hi Das, In general, I will apply them to larger datasets, so I want to use LSH, which is more scaleable than the approaches as you suggested. Have you tried LSH in Spark 2.1.0 before ? If yes, how do you set the parameters/configuration to make it work ? Thanks.
2017-02-10 19:21 GMT+07:00 Debasish Das <debasish.da...@gmail.com>: > If it is 7m rows and 700k features (or say 1m features) brute force row > similarity will run fine as well...check out spark-4823...you can compare > quality with approximate variant... > On Feb 9, 2017 2:55 AM, "nguyen duc Tuan" <newvalu...@gmail.com> wrote: > >> Hi everyone, >> Since spark 2.1.0 introduces LSH (http://spark.apache.org/docs/ >> latest/ml-features.html#locality-sensitive-hashing), we want to use LSH >> to find approximately nearest neighbors. Basically, We have dataset with >> about 7M rows. we want to use cosine distance to meassure the similarity >> between items, so we use *RandomSignProjectionLSH* ( >> https://gist.github.com/tuan3w/c968e56ea8ef135096eeedb08af097db) instead >> of *BucketedRandomProjectionLSH*. I try to tune some configurations such >> as serialization, memory fraction, executor memory (~6G), number of >> executors ( ~20), memory overhead ..., but nothing works. I often get error >> "java.lang.OutOfMemoryError: Java heap space" while running. I know that >> this implementation is done by engineer at Uber but I don't know right >> configurations,.. to run the algorithm at scale. Do they need very big >> memory to run it? >> >> Any help would be appreciated. >> Thanks >> >
