What other params are you using for the lsh transformer? Are the issues occurring during transform or during the similarity join?
On Fri, 10 Feb 2017 at 05:46, nguyen duc Tuan <newvalu...@gmail.com> wrote: > 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 > > >
