ClickHouse Query Parser Archives

Avoiding Costly Mistakes: Profile Events and Query Traces in a Single ClickHouse Query

Effortlessly Profile Events and Query Traces in a Single ClickHouse Query ClickHouse’s exceptional performance in analytical workloads comes with the challenge of understanding and optimizing complex query execution patterns. When queries don’t perform as expected, […]

When ClickHouse Queries Get “Stuck”

ChistaDATA Inc.

When ClickHouse Queries Get “Stuck”: Detecting, Analyzing, and Safely Killing Problematic Sessions ClickHouse is a high-performance columnar database management system designed for online analytical processing (OLAP). While it excels at handling large-scale data analytics workloads, […]

Monitoring Merge Queues in ClickHouse

Shiv Iyer

Monitoring Merge Queues in ClickHouse: Detecting and Resolving Merge Pressure ClickHouse, renowned for its high-performance columnar storage and analytical query capabilities, relies heavily on its underlying Log-Structured Merge-Tree (LSM-Tree) architecture to achieve exceptional write throughput […]

Using system.query_thread_log for Low-Level ClickHouse Performance Insights

ChistaDATA Inc.

Using system.query_thread_log in ClickHouse for Low-Level Performance Insights Introduction to Thread-Level Query Analysis In the realm of high-performance analytical databases, ClickHouse stands out for its ability to process massive datasets at lightning speed. However, as […]

Setting Up ClickHouse Alerts: Detecting Performance Degradation Before It Impacts Users

ChistaDATA Inc.

Setting Up ClickHouse Alerts: Detecting Performance Degradation Before It Impacts Users ClickHouse is a powerful column-oriented database management system designed for real-time analytical processing, capable of handling massive datasets with exceptional speed. However, like any […]

ClickHouse Complete Guide to LowCardinality

ChistaDATA Inc.

ClickHouse Complete Guide to LowCardinality: When and How to Use It Introduction ClickHouse, the high-performance columnar database management system, offers a specialized data type called LowCardinality that serves as a powerful optimization tool for specific […]

Troubleshooting Disk Space in ClickHouse

ChistaDATA Inc.

Troubleshooting Disk Space Issues: Managing ClickHouse Log Tables ClickHouse is a high-performance, columnar database management system optimized for real-time analytical workloads. While its speed and scalability make it ideal for large-scale data processing, one common […]

ClickHouse Search: Case-sensitive Searches with UPPER & LOWER Functions

Boosting Materialized View Performance

ChistaDATA Inc.

Aggregation States: Boosting Materialized View Performance by 10x in ClickHouse ClickHouse has long been celebrated for its lightning-fast analytical query performance, especially when handling massive datasets in real time. One of the most powerful features […]

ClickHouse Performance: Estimated & Actual Row Counts in Execution Plans

PREWHERE vs WHERE in ClickHouse Queries

ChistaDATA Inc.

PREWHERE vs WHERE: When and How to Optimize Your ClickHouse Queries Introduction Query performance in ClickHouse often hinges on how efficiently you filter data. While most SQL users are familiar with the WHERE clause, ClickHouse […]

Troubleshooting Underutilized ClickHouse Index

Unlock Blistering Query Speed with ClickHouse LowCardinality

ChistaDATA Inc.

Unlock Blistering Query Speed with ClickHouse LowCardinality: A ChistaDATA Guide ClickHouse is famous for raw speed, but some of its most powerful tools remain under-documented. Among them, the LowCardinality data type is arguably the simplest […]

* 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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