ChistaDATA Archives | Page 2 of 34

Vector Database Indexing: HNSW, IVF, PQ, OPQ, and ScaNN Explained

A practitioner’s guide to vector database indexing — HNSW, IVF, PQ, OPQ, and ScaNN. Tuning, trade-offs, and architecture patterns from MinervaDB engineers.

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Vector ClickHouse Pipeline: Observability Worth the Effort

ChistaDATA Inc.

Vector ClickHouse pipeline: a practical look at how it works, what to tune, and the pitfalls that trip up production ClickHouse.

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Time-Series Analytics at Scale with ClickHouse

ChistaDATA Inc.

Time-Series Analytics at Scale with ClickHouse Time-series data — metrics, logs, traces, IoT telemetry, financial ticks — is the dominant analytics workload in modern systems. ClickHouse has become the default engine for time-series analytics at […]

ClickHouse Query Optimization for Petabyte-Scale Analytics

ChistaDATA Inc.

ClickHouse Query Optimization for Petabyte-Scale Analytics Query optimization is where ClickHouse either sings or suffers. A well-designed query on a well-designed schema scans terabytes in milliseconds; a poorly designed query scans the same data in […]

eBPF | ClickHouse Performance | ChistaDATA

Advanced eBPF-Based Performance Analysis for ClickHouse

ChistaDATA Inc.

Advanced eBPF-Based Performance Analysis for ClickHouse: Kernel-Level Observability Techniques eBPF (Extended Berkeley Packet Filter) delivers comprehensive, zero-overhead kernel-space observability for ClickHouse performance analysis. Through dynamic instrumentation of kernel tracepoints, kprobes, uprobes, and hardware performance monitoring […]

ClickHouse Performance Detective’s Toolkit

ChistaDATA Inc.

ClickHouse Performance Detective’s Toolkit: CLI Strategies for Common ClickHouse Bottlenecks When your ClickHouse database starts showing signs of performance degradation, the command-line interface becomes your most powerful diagnostic tool. Unlike traditional databases that rely heavily […]

ClickHouse Thread Contention and Troubleshooting

ChistaDATA Inc.

ClickHouse Thread Contention and Troubleshooting: A Comprehensive Guide ClickHouse has established itself as one of the most powerful open-source columnar database management systems designed for online analytical processing (OLAP). Its exceptional performance stems from a […]

When Not to Use ClickHouse

ChistaDATA Inc.

When Not to Use ClickHouse: Understanding the Limits of a Powerful Analytics Engine ClickHouse has emerged as a powerhouse in the world of analytics databases, renowned for its blistering speed in processing large-scale analytical queries. […]

Monitoring Merge Queues in ClickHouse

Shiv Iyer

Monitoring Merge Queues in ClickHouse: Detecting and Resolving Merge Pressure ClickHouse, renowned for its high-performance columnar storage and analytical query capabilities, relies heavily on its underlying Log-Structured Merge-Tree (LSM-Tree) architecture to achieve exceptional write throughput […]

Using system.query_thread_log for Low-Level ClickHouse Performance Insights

ChistaDATA Inc.

Using system.query_thread_log in ClickHouse for Low-Level Performance Insights Introduction to Thread-Level Query Analysis In the realm of high-performance analytical databases, ClickHouse stands out for its ability to process massive datasets at lightning speed. However, as […]

* 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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