Infrastructure for automating rollout of database changes

    公开(公告)号:US11971783B1

    公开(公告)日:2024-04-30

    申请号:US18340528

    申请日:2023-06-23

    Applicant: Snowflake Inc.

    CPC classification number: G06F11/1433 G06F11/3414 G06F16/213 G06F2201/80

    Abstract: A method includes decoding, by at least one hardware processor, a notification of a changed database code of a database. A query is executed responsive to the notification. The query indicates a data processing command and a data object in the database. A regression in the changed database code is detected based on multiple regression testing operations applied to the data processing command and the data object. Analysis of the regression is performed to detect a rollout parameter of a plurality of rollout parameters as a root cause of the regression. The plurality of rollout parameters are associated with the changed database code. A determination is made on whether to perform a mitigation action for the regression based on the rollout parameter.

    Function memoization in query processing system

    公开(公告)号:US11809425B1

    公开(公告)日:2023-11-07

    申请号:US17819758

    申请日:2022-08-15

    Applicant: Snowflake Inc.

    CPC classification number: G06F16/24539 G06F21/53 G06F2221/034

    Abstract: A data platform that implements memoizable functions for database objects. The data platform detects a first execution of a memoizable function and generates a first key based on metadata of one or more database objects operated on by the memoizable function and generates a first result for the memoizable function based on the one or more database objects. The data platform detects a second execution of the memoizable function and generates a second key based on the metadata of the one or more database objects operated on by the memoizable function. When the first key and the second key are equal, the data platform reuses the first result of the memoizable function. When the first key and second key do not match, the data platform generates a second result for the second execution of the memoizable function.

    Infrastructure for automating rollout of database changes

    公开(公告)号:US11734116B1

    公开(公告)日:2023-08-22

    申请号:US18060848

    申请日:2022-12-01

    Applicant: Snowflake Inc.

    CPC classification number: G06F11/1433 G06F11/3414 G06F16/213 G06F2201/80

    Abstract: Provided herein are systems and methods for automating the rollout of database changes. For example, a method includes detecting a change in database code of a database resulting in a changed database code. The change in the database code includes a parameter rollout for at least one parameter setting of the database. Execution of a query associated with the change in the database code is monitored. The query uses the at least one parameter setting. A regression in the changed database code is detected based on the monitoring. Impact analysis is performed to determine a scope of impact of the regression on at least another query using the at least one parameter setting. A determination is made on whether to perform a rollback of the change in the database code or perform mitigation based on the scope of impact.

    JOIN ELIMINATION
    47.
    发明申请

    公开(公告)号:US20230135440A1

    公开(公告)日:2023-05-04

    申请号:US17932140

    申请日:2022-09-14

    Applicant: Snowflake inc.

    Abstract: Techniques for join elimination are described herein. Join elimination can identify and eliminate unnecessary joins in a query plan node. For example, join elimination can involve a semantic query optimization technique, which removes reference to a table whose columns are only referenced in join predicates if the joins do not filter/expand rows in the result. Such joins can cause significant performance issues on larger datasets if not optimized.

    RESOURCE PROVISIONING IN DATABASE SYSTEMS

    公开(公告)号:US20230071465A1

    公开(公告)日:2023-03-09

    申请号:US18050255

    申请日:2022-10-27

    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.

    CUTOFFS FOR PRUNING OF DATABASE QUERIES

    公开(公告)号:US20220405285A1

    公开(公告)日:2022-12-22

    申请号:US17822264

    申请日:2022-08-25

    Applicant: Snowflake Inc.

    Abstract: The subject technology receives, during a query compilation process, a query directed to a set of source tables. The subject technology performs, during the query compilation process, a modification of the query for adjusting at least one pruning operation. The subject technology determines, during a pruning process of a second query, the second query directed to a set of files in a database system and including a set of pruning operations on the set of files, whether to perform a pruning cutoff on the set of pruning operations, the pruning process performing a depth first search of a pruner tree structure, the set of files comprising a set of micro-partitions. The subject technology performs the pruning cutoff based on the determining, the pruning cutoff ceasing at least one pruning operation from the set of pruning operations.

    Aggregation operator optimization during query runtime

    公开(公告)号:US11468063B2

    公开(公告)日:2022-10-11

    申请号:US17232821

    申请日:2021-04-16

    Applicant: Snowflake Inc.

    Abstract: The subject technology provides information, corresponding to properties of a build side of a join operation, to a bloom filter. The subject technology, based at least in part on the information from the bloom filter, determines, during executing of a query plan, at least one property of the join operation to determine whether to switch an aggregation operator to a pass through mode, the at least one property comprising at least a reduction rate. The subject technology, switches, in response to the reduction rate being below a threshold value, the aggregation operator to the pass through mode during runtime of the query plan and, while the aggregation operator is in the pass through mode, an input stream of data goes through the aggregation operator without being analyzed and the input stream of data matches an output stream of data flowing out of the aggregation operator.

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