NON-UNIQUE SECONDARY INDEXING OF SEMI-STRUCTURED DATA IN DATABASES

    公开(公告)号:US20170235845A1

    公开(公告)日:2017-08-17

    申请号:US15386049

    申请日:2016-12-21

    CPC classification number: G06F16/81 G06F16/22

    Abstract: Various embodiments herein each include at least one of systems, methods, and software for generating, storing, and using non-unique secondary indexes of semi-structured data in database tables. One method embodiment includes creating and storing a non-unique secondary index (NUSI) for a database table based on column data stored in rows of the database table. The NUSI in some such embodiments identifying an offset to specific data included in the column data of at least some rows of the database table where the specific data is located. Other embodiments include building a NUSI index that includes values embedded therein.

    METHODS AND SYSTEM TO PROCESS STREAMING DATA
    3.
    发明申请
    METHODS AND SYSTEM TO PROCESS STREAMING DATA 审中-公开
    处理数据流的方法和系统

    公开(公告)号:US20150310069A1

    公开(公告)日:2015-10-29

    申请号:US14263439

    申请日:2014-04-28

    CPC classification number: G06F16/24568 G06F16/2282

    Abstract: Streaming data is populated to an in-memory data table and a continuous query is executed against an in-memory data table using a database interface to perform analytical operations on the populated in-memory data table. Results from the analytical operations performed are streamed to consuming applications.

    Abstract translation: 流数据被填充到内存数据表中,并且使用数据库接口对存储器内数据表执行连续查询,以对填充的内存数据表执行分析操作。 执行的分析操作的结果流式传输到消费应用程序。

    Non-unique secondary indexing of semi-structured data in databases

    公开(公告)号:US11347794B2

    公开(公告)日:2022-05-31

    申请号:US15386049

    申请日:2016-12-21

    Abstract: Various embodiments herein each include at least one of systems, methods, and software for generating, storing, and using non-unique secondary indexes of semi-structured data in database tables. One method embodiment includes creating and storing a non-unique secondary index (NUSI) for a database table based on column data stored in rows of the database table. The NUSI in some such embodiments identifying an offset to specific data included in the column data of at least some rows of the database table where the specific data is located. Other embodiments include building a NUSI index that includes values embedded therein.

    DYNAMIC WORKLOAD BALANCING FOR REAL-TIME STREAM DATA ANALYTICS
    7.
    发明申请
    DYNAMIC WORKLOAD BALANCING FOR REAL-TIME STREAM DATA ANALYTICS 有权
    用于实时流数据分析的动态工作平衡

    公开(公告)号:US20160182588A1

    公开(公告)日:2016-06-23

    申请号:US14580403

    申请日:2014-12-23

    CPC classification number: G06F17/30595 G06F17/30516 H04L65/4076

    Abstract: Router threads read real-time stream data as that data is received at processing nodes along a processing path for the data. The data is initially parsed into workloads. Each router thread maintains real-time analytics for the workloads and reports deviations in the analytics to a workload coordinator. The workload coordinator dynamically and in real time provides updated data distribution maps based on the reported deviations, and the router threads use the data distribution maps to determine a next processing unit to forward the workloads, where the next processing unit includes an instance of a next processing node in the processing path for the workload. The next processing node performs additional processing on the workloads along the processing path.

    Abstract translation: 路由器线程读取实时流数据,因为该数据沿着数据的处理路径在处理节点处被接收。 数据最初被解析为工作负载。 每个路由器线程都会为工作负载维护实时分析,并将分析中的偏差报告给工作负载协调器。 动态和实时的工作负载协调器基于报告的偏差提供更新的数据分布图,并且路由器线程使用数据分布图来确定下一个处理单元来转发工作负载,其中下一个处理单元包括下一个实例 在处理路径中处理节点的工作量。 下一个处理节点对处理路径上的工作负载执行附加处理。

    Spatial joins in multi-processing computing systems including massively parallel processing database systems

    公开(公告)号:US12135720B2

    公开(公告)日:2024-11-05

    申请号:US16718358

    申请日:2019-12-18

    Abstract: Improved techniques for performing Spatial Joins multi-processing computing systems and environments are disclosed. One or more intersection of bounds (or limits) of data sets is determined as a join bounding space. The join bounding space is in a space (Global space or Global universe) where a spatial join between (or for) the data can be performed. The determined join bounding space can be partitioned into sub-partitions of the join bounding space. The sub-partitions of the join bounding space can assigned respectively to multiple processing unit for processing in parallel in. In addition, distribution cost information associated with the cost of distribution of the datasets (and/or their components) to the processing units of a multi-processing system can be provided and/or used to effectively distribute and/or redistribute processing of the Spatial Join between the processing units of a multi-processing system.

    Unbounded analytic framework in a data store system

    公开(公告)号:US11681675B1

    公开(公告)日:2023-06-20

    申请号:US17139917

    申请日:2020-12-31

    Abstract: A data store system may include a storage device configured to store a plurality of data store tables. The data store may further include a plurality of processing units. At least one processing unit from the plurality of processing units may receive an analytic function call. The at least one processing unit may further identify, in the analytic function call, at least one column of a data store table on which to execute an analytic function in the analytic function call and may further identify, in the analytic function call, an identifier column of the data store table. Each row of the at least one column may be associated with a common row value of the identifier column. The at least one processing unit may further identify, in the analytic function call, at least one index column of the data store table. Each value in each at the least one index column may identify an index value on which to index each value of the at least one column with respect to each value of the identifier column. The at least one processing unit may further order values of the at least one column in accordance with the identifier column and the at least one index column, execute the analytic function on the ordered values to generate a result set, and order the result set in accordance with the identifier column and the at least one index column. A computer-readable medium and method are also disclosed.

    Dynamic workload balancing for real-time stream data analytics
    10.
    发明授权
    Dynamic workload balancing for real-time stream data analytics 有权
    用于实时流数据分析的动态工作负载平衡

    公开(公告)号:US09456014B2

    公开(公告)日:2016-09-27

    申请号:US14580403

    申请日:2014-12-23

    CPC classification number: G06F17/30595 G06F17/30516 H04L65/4076

    Abstract: Router threads read real-time stream data as that data is received at processing nodes along a processing path for the data. The data is initially parsed into workloads. Each router thread maintains real-time analytics for the workloads and reports deviations in the analytics to a workload coordinator. The workload coordinator dynamically and in real time provides updated data distribution maps based on the reported deviations, and the router threads use the data distribution maps to determine a next processing unit to forward the workloads, where the next processing unit includes an instance of a next processing node in the processing path for the workload. The next processing node performs additional processing on the workloads along the processing path.

    Abstract translation: 路由器线程读取实时流数据,因为该数据沿着数据的处理路径在处理节点处被接收。 数据最初被解析为工作负载。 每个路由器线程都会为工作负载维护实时分析,并将分析中的偏差报告给工作负载协调器。 动态和实时的工作负载协调器基于报告的偏差提供更新的数据分布图,并且路由器线程使用数据分布图来确定下一个处理单元来转发工作负载,其中下一个处理单元包括下一个实例 在处理路径中处理节点的工作量。 下一个处理节点对处理路径上的工作负载执行附加处理。

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