Alex Klibisz created FLINK-9167:

             Summary: Approximate KNN with Incremental Insertion
                 Key: FLINK-9167
             Project: Flink
          Issue Type: New Feature
          Components: Machine Learning Library
            Reporter: Alex Klibisz

I'm new to Flink, and I'm curious about an extension of approximate KNN to 
supportĀ  incremental insertion to the index.

Consider the case where you build an index from a training set of vectors. As 
your application runs, you ingest a stream of new vectors (e.g. users posting 
new content). For every new vector, you compute its neighbors against the 
existing index. Then you immediately insert the new vector to the index such 
that it can be returned for subsequent queries.

Perhaps this is possible with current components of Flink, or maybe another 
streaming tool already has a comparableĀ  implementation? If so, I would 
appreciate any pointers or links to examples.

If it's not available, is there interest in implementing such a feature? If so, 
I would be interested in making an attempt.

I appreciate any tips or insight. Thanks!

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