How Hologres 4.0 Unifies Real‑Time Data Warehousing and AI‑Native Analytics

Challenges of Multi‑Engine Architecture

Traditional enterprise data platforms often rely on separate engines for OLAP (ClickHouse, Doris), key‑value lookups (Redis, HBase), full‑text search (Elasticsearch), and vector similarity (Milvus, Faiss). Maintaining N engines creates N data pipelines, N metadata stores, high storage and compute costs, and complex operational overhead.

All‑in‑One Architecture of Hologres 4.0

Hologres 4.0 adopts an "All‑in‑One" design that integrates OLAP analytics, high‑QPS point queries, vector search, full‑text search, and semi‑structured data processing within a single unified kernel, achieving the goal of "one data, one compute, multimodal analysis".

Performance Benchmarks

TPC‑H 30 TB benchmark: world‑leading throughput.

ClickBench: internal table query performance ranks among the best in China.

Non‑primary key point queries with global secondary indexes deliver up to 65× speedup.

AI‑Native Capabilities

Hologres 4.0 provides a suite of AI Functions accessible via standard SQL, including ai_embed (vectorization), ai_parse_document (document parsing), ai_chunk (text chunking), and ai_summarize (summary generation). These enable Retrieval‑Augmented Generation (RAG) pipelines to be built entirely inside the database without external orchestration.

Vector Search and Write‑After‑Read

The vector engine achieves 99 % recall and supports "write‑after‑read"—queries can be executed immediately after data ingestion without waiting for index construction. A hybrid memory‑disk index (RapidQ 1‑bit quantization) reduces memory usage by 80 % while keeping performance loss under 5 % .

Full‑Text Search Integration

Built‑in Tantivy engine offers BM25 ranking and Chinese tokenizers (IK, Jieba). Compared with peer products, query latency improves by 151 % . The engine supports three‑way hybrid search (scalar + full‑text + vector) in a single query.

Object Table for Unstructured Data

Unstructured files stored in OSS (images, videos, PDFs) can be mapped to relational tables via the Object Table mechanism. After specifying an OSS path, metadata is auto‑collected and AI Functions can batch‑process the files for feature extraction, description generation, and vectorization, while Dynamic Tables ensure only newly added files trigger AI inference, controlling token costs.

Lake Integration and External Database Support

Hologres supports external lake formats such as Paimon and Iceberg, as well as external sources like MaxCompute, providing unified metadata management and high‑performance direct reads. The lake‑to‑SSD mirroring feature synchronizes hot data to internal storage, delivering internal‑table‑level access speed.

Elastic Resource Governance and Serverless Computing

Through compute‑storage separation and elastic compute groups, Hologres isolates heterogeneous AI workloads (write, online service, ad‑hoc analysis, batch inference) to prevent interference. A per‑query serverless mode allocates resources on demand, offering zero‑reservation cost and ideal elasticity for bursty ETL or exploratory AI analysis.

The query optimizer incorporates HBO (historical‑based optimization) and Adaptive Execution, dynamically adjusting execution plans and parallelism based on past runs to improve stability for complex queries.

Real‑World Use Cases

Intelligent driving: A leading automaker replaced a multi‑engine stack (StarRocks + custom vector store + RocksDB) with Hologres, achieving one‑second data visibility and reducing compute cores from 15 000 to 5 000.

Digital advertising: A gaming client leveraged AI Functions to auto‑generate personalized ad scripts, boosting material production efficiency by 80 % and increasing output volume fivefold, raising ROI by 20 %.

Retail operations: A large convenience‑store chain stored shelf images in OSS, used Object Table and AI Functions for vector inference, achieving 90 % accuracy in compliance and out‑of‑stock detection, shifting the platform focus from data management to content understanding.

Future Direction: Agent‑Ready Platform

The presenter emphasizes that future data platforms will serve not only human analysts but autonomous AI agents. Hologres aims to support end‑to‑end decision loops—query, analysis, inference, action—within hours, positioning itself as an "Agent‑Ready" infrastructure for the AI era.