And to be clear, are you doing a self-join for approx similarity? Or joining to another dataset?
On Thu, 23 Feb 2017 at 02:01, nguyen duc Tuan <newvalu...@gmail.com> wrote: > Hi Seth, > Here's the parameters that I used in my experiments. > - Number of executors: 16 > - Executor's memories: vary from 1G -> 2G -> 3G > - Number of cores per executor: 1-> 2 > - Driver's memory: 1G -> 2G -> 3G > - The similar threshold: 0.6 > MinHash: > - number of hash tables: 2 > SignedRandomProjection: > - Number of hash tables: 2 > > 2017-02-23 0:13 GMT+07:00 Seth Hendrickson <seth.hendrickso...@gmail.com>: > > I'm looking into this a bit further, thanks for bringing it up! Right now > the LSH implementation only uses OR-amplification. The practical > consequence of this is that it will select too many candidates when doing > approximate near neighbor search and approximate similarity join. When we > add AND-amplification I think it will become significantly more usable. In > the meantime, I will also investigate scalability issues. > > Can you please provide every parameter you used? It will be very helfpul > :) For instance, the similarity threshold, the number of hash tables, the > bucket width, etc... > > Thanks! > > On Mon, Feb 13, 2017 at 3:21 PM, Nick Pentreath <nick.pentre...@gmail.com> > wrote: > > The original Uber authors provided this performance test result: > https://docs.google.com/document/d/19BXg-67U83NVB3M0I84HVBVg3baAVaESD_mrg_-vLro > > This was for MinHash only though, so it's not clear about what the > scalability is for the other metric types. > > The SignRandomProjectionLSH is not yet in Spark master (see > https://issues.apache.org/jira/browse/SPARK-18082). It could be there are > some implementation details that would make a difference here. > > By the way, what is the join threshold you use in approx join? > > Could you perhaps create a JIRA ticket with the details in order to track > this? > > > On Sun, 12 Feb 2017 at 22:52 nguyen duc Tuan <newvalu...@gmail.com> wrote: > > After all, I switched back to LSH implementation that I used before ( > https://github.com/karlhigley/spark-neighbors ). I can run on my dataset > now. If someone has any suggestion, please tell me. > Thanks. > > 2017-02-12 9:25 GMT+07:00 nguyen duc Tuan <newvalu...@gmail.com>: > > Hi Timur, > 1) Our data is transformed to dataset of Vector already. > 2) If I use RandomSignProjectLSH, the job dies after I call > approximateSimilarJoin. I tried to use Minhash instead, the job is still > slow. I don't thinks the problem is related to the GC. The time for GC is > small compare with the time for computation. Here is some screenshots of my > job. > Thanks > > 2017-02-12 8:01 GMT+07:00 Timur Shenkao <t...@timshenkao.su>: > > 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 > > > > > > > > >
