ClickHouse Performance Archives | Page 9 of 10

ClickHouse Performance: Estimated & Actual Row Counts in ClickHouse Execution Plans

ClickHouse Execution Plans: Estimated vs Actual Row Counts

Introduction Understanding the difference between the estimated and actual number of rows in ClickHouse execution plans is crucial for query optimization and performance tuning. Overview of Estimated & Actual Row Counts in ClickHouse Here’s an […]

Comprehensive Guide for ClickHouse Horizontal Scaling and Capacity Planning

Shiv Iyer

Calculating your company’s required real-time analytics capacity on ClickHouse involves several steps and considerations. It’s important to assess current and projected data volumes, query complexity, and the expected concurrency of queries.

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ClickHouse Horizontal Scaling: Optimal Read-Write Split Configuration and Execution

Shiv Iyer

Scaling ClickHouse horizontally while optimizing the split between read and write operations is a multi-step process that involves setting up a cluster with sharded and replicated tables.

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ClickHouse Case-Sensitive Search with UPPER & LOWER Functions

Shiv Iyer

Implementing a case-sensitive search in ClickHouse, a column-oriented database management system, can be achieved by using the UPPER or LOWER functions. These functions convert text data to either uppercase or lowercase, respectively, allowing for a consistent comparison.

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ClickHouse High-Volume Data Ingestion: Server Configuration Guide

Shiv Iyer

Optimizing ClickHouse for high-velocity and high-volume data loading involves several server configuration and tuning techniques.

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ClickHouse Index Granularity: Tuning index_granularity for Performance

Shiv Iyer

Understanding index granularity is crucial for troubleshooting performance issues in ClickHouse, especially when it comes to optimizing how ClickHouse uses indexes for query execution.

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ClickHouse vectorized query processing compared with tuple-at-a-time Volcano execution

ClickHouse Vectorized Query Processing: 5 Mechanics, Measured in the Lab

Shiv Iyer

ClickHouse internals · Performance engineering · Lab-measured ClickHouse vectorized query processing is the reason a single CPU core can aggregate billions of values per second. Instead of pushing one row at a time through the […]

Columnar databases vs row-based databases: bytes read for a one-billion-row aggregate, 80.3 GB vs 1.03 GB

Columnar Databases vs Row-Based Databases: Measured on 1 Billion Rows

Shiv Iyer

Columnar databases · Row-based databases · Lab-measured at one billion rows Columnar databases answered a one-billion-row aggregate by reading 1.03 GB. A row store would have read 80 GB. Columnar databases store each column in […]

Comprehensive Guide to ChistaDATA’s ClickHouse Performance Audit

ChistaDATA Inc.

Introduction – ClickHouse Performance Audit ClickHouse is a powerful open source relational database management system that offers high performance, scalability, reliability and data security. As ClickHouse is widely used in various industries, it is important […]

Monitoring ClickHouse Server Load

Shiv Iyer

Introduction What is the Data Load distribution happening to ClickHouse Server? I ask this question often to ClickHouse infrastructure owners. This matrix is used for trending the load and throughput. This will help me identify […]