In Data Platform System with ClickHouse, rather than extracting data from ClickHouse for processing in external systems, we can perform transformations directly within ClickHouse itself. ClickHouse can call any external executable program or script to process data. My idea is using custom **User-Defined Functions (UDFs) written in Rust** to handle data transformations between tables.
Learn how to set up and manage ReplicatedReplacingMergeTree in ClickHouse on Kubernetes. This comprehensive guide covers cluster setup with ClickHouse Operator, data replication, performance tuning, and best practices for high availability deployments.
My favorite ClickHouse table engine is `ReplacingMergeTree`. The main reason is that it is similar to `MergeTree` but can automatically deduplicate based on columns in the `ORDER BY` clause, which is very useful.
After starting this series ClickHouse on Kubernetes, you can now configure your first single-node ClickHouse server. Let's dive into creating your first table and understanding the basic concepts behind the ClickHouse engine, its data storage, and some cool features
Complete guide to monitoring ClickHouse on Kubernetes. Learn about built-in dashboards, Prometheus + Grafana setup, powerful system tables for monitoring queries, and the ClickHouse Monitoring UI dashboard. Includes practical examples, essential monitoring queries, and best practices for production observability.
Dynamic column selection (also known as a `COLUMNS` expression) allows you to match some columns in a result with a re2 regular expression.
Complete guide to deploying ClickHouse on Kubernetes using the Altinity ClickHouse Operator. Learn how to set up your first single-node cluster, configure persistent storage, manage users, and customize ClickHouse versions. Includes practical examples and best practices from production experience managing clusters with trillions of rows.
So, I hadn't really planned on writing a summary post for this year, as lazy as I am, but somehow, here we are.
OpenDAL is a data access layer that allows users to easily and efficiently retrieve data from various storage services in a unified way such as S3, FTP, FS, Google Drive, HDFS, etc. They has been rewritten in Rust for the Core and have a binding from many various language like Python, Node.js, C, etc..
It's been years since I first started using neovim and I've been updating it regularly ever since.