Altinity Blog

ClickHouse and Python: Jupyter Notebooks


Jupyter Notebooks are an indispensable tool for sharing code between users in Python data science. For those unfamiliar with them, notebooks are documents that contain runnable code snippets mixed with documentation. They can invoke Python libraries for numerical processing, machine learning, and visualization. The code output includes not just text output but also graphs from powerful libraries like matplotlib and seaborn. Notebooks are so ubiquitous that it’s hard to think of manipulating data in Python without them.

ClickHouse support for Jupyter Notebooks is excellent. I have spent the last several weeks playing around with Jupyter Notebooks using two community drivers: clickhouse-driver and clickhouse-sqlalchemy. The results are now published on Github at https://github.com/Altinity/clickhouse-python-examples. The remainder of this blog contains tips to help you integrate ClickHouse data to your notebooks.

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ClickHouse Meetup at Cloudflare. Recap


The ClickHouse Meetup at Cloudflare went great! It was a pleasure to see old friends and to meet new people enthusiastic about ClickHouse. Robert Hodges gave an intro talk about the ClickHouse execution model and how it contributes to rapid query responses. Alex Hofsteede walked through how Sentry.io uses ClickHouse and the steps they went through to migrate applications seamlessly onto ClickHouse from other solutions.

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ClickHouse Continues to Crush Time Series

In our previous articles we demonstrated that ClickHouse -- a general purpose analytics DB -- can easily compete with specialized DBMSs for time series data: TimescaleDB and InfluxDB. There were, however, certain queries, pretty typical for time series, where ClickHouse seemed at first glance to be at a disadvantage. The most notable example is returning the latest measurement for particular device. We will take this query and demonstrate how ClickHouse advanced features, namely materialized views and self-aggregating tables, can  dramatically improve performance.

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ClickHouse and Python: Getting to Know the Clickhouse-driver Client

Python is a force in the world of analytics due to powerful libraries like numpy along with a host of machine learning frameworks. ClickHouse is an increasingly popular store of data. As a Python data scientist you may wonder how to connect them. This post contains a review of the clickhouse-driver client.  It’s a solidly engineered module that is easy to use and integrates easily with standard tools like Jupyter Notebooks and Anaconda.  Clickhouse-driver is a great way to jump into ClickHouse Python connectivity.

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