Parallelize multiple http GET requests

Added ThreadPoolExecutor and used to process GET requests in multiple
threads which enables chunks of data to always be available for
processing. Processing of the data can happen as soon as the first chunk
arrives.

Refactored the code to help implement. The idea is the "params" are
built in batch and passed to the thread pool which get the actual
results.

addon.xml

@@ -10,6 +10,7 @@
     <import addon="script.module.six" />
     <import addon="script.module.kodi-six" />
     <import addon="script.module.addon.signals" version="0.0.1"/>
+    <import addon="script.module.futures" version="2.2.0"/>
   </requires>
   <extension    point="xbmc.python.pluginsource"
                 library="default.py">

jellyfin_kodi/downloader.py

@@ -19,6 +19,7 @@ from jellyfin.exceptions import HTTPException
 
 LOG = logging.getLogger("JELLYFIN." + __name__)
 LIMIT = min(int(settings('limitIndex') or 50), 50)
+DTHREADS = int(settings('limitThreads') or 3)
 
 #################################################################################################
 
@@ -243,7 +244,8 @@ def _get_items(query, server_id=None):
     }
 
     url = query['url']
-    params = query.get('params', {})
+    query.setdefault('params', {})
+    params = query['params']
 
     try:
         test_params = dict(params)
@@ -256,21 +258,37 @@ def _get_items(query, server_id=None):
         LOG.exception("Failed to retrieve the server response %s: %s params:%s", url, error, params)
 
     else:
-        index = params.get('StartIndex', 0)
-        total = items['TotalRecordCount']
+        params.setdefault('StartIndex', 0)
 
-        while index < total:
+        def get_query_params(params, start, count):
+            params_copy = dict(params)
+            params_copy['StartIndex'] = start
+            params_copy['Limit'] = count
+            return params_copy
 
-            params['StartIndex'] = index
-            params['Limit'] = LIMIT
-            result = _get(url, params, server_id=server_id) or {'Items': []}
+        query_params = [get_query_params(params, offset, LIMIT) \
+                for offset in xrange(params['StartIndex'], items['TotalRecordCount'], LIMIT)]
 
+        from itertools import izip
+        # multiprocessing.dummy.Pool completes all requests in multiple threads but has to 
+        # complete all tasks before allowing any results to be processed. ThreadPoolExecutor
+        # allows for completed tasks to be processed while other tasks are completed on other
+        # threads. Dont be a dummy.Pool, be a ThreadPoolExecutor
+        import concurrent.futures
+        p = concurrent.futures.ThreadPoolExecutor(DTHREADS)
+
+        results = p.map(lambda params: _get(url, params, server_id=server_id), query_params)
+
+        for params, result in izip(query_params, results):
+            query['params'] = params
+
+            result = result or {'Items': []}
             items['Items'].extend(result['Items'])
+            # Using items to return data and communicate a restore point back to the callee is
+            # a violation of the SRP. TODO: Seperate responsibilities.
             items['RestorePoint'] = query
             yield items
-
             del items['Items'][:]
-            index += LIMIT
 
 
 class GetItemWorker(threading.Thread):