Merge pull request karlseguin#9 from craiglittle/master

Missing apostrophe

en/mongodb.markdown

@@ -110,7 +110,7 @@ First we'll use the global `use` method to switch databases, go ahead and enter
 
 	db.unicorns.insert({name: 'Aurora', gender: 'f', weight: 450})
 
-The above line is executing `insert` against the `unicorns` collection, passing it a single argument. Internally MongoDB uses a binary serialized JSON format. Externally, this means that we use JSON a lot, as is the case with our parameters. If we execute `db.getCollectionNames()` now, we'll actually see two collections: `unicorns` and `system.indexes`. `system.indexes` is created once per database and contains the information on our databases index.
+The above line is executing `insert` against the `unicorns` collection, passing it a single argument. Internally MongoDB uses a binary serialized JSON format. Externally, this means that we use JSON a lot, as is the case with our parameters. If we execute `db.getCollectionNames()` now, we'll actually see two collections: `unicorns` and `system.indexes`. `system.indexes` is created once per database and contains the information on our database's index.
 
 You can now use the `find` command against `unicorns` to return a list of documents:
 
@@ -439,7 +439,7 @@ The message from this chapter is that MongoDB, in most cases, can replace a rela
 \clearpage
 
 ## Chapter 6 - MapReduce ##
-MapReduce is an approach to data processing which has two significant benefits over more traditional solutions. The first, and main, reason it was development is performance. In theory, MapReduce can be parallelized, allowing very large sets of data to be processed across many cores/CPUs/machines. As we just mentioned, this isn't something MongoDB is currently able to take advantage of. The second benefit of MapReduce is that you get to write real code to do your processing. Compared to what you'd be able to do with SQL, MapReduce code is infinitely richer and lets you push the envelope further before you need to use a more specialized solution.
+MapReduce is an approach to data processing which has two significant benefits over more traditional solutions. The first, and main, reason it was developed is performance. In theory, MapReduce can be parallelized, allowing very large sets of data to be processed across many cores/CPUs/machines. As we just mentioned, this isn't something MongoDB is currently able to take advantage of. The second benefit of MapReduce is that you get to write real code to do your processing. Compared to what you'd be able to do with SQL, MapReduce code is infinitely richer and lets you push the envelope further before you need to use a more specialized solution.
 
 MapReduce is a pattern that has grown in popularity, and you can make use of it almost anywhere; C#, Ruby, Java, Python and so on all have implementations. I want to warn you that at first this'll seem very different and complicated. Don't get frustrated, take your time and play with it yourself. This is worth understanding whether you are using MongoDB or not.