db.py

@@ -1,15 +1,15 @@
+import random
 from math import ceil
 
 import aioredis
 import manticoresearch
 import ujson
+from aiokafka import AIOKafkaProducer
 from manticoresearch.rest import ApiException
 from numpy import array_split
 from redis import StrictRedis
 
 import util
-import random
-from aiokafka import AIOKafkaProducer
 
 # Manticore schema
 from schemas import mc_s
@@ -21,6 +21,7 @@ api_instance = manticoresearch.IndexApi(api_client)
 
 # Kafka
 from aiokafka import AIOKafkaProducer
+
 KAFKA_TOPIC = "msg"
 
 log = util.get_logger("db")
@@ -51,7 +52,7 @@ KEYPREFIX = "queue."
 
 async def store_kafka_batch(data):
     print("STORING KAFKA BATCH")
-    producer = AIOKafkaProducer(bootstrap_servers='kafka:9092')
+    producer = AIOKafkaProducer(bootstrap_servers="kafka:9092")
     await producer.start()
     batch = producer.create_batch()
     for msg in data:
@@ -70,27 +71,33 @@ async def store_kafka_batch(data):
                 del msg[key]
             if key in schema:
                 if isinstance(value, int):
-                    if schema[key].startswith("string") or schema[key].startswith("text"):
+                    if schema[key].startswith("string") or schema[key].startswith(
+                        "text"
+                    ):
                         msg[key] = str(value)
         message = ujson.dumps(msg)
         body = str.encode(message)
+        if "ts" not in msg:
+            # print("MSG WITHOUT TS", msg)
+            continue
         metadata = batch.append(key=None, value=body, timestamp=msg["ts"])
         if metadata is None:
             partitions = await producer.partitions_for(KAFKA_TOPIC)
             partition = random.choice(tuple(partitions))
             await producer.send_batch(batch, KAFKA_TOPIC, partition=partition)
-            print("%d messages sent to partition %d"
+            print(
-                  % (batch.record_count(), partition))
+                "%d messages sent to partition %d" % (batch.record_count(), partition)
+            )
             batch = producer.create_batch()
             continue
 
     partitions = await producer.partitions_for(KAFKA_TOPIC)
     partition = random.choice(tuple(partitions))
     await producer.send_batch(batch, KAFKA_TOPIC, partition=partition)
-    print("%d messages sent to partition %d"
+    print("%d messages sent to partition %d" % (batch.record_count(), partition))
-          % (batch.record_count(), partition))
     await producer.stop()
 
+
 # def store_message(msg):
 #     """
 #     Store a message into Manticore
@@ -132,6 +139,7 @@ async def store_kafka_batch(data):
 #     print("Exception when calling IndexApi->bulk: %s\n" % e)
 #     print("ATTEMPT", body_post)
 
+
 async def queue_message(msg):
     """
     Queue a message on the Redis buffer.
@@ -139,9 +147,10 @@ async def queue_message(msg):
     src = msg["src"]
     message = ujson.dumps(msg)
 
-    key = "{KEYPREFIX}{src}"
+    key = f"{KEYPREFIX}{src}"
     await ar.sadd(key, message)
 
+
 async def queue_message_bulk(data):
     """
     Queue multiple messages on the Redis buffer.
@@ -150,7 +159,7 @@ async def queue_message_bulk(data):
         src = msg["src"]
         message = ujson.dumps(msg)
 
-        key = "{KEYPREFIX}{src}"
+        key = f"{KEYPREFIX}{src}"
         await ar.sadd(key, message)
 
 

docker-compose.yml

@@ -19,7 +19,11 @@ services:
       - .env
     volumes_from:
       - tmp
-    # depends_on:
+    depends_on:
+      - broker
+      - kafka
+      - tmp
+      - redis
     #   - db
 
   threshold:
@@ -52,12 +56,16 @@ services:
       - 9093:9090
     environment:
       - DRUID_BROKER_URL=http://broker:8082
+    depends_on:
+      - broker
 
   metabase:
     container_name: metabase
     image: metabase/metabase:latest
     ports:
       - 3001:3000
+    depends_on:
+      - broker
 
   postgres:
     container_name: postgres
@@ -82,6 +90,7 @@ services:
     image: bitnami/kafka
     depends_on:
       - zookeeper
+      - broker
     ports:
       - 29092:29092
       - 9092:9092

monolith.py

@@ -1,11 +1,11 @@
 import asyncio
 from os import getenv
 
+import db
 import util
 from sources.ch4 import Chan4
 from sources.dis import DiscordClient
 from sources.ingest import Ingest
-import db
 
 # For development
 # if not getenv("DISCORD_TOKEN", None):
@@ -27,7 +27,6 @@ async def main(loop):
     log.info("Starting Discord handler.")
     client = DiscordClient()
     loop.create_task(client.start(token))
-    # client.run(token)
 
     log.info("Starting 4chan handler.")
     chan = Chan4()

processing/process.py

@@ -1,7 +1,20 @@
-from concurrent.futures import ProcessPoolExecutor
 import asyncio
 import os
+import random
+
+# For key generation
+import string
+from concurrent.futures import ProcessPoolExecutor
+
+# For timestamp processing
+from datetime import datetime
+from math import ceil
+
 import ujson
+
+# For 4chan message parsing
+from bs4 import BeautifulSoup
+from numpy import array_split
 from siphashc import siphash
 
 import db
@@ -10,19 +23,6 @@ import util
 # 4chan schema
 from schemas.ch4_s import ATTRMAP
 
-# For key generation
-import string
-import random
-
-# For timestamp processing
-import datetime
-
-# For 4chan message parsing
-from bs4 import BeautifulSoup
-
-from numpy import array_split
-from math import ceil
-
 log = util.get_logger("process")
 
 # Maximum number of CPU threads to use for post processing
@@ -30,6 +30,7 @@ CPU_THREADS = os.cpu_count()
 
 p = ProcessPoolExecutor(CPU_THREADS)
 
+
 def get_hash_key():
     hash_key = db.r.get("hashing_key")
     if not hash_key:
@@ -42,31 +43,66 @@ def get_hash_key():
         log.debug(f"Decoded hash key: {hash_key}")
     return hash_key
 
+
 hash_key = get_hash_key()
 
+
+@asyncio.coroutine
 async def spawn_processing_threads(data):
-    print("SPAWN", data)
+    loop = asyncio.get_event_loop()
+    tasks = []
+    oldts = [x["now"] for x in data if "now" in x]
     if len(data) < CPU_THREADS:
         split_data = [data]
     else:
         msg_per_core = int(len(data) / CPU_THREADS)
         print("MSG PER CORE", msg_per_core)
         split_data = array_split(data, ceil(len(data) / msg_per_core))
-    print("SPLIT DATA", split_data)
+    for index, split in enumerate(split_data):
-    for split in split_data:
         print("DELEGATING TO THREAD", len(split))
-        await process_data_thread(split)
+        future = loop.run_in_executor(p, process_data, data)
+        # future = p.submit(process_data, split)
+        tasks.append(future)
+    # results = [x.result(timeout=50) for x in tasks]
+    results = await asyncio.gather(*tasks)
+    print("RESULTS", len(results))
+
+    # Join the results back from the split list
+    flat_list = [item for sublist in results for item in sublist]
+    print("LENFLAT", len(flat_list))
+    print("LENDATA", len(data))
+
+    newts = [x["ts"] for x in flat_list if "ts" in x]
+    print("lenoldts", len(oldts))
+    print("lennewts", len(newts))
+    allts = all(["ts" in x for x in flat_list])
+    print("ALLTS", allts)
+    alllen = [len(x) for x in flat_list]
+    print("ALLLEN", alllen)
+    await db.store_kafka_batch(flat_list)
+
+
+# @asyncio.coroutine
+# def process_data_thread(data):
+#     """
+#     Helper to spawn threads to process a list of data.
+#     """
+#     loop = asyncio.get_event_loop()
+#     if len(data) < CPU_THREADS:
+#         split_data = [data]
+#     else:
+#         msg_per_core = int(len(data) / CPU_THREADS)
+#         print("MSG PER CORE", msg_per_core)
+#         split_data = array_split(data, ceil(len(data) / msg_per_core))
+#     for index, split in enumerate(split_data):
+#         print("DELEGATING TO THREAD", len(split))
+#         #f = process_data_thread(split)
+#         yield loop.run_in_executor(p, process_data, data)
 
-@asyncio.coroutine
-def process_data_thread(data):
-    """
-    Helper to spawn threads to process a list of data.
-    """
-    loop = asyncio.get_event_loop()
-    yield from loop.run_in_executor(p, process_data, data)
 
 def process_data(data):
-    print("PROCESSING DATA", data)
+    print("PROCESS DATA START")
+    # to_store = []
     for index, msg in enumerate(data):
         # print("PROCESSING", msg)
         if msg["src"] == "4ch":
@@ -81,15 +117,18 @@ def process_data(data):
             if key_content:
                 key_content = key_content.decode("ascii")
                 if key_content == hash:
+                    del data[index]
                     continue
                 else:
-                    data[index][index]["type"] = "update"
+                    data[index]["type"] = "update"
             db.r.set(redis_key, hash)
-            for key2, value in list(msg.items()):
+            if "now" not in data[index]:
+                print("NOW NOT IN INDEX", data[index])
+            for key2, value in list(data[index].items()):
                 if key2 in ATTRMAP:
-                    msg[ATTRMAP[key2]] = data[index][key2]
+                    data[index][ATTRMAP[key2]] = data[index][key2]
                     del data[index][key2]
-            if "ts" in msg:
+            if "ts" in data[index]:
                 old_time = data[index]["ts"]
                 # '08/30/22(Tue)02:25:37'
                 time_spl = old_time.split(":")
@@ -100,7 +139,13 @@ def process_data(data):
                 # new_ts = old_ts.isoformat()
                 new_ts = int(old_ts.timestamp())
                 data[index]["ts"] = new_ts
+            else:
+                print("MSG WITHOUT TS PROCESS", data[index])
+                continue
             if "msg" in msg:
                 soup = BeautifulSoup(data[index]["msg"], "html.parser")
                 msg = soup.get_text(separator="\n")
                 data[index]["msg"] = msg
+        # to_store.append(data[index])
+    print("FINISHED PROCESSING DATA")
+    return data

sources/ch4.py

@@ -136,7 +136,7 @@ class Chan4(object):
         # Split into 10,000 chunks
         if not all_posts:
             return
-        self.handle_posts(all_posts)
+        await self.handle_posts(all_posts)
         # threads_per_core = int(len(all_posts) / CPU_THREADS)
         # for i in range(CPU_THREADS):
         #     new_dict = {}
@@ -148,7 +148,6 @@ class Chan4(object):
         #             new_dict[k] = [v]
         # await self.handle_posts_thread(new_dict)
 
-
         # print("VAL", ceil(len(all_posts) / threads_per_core))
         # split_posts = array_split(all_posts, ceil(len(all_posts) / threads_per_core))
         # print("THREADS PER CORE SPLIT", len(split_posts))

sources/ingest.py

@@ -4,24 +4,22 @@ import ujson
 
 import db
 import util
-
 from processing import process
 
-SOURCES = ["irc", "dis", "4ch"]
+SOURCES = ["4ch", "irc", "dis"]
 KEYPREFIX = "queue."
-CHUNK_SIZE = 1000
+CHUNK_SIZE = 90000
 ITER_DELAY = 0.5
 
 
-
-
-
 class Ingest(object):
     def __init__(self):
         name = self.__class__.__name__
         self.log = util.get_logger(name)
 
     async def run(self):
+        # items = [{'no': 23567753, 'now': '09/12/22(Mon)20:10:29', 'name': 'Anonysmous', 'filename': '1644986767568', 'ext': '.webm', 'w': 1280, 'h': 720, 'tn_w': 125, 'tn_h': 70, 'tim': 1663027829301457, 'time': 1663027829, 'md5': 'zeElr1VR05XpZ2XuAPhmPA==', 'fsize': 3843621, 'resto': 23554700, 'type': 'msg', 'src': '4ch', 'net': 'gif', 'channel': '23554700'}]
+        # await process.spawn_processing_threads(items)
         while True:
             await self.get_chunk()
             await asyncio.sleep(ITER_DELAY)
@@ -41,5 +39,3 @@ class Ingest(object):
         if items:
             print("PROCESSING", len(items))
             await process.spawn_processing_threads(items)
-            print("DONE WITH PROCESSING", len(items))
-            await db.store_kafka_batch(items)