Blog:Unlock Powerful ClickHouse Insights – ChistaDATA’s Expert Analytics Blog

Tuning Index Granularity for ClickHouse in E-commerce

Tuning Index Granularity for ClickHouse Performance in E-commerce

Introduction In the world of data analytics and storage, databases are the backbone that supports businesses’ decision-making processes. ClickHouse, an open-source columnar database management system, has gained popularity for its exceptional speed and efficiency in […]

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 […]

ClickHouse vectorized query processing compared with tuple-at-a-time Volcano execution

ClickHouse Vectorized Query Processing: 5 Mechanics, Measured in the Lab

Shiv Iyer

ClickHouse internals · Performance engineering · Lab-measured ClickHouse vectorized query processing is the reason a single CPU core can aggregate billions of values per second. Instead of pushing one row at a time through the […]

Columnar databases vs row-based databases: bytes read for a one-billion-row aggregate, 80.3 GB vs 1.03 GB

Columnar Databases vs Row-Based Databases: Measured on 1 Billion Rows

Shiv Iyer

Columnar databases · Row-based databases · Lab-measured at one billion rows Columnar databases answered a one-billion-row aggregate by reading 1.03 GB. A row store would have read 80 GB. Columnar databases store each column in […]

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

Cassandra 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. […]

ClickHouse vs Hadoop for Real-time Analytics

Shiv Iyer

Introduction This article highlights why Hadoop may not be the best fit for real-time analytics and discusses how ClickHouse emerges as a more suitable solution. 10 Reasons Why Hadoop is Not Recommended for Real-Time Analytics: […]

ClickHouse vs Cassandra for Real-time Analytics

Shiv Iyer

Introduction ClickHouse and Cassandra are both powerful data management systems, but they are designed for different use cases. 1. Data Model 2. Query Language 3. Aggregation 4. Data Compression 5. Indices 6. Throughput Conclusion The […]

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Tuning Index Granularity for ClickHouse Performance in E-commerce

ClickHouse Thread Performance Analysis with eBPF

ClickHouse Data Compression Techniques for Time-series Datasets

ClickHouse Vectorized Query Processing: 5 Mechanics, Measured in the Lab

Columnar Databases vs Row-Based Databases: Measured on 1 Billion Rows

Real-time Analytics with ClickHouse for Risk Management in Banking

Data Compression in ClickHouse vs Cassandra

Cassandra Indexing Limitations for Real-Time Analytics

ClickHouse vs Hadoop for Real-time Analytics

ClickHouse vs Cassandra for Real-time Analytics