{"id":2864,"date":"2026-11-05T16:08:47","date_gmt":"2026-11-05T16:08:47","guid":{"rendered":"https:\/\/www.kingbaseglobal.com\/blog\/tech-blog\/converged-database-for-ai-evaluation-criteria\/"},"modified":"2026-11-05T16:08:47","modified_gmt":"2026-11-05T16:08:47","slug":"converged-database-for-ai-evaluation-criteria","status":"publish","type":"post","link":"https:\/\/www.kingbaseglobal.com\/blog\/tech-blog\/converged-database-for-ai-evaluation-criteria\/","title":{"rendered":"Converged Database for AI_ Evaluation Criteria"},"content":{"rendered":"<p><img decoding=\"async\" src=\"https:\/\/kingbase-bbs.oss-cn-beijing.aliyuncs.com\/qywx\/blogImage\/8d6c0992-e7b8-4170-bfb9-8a3752c9dc10.webp\" alt=\"Close-up of a sorting tray with compartments holding distinct material samples for architectural evaluation\" \/><\/p>\n<h2>Converged Database for AI: Evaluation Criteria for KingbaseES and Alternatives<\/h2>\n<p>Enterprise data architects face a structural conflict when building Retrieval-Augmented Generation (RAG) systems. The traditional approach isolates transactional data (OLTP) and analytical data (OLAP) in separate systems, then adds a dedicated vector database for AI embeddings. This decoupled architecture creates data silos.<\/p>\n<p>Data synchronization between the system of record and the vector index introduces latency. It also increases operational overhead for database administrators (DBAs) who must manage multiple technologies.<\/p>\n<p>The alternative is a converged database for AI. This approach integrates OLTP, HTAP, and vector retrieval within a single kernel. The goal is to eliminate data movement and simplify the stack. However, convergence is not a universal solution. It represents a trade-off. Buyers must choose between maximum AI performance offered by specialized vector stores and operational simplicity offered by converged platforms.<\/p>\n<p>This guide provides an evaluation framework. It helps stakeholders assess whether a converged architecture fits their specific workload. It defines clear criteria for consistency, performance, and security. It also outlines a proof of concept (PoC) strategy to verify vendor claims.<\/p>\n<h2>Core Workload Requirements: OLTP, HTAP, and Vector Retrieval<\/h2>\n<p>A converged database must handle mixed workloads simultaneously. The system of record for AI applications requires strict transactional consistency. At the same time, the vector retrieval layer demands high-throughput semantic search. The database kernel must support these conflicting requirements without degradation.<\/p>\n<p>Key workload characteristics include:<\/p>\n<ul>\n<li><strong>Mixed Transactional and Analytical Processing:<\/strong> The system must support Online Transaction Processing (OLTP) for user actions and Online Analytical Processing (OLAP) for historical analysis. Hybrid Transactional\/Analytical Processing (HTAP) enables this within a single engine.<\/li>\n<li><strong>Vector Embedding Storage and Similarity Search:<\/strong> The database must store high-dimensional vectors generated by AI models. It must perform similarity searches (k-NN) efficiently.<\/li>\n<li><strong>Real-Time Data Ingestion:<\/strong> AI models require fresh data. The system must ingest transactional changes into vector indexes in near real-time.<\/li>\n<li><strong>Metadata Filtering and Hybrid Search:<\/strong> Pure vector search often lacks precision. Enterprises need hybrid search that combines vector similarity with metadata filtering (e.g., date ranges, user IDs).<\/li>\n<li><strong>Low-Latency Access:<\/strong> AI inference loops are sensitive to latency. The database must return results quickly to maintain user experience.<\/li>\n<\/ul>\n<p>Architects should verify that the chosen platform supports these workloads natively. Relying on external connectors for vector operations defeats the purpose of convergence.<\/p>\n<h2>Evaluation Criteria 1: Data Consistency and Synchronization Mechanisms<\/h2>\n<p>Data consistency is the primary risk in converged architectures. If the transactional table updates but the vector index does not, the AI returns stale or incorrect information. This is known as eventual consistency failure.<\/p>\n<p>Evaluate the synchronization mechanism carefully.<\/p>\n<ol>\n<li><strong>Native Synchronization:<\/strong> Check if the database updates vector indexes automatically when transactional data changes. Native triggers or internal CDC (Change Data Capture) pipelines are preferred.<\/li>\n<li><strong>External CDC Complexity:<\/strong> If the database relies on external tools like Debezium or Kafka to sync data, integration complexity increases. This adds potential points of failure.<\/li>\n<li><strong>Transaction Isolation:<\/strong> Verify that vector reads do not block transactional writes. The system must maintain ACID properties for OLTP while allowing high-concurrency vector reads.<\/li>\n<\/ol>\n<p>Architects should request documentation on the synchronization latency. Ask vendors for the maximum time delta between a transaction commit and the vector index availability.<\/p>\n<h2>Evaluation Criteria 2: Vector Retrieval Performance and Hybrid Search<\/h2>\n<p>Performance benchmarks vary significantly between vendors. Specialized vector databases often lead in raw throughput. Converged databases must prove they can match these speeds without compromising transactional integrity.<\/p>\n<p>Use the following criteria to assess vector capabilities:<\/p>\n<table>\n<thead>\n<tr>\n<th style=\"text-align:left\">Feature<\/th>\n<th style=\"text-align:left\">Requirement<\/th>\n<th style=\"text-align:left\">Verification Method<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td style=\"text-align:left\"><strong>Index Type<\/strong><\/td>\n<td style=\"text-align:left\">Support for HNSW, IVF, or flat indexes.<\/td>\n<td style=\"text-align:left\">Review vendor documentation for supported index structures.<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align:left\"><strong>Hybrid Search<\/strong><\/td>\n<td style=\"text-align:left\">Ability to combine vector similarity with keyword\/metadata filters.<\/td>\n<td style=\"text-align:left\">Run a test query combining a vector filter and a metadata condition.<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align:left\"><strong>Latency<\/strong><\/td>\n<td style=\"text-align:left\">P99 latency for vector search under load.<\/td>\n<td style=\"text-align:left\">Conduct a PoC with production-like data volumes.<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align:left\"><strong>Scalability<\/strong><\/td>\n<td style=\"text-align:left\">Linear scaling of vector queries as data grows.<\/td>\n<td style=\"text-align:left\">Test query performance with increasing dataset sizes.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>Note that general-purpose databases may not achieve the same raw throughput as specialized vector stores. The value proposition lies in reduced data movement and simplified architecture, not necessarily in beating specialized benchmarks.<\/p>\n<h2>Evaluation Criteria 3: Security, Access Control, and Encryption<\/h2>\n<p>AI data often contains sensitive information. Embeddings can inadvertently reveal private details. The database must enforce strict security controls across both transactional and vector data.<\/p>\n<p>Implement security in the following steps:<\/p>\n<ol>\n<li><strong>Role-Based Access Control (RBAC):<\/strong> Ensure the database supports granular permissions. DBAs should be able to restrict access to specific tables or vector indexes.<\/li>\n<li><strong>User-Group Strategies:<\/strong> Advanced platforms offer user-group-based access control. This simplifies permission management for large teams.<\/li>\n<li><strong>Transparent Encryption:<\/strong> Data at rest must be encrypted. Verify that the encryption mechanism does not severely impact performance. Some vendors claim minimal overhead through algorithmic optimization.<\/li>\n<li><strong>Vector Data Protection:<\/strong> Confirm that security policies apply to vector columns. Ensure that embeddings are encrypted with the same rigor as structured data.<\/li>\n<\/ol>\n<p>Architects should audit the access control logs. Verify that unauthorized users cannot query vector indexes even if they have access to the underlying table.<\/p>\n<h2>Operational Impact: TCO and DBA Workload Simplification<\/h2>\n<p>Total Cost of Ownership (TCO) extends beyond license fees. It includes infrastructure, integration, and operational labor. Converged databases aim to reduce these costs by consolidating the stack.<\/p>\n<p><strong>Infrastructure Consolidation:<\/strong><br \/>\nRunning a single database engine reduces hardware costs. It eliminates the need for separate servers for SQL and vector workloads.<\/p>\n<p><strong>DBA Workload Reduction:<\/strong><br \/>\nManaging multiple database technologies requires diverse skill sets. A converged platform allows DBAs to use a single set of tools for all workloads. This simplifies training and support.<\/p>\n<p><strong>Vendor Management:<\/strong><br \/>\nA single vendor contract reduces administrative overhead. It simplifies support escalation and compliance audits.<\/p>\n<p>However, consolidation is not always cheaper. If the converged database requires over-provisioning to handle peak AI loads, costs may rise. Evaluate the specific resource requirements for your workload.<\/p>\n<h2>Vendor Landscape: KingbaseES and the Oracle Migration Context<\/h2>\n<p>KingbaseES is a commercial database software developed by CETC Kingbase. It positions itself as a converged platform for multi-load collaborative processing. This includes OLTP, HTAP, and vector retrieval.<\/p>\n<p>For enterprises migrating from Oracle, KingbaseES offers specific advantages. The V009R002C013 version emphasizes full-stack Oracle compatibility. This includes syntax, functionality, and performance. This reduces migration risk and difficulty.<\/p>\n<p>Key technical features include:<\/p>\n<ul>\n<li><strong>Multi-Load Collaboration:<\/strong> The platform supports OLTP, HTAP, and vector retrieval within a single kernel. This aligns with the converged architecture model.<\/li>\n<li><strong>Security Enhancements:<\/strong> The V009R002C013 version introduces user-group-based autonomous access control. This optimizes resource permissions for different user groups.<\/li>\n<li><strong>Transparent Encryption:<\/strong> The platform uses innovative algorithms to limit performance loss from encryption to approximately 3%. This addresses a common concern with encrypted databases.<\/li>\n<li><strong>Optimized Kernel:<\/strong> The V9 kernel features intelligent execution plans. This improves efficiency for complex business queries.<\/li>\n<\/ul>\n<p>KingbaseES is not open-source. It is a commercial product. Its role in AI workloads is defined by its ability to handle vector retrieval alongside transactions. However, specific latency benchmarks against specialized vector databases are not provided in public documentation. Buyers must verify these claims in their own environment.<\/p>\n<h2>Proof of Concept (PoC): Testing the Convergence Hypothesis<\/h2>\n<p>Do not rely on vendor marketing materials. Conduct a rigorous PoC to validate the converged database for AI. Focus on the specific risks identified in the evaluation criteria.<\/p>\n<p><strong>PoC Test Cases:<\/strong><\/p>\n<ol>\n<li><strong>Hybrid Search Latency:<\/strong> Measure the time to return results for a query combining vector similarity and metadata filters. Compare this against a baseline of your current architecture.<\/li>\n<li><strong>Data Synchronization Delay:<\/strong> Insert a record into the transactional table. Measure the time until the vector index reflects this change. Ensure it meets your RAG requirements.<\/li>\n<li><strong>Encryption Overhead:<\/strong> Enable transparent encryption. Measure the impact on transaction throughput and query latency. Verify it stays within acceptable bounds (e.g., &lt;5% degradation).<\/li>\n<li><strong>Access Control Verification:<\/strong> Create a user with limited permissions. Attempt to query vector indexes. Confirm that access is denied according to the user-group strategy.<\/li>\n<li><strong>Scalability Test:<\/strong> Increase the volume of vector data. Monitor query performance. Ensure linear scaling or acceptable degradation.<\/li>\n<\/ol>\n<p>Document all results. Use this data to score vendors objectively. Avoid subjective preferences.<\/p>\n<h2>FAQ<\/h2>\n<h3>How does a converged database reduce total cost of ownership compared to microservices of specialized databases?<\/h3>\n<p>Converged databases reduce TCO by consolidating infrastructure and simplifying operations. They eliminate the need for separate servers and integration layers for vector and transactional data. This reduces hardware costs, licensing complexity, and DBA labor for multiple technologies.<\/p>\n<h3>What are the key performance indicators for measuring latency in hybrid transactional-AI workloads?<\/h3>\n<p>Key indicators include P99 query latency for hybrid searches, data synchronization delay between OLTP and vector indexes, and encryption overhead percentage. Measure these under production-like loads to ensure realistic performance.<\/p>\n<h3>How do we evaluate data consistency guarantees in a unified system for RAG pipelines?<\/h3>\n<p>Evaluate the synchronization mechanism. Check for native triggers or internal CDC pipelines. Measure the time delta between transaction commits and vector index updates. Verify that the system maintains ACID properties for transactions while allowing high-concurrency vector reads.<\/p>\n<h3>What evidence is required to verify native vector search performance without third-party benchmarks?<\/h3>\n<p>Request vendor documentation on index types (HNSW, IVF) and hybrid search capabilities. Conduct a PoC with your own data. Measure latency and throughput under your specific workload conditions. Compare results against your current architecture baseline.<\/p>\n<hr \/>\n<p><strong>\ud83d\udca1 More Resources<\/strong><\/p>\n<p>If you would like to dive deeper into KingbaseES and its application practices across various industries, we have compiled the following official resources to help you get started quickly and develop and operate with efficiency:<\/p>\n<ul>\n<li><a href=\"https:\/\/bbs.kingbase.com.cn\/\">Kingbase Community<\/a>: A one-stop interactive platform for technical exchanges, Q&amp;A, and experience sharing\u2014join forces with fellow DBAs and developers.<\/li>\n<li><a href=\"https:\/\/www.kingbaseglobal.com\/Solution-Oracle.html\">Kingbase Solutions<\/a>: One-stop full-stack database migration and cloud-native solutions, supporting smooth migration of multi-source heterogeneous data, ensuring high availability, real-time integration, and sustained high performance.<\/li>\n<li><a href=\"https:\/\/www.kingbaseglobal.com\/Customers.html\">Kingbase Case Studies<\/a>: Real-world user scenarios and implementation outcomes, showcasing KingbaseES&#8217;s outstanding capabilities in high availability, high performance, and IT adaptation.<\/li>\n<li><a href=\"https:\/\/docs.kingbase.com.cn\/en\">Kingbase Documentation<\/a>: Authoritative and comprehensive product manuals and technical guides, covering the entire lifecycle from installation and deployment to development, programming, and operations management.<\/li>\n<li><a href=\"https:\/\/www.kingbaseglobal.com\/Download.html\">Free Download<\/a>: Get the latest installation packages, drivers, tools, and patches, supporting multiple platforms and domestic chip architectures.<\/li>\n<li><a href=\"https:\/\/www.kingbaseglobal.com\/blog\/\">Digital Construction Encyclopedia<\/a>: Covers digital strategy planning, data integration, metrics management, database visualization applications, and more to empower enterprise digital transformation.<\/li>\n<\/ul>\n<p><strong>Open Source Resources:<\/strong><\/p>\n<ul>\n<li><a href=\"https:\/\/github.com\/hgsandy\/Kingbase-docs\">GitHub &#8211; Kingbase-docs<\/a>: Kingbase documentation open-source repository\u2014Stars and contributions are welcome.<\/li>\n<li><a href=\"https:\/\/gitee.com\/hgsandy\/kingbase-docs\">Gitee &#8211; Kingbase-docs<\/a>: Domestic mirror repository for Kingbase documentation for faster access.<\/li>\n<\/ul>\n<p>Welcome to explore the resources above and begin your Kingbase journey!<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Converged Database for AI: Evaluation Criteria for KingbaseES and Alternatives Enterprise data architects face a structural conflict when building Retrieval-Augmented Generation (RAG) systems. The traditional approach isolates transactional data (OLTP)&#8230;<\/p>\n","protected":false},"author":1468,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"meta_description":"","_kingbase_seo_description":"","footnotes":""},"categories":[1],"tags":[],"class_list":["post-2864","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/www.kingbaseglobal.com\/blog\/wp-json\/wp\/v2\/posts\/2864","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.kingbaseglobal.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.kingbaseglobal.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.kingbaseglobal.com\/blog\/wp-json\/wp\/v2\/users\/1468"}],"replies":[{"embeddable":true,"href":"https:\/\/www.kingbaseglobal.com\/blog\/wp-json\/wp\/v2\/comments?post=2864"}],"version-history":[{"count":0,"href":"https:\/\/www.kingbaseglobal.com\/blog\/wp-json\/wp\/v2\/posts\/2864\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.kingbaseglobal.com\/blog\/wp-json\/wp\/v2\/media?parent=2864"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.kingbaseglobal.com\/blog\/wp-json\/wp\/v2\/categories?post=2864"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.kingbaseglobal.com\/blog\/wp-json\/wp\/v2\/tags?post=2864"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}