Index generation using lazy reassembling of semi-structured data

    公开(公告)号:US11816107B2

    公开(公告)日:2023-11-14

    申请号:US18146912

    申请日:2022-12-27

    Applicant: Snowflake Inc.

    Abstract: A pruning index is generated for a source table organized into a set of batch units. The source table comprises a column of semi-structured data. The pruning index comprises a set of filters that index distinct values in each column of the source table. Rather than reassembling an entire tree structure of the semi-structured data prior to indexing, the generating of the pruning index comprises traversing a reassembly hook object that represents a first portion of the semi-structured data that is subcolumnarized and traversing a residual object that represents a second portion of the semi-structured data that is not subcolumnarized. The reassembly hook object is traversed to identify values corresponding to the first portion of the semi-structured data and the residual object is traversed to identify values corresponding to the second portion. The pruning index is stored with an association with the source table.

    REGULAR EXPRESSION SEARCH QUERY PROCESSING USING PRUNING INDEX

    公开(公告)号:US20230342362A1

    公开(公告)日:2023-10-26

    申请号:US18305993

    申请日:2023-04-24

    Applicant: Snowflake Inc.

    CPC classification number: G06F16/24557 G06F16/9035 G06F16/283 G06F16/2272

    Abstract: A query directed at a source table organized into a set of batch units is received. The query comprises a regular expression search pattern. The regular expression search pattern is converted to a pruning index predicate comprising a set of substring literals extracted from the regular expression search pattern. A set of N-grams is generated based on the set of substring literals extracted from the regular expression search pattern. A pruning index associated with the source table is accessed. The pruning index indexes distinct N-grams in each column of the source table. A subset of batch units to scan for data matching the query are identified based on the pruning index and the set of N-grams. The query is processed by scanning the subset of batch units.

    SCAN SET PRUNING FOR QUERIES WITH PREDICATES ON SEMI-STRUCTURED FIELDS

    公开(公告)号:US20230064151A1

    公开(公告)日:2023-03-02

    申请号:US18047595

    申请日:2022-10-18

    Applicant: Snowflake Inc.

    Abstract: A source table organized into a set of batch units is accessed. The source table comprises a column of data corresponding to a semi-structured data type. One or more indexing transformations for an object in the column are generated. The generating of the one or more indexing transformation includes converting the object to one or more stored data types. A pruning index is generated for the source table based in part on the one or more indexing transformations. The pruning index comprises a set of filters that index distinct values in each column of the source table, and each filter corresponds to a batch unit in the set of batch units. The pruning index is stored in a database with an association with the source table.

    Indexed geospatial predicate search

    公开(公告)号:US12050605B2

    公开(公告)日:2024-07-30

    申请号:US17804248

    申请日:2022-05-26

    Applicant: Snowflake Inc.

    Abstract: Provided herein are systems and methods for indexed geospatial predicate search. An example method performed by at least one hardware processor includes decoding a query with a geospatial predicate. The geospatial predicate is configured between a geography data column and a constant geography object. The method further includes computing a first covering for a data value of a plurality of data values in the geography data column. The first covering includes a first set of cells in a hierarchical grid representation of a geography. The first set of cells represents a surface of the geography associated with the data value. A second covering is computed for the constant geography object. A determination is made on whether to prune at least one partition of a database organized into a set of partitions and including the geography data column based on a comparison between the first covering and the second covering.

    INDEX GENERATION USING LAZY REASSEMBLING OF SEMI-STRUCTURED DATA

    公开(公告)号:US20230139194A1

    公开(公告)日:2023-05-04

    申请号:US18146912

    申请日:2022-12-27

    Applicant: Snowflake Inc.

    Abstract: A pruning index is generated for a source table organized into a set of batch units. The source table comprises a column of semi-structured data. The pruning index comprises a set of filters that index distinct values in each column of the source table. Rather than reassembling an entire tree structure of the semi-structured data prior to indexing, the generating of the pruning index comprises traversing a reassembly hook object that represents a first portion of the semi-structured data that is subcolumnarized and traversing a residual object that represents a second portion of the semi-structured data that is not subcolumnarized. The reassembly hook object is traversed to identify values corresponding to the first portion of the semi-structured data and the residual object is traversed to identify values corresponding to the second portion. The pruning index is stored with an association with the source table.

    Lazy reassembling of semi-structured data

    公开(公告)号:US11567939B2

    公开(公告)日:2023-01-31

    申请号:US17814110

    申请日:2022-07-21

    Applicant: Snowflake Inc.

    Abstract: A pruning index is generated for a source table organized into a set of batch units. The source table comprises a column of semi-structured data. The pruning index comprises a set of filters that index distinct values in each column of the source table. Rather than reassembling an entire tree structure of the semi-structured data prior to indexing, the generating of the pruning index comprises traversing a reassembly hook object that represents a first portion of the semi-structured data that is subcolumnarized and traversing a residual object that represents a second portion of the semi-structured data that is not subcolumnarized. The reassembly hook object is traversed to identify values corresponding to the first portion of the semi-structured data and the residual object is traversed to identify values corresponding to the second portion. The pruning index is stored with an association with the source table.

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