traffic: generate sparser descriptions of conversations

Rather than building all the packets at this point, we stick to the
barest details of the packets (which is all the model gives us
anyway).

The advantage is that will take a lot less memory, which matters
because this process forks into many clients that were sharing and
mutate the conversation list.

Signed-off-by: Douglas Bagnall <douglas.bagnall@catalyst.net.nz>
Reviewed-by: Andrew Bartlett <abartlet@samba.org>

python/samba/emulate/traffic.py

@@ -1293,9 +1293,8 @@ class TrafficModel(object):
 
         return c
 
-    def generate_conversations(self, scale, duration, replay_speed=1,
+    def generate_conversation_sequences(self, scale, duration, replay_speed=1):
-                               server=1, client=2):
+        """Generate a list of conversation descriptions from the model."""
-        """Generate a list of conversations from the model."""
 
         # We run the simulation for ten times as long as our desired
         # duration, and take the section at the end.
@@ -1319,7 +1318,7 @@ class TrafficModel(object):
                % (n_packets, target_packets, len(conversations), scale)),
               file=sys.stderr)
         conversations.sort()  # sorts by first element == start time
-        return seq_to_conversations(conversations)
+        return conversations
 
 
 def seq_to_conversations(seq, server=1, client=2):

python/samba/tests/blackbox/traffic_replay.py

@@ -70,11 +70,6 @@ class TrafficLearnerTests(BlackboxTestCase):
 
         for i, opts in enumerate((["--random-seed=3"],
                                   ["--random-seed=4"],
-                                  ["--random-seed=3",
-                                   "--conversation-persistence=0.5"],
-                                  ["--random-seed=3",
-                                   "--old-scale",
-                                   "--conversation-persistence=0.95"],
                                   )):
             with temp_file(self.tempdir) as output:
                 command = ([SCRIPT, MODEL,

script/traffic_replay

@@ -250,9 +250,9 @@ def main():
             logger.info(("Using the specified model file to "
                          "generate conversations"))
 
-            conversations = model.generate_conversations(opts.scale_traffic,
+            conversations = model.generate_conversation_sequences(opts.scale_traffic,
-                                                         opts.duration,
+                                                                  opts.duration,
-                                                         opts.replay_rate)
+                                                                  opts.replay_rate)
         except ValueError:
             logger.error(("Could not parse %s, which does not seem to be "
                           "a model generated by script/traffic_learner."
@@ -263,7 +263,7 @@ def main():
         conversations = []
 
     if debuglevel > 5:
-        for c in conversations:
+        for c in traffic.seq_to_conversations(conversations):
             for p in c.packets:
                 print("    ", p, file=sys.stderr)
 
@@ -350,7 +350,7 @@ def main():
 
         logger.info("Writing traffic summary")
         summaries = []
-        for c in conversations:
+        for c in traffic.seq_to_conversations(conversations):
             summaries += c.replay_as_summary_lines()
 
         summaries.sort()
@@ -359,7 +359,8 @@ def main():
 
         exit(0)
 
-    traffic.replay(conversations, host,
+    traffic.replay(traffic.seq_to_conversations(conversations),
+                   host,
                    lp=lp,
                    creds=creds,
                    accounts=accounts,