ClickHouse Internals Archives | Page 6 of 18

Conditional Analysis in ClickHouse with ‘IS’ Operator

Introduction The “IS” operator in ClickHouse is used to compare a value against a set of values or conditions and returns a Boolean result. It’s often used to check if a value matches a specific […]

5 Reasons to use ChistaDATA’s ClickHouse for Real-time Analytics

Shiv Iyer

Introduction As technology leaders, understanding the intricacies of data management systems is crucial for making informed decisions. ClickHouse, an open source columnar database management system, offers a unique perspective on data analytics, especially in the […]

From Batch Processing to Real-time Analytics with ChistaDATA’s ClickHouse

Shiv Iyer

Introduction The landscape of data analytics has evolved rapidly, and while batch processing systems like Hadoop have their merits, they can introduce complexities that challenge engineering and operations. In this article, we’ll explore how such […]

ClickHouse Thread Performance Analysis with eBPF

Shiv Iyer

Introduction eBPF (extended Berkeley Packet Filter) is a powerful tool for tracing and profiling various aspects of a running Linux system, including applications like ClickHouse. Leveraging eBPF for monitoring ClickHouse’s thread performance provides deep insights […]

ClickHouse Data Compression Techniques for Time-series Datasets

Shiv Iyer

Introduction ClickHouse, a powerful columnar database management system, employs various techniques to optimize data storage and retrieval. Among these techniques, delta-encoding and dictionary encoding play a pivotal role in enhancing compression, particularly for time-series and […]

Introduction to Vectorized Query Processing in ClickHouse

ClickHouse Performance: Introduction to Vectorized Query Processing

Shiv Iyer

Introduction In the world of data analytics, speed is of the essence. ClickHouse, an innovative columnar database management system, has gained prominence for its exceptional query performance. At the heart of this prowess lies ClickHouse’s […]

Comparing Columnar vs Row-based Databases for Real-time Analytics

Shiv Iyer

Introduction In the dynamic landscape of data management, the choice between columnar stores and ROW-based databases has a profound impact on performance, especially in scenarios requiring rapid data retrieval and analytical prowess. These two approaches, though fundamentally […]

Real-time Analytics in ClickHouse for Risk Management in Banking

Real-time Analytics with ClickHouse for Risk Management in Banking

Shiv Iyer

Introduction In the dynamic realm of consumer banking, where transactions happen at lightning speed and risks lurk around every corner, the ability to predict and manage these risks in real time is not just an […]

Data Compression in ClickHouse vs Cassandra

Shiv Iyer

Introduction ClickHouse and Cassandra: two different databases optimized for different use cases. ClickHouse is a columnar database designed for analytical queries, while Cassandra is a NoSQL database designed for write-heavy workloads and scalability. Because of these fundamental differences in design and use […]

Cassandra’s Indexing Limitations for Real-time Analytics

Shiv Iyer

Introduction Cassandra, a highly-scalable NoSQL database, is renowned for its write-optimized architecture and resilience. With its capacity to handle vast volumes of data across distributed systems, many consider it a go-to choice for various applications. […]

* 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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