ClickHouse DBA Support Archives | Page 19 of 25

Streaming ClickHouse Data to Kafka

Introduction ClickHouse has an inbuilt Kafka table engine which is commonly used to read streaming messages from Apache Kafka and store it in ClickHouse. This is one of the important and widely used features of […]

ClickHouse Live Views to Compute Real-time Moving Averages

ClickHouse Materialized Views: Using Live Views to Compute Real-time Moving Averages

Shiv Iyer

Introduction ClickHouse Live Views can be used to compute real-time moving averages efficiently. Here’s how: Conclusion By using ClickHouse Live Views and defining the appropriate query, settings, and subscription, you can efficiently compute real-time moving […]

Benchmarking ClickHouse using the clickhouse-benchmark Tool

ChistaDATA Inc.

Introduction ClickHouse is a popular open-source analytical database management system that is designed to handle massive amounts of data. It has been adopted by many companies for its excellent performance, low latency, and scalability. However, […]

How does ClickHouse Merge Sort Algorithm work?

Shiv Iyer

Introduction ClickHouse uses a merge-sort algorithm to sort data internally. The merge-sort algorithm works by dividing the data into smaller blocks, sorting each block individually, and then merging the sorted blocks together to produce the […]

ClickHouse Monitoring: Configuring ClickHouse Server Logs in JSON Format

ChistaDATA Inc.

Introduction ClickHouse is a fast, open-source column-oriented database used to analyze large amounts of data. One of the key features of ClickHouse is its support for logging server activity in JSON format, which allows easy […]

How to implement correlated columns in ClickHouse?

Shiv Iyer

Introduction Correlated columns are useful in many situations where you need to retrieve data from multiple tables and use data from one table to filter data in another. Some common use cases include: How are […]

How to Configure ClickHouse for Physical & Logical I/O Performance

Shiv Iyer

Introduction ClickHouse is a high-performance column-oriented database management system. It uses a unique approach to both physical and logical I/O that is optimized for performance and scalability. In addition to the physical and logical I/O […]

ClickHouse Caches: Configuring Buffer Cache for High Performance

Shiv Iyer

Introduction The ClickHouse Buffer Cache works by caching frequently accessed data in memory. This cache reduces disk I/O operations and speeds up query performance. The buffer cache is organized as a pool of memory blocks, […]

ClickHouse Caches: Monitoring Query Plans in Library Cache

Shiv Iyer

Introduction The Library Cache in ClickHouse is a cache of the compiled and optimized query plans used to execute queries. The purpose is to store frequently used query plans to reduce the overhead of parsing, […]

Monitoring Key Activities by ClickHouse Users

Shiv Iyer

Introduction Monitoring key user activity in ClickHouse is quite essential to system health and performance. In this article, we discuss means of doing this, and simple SQL scripts that may be helpful for this purpose. […]

* 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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