> Map-reduce is naturally built into the language I hear this often about both array and functional languages but I think this kind of slogan leads to a misund

> Map-reduce is naturally built into the language I hear this often about both array and functional languages but I think this kind of slogan leads to a misunderstanding of the MapReduce framework (and what's special about it). In a functional or array language, map and reduce have the following signatures: map : key list -> (key -> value) -> value list reduce : value list -> (value, value -> value) -> value These are strictly less powerful than the functions used in MapReduce: read : block -> (input_key, input_value) list map : (input_key, input_value) -> (output_key, output_value) list reduce : (output_key, (output_value list)) -> (output_value, output_value -> output_value) -> (output_key, output_value) Combining this API with a (1) a distributed file system which lets you co-locate computation with data across many computers and (2) a parallel partitioning algorithm enables petabyte-scale computation. It's qualitatively different than in-memory array processing.