Conference Slides. Five Great Ways to Lose Data on Kubernetes (And How to Avoid Them)
As databases and stateful apps rapidly adopt Kubernetes, the reality of accidental data loss isn't far behind. Robert Hodges, Altinity CEO, will give listeners tools to push through the paranoia and ensure happy experiences with cloud native data.
Read MoreMonitoring 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.
ClickHouse Tiered Storage Intro
by Robert Hodges, Altinity CEO
San Francisco ClickHouse Meetup, February 5, 2020
Data warehouse on Kubernetes – gentle intro to Clickhouse Operator
by Robert Hodges, Altinity CEO
San Diego Cloud Native Computing Meetup, January 23, 2020
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 Announces Production-Ready Kubernetes Operator for ClickHouse Data Warehouses
LONDON, November 19, 2019 — Altinity is pleased to announce a production-ready version 0.7.0 of the innovative ClickHouse Kubernetes Operator. The new operator release enables users to confidently use Kubernetes to run ClickHouse, a popular real-time data. warehouse used by a worldwide open source community.
Read MoreAltinity 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.
Read MoreAltinity Has Joined the Cloud Native Computing Foundation
It is my pleasure to announce that Altinity is now a member of the Cloud Native Computing Foundation (CNCF). We will also be a sponsor at Kubecon/CloudNativeCon North America 2019 in San Diego 18-21 November.
Read MoreClickHouse at Data Con LA 2019
On August 17 I had the pleasure of presenting at Data Con LA 2019. My talk was Data Warehouse and Kubernetes: Lessons from the ClickHouse Operator. It described learnings from our work to enable ClickHouse to run easily on Kubernetes. This short article discusses key points from the talk as well as takeaways from the conference itself.
Read MoreData Warehouse on Kubernetes: lessons from Clickhouse Operator
by Robert Hodges, Altinity CEO
Presented at DataCon LA, August 17, 2019
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
June 5, 2019
Speaker: Robert Hodges, Altinity CEO
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.
