With industries ranging from IoT to manufacturing generating and collecting a constantly increasing amount of time-series data, the growth of the time-series database (TSDB) market over the past five years has not come as a surprise. This popularity has resulted in a large number of time series DB solutions coming on the market, sometimes making it difficult to choose the best time series database for a certain business scenario. This article compares two databases that can be used for time-series data processing – Cassandra and TDengine – to help you determine which is right for your use case.
Apache Cassandra is an open-source NoSQL distributed database known for enabling fault tolerance on commodity hardware. Although Cassandra is an Apache Foundation project, a commercial version is also provided by DataStax. While Cassandra is not a time-series database, it does have some characteristics suited for time-series data processing and has been used in similar scenarios.
TDengine is an open-source time-series database that differentiates itself with high performance, a distributed cloud-native architecture, and built-in caching, data subscription, and stream processing that simplify the overall system design.
The following table compares the basic information of Cassandra vs. TDengine.
|Main development language||Java||C|
|Main query language||CQL (proprietary)||Standard SQL|
|Operating systems||Linux, macOS, and Windows||Linux, macOS, and Windows|
The following table indicates specific features supported by Cassandra vs. TDengine.
|System connection management||✅||✅|
|Query task management||✅||✅|
|Telegraf data collection||✅||✅|
|Grafana data visualization||✅||✅|
|Data retention policy||✅||✅|
|Limits and offsets||✅||✅|