Articles by Shiv Iyer

About Shiv Iyer

Open Source Database Systems Engineer with a deep understanding of Optimizer Internals, Performance Engineering, Scalability and Data SRE. Shiv currently is the Founder, Investor, Board Member and CEO of multiple Database Systems Infrastructure Operations companies in the Transaction Processing Computing and ColumnStores ecosystem. He is also a frequent speaker in open source software conferences globally.

Most Common ClickHouse Analytical Models

Shiv Iyer

Unlocking ClickHouse’s Potential: A Deep Dive into the Most Common Analytical Models ClickHouse is a high-performance, columnar database management system designed for real-time analytical workloads. Its architecture allows it to process large datasets at lightning […]

Troubleshooting High CPU Usage in ClickHouse

ClickHouse Storage Engines Explained

Shiv Iyer

Comprehensive Guide to ClickHouse Storage Engines for Optimized Application Performance ClickHouse Storage Engines Overview ClickHouse uses storage engines to manage how data is stored, organized, and queried. Each storage engine is optimized for specific use […]

Tuning index_granularity for ClickHouse Performance

Implementing Self-Joins in ClickHouse: Techniques, Use Cases, and Best Practices

Shiv Iyer

A self-join in ClickHouse joins a table with itself using aliases. This technique helps compare rows within the same table, find relationships between records, and analyze hierarchical data. Let’s explore how to implement self-joins in […]

Benchmarking ClickHouse using the clickhouse-benchmark Tool

Optimizing ClickHouse Thread Pool for High-Concurrency Workloads

Shiv Iyer

Unlocking Performance: How We Optimized ClickHouse Thread Pool for High-Concurrency Workloads Unveiling Hidden Bottlenecks: Optimizing ClickHouse Thread Pool Performance ClickHouse has earned its reputation as a powerhouse for lightning-fast analytics, capable of handling immense volumes […]

Using GROUPBY for Groupings, Rolllups and Cubes in ClickHouse

High-Performance Reads of Parquet Data Using ClickHouse Server Swarms

Shiv Iyer

High-Performance Parquet File Reading with ClickHouse Server Swarms Follow the architecture and optimisation strategies detailed below to achieve high-performance reads of Parquet files using swarms of ClickHouse® servers. This approach leverages ClickHouse’s distributed and columnar […]

Using EXPLAIN to Determine JOIN Order in ClickHouse Query Execution Plan

Optimal Maintenance Plan for ClickHouse Infrastructure Operations

Shiv Iyer

ClickHouse Performance Tuning: Proven Maintenance Plan for Optimal Infrastructure, Scalability, and High Availability Building an optimal maintenance plan for ClickHouse infrastructure operations requires a structured approach to addressing performance, scalability, and high availability. ClickHouse, being […]

ClickHouse Architecture and Query Performance Techniques

Optimizing Data Processing with ClickHouse MergeTree on S3

Shiv Iyer

Optimizing Data Processing with ClickHouse MergeTree on S3: Intro and Architecture ClickHouse® MergeTree on S3 – Intro and Architecture ClickHouse MergeTree is a highly adaptable and robust storage engine optimized for high-performance analytics in contemporary […]

ClickHouse Horizontal Scaling: Optimal Read-Write Split Configuration and Execution

Real-Time Bid Tracking and Optimization with ClickHouse in High-Performance Data Pipelines

Shiv Iyer

Optimizing Real-Time Bidding Efficiency: Harnessing ClickHouse for Advanced Data Pipeline Management In the rapidly evolving landscape of real-time bidding (RTB) platforms, the ability to process and analyze large volumes of data at high speeds is […]

Application Domain Index for Nested Data Structures

Optimizing Revenue for FinTech Platforms Using K-Means Algorithms in Google BigQuery

Shiv Iyer

Harnessing the sophisticated K-Means algorithm within Google BigQuery’s comprehensive analytics ecosystem presents a transformative opportunity to substantially enhance revenue optimization strategies for FinTech platforms. This advanced machine learning technique demonstrates remarkable proficiency in categorizing diverse […]

Understanding ClickHouse MergeTree: Data Organization, Merging, Replication, and Mutations Explained

Shiv Iyer

Understanding ClickHouse MergeTree: Data Organization, Merging, Replication, and Mutations Explained ClickHouse is renowned for its high-performance analytics and its ability to efficiently handle massive amounts of data. At the core of ClickHouse’s data storage and […]

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Articles by Shiv Iyer

Most Common ClickHouse Analytical Models

ClickHouse Storage Engines Explained

Implementing Self-Joins in ClickHouse: Techniques, Use Cases, and Best Practices

Optimizing ClickHouse Thread Pool for High-Concurrency Workloads

High-Performance Reads of Parquet Data Using ClickHouse Server Swarms

Optimal Maintenance Plan for ClickHouse Infrastructure Operations

Optimizing Data Processing with ClickHouse MergeTree on S3

Real-Time Bid Tracking and Optimization with ClickHouse in High-Performance Data Pipelines

Optimizing Revenue for FinTech Platforms Using K-Means Algorithms in Google BigQuery

Understanding ClickHouse MergeTree: Data Organization, Merging, Replication, and Mutations Explained