Latest KDB.AI Release Notes
This page details the latest updates to KDB.AI Cloud UI and Server releases. Includes brief, high-level descriptions of fixes, improvements and any new features added.
KDB.AI Cloud UI v1.6.0 release notes
Release date 2025.01.24
The KDB.AI Cloud UI 1.6.0 release includes the newest capabilities and enhancements introduced in the KDB.AI Server 1.6.0 release.
For a complete list of past features and improvements, refer to previous KDB.AI Cloud UI release notes.
KDB.AI Server v1.6.0 release notes
Release date 2025.01.24
Welcome to the KDB.AI Server 1.6.0 release! Key highlights include:
- Enhanced install experience (Improvement)
- Query batching optimization (Improvement)
- Endpoint for external/reference tables (New)
- Enhanced search and query performance (Improvement)
- Reduced memory usage (Improvement)
- Insert data for partition tables (Bug fix)
- Table search conflict (Known issue)
- Documentation updates (Improvement)
1. Enhanced KDB.AI Server install experience (Improvement)
Experience a quicker and more streamlined KDB.AI Server installation:
- Reduced setup time: We've simplified the setup process to cut the installation time from several days to just 10-20 minutes, ensuring a quick start for all users.
- Simplified process: The installation now requires only 2 Docker commands, minimizing complexity and potential errors.
- Enhanced user experience: Then, you can easily access the quickstart sample, access the KDB.AI features and see the value they can bring to your projects.
Tip: Head to our updated Server Setup page to see how easy it is to get started with KDB.AI. Save time, reduce errors, and enhance your overall experience with our improved installation process.
2. Query batching optimization (Improvement)
Enhance your multi-query search performance with query batching optimization by:
- Batching across the same index: Perform multiple queries within the same index efficiently, reducing the need for repetitive search calls.
- Batching across multiple partitions: Execute queries across different partitions seamlessly, enhancing the flexibility and scalability of your searches.
- Batching across multiple columns: Handle queries involving multiple columns effectively, allowing for more comprehensive data retrieval.
The optimization does not affect how APIs are called or their functionality. They have been optimized to support batch queries without any changes required on your end.
Tip: Use the batching capabilities to streamline your search processes, save time, and improve the efficiency of your data retrieval operations. Perform comprehensive queries across multiple indices, partitions, and columns to enhance the robustness of your data analysis.
3. Endpoint for external/reference tables (New)
We added a new endpoint for external/reference tables: /api/v2/databases/{database}/tables/{table}/load
.
- When to use it: You need to call this endpoint if any changes are made to an external table after the last search or query call.
- Benefit: This ensures that the latest data is loaded and available for subsequent searches or queries, improving data accuracy and consistency.
Tip: Make sure to call the new endpoint /api/v2/databases/{database}/tables/{table}/load
from the q, Python, or REST APIs. This will help keep your database up-to-date and avoid unnecessary reloads, enhancing the efficiency of your operations.
4. Enhanced search and query performance (Improvement)
We have enhanced the search and query performance by optimizing the database load process.
- Previously: The server would load the database on each call, which could slow down operations.
- Now: The backend checks if the database is already loaded and only reloads it if there are changes.
- Result: This improvement ensures faster response times and more efficient search and query experience.
5. Reduced memory usage (Improvement)
Further enhancements to KDB.AI memory management have resulted in improvements to memory usage. KDB.AI Server 1.6.0 uses less memory by implementing a clean-up process after each search or query. This enhancement helps in maintaining optimal memory usage, ensuring that the system remains efficient and responsive even during intensive operations. You can now enjoy a smoother and more reliable performance with reduced memory overhead.
6. Insert data for partition tables (Bug fix)
- Issue: The insert operation was throwing an error when there were missing partitions in the input data.
- Resolution: The insert data bug for partition tables has been resolved, ensuring smooth data insertion even when some partitions are missing.
7. Table search conflict (Known issue)
You may experience issues when searching a table using the same name as a previously deleted table within the same server instance.
Impact: You may see the kdbai-001E
error during search in some cases and in other cases you may receive incorrect search results.
Resolution: Perform the following steps, depending on your use case and restriction:
- Restart the server instance to resolve the issue and create your table again using the same name.
- If you cannot restart the server, then create a table with a new name.
There is no risk of data loss due to this issue as it involves table deletion.
8. Documentation updates (Improvement)
- New page: All integrations.
- Updated pages: API references ( q, Python, REST), Server Setup.
Upgrade procedures
To use the latest version, upgrade your instance or sign up for a free trial.
Summary
We hope you appreciate all the enhancements and the new endpoint we introduced in v1.6.0. Enjoy exploring all the updates! If you need help, reach out to our Support Team at support@kdb.ai or join our Slack channel.
For a complete list of past features and improvements, refer to the previous KDB.AI Server release notes.
Thanks for reading. Stay tuned for the next release!
The KDB.AI Team