Welcome to OmniSci Documentation
Last updated
Last updated
Learn how to use Immerse to gain new insights to your data with fast, responsive graphics and SQL queries.
Learn how to Install and configure your OmniSci instance, then load data for analysis.
Work with JupyterLab and Data Science tools in the Python ecosystem to query and visualize data and build high-performance machine learning workflows.
For more complete release information, see the Release Notes.
New Combo chart type in Immerse provides increased configurability and flexibility.
Immerse chart-specific filters and quick filters add increased flexibility and speed.
Updated Immerse Filter panel provides a Simple mode and Advanced mode for viewing and creating filters.
On multilayer charts, layer visibility can be set by zoom level.
Different map charts can be synced together for pan and zoom actions, regardless of data source.
Array support for the Array type over JDBC.
SELECT DISTINCT in UNION ALL is supported. (UNION ALL is prerelease and must be explicitly enabled.
Support for joins on DECIMAL types.
Performance improvements on CUDA GPUs, particularly Volta and Turing.
NULL support for geospatial types, including in ALTER TABLE ADD COLUMN.
SQL SHOW commands: SHOW TABLES, SHOW DATABASES, SHOW CREATE TABLE, and SHOW USER SESSIONS.
Ability to perform updates and deletes on temporary tables.
Updates to JDBC driver, including escape syntax handling for the fn keyword and added support to get table metadata.
Notable performance improvements, particularly for join queries, projection queries with order by and/or limit, queries with scalar subqueries, and multicolumn group-by queries.
Query interrupt capability improved to allow canceling long-running queries, also supports JDBC now.
Completely overhauled SQL Editor, including query formatting, snippets, history and more.
Database switching from within Immerse, as well as dashboard URLs that contain the database name.
Over 50% reduction in load times for the dashboards list initial load and search.
Cohort builder now supports count (# records) in aggregate filter.
Improved error handling and more meaningful error messages.
Custom logos can now be configured separately for light and dark themes.
Logos can be configured to deep-link to a specific URL.
Added support for UPDATE via JOIN with a subquery in the WHERE clause.
Initial support for TEMPORARY (that is, non-persistent) tables.
Improved performance for multi-column GROUP BY queries, as well as single column GROUP BY queries with high cardinality. Performance improvement varies depending on data volume and available hardware, but most use cases can expect a 1.5 to 2x performance increase over OmniSciDB 5.0.
Improved support for EXISTS and NOT EXISTS subqueries.
Added support for LINESTRING, POLYGON, and MULTIPOLYGON in user defined functions.
Immerse log-ins are fully sessionized and persist across page refreshes.
Pie chart now supports "All Others" and percentage labels.
Cohorts can now be built with aggregation-based filters.
New filter sets can be created through duplicating existing filter sets.
Dashboard URLs now link to individual filter sets.
The new filter panel in Immerse enables the ability to toggle filters on and off, and introduces Filter Sets to provide quick access to different sets of filters in one dashboard.
Immerse now supports using global and cross-filters to interactively build cohorts of interest, and the ability to apply a cohort as a dashboard filter, either within the existing filter set or in a new filter set.
Data Catalog, located within Data Import, is a repository of datasets that users can use to enhance existing analyses.
To see these new features in action, please watch this video from Converge 2019, where Rachel Wang demonstrates how you can use them.
Added support for binary dump and restore of database tables.
Added support for compile-time registered user-defined functions in C++, and experimental support for runtime user-defined SQL functions and table functions in Python via the Remote Backend Compiler.
Support for some forms of correlated subqueries.
Support for update via subquery, to allow for updating a table based on calculations performed on another table.
Multistep queries that generate large, intermediate result sets now execute up to 2.5x faster by leveraging new JIT code generator for reductions and optimized columnarization of intermediate query results.
Frontend-rendered choropleths now support the selection of base map layers.
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