OceanBase Database Community Edition V4.5.0

V4.5.0

Version information

• Release date: December 3, 2025
• Version: V4.5.0_CE
• RPM version: oceanbase-ce-4.5.0.0-100000012025112711

Version overview

OceanBase Database V4.5.0 is an agile iteration version designed for AI scenarios, featuring significant upgrades to its core AI capabilities. This version comprehensively optimizes IVF and IVF_PQ indexes from four dimensions: construction, query, DML operations, and usability. It also introduces support for memory-based sparse vector indexes, delivering superior sparse vector retrieval performance. The hybrid search capabilities have been significantly enhanced, supporting multi-vector hybrid searches and allowing for the weighted fusion and re-ranking of query result sets. Additionally, leveraging its built-in embedding capabilities, the new version allows users to create semantic indexes based on text columns, simplifying the process of using vector indexes.

Key features

Kernel enhancements

• Index Skip Scan optimization

  The new version enhances the performance and stability of the Index Skip Scan feature. Even if the execution plan mistakenly uses Index Skip Scan, the performance will not degrade significantly. Additionally, the Index Skip Scan feature is now enabled by default instead of being disabled by default. After the optimizer collects statistics, it can automatically generate an Index Skip Scan plan if the rules and cost conditions are met.

• Support for Index Merge with JSON multi-valued indexes

  By integrating JSON multi-valued indexes into the Index Merge framework, the new version allows for the merging of scan results from multiple indexes before accessing the table. This significantly reduces the number of table accesses, thereby improving the performance of filtering JSON columns with JSON multi-valued indexes using the member of, json_overlaps, and json_contains expressions.

AI feature enhancements

• IVF vector index performance optimization

  The new version comprehensively upgrades IVF/IVF_PQ indexes from four dimensions: construction, query, DML, and usability. In terms of construction, it optimizes index building time by parallelizing K-means and adapting the domain ID merge framework. For queries, it introduces an iterative filtering algorithm that intelligently selects pre/post filter strategies based on different filtering rates, ensuring result completeness while improving query performance. In DML, it optimizes incremental write efficiency through caching mechanisms. In terms of usability, it adds features such as index rebuild parameter modification, construction progress monitoring, and statement-level nprobes parameter dynamic configuration.

• In-memory sparse vector index (experimental feature)

  The new version supports in-memory sparse vector indexes. When sufficient resources are available, in-memory sparse vector indexes outperform disk-based ones, better meeting business needs for sparse vector retrieval. Users can create in-memory sparse vector indexes by specifying lib=vsag,type=sindi when creating a sparse vector index. It also supports pruning rules to enhance query performance.

• Enhanced automatic partition rebuilding

  Automatic partition rebuilding for vector indexes reduces memory usage. However, in scenarios with a large number of partitions and large data volumes per partition, the single-threaded model used for automatic partition rebuilding is inefficient. To better utilize cluster resources, the new version supports multi-threaded parallel construction for asynchronous indexes, with the number of threads specified by the vector_index_optimization_concurrency parameter.

• Enhanced hybrid search

  The new version enhances the hybrid search feature, supporting hybrid searches with multiple vectors and scalar conditions. It also allows for weighted fusion and re-ranking of query results from multiple vectors.

• Full-text index performance optimization

  The new version further optimizes performance for single full-text index topK pushdown scenarios using adaptive BMM/BMW pruning algorithms.

• AI_Prompt and similarity expressions

  The new version introduces the AI_Prompt function, which upgrades static text prompts to reusable, parameterizable function templates. This supports prompt reuse and dynamic combination with data, enabling efficient prompt engineering. The AI_COMPLETE expression allows calling text generation models to process data tasks using prompts, such as sentiment analysis and summarization. It also supports three expressions for calculating vector similarity: inner_product_similarity, cosine_similarity, and l2_similarity. A value closer to 1 indicates greater similarity between two vectors.

• Semantic index (experimental feature)

  Semantic indexes simplify the process of using vector indexes by leveraging built-in embedding capabilities. This makes vector concepts transparent to users: users can directly write raw data to be stored, which is automatically embedded and indexed within OceanBase Database. During retrieval, users can directly use the raw data they want to query, which is automatically embedded and then queried against the vector index.

Performance improvements

• Multi Get performance optimization

  The new version optimizes Multi Get performance through the following measures:

  • Columnar storage scans support columnar projections.

  • The filter clause utilizes skip index prefixes for optimization.

  • keep order scans support blockscan.

  • Optimizations such as real-time statistics collection at the storage layer.

    These optimizations significantly improve table access performance, especially in scenarios with large data volumes. They also notably enhance the performance of wide table projections involving more than 80 columns, and improve scan performance in vectorized nest loop join scenarios.

• Insert Up performance optimization

  Under the premise of good data locality, where batch primary keys are relatively continuous, the new version merges multiple range point queries into a single range query, reducing the number of ranges and further improving table access performance. Additionally, the new version restructures the main process of the Insert Up operator, optimizes the conflict detection mechanism, and effectively enhances overall performance.

Compatibility changes

View changes

The following changes are introduced:

System variable changes

Parameter changes

The following changes are introduced:

Syntax changes

Recommended versions of components and tools

The following table lists the recommended versions of components and tools for OceanBase Database V4.5.0_CE.