reformat a bit

Author: Justin Lee

streaming/README.md

@@ -7,7 +7,9 @@ Streaming support + MongoDB **requires** your Hadoop distribution include the pa
 * [HADOOP-5450 - Add support for application-specific typecodes to typed bytes](https://issues.apache.org/jira/browse/HADOOP-5450)
 * [MAPREDUCE-764 - TypedBytesInput's readRaw() does not preserve custom type codes](https://issues.apache.org/jira/browse/MAPREDUCE-764)
 
-For the mainline Apache Hadoop distribution, these patches were merged for the 0.21.0 release.  We have verified as well that the [Cloudera](http://cloudera.com) distribution (while based on 0.20.x still) includes these patches in CDH3 Update 1; anecdotal evidence (which needs confirmation) indicates they may have been there since CDH2, and likely exist in CDH3 as well.
+For the mainline Apache Hadoop distribution, these patches were merged for the 0.21.0 release.  We have verified as well that the 
+[Cloudera](http://cloudera.com) distribution (while based on 0.20.x still) includes these patches in CDH3 Update 1; anecdotal evidence 
+(which needs confirmation) indicates they may have been there since CDH2, and likely exist in CDH3 as well.
 
 Building Streaming
 ------------------
@@ -18,25 +20,35 @@ This will create a new “fat” jar in: `streaming/target/mongo-hadoop-streamin
 
 This jar file is runnable with hadoop jar, and contains all of the dependencies necessary to run the job.
 
-Each individual scripting language will have different requirements for working with MongoDB + Hadoop Streaming. Once you have the jar file built for mongo-hadoop-streaming, you will need to build and deploy the support libraries for your chosen language.
+Each individual scripting language will have different requirements for working with MongoDB + Hadoop Streaming. Once you have the jar 
+file built for mongo-hadoop-streaming, you will need to build and deploy the support libraries for your chosen language.
 
-It will also be necessary to ensure these libraries are available on each Hadoop node in your cluster, along with the mongo-hadoop-core driver as outlined in the main setup instructions. However, you do not need to distribute the mongo-hadoop-streaming jar anywhere.
+It will also be necessary to ensure these libraries are available on each Hadoop node in your cluster, along with the mongo-hadoop-core 
+driver as outlined in the main setup instructions. However, you do not need to distribute the mongo-hadoop-streaming jar anywhere.
 
 
 ### Overview
 
-For distributions of Hadoop which support streaming, you can also use MongoDB collections as the input or output for these jobs as well. Here is a description of arguments needed to run a Hadoop streaming job including MongoDB support.
+For distributions of Hadoop which support streaming, you can also use MongoDB collections as the input or output for these jobs as well. 
+Here is a description of arguments needed to run a Hadoop streaming job including MongoDB support.
 
 * Launch the job with `$HADOOP/bin/hadoop jar <location of streaming jar> …` 
-* Depending on which hadoop release you use, the jar needed for streaming may be located in a different directory, commonly it is located somewhere like `$HADOOP_HOME/share/hadoop/tools/lib/hadoop-streaming*.jar` 
-* Provide dependencies for the job by either placing each jar file in one your hadoop's classpath directories, or explicitly list it on the command line using `-libjars <jar file>`. You will need to do this for the Java MongoDB driver as well as the Mongo-Hadoop core library.
-* When using a mongoDB collection as input, add the arguments `-jobconf mongo.input.uri=<input mongo URI>` and `-inputformat com.mongodb.hadoop.mapred.MongoInputFormat`
-* When using a mongoDB collection as output, add the arguments `-jobconf mongo.output.uri=<input mongo URI>` and `-outputformat com.mongodb.hadoop.mapred.MongoOutputFormat`
+* Depending on which hadoop release you use, the jar needed for streaming may be located in a different directory, commonly it is located 
+somewhere like `$HADOOP_HOME/share/hadoop/tools/lib/hadoop-streaming*.jar` 
+* Provide dependencies for the job by either placing each jar file in one your hadoop's classpath directories, or explicitly list it on the
+ command line using `-libjars <jar file>`. You will need to do this for the Java MongoDB driver as well as the Mongo-Hadoop core library.
+* When using a mongoDB collection as input, add the arguments `-jobconf mongo.input.uri=<input mongo URI>` 
+and `-inputformat com.mongodb.hadoop.mapred.MongoInputFormat`
+* When using a mongoDB collection as output, add the arguments `-jobconf mongo.output.uri=<input mongo URI>` 
+and `-outputformat com.mongodb.hadoop.mapred.MongoOutputFormat`
 * When using BSON as input, use `-inputformat com.mongodb.hadoop.mapred.BSONFileInputFormat`.
-* Specify locations for the `-input` and `-output` arguments. Even when using mongoDB for input or output, these are required; you can use temporary directories for these in such a case.
-* Always add the arguments `-jobconf stream.io.identifier.resolver.class=com.mongodb.hadoop.streaming.io.MongoIdentifierResolver` and `-io mongodb` so that decoders/encoders needed by streaming can be resolved at run time.
+* Specify locations for the `-input` and `-output` arguments. Even when using mongoDB for input or output, these are required; you can 
+use temporary directories for these in such a case.
+* Always add the arguments `-jobconf stream.io.identifier.resolver.class=com.mongodb.hadoop.streaming.io.MongoIdentifierResolver` 
+and `-io mongodb` so that decoders/encoders needed by streaming can be resolved at run time.
 * Specify a `-mapper <mapper script>` and `-reducer <reducer script>`.
-* Any other arguments needed, by using `-jobconf`. for example `mongo.input.query` or other options for controlling splitting behavior or filtering.
+* Any other arguments needed, by using `-jobconf`. for example `mongo.input.query` or other options for controlling splitting behavior or 
+filtering.
 
 Here is a full example of a streaming command broken into separate lines for readability:
 
@@ -57,17 +69,20 @@ Here is a full example of a streaming command broken into separate lines for rea
 
 Also, refer to the `TestStreaming` class in the test suite for more concrete examples.
 
-**Important note**: if you need to use `print` or any other kind of text output when debugging streaming Map/Reduce scripts, be sure that you are writing the debug statements to `stderr` or some kind of log file. Using `stdin` or `stdout` for any purpose other than communicating with the Hadoop Streaming layer will interfere with the encoding and decoding of data.
+**Important note**: if you need to use `print` or any other kind of text output when debugging streaming Map/Reduce scripts, be sure that 
+you are writing the debug statements to `stderr` or some kind of log file. Using `stdin` or `stdout` for any purpose other than 
+communicating with the Hadoop Streaming layer will interfere with the encoding and decoding of data.
 
 ### Python
 
 #####Setup
 
-To use Python for streaming first install the python package `pymongo_hadoop` using pip or easy_install (For best performance, ensure that you are using the C extensions for BSON).
+To use Python for streaming first install the python package `pymongo_hadoop` using pip or easy_install (For best performance, ensure that 
+you are using the C extensions for BSON).
 
 #####Mapper
-To implement a mapper, write a function which accepts an iterable sequence of documents and calls `yield` to produce each output document, then call BSONMapper() against that function.
-For example:
+To implement a mapper, write a function which accepts an iterable sequence of documents and calls `yield` to produce each output document, 
+then call BSONMapper() against that function. For example:
 
     from pymongo_hadoop import BSONMapper
     def mapper(documents):
@@ -77,7 +92,8 @@ For example:
     BSONMapper(mapper)
 
 #####Reducer
-To implement a reducer, write a function which accepts two arguments: a key and an iterable sequence of documents matching that key. Compute your reduce output and pass it back to Hadoop with `return`. Then call `BSONReducer()` against this function. For example,
+To implement a reducer, write a function which accepts two arguments: a key and an iterable sequence of documents matching that key. 
+Compute your reduce output and pass it back to Hadoop with `return`. Then call `BSONReducer()` against this function. For example,
 
     from pymongo_hadoop import BSONReducer
 
@@ -104,7 +120,8 @@ Install the nodejs mongo-hadoop lib with `npm install node_mongo_hadoop`.
 
 #####Mapper
 
-Write a function that accepts two arguments: the input document, and a callback function. Call the callback function with the output of your map function. Then pass that function as an argument to node_mongo_hadoop.MapBSONStream. For example:
+Write a function that accepts two arguments: the input document, and a callback function. Call the callback function with the output of 
+your map function. Then pass that function as an argument to node_mongo_hadoop.MapBSONStream. For example:
 
     function mapFunc(doc, callback){
         if(doc.headers && doc.headers.From && doc.headers.To){