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…