Thanks for your reply. For the first question, it's not strictly necessary. But I perfer not to have a TableEnvironment argument in Estimator.fit() or Transformer.transform(), which is not part of machine learning concept, and may make our API not as clean and pretty as other systems do. I would like another way other than introducing flink-table-planner to do this. If it's impossible or severely opposed, I may make the concession to add the argument.
Other than that, "flink-table-api-xxx-bridge"s are still needed. A vary common case is that an algorithm needs to guarantee that it's running under a BatchTableEnvironment, which makes it possible to collect result each iteration. A typical algorithm like this is ALS. By flink1.8, this can be only achieved by converting Table to DataSet than call DataSet.collect(), which is available in flink-table-api-xxx-bridge. Besides, registering UDAGG is also depending on it. In conclusion, '"planner" can be removed from dependencies but introducing "bridge"s are inevitable. Whether and how to acquire TableEnvironment from a Table can be discussed.
