ClickHouse vs. Google Cloud SQL

The most important thing when using ClickHouse is to be clear that the scenarios in which you want to use it really are the right ones. Many users think that when a database is very fast for a specific use case, it can be extrapolated to other contexts (most of the time different) in which a previous analysis has not been carried out.

ClickHouse is an analytical database, as such, it should be used for such purposes, where the information is stored correctly, the data volumes are really large and the queries to be performed are not the typical traditional queries on several columns with multiple aggregations. ClickHouse is not the solution for this.

On the other hand, if your case is not one of the above, it is quite possible that ClickHouse can help you. Where ClickHouse shines is when you are looking for aggregation over a particular column in large volumes of data.

Incentivized

Although Google Cloud SQL has room for improvement by addressing a minor lack of features, its features and services keep it high among other SQL database products. It is very fast compared to others. Since it is cloud-based, maintenance is also easier. Integration capabilities are also more than expected.

Incentivized

• Opensource
• High performance
• Multiple engines to adapt user cases
• Easy configuration of data replication

Incentivized

• Highly scalable without worrying about sudden transaction explosion during peak hours.
• Highly available with multiple geographical locations and regions for nearly 0 downtime to the users.
• Extremely reliable and responsive for high latency applications due to superb networking at the core.

Incentivized

• Avro data manipulation
• Kafka consistency
• DDL operations errors (by replica configuration)

Incentivized

• Increasing support for more database engines may enable a wider range of application needs to be met.
• Implementing and updating cutting-edge security features on a constant basis.
• Streamlining and enhancing the tools for transferring data to Google Cloud SQL from on-premises databases or other cloud providers.

On demand scalability helps us provision extra resources as per our requirement and load on the application. Reduced cost due to fully managed services and no need to manage underlying infrastructure which reduced time of patching and maintaining underlying infrastructure. Easy to configure as per application requirements. Supports monitoring which is not available in custom hosted database instance on own computing infrastructure

Incentivized

GCP support in general requires a support agreement. For small organizations like us, this is not affordable or reasonable. It would help if Google had a support mechanism for smaller organizations. It was a steep learning curve for us because this was our first entry into the cloud database world. Better documentation also would have helped.

Incentivized

ClickHouse was not compared to them as a competitor but as the ideal partner to complete an information analysis system, providing users with the most complete and efficient tools. Therefore, in this case it was considered that it would be the ideal candidate due to its characteristics compared to the other competitors.

Incentivized

Google SQL was great as a first SQL provision. It quickly enabled the apps to be built and scaled as needed for a while. It was robust and adaptable as needed and easy to export as needed when ready, depending on growth. Cost-wise, it's a good choice and requires little investment to get going.

Incentivized

• Queries that used to take more than 2 minutes now take less than 1 second
• Possibility to analyze use cases in real time (before was impossible)
• The applications are more complete and the users decisions are better

Incentivized

• With managed database system, it has given us near 100% data availability
• It has also improved web layer experience with faster processing and authentication using database fields
• Google Cloud SQL also gels up well with Google Analytics and other analytics systems for us to join up different data points and process them for deeper dives and analysis

Incentivized