Databricks, Inc. patent applications on February 20th, 2025
Patent Applications by Databricks, Inc. on February 20th, 2025
Databricks, Inc.: 2 patent applications
Databricks, Inc. has applied for patents in the areas of G06F16/27 (1), G06F16/2455 (1), G06N20/00 (1), G06F18/20 (1), G06F18/2132 (1) G06F16/278 (1), G06N20/00 (1)
With keywords such as: tasks, stateful, rebalancing, models, dataset, cluster, data, across, processing, and building in patent application abstracts.
Patent Applications by Databricks, Inc.
20250061132. STATE REBALANCING IN STRUCTURED STREAMING_simplified_abstract_(databricks, inc.)
Inventor(s): Alexander Balikov of Kirkland WA (US) for databricks, inc., Tathagata Das of New Haven CT (US) for databricks, inc., Karthikeyan Ramasamy of Mountain View CA (US) for databricks, inc.
IPC Code(s): G06F16/27, G06F16/2455
CPC Code(s): G06F16/278
Abstract: a data processing service performs a rebalancing process for rebalancing stateful tasks on a cluster computing system. in one instance, the method for rebalancing stateful tasks is performed such that the per-operator partitions are spread across available executors of a cluster of the cluster computing system with respect to one or more statistics of the tasks. in one instance, the method for rebalancing stateful tasks is also performed such that the total number of stateful tasks are balanced per executor as long as this rebalancing does not imbalance the per-operator placements. in this way, the processing of stateful tasks can be spread across multiple executors in a relatively uniform manner, even though there may be an upfront cost of breaking the local caching on an executor.
Inventor(s): Benjamin Thomas Wilson of Apex NC (US) for databricks, inc., Corey Zumar of Santa Clara CA (US) for databricks, inc.
IPC Code(s): G06N20/00, G06F18/20, G06F18/2132
CPC Code(s): G06N20/00
Abstract: the present application discloses a method, system, and computer system for building a model associated with a dataset. the method includes receiving a data set, the dataset comprising a plurality of keys and a plurality of key-value relationships, determining a plurality of models to build based at least in part on the dataset, wherein determining the plurality of models to build comprises using the dataset format information to identify the plurality of models, building the plurality of models, and optimizing at least one of the plurality of models.
