Snowflake Inc.

Alcatel

Stefan Richter

Ismail Oukid

Thierry Cruanes

Benoit Dageville

Max Heimel

Mahmud Allahverdiyev

Linnea Passing

Juliane K Waack

Pierre Gaillard

Alejandro Salinger

Brian G Risch

Vincent Bourget

Alexander Weiss

Andreas Fleck

Jan Dehnel

Geoffrey Witt

J Boyet Stevens

Christopher L Johnson

Matthew Barber

Marta Garcia

Michael Wittrich

Frank Streichhahn

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.

Regular expression search query processing using pruning index

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.

Scan set pruning for queries with predicates on semi-structured fields

A top K query directed at a table is received. The table is organized into multiple storage units. The top K query comprises a first clause to sort a result set in order and a second clause that specifies a limit on a number of results provided in response to the query. A table scan operator identifies a first set of rows from the table based on a scan set determined for the table and provides the first set of rows to a top K operator. The top K operator determines a current boundary based on the first set of rows and provides the current boundary to the table scan operator. The table scan operator prunes the scan set based on the current boundary and identifies a second set of rows from the table based on the pruning.

Pruning data based on state of top K operator

A source table organized into a set of micro-partitions is accessed by a network-based data warehouse. A pruning index is generated based on the source table. The pruning index comprises a set of filters that indicate locations of distinct values in each column of the source table. A query directed at the source table is received at the network-based data warehouse. The query is processed using the pruning index. The processing of the query comprises pruning the set of micro-partitions of the source table to scan for data matching the query, the pruning of the plurality of micro-partitions comprising identifying, using the pruning index, a sub-set of micro-partitions to scan for the data matching the query.

Pruning index generation and enhancement

A table organized into a set of batch units is accessed. A set of N-grams are generated for a data value in the source table. The set of N-grams include a first N-gram of a first length and a second N-gram of a second length where the first N-gram corresponds to a prefix of the second N-gram. A set of fingerprints are generated for the data value based on the set of N-grams. The set of fingerprints include a first fingerprint generated based on the first N-gram and a second fingerprint generated based on the second N-gram and the first fingerprint. A pruning index that indexes distinct values in each column of the source table is generated based on the set of fingerprints and stored in a database with an association with the source table.

Processing queries using an index generated based on data segments

Indexed regular expression search with N-grams

A query directed at a table organized into a set of batch units is received. The query comprises a predicate for which values are unknown prior to runtime. A set of values for the predicate are determined based on the query. An index access plan is created based on the set of values. Based on the index access plan, the set of batch units are pruned using a pruning index associated with the table. The pruning index comprises a set of filters that index distinct values in each column of the table. The pruning of the set of batch units comprises identifying a subset of batch units to scan for data that satisfies the query. The subset of batch units of the table are scanned to identify data that satisfies the query.

Join query processing using pruning index

A query directed at a source table organized into a set of batch units is received. The query includes a pattern matching predicate that specifies a search pattern. A set of N-grams are generated based on the search pattern. A pruning index associated with the source table is accessed. The pruning index comprises a set of filters that index distinct N-grams in each column of the source table. The pruning index is used to identify a subset of batch units to scan for matching data based on the set of N-grams generated for the search pattern. The query is processed by scanning the subset of batch units.

Index to support processing of pattern matching queries

Processing queries on semi-structured data columns

Pruning using prefix indexing

Processing techniques for queries where predicate values are unknown until runtime

Pruning index generation for pattern matching queries

Pruning index maintenance

Pruning index to support semi-structured data types

Prefix N-gram indexing

Growing community of inventors

Berlin, Germany

Stefan Richter