Salesforce Com, Inc.

Salesforce, Inc.

Yogesh Patel

Shaahin Mehdinezhad Rushan

Mahalaxmi Sanathkumar

Rajkumar Pellakuru

Shreedhar Sundaram

William Hackett

William Victor Gray

Percy Dara Mehta

Shan-Cheng Ho

Johannes Kienzle

Mattia Padovani

Anjani Gupta

Bhaves Patel

Birva Joshi

Bertha Ching Wai Lam

Kshama Lalit Thacker

Chandra S Chadalavada

Zhidong Ke

Utsavi Benani

Heng Zhang

Kevin Terusaki

Jayanth Parayil Kumarji

Shardul Vikram

Priya Sethuraman

Yifeng Liu

Mugdha Choudhari

Clinton Tran

Priyadarshini Mitra

Aaron Zhang

Wing Hing Ku

Evan Jiang

Lucas Ung

Syed Mohammad Raza Hussain

Violet Gong

Various techniques and mechanisms for sharing remote resources among a trusted group are disclosed. A credential management agent utilizes a resource credential for a first user to access a secure resource corresponding to the first user for a second user by at least validating a second user and validating a consent of the first user to allow the second user to access the secure resource using the resource credential for the first user. The secure resource resides on a remote server system accessible via one or more application program interfaces (APIs). A platform management agent provides an interface for shared resource-agnostic credential sharing. The platform management agent validates credentials for the second user as belonging to a trusted group and forwards a request for access to the secure resource for the second user to the credential management agent. In response to receiving the resource credential for the first user to access the secure resource, the platform management agent accesses the secure resource on behalf of the second user to allow the second user to utilize the secure resource.

Techniques and architectures for sharing remote resources among a trusted group of users

An indication to migrate requested data objects stored in a source database environment to a destination database environment is received. Some of data objects have many-to-one relationships with other data objects in the source database environment. At least one snapshot file generated by the source database environment is transferred to a destination database environment in bulk transfer mode. Subsequent incoming data received during bulk transfer mode, after the indication is stored in a temporary table. Upon completion of migration of the requested data objects the system transitions from bulk transfer mode to streaming mode. The subsequent incoming data from the temporary table is transferred to the destination database environment in response to the transition to streaming mode. Additional data received after the temporary table is empty is transferred from the source database environment to the destination database environment without use of the temporary table.

Data transfer resiliency during bulk to streaming transition

A method of syncing data across multiple systems includes: receiving a plurality of calendar events from a plurality of independent calendar systems that use different calendar system specific schemas; aggregating the calendar events at a unifying communication system; converting the calendar events from a calendar system specific schema to a unifying communication system specific schema; storing the plurality of calendar events in the unifying communication system specific schema; converting a calendar event received from a non-master calendar system to the master calendar system specific schema; and sending the converted calendar event to the master calendar system; wherein copies of the received calendar events that are formatted according to the calendar system specific schema of the master calendar system are stored with the master calendar system, and copies of the calendar events that are formatted according to the unifying communication system specific schema are stored with the unifying communication system.

System and method for augmenting synced data across multiple systems to facilitate data cleansing

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 and system that perform cluster node work assignments for tenants in a multi-tenant system. The method includes selecting a partition of a cluster tenant tracking structure having a hierarchical namespace and virtual nodes representing each tenant by a cluster node foreman controller, searching the selected partition for a tenant that is unlocked in the cluster tenant tracking structure, and initiating work for the tenant by the cluster node.

Mechanism for a work node scan process to facilitate cluster scaling

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 method and system manage synchronization of data streams in a multi-tenant system. The method includes tracking by a flow control mechanism a recent flow rate of at least one data stream, determining by the flow control mechanism whether the flow rate of the at least one data stream varies from at least one related data stream, and adjusting by the flow control mechanism a throughput of the at least one data stream to correlate with the at least one related data stream, in response to determining that the at least one data stream has varied from the at least one related data stream.

Mechanism to synchronize, control, and merge data streams of disparate flow characteristics

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

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.

Growing community of inventors

Dublin, CA, United States of America

Yogesh Patel