Patent Grant
10275281
A portion of the disclosure of this patent document contains material, which is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent file or records, but otherwise reserves all copyright rights whatsoever.
This patent document generally relates to log files in a computing environment and, more specifically, to techniques for scheduling jobs to process log files.
“Cloud computing” services provide shared resources, software, and information to computers and other devices upon request. In cloud computing environments, software can be accessible over the Internet rather than installed locally on in-house computer systems. Cloud computing typically involves over-the-Internet provision of dynamically scalable and often virtualized resources. Technological details can be abstracted from the users, who no longer have need for expertise in, or control over, the technology infrastructure “in the cloud” that supports them.
The included drawings are for illustrative purposes and serve only to provide examples of possible structures and operations for the disclosed inventive systems, apparatus, methods and computer program products for scheduling jobs to process log files. These drawings in no way limit any changes in form and detail that may be made by one skilled in the art without departing from the spirit and scope of the disclosed implementations.
Examples of systems, apparatus, methods and computer program products according to the disclosed implementations are described in this section. These examples are being provided solely to add context and aid in the understanding of the disclosed implementations. It will thus be apparent to one skilled in the art that implementations may be practiced without some or all of these specific details. In other instances, certain operations have not been described in detail to avoid unnecessarily obscuring implementations. Other applications are possible, such that the following examples should not be taken as definitive or limiting either in scope or setting.
In the following detailed description, references are made to the accompanying drawings, which form a part of the description and in which are shown, by way of illustration, specific implementations. Although these implementations are described in sufficient detail to enable one skilled in the art to practice the disclosed implementations, it is understood that these examples are not limiting, such that other implementations may be used and changes may be made without departing from their spirit and scope. For example, the operations of methods shown and described herein are not necessarily performed in the order indicated. It should also be understood that the methods may include more or fewer operations than are indicated. In some implementations, operations described herein as separate operations may be combined. Conversely, what may be described herein as a single operation may be implemented in multiple operations.
Some implementations described or referenced herein are directed to different systems, methods, apparatus and computer program products for scheduling jobs to process log files. In some but not all implementations, a database system is used to maintain one or more log files, and the database system can be in the form of a multi-tenant database system. The multiple tenants of the system may include various customer organizations of users who interact with cloud-based applications running on the database system or on a platform associated with the database system. In such systems and in other non-multi-tenant and non-database oriented computing systems and environments in which the present techniques can be implemented, the actions of users when interacting with cloud-based applications may cause data to be generated and/or may cause system events to occur, where some or all of such actions, data, and events can be systematically identified in log files maintained in a database or other repository. Non-limiting examples of system events corresponding to user activity include, by way of illustration, a download, a page load, a login or a logout, a URI which may represent a page click and view, an API call, a report, a record access, an export, or a page request. A system event may be generated in response to any type of user interaction. Such log files can be accessed and analyzed as desired to better understand a history of user activity and/or system events. By way of non-limiting example, a multi-tenant database system may be configured to add, to a log file, data entries identifying corresponding user actions as such actions occur so a system administrator can later analyze the log data for debugging and other analytical purposes.
In some instances, a user affiliated with a tenant organization may want to review some of the log data in a log file. For example, it may be desirable to access a log file storing a history of user login events, where each entry in the log file identifies a user's location at the time the user logged into a system, for example, for the purpose of plotting the locations on a map. However, log files maintained in the same multi-tenant database system may include log data for multiple different tenant organizations. The system administrator may not want to allow one tenant organization to have access to data identifying user activity of another tenant organization. Moreover, the system administrator may set up the log file to generate additional proprietary data of one tenant, such as system performance details or other internal metrics, which should not be shared with other tenants. Accordingly, in some of the disclosed implementations, a server may be configured to parse through a log file maintained by a multi-tenant database service and create different customer-facing log files, where each customer-facing log file has data specific to a particular tenant and is not shared with other tenants. Some types of log entries as well as some types of data fields of the log entries can be automatically excluded from a particular customer-facing log file.
A server generating a customer-facing log file may use a metadata file to identify specific log entry types and data fields to include in the customer-facing log file. In some implementations, a system administrator can specify that only particular types of log entries are provided to the tenant organizations in customer-facing log files. Also or alternatively, only particular fields of the log entries may be provided to the tenant organizations. A database system server can parse through the log files and generate customer-facing log files with log entries specific to a tenant's applications and with the fields of the log entries approved by the developer. Accordingly, a large amount of log file data may be reduced such that each tenant receives a smaller and tailored amount of log file data meaningful to the tenant.
In some implementations, an event log file system provides a declarative metadata framework for providing instructions to a log processing system to process log files for an application. As an example, a Hadoop system including a Hadoop Distributed File System (HDFS) component can be included for storing the log files, and a MapReduce component can be included for processing the log files to generate customer-facing log files. The event log file system may receive a log metadata file that contains instructions for how to process application log files for an application in order to generate customer-facing log files having a particular set of log entries and log entry fields that are designated by the log metadata file. For example, the log metadata file may indicate what types of log entries to include in the customer-facing log file. The log metadata file may also indicate the names and positions of the log entry fields that are to be included in the customer-facing log file. The event log file system then uses this log metadata file to determine how to process the application log files that the event log file system receives.
In some implementations, the log metadata file may be automatically generated based on log entry definition metadata provided by a developer. The log entry definition metadata may be provided to an application server in a log entry definition file. The log entry definition file may contain a description of different types of log entries that may appear in the application log files, as well as the particular fields that appear in each type of log entry. The log entry definition file may also indicate which log entry types and fields should be provided to the customer in a customer-facing log file. The log metadata file may be generated based on the log entry definition file and provided to a job scheduler to perform the log processing.
In some implementations, it is desirable to promote reliability and eventual consistency of customer-facing log files delivered in a multi-tenant database system. Eventual consistency can be based on a consistency model with the goal that if no new updates are made to a given data item, eventually all accesses to that item will return the last updated value. Eventual consistency is often deployed in distributed systems such as multi-tenant database systems.
It is possible that some input log files to be processed to generate customer-facing log files are saved to a database or otherwise identified at a later time than when the input log files were generated. For example, this scenario can result from an application server going offline during some part of the day and coming back online after an initial batch of input log files are streamed for a given hour or other timeframe. In such a scenario, log files may be considered eventually consistent based on a log streaming service picking up a past input log file for a future job to process the log file. In some implementations, a look-back functionality is incorporated in a scheduler to address this scenario. For example, when a new job is defined, a scheduler can check to see if new input log files have been identified for an earlier timeframe and generate new event log file content.
In some implementations, a scheduler for defining and scheduling jobs to process input log files is configurable with a specialized functionality as opposed to a general purpose scheduler like Cron, an open source scheduler generally known to those skilled in the art. For example, a specialized scheduler as disclosed can be configured to run repeatedly on an hourly basis or at another designated time interval for event log file processing. Some implementations of schedulers disclosed herein can be implemented using one or more processors of a server system and can be configured to repeatedly define and schedule new jobs to output customer-facing log files in a manner that customers do not have to de-duplicate log lines of the files that may arrive later than when a customer expects. Some implementations provide for one or more of the following abilities: reliably tracking partially successful jobs, adding eventually consistent log files using a sequence to prevent duplicate log lines, looking back to determine if a new log file from a previous period should be processed, and/or intelligently scheduling new jobs based on the states of multi-tenant organizations and previous job runs.
In some implementations of the disclosed schedulers, a current job can be scheduled in response to a previous job failing, in response to a previous job being partially successful in the context of a multi-tenant database system, for instance, if the work included in the previous job was successfully completed for 90 tenants out of 100 tenants but failed for the remaining 10 tenants, and/or when additional resources are available. In some implementations, a scheduler can also be configured to generate appropriate jobs for processing log files in one or more scenarios including: the scheduler running for the first time, some of the previous job(s) still running or waiting, all previous jobs being successful, no input log files for a given hour, and/or jobs being manually scheduled. Job type metadata can be generated, stored in or otherwise linked with a job to indicate whether a given job is a new job, a re-run in the case of a partial success, or a failure. In some implementations, a retry limit is imposed on the number of times a partially successful job can be re-run, for instance, to avoid processing log files associated with a corrupt organization. In some implementations, a new base platform object (BPO), described in greater detail below, may be created for each job to identify relevant customer-facing log files for a given job rather than updating a previous BPO. In some implementations, a BPO is treated as immutable, with a new BPO being created each time the scheduler schedules a job. Thus, in most scenarios, customers do not need to apply de-duplication logic to the customer-facing log files.
By way of illustration, an example of a scheduler disclosed herein is configured to run on an hourly basis to make logs available to customers. A sequence of hourly timeframes is used to categorize input log files saved to a database or otherwise identified, e.g., 7:00 am-8:00 am, 8:00 am-9:00 am, 9:00 am-10:00 am, etc. The scheduler runs every hour at a 30 minute offset from the end of each timeframe, e.g., at 8:30 am, 9:30 am, 10:30 am, etc. In some implementations, the scheduler is configured to only process log files created in a specified timeframe, such as 7:00 am-8:00 am. Each time the scheduler runs, the scheduler can include in a job any and all newly identified log files created during the 7:00 am-8:00 am timeframe. For instance, a log file created during the 7:00 am-8:00 am timeframe may not be identified until 9:45 am, so the scheduler will include that log file in the 10:30 am job. The scheduler can be configured to look back a specified number of hours, days, weeks, etc. for input log files created for the timeframe of interest.
In some implementations, the scheduler is configured to run only one job per timeframe, e.g., for a given hour of an hourly sequence. For instance, a single job would be scheduled at 10:30 am to process input log files identified in the timeframe of 9:00 am-10:00 am. In some implementations, if no new input log files are identified in a given timeframe, e.g., 9:00 am-10:00 am, the scheduler skips scheduling a job for that timeframe. Thus, in the current example, there would be no job scheduled at 10:30 am, and the scheduler would run again at 11:30 am.
In some implementations, if a previous job is still running or in a waiting state, and even if an input log file is newly identified, the scheduler will not schedule a new job since invariants could be invalidated if the previous job eventually fails. So at 10:30 am, 11:30 am, 12:30 pm, etc., the scheduler will check whether the previous job has reached its final state before scheduling another job.
User systems 110a and 110b may be any type of computing device. For example, user systems 110a and 110b may be portable electronic devices such as smartphones, tablets, laptops, wearable devices (e.g., smart watches), etc. User systems 110a and 110b may be another server or a desktop computer. Additionally, user systems 110a and 110b may be different types of computing devices. For example, user system 110a may be a desktop computer whereas user system 110b may be a smartphone. In some implementations, user systems 110a and/or 110b may be an integration service.
In some implementations, application server 120 may include applications used by different tenants of application server 120. As each client of each tenant interacts with the applications, log entries corresponding to the interactions may be generated by log writer 130 and saved in application log files 125, which may be a content management system, document repository, database or other storage mechanism for log files. At certain times, log streaming service 135 may send one or more log files 125 to the log processing server 105. In some implementations, log files 125, log writer 130, and log streaming service 135 may be integrated within application server 120.
For example, if a tenant's client logs into an application, a corresponding log entry may be stored in a log file in log files 125. The log entry may include a variety of data such as a tenant ID (i.e., a unique identifier associated with the tenant), event type (i.e., a login), location (i.e., the geographic location from which the client logged into the application), timestamp (i.e., when the login occurred), and internal system information (e.g., a server load associated with the login). If another client of another tenant logs into the application, another log entry may be stored in the same log file.
As another example, if a client downloads a file, another log entry may be generated in the same log file or in another log file in log files 125. The new log entry may include data such as the tenant ID, event type (i.e., a download), timestamp (i.e., when the event, or download, occurred), the file name of the downloaded file, and internal system information (e.g., the bandwidth used by the system to provide the download).
At an hourly or other designated interval, such as every 30 minutes, every 2 hours, every 4 hours, etc., log streaming service 135 may obtain the log files 125 and provide the log files to log processing server 105. The log processing server 105 may also receive a log metadata file 140 from the application server. The log metadata file 140 may provide information to the log processing server 105 about the structure of the log files 125 received from the log streaming service 135. Additionally, log metadata file 140 may specify algorithms to be executed to derive data for new data fields from existing data fields in the log entries. The log metadata file 140 can also include algorithms describing the functionality or operations to derive data for the new data fields, as discussed later herein.
Log processing server 105 may receive the log files from log streaming service 135 and, using the log metadata file, parse through the log files and generate customer-facing log files 115 for each of the tenants to be stored in an appropriate database. In some implementations, application server 120 may receive the customer-facing log files 115 from log processing server 105 and then store them in a database. That is, customer-facing log files 115 may include log files specific for each tenant based on the log files provided by application server 120 and the log metadata file. Accordingly, co-mingled data associated with multiple tenants may be split into separate log files.
For example, log files 125 may each include log entries associated with different events. Additionally, each log entry may include a variety of fields associated with the event. As an example, as previously discussed, a download event type log entry may include fields providing data such as the tenant ID, event type, timestamp, file name, and bandwidth information. The log metadata file 140 may indicate which types of log entries (e.g., log entries associated with download events) may be used to generate the customer-facing log files 115. The log metadata file 140 may also indicate which types of fields of the log entries (e.g., event type, timestamp, and file name) may be used to generate the customer-facing log files. That is, the log metadata file 140 may indicate data fields that can be represented in the customer-facing log files and/or the fields that may be purposefully kept away from the tenants.
As such, in some implementations, log processing server 105 receives log files from application server 120 and, using the log metadata file 140, parses through and pares down the data in log files to generate smaller, customer-facing log files with some potentially new types of data that are derived. Log processing server 105 or application server 120 may store the customer-facing log files in customer-facing log files 115, which may be a database or other type of storage. User system 110a and user system 110b may be able to access their respective log files by application server 120. For example, user system 110a may be able to access its own customer-facing log file providing details based on the actions of its clients from log processing server 105. Likewise, user system 110b may be able to access its own customer-facing log file providing details of its clients from log processing server 105. As such, a subset of the data from log files of a multi-tenant database system may be provided to the corresponding individual tenants. Additionally, new types of data can be derived. User-side customer-facing log files 190 may provide log processing activities such as scheduling, generating hash, and event storage at the user-side. In some implementations, customer-facing log files 190 may be integrated with one or both of user systems 110a and 110b.
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The log files may be stored as one or more data objects in a database. By way of example, the user activity and corresponding system events may be associated with an on-demand application hosted by a database system.
For example, at a time when the database system is relatively idle, log files may be transferred from application server 120 of
In this example, each log entry generated upon a client logging in includes five data fields: tenant ID, event, location, timestamp, and server load, as depicted for log entry D in
Log file 310 includes five log entries: log entry E, log entry F, log entry G, log entry H, and log entry X. Each of the five log entries in log file 310 may also be generated upon a client action by the multi-tenant system, similar to log file 305. However, rather than each log entry in log file 310 being generated upon a client logging into an application, log entries E-H in log file 310 may be generated upon a client downloading a file, and log entry X may be generated upon a client using an API. Accordingly, log file 310 includes co-mingled data from multiple tenants as well as co-mingled log entries of different types (e.g., download and API event types). Each of the log entries E-H in log file 310 includes five data fields: tenant ID, event, timestamp, file, and bandwidth. Tenant ID may indicate the particular tenant associated with the client performing the action that result in the generated log entries. “Event” may indicate a type of action that led to the generation of the log entry, for example, “download.” Similar to log file 305, timestamp may be the time when the action was performed. The file data field may indicate the name of the file that was downloaded by the client. Lastly, the bandwidth data field may indicate the bandwidth used by the system to allow for the client to download the file. By contrast, log entry X may include different data fields than log entries E-H because log entry X is for a different event type (i.e., an API use in this example).
A server of the database system generates or updates a metadata file. The metadata file includes information indicating one or more approved entry types and approved data associated with each approved entry type. In some implementations, the metadata file is an extensible markup language (XML) file. The metadata file may correspond to particular log files generated from user interactions with an application.
In some implementations, the metadata file is generated by an application server based on a log entry definition file comprising descriptive data describing each entry of the log file and comprising approval data identifying approved entry types and approved data. In some implementations, the log entry definition file may be an XML file provided by a developer or administrator of the on-demand application provided by the database system.
As an example, a portion of a log entry definition file that may be used to generate the metadata file exemplified above may be the following:
In this example, the log entry definition file provides information for all of the fields that appear in an APEX_CALLOUT_EVENT log entry, as well as the order in which those fields appear. The five fields in this log entry are, in the following order, “type,” “success,” “statusCode,” “responseSize,” and “url.” The log entry definition file also indicates which of these fields should be included in the customer-facing log file by providing the attribute “event_log_field” in the <field> tags for the desired fields. The fields that include an “event_log_field” attribute will appear in the metadata file in a <field> section, and the <field> section of the metadata file will have the value of the “event_log_field” attribute as the <name>. For example, the log entry definition file above indicates that the first (“type”), second (“success”), and fifth (“url”) fields of the log entry should be provided in the metadata file with the names, “TYPE,” “SUCCESS,” and “URL,” respectively. Moreover, the <position> value in the resulting metadata file is based on the position of the <field> tag in the <log-record> definition of the log entry definition file. As such, the positions for the fields indicated in the metadata file above are 1, 2, and 5, respectively.
In some implementations, returning to
A server of the database system generates or updates, based at least on the log file and the metadata file, one or more customer-facing log files. The customer-facing log files may be stored in a content file system, and one or more pointers to the customer-facing log files may be stored as one or more data objects in a database of the database system. Each customer-facing log file may be associated with a corresponding customer entity capable of being serviced by the database system, and each customer-facing log file may include a subset of the entries and a subset of the data items of at least one of the entries. In some implementations, each customer-facing log file is associated with a version of the application hosted by the database system.
In some implementations, the customer entity may be a tenant of the multi-tenant database system hosting the on-demand application that users of the tenant are interacting with. The tenant may be provided with a customer-facing log file that contains a relevant subset of the log entries and log entry data, which is indicated by the approved entry types and approved data provided in the metadata file. As described above, the metadata file provides instructions to the log processing server for determining which log entries to select from the log file, and which fields to select from each log entry.
A server of a database system captures a series of system events as the entries of a log file. The various types of system events that may be captured in the log file are described above. In some implementations, the database is a multi-tenant system, in which the clients (or users) of the tenants using the applications hosted by the multi-tenant system are performing actions that may result in a log entry being generated in a log file hosted by the system. Each log entry may appear as a line in the log file.
The server of the database system access a log file storing data entries identifying system events corresponding to user activity, as generally described above. The server of the database system identifies a log entry definition file associated with the log file. The log entry definition file may be stored as a data object in a database of the database system. The log entry definition file includes descriptive data describing each entry of the log file and includes approval data identifying approved entry types and approved data. As discussed above, in some implementations, the descriptive data may provide a description of log entry types that may appear in the log files for a particular application. The description of a log entry type may include a list of fields that appear in order in a log entry having the log entry type. The server of the database system generates or updates the metadata file based on the descriptive data and approval data of the log entry definition file, as generally described above. The server of the database system selects a subset of the log file entries based on the approved entry types and based on a corresponding customer entity.
In some implementations, the selection of the subset of entries may be initiated by scheduler 415 of
For each selected entry, the server of the database system selects a subset of the data items based on the approved data. For example, in
In some implementations, customer-facing log files may then be generated. In particular, customer-facing log files corresponding to the tenants may be generated based on the selected log entries and data fields.
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The server of the database system stores pointers identifying the customer-facing log files as at least one data object in a database. Each pointer stored by the database system may indicate a location of a corresponding customer-facing log file.
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In some implementations, the customer entities may be capable of accessing the customer-facing log files using an API providing access to the pointers. For example, the tenant may access the customer-facing log files by using API 435. For example, the tenant may use the API 435 to contact BPO 425 to find the pointers for its customer-facing log files based on determining the pointers in a row with a corresponding tenant ID. The tenant may then be provided the customer-facing log files from customer-facing log files 115 based on using the pointers. As a result, BPO 425 allows a user to access, via the API, a database table with the pointers to the stored customer-facing log files. In some implementations, in addition to the pointers, users may also be provided attributes of the customer-facing log files such length (i.e., the file size), log date, and type of log.
In some implementations, the customer-facing log files generated by map reduce logic 410 may be CSV files with each log entry on its own line (e.g., of a text file) with each data field separated by a comma. As a result, tenants may receive the CSV files with the log entries and data fields and use the data to develop their own applications. For example, tenants may be able to plot on a map the geographical locations where clients are downloading files from and determine whether data leakage problems exist, for example, by finding out that a file was downloaded from an unsecure location. Tenants may also use the customer-facing log files for compliance and auditing purposes. Additionally, comingled data may be split into tenant-specific data in tenant-specific customer-facing log files. As such, the customer-facing log files may be integrated into third-party applications developed by applications developed by the tenants.
In some implementations, only specific tenants may be provided with customer-facing log files. For example, tenants may pay to receive customer-facing log files, and therefore, the tenant ID data field in log entries may be analyzed to determine whether the tenant ID belongs to a tenant that pays for the service. Tenants who pay for the service may have their customer-facing log files stored in customer-facing log files 115 and access the logs through API 435. Clients who do not pay may not have any log files in customer-facing log files 115, or may not be able to access any sort of log file in customer-facing log files 115.
The data processed from the customer-facing log files can be visualized, for example, in graphs, charts, infographics, text, etc. in an analytics application. The visualizations can be updated to reflect the data in the new log entries. As a result, the analytics application can be provided with the latest data from the customer-facing log files.
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When current job G1 is created, the input log files to be processed in job G1 are identified in column 624 for row 604 of
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A user system 12 may be implemented as any computing device(s) or other data processing apparatus such as a machine or system used by a user to access a database system 16. For example, any of user systems 12 can be a handheld and/or portable computing device such as a mobile phone, a smartphone, a laptop computer, or a tablet. Other examples of a user system include computing devices such as a work station and/or a network of computing devices. As illustrated in
An on-demand database service, implemented using system 16 by way of example, is a service that is made available to users who do not need to necessarily be concerned with building and/or maintaining the database system. Instead, the database system may be available for their use when the users need the database system, i.e., on the demand of the users. Some on-demand database services may store information from one or more tenants 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 (RDBMS) or the equivalent may execute storage and retrieval of information against the database object(s). Application platform 18 may be a framework that allows the applications of system 16 to run, such as the hardware and/or software, e.g., the operating system. In some implementations, application platform 18 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 12, or third party application developers accessing the on-demand database service via user systems 12.
The users of user systems 12 may differ in their respective capacities, and the capacity of a particular user system 12 might be entirely determined by permissions (permission levels) for the current user. For example, when a salesperson is using a particular user system 12 to interact with system 16, the user system has the capacities allotted to that salesperson. However, while an administrator is using that user system to interact with system 16, 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, also called authorization.
Network 14 is any network or combination of networks of devices that communicate with one another. For example, network 14 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. Network 14 can include a TCP/IP (Transfer Control Protocol and Internet Protocol) network, such as the global internetwork of networks often referred to as the Internet. The Internet will be used in many of the examples herein. However, it should be understood that the networks that the present implementations might use are not so limited.
User systems 12 might communicate with system 16 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 12 might include an HTTP client commonly referred to as a “browser” for sending and receiving HTTP signals to and from an HTTP server at system 16. Such an HTTP server might be implemented as the sole network interface 20 between system 16 and network 14, but other techniques might be used as well or instead. In some implementations, the network interface 20 between system 16 and network 14 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 for users accessing system 16, each of the plurality of servers has access to the MTS' data; however, other alternative configurations may be used instead.
In one implementation, system 16, shown in
One arrangement for elements of system 16 is shown in
Several elements in the system shown in
According to one implementation, each user system 12 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 Pentium® processor or the like. Similarly, system 16 (and additional instances of an MTS, where more than one is present) and all of its components might be operator configurable using application(s) including computer code to run using processor system 17, which may be implemented to include a central processing unit, which may include an Intel Pentium® processor or the like, and/or multiple processor units. Non-transitory computer-readable media can have instructions stored thereon/in, that can be executed by or used to program a computing device to perform any of the methods of the implementations described herein. Computer program code 26 implementing instructions for operating and configuring system 16 to intercommunicate and to process web pages, applications and other data and media content as described herein is preferably downloadable 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 other type of computer-readable medium 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 the disclosed implementations can be realized 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 some implementations, each system 16 is configured to provide web pages, forms, applications, data and media content to user (client) systems 12 to support the access by user systems 12 as tenants of system 16. As such, system 16 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 refer to one type of computing device such as a system including processing hardware and process space(s), an associated storage medium such as a memory device or database, and, in some instances, a 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 objects 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 12, network 14, system 16, tenant data storage 22, and system data storage 24 were discussed above in
Application platform 18 includes an application setup mechanism 38 that supports application developers' creation and management of applications, which may be saved as metadata into tenant data storage 22 by save routines 36 for execution by subscribers as one or more tenant process spaces 54 managed by tenant management process 60 for example. Invocations to such applications may be coded using PL/SOQL 34 that provides a programming language style interface extension to API 32. A detailed description of some PL/SOQL language implementations is discussed in commonly assigned U.S. Pat. No. 7,730,478, titled METHOD AND SYSTEM FOR ALLOWING ACCESS TO DEVELOPED APPLICATIONS VIA A MULTI-TENANT ON-DEMAND DATABASE SERVICE, by Craig Weissman, issued on Jun. 1, 2010, and hereby incorporated by reference in its entirety and for all purposes. Invocations to applications may be detected by one or more system processes, which manage retrieving application metadata 66 for the subscriber making the invocation and executing the metadata as an application in a virtual machine.
Each application server 50 may be communicably coupled to database systems, e.g., having access to system data 25 and tenant data 23, via a different network connection. For example, one application server 501 might be coupled via the network 14 (e.g., the Internet), another application server 50N-1 might be coupled via a direct network link, and another application server 50N 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 50 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 implementations, each application server 50 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 50. In one implementation, therefore, an interface system implementing a load balancing function (e.g., an F5 Big-IP load balancer) is communicably coupled between the application servers 50 and the user systems 12 to distribute requests to the application servers 50. In one implementation, the load balancer uses a least connections algorithm to route user requests to the application servers 50. Other examples of load balancing algorithms, such as round robin and observed response time, also can be used. For example, in certain implementations, three consecutive requests from the same user could hit three different application servers 50, and three requests from different users could hit the same application server 50. In this manner, by way of example, system 16 is multi-tenant, wherein system 16 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 system 16 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 22). 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 system 16 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, system 16 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 implementations, user systems 12 (which may be client systems) communicate with application servers 50 to request and update system-level and tenant-level data from system 16 that may involve sending one or more queries to tenant data storage 22 and/or system data storage 24. System 16 (e.g., an application server 50 in system 16) 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 24 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 according to some implementations. 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 case, account, contact, lead, and opportunity data objects, 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. Commonly assigned U.S. Pat. No. 7,779,039, titled CUSTOM ENTITIES AND FIELDS IN A MULTI-TENANT DATABASE SYSTEM, by Weissman et al., issued on Aug. 17, 2010, and hereby incorporated by reference in its entirety and for all purposes, teaches systems and methods for creating custom objects as well as customizing standard objects in a multi-tenant database system. In certain implementations, 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.
As shown in
Moreover, one or more of the devices in the on-demand database service environment 900 may be implemented on the same physical device or on different hardware. Some devices may be implemented using hardware or a combination of hardware and software. Thus, terms such as “data processing apparatus,” “machine,” “server” and “device” as used herein are not limited to a single hardware device, but rather include any hardware and software configured to provide the described functionality.
The cloud 904 is intended to refer to a data network or combination of data networks, often including the Internet. Client machines located in the cloud 904 may communicate with the on-demand database service environment to access services provided by the on-demand database service environment. For example, client machines may access the on-demand database service environment to retrieve, store, edit, and/or process information.
In some implementations, the edge routers 908 and 912 route packets between the cloud 904 and other components of the on-demand database service environment 900. The edge routers 908 and 912 may employ the Border Gateway Protocol (BGP). The BGP is the core routing protocol of the Internet. The edge routers 908 and 912 may maintain a table of IP networks or ‘prefixes’, which designate network reachability among autonomous systems on the Internet.
In one or more implementations, the firewall 916 may protect the inner components of the on-demand database service environment 900 from Internet traffic. The firewall 916 may block, permit, or deny access to the inner components of the on-demand database service environment 900 based upon a set of rules and other criteria. The firewall 916 may act as one or more of a packet filter, an application gateway, a stateful filter, a proxy server, or any other type of firewall.
In some implementations, the core switches 920 and 924 are high-capacity switches that transfer packets within the on-demand database service environment 900. The core switches 920 and 924 may be configured as network bridges that quickly route data between different components within the on-demand database service environment. In some implementations, the use of two or more core switches 920 and 924 may provide redundancy and/or reduced latency.
In some implementations, the pods 940 and 944 may perform the core data processing and service functions provided by the on-demand database service environment. Each pod may include various types of hardware and/or software computing resources. An example of the pod architecture is discussed in greater detail with reference to
In some implementations, communication between the pods 940 and 944 may be conducted via the pod switches 932 and 936. The pod switches 932 and 936 may facilitate communication between the pods 940 and 944 and client machines located in the cloud 904, for example via core switches 920 and 924. Also, the pod switches 932 and 936 may facilitate communication between the pods 940 and 944 and the database storage 956.
In some implementations, the load balancer 928 may distribute workload between the pods 940 and 944. Balancing the on-demand service requests between the pods may assist in improving the use of resources, increasing throughput, reducing response times, and/or reducing overhead. The load balancer 928 may include multilayer switches to analyze and forward traffic.
In some implementations, access to the database storage 956 may be guarded by a database firewall 948. The database firewall 948 may act as a computer application firewall operating at the database application layer of a protocol stack. The database firewall 948 may protect the database storage 956 from application attacks such as structure query language (SQL) injection, database rootkits, and unauthorized information disclosure.
In some implementations, the database firewall 948 may include a host using one or more forms of reverse proxy services to proxy traffic before passing it to a gateway router. The database firewall 948 may inspect the contents of database traffic and block certain content or database requests. The database firewall 948 may work on the SQL application level atop the TCP/IP stack, managing applications' connection to the database or SQL management interfaces as well as intercepting and enforcing packets traveling to or from a database network or application interface.
In some implementations, communication with the database storage 956 may be conducted via the database switch 952. The multi-tenant database storage 956 may include more than one hardware and/or software components for handling database queries. Accordingly, the database switch 952 may direct database queries transmitted by other components of the on-demand database service environment (e.g., the pods 940 and 944) to the correct components within the database storage 956.
In some implementations, the database storage 956 is an on-demand database system shared by many different organizations. The on-demand database service may employ a multi-tenant approach, a virtualized approach, or any other type of database approach. On-demand database services are discussed in greater detail with reference to
The content batch servers 964 may handle requests internal to the pod. These requests may be long-running and/or not tied to a particular customer. For example, the content batch servers 964 may handle requests related to log mining, cleanup work, and maintenance tasks.
The content search servers 968 may provide query and indexer functions. For example, the functions provided by the content search servers 968 may allow users to search through content stored in the on-demand database service environment.
The file servers 986 may manage requests for information stored in the file storage 998. The file storage 998 may store information such as documents, images, and basic large objects (BLOBs). By managing requests for information using the file servers 986, the image footprint on the database may be reduced.
The query servers 982 may be used to retrieve information from one or more file systems. For example, the query system 982 may receive requests for information from the app servers 988 and then transmit information queries to the NFS 996 located outside the pod.
The pod 944 may share a database instance 990 configured as a multi-tenant environment in which different organizations share access to the same database. Additionally, services rendered by the pod 944 may call upon various hardware and/or software resources. In some implementations, the ACS servers 980 may control access to data, hardware resources, or software resources.
In some implementations, the batch servers 984 may process batch jobs, which are used to run tasks at specified times. Thus, the batch servers 984 may transmit instructions to other servers, such as the app servers 988, to trigger the batch jobs.
In some implementations, the QFS 992 may be an open source file system available from Sun Microsystems® of Santa Clara, Calif. The QFS may serve as a rapid-access file system for storing and accessing information available within the pod 944. The QFS 992 may support some volume management capabilities, allowing many disks to be grouped together into a file system. File system metadata can be kept on a separate set of disks, which may be useful for streaming applications where long disk seeks cannot be tolerated. Thus, the QFS system may communicate with one or more content search servers 968 and/or indexers 994 to identify, retrieve, move, and/or update data stored in the network file systems 996 and/or other storage systems.
In some implementations, one or more query servers 982 may communicate with the NFS 996 to retrieve and/or update information stored outside of the pod 944. The NFS 996 may allow servers located in the pod 944 to access information to access files over a network in a manner similar to how local storage is accessed.
In some implementations, queries from the query servers 922 may be transmitted to the NFS 996 via the load balancer 928, which may distribute resource requests over various resources available in the on-demand database service environment. The NFS 996 may also communicate with the QFS 992 to update the information stored on the NFS 996 and/or to provide information to the QFS 992 for use by servers located within the pod 944.
In some implementations, the pod may include one or more database instances 990. The database instance 990 may transmit information to the QFS 992. When information is transmitted to the QFS, it may be available for use by servers within the pod 944 without using an additional database call.
In some implementations, database information may be transmitted to the indexer 994. Indexer 994 may provide an index of information available in the database 990 and/or QFS 992. The index information may be provided to file servers 986 and/or the QFS 992.
In some implementations, one or more application servers or other servers described above with reference to
While some of the disclosed implementations may be described with reference to a system having an application server providing a front end for an on-demand database service capable of supporting multiple tenants, the disclosed implementations are not limited to multi-tenant databases nor deployment on application servers. Some implementations may be practiced using various database architectures such as ORACLE®, DB2® by IBM and the like without departing from the scope of the implementations claimed.
It should be understood that some of the disclosed implementations can be embodied in the form of control logic using hardware and/or computer software in a modular or integrated manner. Other ways and/or methods are possible using hardware and a combination of hardware and software.
Any of the disclosed implementations may be embodied in various types of hardware, software, firmware, and combinations thereof. For example, some techniques disclosed herein may be implemented, at least in part, by computer-readable media that include program instructions, state information, etc., for performing various services and operations described herein. Examples of program instructions include both machine code, such as produced by a compiler, and files containing higher-level code that may be executed by a computing device such as a server or other data processing apparatus using an interpreter. Examples of computer-readable media include, but are not limited to: magnetic media such as hard disks, floppy disks, and magnetic tape; optical media such as flash memory, compact disk (CD) or digital versatile disk (DVD); magneto-optical media; and hardware devices specially configured to store program instructions, such as read-only memory (ROM) devices and random access memory (RAM) devices. A computer-readable medium may be any combination of such storage devices.
Any of the operations and techniques described in this application may be implemented as software code to be executed by a processor using any suitable computer language such as, for example, Java, C++ or Perl using, for example, object-oriented techniques. The software code may be stored as a series of instructions or commands on a computer-readable medium. Computer-readable media encoded with the software/program code may be packaged with a compatible device or provided separately from other devices (e.g., via Internet download). Any such computer-readable medium may reside on or within a single computing device or an entire computer system, and may be among other computer-readable media within a system or network. A computer system or computing device may include a monitor, printer, or other suitable display for providing any of the results mentioned herein to a user.
While various implementations have been described herein, it should be understood that they have been presented by way of example only, and not limitation. Thus, the breadth and scope of the present application should not be limited by any of the implementations described herein, but should be defined only in accordance with the following and later-submitted claims and their equivalents.
| 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 |
| 5983227 | Nazem et al. | Nov 1999 | A |
| 6092083 | Brodersen et al. | Jul 2000 | A |
| 6161149 | Achacoso et al. | Dec 2000 | A |
| 6169534 | Raffel et al. | Jan 2001 | B1 |
| 6178425 | Brodersen et al. | Jan 2001 | B1 |
| 6189011 | Lim et al. | Feb 2001 | B1 |
| 6216133 | Masthoff | Apr 2001 | B1 |
| 6216135 | Brodersen et al. | Apr 2001 | B1 |
| 6233617 | Rothwein et al. | May 2001 | B1 |
| 6236978 | Tuzhilin | May 2001 | B1 |
| 6266669 | Brodersen et al. | Jul 2001 | B1 |
| 6288717 | Dunkle | Sep 2001 | B1 |
| 6295530 | Ritchie et al. | Sep 2001 | B1 |
| 6324568 | Diec et al. | Nov 2001 | B1 |
| 6324693 | Brodersen et al. | Nov 2001 | B1 |
| 6336137 | Lee et al. | Jan 2002 | B1 |
| 6347374 | Drake | Feb 2002 | B1 |
| D454139 | Feldcamp et al. | Mar 2002 | S |
| 6367077 | Brodersen et al. | Apr 2002 | B1 |
| 6393605 | Loomans | May 2002 | B1 |
| 6405220 | Brodersen et al. | Jun 2002 | B1 |
| 6411949 | Schaffer | Jun 2002 | B1 |
| 6434550 | Warner et al. | Aug 2002 | B1 |
| 6446089 | Brodersen et al. | Sep 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 et al. | 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 et al. | 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 |
| 6772229 | Achacoso et al. | Aug 2004 | B1 |
| 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 | 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 |
| 6907566 | McElfresh et al. | Jun 2005 | B1 |
| 6973491 | Staveley | Dec 2005 | B1 |
| 7062502 | Kesler | Jun 2006 | B1 |
| 7069231 | Cinarkaya et al. | Jun 2006 | B1 |
| 7069497 | Desai | Jun 2006 | B1 |
| 7100111 | McElfresh et al. | Aug 2006 | B2 |
| 7181758 | Chan | Feb 2007 | B1 |
| 7269590 | Hull et al. | Sep 2007 | B2 |
| 7289976 | Kihneman et al. | Oct 2007 | B2 |
| 7340411 | Cook | Mar 2008 | B2 |
| 7356482 | Frankland et al. | Apr 2008 | B2 |
| 7373599 | McElfresh et al. | May 2008 | B2 |
| 7401094 | Kesler | Jul 2008 | B1 |
| 7406501 | Szeto et al. | Jul 2008 | B2 |
| 7412455 | Dillon | Aug 2008 | B2 |
| 7454509 | Boulter et al. | Nov 2008 | B2 |
| 7508789 | Chan | Mar 2009 | B2 |
| 7599935 | La Rotonda et al. | Oct 2009 | B2 |
| 7603331 | Tuzhilin et al. | Oct 2009 | B2 |
| 7603483 | Psounis et al. | Oct 2009 | B2 |
| 7620655 | Larsson et al. | Nov 2009 | B2 |
| 7644122 | Weyer et al. | Jan 2010 | B2 |
| 7668861 | Steven | Feb 2010 | B2 |
| 7698160 | Beaven et al. | Apr 2010 | B2 |
| 7730478 | Weissman | Jun 2010 | B2 |
| 7747648 | Kraft et al. | Jun 2010 | B1 |
| 7779039 | Weissman et al. | Aug 2010 | B2 |
| 7779475 | Jakobson et al. | Aug 2010 | B2 |
| 7827208 | Bosworth et al. | Nov 2010 | B2 |
| 7853881 | Aly Assal et al. | Dec 2010 | B1 |
| 7945653 | Zukerberg et al. | May 2011 | B2 |
| 7991744 | Saha | Aug 2011 | B2 |
| 8005896 | Cheah | Aug 2011 | B2 |
| 8014943 | Jakobson | Sep 2011 | B2 |
| 8015495 | Achacoso et al. | Sep 2011 | B2 |
| 8032297 | Jakobson | Oct 2011 | B2 |
| 8073850 | Hubbard et al. | Dec 2011 | B1 |
| 8082301 | Ahlgren et al. | Dec 2011 | B2 |
| 8095413 | Beaven | Jan 2012 | B1 |
| 8095531 | Weissman et al. | Jan 2012 | B2 |
| 8095594 | Beaven et al. | Jan 2012 | B2 |
| 8103611 | Tuzhilin et al. | Jan 2012 | B2 |
| 8150913 | Cheah | Apr 2012 | B2 |
| 8209308 | Rueben et al. | Jun 2012 | B2 |
| 8209333 | Hubbard et al. | Jun 2012 | B2 |
| 8275836 | Beaven et al. | Sep 2012 | B2 |
| 8457545 | Chan | Jun 2013 | B2 |
| 8484111 | Frankland et al. | Jul 2013 | B2 |
| 8490025 | Jakobson et al. | Jul 2013 | B2 |
| 8504945 | Jakobson et al. | Aug 2013 | B2 |
| 8510045 | Rueben et al. | Aug 2013 | B2 |
| 8510664 | Rueben et al. | Aug 2013 | B2 |
| 8566301 | Rueben et al. | Oct 2013 | B2 |
| 8646103 | Jakobson et al. | Feb 2014 | B2 |
| 8973106 | Warshavsky et al. | Mar 2015 | B2 |
| 20010044791 | Richter et al. | Nov 2001 | A1 |
| 20020072951 | Lee et al. | Jun 2002 | A1 |
| 20020082892 | Raffel et al. | Jun 2002 | A1 |
| 20020129352 | Brodersen et al. | Sep 2002 | A1 |
| 20020138321 | Yuan | Sep 2002 | A1 |
| 20020140731 | Subramaniam et al. | Oct 2002 | A1 |
| 20020143997 | Huang et al. | Oct 2002 | A1 |
| 20020162090 | Parnell et al. | Oct 2002 | A1 |
| 20020165742 | Robbins | Nov 2002 | A1 |
| 20030004971 | Gong | Jan 2003 | A1 |
| 20030018705 | Chen et al. | Jan 2003 | A1 |
| 20030018830 | Chen et al. | Jan 2003 | A1 |
| 20030066031 | Laane et al. | Apr 2003 | A1 |
| 20030066032 | Ramachandran 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 et al. | Apr 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 et al. | Oct 2003 | A1 |
| 20030189600 | Gune 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 et al. | 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 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 |
| 20050262170 | Girkar | Nov 2005 | A1 |
| 20050273655 | Chow | Dec 2005 | A1 |
| 20070168704 | Connolly | Jul 2007 | A1 |
| 20080249972 | Dillon | Oct 2008 | A1 |
| 20090063415 | Chatfield et al. | Mar 2009 | A1 |
| 20090100342 | Jakobson | Apr 2009 | A1 |
| 20090177744 | Marlow et al. | Jul 2009 | A1 |
| 20110218958 | Warshavsky et al. | Sep 2011 | A1 |
| 20110247051 | Bulumulla et al. | Oct 2011 | A1 |
| 20120030172 | Pareek | Feb 2012 | A1 |
| 20120042218 | Cinarkaya et al. | Feb 2012 | A1 |
| 20120150925 | Gupta | Jun 2012 | A1 |
| 20120233137 | Jakobson et al. | Sep 2012 | A1 |
| 20120290407 | Hubbard et al. | Nov 2012 | A1 |
| 20130212497 | Zelenko et al. | Aug 2013 | A1 |
| 20130218948 | Jakobson | Aug 2013 | A1 |
| 20130218949 | Jakobson | Aug 2013 | A1 |
| 20130218966 | Jakobson | Aug 2013 | A1 |
| 20130247216 | Cinarkaya et al. | Sep 2013 | A1 |
| 20140006441 | Torman et al. | Jan 2014 | A1 |
| 20140013400 | Warshavsky et al. | Jan 2014 | A1 |
| 20140074905 | Schincariol | Mar 2014 | A1 |
| 20140095553 | Panteleenko | Apr 2014 | A1 |
| 20140156722 | Hinni | Jun 2014 | A1 |
| 20140188986 | Levy | Jul 2014 | A1 |
| 20140214765 | Jiang | Jul 2014 | A1 |
| 20140359537 | Jakobson et al. | Dec 2014 | A1 |
| 20150006289 | Jakobson et al. | Jan 2015 | A1 |
| 20150007050 | Jakobson et al. | Jan 2015 | A1 |
| 20150095162 | Jakobson et al. | Apr 2015 | A1 |
| 20150106736 | Torman et al. | Apr 2015 | A1 |
| 20150127670 | Torman et al. | May 2015 | A1 |
| 20150142596 | Jakobson et al. | May 2015 | A1 |
| 20150142844 | Bruce | May 2015 | A1 |
| 20150172563 | Jakobson et al. | Jun 2015 | A1 |
| 20160041976 | Pradeep et al. | Feb 2016 | A1 |
| 20160105409 | Torman et al. | Apr 2016 | A1 |
| 20170075922 | Torman et al. | Mar 2017 | A1 |
| 20170076012 | Sreenivasa et al. | Mar 2017 | A1 |
| 20180025113 | Torman et al. | Jan 2018 | A1 |
| Entry |
|---|
| U.S. Appl. No. 15/097,090, filed Apr. 12, 2016, Torman, et al. |
| U.S. Appl. No. 15/097,102, filed Apr. 12, 2016, Sreenivasa, et al. |
| U.S. Appl. No. 15/218,468, filed Jul. 25, 2016, Torman, et al. |
| “Google Plus Users”, Google+Ripples, Oct. 31, 2011 [retrieved on Feb. 21, 2012 from Internet at http://www.googleplusers.com/google-ripples.html], 3 pages. |
| Number | Date | Country | |
|---|---|---|---|
| 20180095790 A1 | Apr 2018 | US |
