Salesforce Com, Inc.

Salesforce, Inc.

Oracle International Corporation

Percy Dara Mehta

Shan-Cheng Ho

Yogesh Patel

Mattia Padovani

Shaahin Mehdinezhad Rushan

Jayanth Parayil Kumarji

Johannes Kienzle

Clinton Tran

Kevin Terusaki

Birva Joshi

Shardul Vikram

Kshama Lalit Thacker

Mugdha Choudhari

Wing Hing Ku

Jesse Yarbro Collins

Ramesh Vasudevan

Wenhao Liu

Rajkumar Pellakuru

Shreedhar Sundaram

Zhidong Ke

Kayvaan Ghassemieh

Utsavi Benani

Heng Zhang

Alexis Roos

Noah William Burbank

Rohit Deshpande

Priya Sethuraman

Robert Christopher Ames

Yifeng Liu

Aaron Zhang

Madeleine Mary Gill

Joseph Gerald Keller

William Christopher Fama Roller

Alex Michael Noe

Madhav Vaidyanathan

Xu Yang

Janelle Wen Hui Teng

Abraham Dio Suharli

Nelson Esteban Acevedo

Sandeep Raju Prabhakar

Lucas Ung

Nishkam Agrawal

Sammy Adnan Nammari

Ascander Dost

Shuvajit Das

Oliver Qian Tang

Bhaskar Garg

A method and system uses a test cloud system for monitoring cloud to cloud performance. The method includes initiating a first trust relationship by the test cloud system with a first target cloud system, receiving a success indicator for the trust relationship, in response to the first target cloud verifying the test cloud system is whitelisted, generating a security token using a private key of the test cloud system, and invoking a function of an application programming interface of the first target cloud using the security token to validate functionality of the function of the application programming interface of the first target cloud.

Cloud to cloud test set up for authentication and monitoring

A system and method for rotating private encryption keys for tenants of a database system has been developed. First, three separate public-private encryption keys are generated for a tenant of the database system. The three separate private encryption keys for the tenant are then stored in cloud-based storage. A defined cadence is created to rotate the private encryption keys for the tenant. The three separate private encryption keys for the tenant are defined as a a past private key, a present private key and a future private key. Next, the public encryption key is stored for the tenant in a global tenant directory. The present private key and the public encryption key are retrieved to encrypt and decrypt data from the tenant. The three separate private encryption keys are rotated at the defined cadence, where the past private key is discarded, the present private key becomes a new past private key, the future private key becomes a new present private key, and a new future private key is generated. The new past private key, the new present private key and the new future private key for the tenant are then stored in cloud-based storage.

Centrally rotating private/public encryption keys in a large scale system

The techniques may include maintaining a thread of events for a plurality of users, where each element of the thread corresponds to an event/activity and includes at least a next field that includes a first subset of the plurality of users and a previous field that includes a second subset of the plurality of users. These techniques may allow new events to be quickly added and prior elements may be updated to reflect the addition. Further, the thread elements may allow the thread to be quickly traversed to identify queried information such that the information may be displayed to a user.

User specific event threading

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

Managing mutations in a data lake environment. A mutation request to cause write operations that modify data objects or structures within an environment for collecting unformatted raw data is received. The environment has at least a data table and a notification table. An entry is written to the data table with a streaming job configured to receive and process the mutation request. Entries to the data table specify at least records indicating changes to objects in the environment based on ingestion processing for the environment for collecting unformatted raw data and based on the mutation request. A corresponding entry is written to the notification table in response to a successful write attempt to the data table. The notification table entry has information about data table entries for a specified period. At least one data consumer is notified that the data table has been modified.

Techniques and architectures for managing cascading mutations in an environment having a data lake

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

Methods, systems, and devices for analyzing communication messages (e.g., emails) and selecting corresponding actions are described. In some database systems, a user may receive multiple messages at a user device. To efficiently determine responses to these messages, the user device may send the messages to a backend server for analysis. The server may perform natural language processing (NLP) to classify the message with one or more binary classifications and may extract metadata from each message. Based on the classifications and the metadata, the server may determine one or more actions the user device may perform to respond to each message. The server may send instructions to the user device indicating the suggested actions, and the user device may display these actions as options to a user. Additionally, the user device may use the classifications and metadata to automatically generate one or more communication templates in response to the message.

Action response selection based on communication message analysis

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 computer that is programmed with collaboration software receives a new value of an attribute (or property or characteristic) which is specific to a label (or tag or category) as applied to an object managed by the computer. The computer does not use the new value to overwrite a corresponding old value in existing metadata. Instead, the computer is programmed to hold the new value in an incremental label application for the object. The computer is further programmed to respond to a request for the object's label application, by retrieving the incremental label application, and a full application of said label to said object. The computer uses attribute values in the incremental label application to update corresponding attribute values in a copy of the full label application, in a time sequence based on the time of creation of incremental label applications, thereby to generate a current label application.

Shared storage of categorization, labeling or tagging of objects in a collaboration system

The technology performs database access control in a manner that decreases computational cost of the database access control with an object type definition of a database object that permit multiple parent objects. The system determines whether to grant a user access to a database object via a first set of access control paths that do not rely on whether the user has permission to access a minimum number of parent objects of the database object. Responsive to a determination not to grant the user access via the first set of access control paths, the system determines whether to grant the user access to the database object via a second set of access control paths that determine whether the user has permission to access the minimum number of parent objects of the database object.

Determining accessibility of database objects with multiple parents

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Foster City, CA, United States of America

Percy Dara Mehta