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HomeClickHouse DBA Support

ClickHouse DBA Support

ClickHouse Performance

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
ClickHouse

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
ClickHouse Performance

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 […]
When to Avoid Indexing in ClickHouse for Optimal Performance
ClickHouse Security

Implementing Custom Access Policies in ClickHouse: A Comprehensive Guide

Shiv Iyer
Custom Access Policies in ClickHouse Implementing access policies in ClickHouse similar to SQL Server’s Purview Access Policies requires combining ClickHouse’s built-in access control mechanisms with additional scripting and possibly external tools. ClickHouse does not have […]
ClickHouse Backup & DR

Implementing an Oracle RMAN-Like Backup and Recovery Toolkit for ClickHouse

Shiv Iyer
ClickHouse Backup Toolkit Implementing a comprehensive backup and recovery toolkit for ClickHouse is essential to ensure data integrity, consistency, and reliability, forming the core of ClickHouse Data Reliability Engineering. While ClickHouse lacks a built-in tool […]
ClickHouse Redo Operations for Data Reliability
ClickHouse Performance

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 […]
Troubleshooting High CPU Usage in ClickHouse
ClickHouse Security

Securing ClickHouse Data at Rest: A Guide to Implementing Filesystem-Level Encryption

Shiv Iyer
ClickHouse Data at Rest ClickHouse does not directly support Transparent Data Encryption (TDE) in the same way that some other database systems do, such as Oracle or SQL Server, which provide built-in TDE capabilities to […]
ClickHouse Performance

Strategic Considerations for Integrating ClickHouse with Row-based Systems: Balancing Performance and Architecture

Shiv Iyer
Introduction: ClickHouse Integration Strategy Switching from a row-based to a column-based database system like ClickHouse involves significant architectural changes and strategic planning. This transition can offer substantial performance benefits, especially for analytics and read-heavy operations, […]
ClickHouse

Enhancing ClickHouse Performance: Strategic Insights on Partitioning, Indexing, and Monitoring

Shiv Iyer
ClickHouse Performance tuning Optimizing ClickHouse performance involves a multi-faceted approach that includes effective partitioning, strategic indexing, and diligent system monitoring. Each of these areas plays a crucial role in enhancing the efficiency and speed of […]
ClickHouse Performance

Optimizing Query Performance: Understanding Criterion Indexability in ClickHouse

Shiv Iyer
Criterion Indexability in ClickHouse Criterion indexability in ClickHouse refers to the database’s ability to utilise indexes for filtering data based on query conditions efficiently. ClickHouse, designed for fast analytical queries over large datasets, employs various […]

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* 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. 

Recent Posts from ChistaDATA

  • Designing ClickHouse for Mixed Workloads
  • Designing ClickHouse Schemas for 1B+ Row Tables
  • Avoiding Costly Mistakes: Profile Events and Query Traces in a Single ClickHouse Query
  • When ClickHouse Queries Get “Stuck”
  • SQL Antipatterns in ClickHouse

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Table of Contents

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  • Criterion Indexability in ClickHouse
  • Types of Indexes in ClickHouse
  • Criterion Indexability Factors
  • Designing Indexable Queries in ClickHouse
  • Limitations and Considerations
  • Further Reading
→ Index