This article presents a detailed performance comparison of ClickHouse using two real‑world cases, analyzes query timings, explores parameter tuning, schema and SQL adjustments, and summarizes practical conclusions for achieving sub‑second analytics on billion‑row datasets.
This article explains how to handle high‑volume advertising monitoring by storing raw request logs in ClickHouse, enabling sampling and materialized views, and using TP999 metrics, aggregating tables, and Grafana queries to achieve fast, flexible, and low‑impact real‑time analytics on billions of events.
This article explains how Ctrip’s hotel data intelligence platform handles over ten billion daily data updates and nearly a million queries by adopting ClickHouse, detailing the system's background, the reasons for choosing ClickHouse over other solutions, the data ingestion pipelines, monitoring strategies, operational practices, and performance outcomes.
The article presents Renrenche’s end‑to‑end mobile data platform, detailing its overall architecture, real‑time Spark‑based computation engine, Web IDE, metadata management, BI reporting built on ClickHouse, and how data‑driven practices empower both online and offline business operations.
The article details JD's 2018 upgrade of its Business Intelligence platform, describing how unified offline OLAP with ClickHouse, Spark, and Scala, timeliness optimizations, and a React‑based visualization component library together improve data consistency, performance, and user experience for merchants.
This article details how Beike Zhaofang leveraged the ClickHouse columnar OLAP database for large‑scale user behavior analysis, covering its architecture, key features, performance benchmarks against other engines, data ingestion pipelines, custom UDFs for funnel and retention metrics, deployment setup, and future enhancements.
This article introduces ClickHouse as a high‑performance, column‑oriented database designed for real‑time big‑data analytics, outlines its key features, performance characteristics, supported interfaces, differences from Hadoop, and explains its main storage engines—MergeTree and Distributed—while also noting its current limitations.
This article examines the evolution and classification of distributed databases for OLAP workloads, comparing traditional RDBMS, MPP solutions such as Teradata and Greenplum, Hadoop‑based ecosystems, and newer architectures like ClickHouse and Palo, while highlighting their architectural traits, strengths, and limitations.
