TRANSIENT MATERIALIZED VIEW REWRITE

    公开(公告)号:US20220092068A1

    公开(公告)日:2022-03-24

    申请号:US17538219

    申请日:2021-11-30

    Applicant: Snowflake Inc.

    Abstract: Queries executed against a materialized view can execute up to orders of magnitude faster than equivalent queries on a source (or base) table. However, although a query can reference a materialized view directly, a user (e.g., query author) may not know about a relevant materialized view. Moreover, if a source table has multiple materialized views generated, the user may not know which materialized view to reference in the query. Thus, embodiments of the present disclosure provide techniques for automatically rewriting queries directed to a source table to utilize existing materialized views.

    INTERNAL RESOURCE PROVISIONING IN DATABASE SYSTEMS

    公开(公告)号:US20220004552A1

    公开(公告)日:2022-01-06

    申请号:US17477663

    申请日:2021-09-17

    Applicant: Snowflake Inc.

    Abstract: Resource provisioning systems and methods are described. In an embodiment, a system includes a plurality of shared storage devices collectively storing database data, an execution platform, and a compute service manager. The compute service manager is configured to determine a task to be executed in response to a trigger event and determine a query plan for executing the task, wherein the query plan comprises a plurality of discrete subtasks. The compute service manager is further configured to assign the plurality of discrete subtasks to one or more nodes of a plurality of nodes of the execution platform, determine whether execution of the task is complete, and in response to determining the execution of the task is complete, store a record in the plurality of shared storage devices indicating the task was completed.

    Transient materialized view rewrite

    公开(公告)号:US11216462B1

    公开(公告)日:2022-01-04

    申请号:US16994024

    申请日:2020-08-14

    Applicant: Snowflake Inc.

    Abstract: Queries executed against a materialized view can execute up to orders of magnitude faster than equivalent queries on a source (or base) table. However, although a query can reference a materialized view directly, a user (e.g., query author) may not know about a relevant materialized view. Moreover, if a source table has multiple materialized views generated, the user may not know which materialized view to reference in the query. Thus, embodiments of the present disclosure provide techniques for automatically rewriting queries directed to a source table to utilize existing materialized views.

    FLEXIBLE COMPUTING
    55.
    发明申请

    公开(公告)号:US20210357263A1

    公开(公告)日:2021-11-18

    申请号:US17342713

    申请日:2021-06-09

    Applicant: Snowflake Inc

    Abstract: Embodiments of the present disclosure may provide dynamic and fair assignment techniques for allocating resources on a demand basis. Assignment control may be separated into at least two components: a local component and a global component. Each component may have an active dialog with each other; the dialog may include two aspects: 1) a demand for computing resources, and 2) a total allowed number of computing resources. The global component may allocate resources from a pool of resources to different local components, and the local components in turn may assign their allocated resources to local competing requests. The allocation may also be throttled or limited at various levels.

    Reclustering of database tables based on peaks and widths

    公开(公告)号:US11163746B2

    公开(公告)日:2021-11-02

    申请号:US17249796

    申请日:2021-03-12

    Applicant: Snowflake Inc.

    Abstract: The subject technology determines whether a table is sufficiently clustered. The subject technology in response to determining the table is not sufficiently clustered, selects one or more micro-partitions of the table to be reclustered. The subject technology constructs a data structure for the table. The subject technology extracts minimum and maximum endpoints for each micro-partition in the data structure. The subject technology sorts each of one or more peaks in the data structure based on height. The subject technology sorts overlapping micro-partitions based on width. The subject technology selects based on which micro-partitions are within the tallest peaks of the one or more peaks and further based on which of the overlapping micro-partitions have the widest widths.

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