Thoughtspot, Inc.

Snowflake Inc.

Naresh Kumar

Srinath Shankar

Rakesh Kothari

Sandeep Kumar

Andong Zhan

Srilakshmi Chintala

Satyam Shekhar

Tushar Mahale

Siva Singaram

Jianzhun Du

Jasmeet Singh Jaggi

Rahul Paliwal

Eric Shawn Vandenberg

Leonhard Franz Spiegelberg

Yun Zou

Amit Prakash

Manikanta Balakavi

Abhishek Rai

Priyendra Singh Deshwal

Rahul Manikanta Balakavi

Nitish Rajguru

Mayank Raj

Peter Kuimelis

A method provides deterministic row ordering for query result sets in a database system. A framework receives a query referencing a database table and generating a row index column in response. The framework generates the row index column that involves determining an order associated with source files underlying the referenced table and assigns each table row a unique index value based on the file order and row position within the files. The framework executes the received query to produce a result set ordered by the generated row index column. The framework determines row order based on analyzing underlying data file metadata and facilitates returning the result set with consistent, reliable ordering, enabling stable positional-based access semantics when querying distributed database tables.

Dataframe workloads using read-only data snapshots

A method includes receiving instructions to perform order-dependent DataFrame operation on data. In response to receiving the instructions, the framework analyzes the instructions to identify the order-dependent DataFrame operation, and generates an executable query corresponding to the identified order-dependent DataFrame operation. The framework executes the generated executable query on the data stored in the first database in the cloud data platform, creates a row position column that generates for each row a unique integer identifier, reproducible across multiple sessions and/or queries for the same underlying data to make the data accessible via positional indexing, and assigns each row of the first database a unique row index value based on the row position column and the row position column order. The framework orders a result from performing the order-dependent DataFrame operation based on the unique row index value assigned to each row of the first database, and returns the ordered result to the user.

Row-order dependent dataframe workloads

A method and system may be implemented for automatically analyzing data in a database. A method for use in a low-latency database analysis system may include generating a schema. The schema may be based on a portion of an external database. The method may include storing the schema in an in-memory database. The method may include receiving a data-query. The method may include generating a resolved-request. The resolved-request may be based on the data-query and the stored schema. The stored schema may be used for executing the query on the external database. The method may include receiving results data responsive to the data-query from the external database. The method may include outputting the results data for display on a user interface.

Low-latency database analysis using external data sources

Improved systems and methods for database analysis are described herein. A method includes generating a graph-based ontological data structure including nodes connected by edges in a low-latency database analysis system, wherein each node represents a respective analytical-object in the low-latency database analysis system, maintaining versions for each of the nodes in the graph-based ontological data structure, maintaining versions for each of the edges in the graph-based ontological data structure, maintaining a transaction log for each transaction with respect to the graph-based ontological data structure, reverting to an earlier version of at least a portion of the graph-based ontological data structure using the transaction log, versioned nodes, and versioned edges in response to an event, and outputting a version of the graph-based ontological data structure in a defined form for presentation to a user or for use by a client.

Systems and methods for database analysis

Index sharding in a low-latency database analysis system includes obtaining index configuration data for indexing constituent data, the constituent data including a plurality of logical tables, and indexing, by an indexing unit, the constituent data by partitioning the constituent data based on a characteristic of the constituent data into at least a first partition and a second partition, segmenting the first partition into a first segment of the first partition, sharding the first segment into a first shard of the first segment of the first partition, segmenting, using hash-partitioning, the second partition into one or more segments of the second partition, and for each segment of the second partition, sharding the segment into one or more respective shards.

Index sharding

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Santa Clara, CA, United States of America

Naresh Kumar