-1

I am creating a (semi) big data analysis app. I am utilizing apache-mahout. I am concerned about the fact that with java, I am limited to 4gb of memory. This 4gb limitation seems somewhat wasteful of the memory modern computers have at their disposal. As a solution, I am considering using something like RMI or some form of MapReduce. (I, as of yet, have no experience with either)

First off: is it plausible to have multiple JVM's running on one machine and have them talk? and if so, am I heading in the right direction with the two ideas alluded to above?

Furthermore,

In attempt to keep this an objective question, I will avoid asking "Which is better" and instead will ask:

1) What are key differences (not necessarily in how they work internally, but in how they would be implemented by me, the user)

2) Are there drawbacks or benefits to one or the other and are there certain situations where one or the other is used?

3) Is there another alternative that is more specific to my needs?

Thanks in advance

sbc
  • 1
  • 2

1 Answers1

0

First, re the 4GB limit, check out Understanding max JVM heap size - 32bit vs 64bit . On a 32 bit system, 4GB is the maximum, but on a 64 bit system the limit is much higher.

It is a common configuration to have multiple jvm's running and communicating on the same machine. Two good examples would be IBM Websphere and Oracle's Weblogic application servers. They run the administrative console in one jvm, and it is not unusual to have three or more "working" jvm's under its control.

This allows each JVM to fail independently without impacting the overall system reactiveness. Recovery is transparent to the end users because some fo the "working" jvm's are still doing their thing while the support team is frantically trying to fix things.

You mentioned both RMI and MapReduce, but in a manner that implies that they fill the same slot in the architecture (communication). I think that it is necessary to point out that they fill different slots - RMI is a communications mechanism, but MapReduce is a workload management strategy. The MapReduce environment as a whole typically depends on having a (any) communication mechanism, but is not one itself.

For the communications layer, some of your choices are RMI, Webservices, bare sockets, MQ, shared files, and the infamous "sneaker net". To a large extent I recommend shying away from RMI because it is relatively brittle. It works as long as nothing unexpected happens, but in a busy production environment it can present challenges at unexpected times. With that said, there are many stable and performant large scale systems built around RMI.

The direction the world is going this week for cross-tier communication is SOA on top of something like spring integration or fuse. SOA abstracts the mechanics of communication out of the equation, allowing you to hook things up on the fly (more or less).

MapReduce (MR) is a way of organizing batched work. The MR algorithm itself is essentially turn the input data into a bunch of maps on input, then reduce it to the minimum amount necessary to produce an output. The MR environment is typically governed by a workload manager which receives jobs and parcels out the work in the jobs to its "worker bees" splattered around the network. The communications mechanism may be defined by the MR library, or by the container(s) it runs in.

Does this help?

Community
  • 1
  • 1
pojo-guy
  • 966
  • 1
  • 12
  • 39