公开(公告)号：US20250086155A1

公开(公告)日：2025-03-13

申请号：US18772758

申请日：2024-07-15

Applicant: Databricks, Inc.

Inventor： Bart Samwel ,  Prakhar Jain

IPC: G06F16/22 ,  G06F16/28

Abstract: A system for clustering data into corresponding files comprises one or more processors and a memory. The one or more processors is/are configured to: 1) determine to cluster a set of data into a set of files; 2) determine a set of split points in a corresponding set of dimensions of the set of data to determine the set of files, wherein each file of the set of files has an approximate target size; and 3) store one or more items of the set of data into a corresponding file of the set of files based at least in part on the set of split points. The memory is coupled to the one or more processors and configured to provide the processor with instructions.

公开(公告)号：US12229169B1

公开(公告)日：2025-02-18

申请号：US18501830

申请日：2023-11-03

Applicant: Databricks, Inc.

Inventor： Terry Kim ,  Lin Ma ,  Rahul Shivu Mahadev ,  Rahul Potharaju

IPC: G06F16/28 ,  G06F16/21 ,  G06F16/22

Abstract: The disclosed configurations provide a method (and/or a computer-readable medium or system) for determining, from a table schema describing keys of a data table, one or more clustering keys that can be used to cluster data files of a data table. The method includes generating features for the data table, generating tokens from the features, generating a prediction for each token by applying to the token a machine-learned transformer model trained to predict a likelihood that the key associated with the token is a clustering key for the data table, determining clustering keys based on the predictions, and clustering data records of the data table into data files based on key-values for the clustering keys.

公开(公告)号：US12229137B1

公开(公告)日：2025-02-18

申请号：US18412438

申请日：2024-01-12

Applicant: Databricks, Inc.

Inventor： Xinyang Ge ,  Lixiang Ao ,  Haonan Jing ,  Aaron Daniel Davidson

IPC: G06F16/2453

Abstract: A system performs efficient startup of executors of a distributed computing engine used for processing queries, for example, database queries. The system starts an executor node and processes a set of queries using the executor node to warm up the executor node. The system performs a checkpoint of the warmed-up executor node to create an image. The image is restored in the target executor nodes. The system may store a checkpoint image for each configuration of an executor node. The configuration is determined based on various factors including the hardware of the executor node, memory allocation of the processes, and so on. The user or restore based on checkpoint images improves efficiency of execution of the startup of executor nodes.

公开(公告)号：US12189625B2

公开(公告)日：2025-01-07

申请号：US18222343

申请日：2023-07-14

Applicant: Databricks, Inc.

Inventor： Bogdan Ionut Ghit ,  Saksham Garg ,  Christian Stuart ,  Christopher Stevens

IPC: G06F16/24 ,  G06F16/2453 ,  G06F16/25 ,  G06F16/28

Abstract: A multi-cluster computing system which includes a query result caching system is presented. The multi-cluster computing system may include a data processing service and client devices communicatively coupled over a network. The data processing service may include a control layer and a data layer. The control layer may be configured to receive and process requests from the client devices and manage resources in the data layer. The data layer may be configured to include instances of clusters of computing resources for executing jobs. The data layer may include a data storage system, which further includes a remote query result cache Store. The query result cache store may include a cloud storage query result cache which stores data associated with results of previously executed requests. As such, when a cluster encounters a previously executed request, the cluster may efficiently retrieve the cached result of the request from the in-memory query result cache or the cloud storage query result cache.

公开(公告)号：US20240412095A1

公开(公告)日：2024-12-12

申请号：US18206460

申请日：2023-06-06

Applicant: Databricks, Inc.

Inventor： Matei Zaharia ,  Avesh Singh ,  Mani Parkhe ,  Maxim Lukiyanov ,  Xiangrui Meng ,  Aakrati Talati ,  Chenen Liang ,  Kasey Uhlenhuth

IPC: G06N20/00

Abstract: A system performs training and execution of machine learning models that use on-demand features using feature functions. The system receives commands for registering metadata associated with a machine learning model. The machine learning model may process a set of features including on-demand features as well as other features such as batch features. The system executes the command by storing an association between the machine learning model and the feature functions associated with any on-demand features processed by the machine learning model. The feature functions are executed using an end point of a data asset service. The use of the data asset service for invoking the feature functions ensures that the same set of instructions is executed during model training and model inferencing, thereby avoiding model skew.

公开(公告)号：US20240378181A1

公开(公告)日：2024-11-14

申请号：US18144647

申请日：2023-05-08

Applicant: Databricks, Inc.

Inventor： Vijayan Prabhakaran ,  Himanshu Raja ,  Rahul Potharaju ,  Naga Raju Bhanoori ,  Lin Ma ,  Rajesh Parangi Sharabhalingappa ,  Jintian Liang ,  Zach Schuermann ,  Kam Cheung Ting

IPC: G06F16/21 ,  G06F11/34 ,  G06F16/22

Abstract: Disclosed is a configuration for managing the organization of data tables in cloud-based storage. The configuration receives metrics for data processing operations on the data table. Metrics include at least one of a size of the data table, a size of each file in the data table, and metadata describing the data table. The configuration automatically executes a cost-benefit analysis based on the one or more metrics for each candidate maintenance operation in a plurality of candidate maintenance operations. The configuration automatically selects a maintenance operation from the candidate maintenance operations to automate based on the cost-benefit analysis of the one or more candidate maintenance operations. The selected maintenance operation is automated and scheduled on the data table.

公开(公告)号：US20240346007A1

公开(公告)日：2024-10-17

申请号：US18135078

申请日：2023-04-14

Applicant: Databricks, Inc.

Inventor： Zhaoxing Li ,  Rayman Preet Singh ,  Fuat Can Efeoglu ,  Daniel Tenedorio ,  Sarah Cai

IPC: G06F16/23 ,  G06F16/2455

CPC classification number: G06F16/2365 ,  G06F16/24552

Abstract: A system for retrieving and caching metadata from a remote data source is described.
The system may receive a request from a client device. The request is to perform a query operation on a set of data objects stored in the remote data source. The system may access a metadata cache storing metadata information on one or more data objects of the remote data source and identify metadata corresponding to the set of data objects for the query operation in the metadata cache. The system may determine whether the identified metadata for the set of data objects meets an update condition. In response to the identified metadata meeting the update condition, the system may fetch updated metadata for at least the set of data objects from the remote data source, and store the updated metadata in the metadata cache.

公开(公告)号：US12117983B2

公开(公告)日：2024-10-15

申请号：US18512028

申请日：2023-11-17

Applicant: Databricks, Inc.

Inventor： Aaron Daniel Davidson ,  Clemens Mewald ,  Tomas Nykodym

IPC: G06F16/00 ,  G06F16/21 ,  G06F16/955 ,  G06N5/022

CPC classification number: G06F16/219 ,  G06F16/955 ,  G06N5/022

Abstract: A system includes an interface, a processor, and a memory. The interface is configured to receive a version of a model from a model registry. The processor is configured to store the version of the model, start a process running the version of the model, and update a proxy with version information associated with the version of the model, wherein the updated proxy indicates to redirect an indication to invoke the version of the model to the process. The memory is coupled to the processor and configured to provide the processor with instructions.

公开(公告)号：US12072880B2

公开(公告)日：2024-08-27

申请号：US17892376

申请日：2022-08-22

Applicant: Databricks, Inc.

Inventor： Prashanth Menon ,  Alexander Behm ,  Sriram Krishnamurthy

IPC: G06F9/00 ,  G06F16/2453 ,  G06F16/28

CPC classification number: G06F16/24542 ,  G06F16/285

Abstract: The present application discloses a method, system, and computer system for parsing files. The method includes receiving an indication that a first file is to be processed, determining to begin processing the first file using a first processing engine based at least in part on one or more predefined heuristics, indicating to process the first file using a first processing engine, determining whether a particular error in processing the first file using the first processing engine has been detected, in response to determining that the particular error has been detected, indicate to stop processing the first file using the first processing engine and indicate to continue processing using a second processing engine, and storing in memory information obtained based on processing the first file by one or more of the first processing engine and the second processing engine.

公开(公告)号：US20240265011A1

公开(公告)日：2024-08-08

申请号：US18222343

申请日：2023-07-14

Applicant: Databricks, Inc.

Inventor： Saksham Garg ,  Bogdan Ionut Ghit ,  Christopher Stevens ,  Christian Stuart

IPC: G06F16/2453

CPC classification number: G06F16/24539

Abstract: A multi-cluster computing system which includes a query result caching system is presented. The multi-cluster computing system may include a data processing service and client devices communicatively coupled over a network. The data processing service may include a control layer and a data layer. The control layer may be configured to receive and process requests from the client devices and manage resources in the data layer. The data layer may be configured to include instances of clusters of computing resources for executing jobs. The data layer may include a data storage system, which further includes a remote query result cache Store. The query result cache store may include a cloud storage query result cache which stores data associated with results of previously executed requests. As such, when a cluster encounters a previously executed request, the cluster may efficiently retrieve the cached result of the request from the in-memory query result cache or the cloud storage query result cache.