readme pt. 1

distroduce/configuration/launch_local_kafka.sh

@@ -0,0 +1,4 @@
+#!/bin/bash
+bin/zookeeper-server-start.sh config/zookeeper.properties &
+bin/kafka-server-start.sh config/server.properties &
+bin/kafka-topics.sh --create --bootstrap-server localhost:9092 --replication-factor 1 --partitions 1 --topic test

distroduce/local_messaging_sim.py

@@ -0,0 +1,36 @@
+from datetime import datetime
+
+from cadCAD import configs
+from cadCAD.configuration.utils import config_sim
+from cadCAD.engine import ExecutionMode, ExecutionContext, Executor
+
+from distroduce.simulation import main, sim_composition
+from distroduce.spark.session import sc
+from distroduce.executor.spark import distributed_produce
+
+if __name__ == "__main__":
+    intitial_conditions = {
+        'record_creation': datetime.now(),
+        'client_a': {'users': [], 'messages': [], 'msg_count': 0, 'send_time': 0.0},
+        'client_b': {'users': [], 'messages': [], 'msg_count': 0, 'send_time': 0.0},
+        'total_msg_count': 0,
+        'total_send_time': 0.000000
+    }
+
+    # N = 5 = 10000 / (500 x 4)
+    # T = 500
+    sim_config = config_sim(
+        {
+            "N": 1,
+            "T": range(10),
+            # "T": range(5000),
+        }
+    )
+
+    exec_mode = ExecutionMode()
+    kafkaConfig = {'send_topic': 'test', 'producer_config': {'bootstrap_servers': f'localhost:9092', 'acks': 'all'}}
+    # kafkaConfig = {'send_topic': 'test', 'producer_config': {'bootstrap_servers': f'{sys.argv[1]}:9092', 'acks': 'all'}}
+    dist_proc_ctx = ExecutionContext(context=exec_mode.dist_proc, method=distributed_produce, kafka_config=kafkaConfig)
+    run = Executor(exec_context=dist_proc_ctx, configs=configs, spark_context=sc)
+
+    main(run, sim_config, intitial_conditions, sim_composition)

distroduce/messaging_sim.py

@@ -1,54 +1,14 @@
-import sys
-import pandas as pd
 from datetime import datetime
-from tabulate import tabulate
 
 from cadCAD import configs
-from cadCAD.utils import arrange_cols
-from cadCAD.configuration import append_configs
 from cadCAD.configuration.utils import config_sim
 from cadCAD.engine import ExecutionMode, ExecutionContext, Executor
 
+from distroduce.simulation import main, sim_composition
 from distroduce.spark.session import sc
 from distroduce.executor.spark import distributed_produce
-from distroduce.action_policies import enter_action, message_actions, exit_action
-from distroduce.state_updates import send_message, count_messages, add_send_time, current_time
-
-# State Updates
-variables = {
-    'client_a': send_message('client_a'),
-    'client_b': send_message('client_b'),
-    'total_msg_count': count_messages,
-    'total_send_time': add_send_time,
-    'record_creation': current_time('record_creation')
-}
-
-# Action Policies
-policy_group_1 = {
-    "action_1": enter_action('server', 'room_1', 'A'),
-    "action_2": enter_action('server', 'room_1', 'B')
-}
-
-policy_group_2 = {
-    "action_1": message_actions('client_A', 'room_1', "Hi B", 'A', 'B'),
-    "action_2": message_actions('client_B', 'room_1', "Hi A", 'B', 'A')
-}
-
-policy_group_3 = {
-    "action_1": message_actions('client_A', 'room_1', "Bye B", 'A', 'B'),
-    "action_2": message_actions('client_B', 'room_1', "Bye A", 'B', 'A')
-}
-
-policy_group_4 = {
-    "action_1": exit_action('server', 'room_1', 'A'),
-    "action_2": exit_action('server', 'room_1', 'B')
-}
-
-policy_groups = [policy_group_1, policy_group_2, policy_group_3, policy_group_4]
-sim_composition = [{'policies': policy_group, 'variables': variables} for policy_group in policy_groups]
 
 if __name__ == "__main__":
-    print("Distributed Simulation: Chat Clients")
     intitial_conditions = {
         'record_creation': datetime.now(),
         'client_a': {'users': [], 'messages': [], 'msg_count': 0, 'send_time': 0.0},
@@ -56,48 +16,17 @@ if __name__ == "__main__":
         'total_msg_count': 0,
         'total_send_time': 0.000000
     }
-    exec_mode = ExecutionMode()
 
-
-    # N = 5 = 10000 / (500 x 4)
-    # T = 500
     sim_config = config_sim(
         {
             "N": 1,
-            "T": range(10),
+            "T": range(5000),
-            # "T": range(5000),
         }
     )
 
-    append_configs(
+    exec_mode = ExecutionMode()
-        user_id='Joshua',
-        sim_configs=sim_config,
-        initial_state=intitial_conditions,
-        partial_state_update_blocks=sim_composition,
-        policy_ops=[lambda a, b: a + b]
-    )
-
     kafkaConfig = {'send_topic': 'test', 'producer_config': {'bootstrap_servers': f'{sys.argv[1]}:9092', 'acks': 'all'}}
     dist_proc_ctx = ExecutionContext(context=exec_mode.dist_proc, method=distributed_produce, kafka_config=kafkaConfig)
     run = Executor(exec_context=dist_proc_ctx, configs=configs, spark_context=sc)
 
-    i = 0
+    main(run, sim_config, intitial_conditions, sim_composition)
-    for raw_result, tensor_field in run.execute():
-        result = arrange_cols(pd.DataFrame(raw_result), False)[
-            [
-                'run_id', 'timestep', 'substep',
-                'record_creation', 'total_msg_count', 'total_send_time'
-            ]
-        ]
-        print()
-        if i == 0:
-            print(tabulate(tensor_field, headers='keys', tablefmt='psql'))
-        last = result.tail(1)
-        last['msg_per_sec'] = last['total_msg_count']/last['total_send_time']
-        print("Output: Head")
-        print(tabulate(result.head(5), headers='keys', tablefmt='psql'))
-        print("Output: Tail")
-        print(tabulate(result.tail(5), headers='keys', tablefmt='psql'))
-        print(tabulate(last, headers='keys', tablefmt='psql'))
-        print()
-        i += 1