Tdengine Database Becoming A Spiritual Scientist: Your Renewable Energy Source

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Separation of compute and storage: In containerized environments, specific containers may be up or down at any time, but stored data is persistent. Traditional database designs are unable to cope with this because they store data locally. Furthermore, to run a complicated query or do batch processing, more compute nodes need to be added dynamically to speed up the process.
Elasticity: The system must be able to adjust storage and compute resources based on workload and latency requirements. For compute resources, this is not a hard decision for the system to make. But storage resources are another story. To scale a distributed database up or down, its shards have to be merged or split while live data is coming or queries are running. Designing a system that can accomplish this is no easy task.
Observability: The status of a time-series database must be monitored together with the other components of the system, so a good database needs to provide full observability to make operations and management simpler.
At this point, any newly developed time-series database must be cloud native. While TDengine was designed from day one with a highly scalable distributed architecture, as of version 3.0 we now fully support the separation of compute and storage.

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