Rescuing ClickHouse® from the Linux OOM Killer
The Linux OOM killer terminates ClickHouse servers that use too much memory. Learn how to detect and prevent it in your environment.
ClickHouse® Black Magic, Part 2: Bloom Filters
Bloom filters are an important ClickHouse index type with mysterious parameters. Take a closer look at the theory behind bloom filters, parameter selection using queries on a test dataset, and effective tuning.
Altinity ClickHouse® Knowledge Base
We are pleased to announce a new tool for ClickHouse users: the Altinity Knowledge Base. The ClickHouse Knowledge Base is maintained by our fantastic team of engineers here at Altinity. Here you’ll find quick answers to common questions involving ClickHouse…
ClickHouse® Kafka Engine FAQ
Kafka is a popular way to stream data into ClickHouse. ClickHouse has a built-in connector for this purpose — the Kafka engine. This article collects typical questions that we get in our support cases regarding the Kafka engine usage. We…
Amplifying ClickHouse® Capacity with Multi-Volume Storage (Part 2)
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…
Amplifying ClickHouse® Capacity with Multi-Volume Storage (Part 1)
As longtime users know well, ClickHouse has traditionally had a basic storage model. Each ClickHouse server is a single process that accesses data located on a single storage device. The design offers operational simplicity–a great virtue–but restricts users to a…
clickhouse-local: The power of ClickHouse® SQL in a single command
June 11, 2019The most interesting innovations in databases come from asking simple questions. For example: what if you could run ClickHouse queries without a server or attached storage? It would just be SQL queries and the rich ClickHouse function library….
ClickHouse® In the Storm. Part 2: Maximum QPS for key-value lookups
May 3, 2019The previous post surveyed connectivity benchmarks for ClickHouse to estimate general performance of server concurrency. In this next post we will take on real-life examples and explore concurrency performance when actual data are involved.
ClickHouse® In the Storm. Part 1: Maximum QPS estimation
May 2, 2019ClickHouse is an OLAP database for analytics, so the typical use scenario is processing a relatively small number of requests — from several per hour to many dozens or even low hundreds per second –affecting huge ranges of…
Do-It-Yourself Multi-Volume Storage in ClickHouse®
Many applications have very different requirements for acceptable latencies / processing speed on different parts of the database. In time-series use cases most of your requests touch only the last day of data (‘hot’ data). Those queries should run very…