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MySQL HeatWave User Guide  /  HeatWave ML  /  Limitations

3.12 Limitations

HeatWave ML has the following limitations:

  • The ML_TRAIN routine does not support MySQL user names that contain a period; for example, a user named 'joe.smith'@'%' cannot run the ML_TRAIN routine. The model catalog schema created by the ML_TRAIN procedure incorporates the user name in the schema name (e.g., ML_SCHEMA_joesmith), and a period is not a permitted schema name character.

  • The table used to train a model (the training dataset) cannot exceed 10 GB, 100 million rows, or 900 columns.

  • To avoid taking up too much space in memory, the number of loaded models should be limited to three.

  • Bring your own model is not supported. Use of non-HeatWave ML models or manually modified HeatWave ML models can cause undefined behavior.

  • Models greater than 900 MB in size are not supported. If a model being trained by the ML_TRAIN routine exceeds 900 MB, the ML_TRAIN query fails with an error.

  • There is currently no way to monitor HeatWave ML query progress. ML_TRAIN is typically the most time consuming routine. The time required to train a model depends on the number of rows and columns in the dataset and the specified ML_TRAIN parameters and options.

    ML_EXPLAIN_TABLE and ML_PREDICT_TABLE are compute intensive processes, with ML_EXPLAIN_TABLE being the most compute intensive. Limiting operations to batches of 10 to 100 rows by splitting large tables into smaller tables is recommended.

  • Ctrl+C interruption is supported only for ML_TRAIN, ML_EXPLAIN_ROW, and ML_EXPLAIN_TABLE. supported.

  • ML_EXPLAIN_* routines limit explanations to the 100 most relevant features.

  • Concurrent HeatWave analytics and HeatWave ML queries are not supported. A HeatWave ML query must wait for HeatWave analytics queries to finish, and vice versa. HeatWave analytics queries are given priority over HeatWave ML queries.