In this blog, we’ll look at ClickHouse on its one year anniversary.

It’s been a year already since the Yandex team released ClickHouse as open source software. I’ve had an interest in this project from the very start, as I didn’t think there was an open source analytical database that could compete with industry leaders like Vertica (for example).

This was an exciting year for ClickHouse early adopters. Let’s look at what it accomplished so far.

ClickHouse initially generated interest due to the Yandex name – the most popular search engine in Russia. It wasn’t long before jaw-dropping responses popped up: guys, this thing is crazy fast! Many early adopters who tried ClickHouse were really impressed.

Fast doesn’t mean convenient …

“Spark and Ignite can complement each other very well. Ignite can provide shared storage for Spark so state can be passed from one Spark application or job to another. Ignite can also be used to provide distributed SQL with indexing that accelerates Spark SQL by up to 1,000x.”–Nikita Ivanov.

I have interviewed Nikita Ivanov,CTO of GridGain.
Main topics of the interview are Apache Ignite, Apache Spark and MySQL, and how well they perform on big data analytics.

RVZ

Q1. What are the main technical challenges of SaaS development projects?

Nikita Ivanov: SaaS requires that the applications be highly responsive, reliable and web-scale. SaaS development projects face many of the same challenges as …

In this blog post, we’ll look at how ClickHouse performs in a general analytical workload using the star schema benchmark test.

We have mentioned ClickHouse in some recent posts (ClickHouse: New Open Source Columnar Database, Column Store Database Benchmarks: MariaDB ColumnStore vs. Clickhouse vs. Apache Spark), where it showed excellent results. ClickHouse by itself seems to be event-oriented RDBMS, as its name suggests (clicks). Its primary purpose, using Yandex Metrica (the system similar to Google Analytics), also points to an event-based nature. We also can see there is …

This blog shares some column store database benchmark results, and compares the query performance of MariaDB ColumnStore v. 1.0.7 (based on InfiniDB), Clickhouse and Apache Spark.

I’ve already written about ClickHouse (Column Store database).

The purpose of the benchmark is to see how these three solutions work on a single big server, with many CPU cores and large amounts of RAM. Both systems are massively parallel (MPP) database systems, so they should use many cores for SELECT queries.

For the benchmarks, I chose …