  In recent years, machine learning (ML) research methods have received
increasing attention in the tokamak community. The conventional database (i.e.,
MDSplus for tokamak) of experimental data has been designed for small group
consumption and is mainly aimed at simultaneous visualization of a small amount
of data. The ML data access patterns fundamentally differ from traditional data
access patterns. The typical MDSplus database is increasingly showing its
limitations. We developed a new data management system suitable for tokamak
machine learning research based on Experimental Advanced Superconducting
Tokamak (EAST) data. The data management system is based on MongoDB and
Hierarchical Data Format version 5 (HDF5). Currently, the entire data
management has more than 3000 channels of data. The system can provide highly
reliable concurrent access. The system includes error correction, MDSplus
original data conversion, and high-performance sequence data output. Further,
some valuable functions are implemented to accelerate ML model training of
fusion, such as bucketing generator, the concatenating buffer, and distributed
sequence generation. This data management system is more suitable for fusion
machine learning model R\&D than MDSplus, but it can not replace the MDSplus
database. The MDSplus database is still the backend for EAST tokamak data
acquisition and storage.
