update docs to point to druid-io/druid

docs/_includes/page_header.html

@@ -10,7 +10,7 @@
     </div>
     <div class="navbar-collapse collapse">
       <ul class="nav navbar-nav">
-        <li {% if page.sectionid == 'docs' %} class="active"{% endif %}><a href="https://github.com/metamx/druid/wiki">Documentation</a></li>
+        <li {% if page.sectionid == 'docs' %} class="active"{% endif %}><a href="https://github.com/druid-io/druid/wiki">Documentation</a></li>
       </ul>
     </div>
   </div>

docs/content/Booting-a-production-cluster.md

@@ -25,7 +25,7 @@ Clone the code from [https://github.com/druid-io/whirr](https://github.com/druid
     git checkout trunk
     mvn clean install -Dmaven.test.failure.ignore=true
 
-In order to run the test below, you'll also need two files that available only from a [standard install of Druid](http://druid.io/downloads.html) or the [Druid repo](https://github.com/metamx/druid/tree/master/examples/bin/examples):
+In order to run the test below, you'll also need two files that available only from a [standard install of Druid](http://druid.io/downloads.html) or the [Druid repo](https://github.com/druid-io/druid/tree/master/examples/bin/examples):
 
 * `druid/examples/bin/examples/wikipedia/wikipedia_realtime.spec`
 * `druid/examples/bin/examples/indexing/wikipedia_realtime_task.json`

docs/content/Build-from-source.md

@@ -6,7 +6,7 @@ layout: doc_page
 The other way to setup Druid is from source via git. To do so, run these commands:
 
 ```
-git clone git@github.com:metamx/druid.git
+git clone git@github.com:druid-io/druid.git
 cd druid
 ./build.sh
 ```

docs/content/Examples.md

@@ -4,7 +4,7 @@ layout: doc_page
 Examples
 ========
 
-The examples on this page are setup in order to give you a feel for what Druid does in practice. They are quick demos of Druid based on [CliRealtimeExample](https://github.com/metamx/druid/blob/master/services/src/main/java/io/druid/cli/CliRealtimeExample.java). While you wouldn’t run it this way in production you should be able to see how ingestion works and the kind of exploratory queries that are possible. Everything that can be done on your box here can be scaled out to 10’s of billions of events and terabytes of data per day in a production cluster while still giving the snappy responsive exploratory queries.
+The examples on this page are setup in order to give you a feel for what Druid does in practice. They are quick demos of Druid based on [CliRealtimeExample](https://github.com/druid-io/druid/blob/master/services/src/main/java/io/druid/cli/CliRealtimeExample.java). While you wouldn’t run it this way in production you should be able to see how ingestion works and the kind of exploratory queries that are possible. Everything that can be done on your box here can be scaled out to 10’s of billions of events and terabytes of data per day in a production cluster while still giving the snappy responsive exploratory queries.
 
 Installing Standalone Druid
 ---------------------------
@@ -16,7 +16,7 @@ There are two options for installing standalone Druid. Building from source, and
 Clone Druid and build it:
 
 ``` bash
-git clone https://github.com/metamx/druid.git druid
+git clone https://github.com/druid-io/druid.git druid
 cd druid
 git fetch --tags
 git checkout druid-0.6.160
@@ -37,7 +37,7 @@ Twitter Example
 
 For a full tutorial based on the twitter example, check out this [Twitter Tutorial](Twitter-Tutorial.html).
 
-This Example uses a feature of Twitter that allows for sampling of it’s stream. We sample the Twitter stream via our [TwitterSpritzerFirehoseFactory](https://github.com/metamx/druid/blob/master/examples/src/main/java/druid/examples/twitter/TwitterSpritzerFirehoseFactory.java) class and use it to simulate the kinds of data you might ingest into Druid. Then, with the client part, the sample shows what kinds of analytics explorations you can do during and after the data is loaded.
+This Example uses a feature of Twitter that allows for sampling of it’s stream. We sample the Twitter stream via our [TwitterSpritzerFirehoseFactory](https://github.com/druid-io/druid/blob/master/examples/src/main/java/druid/examples/twitter/TwitterSpritzerFirehoseFactory.java) class and use it to simulate the kinds of data you might ingest into Druid. Then, with the client part, the sample shows what kinds of analytics explorations you can do during and after the data is loaded.
 
 ### What you’ll learn
 * See how large amounts of data gets ingested into Druid in real-time

docs/content/Realtime.md

@@ -37,7 +37,7 @@ Extending the code
 Realtime integration is intended to be extended in two ways:
 
 1.  Connect to data streams from varied systems ([Firehose](https://github.com/druid-io/druid-api/blob/master/src/main/java/io/druid/data/input/FirehoseFactory.java))
-2.  Adjust the publishing strategy to match your needs ([Plumber](https://github.com/metamx/druid/blob/master/server/src/main/java/io/druid/segment/realtime/plumber/PlumberSchool.java))
+2.  Adjust the publishing strategy to match your needs ([Plumber](https://github.com/druid-io/druid/blob/master/server/src/main/java/io/druid/segment/realtime/plumber/PlumberSchool.java))
 
 The expectations are that the former will be very common and something that users of Druid will do on a fairly regular basis. Most users will probably never have to deal with the latter form of customization. Indeed, we hope that all potential use cases can be packaged up as part of Druid proper without requiring proprietary customization.
 

docs/content/Recommendations.md

@@ -16,20 +16,20 @@ Druid is not perfect in how it handles mix-cased dimension and metric names. Thi
 # SSDs
 
 SSDs are highly recommended for historical and real-time nodes if you are not running a cluster that is entirely in memory. SSDs can greatly mitigate the time required to page data in and out of memory.
- 
+
 # Provide Columns Names in Lexicographic Order
 
-Although Druid supports schema-less ingestion of dimensions, because of [https://github.com/metamx/druid/issues/658](https://github.com/metamx/druid/issues/658), you may sometimes get bigger segments than necessary. To ensure segments are as compact as possible, providing dimension names in lexicographic order is recommended. 
- 
- 
+Although Druid supports schema-less ingestion of dimensions, because of [https://github.com/druid-io/druid/issues/658](https://github.com/druid-io/druid/issues/658), you may sometimes get bigger segments than necessary. To ensure segments are as compact as possible, providing dimension names in lexicographic order is recommended.
+
+
 # Use Timeseries and TopN Queries Instead of GroupBy Where Possible
- 
-Timeseries and TopN queries are much more optimized and significantly faster than groupBy queries for their designed use cases. Issuing multiple topN or timeseries queries from your application can potentially be more efficient than a single groupBy query.  
- 
+
+Timeseries and TopN queries are much more optimized and significantly faster than groupBy queries for their designed use cases. Issuing multiple topN or timeseries queries from your application can potentially be more efficient than a single groupBy query.
+
 # Read FAQs
 
 You should read common problems people have here:
 
 1) [Ingestion-FAQ](Ingestion-FAQ.html)
 
-2) [Performance-FAQ](Performance-FAQ.html)
+2) [Performance-FAQ](Performance-FAQ.html)

docs/content/Stand-Alone-With-Riak-CS.md

@@ -110,7 +110,7 @@ This just walks through getting the relevant software installed and running.  Yo
 
 1. Clone the git repository for druid, checkout a "stable" tag and build 
 
-        git clone https://github.com/metamx/druid.git druid
+        git clone https://github.com/druid-io/druid.git druid
         pushd druid
         git checkout druid-0.4.12
         export LANGUAGE=C