Implement indexing into Apache Druid #1
179
db.py
179
db.py
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@ -1,15 +1,15 @@
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import random
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from math import ceil
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import aioredis
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import manticoresearch
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import ujson
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from aiokafka import AIOKafkaProducer
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from manticoresearch.rest import ApiException
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from numpy import array_split
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from redis import StrictRedis
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import util
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import random
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from aiokafka import AIOKafkaProducer
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# Manticore schema
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from schemas import mc_s
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@ -21,6 +21,7 @@ api_instance = manticoresearch.IndexApi(api_client)
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# Kafka
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from aiokafka import AIOKafkaProducer
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KAFKA_TOPIC = "msg"
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log = util.get_logger("db")
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@ -51,7 +52,7 @@ KEYPREFIX = "queue."
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async def store_kafka_batch(data):
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print("STORING KAFKA BATCH")
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producer = AIOKafkaProducer(bootstrap_servers='kafka:9092')
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producer = AIOKafkaProducer(bootstrap_servers="kafka:9092")
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await producer.start()
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batch = producer.create_batch()
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for msg in data:
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@ -70,67 +71,74 @@ async def store_kafka_batch(data):
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del msg[key]
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if key in schema:
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if isinstance(value, int):
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if schema[key].startswith("string") or schema[key].startswith("text"):
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if schema[key].startswith("string") or schema[key].startswith(
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"text"
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):
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msg[key] = str(value)
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message = ujson.dumps(msg)
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body = str.encode(message)
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if "ts" not in msg:
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# print("MSG WITHOUT TS", msg)
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continue
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metadata = batch.append(key=None, value=body, timestamp=msg["ts"])
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if metadata is None:
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partitions = await producer.partitions_for(KAFKA_TOPIC)
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partition = random.choice(tuple(partitions))
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await producer.send_batch(batch, KAFKA_TOPIC, partition=partition)
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print("%d messages sent to partition %d"
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% (batch.record_count(), partition))
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print(
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"%d messages sent to partition %d" % (batch.record_count(), partition)
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)
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batch = producer.create_batch()
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continue
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partitions = await producer.partitions_for(KAFKA_TOPIC)
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partition = random.choice(tuple(partitions))
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await producer.send_batch(batch, KAFKA_TOPIC, partition=partition)
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print("%d messages sent to partition %d"
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% (batch.record_count(), partition))
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print("%d messages sent to partition %d" % (batch.record_count(), partition))
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await producer.stop()
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# def store_message(msg):
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# """
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# Store a message into Manticore
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# :param msg: dict
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# """
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# store_kafka(msg)
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# # Duplicated to avoid extra function call
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# if msg["type"] in TYPES_MAIN:
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# index = "main"
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# schema = mc_s.schema_main
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# elif msg["type"] in TYPES_META:
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# index = "meta"
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# schema = mc_s.schema_meta
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# elif msg["type"] in TYPES_INT:
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# index = "internal"
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# schema = mc_s.schema_int
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# # normalise fields
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# for key, value in list(msg.items()):
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# if value is None:
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# del msg[key]
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# if key in schema:
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# if isinstance(value, int):
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# if schema[key].startswith("string") or schema[key].startswith("text"):
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# msg[key] = str(value)
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# # Duplicated to avoid extra function call
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# if msg["type"] in TYPES_MAIN:
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# index = "main"
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# schema = mc_s.schema_main
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# elif msg["type"] in TYPES_META:
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# index = "meta"
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# schema = mc_s.schema_meta
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# elif msg["type"] in TYPES_INT:
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# index = "internal"
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# schema = mc_s.schema_int
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# # normalise fields
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# for key, value in list(msg.items()):
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# if value is None:
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# del msg[key]
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# if key in schema:
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# if isinstance(value, int):
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# if schema[key].startswith("string") or schema[key].startswith("text"):
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# msg[key] = str(value)
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# body = [{"insert": {"index": index, "doc": msg}}]
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# body_post = ""
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# for item in body:
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# body_post += ujson.dumps(item)
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# body_post += "\n"
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# body = [{"insert": {"index": index, "doc": msg}}]
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# body_post = ""
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# for item in body:
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# body_post += ujson.dumps(item)
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# body_post += "\n"
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# # print(body_post)
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# try:
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# # Bulk index operations
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# print("FAKE POST")
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# #api_response = api_instance.bulk(body_post) # , async_req=True
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# # print(api_response)
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# except ApiException as e:
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# print("Exception when calling IndexApi->bulk: %s\n" % e)
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# print("ATTEMPT", body_post)
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# # print(body_post)
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# try:
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# # Bulk index operations
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# print("FAKE POST")
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# #api_response = api_instance.bulk(body_post) # , async_req=True
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# # print(api_response)
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# except ApiException as e:
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# print("Exception when calling IndexApi->bulk: %s\n" % e)
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# print("ATTEMPT", body_post)
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async def queue_message(msg):
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"""
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@ -139,9 +147,10 @@ async def queue_message(msg):
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src = msg["src"]
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message = ujson.dumps(msg)
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key = "{KEYPREFIX}{src}"
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key = f"{KEYPREFIX}{src}"
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await ar.sadd(key, message)
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async def queue_message_bulk(data):
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"""
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Queue multiple messages on the Redis buffer.
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@ -150,7 +159,7 @@ async def queue_message_bulk(data):
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src = msg["src"]
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message = ujson.dumps(msg)
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key = "{KEYPREFIX}{src}"
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key = f"{KEYPREFIX}{src}"
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await ar.sadd(key, message)
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@ -176,50 +185,50 @@ def queue_message_bulk_sync(data):
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# return
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# for msg in data:
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# store_kafka(msg)
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# # 10000: maximum inserts we can submit to
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# # Manticore as of Sept 2022
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# split_posts = array_split(data, ceil(len(data) / 10000))
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# for messages in split_posts:
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# total = []
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# for msg in messages:
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# # Duplicated to avoid extra function call (see above)
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# if msg["type"] in TYPES_MAIN:
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# index = "main"
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# schema = mc_s.schema_main
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# elif msg["type"] in TYPES_META:
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# index = "meta"
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# schema = mc_s.schema_meta
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# elif msg["type"] in TYPES_INT:
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# index = "internal"
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# schema = mc_s.schema_int
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# # normalise fields
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# for key, value in list(msg.items()):
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# if value is None:
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# del msg[key]
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# if key in schema:
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# if isinstance(value, int):
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# if schema[key].startswith("string") or schema[key].startswith(
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# "text"
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# ):
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# msg[key] = str(value)
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# # 10000: maximum inserts we can submit to
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# # Manticore as of Sept 2022
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# split_posts = array_split(data, ceil(len(data) / 10000))
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# for messages in split_posts:
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# total = []
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# for msg in messages:
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# # Duplicated to avoid extra function call (see above)
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# if msg["type"] in TYPES_MAIN:
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# index = "main"
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# schema = mc_s.schema_main
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# elif msg["type"] in TYPES_META:
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# index = "meta"
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# schema = mc_s.schema_meta
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# elif msg["type"] in TYPES_INT:
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# index = "internal"
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# schema = mc_s.schema_int
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# # normalise fields
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# for key, value in list(msg.items()):
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# if value is None:
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# del msg[key]
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# if key in schema:
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# if isinstance(value, int):
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# if schema[key].startswith("string") or schema[key].startswith(
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# "text"
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# ):
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# msg[key] = str(value)
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# body = {"insert": {"index": index, "doc": msg}}
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# total.append(body)
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# body = {"insert": {"index": index, "doc": msg}}
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# total.append(body)
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# body_post = ""
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# for item in total:
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# body_post += ujson.dumps(item)
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# body_post += "\n"
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# body_post = ""
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# for item in total:
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# body_post += ujson.dumps(item)
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# body_post += "\n"
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# # print(body_post)
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# try:
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# # Bulk index operations
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# print("FAKE POST")
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# #api_response = api_instance.bulk(body_post) # , async_req=True
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# #print(api_response)
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# except ApiException as e:
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# print("Exception when calling IndexApi->bulk: %s\n" % e)
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# print("ATTEMPT", body_post)
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# # print(body_post)
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# try:
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# # Bulk index operations
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# print("FAKE POST")
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# #api_response = api_instance.bulk(body_post) # , async_req=True
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# #print(api_response)
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# except ApiException as e:
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# print("Exception when calling IndexApi->bulk: %s\n" % e)
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# print("ATTEMPT", body_post)
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# def update_schema():
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@ -243,5 +252,5 @@ def queue_message_bulk_sync(data):
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# util_instance.sql(create_query)
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#create_index(api_client)
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#update_schema()
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# create_index(api_client)
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# update_schema()
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@ -19,7 +19,11 @@ services:
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- .env
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volumes_from:
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- tmp
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# depends_on:
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depends_on:
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- broker
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- kafka
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- tmp
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- redis
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# - db
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threshold:
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@ -52,12 +56,16 @@ services:
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- 9093:9090
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environment:
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- DRUID_BROKER_URL=http://broker:8082
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depends_on:
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- broker
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metabase:
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container_name: metabase
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image: metabase/metabase:latest
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ports:
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- 3001:3000
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depends_on:
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- broker
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postgres:
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container_name: postgres
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@ -82,6 +90,7 @@ services:
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image: bitnami/kafka
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depends_on:
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- zookeeper
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- broker
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ports:
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- 29092:29092
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- 9092:9092
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@ -1,11 +1,11 @@
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import asyncio
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from os import getenv
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import db
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import util
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from sources.ch4 import Chan4
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from sources.dis import DiscordClient
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from sources.ingest import Ingest
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import db
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# For development
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# if not getenv("DISCORD_TOKEN", None):
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@ -27,7 +27,6 @@ async def main(loop):
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log.info("Starting Discord handler.")
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client = DiscordClient()
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loop.create_task(client.start(token))
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# client.run(token)
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log.info("Starting 4chan handler.")
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chan = Chan4()
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@ -1,7 +1,20 @@
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from concurrent.futures import ProcessPoolExecutor
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import asyncio
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import os
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import random
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# For key generation
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import string
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from concurrent.futures import ProcessPoolExecutor
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# For timestamp processing
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from datetime import datetime
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from math import ceil
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import ujson
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# For 4chan message parsing
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from bs4 import BeautifulSoup
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from numpy import array_split
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from siphashc import siphash
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import db
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@ -10,19 +23,6 @@ import util
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# 4chan schema
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from schemas.ch4_s import ATTRMAP
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# For key generation
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import string
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import random
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# For timestamp processing
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import datetime
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# For 4chan message parsing
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from bs4 import BeautifulSoup
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from numpy import array_split
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from math import ceil
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log = util.get_logger("process")
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# Maximum number of CPU threads to use for post processing
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@ -30,6 +30,7 @@ CPU_THREADS = os.cpu_count()
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p = ProcessPoolExecutor(CPU_THREADS)
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def get_hash_key():
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hash_key = db.r.get("hashing_key")
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if not hash_key:
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@ -42,33 +43,68 @@ def get_hash_key():
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log.debug(f"Decoded hash key: {hash_key}")
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return hash_key
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hash_key = get_hash_key()
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@asyncio.coroutine
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async def spawn_processing_threads(data):
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print("SPAWN", data)
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loop = asyncio.get_event_loop()
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tasks = []
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oldts = [x["now"] for x in data if "now" in x]
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if len(data) < CPU_THREADS:
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split_data = [data]
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else:
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msg_per_core = int(len(data) / CPU_THREADS)
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print("MSG PER CORE", msg_per_core)
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split_data = array_split(data, ceil(len(data) / msg_per_core))
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print("SPLIT DATA", split_data)
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for split in split_data:
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for index, split in enumerate(split_data):
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print("DELEGATING TO THREAD", len(split))
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await process_data_thread(split)
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future = loop.run_in_executor(p, process_data, data)
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# future = p.submit(process_data, split)
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tasks.append(future)
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# results = [x.result(timeout=50) for x in tasks]
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results = await asyncio.gather(*tasks)
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print("RESULTS", len(results))
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# Join the results back from the split list
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flat_list = [item for sublist in results for item in sublist]
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print("LENFLAT", len(flat_list))
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print("LENDATA", len(data))
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newts = [x["ts"] for x in flat_list if "ts" in x]
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print("lenoldts", len(oldts))
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print("lennewts", len(newts))
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allts = all(["ts" in x for x in flat_list])
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print("ALLTS", allts)
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alllen = [len(x) for x in flat_list]
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print("ALLLEN", alllen)
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await db.store_kafka_batch(flat_list)
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|
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# @asyncio.coroutine
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# def process_data_thread(data):
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# """
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# Helper to spawn threads to process a list of data.
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# """
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# loop = asyncio.get_event_loop()
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# if len(data) < CPU_THREADS:
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# split_data = [data]
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# else:
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# msg_per_core = int(len(data) / CPU_THREADS)
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# print("MSG PER CORE", msg_per_core)
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# split_data = array_split(data, ceil(len(data) / msg_per_core))
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# for index, split in enumerate(split_data):
|
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# print("DELEGATING TO THREAD", len(split))
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# #f = process_data_thread(split)
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# yield loop.run_in_executor(p, process_data, data)
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|
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@asyncio.coroutine
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def process_data_thread(data):
|
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"""
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Helper to spawn threads to process a list of data.
|
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"""
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loop = asyncio.get_event_loop()
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yield from loop.run_in_executor(p, process_data, data)
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def process_data(data):
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print("PROCESSING DATA", data)
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print("PROCESS DATA START")
|
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# to_store = []
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for index, msg in enumerate(data):
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#print("PROCESSING", msg)
|
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# print("PROCESSING", msg)
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if msg["src"] == "4ch":
|
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board = msg["net"]
|
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thread = msg["channel"]
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|
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@ -81,15 +117,18 @@ def process_data(data):
|
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if key_content:
|
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key_content = key_content.decode("ascii")
|
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if key_content == hash:
|
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del data[index]
|
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continue
|
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else:
|
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data[index][index]["type"] = "update"
|
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data[index]["type"] = "update"
|
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db.r.set(redis_key, hash)
|
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for key2, value in list(msg.items()):
|
||||
if "now" not in data[index]:
|
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print("NOW NOT IN INDEX", data[index])
|
||||
for key2, value in list(data[index].items()):
|
||||
if key2 in ATTRMAP:
|
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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
|
||||
|
|
|
|||
|
|
@ -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 = {}
|
||||
|
|
@ -146,8 +146,7 @@ class Chan4(object):
|
|||
# new_dict[k].append(v)
|
||||
# else:
|
||||
# new_dict[k] = [v]
|
||||
#await self.handle_posts_thread(new_dict)
|
||||
|
||||
# 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))
|
||||
|
|
|
|||
|
|
@ -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)
|
||||
|
|
@ -33,13 +31,11 @@ class Ingest(object):
|
|||
chunk = await db.ar.spop(key, CHUNK_SIZE)
|
||||
if not chunk:
|
||||
continue
|
||||
#self.log.info(f"Got chunk: {chunk}")
|
||||
# self.log.info(f"Got chunk: {chunk}")
|
||||
for item in chunk:
|
||||
item = ujson.loads(item)
|
||||
#self.log.info(f"Got item: {item}")
|
||||
# self.log.info(f"Got item: {item}")
|
||||
items.append(item)
|
||||
if items:
|
||||
print("PROCESSING", len(items))
|
||||
await process.spawn_processing_threads(items)
|
||||
print("DONE WITH PROCESSING", len(items))
|
||||
await db.store_kafka_batch(items)
|
||||
|
|
|
|||
Loading…
Reference in New Issue