Snowflake Inc.

Varun Ganesh

Jiaqi Yan

Libo Wang

Thierry Cruanes

Igor Demura

Prasanna Rajaperumal

Benoit Dageville

Haowei Yu

Jiaxing Liang

Jiansheng Huang

Scott Ziegler

Ryan Michael Thomas Shelly

Yizhi Zhu

Saiyang Gou

Wenhao Song

Yi Fang

Xinglian Liu

Donghe Kang

Alvin E Jou

A method for selecting micro-partitions for a clustering operation includes: storing table data in a plurality of micro-partitions of a storage device, wherein each of the plurality of micro-partitions comprises a portion of the table data, wherein subsets of the plurality of micro-partitions are associated with a respective one of a plurality of expression property (EP) files, and wherein each of the plurality of EP files comprises an EP data region that represents the portions of the table data of the subset of the plurality of micro-partitions associated with the EP file; determining sub-ranges of the table data based on the EP data regions of the plurality of EP files; selecting a subset of the plurality of EP files for a clustering operation based on the sub-ranges of the table data; and performing the clustering operation on the micro-partitions associated with the subset of the EP files.

Micro-partition clustering based on expression property metadata

Systems, methods, and devices for batch ingestion of data into a table of a database. A method includes determining a notification indicating a presence of a user file received from a client account to be ingested into a database. The method includes identifying data in the user file and identifying a target table of the database to receive the data in the user file. The method includes generating an ingest task indicating the data and the target table. The method includes assigning the ingest task to an execution node of an execution platform, wherein the execution platform comprises a plurality of execution nodes operating independent of a plurality of shared storage devices collectively storing database data. The method includes registering metadata concerning the target table in a metadata store after the data has been fully committed to the target table by the execution node.

Batch data ingestion in database systems

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

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

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

Embodiments of the present disclosure describe systems, methods, and computer program products for improving query processing of a database. An example method can include: storing table data for a table in a plurality of micro-partitions, each micro-partition comprising a portion of the table data for the table; for each micro-partition of the plurality of micro-partitions, storing metadata for the micro-partition in at least one of a plurality of expression properties; and selecting, by a processing device, a subset of the plurality of expression properties to be grouped into a grouping expression property based at least partially on the metadata of the subset of the plurality of the expression properties. The grouping expression property may include cumulative metadata associated with the metadata of the subset of the plurality of expression properties.

Metadata clustering

A cluster view method of a database to perform compaction and clustering of database objects, such as database materialized view is shown. The database can comprise a cache to store changes to storage units of tables of the database objects. The cluster view method can implement clustering to remove data based on the cache and clustering to group the data of the materialized view.

Maintenance of clustered materialized views on a database system

The subject technology receives a query plan corresponding to a query. The subject technology executes the query based at least in part on the query plan, the executing including: filtering a first set of files that are to be modified by a merge statement, performing a split operation to send information related to a second set of files to a scan set builder operation in a first portion of the query plan and scan back operation in a second portion of the query plan, performing the scan set builder operation to remove the second set of files from the first set of files, performing a table scan operation based on a third set of files, and performing a first union all operation to combine the first set of data with a second set of data as a first set of combined data.

Growing community of inventors

San Mateo, CA, United States of America

Varun Ganesh