kdb+ vs. QuestDB

kdb is well suited for real time tick data and time series analytics.

Incentivized

QuestDB is well suited for any use case where you need to store large amount of data and the performance is the key factor - for both reads and writes. So use cases like market data storage in financial industry, any kind of telemetry, etc.

• Time series analysis. The built-in vector operations are extremely fast. Also with the q language you can code up any customized analytical ideas quickly.
• The database are all file based, very easy to maintain.
• Very solid and fast interface to websocket, so you can interface with javascript easily.

Incentivized

• Extreme performance.
• Super easy to use.
• Compatibility with Influx line protocol.
• PostgreSQL compatibility.
• Out of order timestamps.
• Support for multiple records with same timestamp.
• Integration with Grafana.
• Team responsiveness.

• Run time error message readability, particularly for new users.
• Backwards compatibility between versions.

Incentivized

• New project so needs a bit polishing.

switching costs

Incentivized

its ok

Incentivized

We don't use it.

Incentivized

Python is very commonly used for large data analysis and in general is much easier to pickup than kdb+. The biggest drawback of kdb+ is the learning curve.

Incentivized

We were looking for time series database that will be able to handle L2 market data and came across QuestDB. From the beginning we were impressed how well the QuestDB performs and that it actually significantly outperforms all other open source TSDB on market like InfluxDB, ClickHouse, Timescale, etc. Apart from the excellent performance it is also super easy to use and deploy which makes the experience of using the database very pleasant - we were able to be up and running and storing data within few hours. Topic itself is the QuestDB team that is super responsive on their slack channel and always ready to help with any query. They are constantly improving the product and if there is some missing feature that is blocking you from usage they always try the best to implement such feature asap and release a new version - one of the best support I have ever seen so far in open source community.

• It perfectly solves most of our real time tick data needs.
• Finding good kdb resources is slightly difficult. Also new people trying to learn kdb experience a relatively longer learning curve.

Incentivized

• Reduced cost.
• Increased efficiency.
• Faster time to market.