TDengine vs Alopex Skulk

What TDengine is

TDengine is an open source time-series database built for IoT, connected vehicles, the industrial internet and similar scenarios.

How it works

Data model: one SQL table per data collection point, with super tables grouping collection points of the same kind.
SQL: queries in SQL with time-series extensions.
Cluster: a TDengine cluster is made of processes called data nodes (dnodes).

Main features

Stream processing with windows.
Data subscription that works like a message queue.
Official connectors for C/C++, Java, Python, Go, Node.js, C# and Rust.

Limits

TDengine runs as a cluster separate from the application. Data has to be designed around its own model of per-collection-point SQL tables and super tables.

How it differs from Alopex Skulk

Both store time series from devices and sensors and aggregate them with SQL.

Alopex Skulk is a time-series engine written in Rust that grows from embedded to server to cluster on the same engine. Start small by embedding it in your application, then move the same application to a server with Prometheus-compatible endpoints and to a cluster that scales out with sharding and replication. In every form it ingests time series, queries them with PromQL and SQL, stores them in time-partitioned, columnar Parquet files, and drops expired partitions automatically. Tagged metrics are written as they are, with no SQL table per collection point. It is embedded in the application on edge devices and runs as a server or cluster where data is gathered, on the same engine.

Try Alopex Skulk to connect applications on devices and the cluster that gathers their data with one time-series engine. Start from Alopex Skulk.

References

TDengine Documentation, Data Modeling, Stream Processing, Data Subscription, Connectors, Manage Nodes