Snowflake Inc.

Prasanna Rajaperumal

Jiaqi Yan

Thierry Cruanes

Varun Ganesh

Igor Demura

Libo Wang

Allison Waingold Lee

Tyler Jones

Tyler Arthur Akidau

Daniel E Sotolongo

Daniel Mills

Leon Papke

Benoit Dageville

William H Waddington

Abdul Q Munir

Yongsik Yoon

Jeffrey Rosen

Shrainik Jain

Boyung Lee

Fabian Hueske

Saiyang Gou

Wenhao Song

Systems, methods, and devices for sharing materialized views in multiple tenant database systems. A method includes defining a materialized view over a source table that is associated with a first account of a multiple tenant database. The method includes defining cross-account access rights to the materialized view to a second account such that that second account can read the materialized view without copying the materialized view. The method includes modifying the source table for the materialized view. The method includes identifying whether the materialized view is stale with respect to the source table by merging the materialized view and the source table.

Sharing materialized views in multiple tenant database systems

A system for a materialized table (MT) refresh using multiple processing pipelines includes at least one hardware processor coupled to memory storing instructions. The instructions cause the at least one hardware processor to perform operations including determining dependencies among a plurality of intermediate MTs generated from a source MT. The source MT uses a table definition with a query on one or more base tables and a lag duration value. A graph snapshot of dependencies among the plurality of intermediate MTs is generated. Processing pipelines are configured. Each of the processing pipelines corresponds to a subset of the plurality of intermediate MTs indicated by the graph snapshot. Responsive to detecting an instruction for a refresh operation on the source MT, refreshes on corresponding intermediate MTs of the plurality of intermediate MTs in each processing pipeline of the processing pipelines are performed to complete the refresh operation on the source MT.

Materialized table refresh using multiple processing pipelines

Embodiments of the present disclosure may provide a dynamic query execution model. This query execution model may provide acceleration by scaling out parallel parts of a query (also referred to as a fragment) to additional computing resources, for example computing resources leased from a pool of computing resources. Execution of the parts of the query may be coordinated by a parent query coordinator, where the query originated, and a fragment query coordinator.

Scalable query processing

A method includes retrieving a plurality of materialized tables (MTs). Each of the plurality of MTs includes a lag duration and refers to a corresponding base table of a plurality of base tables. The lag duration indicates a maximum time period that a result of a prior refresh of a query on the corresponding base table can lag behind a current time instance. A plurality of time instances for the MT is determined based on the lag duration and a number of prior refreshes of the corresponding base table. A plurality of aligned time instances for the plurality of MTs is determined based on the plurality of time instances for each of the plurality of MTs. Refresh operations are scheduled for the plurality of MTs at one or more of the plurality of aligned time instances that are within the maximum time period.

Query execution using materialized tables

Methods, systems, and computer programs are presented for providing a cluster view method of a database to perform compaction and clustering of database objects, such as database materialized view. A cluster view system identifies a materialized view including data from one or more base tables, a portion of the data of the materialized view including stale data. The cluster view system performs an integrated task within a maintenance operation on a database, the integrated task including compacting the materialized view, the maintenance operation including clustering the materialized view, and stores the compacted and clustered materialized view in the database.

Clustering and compaction of materialized views on a database system

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.

Flexible computing

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.

Transient materialized view rewrite

Embodiments of the present disclosure may provide a dynamic query execution model with fault tolerance and failure recovery techniques. Embodiments of the present disclosure may utilize checkpoints to map processed output files to their corresponding input files. Therefore, if an error occurs in processing one or more files, the system may only need to reschedule processing of selected file(s).

Checkpoints in batch file processing

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.

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Bengaluru, India

Prasanna Rajaperumal