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Posts in Benchmarks
Handling Variable Time Series Efficiently in ClickHouse

May 23, 2019

ClickHouse offers incredible flexibility to solve almost any business problem in a multiple of ways. Schema design plays a major role in this. For our recent benchmarking using the Time Series Benchmark Suite (TSBS) we replicated TimescaleDB schema in order to have fair comparisons. In that design every metric is stored in a separate column. This is the best for ClickHouse from a performance perspective, as it perfectly utilizes column store and type specialization.

Sometimes, however, schema is not known in advance, or time series data from multiple device types needs to be stored in the same table. Having a separate column per metric may be not very convenient, hence a different approach is required. In this article we discuss multiple ways to design schema for time series, and do some benchmarking to validate each approach.

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Introducing ClickHouse IPv4 and IPv6 Domains for IP Address Handling

May 21, 2019

One of our customers recently had a problem using CickHouse: the simple workflow of load-analyze-present wasn't as efficient as they were expecting. The body of the problem was with loading and presenting IPv4 and IPv6 addresses, which are traditionally stored in ClickHouse as UInt32 and FixedString(16) columns. These types have many advantages, like compact footprint and ease of comparing values. But they also have shortcomings that prompted us to seek a better solution.

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ClickHouse In the Storm. Part 1: Maximum QPS estimation

May 2, 2019

ClickHouse 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 data (gigabytes/millions of rows).

But how it will behave in other scenarios? Let's try to use a steam-hammer to crack nuts, and check how ClickHouse will deal with thousands of small requests per second. This will help us to understand the range of possible use cases and limitations better.

This post has two parts. The first part covers connectivity benchmarks and test setup. The next part covers maximum QPS in scenarios involving actual data.

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ClickHouse Crushing Time Series

Nov 15, 2018

Once upon a time we spotted TSBS (https://github.com/timescale/tsbs) -- Time Series Benchmark Suite, started by InfluxDB engineers and polished to perfection by TimescaleDB team. The suite allows to compare apples-to-apples when testing different databases: it is a framework to generate test data, load it to different databases, run test queries, and collect statistics to analyse. We could not resist adding ClickHouse to the list of supported databases. It turned out that ClickHouse --- being a general purpose analytical DBMS -- stands very well against proven time series databases. Those benchmarks highlighted strengths and weaknesses of different technologies. Interested? Let’s dig into details.

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Aggregate MySQL data at high speed with ClickHouse

Feb 12, 2018
There are multiple ways how ClickHouse and MySQL can work together. External Dictionaries, ProxySQL support or [realtime streaming] of MySQL binary logs into ClickHouse. A few weeks ago ClickHouse team has released mysql() table function that allows to access MySQL data directly from ClickHouse. This opens up a number of interesting capabilities. Accidentally we have found [a blog article in Japanese] by Mikage Sawatari‏ , that tests a new way of integration and translated it for our blog with some minor edits.

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ClickHouse on the Cloud benchmarks

Jan 4, 2018
It's been a while since Altinity announced a partnership with Kodiak Data, a cloud-infrastructure company. Despite that, we have never written about Kodiak Data and how they help with ClickHouse deployments. Now there are several companies already using ClickHouse at Kodiak Data MemCloud(TM), so it's time to explain why. In this article, we test ClickHouse performance at various AWS and Kodiak Data cloud instances as well as add RedShift to complete the picture. 

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