Address comments

docs/configuration.md

@@ -189,7 +189,9 @@ of the most common options to set are:
     limited to this amount. If not set, Spark will not limit Python's memory use
     and it is up to the application to avoid exceeding the overhead memory space
     shared with other non-JVM processes. When PySpark is run in YARN or Kubernetes, this memory
-    is added to executor resource requests. This configuration is not supported on Windows.
+    is added to executor resource requests.
+
+    NOTE: This configuration is not supported on Windows.
   </td>
 </tr>
 <tr>

python/pyspark/worker.py

@@ -22,12 +22,12 @@
 import os
 import sys
 import time
-# 'resource' is a Unix specific package.
-has_resource_package = True
+# 'resource' is a Unix specific module.
+has_resource_module = True
 try:
     import resource
 except ImportError:
-    has_resource_package = False
+    has_resource_module = False
 import socket
 import traceback
 
@@ -274,8 +274,8 @@ def main(infile, outfile):
         # set up memory limits
         memory_limit_mb = int(os.environ.get('PYSPARK_EXECUTOR_MEMORY_MB', "-1"))
         # 'PYSPARK_EXECUTOR_MEMORY_MB' should be undefined on Windows because it depends on
-        # resource package which is a Unix specific package.
-        if memory_limit_mb > 0 and has_resource_package:
+        # resource module which is a Unix specific module.
+        if memory_limit_mb > 0 and has_resource_module:
             total_memory = resource.RLIMIT_AS
             try:
                 (soft_limit, hard_limit) = resource.getrlimit(total_memory)