Re: Comparative study

[Kevin Markey]

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. Where allowed by local law, electronic communications with Accenture and its affiliates, including e-mail and instant messaging (including content), may be scanned by our systems for the purposes of information security and assessment of internal compliance with Accenture policy. ______________________________________________________________________________________ www.accenture.com -- Daniel Siegmann, Software Developer Velos Accelerating Machine Learning 440 NINTH AVENUE, 11TH FLOOR, NEW YORK, NY 10001 E: daniel.siegm...@velos.io W: www.velos.io --                                                              SUREN HIRAMAN, VP TECHNOLOGY Velos Accelerating Machine Learning 440 NINTH AVENUE, 11TH FLOOR NEW YORK, NY 10001 O: (917) 525-2466 ext. 105 F: 646.349.4063 E: suren.hiraman@velos.io W: www.velos.io