Hi Qiao, I’m very glad to learn about TsFile’s design for supporting AI in the future.
TsFile already demonstrates strong advantages in time-series scenarios, and the next step is to further adapt it to the evolving requirements of AI-driven time-series workloads. In my view, TsFile could be enhanced in several key areas: 1. providing more efficient batch read capabilities to better leverage modern CPUs and GPUs; 2. optimizing read mechanisms for random, unfiltered, small-batch query patterns; 3. offering richer and more user-friendly statistical metadata to facilitate data analysis. I look forward to TsFile becoming a core foundation for time-series model training and dataset construction. Thanks, Colin Lee. On 2025/12/30 12:37:20 Jialin Qiao wrote: > Hi all, > > With the release of TsFile 2.2.0, the project now offers > multi-language SDKs (Python, Java, C++, C), enabling seamless data > storage for terminal devices, real-time edge-side processing, and > cloud-based data analysis. Its support for table models further > simplifies data analysis and model training in Python. > > As AI continues to gain momentum, TsFile can serve as a foundational > format for building industrial time-series datasets in the AI era. > > Here are some potential work we could do > 1. Deeper alignment with the Python ecosystem, such as Pandas & DataFrame. > 2. Integration with HuggingFace Datasets. > 3. Viewer of a TsFile. > 4. Converter between other formats(such as Parquet, CSV, HDF5) and TsFile. > > Welcome further ideas to advance the TsFile community :-) > > Thanks, > Jialin Qiao >
