handling large data on single computer doesn' yet support block matrix inversion you can find more general information on matrix arithmetic on the wikipedia page at (mathematicsmapreduce mapreduce algorithms are easy to understand with an analogyimagine that you were asked to count all the votes for the national elections your country has parties , voting officesand million people you could choose to gather all the voting tickets from every office individually and count them centrallyor you could ask the local offices to count the votes for the parties and hand over the results to youand you could then aggregate them by party map reducers follow similar process to the second way of working they first map values to key and then do an aggregation on that key during the reduce phase have look at the following listing' pseudo code to get better feeling for this listing mapreduce pseudo code example for each person in voting officeyield (voted_party for each vote in voting officeadd_vote_to_party(one of the advantages of mapreduce algorithms is that they're easy to parallelize and distribute this explains their success in distributed environments such as hadoopbut they can also be used on individual computers we'll take more in-depth look at them in the next and an example (javascriptimplementation is also provided in when implementing mapreduce in pythonyou don' need to start from scratch number of libraries have done most of the work for yousuch as hadoopyoctopydiscoor dumbo choosing the right data structure algorithms can make or break your programbut the way you store your data is of equal importance data structures have different storage requirementsbut also influence the performance of crud (createreadupdateand deleteand other operations on the data set figure shows you have many different data structures to choose fromthree of which we'll discuss heresparse datatree dataand hash data let' first have look at sparse data sets sparse data sparse data set contains relatively little information compared to its entries (observationslook at figure almost everything is " with just single " present in the second observation on variable data like this might look ridiculousbut this is often what you get when converting textual data to binary data imagine set of , completely unrelated twitter