extend metrics documentation and outline of what should be included

2 files changed, 65 insertions, 15 deletions

diff --git a/site/src/main/jekyll/assets/img/diagrams/metric-collection-concepts.png b/site/src/main/jekyll/assets/img/diagrams/metric-collection-concepts.png
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diff --git a/site/src/main/jekyll/core/metrics.md b/site/src/main/jekyll/core/metrics.md
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--- a/site/src/main/jekyll/core/metrics.md
+++ b/site/src/main/jekyll/core/metrics.md
@@ -12,32 +12,82 @@ Philosophy
 ----------
 
 Back in the day, the most common approach to get metrics out of an Akka/Spray application for production monitoring was
-doing manual instrumentation: select your favorite metrics collection library, wrap you messages with some useful metadata,
-wrap your actor's receive function with some metrics measuring code and, finally, push that metrics data out to somewhere
-you can keep it, graph it and analyze it whenever you want.
+doing manual instrumentation: select your favorite metrics collection library, wrap you messages with some useful
+metadata, wrap your actor's receive function with some metrics measuring code and, finally, push that metrics data out
+to somewhere you can keep it, graph it and analyse it whenever you want.
 
-Each metrics collection library has it's own strengths and weaknesses, and each developer has to choose wisely according to the
-requirements they have in hand, leading them in different paths as they progress with their applications. Each path has
-different implications with regards to introduced overhead and latency, metrics data accuracy and memory consumption. Kamon takes this
-responsibility out of the developer and tries to make the best choice to provide high performance metrics collection instruments
-while keeping the inherent overhead as low as possible.
+Each metrics collection library has it's own strengths and weaknesses, and each developer has to choose wisely according
+to the requirements they have in hand, leading them in different paths as they progress with their applications. Each
+path has different implications with regards to introduced overhead and latency, metrics data accuracy and memory
+consumption. Kamon takes this responsibility out of the developer and tries to make the best choice to provide high
+performance metrics collection instruments while keeping the inherent overhead as low as possible.
 
 Kamon tries to select the best possible approach, so you don't have to.
 
 
-
-
 Metrics Collection and Flushing
 -------------------------------
 
-All the metrics infrastructure in Kamon lives around two concepts: collection and flushing. Metrics collection happens in real time, as soon
-as the information is available for being recorded. Let's see a simple example: as soon as a actor finishes processing a
-message, Kamon knows the elapsed time for processing that specific message and it is recorded right away. If you have millions
-of messages passing through your system, then millions of measurements will be taken.
+All the metrics infrastructure in Kamon was designed around two concepts: collection and flushing. Metrics collection
+happens in real time, as soon as the information is available for being recorded. Let's see a simple example: as soon as
+a actor finishes processing a message, Kamon knows the elapsed time for processing that specific message and it is
+recorded right away. If you have millions of messages passing through your system, then millions of measurements will be
+taken.
 
 Flushing happens recurrently after a fixed amount of time has passed, a tick. Upon each tick, Kamon will collect all
 measurements recorded since the last tick, flush the collected data and reset all the instruments to zero. Let's explore
 a little bit more on how this two concepts are modeled inside Kamon.
 
-SIMPLE CLASS DIAGRAM WITH COLLECTION AND FLUSHING SIDES
+<img class="img-responsive" src="/assets/img/diagrams/metric-collection-concepts.png">
+
+A metric group contains various individual metrics that are related to the same entity, for example, if the entity we
+are talking about is an actor, the metrics related to processing time, mailbox size and time in mailbox for that
+specific actor are grouped inside a single metric group, and each actor gets its own metric group. As you might disguise
+from the diagram above, on the left we have the mutable side of the process that is constantly recoding measurements as
+the events flow through your application and on the right we have the immutable side, containing snapshots representing
+all the measurements taken during a specific period on time for a metric group.
+
+
+Filtering Entities
+------------------
+
+By default Kamon will not include any entity for metrics collection and you will need to explicitly include all the
+entities you are interested in, be it a actor, a trace, a dispatcher or any other entity monitored by Kamon. The
+`kamon.metrics.filters` key on your application's configuration controls which entities must be included/excluded from
+the metrics collection infrastructure. Includes and excludes are provided as lists of strings containing the
+corresponding GLOB patterns for each group, and the logic behind is simple: include everything that matches at least one
+`includes` pattern and does not match any of the `excludes` patterns. The following configuration file sample includes
+the `user/job-manager` actor and all the worker actors, but leaves out all system actors and the `user/worker-helper`
+actor.
+
+```
+kamon {
+  metrics {
+    filters = [
+      {
+        actor {
+          includes = [ "user/job-manager", "user/worker-*" ]
+          excludes = [ "system/*", "user/worker-helper" ]
+        }
+      },
+      {
+        trace {
+          includes = [ "*" ]
+          excludes = []
+        }
+      }
+    ]
+  }
+}
+```
+
+Instruments
+-----------
+
+Talk about how HDR Histogram works and how we use it.
+
+
+Subscription protocol
+---------------------
 
+Explain how to subscribe for metrics data and provide a simple example.