Salesforce Com, Inc.

Salesforce, Inc.

Siddhi Mehta

Aakash Pradeep

Samarpan Jain

Jan Asita Fernando

Jonathan Bruce

Jay Hurst

Brian Esserlieu

Brian Jaffery Tajuddin

Reuben Cornel

Gopi Krishnan Nambiar

Shangkar Mayanglambam

Zuye Zheng

Thomas J Tobin

Arunkumaran Varadharajan

Jeremiah Lu

Vladislav Eroshin

Daphne Kao

Venkata Vepa

Benson Qiu

Techniques and architectures for data ingestion in an environment having a distributed data storage system. A request to import data into the environment. The data to be imported from an external source through an application programming interface (API). The request is analyzed to determine if the request corresponds to a request to import a large data load. The data is staged if the request is for the large data load. Data management jobs are created to cause the data to be stored in one or more nodes of the distributed data storage system. The data management jobs are transmitted to corresponding nodes in the distributed data storage system. The jobs are executed asynchronously to cause the data to be stored in the nodes.

Techniques and architectures for importation of large data load

Big object bulk data ingest

Disclosed are some implementations of systems, apparatus, methods and computer program products for facilitating policy-based file deletion. Policy-based file deletion is implemented via a tiered system that includes a master computing system and a plurality of slave computing systems. The master computing system distributes policies among the slave computing systems, which each applies assigned policies to cause deletion of files that satisfy those policies.

Memory efficient policy-based file deletion system

A server includes a processing device to execute a resource manager to receive, from a client device, a job to complete a data-processing task using processing resources of a data-processing cluster, and configure a scheduler to be associated with the data-processing cluster and to manage sharing the processing resources with at least a second job. The scheduler includes a job queue. The processing device is further to partition the job queue into a delegator queue and an application queue, wherein the delegator queue is associated with a delegator container and the application queue is associated with a child application container. The processing device is further to manage, in completion of the job, the processing resources of the data-processing cluster according to capacities allocated to the delegator queue and to the application queue, respectively.

Scheduling capacity in a data-processing cluster to an application queue by repurposing monitoring-based capacity of a delegator queue for job execution in the application queue

Systems and methods are described for registering a new column in an existing table on a distributed file system as a partition. In response to receiving a request to add a new column to a selected table, a new table may be created that includes all fields of the selected table, the new column, and a new partition, and the selected table may be renamed within the distributed file system. A view may then be created that points to data in the new table and the renamed selected table, the created view being a virtual table that receives and processes queries. All data from the renamed selected table may be transferred the new table. When the transfer is complete, the new table may be renamed to an original name of the selected table, and any data received after the renaming may be included in the renamed new table.

Adding a partition to an existing table stored on a distributed file system

In accordance with embodiments, there are provided mechanisms and methods for facilitating dynamic cross-block joining of reports in an on-demand services environment in a multi-tenant environment according to one embodiment. In one embodiment and by way of example, a method includes receiving, at a server computing device, a joining request to merge a plurality of reports into a joined report, each of the plurality of reports having data including customer relationship management (CRM) data, where the request is placed at a client computing device, and merging the plurality of report into the joined report. The joined report comprises a single report representing the merged plurality of reports. The method may further include facilitating access, via a user interface at the client computing device, to the plurality of reports represented as the joined report.

Facilitating dynamic cross-block joining of reports in an on-demand services environment

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San Francisco, CA, United States of America

Siddhi Mehta