ClickHouse Performance Archives | Page 18 of 32

ClickHouse Replication: Understanding High Watermark Mechanism for Horizontal Scaling

Introduction In ClickHouse, the term “high watermark” refers to a mechanism used to track the progress of data replication in a distributed environment. It helps ensure data consistency and integrity across multiple replicas of a ClickHouse […]

Leveraging ClickHouse for Real-time Analytics on Blockchain Data

ChistaDATA Inc.

Introduction With one of our proof-of-concepts (POC), we have implemented ClickHouse to handle Ethereum blockchain transaction data for processing purposes. Through the cumulative aggregation and analysis of this data, we can extract valuable insights […]

From Snowflake to ClickHouse: How ChistaDATA Enabled the World’s Largest Ad Tech Platform’s Migration and Built an Optimal Real-Time Analytics Infrastructure

Shiv Iyer

Introduction The world of advertising technology is fast-paced, data-intensive, and requires real-time insights for efficient decision-making. In this case study, we explore how ChistaDATA, a leading provider of advanced analytics solutions, helped the world’s largest […]

Direct Path Load and Space Management in ClickHouse

Direct Path Load and Space Management for Optimal ClickHouse Storage & Ingestion

Shiv Iyer

Introduction Space management and direct path load are important considerations in ClickHouse for optimizing storage efficiency and data loading performance. Here are some tips and tricks for space management and direct path load in ClickHouse: […]

Accuracy of Cardinality Estimates in ClickHouse Execution Plans

ClickHouse Performance: How to assess Accuracy of Cardinality Estimates in Execution Plans

Shiv Iyer

Introduction In ClickHouse, evaluating the accuracy of cardinality estimates in a query plan can be challenging since ClickHouse relies on different heuristics and sampling techniques to estimate cardinalities. Accuracy of Cardinality Estimates However, you can […]

How to drop an Existing Histogram from a ClickHouse Column?

Shiv Iyer

Introduction To drop an existing histogram on a ClickHouse column and prevent the Auto Stats gathering job from creating it in the future, you can follow these steps: For example: Identify the column name that […]

ClickHouse EXPLAIN: Display & Analyze Execution Plans

Shiv Iyer

Introduction To display and read the execution plans for a SQL statement in ClickHouse, you can follow these steps using real-life data sets: For this example, we’ll use a simple SELECT statement to retrieve data […]

How to Identify Overlapping Date Ranges in ClickHouse

ClickHouse SQL Engineering: How to Identify Overlapping Date Ranges

Shiv Iyer

To identify overlapping date ranges in ClickHouse, you can use SQL queries that compare the start and end dates of each range to determine if there are any overlaps. Example to identify Overlapping Date Ranges […]

ClickHouse Horizontal Scaling: Implementing Parallel Replicas with Dynamic Shards

ChistaDATA Inc.

Introduction ClickHouse is known for its exceptional performance, scalability, and flexibility. With its ability to handle massive amounts of data and process queries in real-time, ClickHouse is becoming increasingly popular among data analysts, developers, and […]

Using GROUPBY for Groupings, Rolllups and Cubes in ClickHouse

Using GROUPBY for Groupings, Rollups and Cubes in ClickHouse

Shiv Iyer

Introduction Grouping, rollup, and cube are SQL query operations that allow for grouping and aggregation of data based on multiple dimensions or attributes. In ClickHouse, these operations are implemented using the GROUP BY clause, which […]

* 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.

Licensed under the Apache License, Version 2.0 (the “License”); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an “AS IS” BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

PostgreSQL is a registered trademark of the PostgreSQL Community Association. ClickHouse is a registered trademark of ClickHouse, Inc. MongoDB is a registered trademark of MongoDB, Inc. Couchbase is a registered trademark of Couchbase, Inc. Redis is a registered trademark of Redis Ltd. Apache Cassandra is a registered trademark of the Apache Software Foundation. Milvus is a registered trademark of Zilliz. MinIO is a registered trademark of MinIO, Inc. Amazon Redshift and Amazon Aurora are registered trademarks of [Amazon.com](http://amazon.com/), Inc. Google Cloud is a registered trademark of Google LLC. Snowflake is a registered trademark of Snowflake Inc. Databricks is a registered trademark of Databricks, Inc. MySQL and InnoDB are registered trademarks of Oracle Corporation. MariaDB is a trademark of MariaDB Corporation Ab. All other trademarks are the property of their respective owners. Any other product or company names mentioned may be trademarks or trade names of their respective owners. Copyright © 2010–2026. All Rights Reserved by ChistaDATA®.

→ Index