Merge pull request apache#35 from mesosphere/edits-to-job-scheduling-doc

Infinitesimal changes

docs/job-scheduling.md

@@ -1,78 +1,126 @@
-This page is a simple overview of material described in greater detail
-in the Apache Spark documentation [here][1] and [here][2].
+This document is a simple overview of material described in greater
+detail in the Apache Spark documentation [here][1] and [here][2].
 
 # Modes
-
+ 
 Spark on Mesos supports two "modes" of operation: coarse-grained mode
-and fine-grained mode.  Coarse-grained mode provides lower latency,
-whereas fine-grained mode provides higher utilization.  More info
+and fine-grained mode. Coarse-grained mode provides lower latency,
+whereas fine-grained mode provides higher utilization. More info
 [here][2].
 
-## Coarse-grained mode
-
+# Coarse-grained mode
+ 
 "Coarse-grained" mode is so-called because each Spark **executor** is
-represented by a single Mesos task.  As a result, executors have a
+represented by a single Mesos task. As a result, executors have a
 constant size throughout their lifetime.
 
-* **Executor memory**: `spark.executor.memory`
-* **Executor CPUs**: `spark.executor.cores`, or all the cores in the offer.
-* **Number of Executors**: `spark.cores.max` / `spark.executor.cores`.
-  Executors are brought up until `spark.cores.max` is reached.
-  Executors survive for duration of the job.
-* **Executors per agent**: Multiple
+*   **Executor memory**: `spark.executor.memory`
+*   **Executor CPUs**: `spark.executor.cores`, or all the cores in the
+offer.
+*   **Number of Executors**: `spark.cores.max` /
+`spark.executor.cores`. Executors are brought up until
+`spark.cores.max` is reached. Executors survive for duration of the
+job.
+*   **Executors per agent**: Multiple
 
 Notes:
 
-- We highly recommend you set `spark.cores.max`.  If you do not,
-  your Spark job may consume all available resources in your cluster,
-  resulting in unhappy peers.
-
-## Fine-grained mode
+*   We highly recommend you set `spark.cores.max`. If you do not, your
+Spark job may consume all available resources in your cluster,
+resulting in unhappy peers.
 
+# Fine-grained mode
+
 In "fine-grained" mode, each Spark **task** is represented by a single
-Mesos task.  When a Spark task finishes, the resources represented by
-a its Mesos task are relinquished.  This enables finer-grained
-resource allocation, at the cost of task startup latency.
+Mesos task. When a Spark task finishes, the resources represented by
+its Mesos task are relinquished. Fine-grained mode enables
+finer-grained resource allocation at the cost of task startup latency.
 
-* **Executor memory**: `spark.executor.memory`
-* **Executor CPUs**: Increases and decreases as tasks start and terminate.
-* **Number of Executors**: Increases and decreases as tasks start and terminate.
-* **Executors per agent**: At most 1
+*   **Executor memory**: `spark.executor.memory`
+*   **Executor CPUs**: Increases and decreases as tasks start and
+terminate.
+*   **Number of Executors**: Increases and decreases as tasks start
+and terminate.
+*   **Executors per agent**: At most 1
 
 # Properties
 
 The following is a description of the most common Spark on Mesos
-scheduling properties.  For a full list, see the
-[Spark configuration page][1] and the
-[Spark on Mesos configuration page][2].
+scheduling properties. For a full list, see the [Spark configuration
+page][1] and the [Spark on Mesos configuration page][2].
 
-<table>
-<tr>
-<th>property</th>
-<th>default</th>
-<th>description</th>
-<tr>
-<td>spark.mesos.coarse</td>
-<td>true</td>
-<td>Described above</td>
-</tr>
-<tr>
-<td>spark.executor.memory</td>
-<td>1g</td>
-<td>Executor memory allocation</td>
-</tr>
-<tr>
-<td>spark.executor.cores</td>
-<td>All available cores in the offer</td>
-<td>Coarse-grained mode only. DCOS Spark >= 1.6.1.  Executor CPU allocation.</td>
-</tr>
-<tr>
-<td>spark.cores.max</td>
-<td>unlimited</td>
-<td>Maximum total number of cores to allocate.</td>
-<tr> 
-</tr>
-</table>
+<table class="table">
+  <tr>
+    <th>
+      property
+    </th>
+
+    <th>
+      default
+    </th>
+
+    <th>
+      description
+    </th>
+
+    <tr>
+      <td>
+        spark.mesos.coarse
+      </td>
+      
+      <td>
+        true
+      </td>
+      
+      <td>
+        Described above.
+      </td>
+    </tr>
+
+    <tr>
+      <td>
+        spark.executor.memory
+      </td>
+      
+      <td>
+        1g
+      </td>
+      
+      <td>
+        Executor memory allocation.
+      </td>
+    </tr>
+
+    <tr>
+      <td>
+        spark.executor.cores
+      </td>
+      
+      <td>
+        All available cores in the offer
+      </td>
+      
+      <td>
+        Coarse-grained mode only. DCOS Spark >= 1.6.1. Executor CPU
+allocation.
+      </td>
+    </tr>
+
+    <tr>
+      <td>
+        spark.cores.max
+      </td>
+
+      <td>
+        unlimited
+      </td>
+
+      <td>
+        Maximum total number of cores to allocate.
+      </td>
+
+      <tr>
+      </tr></table>
 
-[1]: http://spark.apache.org/docs/latest/configuration.html
-[2]: http://spark.apache.org/docs/latest/running-on-mesos.html
+ [1]: http://spark.apache.org/docs/latest/configuration.html
+ [2]: http://spark.apache.org/docs/latest/running-on-mesos.html