Right, with Hadoop, all that hard work is done for you.
Hm. Only once you have turned over your cluster to single purpose, Java-based HDFS monoculture. And if all your processing needs can be force fitted into that monoculture's way of working.
But, if your hardware resources have to serve a variety of needs and uses...
Besides, it does not have to be "hard work". There is a very simple pattern to be followed, exemplified by a server instance something like:
And a client template:
The work items distributed by the server can be anything you like besides filenames. The processing inside the client is the same bit you'd have to write yourself for you hadoop application. Hardly onerous, but very flexible.
Except that now the hardware can (realistically) support the volumes of data being processed.
The scale of things is relative. We didn't have as much data to process back then as now, but disks were smaller and machines slower and more expensive. But the trade-offs of monocultural versus flexible remain the same.
Transaction engines like CICS--the hadoop's of that time--could process prodigious volumes of data through a very specific small band of processing requirements, but couldn't handle the variety of general purpose programming requirements and applications that arose.
The same problem arises today with map/reduce. If you're requirements fit its way of working--datasets that are infinitely partitionable into fixed-sized chunks that each take the same amount of time to process and don't require feedback loops, you're laughing.
But, if your images can vary widely in size and so do not fit into the fix-sized chunks of HDFS; and processing times vary greatly with size--many image processing algorithms increase exponentially with the size of the image--then map/reduce scheduling algorithms get tied in knots.
You should check it out.
My needs, resources and pockets do not lend themselves to such.
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In reply to Re^7: randomising file order returned by File::Find