Suning’s senior architect Yang Zhaohui explains how his team rebuilt the tag platform with ClickHouse, using RoaringBitmap and custom optimizations to achieve second-level queries on billions of user tags, dramatically cutting response time, reducing hardware costs, and enabling real-time marketing insights.
This article describes how 58.com designed and optimized a ClickHouse‑based OLAP platform for massive user‑behavior data, covering the reasons for choosing ClickHouse, its key features, multi‑layer architecture, configuration management, automation scripts, monitoring, performance benchmarks, and future improvement plans.
This article provides a comprehensive technical comparison of HBase, Kudu, and ClickHouse—covering installation dependencies, architecture, basic read/write and query operations, real‑world use cases at Didi, a Kudu‑based real‑time data warehouse, and ClickHouse log‑analysis practices—highlighting each system’s strengths and trade‑offs for big‑data workloads.
This article explains how Alibaba's traffic analysis platform evolved to require real‑time analytics, outlines the business background, data model and metric system, discusses the difficulties of big‑data processing, and describes why ClickHouse was chosen and how its features solve those challenges.
This article compares Apache Kylin and ClickHouse, two popular big‑data OLAP engines, by examining their technical principles, storage structures, optimization techniques, and ideal application scenarios to help readers make an informed technology selection.
This article explains how Suning built a high‑performance user‑tag platform on ClickHouse, replacing Elasticsearch with bitmap‑based storage and a new architecture that delivers sub‑second profiling queries for over 600 million users, detailing the design, implementation, and future enhancements.
This article explains how Suning built a massive user‑tag platform using ClickHouse and RoaringBitmap to enable second‑level user profiling for over 600 million users, covering tag generation, storage, query architecture, and performance optimizations for marketing campaigns.
This article examines the data models and index structures of the three leading OLAP engines—Kylin, Druid, and ClickHouse—offering technical explanations, code examples, and a concise comparison to help readers understand their strengths, limitations, and suitable use cases.
This article explains how to build a real‑time click‑stream data warehouse using Flink for stream processing and ClickHouse for near‑real‑time OLAP, covering click‑stream characteristics, dimensional modeling, layered warehouse design, async dimension joins, sink implementation, and data rebalancing strategies.
This article introduces ClickHouse, compares column‑store and row‑store databases, shows how migrating a 50‑million‑row MySQL table to ClickHouse reduced query time from minutes to under one second, and shares practical installation, migration, performance testing, and synchronization tips.
This article explains the concept of sparse indexes in database systems, compares them with dense indexes, and demonstrates their use in Kafka log files and ClickHouse MergeTree tables, highlighting implementation details, lookup procedures, and configuration parameters.
This article introduces ClickHouse, compares column‑ and row‑oriented storage, explains a real‑world migration from MySQL to ClickHouse that reduced a 3‑minute query to under one second, details installation, migration methods, performance results, synchronization options, and common pitfalls.
Ctrip’s hotel data team tackled a 3 TB daily data load by building a ClickHouse cluster on VMware, creating custom sync and execution tools, applying query optimizations, and handling merge and memory errors, ultimately achieving over 400% performance gains across multiple reporting themes.
This article explains what ClickHouse is, compares column‑store and row‑store databases, shows how to migrate large MySQL tables to ClickHouse, presents performance test results, discusses data synchronization methods, highlights why ClickHouse is fast, and shares common migration pitfalls.
This article explains how JD.com’s JUST platform leverages the open‑source columnar database ClickHouse to store, query and analyze massive time‑series data, covering data modeling, lifecycle management, system goals, technology selection, cluster architecture, deployment, scaling and future enhancements.
ClickHouse is a PB‑scale, open‑source columnar OLAP database that uses a ZooKeeper‑coordinated sharded cluster, columnar storage, vectorized execution, advanced compression, data‑skipping indexes, and materialized views to deliver high‑performance interactive analytics, yet it requires manual shard management, lacks a mature MPP optimizer, and handles real‑time single‑row writes poorly.
This article dissects ClickHouse's Distributed table write path, detailing how the Distributed engine creates tables, decides between synchronous and asynchronous insertion, manages local and remote shards, handles sharding keys, and ensures atomic data distribution across the cluster.
This article explains how ClickHouse’s column‑oriented storage, vectorized execution engine, rich DBMS capabilities, flexible table engines, and carefully designed distributed architecture enable it to handle massive workloads with sub‑second query latency, making it a standout OLAP solution.
This article introduces ClickHouse as an OLAP columnar database, explains the differences between row‑ and column‑oriented storage, details a real‑world migration from MySQL using a CREATE TABLE AS SELECT approach, presents performance comparisons, discusses synchronization methods, and shares common pitfalls and solutions.
This article explains how JD Cloud’s JCHDB, built on ClickHouse, achieves millisecond‑level queries on billions of rows through columnar storage, distributed multi‑master architecture, SIMD vector engine, sparse indexing, and specialized table engines, and outlines the ideal use cases and deployment details.
Facing growing log volumes and high Elasticsearch costs, Ctrip migrated its 200 TB daily logs to ClickHouse, achieving up to 38× faster queries, 60 % lower server resources, and simplified operations through columnar storage, sharding, and custom dashboards, while detailing deployment, tuning, and common pitfalls.
This article explains why ClickHouse, the high‑performance columnar OLAP database, can return results on billions of rows within seconds, detailing its columnar storage, MergeTree engine, and how JD Cloud deploys and optimizes it on Kubernetes for scalability and reliability.
This article introduces ClickHouse, a column‑store OLAP database, explains its advantages over row‑store systems, guides installation on macOS, details migration strategies from MySQL, presents performance benchmarks showing 200‑fold speedups, and discusses synchronization methods, pitfalls, and practical tips.
This article details Ctrip's practical implementation of ClickHouse for its hotel data warehouse, covering background, technical selection, integration tooling, code optimization techniques, exception and server fault handling, and the resulting performance gains.
This article introduces ClickHouse as a high‑performance columnar OLAP database, compares it with row‑based systems, details migration methods from MySQL, presents performance benchmarks showing dramatic speed gains, discusses data synchronization strategies, explains why ClickHouse is fast, and shares common pitfalls and solutions.
This article explains ClickHouse's strengths as a high‑performance MPP database, details hardware selection, read/write separation, shard expansion steps, batch‑size tuning, and presents a three‑layer monitoring model, while also describing its practical application in Tencent's game analytics platform.
This article introduces ClickHouse as an OLAP columnar database, compares its storage model with row‑based databases, details migration from MySQL, presents performance benchmarks showing query speeds under one second for 50 million rows, and shares practical deployment tips and pitfalls.
This guide walks through deploying clickhouse-exporter, configuring Prometheus to scrape its metrics, and importing a Grafana dashboard to monitor ClickHouse single‑node or cluster performance, providing a practical monitoring solution for the database.
Tencent’s game marketing analysis system “EAS” evolved from inefficient TDW HiveSQL jobs and file‑heavy real‑time pipelines to a scalable ClickHouse‑based deduplication service that processes hundreds of thousands of daily activity counts in sub‑second time, offering fast, reliable, and maintainable participant deduplication for massive marketing campaigns.
JD’s Algorithmic Intelligence team built a ClickHouse‑based real‑time analytics engine that ingests Kafka and offline data, uses MergeTree tables with strategic partitioning and sorting, and employs batch writes, materialized views, and monitoring to achieve second‑level queries over billions of rows.
This article explains how Suning's big‑data center leverages ClickHouse’s columnar OLAP engine and a full‑chain monitoring platform to achieve real‑time query tracing, slow‑query analysis, cluster health checks, and resource‑level alerts across diverse business scenarios.
This article explains how ClickHouse's MergeTree engine creates partition directories based on a partition key, details the naming convention PartitionID_MinBlockNum_MaxBlockNum_Level, and describes the automatic and manual merge processes that consolidate partitions for efficient storage.
Tencent Cloud’s managed ClickHouse service now includes an automated data‑balancing feature that, after user authorization and bandwidth configuration, creates migration plans to redistribute tables across new or decommissioned nodes, eliminating manual rebalancing, reducing operational overhead, and ensuring balanced storage during elastic scaling.
This article explains ClickHouse's four composite data types—Array, Tuple, Enum, and Nested—detailing their definitions, type inference rules, practical SQL examples, and common pitfalls to help developers model complex data structures efficiently.
This tutorial walks through building a three‑node ClickHouse cluster, configuring host mappings and XML files, setting up ZooKeeper for internal replication, creating local and distributed tables, testing data insertion and queries, and demonstrating how to achieve high availability with replicated tables.
This article demonstrates how to leverage ClickHouse’s parametric aggregate and higher‑order functions to perform path matching, intelligent path detection, ordered funnel conversion, retention calculation, and session statistics for user behavior analysis in a big‑data environment.
This article details ByteDance's engineering practices for using ClickHouse to ingest, store, and query massive real‑time recommendation and advertising data, covering early external‑transaction mechanisms, the risks of direct INSERTs, the design and evaluation of Kafka Engine versus Flink pipelines, and a series of performance and reliability improvements implemented to support high‑frequency workloads.
This article describes how a data management platform (DMP) at Beike leverages ClickHouse bitmap structures and Spark pipelines to generate global numeric user IDs, design tag-specific bitmap rules for enum, continuous, and date attributes, handle boundary cases, and produce high‑performance bitmap SQL for real‑time user group estimation and complex segment logic.
This article compares the data modeling and indexing techniques of three mainstream OLAP engines—Kylin, Druid, and ClickHouse—explaining their conversion processes, storage formats, query execution paths, and summarizing their strengths and limitations for aggregation and detail query scenarios.
This article explains how ClickHouse's MergeTree engine creates partition directories, the naming convention for partitions, and the automatic and manual merging processes that consolidate parts while tracking block numbers and merge levels.
This article introduces ClickHouse, an open‑source column‑oriented OLAP database, detailing its key features, appropriate use cases, installation steps, performance benchmark queries, and how it compares with other columnar storage solutions while highlighting its adoption by major internet companies.
ClickHouse, the high‑performance columnar OLAP DBMS behind Yandex.Metrica, combines complete DBMS capabilities, column‑oriented storage with compression, vectorized execution, flexible table engines, multi‑master clustering, and extensive SQL support, offering fast online queries and scalable distributed processing for massive data workloads.
This article details how ByteDance leverages ClickHouse to power large‑scale advertising audience estimation, profiling, and statistical analysis, describing the challenges of massive data, strict latency requirements, and the evolution from a simple tag‑uid table to a bitmap‑based architecture with extensive parallel and cache optimizations.
This article explains how ClickHouse can act as a MySQL replica, covering full and incremental synchronization, supported MySQL versions, DDL compatibility, handling of DELETE/UPDATE events, the underlying binlog mechanism, and provides complete code examples to set up both MySQL master and ClickHouse slave.
QQ Music replaced its slow Hive warehouse with a massive ClickHouse cluster, achieving sub‑second to ten‑second query latency on petabyte‑scale data, enabling real‑time analytics for non‑technical users, and following five operational best practices—ZooKeeper planning, idempotent writes, sensible partitions, read‑write separation, and localized joins—while leveraging Tencent Cloud’s managed ClickHouse service.
This article provides a comprehensive technical overview of ClickHouse, covering its complete DBMS capabilities, column‑oriented storage and compression, vectorized execution engine, relational SQL support, diverse table engines, multi‑master clustering, sharding, and the design philosophies that make it exceptionally fast for large‑scale analytical workloads.
The article describes how a lightweight big‑data analysis platform called MVP was built from scratch—using a User‑Event‑Config model, HDFS + ClickHouse + Spark, and four modules for metric monitoring, root‑cause alerts, deep growth analysis, and A/B testing—enabling real‑time insights in seconds instead of days and dramatically accelerating product‑growth operations.
This article describes how JD.com’s Algorithmic Intelligence team built a ClickHouse‑based real‑time analytics engine, covering ClickHouse fundamentals, MergeTree table design, Kafka‑Flink data pipelines, JDBC batch loading, query‑optimization techniques, and monitoring for handling billions of rows with sub‑second response times.
This article explains how FunTouTiao builds a high‑performance real‑time analytics pipeline using Flink, Hive, and ClickHouse, covering business scenarios, hour‑level and second‑level Flink‑to‑Hive architectures, streaming file sink mechanics, multi‑user permissions, ClickHouse performance tricks, and future roadmap for unified stream‑batch storage.
This article describes how a company replaced its Zabbix‑based monitoring with a scalable, Prometheus‑driven alert engine that leverages ClickHouse for storage, remote‑storage integration via Prom2Click, and materialized views to provide flexible, SQL‑based business metric alerts.
QuTouTiao leverages Flink and ClickHouse to build a high‑performance real‑time analytics platform that supports hourly Hive pipelines and sub‑second ClickHouse queries, achieving sub‑second response for 80% of requests through streaming ingestion, exactly‑once semantics, multi‑cluster coordination, and optimized ClickHouse storage and connector designs.
QQ Music’s data team tackled massive PB‑scale, real‑time analytics challenges by migrating from Hive to a ClickHouse‑based OLAP platform integrated with Tencent Cloud EMR and Superset, achieving low‑latency, high‑availability data processing, self‑service visualization, and efficient read/write scaling for billions of daily events.
QQ Music’s new real‑time OLAP platform, built on ClickHouse, Superset and Tencent Cloud EMR, ingests petabyte‑scale streaming and batch data with SSD‑backed ZooKeeper, load‑balanced writes, optimized partitions and read/write separation, delivering second‑level query responses that are several times faster than Hive, Presto or SparkSQL and enabling self‑service BI for thousands of users.
ClickHouse is a column‑oriented, open‑source OLAP database developed by Yandex that offers high‑compression columnar storage, vectorized execution, and massive read/write throughput, making it ideal for large‑scale analytics while having specific usage scenarios and notable limitations such as lack of true transactions and secondary indexes.
This article details Dada Group's development of the Dada Flink SQL engine, describing its background, architecture, parser design, dimension‑table join strategies, numerous enhancements such as HA support, Kafka keyword handling, metadata integration, Redis and ClickHouse sinks, BINLOG simplification, and future migration plans toward Flink 1.10.
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.
This article details Ctrip's migration of large‑scale log processing from Elasticsearch to ClickHouse, explaining why ClickHouse was chosen, the high‑availability deployment architecture, data ingestion strategies, dashboard integration, performance gains, operational practices, and overall cost and reliability improvements.
This article examines the shortcomings of a Storm‑based real‑time metric platform, outlines the requirements for a stable, SQL‑driven, fast engine, and explains why ClickHouse was chosen over Presto, detailing performance benchmarks, architectural advantages, cluster configuration, engine options, best practices, and common operational issues.
This article provides a comprehensive overview of ClickHouse, covering its typical application scenarios, inherent limitations, common SQL syntax, default values, data types, materialized and expression columns, and detailed explanations of its various storage engines such as TinyLog, Log, Memory, Merge, Distributed, Null, Buffer, Set, MergeTree, ReplacingMergeTree, SummingMergeTree, AggregatingMergeTree, and CollapsingMergeTree, accompanied by practical code examples.
This article outlines ClickHouse’s columnar OLAP architecture, dual‑center design, storage and write stability strategies, performance testing results, and practical query and system optimizations for handling petabyte‑scale data with high throughput and low latency requirements.
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.
