Design and Optimization of Ctrip's Hotel Data Intelligence Platform Using ClickHouse
This article describes how Ctrip built a unified hotel data intelligence platform, evaluated various database solutions, selected ClickHouse as the primary engine, and implemented performance, high‑availability, and monitoring strategies to handle billions of records and thousands of concurrent queries.
Background: With the rapid integration of big data into business, Ctrip needed a unified data platform to monetize data assets, provide valuable insights, and support real‑time and historical analytics for hotel operations.
Why build the platform: Existing fragmented tools caused data silos, inconsistent logic, shallow insights, slow queries, and delayed reporting, especially during peak order periods.
Solution selection: After testing multiple databases, ClickHouse was chosen for its fast query speed despite limited concurrency, while other technologies (ElasticSearch, CrateDB, MongoDB, Hive, etc.) were used for complementary scenarios.
Implementation details: The platform uses a multi‑node ClickHouse cluster with ElasticSearch as a secondary engine. Optimizations include query monitoring, asynchronous requests, partitioned tables for recent data, caching strategies (fixed and dynamic), and traffic sharding between MySQL and ClickHouse based on data size.
High‑availability measures: Process monitoring for data pipelines, DR mechanisms across data centers, virtual clusters for load balancing, CPU/IO alerting, data consistency checks, and comprehensive error notifications ensure stable operation of over 2000 daily data jobs and millions of queries.
Architecture and results: The system consists of multiple virtual clusters that can scale horizontally. Current data volume exceeds 10 billion rows, with 2000+ daily data flows and 300 k+ ClickHouse queries per day. Performance metrics show <1 s response rates improving from 75 % to over 93 % across several months.
Frontend enhancements: Asynchronous page loading, custom visualizations using modified ECharts/Highcharts, and UI improvements provide intuitive data exploration without page refreshes.
Future plans: Continue performance monitoring, extend data warehouse integration, simplify the data ecosystem, and enhance platform features and cross‑system interactions.
Recruitment: Ctrip is hiring engineers interested in big‑data technologies for the hotel data visualization platform (contact: [email protected]).
Recommended reading:
Every Billion‑Row Daily Update: ClickHouse in Ctrip Hotels
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