How ByteHouse Redefines Real‑Time Multimodal Analytics with a Cloud‑Native Data Warehouse

Background

ByteHouse is a cloud‑native data‑warehouse engine developed by ByteDance. It was accepted to SIGMOD 2026 under the title “ByteHouse: ByteDance's Cloud‑Native Data Warehouse for Real‑Time Multimodal Data Analytics.”

Scale

Deployed on more than 25,000 nodes, ByteHouse manages over 800 PB of data and serves hundreds of business lines. Existing open‑source warehouses could not simultaneously satisfy the high concurrency, low latency, and multimodal analysis demands of ByteDance’s workloads, motivating a full‑stack redesign.

Architecture Overview

ByteHouse follows a cloud‑native, shared‑storage architecture that fully decouples control, compute, and storage layers, allowing independent scaling.

Storage Layer

Key innovations:

Sniffer file format : a self‑describing file that packs data blocks, layout indexes, Bloom filters, and metadata together. This eliminates external metadata lookups and improves point‑lookup latency. The format dynamically selects optimal encodings (e.g., FSST for text, ALP for floating‑point numbers) based on data distribution.

CrossCache : a distributed SSD‑accelerated cache with chunk‑level granularity. By caching at the chunk level, read amplification is reduced and the performance penalty of storage‑compute separation is mitigated.

Compute Layer

ByteHouse provides three execution modes that share a common optimizer:

APM (Analytic Pipeline Mode) : vectorized pipeline optimized for low‑latency interactive queries.

SBM (Staged Batch Mode) : supports long‑running ETL jobs with intermediate materialization and task‑level retries.

IPM (Incremental Processing Mode) : processes only data deltas using row‑level lineage, avoiding full recomputation. In TPC‑H‑style join workloads with a 2.5 % update rate, IPM reduces CPU consumption by 28.4 %–69.2 % compared with full recompute.

AI‑Driven Query Optimization

Beyond traditional cost‑based optimization, ByteHouse incorporates deep‑learning models that generalize to unseen query patterns. The optimizer parses WHERE clauses into abstract syntax trees (AST) and extracts features via max/avg pooling layers, feeding them to neural networks that predict I/O cost and guide predicate pushdown.

Additional AI components:

Join Side Selection (JSS) : a binary‑classification model that chooses the build side of a hash join to avoid memory blow‑up on skewed data.

Predicate Pushdown (PPS) : a learned model that decides when early pushdown is beneficial, preventing unnecessary I/O.

Production tests on 1,000 real long‑tail queries show a 15 %–45 % reduction in the 95th–99th percentile latency when the AI models are enabled.

Native Vector Search Integration

Vector indexes are built directly into the engine, removing the need for external plugins. ByteHouse implements a tiered indexing strategy:

Online serving : high‑performance HNSW with scalar quantization (SQ) for millisecond‑level response.

Massive datasets : DiskANN that stores the full‑precision graph on SSD, drastically lowering memory usage.

Runtime filtering generates scalar predicates (e.g., category = 'news') as filters that prune the vector search space before similarity computation.

SQL‑level fusion operators such as RANK_FUSION enable combined keyword and vector similarity ranking (e.g., Reciprocal Rank Fusion) within a single query.

Performance Evaluation

In mixed‑query benchmarks using the Cohere and C4 datasets, ByteHouse’s tiered indexing and runtime filtering achieve over 50 % higher QPS at 99 % recall compared with dedicated vector databases such as Milvus and pgvector.

Conclusion

ByteHouse demonstrates that a cloud‑native, AI‑augmented data‑warehouse can break the storage‑compute bottleneck, support real‑time multimodal analytics at petabyte scale, and serve as a robust foundation for next‑generation AI applications. The system is open‑sourced through ByteDance’s Volcano Engine for enterprise deployment.

Paper PDF: https://arxiv.org/pdf/2602.08226