Salesforce Com, Inc.

Shaahin Mehdinezhad Rushan

Yogesh Patel

Shreedhar Sundaram

Mahalaxmi Sanathkumar

Rajkumar Pellakuru

William Hackett

William Victor Gray

Johannes Kienzle

Anjani Gupta

Percy Dara Mehta

Shan-Cheng Ho

Bhaves Patel

Mattia Padovani

Bertha Ching Wai Lam

Kshama Lalit Thacker

Chandra S Chadalavada

Nima Motamedi

Birva Joshi

Ashwini Govindaraman

Gregory Lapouchnian

Mugdha Choudhari

Clinton Tran

Aaron Zhang

James Fai-Kuen Tam

Tam Duc Tran

Alireza Bakhtiari Koohsorkhi

A method and system of data deduplication for data streams in a multi-tenant system. The method receives, at a data accuracy manager, an event from an activity tracking component, determine whether the event is recorded in a probabilistic model that tracks previously received events from the activity tracking component, where the probabilistic model can accurately identify the event has not been previously received with a possible false positive response where the event has been previously received, determines whether information for the event is stored in a metric storage, where the metric storage is a database of metrics derived from the previously received events, and discards the event in response to determining that the event is recorded in the probabilistic model and in the metric storage.

Mechanism for stream processing efficiency using probabilistic model to reduce data redundancy

A method enables data aggregation in a multi-tenant system. The method includes receiving, at a data aggregation manager, an event from an activity tracking component, generating, by the data aggregation manager, a first hash value based on a first identifier in the event, generating, by the data aggregation manager, a second hash value based on a second identifier in the event, and storing event message information to an entry of a metric storage database in a partition according the first hash value and the second hash value.

Multidimensional partition of data to calculate aggregation at scale

A total number of messages in a set of messages that are processed by a computing node in a virtual computer cluster during a time interval is determined. The virtual computer cluster is deployed with a cloud computing service and includes the computing node and other computing nodes at an end time of the time interval. It is determined whether the total number of messages in the set of messages processed by the computing node is no less than a maximum per-interval message number threshold. In response to determining that the total number of messages is no less than a maximum per-interval message number threshold, the cloud computing service is caused to start an additional computing node in the virtual computer cluster after the time interval, wherein the additional computing node is used to process one or more subsequent messages to be processed after the time interval.

Streaming traffic pattern for public cloud auto scaling

Some embodiments of the present invention include a method for creating, via a first user interface, at least one link in a performance summary to link to supporting information of an employee's work associated with the performance summary. The performance summary including the at least one link is then displayed via the first user interface. The method then detects, via the first user interface, when the at least one link in the performance summary is selected. Responsive to the at least one link being selected, the supporting information of the employee's work associated with the performance summary is displayed via the first user interface.

Systems and methods for performance summary citations

Techniques are disclosed for generating metrics for a stream of interaction data. In some embodiments, a computing system receives a stream of interaction data that is based on user interactions with one or more computing systems, where the stream of interaction data includes information for multiple interaction characteristics including an interaction category. In response to receiving additional interaction information in the stream of interaction data, the computing system may generate cumulative metric values for one or more interaction categories. Generating cumulative metric values may include: sharding the additional interaction information based on multiple characteristics including interaction category, storing the sharded information in a non-relational database, determining respective metric values for shards modified by the storing, storing the respective metric values and shard information for each of the modified shards in a relational database, querying the relational database using one or more interaction categories corresponding to the modified shards, and determining the cumulative metric values based on the results of the querying.

Metric determination for an interaction data stream using multiple databases

A method by a computing system implementing a node within a cluster of nodes to load balance among the cluster of nodes using retroactive refusal. Each of the nodes within the cluster of nodes is capable of processing work items. The method includes accepting a work item to be processed by the node, monitoring a resource utilization of the node, determining whether the resource utilization of the node exceeds a predetermined threshold, and retroactively refusing the work item in response to a determination that the resource utilization of the node exceeds the predetermined threshold, wherein the retroactive refusal causes the work item to be reassigned from the node to another node within the cluster of nodes.

Proactive load-balancing using retroactive work refusal

Dynamic selection of data sources for streaming dynamic data is described. A data streaming service receives a dynamic selection of a data source after the data streaming service begins executing, the dynamic selection of the data source including an identifier of a host of a multi-tenant database and an identifier of a tenant of multiple tenants storing data in the multi-tenant database. The data streaming service streams dynamic data from the data source to a data destination.

Dynamic selection of data sources for streaming dynamic data

A cloud computing service is used to deploy a virtual computer cluster. The virtual computer cluster is initialized with a set of one or more streaming nodes for processing first messages of one or more streaming jobs. It is determined whether the virtual computer cluster is to process second messages of a non-streaming job. In response to determining that the virtual computer cluster is to process the second messages of the non-streaming job, for example using control messages, the cloud computing service is caused to start a non-streaming node in the virtual computer cluster. The non-streaming node is tasked to process the second messages of the non-streaming job.

Control message from streaming source to facilitate scaling

Embodiments regard transfer of data streaming services to provide continuous data flow. An embodiment of an apparatus includes a processor to process data for streaming to one or more organizations; and a memory to store data for streaming to the one or more organizations, wherein the apparatus is to provide a centralized work distribution service to track status of each of a plurality of data streams to the one or more organizations, and a plurality of nodes, each node being a virtual machine to stream one or more data streams to the one or more organizations, each node including a first daemon service to monitor connectivity of the node to dependency services for the node and, upon detecting a loss of connection to one or more of the dependency services, the node to discontinue ownership of the one or more data streams of the node and a second daemon service to poll the centralized work distribution service for data streams that are not assigned.

Transfer of data streaming services to provide continuous data flow

Methods, systems, and devices for asynchronous deletion at a non-relational database are described. The non-relational database may store a set of data object associations. The non-relational database may receive a read request indicating a first data object identifier. Based on the first data object identifier indicated in the read request, the non-relational database may identify a second data object identifier according to a first data object association of the stored set of data object associations that is indexed in the non-relational database with the first data object identifier. The non-relational database may determine that a last data entry that is indexed in the non-relational database with the identified second data object identifier and is associated with the first data object identifier has been deleted from the non-relational database. The non-relational database may delete the first data object association from the non-relational database.

Asynchronous deletion in non-relational databases

A method is described for collapsing a plurality of detected changes to data in a database into a reduced set of changes. The method includes detecting a plurality of changes to the database; determining that one or more changes in the plurality of changes are superfluous to demonstrating the current state of data in the database to an external system; determining, in response to determining that one or more changes in the plurality of changes are superfluous, a reduced set of changes that demonstrates the current state of data in the database based on the plurality of changes, wherein a number of changes in the plurality of changes is greater than a number of changes in the reduced set of changes; and determining a set of messages to generate based on the reduced set of changes, wherein the set of messages demonstrate the current state of data.

Collapsing multiple changes in a database for generating a reduced number of messages for an external system

In accordance with embodiments, there are provided mechanisms and methods for facilitating smart streaming of data between external systems and service providers in an on-demand services environment according to one embodiment. In one embodiment and by way of example, a method comprises splitting, by a streaming server computing device ('streaming device'), a file including messages associated with multiple tenants in a multitenant environment based on growth of the file over a time-period. The method may further include grouping, by the streaming device, the messages into multiple files based on intelligence fields associated with one or more of the messages and the tenants, where each file of the multiple files corresponds to a tenant of the multiple tenants. The method may further include uploading, by the streaming device, the multiple files to a database device associated with a service provider and coupled to the streaming server device.

Smart streaming of data between external systems and service providers in an on-demand environment

Techniques are disclosed relating to bulk data extraction systems. In some embodiments, a streaming server system may receive a first request, from a data storage system, that is sent prior to initiation of a bulk data extraction for a first group of users. In response to the first request, the streaming server system may receive, from the data storage system, a first notification message that includes a particular event identifier for a most recent data event generated at the data storage system. The streaming server system may receive, from the data storage system, those messages associated with the bulk data extraction for the first group. Subsequent to completion of the bulk data extraction, the streaming server system may send, to the data storage system, a request to subscribe to notification messages for data events associated with the first group.

Growing community of inventors

Dublin, CA, United States of America

Shaahin Mehdinezhad Rushan