, In Proceedings of the 15th International Software Product Line Conference, vol. 2 (SPLC '11), Ina Schaefer, Isabel John, and Klaus Schmid (Eds.). ACM, New York, NY, USA, Article 14, 6 pages. DOI=http://dx.DOI.org/10.1145/2019136.2019152. |
Li, Fei, et al., “Constructing an Interactive Natural Language Interface for Relational Databases”, Proc. VLDB Endow. 8, 1 (Sep. 2014), 73-84. DOI=http://dx.DOI.org/10.14778/2735461.2735468. |
Lin, Xue “Query Optimization Strategies and Implementation Based on Distributed Database”, Computer Science and Information Technology, 2009. ICCSIT 2009. 2nd IEEE International Conference on, Beijing, 2009, pp. 480-484. DOI: 10.1109/ICCSIT.2009.5234507, URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5234507&isnumber=5234374. |
Manegold, Stefan, et al., “A Multi-Query Optimizer for Monet”, In BNCOD, 2000, 15 pgs. |
Mekky, Hesham et al., “Application-Aware Data Plane Processing in SDN”, In Proceedings of the third workshop on Hot topics in software defined networking (HotSDN '14). ACM, New York, NY, USA, 13-18. DOI: http://dx.DOI.org/10.1145/2620728.2620735. |
Narasayya, Vivek, et al., “SQLVM: Performance Isolation in Multi-Tenant Relational Database-as-a-Service”, In Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data (SIGMOD '13). ACM, New York, NY, USA, 1077-1080. DOI: http://dx.DOI.org/10.1145/2463676.2463686. |
Re, Christopher, et al., “Machine Learning and Databases: The Sound of Things to Come or a Cacophony of Hype?”, In Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data (SIGMOD '15). ACM, New York, NY, USA, 283-284. DOI: http://dx.DOI.org/10.1145/2723372.2742911. |
Sirohi, Arjun K., et al., “Context Sensitive Indexes”, May 2016. United States Patent Publication—US 2016-0140178 A1 titled “Context-Sensitive Indexes”, Publication No. “2016-0140178” at http://portal.uspto.gov/pair/PublicPair. |
Sirohi, Arjun K., et al., “Context-Sensitive Indexes in RDBMS for Performance Optimization of SQL Queries in Multi-Tenant/Multi-Application Environments”, In Proceedings of the 17th International Conference on Enterprise Information Systems (ICEIS 2015), Barcelona, Spain, Apr. 27-30, 2015. ISBN: 978-989-758-096-3, pp. 259-270 DOI: 10.5220/0005350802590270. |
Taft, Rebecca et al., “STeP: Scalable Tenant Placement for Managing Database-as-a-Service Deployments”, In Proceedings of the Seventh ACM Symposium on Cloud Computing (SoCC '16), Marcos K. Aguilera, Brian Cooper, and Yanlei Diao (Eds.). ACM, New York, NY, USA, 388-400. DOI: http://dx.DOI.org/10.1145/2987550.2987575. |
Wu, Yongwei et al., “Adapting to Application Workflow in Processing Data Integration Queries”, Sixth International Conference on Grid and Cooperative Computing (GCC 2007), Los Alamitos, CA, 2007, pp. 745-748. DOI: 10.1109/GCC.2007.38 URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4293857&isnumber=4293747. |
Xiong, Pengcheng “Dynamic Manegement of Resources and Workloads for RDBMS in CLoud: a Control-Theoretic Approach”, In Proceedings of the on SIGMOD/PODS 2012 PhD Symposium (PhD '12). ACM, New York, NY, USA, 63-68. DOI=http://dx.DOI.org/10.1145/2213598.2213614. |
Zeng, Qiang et al., “QuickFOIL: Scalable Inductive Logic Programming”, Proc. VLDB Endow. 8, 3 (Nov. 2014), 197-208. DOI=http://dx.DOI.org/10.14778/2735508.2735510. |
Zhang, Aston et al., “Towards Mobile Query Auto-Completion: An Efficient Mobile Application-Aware Approach”, In Proceedings of the 25th International Conference on World Wide Web (WWW '16). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland, 579-590. DOI=http://dx.DOI.org/10.1145/2872427.2882977. |
Zhao, Shuai et al., “Building Application-Aware Network Environments Using SDN for Optimizing Hadoop Applications”, In Proceedings of the 2016 conference on ACM SIGCOMM 2016 Conference (SIGCOMM '16). ACM, New York, NY, USA, 583-584. DOI: http://dx.DOI.org/10.1145/2934872.2959059. |
Non-Final Office Action for U.S. Appl. No. 15/396,291 dated Oct. 5, 2018, 18 pages. |
Final Office Action for U.S. Appl. No. 15/396,291 dated Mar. 21, 2019, 20 pages. |
Non-Final Office Action for U.S. Appl. No. 15/396,291 dated Sep. 18, 2019, 13 pages. |
Final Office Action for U.S. Appl. No. 15/396,291 dated Mar. 5, 2020, 16 pages. |
Number | Date | Country | |
---|---|---|---|
20180189348 A1 | Jul 2018 | US |