General
Hybrid Partitioned Tables with Interval and Auto-List Partitioning
You can create Hybrid Partitioned Tables using single-level partitioning with interval and automatic list partitioning. This is in addition to existing support for single-level partitioning and range and list partitioning.
These extensions to Hybrid Partitioned Tables in Oracle Database provide a user-friendly partitioning strategy.
Data Quality Operators in Oracle Database
This release introduces the following two new string matching operators based on approximate or "fuzzy" string matching.
PHONIC_ENCODEconverts words or phrases into language-specific codes based on pronunciation.FUZZY_MATCH, which is language-neutral, gauges the textual similarity between two strings.
The new phonic encoding and fuzzy matching methods enable more sophisticated matching algorithms to be run directly on data in the database rather than only in external applications, providing improved matching performance and efficiency, for example in data de-duplication, linking or enhancement.
Automatic Data Clustering
Oracle Database automatically and transparently clusters storage-based data in response to the type of queries used by the application workload. This allows the workload to make more efficient use of data access optimizations, such as storage indexes, zone maps, and join zone maps.
This feature significantly improves performance for data warehousing workloads based on zone maps or storage indexes. Once data is clustered, the performance of data-scanning queries improves because larger contiguous areas (or zones) of storage are pruned or skipped when they do not contain the data being matched by a particular query.
Extended Support and Faster Performance for JSON Materialized Views
Materialized views of JSON tables have been enhanced with the ability to fast refresh more types of Materialized Views of JSON tables as well as Query Rewrite support for these Materialized Views.
The performance for JSON table Materialized Views is significantly improved through better fast refresh capabilities and better query rewrite capabilities for more workloads. You can use JSON table Materialized Views more broadly in your applications, with better performance and less resource utilization.
Oracle SQL Access to Kafka
Oracle SQL Access to Kafka (DBMS_KAFKA) provides efficient, reliable, and scalable access to data streams from Apache Kafka and OCI Streaming Service. Streaming data can be queried via SQL or loaded into Oracle database tables.
Oracle Database provides efficient, reliable, and scalable integration with Apache Kafka using the DBMS_KAFKA APIs. This API enables Oracle Database to consume data from external data streams without the need for costly, complex direct application connections using proprietary interfaces. Oracle SQL Access to Kafka enables you to use Oracle Databases rich analytic capabilities across all your data.