Non-disruptive dynamic ad-hoc database catalog services

    公开(公告)号:US11200234B2

    公开(公告)日:2021-12-14

    申请号:US16441989

    申请日:2019-06-14

    Abstract: Approaches herein transparently delegate data access from a relational database management system (RDBMS) onto an offload engine (OE). The RDBMS receives a database statement referencing a user defined function (UDF). In an execution plan, the RDBMS replaces the UDF reference with an invocation of a relational operator in the OE. Execution invokes the relational operator in the OE to obtain a result based on data in the OE. Thus, the UDF is bound to the OE, and almost all of the RDBMS avoids specially handling the UDF. The UDF may be a table function that offloads a relational table for processing. User defined objects such as functions and types provide metadata about the table. Multiple tables can be offloaded and processed together, such that some or all offloaded tables are not materialized in the RDBMS. Offloaded tables may participate in standard relational algebra such as in a database statement.

    Code dictionary generation based on non-blocking operations

    公开(公告)号:US11126611B2

    公开(公告)日:2021-09-21

    申请号:US15897375

    申请日:2018-02-15

    Abstract: Techniques related to code dictionary generation based on non-blocking operations are disclosed. In some embodiments, a column of tokens includes a first token and a second token that are stored in separate rows. The column of tokens is correlated with a set of row identifiers including a first row identifier and a second row identifier that is different from the first row identifier. Correlating the column of tokens with the set of row identifiers involves: storing a correlation between the first token and the first row identifier, storing a correlation between the second token and the second row identifier if the first token and the second token have different values, and storing a correlation between the second token and the first row identifier if the first token and the second token have identical values. After correlating the column of tokens with the set of row identifiers, duplicate correlations are removed.

    PARTIAL GROUP BY FOR EAGER GROUP BY PLACEMENT QUERY PLANS

    公开(公告)号:US20210263930A1

    公开(公告)日:2021-08-26

    申请号:US16797507

    申请日:2020-02-21

    Abstract: A partial group by operator is a group by operator that implements a fallback mechanism. The fallback mechanism is triggered whenever memory pressure reaches a certain threshold. When the fallback mechanism is triggered, a row is included in an output of the partial group by operator without including an aggregation value for a grouping value for the row to an aggregation data structure. A final group by operator computes a final aggregate value of all results, including pre-grouped results and passed through results, from the partial group by operator.

    DRIVING MASSIVE SCALE OUT THROUGH REWRITES OF ANALYTICAL FUNCTIONS

    公开(公告)号:US20210019315A1

    公开(公告)日:2021-01-21

    申请号:US16516898

    申请日:2019-07-19

    Abstract: According to an embodiment, a method includes rewriting a particular query to generate a rewritten query. The particular query specifies a window function operator, a particular input to the window function operator, and an analytical function. Rewriting the particular query includes assigning the particular input to an intermediate relation and replacing the window function operator with a replacement operator. The method further includes assigning to the replacement operator an aggregate function corresponding to the analytical function, and the intermediate relation. In this embodiment, the method also includes placing a join operator that joins the intermediate relation with an output of the replacement operator.

    Computing columnar information during join enumeration

    公开(公告)号:US10783143B2

    公开(公告)日:2020-09-22

    申请号:US15791712

    申请日:2017-10-24

    Abstract: Techniques are described herein for computing columnar information during join enumeration in a database system. The computation occurs in two phases: the first phase involves a pre-computational phase that is only run once per query block to initialize and prepare a set of data structures. The second phase is an incremental approach that takes place for every query sub-plan. Upon completion of the second phase, the generated projected attributes of a query sub-plan are associated as columnar information associated with the query sub-plan, and used to compute the query execution cost. Subsequently, based on the computed query execution cost, the query sub-plan may be executed as part of the query execution plan.

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