View source for Databricks, Inc. patent applications on August 1st, 2024

=Patent Applications by Databricks, Inc. on August 1st, 2024=



[[:Category:Databricks, Inc.|Databricks, Inc.]]: 7 patent applications[[Category:Databricks, Inc.]]

Databricks, Inc. has applied for patents in the areas of [[:Category:G06F16/22|G06F16/22]] (5), [[:Category:G06F11/34|G06F11/34]] (3), [[:Category:G06F16/2453|G06F16/2453]] (2), [[:Category:G06F16/2455|G06F16/2455]] (2), [[:Category:G06F9/50|G06F9/50]] (1)
[[:Category:CPC_G06F16/24558|G06F16/24558]] (2), [[:Category:CPC_G06F9/5077|G06F9/5077]] (1), [[:Category:CPC_G06F11/3612|G06F11/3612]] (1), [[:Category:CPC_G06F16/2448|G06F16/2448]] (1), [[:Category:CPC_G06F16/24539|G06F16/24539]] (1)

With keywords such as: query, database, column, data, udf, based, value, dictionary, dataset, and filter in patent application abstracts.




====Patent Applications by Databricks, Inc.====


[[20240256360. NUMA AWARENESS ARCHITECTURE FOR VM-BASED CONTAINER IN KUBERNETES ENVIRONMENT_simplified_abstract_(databricks, inc.)]]

Inventor(s): Shuo Chen of San Francisco CA (US) for databricks, inc., Yuming Qiao of San Francisco CA (US) for databricks, inc., Anders Liu of San Francisco CA (US) for databricks, inc.

IPC Code(s): G06F9/50, G06F9/455

CPC Code(s): G06F9/5077




Abstract: disclosed herein is a method for resource management in a web-based container orchestrating environment. a disclosed method includes initializing a set of micro-virtual machines (vms) within a macro-vm environment. the method each container within a micro-vm based sandbox. the method assigns a virtual central processing unit (vcpu) to a micro-vm based on an estimated memory required by the micro-vm and the estimated available memory associated with the vcpu. the method pins the vcpu with a physical cpu based on the pod location of the physical cpu and an estimated available memory associated with the vcpu and an available local memory of the physical cpu. the method maintains a state of the vcpu and the physical cpu in a resource manager.[[Category:G06F9/50]][[Category:G06F9/455]][[Category:CPC_G06F9/5077]][[Category:databricks, inc.]]



[[20240256426. RUNTIME ERROR ATTRIBUTION FOR DATABASE QUERIES SPECIFIED USING A DECLARATIVE DATABASE QUERY LANGUAGE_simplified_abstract_(databricks, inc.)]]

Inventor(s): Gengliang Wang of San Francisco CA (US) for databricks, inc., Wenchen Fan of Hangzhou  (CN) for databricks, inc., Serge Rielau of San Francisco CA (US) for databricks, inc., Entong Shen of San Francisco CA (US) for databricks, inc.

IPC Code(s): G06F11/36, G06F16/25, G06F16/901

CPC Code(s): G06F11/3612




Abstract: a system executes database queries specified using a declarative database query language such as the structured query language (sql). the system determines whether a runtime error is encountered during execution of a query, for example, a division by zero error, resource usage errors such as out of memory error, time out error, and so on. the system reports such runtime errors encountered during execution of a database query. the system identifies one or more origins of the runtime error in the database query. the origin identifies a portion of the database query that represents a cause of the runtime error. reporting the origin of a runtime error in the database query simplifies the task of development and testing of database queries.[[Category:G06F11/36]][[Category:G06F16/25]][[Category:G06F16/901]][[Category:CPC_G06F11/3612]][[Category:databricks, inc.]]



[[20240256531. EXECUTION AND ATTESTATION OF USER DEFINED FUNCTIONS IN DATABASES_simplified_abstract_(databricks, inc.)]]

Inventor(s): Martin Grund of San Francisco CA (US) for databricks, inc., Herman Rudolf Petrus Catharina van Hövell tot Westerflier of San Francisco CA (US) for databricks, inc., Stefania Leone of San Francisco CA (US) for databricks, inc.

IPC Code(s): G06F16/242, G06F16/22, G06F21/60

CPC Code(s): G06F16/2448




Abstract: a system executes user defined functions (udfs) invoked by database queries. the udf includes udf code specified using a programing language distinct from a database query language. a hash value from the udf code provided by a client application for creating the udf is compared with a hash value generated from udf code invoked by database queries to determine whether the two udf codes match. if the two hash values fail to match, the system takes an action, for example, storing an indication of udf code mismatch or disabling subsequent executions of the database queries invoking the udf. the system may use encoded udf code that is decoded by the system at runtime using a key obtained from a separate system such as the client application. the client application can disable execution of database queries executing the udf code by refusing to provide the key.[[Category:G06F16/242]][[Category:G06F16/22]][[Category:G06F21/60]][[Category:CPC_G06F16/2448]][[Category:databricks, inc.]]



[[20240256539. STATIC APPROACH TO LAZY MATERIALIZATION IN DATABASE SCANS USING PUSHED FILTERS_simplified_abstract_(databricks, inc.)]]

Inventor(s): Shoumik Palkar of San Francisco CA (US) for databricks, inc., Alexander Behm of San Francisco CA (US) for databricks, inc., Mostafa Mokhtar of San Francisco CA (US) for databricks, inc., Sriram Krishnamurthy of San Francisco CA (US) for databricks, inc.

IPC Code(s): G06F16/2453, G06F16/22

CPC Code(s): G06F16/24539




Abstract: disclosed herein is a method for determining whether to apply a lazy materialization technique to a query run. the method includes receiving a request to perform a new query in a columnar database containing a plurality of columns. a step in the method includes accessing a set of data in a column of the plurality of columns based on the query. the method includes generating an input to a machine-learned model comprising characteristics of the set of data in the column. from the machine-learned model, the method includes generating a likelihood value indicative of whether a filter of a first portion of the set of data in the column has greater efficiency than a download followed by a filter of the set of data in the column. the method further includes comparing the likelihood value to a threshold value. based on the comparison, the method includes filtering the first portion of the set of data before downloading the set of data if the likelihood value is equal to or above the threshold value.[[Category:G06F16/2453]][[Category:G06F16/22]][[Category:CPC_G06F16/24539]][[Category:databricks, inc.]]



[[20240256543. Adaptive Approach to Lazy Materialization in Database Scans using Pushed Filters_simplified_abstract_(databricks, inc.)]]

Inventor(s): Shoumik Palkar of San Francisco CA (US) for databricks, inc., Alexander Behm of San Francisco CA (US) for databricks, inc., Mostafa Mokhtar of San Francisco CA (US) for databricks, inc., Sriram Krishnamurthy of San Francisco CA (US) for databricks, inc.

IPC Code(s): G06F16/2453, G06F11/34, G06F16/22

CPC Code(s): G06F16/24545




Abstract: disclosed herein is a method for determining whether to apply a lazy materialization technique to a query run. a data processing service receives a request to perform a query identifying a filter column and a non-filter column in a columnar database. the data processing service accesses a first task of contiguous rows in the filter column from a cloud-based object storage. the data processing service applies a filter defined by the query to the first task. the data processing service generates filter results for the first task that may include a percentage of the first task discarded and a run-time. the data processing service determines, based on the filter results for the first task, a likelihood value that indicates a likelihood of gaining a performance benefit by applying the lazy materialization technique to a second task of the query.[[Category:G06F16/2453]][[Category:G06F11/34]][[Category:G06F16/22]][[Category:CPC_G06F16/24545]][[Category:databricks, inc.]]



[[20240256549. Evaluating Expressions Over Dictionary Data_simplified_abstract_(databricks, inc.)]]

Inventor(s): Utkarsh Agarwal of San Francisco CA (US) for databricks, inc., Shoumik Palkar of San Francisco CA (US) for databricks, inc., Alexander Behm of San Francisco CA (US) for databricks, inc., Sriram Krishnamurthy of San Francisco CA (US) for databricks, inc.

IPC Code(s): G06F16/2455, G06F11/34, G06F16/22

CPC Code(s): G06F16/24558




Abstract: disclosed herein is a method, system, or non-transitory computer readable medium for evaluating a query on a columnar dataset comprising one or more dictionaries associated with columns in the dataset. the method includes receiving a request to perform a query comprising at least an operator for a columnar dataset on cloud storage. at least one column in the dataset is based on a dictionary, and the dictionary maps one or more values for a column to one or more respective identifiers. the method evaluates the operator on one or more values of the dictionary to generate an updated dictionary comprising updated values. the method may decode the updated dictionary into an updated column comprising updated data values.[[Category:G06F16/2455]][[Category:G06F11/34]][[Category:G06F16/22]][[Category:CPC_G06F16/24558]][[Category:databricks, inc.]]



[[20240256550. Dictionary Filtering and Evaluation in Columnar Databases_simplified_abstract_(databricks, inc.)]]

Inventor(s): Utkarsh Agarwal of San Francisco CA (US) for databricks, inc., Shoumik Palkar of San Francisco CA (US) for databricks, inc., Alexander Behm of San Francisco CA (US) for databricks, inc., Sriram Krishnamurthy of San Francisco CA (US) for databricks, inc.

IPC Code(s): G06F16/2455, G06F11/34, G06F16/22

CPC Code(s): G06F16/24558




Abstract: disclosed herein is a method, system, or non-transitory computer readable medium for evaluating a query on a columnar dataset comprising one or more dictionaries associated with columns in the dataset. the method includes receiving a request to perform a query comprising at least a operator and a request to return information about a value of interest in a columnar dataset stored on cloud storage. at least one column in the columnar dataset is based on a dictionary. the dictionary maps one or more values for a column to one or more respective identifiers. the method determines whether to perform dictionary filtering for the query by calculating a metric based on one or more factors. responsive to the metric being below a threshold, which may be predetermined, the method performs the dictionary filtering.[[Category:G06F16/2455]][[Category:G06F11/34]][[Category:G06F16/22]][[Category:CPC_G06F16/24558]][[Category:databricks, inc.]]



[[Databricks, Inc. patent applications on August 1st, 2024]]



[[Category:Databricks, Inc.]] [[Category:G06F16/22]] [[Category:G06F11/34]] [[Category:G06F16/2453]] [[Category:G06F16/2455]] [[Category:G06F9/50]]