4 ms·

Outside of Google, Facebook and another top 10 internet businesses, can someone share a realistic example of employing M/R in a typical corporate environment?

• seiji 15y ago
  Read http://infolab.stanford.edu/~ullman/mmds.html http://infolab.stanford.edu/~ullman/mmds.html a few times and you'll know more about manipulating large data sets than most people. If you're just looking for a quick example: imagine you want to parse 6,000 webserver logs (each being 100+GB) to determine the most popular pages, referrers and user agents. The map phase extracts all fields you want and the reduce phase counts all the extracted unique values. "mapreduce" is about processing at scale. You can always do the same thing on a smaller set of data locally (and usually with just sed, awk, sort, and uniq).
• patio11 15y ago
  Most of the Fortune 500s have enough data to theoretically have something to throw M/R at, though whether they'd gain from any particular project is anyone's guess. e.g. At a national bank, determine whether distance to the nearest branch or ATM correlates with deposit frequency or average customer relationship value. Your inputs are a) 10 billion timestamped transactions, b) 50 million accounts, c) 200 million addresses and dates for which they began and entered service, and d) a list of 25,000 branch/ATM locations and the dates they entered service. This is fairly straightforward to describe as a map/reduce job. You could do it on one machine with a few nested loops and some elbow grease, too, but the mucky mucks might want an answer this quarter. I know the feeling, though: I keep wanting to try it, but haven't been able to find a good excuse in my own business yet.
  • Radim 15y ago
    I doubt such companies would put their data "in the cloud". And if they don't, the cost of supporting this "straightforward" Hadoop infrastructure, both in terms of hardware, engineering and support, is so massive that the little elbow grease for a simple I-know-what-it-does solution may well be worth it. In other words, I share alexro's concerns. If you're buying into the M/R hype and process your blog logs in the cloud, that's one thing. But legitimate business use-cases are probably not as common as people may expect/hope.
    • ajessup 15y ago
      Hadoop is not a cloud solution - most organizations deploy it on their own infrastructure. Some folks (eg. Amazon AWS) offer a "hosted" version.
      • Radim 15y ago
        Sure, but that's not what this article is about ("5 min setup, for the masses").
    • nl 15y ago
      And if they don't, the cost of supporting this "straightforward" Hadoop infrastructure, both in terms of hardware, engineering and support, is so massive that the little elbow grease for a simple I-know-what-it-does solution may well be worth it. That's not true. A small cluster and support from a company like Cloudera is much less than an new Oracle install.
  • ericlavigne 15y ago
    The mucky mucks can have their answer today with a random subset of that data: 10,000 accounts and the corresponding addresses and transactions. We'll need map-reduce tomorrow if they want precision in the last decimal point.
    • rmc 15y ago
      You might be able to do it with a random subset, but your accuracy would not be as good. For companies with this much data, 0.1% increase (or decrease) can cost millions. If you use a random subset, you won't be able to accurately find this teeny little variation/
• pork 15y ago
  If you have tons of data (logs, for example), on the order or tens or more terabytes, and want the benefit of SQL in addition to MR to query and crunch that data offline, you can use something like Hive which runs on top of Hadoop, gives you all the power and familiarity of SQL, and lets you "drop down" to MR if you need the extra power.
• jchrisa 15y ago
  map reduce can be used for garden variety stuff, for instance my blog index is the result of a map reduce index of recent posts (in this case those tagged html5) http://bit.ly/s7NHYM http://bit.ly/s7NHYM
• ajessup 15y ago
  I think any corporate data warehouse is an area that Hadoop+Pig/Hive could add a huge amount of value. Here's why. Conventional data warehouses are built from running ETL transforms against existing sources, where you are carefully selecting which attributes you want to transform in. These transforms are tricky to set up and even trickier to change if you decide you need a new piece of data (which happens every day in big BI implementations). And if the source system isn't permanently preserving the data (eg. log files) then pulling in data retrospectively often isn't possible. With a HDFS cluster you can cost effectively dump undifferentiated data from existing sources into a "data lake" without worrying about complex and highly selective transforms, and then use tools like Pig and Hive to do ad-hoc interrogations of the data. Most data warehouse implementations fail to a large extent because of the ETL problem - Hadoop could help solve that in a big way. Further reading (no connection to me) http://www.pentaho.com/hadoop/ http://www.pentaho.com/hadoop/
• grammr 15y ago
  Many people mistakenly assume that MapReduce is only useful when dealing with Google or Facebook scale datasets. Nowadays, MapReduce is simply a convenient data access pattern, particularly when your data is spread across multiple machines. For example, Riak employs the MapReduce paradigm as its "primary querying and data-processing system." [0] Clearly, Riak does not expect each and every query to run for hours and involve TBs of data. MapReduce is just a straightforward way to process distributed data. [0] http://wiki.basho.com/MapReduce.html http://wiki.basho.com/MapReduce.html
• nl 15y ago
  The NY Times, back in 2007 shared how they generate PDFs using Map/Reduce (on Amazon infrastructure)[1] Hadoop has a long, long list of companies using it[2]. Cloudera has a similar list[3] [1] http://open.blogs.nytimes.com/2007/11/01/self-service-prorated-super-computing-fun/ http://open.blogs.nytimes.com/2007/11/01/self-service-prorat... [2] http://wiki.apache.org/hadoop/PoweredBy http://wiki.apache.org/hadoop/PoweredBy [3] http://www.cloudera.com/customers/ http://www.cloudera.com/customers/
• nl 15y ago
  Splunk is killing it in the "Map/Reduce for enterprise" market. See http://www.splunk.com/industries http://www.splunk.com/industries