How do you do distributed processing without a distributed filesystem? Do you mean you'd load the filesystem into memory and send it to the "processors"?
The data could be stored on a network device, such as a file server or database, for example. It could indeed be local, but it needn't be distributed.
In the example GP gave, the data could possibly have been stored in a database queried using segmentation via consistent hashing (a basic way to distribute jobs across a known number of workers).
...defeating the entire purpose: of large scale parallelism on commodity machines. OTOH if you have a way of achieving order 500X parallelism with a centralized commodity server or database, I would love to hear.
EDIT @supermatt Ah I see, we differ in the definition then, to me it isnt bigdata/largescale unless it churns through big amounts of stored data. Bitcoin mining is no where in the ball park of this, its an append only log of solutions computed in parallel.
How on earth do you think bitcoin mining pools work (as an extremely trivial example). They coordinate ranges between a number of workers. The stored size of those ranges is miniscule in comparison to the data of the hashes between those ranges calculated on each 'miner'. These 'coordinators' absolutely work as a centralised 'commodity' storage server (or database) resource for 500x+ parallelism.
'Big Data' means 'Big Data', not 'Big Storage'. They are completely different things.
The bitcoin example may be a bit oversimplified, and may indeed lean more towards HPC. The example was intended to illustrate data locality (as per the parent question), not the actual computation.
Big Data may incorporate data from various 3rd party, remote, local, or even random sources. For example, testing whether URLs in a search engines index are currently available. This may be a map/reduce job, it may utilize a local source of urls, but it will also incorporate a remote check of the url.
As I said a few links up: DFS is not a requirement for map/reduce.
All MapReduce frameworks I know about today are built on DFSs. There are definitely plenty of frameworks that support map and reduce that don't (e.g. MPI), but these aren't systems based on what was described in the OSDI 2004 paper where the word MapReduce was introduced.
I guess people just fixate on the terms map and reduce when the focus of MapReduce really was....shuffle.
I think the problem is that we are talking about two different things.
The very start of the paper describes the term and it's methodology (which is what we are discussing), and then goes on to explain googles own implementation using GFS (which you seem to be getting hung up on.)
Keep in mind that this whole thread is about "MapReduce", which Holzle was talking about, not the more generic map and reduce that has been around since the 1800s (and they will continue to mapping and reducing in their new dataflow framework, they just won't be using MapReduce). Now for the paper:
> Our abstraction is inspired by the map and reduce primitives present in Lisp and many other functional languages.
Inspired doesn't mean equivalent.
> Our use of a functional model with user specified map and reduce operations allows us to parallelize large computations easily and to use re-execution as the primary mechanism for fault tolerance.
They are using map and reduce as a tool to get something else.
> The major contributions of this work are a simple and powerful interface that enables automatic parallelization and distribution of large-scale computations, combined with an implementation of this interface that achieves high performance on large clusters of commodity PCs.
They are very specific about what the contribution is. All work that has claimed to be an implementation of MapReduce has followed their core tenants. Even if MPI has a reduce function, it is not MapReduce because it is based on other techniques.
I'm really tired of people who claim there is nothing new or even significant when there clearly was. Ya, everything is built on something these days, but so what? In the systems community, MapReduce has been a huge advance, and now we are moving on (at least for streaming).
I'm still in the camp of there being nothing new here. Now gfs may be a different matter, but that was part of a different paper, and not a requirement of this one. Which is why I have kept stating that a dfs is not a requirement.
If that's what you believe, then you are going to miss out on the last 10 or so years of systems research and improvements. And when Google stops using MapReduce but the new thing still uses map and reduce, you are going to be kind of confused.
I've seen MapReduce done against fairly significant amounts of data stored (10s of TBs per run) on a SAN running over fibre. The compute nodes weren't particularly cheap either - I guess they were commodity machines, but quite a long way from the "cheapest possible" things Google uses.
But it was still useful: it was a good computing model for letting as many compute nodes as possible process data.
That might not be what Google was trying to achieve, but it's difficult to argue that it isn't MapReduce.
Databases we should be so lucky :-) this was old school ISAM files updated with Fortran 77 and 4 different log files all with multiple types of records.
Our "Mappers" did quite a lot of work compared most modern map functions
