The COVID-19 outbreak has placed unprecedented stress on individuals and businesses worldwide. We would like to share a few words about how the Altinity team is dealing with this outbreak in the hope it may help your own planning.Read More
We are delighted to announce that Altinity Stable Release is updated to 22.214.171.124. The release includes several important bug fixes not present in the previous 126.96.36.199, and one compatibility fix that is important for future upgrades.Read More
In data analytics, analysts often use moving averages. Moving averages help to smooth data series as well as identify long term trends. New Live View tables start to bring real-time capabilities to ClickHouse.
One of the applications of Live View tables is a calculation of real-time metrics on the event data. Readings from IoT sensors, price ticks from the stock exchange, or some metrics from your production servers are some of the examples of event data streams. ClickHouse can store all this data with a good compression ratio and excellent analytical query performance.Read More
Important notice for our beloved Apache Kafka users. We continue to improve Kafka engine reliability, performance and usability, and as a part of this entertaining process we have released 188.8.131.52 Altinity Stable ClickHouse release. This release supersedes the previous stable release 184.108.40.206, and addresses following problems:Read More
Welcome to the year 2020! New Year came and it's a good time to update your ClickHouse to the latest Altinity stable version which we are presenting today. As always, Altinity Stable is a version we know is good and recommended for production use. You can use a newer version in your lab environment to try the latest features.
Jan 1, 2020
Cost-efficiency and performance are critical for big data analytics. For this reason a recent blog post from ScyllaDB guys caught our attention. They collected over 500 billion data points and were able to query it with 1B rows/sec query scan performance. The test rig was a beefy and expensive packet.com cluster: 83 n2.xlarge.x86 instances, 28 cores and 384RAM each. This is a nice demo of ScyllaDB cluster management. But looking at the numbers we realized it’s not very impressive as an example of efficient analytics. We can prove that using ClickHouse.
Dec 28, 2019
Grafana is a popular tool to create dashboards of time series data. It features outstanding graphics, interactive displays that zoom in on data, and support for a wide range of data sources. It turns out that one of those data sources is ClickHouse, and Grafana is a great way to visualize ClickHouse data.
Many years ago a wise customer once said, “every report wants to be an Excel spreadsheet when it grows up!” I have always had a soft spot for Excel--it’s one of the most useful programs that Microsoft ever released. To honor that long lost user this article will show how to bring joy to your ClickHouse reporting by pulling data into Excel.
The year 2019 ended with a burst of ClickHouse community events in the US and Europe. There were many great talks about ClickHouse from users exploring new use cases as well as operating at large scale. Here is a quick rundown.Read More
In the couple of previous blog posts, I have introduced Live Views tables and covered basic usage. Now, in this post, we will take a closer look at Live View tables. Specifically, we will look at the options available for the WATCH query, then introduce temporary Live Views, as well as look at the new JSONEachRowWithProgress format.
This article is a continuation of the series describing multi-volume storage, which greatly increases ClickHouse server capacity using tiered storage. In the previous article we introduced why tiered storage is important, described multi-volume organization in ClickHouse, and worked through a concrete example of setting up disk definitions.