Monitoring ClickHouse on Kubernetes with Prometheus and Grafana

Monitoring ClickHouse on Kubernetes with Prometheus and Grafana

The ClickHouse Kubernetes operator is great at spinning up data warehouse clusters on Kubernetes. Once they are up, though, how can you see what they are actually doing? It’s time for monitoring!
In this article we’ll explore how to configure two popular tools for building monitoring systems: Prometheus and Grafana. The ClickHouse Kubernetes operator includes scripts to set these up quickly and add a basic dashboard for clusters.

Altinity and ClickHouse at KubeCon 2019

Altinity and ClickHouse at KubeCon 2019

The Altinity team just returned from sponsoring at KubeCon North America in San Diego November 19-21. I would like to thank all the people who visited our booth, especially current customers and ClickHouse users. It’s starting to be a real crowd!

Altinity ClickHouse Operator at Red Hat’s OperatorHub.io

Altinity ClickHouse Operator at Red Hat’s OperatorHub.io

CoreOS introduced operators for Kubernetes in 2016. A Kubernetes Operator is an application that integrates into Kuberentes and manages other applications. It significantly simplifies deployment and management of cloud native apps by leveraging application specific operational knowledge. Since then operators have been developed for many applications in Kubernetes, including some databases. As a result, operators became a successful pattern for managing cloud native applications. 

Webinar Slides: ClickHouse and the Magic of Materialized Views

By Robert Hodges and Altinity Engineering Team
Slides for the Webinar, presented on June 26, 2019 
Webinar recording is available here

Materialized views are a killer feature of ClickHouse that can speed up queries 200X or more. Our webinar will teach you how to use this potent tool starting with how to create materialized views and load data. We’ll then walk through cookbook examples to solve practical problems like deriving aggregates that outlive base data, answering last point queries, and using AggregateFunctions to handle problems like counting unique values, which is a special ClickHouse feature. There will be time for Q&A at the end. At that point you’ll be a wizard of ClickHouse materialized views and able to cast spells of your own.

Webinar: Analyzing Billion Row Datasets with ClickHouse

Webinar: Analyzing Billion Row Datasets with ClickHouse

This talk shows how to get a sub-second response from datasets containing a billion rows or more. We’ll start with defining schema and loading quickly data in parallel. We will then introduce tricks like LowCardinality datatype, ASOF joins, and materialized views that can reduce query response to thousandths of seconds. Finally, we’ll show you metrics and logging to analyze query performance. After this talk you’ll be ready for your first billion rows and many more afterwards