Snowflake Inc. patent applications on July 11th, 2024
Patent Applications by Snowflake Inc. on July 11th, 2024
Snowflake Inc.: 9 patent applications
Snowflake Inc. has applied for patents in the areas of G06F16/2455 (3), G06F16/23 (2), G06F16/25 (2), G06F11/14 (1), H04L41/5025 (1) G06F11/1433 (1), G06F16/2365 (1), G06F16/2379 (1), G06F16/24537 (1), G06F16/254 (1)
With keywords such as: data, result, processing, query, execution, subject, error, input, technology, and columns in patent application abstracts.
Patent Applications by Snowflake Inc.
Inventor(s): Vlad Bunescu of Morgan Hill CA (US) for snowflake inc., Yan Huang of Bellevue WA (US) for snowflake inc., Jaeha Lee of Redmond WA (US) for snowflake inc., Shiyu Qu of Bellevue WA (US) for snowflake inc., Jiaqi Yan of Menlo CA (US) for snowflake inc.
IPC Code(s): G06F11/14, G06F11/34, G06F16/21
CPC Code(s): G06F11/1433
Abstract: a method includes updating, by at least one hardware processor, a table with a detected regression associated with database code of a database. a first stored procedure is performed to determine a root cause of the detected regression. a second stored procedure is performed to determine an impact of the detected regression based at least on the root cause. a determination is made on whether to perform mitigation of the detected regression based on the impact.
Inventor(s): Abdullah Al Mahmood of Bothell WA (US) for snowflake inc., Ruta Dhaneshwar of Redmond WA (US) for snowflake inc., Max Heimel of Berlin (DE) for snowflake inc., Xin Huang of Bellevue WA (US) for snowflake inc., Canzhou Qu of Bellevue WA (US) for snowflake inc., Purav B. Saraiya of Kirkland WA (US) for snowflake inc., Konstantinos Zoumpatianos of Berlin (DE) for snowflake inc.
IPC Code(s): G06F16/23, G06F16/25
CPC Code(s): G06F16/2365
Abstract: a data platform including an error handling framework for loading of input data. the data platform generates input data columns based on an input file and generates result data columns based on the input data columns and evaluating expressions. the data platform detects projection errors during the generating of the result data columns and stores result error indicators in error indicator arrays of the result data columns based on the projection errors. the data platform generates filtered result data columns based on the result data columns and the result error indicator arrays of the result data columns and stores the filtered result data columns in a database of the data platform.
20240232173. DISTRIBUTED EXECUTION OF TRANSACTIONAL QUERIES_simplified_abstract_(snowflake inc.)
Inventor(s): Thierry Cruanes of San Mateo CA (US) for snowflake inc., Moritz Eyssen of Berlin (DE) for snowflake inc., Max Heimel of Berlin (DE) for snowflake inc., Lishi Jiang of Bellevue WA (US) for snowflake inc., Alexander Miller of San Mateo CA (US) for snowflake inc.
IPC Code(s): G06F16/23, G06F16/2455
CPC Code(s): G06F16/2379
Abstract: the subject technology receives, at a first execution node, a first transaction, the first transaction to be executed on linearizable storage. the subject technology determines whether the first execution node corresponds to a rank indicating a leader worker. the subject technology, in response to the first execution node corresponding to the rank indicating the leader worker, performs, by the first execution node, an initialization process for executing the first transaction. the subject technology broadcasts a first read timestamp associated with the first transaction to a set of execution nodes, the set of execution nodes being different than the first execution node. the subject technology executes, by the first execution node, at least a first operation from the first transaction.
Inventor(s): Xinzhu Cai of San Mateo CA (US) for snowflake inc., Florian Andreas Funke of San Francisco CA (US) for snowflake inc.
IPC Code(s): G06F16/2453, G06F16/22
CPC Code(s): G06F16/24537
Abstract: provided herein are systems and methods for handling build-side skew. for example, a method includes computing a plurality of hash values for a join operation. the join operation uses a corresponding plurality of row sets. the plurality of hash values are sampled to detect a frequent hash value. a build-side row set is partitioned using the frequent hash value to generate a partitioned build-side row set. the build-side row set is selected from the plurality of row sets. the partitioned build-side row set is distributed to a plurality of hash-join-build (hjb) instances executing at a corresponding plurality of servers.
20240232213. FUTURE SCHEDULER FOR DATABASE SYSTEMS_simplified_abstract_(snowflake inc.)
Inventor(s): Marco H. Kroll et al. of Berlin (DE) for snowflake inc., Mariusz Kwiczala of Berlin (DE) for snowflake inc., Prayag Chandran Nirmala of Seattle WA (US) for snowflake inc., William Waddington of Stateline NV (US) for snowflake inc., Shijie Xu of Seattle WA (US) for snowflake inc.
IPC Code(s): G06F16/25, G06F9/48, G06F16/2455
CPC Code(s): G06F16/254
Abstract: the subject technology receives a query, the query comprising a set of query statements. the subject technology determines that a set of resources to be utilized by the query during execution is provided by a slot, the slot comprising an allocation of resources provided by a virtual warehouse. the subject technology performs a first scheduling process for execution of the query using the slot. the subject technology determines that an available slot is provided by the virtual warehouse. the subject technology executes, using the virtual warehouse, the query using the available slot.
Inventor(s): Tyler Arthur Akidau of Seattle WA (US) for snowflake inc., Istvan Cseri of Seattle WA (US) for snowflake inc., Tyler Jones of Redwood City CA (US) for snowflake inc., Dinesh Chandrakant Kulkarni of Sammamish WA (US) for snowflake inc., Daniel Mills of Seattle WA (US) for snowflake inc., Daniel E. Sotolongo of Seattle WA (US) for snowflake inc., Di Fei Zhang of Redmond WA (US) for snowflake inc.
IPC Code(s): G06F16/27
CPC Code(s): G06F16/273
Abstract: techniques for triggering pipeline execution based on data change (transaction commit) are described. the pipelines can be used for data ingestion or other specified tasks. these tasks can be operational across account, organization, cloud region, and cloud provider boundaries. the tasks can be triggered by commit post-processing. gates in the tasks can be set up to reference change data capture information. if the gate is satisfied, tasks can be executed to set up data pipelines.
20240232226. MULTI-CLUSTER WAREHOUSE_simplified_abstract_(snowflake inc.)
Inventor(s): Thierry Cruanes of San Mateo CA (US) for snowflake inc., Benoit Dageville of Foster City CA (US) for snowflake inc., Florian Andreas Funke of San Francisco CA (US) for snowflake inc., Peter Povinec of Redwood City CA (US) for snowflake inc.
IPC Code(s): G06F16/28, G06F9/50, G06F16/2455, H04L41/0896, H04L41/5025, H04L43/0817, H04L67/1008, H04L67/1097
CPC Code(s): G06F16/283
Abstract: a method implementing a fault-tolerant data warehouse including allocating a plurality of processing units to a data warehouse, the processing units located in different availability zones, an availability zone comprising one or more data centers. the method further includes, as a result of monitoring a number of queries running at an input degree of parallelism on the plurality of processing units of the data warehouse, determining that the number of queries is serviceable by one fewer processing units. the method further includes routing a query from a first processing unit to a second processing unit within the data warehouse, the query having a common session identifier with another query previously provided to the second processing unit, the second processing unit determined to be caching a data segment associated with a cloud storage resource, usable by the query, and removing the first processing unit from the data warehouse.
Inventor(s): Orestis Kostakis of Redmond WA (US) for snowflake inc., Qiming Jiang of Redmond WA (US) for snowflake inc., Boxin Jiang of Sunnyvale CA (US) for snowflake inc.
IPC Code(s): G06N20/00, G06F16/24
CPC Code(s): G06N20/00
Abstract: techniques for managing input and output error of a machine learning (ml) model in a database system are presented herein. test data is generated from successive versions of a database system, the database system comprising a machine learning (ml) model to generate an output corresponding to a function of the database system the test data is used to train an error model to determine an error associated with the output of or an input to the ml model between the successive versions of the database system. in response to the ml model generating a first output based on a first input: the error model adjusts the first output when the error is associated with the output to the ml model and adjusts the first input when the error is associated with the input to the ml model.
Inventor(s): Edward J. Fron of Denver CO (US) for snowflake inc., Nicholas A. Goodman of Durango CO (US) for snowflake inc., Kristl Smith Tyler of Spokane WA (US) for snowflake inc.
IPC Code(s): G06Q50/20, G06F21/54
CPC Code(s): G06Q50/205
Abstract: provided herein are systems and methods for automated, secure, and credential-less evaluation (e.g., grading) of data processing task results (e.g., student/learner data processing lab result or assignment completion result) in a cloud environment using a learning management system (lms). for example, a method includes detecting verification request code received from an account of a data consumer. the verification request code includes a call to an external function and a query statement associated with a task result obtained after completion of a data processing task. the verification request code is revised with metadata to obtain revised verification request code. the call to the external function is executed to cause an evaluation of the task result. a notification of a result of the evaluation is communicated to the account of the data consumer.
- Snowflake Inc.
- G06F11/14
- G06F11/34
- G06F16/21
- CPC G06F11/1433
- Snowflake inc.
- G06F16/23
- G06F16/25
- CPC G06F16/2365
- G06F16/2455
- CPC G06F16/2379
- G06F16/2453
- G06F16/22
- CPC G06F16/24537
- G06F9/48
- CPC G06F16/254
- G06F16/27
- CPC G06F16/273
- G06F16/28
- G06F9/50
- H04L41/0896
- H04L41/5025
- H04L43/0817
- H04L67/1008
- H04L67/1097
- CPC G06F16/283
- G06N20/00
- G06F16/24
- CPC G06N20/00
- G06Q50/20
- G06F21/54
- CPC G06Q50/205
