Hello, 1) Are you sure that your data is "clean"? No unexpected missing values? No strings in unusual encoding? No additional or missing columns ? 2) How long does your job run? What about garbage collector parameters? Have you checked what happens with jconsole / jvisualvm ?
Sincerely yours, Timur On Sat, Feb 11, 2017 at 12:52 AM, nguyen duc Tuan <newvalu...@gmail.com> wrote: > Hi Nick, > Because we use *RandomSignProjectionLSH*, there is only one parameter for > LSH is the number of hashes. I try with small number of hashes (2) but the > error is still happens. And it happens when I call similarity join. After > transformation, the size of dataset is about 4G. > > 2017-02-11 3:07 GMT+07:00 Nick Pentreath <nick.pentre...@gmail.com>: > >> 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 >>> >>> >>> >
