See how thousands of Engineering, Product and Marketing Teams are accelerating their growth with Zipy.
When a feature request from the product team doesn't work with the existing system, it is nothing short of a nightmare. Due to the need for several complex aggregated functions, a row-based database was unable to provide the desired results for this feature request.
Instead, we turned to columnar databases. That's when ClickHouse caught our attention, and we began analyzing ClickHouse closely using a number of criteria that I've explained below.
Fast, column-oriented, open-source ClickHouse SQL database (db) is great for real-time and data analysis. ClickHouse is appropriate for applications that need analytical findings in less than a second since it supports real-time query processing.
ClickHouse supports a wide variety of database and table engines.
Our system's use case did not align with the use case of other database engines, thus after reading and conducting a Proof of Concept, we determined that Atomic is the most suitable database engine for ClickHouse. The most reliable engine offered by ClickHouse is Atomic. Database engines, such as MYSQL, SQLite, PostgreSQL, and others, are highly specialized for connecting to and establishing handshakes with these kinds of databases.
ClickHouse supports a number of table engines, however MergeTree worked best for us. When a standard insert is needed, the MergeTree table engine is recommended. Since ClickHouse uses a columnar database and writing takes time, it writes data piecemeal and handles merging afterwards. This is because data in.bin files is compressed, making real-time insertion laborious.
As mentioned earlier ClickHouse is a column-oriented SQL database.ClickHouse is an open-source database with an Apache2.0 license which makes it more reliable. The ClickHouse team has implemented the support for ML algorithms, which makes it much easier and faster to run ML over ClickHouse data.
Just like other columnar databases ClickHouse is also read optimized. ClickHouse support for real-time query processing makes it suitable for applications that require sub-second analytical results.
Also, ClickHouse supports partitioning, indexing, joins, and other DML statements like update, and delete. It gives support to batch processing.
As the famous saying goes, “Nothing is perfect in this world, everything has its pros and cons…”
If someone needs any of the below properties then one should not consider ClickHouse.
ClickHouse is a very robust database with a variety of table engines. It supports mostly all DML, DDL, and DCL which MySQL supports. ClickHouse gains more popularity because of its open-source nature. Columnar DBs come with some overhead cost of time when it performs DML, so we have to choose wisely.
Feel free to comment or write to us in case you have any further questions at support@zipy.ai. We would be happy to help you. In case you want to explore for your app, you can sign up or book a demo.
Zipy provides you with full customer visibility without multiple back and forths between Customers, Customer Support and your Engineering teams.