Using ClickHouse as an Analytic Extension for MySQL
With Altinity and Percona’s partnership, building more powerful, cost-efficient applications using ClickHouse as an analytic extension for MySQL is now a reality.
With Altinity and Percona’s partnership, building more powerful, cost-efficient applications using ClickHouse as an analytic extension for MySQL is now a reality.
Polyscale.ai is an Altinity.Cloud customer and recently documented their journey into ClickHouse materialized views on the Polyscale Blog. We’re delighted to repost the article.
JSON data type is one of the most popular ClickHouse additions of 2022. It offers simple storage and easy query syntax. What’s not to like?
ClickHouse Live Views and Window Views are alternatives to query newly arrived data from event streams. In this article, we break down these experimental features and compare and contrast usage.
Want fast MySQL analytics? Then check out the Altinity Sink Connector for ClickHouse. Merging the power of ClickHouse with MySQL, the sink connector lets you replicate data from MySQL to ClickHouse in real-time.
Can your Snowflake application run on ClickHouse? Learn how to migrate data from Snowflake to ClickHouse via S3 object storage and Parquet. Low latency response, freedom to run anywhere, and outstanding cost efficiency await.
Get a quick low-down of recent ClickHouse meetups and upcoming community events. Videos and slide decks are included!
Window functions have arrived in ClickHouse! Our webinar will start with an introduction to standard window function syntax and show how it is implemented in ClickHouse. We’ll next show you problems that you can now solve easily using window functions. Finally, we’ll compare window functions to arrays, another powerful ClickHouse feature.
Window functions have arrived in ClickHouse! Our webinar will start with an introduction to standard window function syntax and show how it is implemented in ClickHouse. We’ll next show you problems that you can now solve easily using window functions. Finally, we’ll compare window functions to arrays, another powerful ClickHouse feature.
Window functions have long been a top feature request in ClickHouse. Thanks to excellent work by Alexander Kuzmenkov, ClickHouse now has experimental support, and users can begin to try them out.
Learn about this long-awaited feature and how it works, based on extensive QA work at Altinity.
My name is Meo and I’ve been a DataBase Administrator (DBA) for the last 35 years of my life. After getting a degree at the University in Turin (Italy) many years ago, I started working with relational databases, and I’m still enjoying it! Sometimes I worked as a programmer, and later as an analyst, but my favourite job is the DBA. I’ve worked with Oracle, PostgreSQL, MySQL, and now with ClickHouse in XeniaLab.
The DBA uses particular SQL statements to extract the most interesting information from the databases it manages. This article describes some useful SQL commands a DBA must know to manage a ClickHouse database, like checking sessions, locks, disk space usage, performance and replication. This is my personal “Run Book,” and I am happy to share it in the Altinity blog.
May 21, 2019
One of our customers recently had a problem using CickHouse: the simple workflow of load-analyze-present wasn’t as efficient as they were expecting. The body of the problem was with loading and presenting IPv4 and IPv6 addresses, which are traditionally stored in ClickHouse as UInt32 and FixedString(16) columns. These types have many advantages, like compact footprint and ease of comparing values. But they also have shortcomings that prompted us to seek a better solution.