Merge branch 'preprocessing' into integration_2

clickhouse/README.md

@@ -74,7 +74,7 @@ docker exec -it clickhouse-kafka-1 kafka-console-consumer --bootstrap-server kaf
 
 Get into the ClickHouse client:  
 ```bash
-docker exec -it clickhouse-client clickhouse-client
+docker exec -it <server1's container ID from docker ps> clickhouse-client
 ```  
 Check if tables are available:  
 ```bash

clickhouse/clickhouse_data/data/preprocessed_configs/config.xml

@@ -21,7 +21,7 @@
 
     <keeper_server>
         <tcp_port>9181</tcp_port>
-        <server_id>3</server_id>
+        <server_id>2</server_id>
         <log_storage_path>/var/lib/clickhouse/coordination/log</log_storage_path>
         <snapshot_storage_path>/var/lib/clickhouse/coordination/snapshots</snapshot_storage_path>
         <coordination_settings>

clickhouse/docker-compose.yml

@@ -125,7 +125,7 @@ services:
         condition: on-failure
 
   pcap_streamer:
-    image: levenshtein/streamer_test4:latest
+    image: levenshtein/streamer_test7:latest
     depends_on:
       - kafka
     networks:
@@ -133,10 +133,9 @@ services:
         aliases:
           - pcap_streamer
     volumes:
-      - "/host_mnt/c/Users/akash/storage/Asu/sem3/dds/project:/data/pcap"
+      #- "/host_mnt/c/Users/akash/storage/Asu/sem3/dds/project:/data/pcap"
-    environment:
+      - "/host_mnt/c/Users/akash/storage/Asu/sem3/dds/project/project_github/real-time-traffic-analysis-clickhouse/preprocessing:/data/pcap"
-      PCAP_FILE: /data/pcap/202310081400.pcap
+    command: ["sh", "-c", "sleep 60 && python /app/pcap_processor.py -c /data/pcap/sample_output.csv -s --stream_size 1000"]
-    command: ["sh", "-c", "sleep 30 && python /app/pcap_processor.py -f /data/pcap/202310081400.pcap -s --stream_size 1000"]
     deploy:
       replicas: 1
       restart_policy:

preprocessing/README.md

@@ -49,3 +49,4 @@
   
 
 python pcap_processor.py -f C:/Users/akash/storage/Asu/sem3/dds/project/202310081400.pcap -s --stream_size 1000
+python pcap_procesor.py -c sample_output.csv -s --stream_size 1000

preprocessing/ip2loc_prep.py

@@ -5,14 +5,15 @@ import csv
 sample_size = 100
 batch_size = 10000
 
-sample = True
+sample = False
 
 
 def int_to_ipv4(num: int) -> str:
     return socket.inet_ntoa(struct.pack("!L", num))
 
 
-with open("IP2LOCATION-LITE-DB3.csv", "r") as input_file, open(
+# with open("IP2LOCATION-LITE-DB3.csv", "r") as input_file, open(
+with open("IP2LOCATION-LITE-DB1.csv", "r") as input_file, open(
     "geoip.csv", "w", newline=""
 ) as output_file:
     reader = csv.reader(input_file)
@@ -21,11 +22,11 @@ with open("IP2LOCATION-LITE-DB3.csv", "r") as input_file, open(
     # header row
     writer.writerow(
         [
-            "ip_from",
+            "ip_range_start",
-            "ip_to",
+            "ip_range_end",
             "country",
-            "region",
+            # "region",
-            "city",
+            # "city",
         ]
     )
 
@@ -35,8 +36,8 @@ with open("IP2LOCATION-LITE-DB3.csv", "r") as input_file, open(
             int_to_ipv4(int(record[0])),
             int_to_ipv4(int(record[1])),
             record[3],
-            record[4],
+            # record[4],
-            record[5],
+            # record[5],
         ]
         records.append(new_record)
         if sample and idx > sample_size:

preprocessing/pcap_processor.py

@@ -19,7 +19,7 @@ class KafkaClient:
         self.topic_name = topic_name
         if mode == 'producer':
             self.client = KafkaProducer(
-                bootstrap_servers=['localhost:9092'],
+                bootstrap_servers=['kafka:9092'],
                 max_request_size = 200000000,
                 #api_version=(0,11,5),
                 value_serializer=lambda x: json.dumps(x).encode('utf-8'))
@@ -192,11 +192,12 @@ if __name__ == "__main__":
 
     DEBUG = args._debug
 
-    sample_size = int(args._samplesize) #1000000
+    sample_size = int(args._streamsize)  # 100000
     batch_size = 100 #100000
 
     # if preprocessed data ready for streaming
     if csv_file:
+        #print("true")
         with open(csv_file, newline="") as f:
             csv_rdr = csv.reader(f)
             next(csv_rdr)  # skip headers
@@ -206,10 +207,10 @@ if __name__ == "__main__":
                 # direct streaming to kafka goes here
                 producer.client.send(KAFKA_TOPIC, row_to_dict(row))
                 dbg_print(row_to_dict(row))
-                dbg_print("streamed packet", idx)
+                print("streamed packet", idx)
                 if idx > sample_size:
                     break
-            dbg_print(f"total streamed: {idx}")
+            print(f"total streamed: {idx}")
 
     # otherwise, process packets
     else: