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MergeTree

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YunZhu Net Technology Team
YunZhu Net Technology Team
Jun 24, 2021 · Databases

Introduction to ClickHouse and Step‑by‑Step Cluster Deployment Guide

This article provides a comprehensive overview of ClickHouse, covering its columnar OLAP architecture, key features such as data compression, vectorized processing, distributed query handling, and SQL support, followed by detailed step‑by‑step instructions for deploying a multi‑node ClickHouse cluster with MergeTree and ReplicatedMergeTree engines, configuration files, and Java MyBatis integration.

ClickHouseCluster DeploymentColumnar Database
0 likes · 10 min read
Introduction to ClickHouse and Step‑by‑Step Cluster Deployment Guide
Youzan Coder
Youzan Coder
Jan 25, 2021 · Big Data

ClickHouse: Principles, Architecture, and Deployment at Youzan

The article explains ClickHouse’s high‑performance columnar OLAP design, its vectorized execution, sparse primary‑key indexes and MergeTree engines, contrasts it with ROLAP/MOLAP approaches, and details Youzan’s large‑scale deployment—including dual‑replica clusters, ingestion pipelines, routing architecture, current challenges, and future container‑based expansion plans.

Big DataClickHouseMergeTree
0 likes · 22 min read
ClickHouse: Principles, Architecture, and Deployment at Youzan
Aikesheng Open Source Community
Aikesheng Open Source Community
Sep 3, 2020 · Databases

Understanding ClickHouse MergeTree Partitioning and Merge Rules

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.

ClickHouseData PartitioningDatabase
0 likes · 8 min read
Understanding ClickHouse MergeTree Partitioning and Merge Rules
JD Retail Technology
JD Retail Technology
Jul 13, 2020 · Databases

Real‑Time Analytics Engine Based on ClickHouse: Architecture, MergeTree, Data Ingestion, and Query Optimization

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.

Big DataClickHouseData ingestion
0 likes · 14 min read
Real‑Time Analytics Engine Based on ClickHouse: Architecture, MergeTree, Data Ingestion, and Query Optimization