Salesforce Com, Inc.

Salesforce, Inc.

Johannes Kienzle

Yogesh Patel

Shaahin Mehdinezhad Rushan

Percy Dara Mehta

Shan-Cheng Ho

Rajkumar Pellakuru

Mattia Padovani

William Victor Gray

Mahalaxmi Sanathkumar

Shreedhar Sundaram

William Hackett

Bhaves Patel

Bertha Ching Wai Lam

Birva Joshi

Clinton Tran

Mugdha Choudhari

Aaron Zhang

Syed Mohammad Raza Hussain

Violet Gong

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 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

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 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

Growing community of inventors

Oakland, CA, United States of America

Johannes Kienzle