ClickHouse DBA Script Archives | Page 5 of 10

ClickHouse vs Cassandra for Real-time Analytics

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 […]

🚀 Exciting Plans for MinervaDB Inc. and ChistaDATA Inc.! 🌟

Shiv Iyer

Empowering Data Management: The Vision for MinervaDB and ChistaDATA As the founder of MinervaDB Inc. and ChistaDATA Inc., I am thrilled to share our vision for the next two years. Our companies have been established […]

ClickHouse Installation: Cluster Setup and Configuration

ClickHouse Resource Safety: Implementing RAII and Destructors

Shiv Iyer

Introduction In ClickHouse’s C++ codebase, efficient resource management and preventing resource leaks are crucial for ensuring stable and high-performance data processing. C++ provides the powerful RAII (Resource Acquisition Is Initialization) idiom, which allows resources to […]

ClickHouse Performance: Real-Time Monitoring of Expensive Queries

Shiv Iyer

Introduction Expensive queries in ClickHouse can have a significant impact on overall system performance, consuming valuable resources like CPU, memory, and disk I/O. These resource-intensive queries can lead to increased latency and reduced throughput, hampering […]

ClickHouse Row Number Functions for High Performance

Shiv Iyer

Introduction Row numbering methods play a crucial role in ClickHouse’s performance, particularly when dealing with extensive datasets and intricate queries. Different approaches, such as using the ROW_NUMBER() function, ARRAY JOIN clause, WITH ORDINALITY syntax, or […]

Real-time Analytics in Modern Banking with ChistaDATA’s ClickHouse

Shiv Iyer

Introduction In the fast-paced world of modern banking, making accurate and timely decisions is crucial for success. Traditional data analysis methods are no longer sufficient to keep up with the ever-increasing volumes of data and […]

eBPF

ClickHouse Troubleshooting: How to use eBPF to Profile Performance

Shiv Iyer

Introduction Using the eBPF-based tool “perf” for troubleshooting ClickHouse involves several steps to collect and analyze performance data. Here’s a general guide on how to utilize “perf” for ClickHouse troubleshooting: I/O Profiling: It’s important to […]

ChistaDATA’s ClickHouse v/s Hadoop for Real-time Analytics

Shiv Iyer

Complexities in Cloudera Hadoop Infrastructure Operations Management: Why is Hadoop not scalable in modern real-time analytics? Why do corporations globally engage ChistaDATA for real-time analytics on ClickHouse? Conclusion By engaging ChistaDATA for real-time analytics on […]

Python Script to Monitor Kafka Producer Memory Leak in ClickHouse

Transforming Mobile Gaming with Machine Learning and AI Infrastructure on ClickHouse

Shiv Iyer

Introduction The mobile gaming industry is experiencing tremendous growth, and the adoption of data-driven technologies like machine learning (ML) and artificial intelligence (AI) has become essential for delivering personalized gaming experiences, optimizing monetization strategies, and […]

How to implement Lazy Expressions in ClickHouse for Query Performance?

Shiv Iyer

Introduction Lazy expression evaluation has several use cases in data processing and analytics. Here are some common scenarios where lazy evaluation is beneficial: Lazy expression evaluation is widely used in various data processing systems, including […]

* 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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