This article details Haijing Technology's challenges with real‑time telecom data analysis, explains why traditional Hadoop and ClickHouse solutions fell short, and shows how ByConity's unified engine, multi‑table joins, and elastic scaling enable efficient, low‑latency analytics across complex B‑O‑M domains.
Haijing Technology faced Hadoop's real‑time limits and ClickHouse's operational pain points, so it adopted the open‑source ByConity platform, which provides a unified table engine, fast multi‑table joins, and seamless scaling to deliver a carrier‑grade real‑time analytics solution.
MetaApp replaced ClickHouse with the open‑source cloud‑native warehouse ByConity, achieving more than 50% reduction in resource costs while delivering comparable or faster OLAP query performance across distinct, retention, conversion, and point‑lookup workloads, thanks to compute‑storage separation, read/write isolation, and minute‑level elastic scaling.
MetaApp replaced ClickHouse with the open‑source cloud‑native data warehouse ByConity, achieving over 50% cost reduction and faster, more stable OLAP queries by separating storage and compute, simplifying scaling, and improving resource utilization across a range of analytics workloads such as deduplication, retention, conversion and point‑lookup.
ByConity 0.3.0 introduces a new inverted index supporting Chinese tokenization, a shared‑storage leader election mechanism, enhanced cold‑read performance via Prefetch and adaptive mark allocation, and an upgraded ELT pipeline with BSP mode, all detailed with implementation steps, code examples, and future roadmap.
This article explains how ByConity uses a high‑availability shared KV store and CAS operations to implement a lightweight, fault‑tolerant leader election mechanism that eliminates the need for external services like Zookeeper, simplifies node management, and ensures safe leader transitions in a cloud‑native data warehouse.
This article compares ByConity and ClickHouse from a usage perspective, detailing their architectural differences, core components, basic operations such as table creation, data import and query, distributed transaction support, special table engines, scaling strategies, and deployment requirements.
ByConity is a cloud‑native, storage‑compute separated data warehouse engine that supports multi‑tenant isolation, high performance, and elastic scaling; this guide explains its three‑layer architecture, hardware requirements, Helm‑based Kubernetes deployment, dynamic scaling, and practical SQL testing steps.
Using the TPC‑DS benchmark’s 99 queries on a 1 TB dataset, this study evaluates the performance of four open‑source OLAP engines—ClickHouse, Doris, Presto, and ByConity—across basic, join, aggregation, subquery, and window‑function scenarios, revealing ByConity’s superior speed and the limitations of ClickHouse.
