ClickHouse Performance Archives | Page 6 of 31

Using GROUPBY for Groupings, Rolllups and Cubes in ClickHouse

High-Performance Reads of Parquet Data Using ClickHouse Server Swarms

High-Performance Parquet File Reading with ClickHouse Server Swarms Follow the architecture and optimisation strategies detailed below to achieve high-performance reads of Parquet files using swarms of ClickHouse® servers. This approach leverages ClickHouse’s distributed and columnar […]

Using EXPLAIN to Determine JOIN Order in ClickHouse Query Execution Plan

Optimal Maintenance Plan for ClickHouse Infrastructure Operations

Shiv Iyer

ClickHouse Performance Tuning: Proven Maintenance Plan for Optimal Infrastructure, Scalability, and High Availability Building an optimal maintenance plan for ClickHouse infrastructure operations requires a structured approach to addressing performance, scalability, and high availability. ClickHouse, being […]

Understanding ClickHouse MergeTree: Data Organization, Merging, Replication, and Mutations Explained

Shiv Iyer

Understanding ClickHouse MergeTree: Data Organization, Merging, Replication, and Mutations Explained ClickHouse is renowned for its high-performance analytics and its ability to efficiently handle massive amounts of data. At the core of ClickHouse’s data storage and […]

Why Delta Updates Are Not Recommended in OLAP Databases: A Performance and Efficiency Perspective

Shiv Iyer

Why Delta Updates Are Not Recommended in OLAP Databases: A Performance and Efficiency Perspective Delta Updates in OLAP (Online Analytical Processing) databases are not recommended due to the fundamental design and architecture of these systems, […]

ClickHouse Search: Manticore Full Text Search with Plain Index

Mastering User Management in ClickHouse: A Complete Guide to Authentication, Authorization, and Future Security Enhancements

Shiv Iyer

User Management in ClickHouse: A Comprehensive Guide Introduction User management is a critical aspect of any analytical application, as it ensures secure access to data while maintaining flexibility for various users. In ClickHouse, user management […]

Integrating Parquet File Ingestion into ClickHouse Using Kafka: A Step-by-Step Guide

Shiv Iyer

Unlock the Power of Data: Seamlessly Integrate Parquet File Ingestion into ClickHouse with Kafka – Your Ultimate Step-by-Step Guide to Optimized Performance! ClickHouse Parquet Ingestion enables a seamless, high-performance workflow for moving data from distributed […]

ClickHouse Data Compression Techniques for Time-series Datasets

Optimizing Non-SARGable Predicates in ClickHouse for Improved Query Performance

Shiv Iyer

ClickHouse Query Optimization Non-SARGable (Search ARGument ABLE) predicates are conditions in SQL queries that prevent the database engine from using indexes efficiently, leading to full table scans and degraded query performance. Implementing and handling Non-SARGable […]

Tuning ClickHouse for High-Velocity Data Ingestion in Distributed Tables

Implementing Tiered Storage in ClickHouse: Leveraging S3 for Efficient Data Archival and Compliance

Shiv Iyer

ClickHouse S3 Archival is a common strategy for handling large volumes of data efficiently, particularly for compliance purposes where data must be retained but is queried infrequently. Using tiered storage like S3 for archiving data […]

Optimising ClickHouse Queries – Turbocharge your queries

ChistaDATA Inc.

Pic Courtesy – Pexels ClickHouse Query Optimization ClickHouse is an open-source online analytical database system for real-time analytics and super-fast queries on large datasets. While ClickHouse is inherently optimized for read speeds, it is still […]

ClickHouse Redo Operations for Data Reliability

Efficient Strategies for Purging Data in ClickHouse: Real-Life Use Cases and Detailed Implementation

Shiv Iyer

Clickhouse Data Purging Efficiently purging data from ClickHouse is crucial for maintaining performance and managing storage costs, especially when dealing with large, real-life datasets. Here are some detailed strategies, complete with real-life data sets and […]

* Everything changes over time – Our blogs/posts and comments changes over time, That’s how it should be! Whatever we comment from ChistaDATA Inc. Teams (including Shiv Iyer) and other stakeholders or guest bloggers posted here are never permanent, These things worked for us. But, there is no guarantee they will work for you too, When using the recommendations from ChistaDATA or MinervaDB or MinervaSQL or any other online resources / Google, You must test the advice before applying them to your production systems, and always invest for a robust Database DR solution, Thank you for understanding.

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