To clarify, we are not persisting to disk. That was just one of the experiments we did because of some issues we had along the way.
At this time, we are NOT using persist but cannot get the flow to complete in Standalone Cluster mode. We do not have a YARN-capable cluster at this time. We agree with what you're saying. Your results are what we were hoping for and expecting. :-) Unfortunately we still haven't gotten the flow to run end to end on this relatively small dataset. It must be something related to our cluster, standalone mode or our flow but as far as we can tell, we are not doing anything unusual. Did you do any custom configuration? Any advice would be appreciated. -Suren On Tue, Jul 8, 2014 at 1:54 PM, Kevin Markey <kevin.mar...@oracle.com> wrote: > It seems to me that you're not taking full advantage of the lazy > evaluation, especially persisting to disk only. While it might be true > that the cumulative size of the RDDs looks like it's 300GB, only a small > portion of that should be resident at any one time. We've evaluated data > sets much greater than 10GB in Spark using the Spark master and Spark with > Yarn (cluster -- formerly standalone -- mode). Nice thing about using Yarn > is that it reports the actual memory *demand*, not just the memory > requested for driver and workers. Processing a 60GB data set through > thousands of stages in a rather complex set of analytics and > transformations consumed a total cluster resource (divided among all > workers and driver) of only 9GB. We were somewhat startled at first by > this result, thinking that it would be much greater, but realized that it > is a consequence of Spark's lazy evaluation model. This is even with > several intermediate computations being cached as input to multiple > evaluation paths. > > Good luck. > > Kevin > > > > On 07/08/2014 11:04 AM, Surendranauth Hiraman wrote: > > I'll respond for Dan. > > Our test dataset was a total of 10 GB of input data (full production > dataset for this particular dataflow would be 60 GB roughly). > > I'm not sure what the size of the final output data was but I think it > was on the order of 20 GBs for the given 10 GB of input data. Also, I can > say that when we were experimenting with persist(DISK_ONLY), the size of > all RDDs on disk was around 200 GB, which gives a sense of overall > transient memory usage with no persistence. > > In terms of our test cluster, we had 15 nodes. Each node had 24 cores > and 2 workers each. Each executor got 14 GB of memory. > > -Suren > > > > On Tue, Jul 8, 2014 at 12:06 PM, Kevin Markey <kevin.mar...@oracle.com> > wrote: > >> When you say "large data sets", how large? >> Thanks >> >> >> On 07/07/2014 01:39 PM, Daniel Siegmann wrote: >> >> From a development perspective, I vastly prefer Spark to MapReduce. The >> MapReduce API is very constrained; Spark's API feels much more natural to >> me. Testing and local development is also very easy - creating a local >> Spark context is trivial and it reads local files. For your unit tests you >> can just have them create a local context and execute your flow with some >> test data. Even better, you can do ad-hoc work in the Spark shell and if >> you want that in your production code it will look exactly the same. >> >> Unfortunately, the picture isn't so rosy when it gets to production. In >> my experience, Spark simply doesn't scale to the volumes that MapReduce >> will handle. Not with a Standalone cluster anyway - maybe Mesos or YARN >> would be better, but I haven't had the opportunity to try them. I find jobs >> tend to just hang forever for no apparent reason on large data sets (but >> smaller than what I push through MapReduce). >> >> I am hopeful the situation will improve - Spark is developing quickly - >> but if you have large amounts of data you should proceed with caution. >> >> Keep in mind there are some frameworks for Hadoop which can hide the >> ugly MapReduce with something very similar in form to Spark's API; e.g. >> Apache Crunch. So you might consider those as well. >> >> (Note: the above is with Spark 1.0.0.) >> >> >> >> On Mon, Jul 7, 2014 at 11:07 AM, <santosh.viswanat...@accenture.com> >> wrote: >> >>> Hello Experts, >>> >>> >>> >>> I am doing some comparative study on the below: >>> >>> >>> >>> Spark vs Impala >>> >>> Spark vs MapREduce . Is it worth migrating from existing MR >>> implementation to Spark? >>> >>> >>> >>> >>> >>> Please share your thoughts and expertise. >>> >>> >>> >>> >>> >>> Thanks, >>> Santosh >>> >>> ------------------------------ >>> >>> This message is for the designated recipient only and may contain >>> privileged, proprietary, or otherwise confidential information. If you have >>> received it in error, please notify the sender immediately and delete the >>> original. Any other use of the e-mail by you is prohibited. 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