ChistaDATA Performance Archives | Page 3 of 6

Using EXPLAIN to Determine JOIN Order in ClickHouse Query Execution Plan

ClickHouse EXPLAIN: Determine JOIN Order in Query Execution Plan

Introduction In ClickHouse, the join order in an execution plan is determined by the query optimizer, which analyzes the query and generates an optimal plan for executing the query. The optimizer uses statistics about the […]

ClickHouse Troubleshooting: How to Identify Blocks Causing Latch Contention

Shiv Iyer

Introduction Latch contention in ClickHouse can have a significant impact on performance. A latch is a synchronization mechanism that allows multiple threads to access a shared resource, such as a data block, in a controlled […]

ClickHouse Performance: Implementing Dynamic Disks in ClickHouse 23.2

Shiv Iyer

Introduction Dynamic disks in ClickHouse 23.2 refer to a feature that allows for more efficient use of disk space and improved performance when working with large amounts of data. Traditionally, ClickHouse used a file-based storage […]

Overview of 5 Key ClickHouse Configuration Parameters

ChistaDATA Inc.

Introduction ClickHouse is a column-oriented database management system that is designed for high-performance analytics. One of the critical features of ClickHouse is its ability to handle large amounts of data quickly and efficiently. This is […]

Monitoring ClickHouse for Blocked Queries

Shiv Iyer

Introduction Blocked queries in ClickHouse can negatively impact performance by causing delays in query execution. When a query is blocked, it is not able to proceed until the resource it is waiting for is released […]

Migrating from Hadoop to ChistaDATA's ClickHouse

Hadoop vs ClickHouse: Comparison of Key Features

Shiv Iyer

Introduction Hadoop and ClickHouse are both big data processing platforms, but they have different design goals and use cases. Here is a comparison of some key features. Hadoop vs ClickHouse: Comparison of Key Features Data […]

Tuning ClickHouse Configuration Parameters for High Performance

Shiv Iyer

Introduction Tuning ClickHouse configuration parameters for performance can be a complex task, but there are a few key parameters that can have a significant impact on performance. Here is a general guide for tuning ClickHouse […]

How data compression is implemented in ClickHouse?

How are Data Compression Algorithms implemented in ClickHouse?

Shiv Iyer

Introduction ClickHouse implements data compression in several ways to reduce storage space and improve query performance. Here are a few examples of how data compression is implemented in ClickHouse: (1) Dictionary encoding ClickHouse uses dictionary […]

How does Memory Management work in ClickHouse?

Shiv Iyer

Introduction In ClickHouse, memory management is handled by the MemoryTracker class, which is responsible for allocating and deallocating memory to different parts of the system. The MemoryTracker class uses a hierarchical approach to manage memory, […]

Algorithm of Log Structure Merge Tree (LSM-Tree)

Shiv Iyer

How does the Log Structure Merge Tree (LSM-Tree) work? Log Structured Merge Tree (LSM-Tree) is a data structure that is used to implement high-performance, disk-based storage systems such as RocksDB. The algorithm of LSM-Tree is […]

* 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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