Patent application number | Description | Published |
20080209428 | RESOURCE GOVERNOR CONFIGURATION MODEL - A database can have multiple requests applied at one time. Each of these requests requires a specific amount of server resources. There can be a differentiation of user-submitted workloads between each other. These workloads are a set of queries submitted by different users. Each query can have specific resource limits. In addition, each set can have specific resource limits. | 08-28-2008 |
20080215556 | DYNAMIC FILTERS FOR RELATIONAL QUERY PROCESSING - Systems and methods that eliminate non-qualifying data for queries against data warehouses and improve execution time, via a dynamic filter component(s). In general, such dynamic filter components are derived from data during processing of the query and without being explicitly defined by the users within a query forwarded to the data warehouse. Moreover, an evaluation component can monitor efficiency of filter components (e.g., number of rows that can be eliminated), and dynamically change and/or update the evaluation order of such filters. | 09-04-2008 |
20110231389 | ADAPTIVE ROW-BATCH PROCESSING OF DATABASE DATA - Architecture that provides for greater interoperability between column stores and row stores by leveraging the advantages both have to offer. The architecture operates automatically (e.g., dynamically) to move between row oriented processing mode and batch processing mode, and the combination thereof, when it is more beneficial to run in one mode relative to the other mode, or both modes. The auto-switching of data processing between batch and row oriented mode occurs during the execution of a single query. The architecture can automatically modify an operator in the query tree and/or remove an operator if desired at runtime for more efficient processing. This approach also accounts for memory constraints for either of row or column processing. | 09-22-2011 |
20110231403 | SCALABLE INDEX BUILD TECHNIQUES FOR COLUMN STORES - Architecture that includes an index creation algorithm that utilizes available resources and dynamically adjusts to successfully scale with increased resources and be able to do so for any data distribution. The resources can be processing resources, memory, and/or input/output, for example. A finer level of granularity, called a segment, is utilized to process tuples in a partition while creating an index. The segment also aligns with compression techniques for the index. By choosing an appropriate size for a segment and using load balancing the overall time for index creation can be reduced. Each segment can then be processed by a single thread thereby limiting segment skew. Skew is further limited by breaking down the work done by a thread into parallelizable stages. | 09-22-2011 |
20110276607 | NORMALIZING DATA FOR FAST SUPERSCALAR PROCESSING - A data normalization system is described herein that represents multiple data types that are common within database systems in a normalized form that can be processed uniformly to achieve faster processing of data on superscalar CPU architectures. The data normalization system includes changes to internal data representations of a database system as well as functional processing changes that leverage normalized internal data representations for a high density of independently executable CPU instructions. Because most data in a database is small, a majority of data can be represented by the normalized format. Thus, the data normalization system allows for fast superscalar processing in a database system in a variety of common cases, while maintaining compatibility with existing data sets. | 11-10-2011 |
20140129525 | NORMALIZING DATA FOR FAST SUPERSCALAR PROCESSING - A data normalization system is described herein that represents multiple data types that are common within database systems in a normalized form that can be processed uniformly to achieve faster processing of data on superscalar CPU architectures. The data normalization system includes changes to internal data representations of a database system as well as functional processing changes that leverage normalized internal data representations for a high density of independently executable CPU instructions. Because most data in a database is small, a majority of data can be represented by the normalized format. Thus, the data normalization system allows for fast superscalar processing in a database system in a variety of common cases, while maintaining compatibility with existing data sets. | 05-08-2014 |
20140149355 | STREAMING RESTORE OF A DATABASE FROM A BACKUP SYSTEM - A distributed data warehouse system may maintain data blocks on behalf of clients in multiple clusters in a data store. Each cluster may include a single leader node and multiple compute nodes, each including multiple disks storing data. The warehouse system may store primary and secondary copies of each data block on different disks or nodes in a cluster. Each node may include a data structure that maintains metadata about each data block stored on the node, including its unique identifier. The warehouse system may back up data blocks in a remote key-value backup storage system with high durability. A streaming restore operation may be used to retrieve data blocks from backup storage using their unique identifiers as keys. The warehouse system may service incoming queries (and may satisfy some queries by retrieving data from backup storage on an as-needed basis) prior to completion of the restore operation. | 05-29-2014 |
20140149590 | SCALING COMPUTING CLUSTERS IN A DISTRIBUTED COMPUTING SYSTEM - A currently operating computing cluster that has multiple nodes storing cluster data may be scaled. A cluster scaling request may be received for the current cluster indicating a change in a number or type of nodes in the current cluster. In response to receiving the cluster scaling request, a new cluster may be created as indicated in the cluster scaling request, a copy of the cluster data from the current cluster to the nodes in the new cluster may be initiated, a network endpoint for the current cluster may be moved to the new cluster, and the current cluster may be disable. The current cluster may, in some embodiments, respond to read access requests during the copy of the cluster data. | 05-29-2014 |