This release improves UI, AI, and database management. Highlights include reorganized menus, per-table RowActionDisplay, child-table search, structured property configuration, and AI Agents. Backend updates add Docker deployments, SQL health monitoring, multi-database maintenance overhaul, bulk operations, and Postgres support for better performance and scalability. Other improvements include validation enhancements, new tags for table fields, updated table filter visuals, and key bug fixes for maintenance routines, translations, and form behavior.
A new Validation Rule 71 warns when a replica field is filled by a formula referencing another replica. Users can choose to address or ignore these cases, ensuring they are alerted to potential issues while still managing the data.
RowActionDisplay can now be configured per table, allowing developers to set layout behavior directly in forms and menus without using Altwins.
Previously global-only, the setting now supports a default value with per-table overrides, making UI behavior more flexible and maintainable.
To configure, open the form containing the table, go to the Other Properties section, and set the desired RowActionDisplay value.
Users can now search records using tags from related (child) tables. If at least one tag matches, the full record is returned along with all its related tags. This improves usability by allowing direct searches on related data, providing full context without extra navigation or browsing sub-tables. To configure, create a column with the Column from the below table option checked. Search is enabled by default but can be disabled if needed.
AI Agents enable analytical and validation tasks to be modeled directly in Genio, allowing fields to be automatically analyzed, processed, and populated based on system data. The agent gathers model data, builds a prompt, calls the AI service, and maps the response back to configured fields. Configuration is done in the AI Agents table, where users define the system prompt, user prompt, base area, and response mappings. Agents can be triggered via a Button to AI Agent or automatic triggers. This allows applications to leverage GenAI without custom code. Service abstraction manages keys, configurations, and model selection, while all calls are traced and measured for cost, duration, and analysis.
Properties can now be defined using a structured table instead of manual code, making them easier to identify, manage, and generate. Each property includes an Id, Label, Type (Text, Numeric, Logical, Password), optional Default value, and Notes. Defined properties are generated in WebAdmin’s system configuration for user setup. This improves usability, enables automatic generation support, and enforces proper data type validation in the UI.
Business rules were previously executed row by row. While suitable for UI workloads, this approach does not scale for routines that create or update large volumes of data, often resulting in exponential query growth and heavy database roundtrips.
Bulk Operations introduce an API capable of processing blocks of rows for Insert, Update, and Delete operations. Propagations are calculated based on changed fields and handled recursively using block updates, leveraging existing Calc_block procedures.
This approach significantly reduces database roundtrips and takes advantage of SQL vendor mechanisms such as table-valued parameters, bulk inserts, and multi-row updates, resulting in substantial performance improvements for large-scale routines.
Data Systems (formerly “Database Years”) allow records to be split across multiple databases, improving performance and organization in large or long-running projects. However, maintaining multi-system applications required repetitive and manual reindexation through WebAdmin. The maintenance workflow has been simplified. Single-system applications remain unaffected. For multi-system applications, the data system dropdown was removed, and the data systems table is now a checklist displaying information about the latest maintenance job per system. Users can now:
QuidDBTools now includes structured SQL monitoring to ensure smooth server operation and optimal performance. By integrating Brent Ozar scripts, scheduled SQL jobs, and automated data purging, the system detects slow queries, indexing issues, misconfigurations, and potential bottlenecks while keeping historical data efficiently managed. Key benefits include proactive issue detection, automated maintenance and cleanup, faster troubleshooting with detailed performance insights, and continuous optimization through scheduled jobs.
Projects can now be deployed using Docker in a Linux environment, providing a modern alternative to Windows/IIS deployments. This approach allows applications to run in isolated, consistent environments, eliminating the need for manual deployments or resource-heavy VMs.
Advantages include one-click deployments, automated processes, better resource management, easier scaling, version control and rollbacks, lower infrastructure costs, and minimal redeployment effort in case of failure. All dependencies are documented in code, and Docker images are fully compatible with Kubernetes, simplifying setup for new Genio users.
Genio now supports Postgres, providing a free, open-source alternative to SQL Server and MySQL with strong SQL procedure support. Key features include schema updates, standard Genio functions, database seeding, manual routines, table and index checks, formula recalculation, view generation, and block/daily calculations.
Postgres is strongly typed, case-sensitive, and enforces strict dependency checks. Experimental features include formula calculations via views and database-side primary key generation, simplifying insert procedures.
logReindex.xml was empty, which caused infinite loading.