Implement sentiment/NLP annotation and optimise processing
This commit is contained in:
parent
f432e9b29e
commit
a89b5a8b6f
198
db.py
198
db.py
|
|
@ -1,28 +1,15 @@
|
|||
import random
|
||||
from math import ceil
|
||||
|
||||
import aioredis
|
||||
import manticoresearch
|
||||
import ujson
|
||||
import orjson
|
||||
|
||||
# Kafka
|
||||
from aiokafka import AIOKafkaProducer
|
||||
from manticoresearch.rest import ApiException
|
||||
from numpy import array_split
|
||||
from redis import StrictRedis
|
||||
|
||||
import util
|
||||
|
||||
# Manticore schema
|
||||
from schemas import mc_s
|
||||
|
||||
# Manticore
|
||||
configuration = manticoresearch.Configuration(host="http://monolith-db-1:9308")
|
||||
api_client = manticoresearch.ApiClient(configuration)
|
||||
api_instance = manticoresearch.IndexApi(api_client)
|
||||
|
||||
# Kafka
|
||||
from aiokafka import AIOKafkaProducer
|
||||
|
||||
KAFKA_TOPIC = "msg"
|
||||
# KAFKA_TOPIC = "msg"
|
||||
|
||||
log = util.get_logger("db")
|
||||
|
||||
|
|
@ -51,103 +38,62 @@ KEYPREFIX = "queue."
|
|||
|
||||
|
||||
async def store_kafka_batch(data):
|
||||
print("STORING KAFKA BATCH")
|
||||
log.debug(f"Storing Kafka batch of {len(data)} messages")
|
||||
producer = AIOKafkaProducer(bootstrap_servers="kafka:9092")
|
||||
await producer.start()
|
||||
batch = producer.create_batch()
|
||||
for msg in data:
|
||||
if msg["type"] in TYPES_MAIN:
|
||||
index = "main"
|
||||
schema = mc_s.schema_main
|
||||
# schema = mc_s.schema_main
|
||||
elif msg["type"] in TYPES_META:
|
||||
index = "meta"
|
||||
schema = mc_s.schema_meta
|
||||
# schema = mc_s.schema_meta
|
||||
elif msg["type"] in TYPES_INT:
|
||||
index = "internal"
|
||||
schema = mc_s.schema_int
|
||||
# schema = mc_s.schema_int
|
||||
|
||||
KAFKA_TOPIC = index
|
||||
# normalise fields
|
||||
for key, value in list(msg.items()):
|
||||
if value is None:
|
||||
del msg[key]
|
||||
if key in schema:
|
||||
if isinstance(value, int):
|
||||
if schema[key].startswith("string") or schema[key].startswith(
|
||||
"text"
|
||||
):
|
||||
msg[key] = str(value)
|
||||
message = ujson.dumps(msg)
|
||||
body = str.encode(message)
|
||||
# if key in schema:
|
||||
# if isinstance(value, int):
|
||||
# if schema[key].startswith("string") or schema[key].startswith(
|
||||
# "text"
|
||||
# ):
|
||||
# msg[key] = str(value)
|
||||
body = orjson.dumps(msg)
|
||||
# orjson returns bytes
|
||||
# body = str.encode(message)
|
||||
if "ts" not in msg:
|
||||
# print("MSG WITHOUT TS", msg)
|
||||
continue
|
||||
raise Exception("No TS in msg")
|
||||
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" % (batch.record_count(), partition)
|
||||
)
|
||||
log.debug(f"{batch.record_count()} messages sent to partition {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" % (batch.record_count(), partition))
|
||||
log.debug(f"{batch.record_count()} messages sent to partition {partition}")
|
||||
await producer.stop()
|
||||
|
||||
|
||||
# def store_message(msg):
|
||||
# """
|
||||
# Store a message into Manticore
|
||||
# :param msg: dict
|
||||
# """
|
||||
# store_kafka(msg)
|
||||
# # Duplicated to avoid extra function call
|
||||
# if msg["type"] in TYPES_MAIN:
|
||||
# index = "main"
|
||||
# schema = mc_s.schema_main
|
||||
# elif msg["type"] in TYPES_META:
|
||||
# index = "meta"
|
||||
# schema = mc_s.schema_meta
|
||||
# elif msg["type"] in TYPES_INT:
|
||||
# index = "internal"
|
||||
# schema = mc_s.schema_int
|
||||
# # normalise fields
|
||||
# for key, value in list(msg.items()):
|
||||
# if value is None:
|
||||
# del msg[key]
|
||||
# if key in schema:
|
||||
# if isinstance(value, int):
|
||||
# if schema[key].startswith("string") or schema[key].startswith("text"):
|
||||
# msg[key] = str(value)
|
||||
|
||||
# body = [{"insert": {"index": index, "doc": msg}}]
|
||||
# body_post = ""
|
||||
# for item in body:
|
||||
# body_post += ujson.dumps(item)
|
||||
# body_post += "\n"
|
||||
|
||||
# # print(body_post)
|
||||
# try:
|
||||
# # Bulk index operations
|
||||
# print("FAKE POST")
|
||||
# #api_response = api_instance.bulk(body_post) # , async_req=True
|
||||
# # print(api_response)
|
||||
# except ApiException as e:
|
||||
# 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.
|
||||
"""
|
||||
src = msg["src"]
|
||||
message = ujson.dumps(msg)
|
||||
message = orjson.dumps(msg)
|
||||
|
||||
key = f"{KEYPREFIX}{src}"
|
||||
# log.debug(f"Queueing single message of string length {len(message)}")
|
||||
await ar.sadd(key, message)
|
||||
|
||||
|
||||
|
|
@ -155,102 +101,10 @@ async def queue_message_bulk(data):
|
|||
"""
|
||||
Queue multiple messages on the Redis buffer.
|
||||
"""
|
||||
# log.debug(f"Queueing message batch of length {len(data)}")
|
||||
for msg in data:
|
||||
src = msg["src"]
|
||||
message = ujson.dumps(msg)
|
||||
message = orjson.dumps(msg)
|
||||
|
||||
key = f"{KEYPREFIX}{src}"
|
||||
await ar.sadd(key, message)
|
||||
|
||||
|
||||
# For now, make a normal function until we go full async
|
||||
def queue_message_bulk_sync(data):
|
||||
"""
|
||||
Queue multiple messages on the Redis buffer.
|
||||
"""
|
||||
for msg in data:
|
||||
src = msg["src"]
|
||||
message = ujson.dumps(msg)
|
||||
|
||||
key = "{KEYPREFIX}{src}"
|
||||
r.sadd(key, message)
|
||||
|
||||
|
||||
# def store_message_bulk(data):
|
||||
# """
|
||||
# Store a message into Manticore
|
||||
# :param msg: dict
|
||||
# """
|
||||
# if not data:
|
||||
# return
|
||||
# for msg in data:
|
||||
# store_kafka(msg)
|
||||
# # 10000: maximum inserts we can submit to
|
||||
# # Manticore as of Sept 2022
|
||||
# split_posts = array_split(data, ceil(len(data) / 10000))
|
||||
# for messages in split_posts:
|
||||
# total = []
|
||||
# for msg in messages:
|
||||
# # Duplicated to avoid extra function call (see above)
|
||||
# if msg["type"] in TYPES_MAIN:
|
||||
# index = "main"
|
||||
# schema = mc_s.schema_main
|
||||
# elif msg["type"] in TYPES_META:
|
||||
# index = "meta"
|
||||
# schema = mc_s.schema_meta
|
||||
# elif msg["type"] in TYPES_INT:
|
||||
# index = "internal"
|
||||
# schema = mc_s.schema_int
|
||||
# # normalise fields
|
||||
# for key, value in list(msg.items()):
|
||||
# if value is None:
|
||||
# del msg[key]
|
||||
# if key in schema:
|
||||
# if isinstance(value, int):
|
||||
# if schema[key].startswith("string") or schema[key].startswith(
|
||||
# "text"
|
||||
# ):
|
||||
# msg[key] = str(value)
|
||||
|
||||
# body = {"insert": {"index": index, "doc": msg}}
|
||||
# total.append(body)
|
||||
|
||||
# body_post = ""
|
||||
# for item in total:
|
||||
# body_post += ujson.dumps(item)
|
||||
# body_post += "\n"
|
||||
|
||||
# # print(body_post)
|
||||
# try:
|
||||
# # Bulk index operations
|
||||
# print("FAKE POST")
|
||||
# #api_response = api_instance.bulk(body_post) # , async_req=True
|
||||
# #print(api_response)
|
||||
# except ApiException as e:
|
||||
# print("Exception when calling IndexApi->bulk: %s\n" % e)
|
||||
# print("ATTEMPT", body_post)
|
||||
|
||||
|
||||
# def update_schema():
|
||||
# pass
|
||||
|
||||
|
||||
# def create_index(api_client):
|
||||
# util_instance = manticoresearch.UtilsApi(api_client)
|
||||
# schemas = {
|
||||
# "main": mc_s.schema_main,
|
||||
# "meta": mc_s.schema_meta,
|
||||
# "internal": mc_s.schema_int,
|
||||
# }
|
||||
# for name, schema in schemas.items():
|
||||
# schema_types = ", ".join([f"{k} {v}" for k, v in schema.items()])
|
||||
|
||||
# create_query = (
|
||||
# f"create table if not exists {name}({schema_types}) engine='columnar'"
|
||||
# )
|
||||
# print("Schema types", create_query)
|
||||
# util_instance.sql(create_query)
|
||||
|
||||
|
||||
# create_index(api_client)
|
||||
# update_schema()
|
||||
|
|
|
|||
|
|
@ -20,10 +20,14 @@ services:
|
|||
volumes_from:
|
||||
- tmp
|
||||
depends_on:
|
||||
- broker
|
||||
- kafka
|
||||
- tmp
|
||||
- redis
|
||||
broker:
|
||||
condition: service_started
|
||||
kafka:
|
||||
condition: service_healthy
|
||||
tmp:
|
||||
condition: service_started
|
||||
redis:
|
||||
condition: service_healthy
|
||||
# - db
|
||||
|
||||
threshold:
|
||||
|
|
@ -46,8 +50,10 @@ services:
|
|||
volumes_from:
|
||||
- tmp
|
||||
depends_on:
|
||||
- tmp
|
||||
- redis
|
||||
tmp:
|
||||
condition: service_started
|
||||
redis:
|
||||
condition: service_healthy
|
||||
|
||||
turnilo:
|
||||
container_name: turnilo
|
||||
|
|
@ -102,6 +108,17 @@ services:
|
|||
KAFKA_INTER_BROKER_LISTENER_NAME: PLAINTEXT
|
||||
KAFKA_OFFSETS_TOPIC_REPLICATION_FACTOR: 1
|
||||
ALLOW_PLAINTEXT_LISTENER: yes
|
||||
# healthcheck:
|
||||
# test: ["CMD-SHELL", "kafka-topics.sh --bootstrap-server 127.0.0.1:9092 --topic main --describe"]
|
||||
# interval: 2s
|
||||
# timeout: 2s
|
||||
# retries: 15
|
||||
healthcheck:
|
||||
test: ["CMD", "kafka-topics.sh", "--list", "--bootstrap-server", "kafka:9092"]
|
||||
start_period: 15s
|
||||
interval: 2s
|
||||
timeout: 5s
|
||||
retries: 30
|
||||
|
||||
coordinator:
|
||||
image: apache/druid:0.23.0
|
||||
|
|
@ -230,6 +247,11 @@ services:
|
|||
- ${PORTAINER_GIT_DIR}/docker/redis.conf:/etc/redis.conf
|
||||
volumes_from:
|
||||
- tmp
|
||||
healthcheck:
|
||||
test: "redis-cli -s /var/run/redis/redis.sock ping"
|
||||
interval: 2s
|
||||
timeout: 2s
|
||||
retries: 15
|
||||
|
||||
networks:
|
||||
default:
|
||||
|
|
|
|||
|
|
@ -16,7 +16,7 @@ COPY requirements.txt /code/
|
|||
COPY discord-patched.tgz /code/
|
||||
|
||||
RUN python -m venv /venv
|
||||
RUN . /venv/bin/activate && pip install -r requirements.txt
|
||||
RUN . /venv/bin/activate && pip install -r requirements.txt && python -m spacy download en_core_web_sm
|
||||
|
||||
RUN tar xf /code/discord-patched.tgz -C /venv/lib/python3.10/site-packages
|
||||
|
||||
|
|
|
|||
|
|
@ -4,8 +4,18 @@ redis
|
|||
siphashc
|
||||
aiohttp[speedups]
|
||||
python-dotenv
|
||||
manticoresearch
|
||||
#manticoresearch
|
||||
numpy
|
||||
ujson
|
||||
aioredis[hiredis]
|
||||
aiokafka
|
||||
vaderSentiment
|
||||
polyglot
|
||||
pyicu
|
||||
pycld2
|
||||
morfessor
|
||||
six
|
||||
nltk
|
||||
spacy
|
||||
python-Levenshtein
|
||||
orjson
|
||||
|
|
|
|||
|
|
@ -1,19 +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
|
||||
|
||||
# For development
|
||||
# if not getenv("DISCORD_TOKEN", None):
|
||||
# print("Could not get Discord token, attempting load from .env")
|
||||
# from dotenv import load_dotenv
|
||||
|
||||
# load_dotenv()
|
||||
|
||||
log = util.get_logger("monolith")
|
||||
|
||||
modules_enabled = getenv("MODULES_ENABLED", False)
|
||||
|
|
|
|||
|
|
@ -4,25 +4,73 @@ import random
|
|||
|
||||
# For key generation
|
||||
import string
|
||||
|
||||
# Squash errors
|
||||
import warnings
|
||||
from concurrent.futures import ProcessPoolExecutor
|
||||
|
||||
# For timestamp processing
|
||||
from datetime import datetime
|
||||
from math import ceil
|
||||
|
||||
import ujson
|
||||
import orjson
|
||||
|
||||
# Tokenisation
|
||||
import spacy
|
||||
|
||||
# For 4chan message parsing
|
||||
from bs4 import BeautifulSoup
|
||||
from numpy import array_split
|
||||
from polyglot.detect.base import logger as polyglot_logger
|
||||
|
||||
# For NLP
|
||||
from polyglot.text import Text
|
||||
from pycld2 import error as cld2_error
|
||||
from siphashc import siphash
|
||||
|
||||
# For sentiment
|
||||
from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
|
||||
|
||||
import db
|
||||
import util
|
||||
|
||||
# 4chan schema
|
||||
from schemas.ch4_s import ATTRMAP
|
||||
|
||||
# For tokenisation
|
||||
# from gensim.parsing.preprocessing import (
|
||||
# strip_tags,
|
||||
# strip_punctuation,
|
||||
# strip_numeric,
|
||||
# stem_text,
|
||||
# strip_multiple_whitespaces,
|
||||
# strip_non_alphanum,
|
||||
# remove_stopwords,
|
||||
# strip_short,
|
||||
# preprocess_string,
|
||||
# )
|
||||
|
||||
# CUSTOM_FILTERS = [
|
||||
# lambda x: x.lower(),
|
||||
# strip_tags, #
|
||||
# strip_punctuation, #
|
||||
# strip_multiple_whitespaces,
|
||||
# strip_numeric,
|
||||
# remove_stopwords,
|
||||
# strip_short,
|
||||
# #stem_text,
|
||||
# strip_non_alphanum, #
|
||||
# ]
|
||||
|
||||
# Squash errors
|
||||
polyglot_logger.setLevel("ERROR")
|
||||
warnings.filterwarnings("ignore", category=UserWarning, module="bs4")
|
||||
|
||||
|
||||
TAGS = ["NOUN", "ADJ", "VERB", "ADV"]
|
||||
nlp = spacy.load("en_core_web_sm", disable=["parser", "ner"])
|
||||
|
||||
|
||||
log = util.get_logger("process")
|
||||
|
||||
# Maximum number of CPU threads to use for post processing
|
||||
|
|
@ -49,67 +97,44 @@ hash_key = get_hash_key()
|
|||
|
||||
@asyncio.coroutine
|
||||
async def spawn_processing_threads(data):
|
||||
len_data = len(data)
|
||||
log.debug(f"Spawning processing threads for batch of {len_data} messages")
|
||||
|
||||
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))
|
||||
for index, split in enumerate(split_data):
|
||||
print("DELEGATING TO THREAD", len(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))
|
||||
log.debug(f"Delegating processing of {len(split)} messages to thread {index}")
|
||||
task = loop.run_in_executor(p, process_data, data)
|
||||
tasks.append(task)
|
||||
|
||||
results = [await task for task in tasks]
|
||||
log.debug(f"Results from processing of {len_data} messages: {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)
|
||||
log.debug(f"Finished processing {len_data} messages")
|
||||
|
||||
|
||||
def process_data(data):
|
||||
print("PROCESS DATA START")
|
||||
# to_store = []
|
||||
for index, msg in enumerate(data):
|
||||
# print("PROCESSING", msg)
|
||||
to_store = []
|
||||
|
||||
# Initialise sentiment analyser
|
||||
analyzer = SentimentIntensityAnalyzer()
|
||||
for msg in data:
|
||||
if msg["src"] == "4ch":
|
||||
board = msg["net"]
|
||||
thread = msg["channel"]
|
||||
|
||||
# Calculate hash for post
|
||||
post_normalised = ujson.dumps(msg, sort_keys=True)
|
||||
post_normalised = orjson.dumps(msg, option=orjson.OPT_SORT_KEYS)
|
||||
hash = siphash(hash_key, post_normalised)
|
||||
hash = str(hash)
|
||||
redis_key = f"cache.{board}.{thread}.{msg['no']}"
|
||||
|
|
@ -117,19 +142,17 @@ def process_data(data):
|
|||
if key_content:
|
||||
key_content = key_content.decode("ascii")
|
||||
if key_content == hash:
|
||||
del data[index]
|
||||
# This deletes the message since the append at the end won't be hit
|
||||
continue
|
||||
else:
|
||||
data[index]["type"] = "update"
|
||||
msg["type"] = "update"
|
||||
db.r.set(redis_key, hash)
|
||||
if "now" not in data[index]:
|
||||
print("NOW NOT IN INDEX", data[index])
|
||||
for key2, value in list(data[index].items()):
|
||||
for key2, value in list(msg.items()):
|
||||
if key2 in ATTRMAP:
|
||||
data[index][ATTRMAP[key2]] = data[index][key2]
|
||||
del data[index][key2]
|
||||
if "ts" in data[index]:
|
||||
old_time = data[index]["ts"]
|
||||
msg[ATTRMAP[key2]] = msg[key2]
|
||||
del msg[key2]
|
||||
if "ts" in msg:
|
||||
old_time = msg["ts"]
|
||||
# '08/30/22(Tue)02:25:37'
|
||||
time_spl = old_time.split(":")
|
||||
if len(time_spl) == 3:
|
||||
|
|
@ -138,14 +161,42 @@ def process_data(data):
|
|||
old_ts = datetime.strptime(old_time, "%m/%d/%y(%a)%H:%M")
|
||||
# new_ts = old_ts.isoformat()
|
||||
new_ts = int(old_ts.timestamp())
|
||||
data[index]["ts"] = new_ts
|
||||
msg["ts"] = new_ts
|
||||
else:
|
||||
print("MSG WITHOUT TS PROCESS", data[index])
|
||||
continue
|
||||
raise Exception("No TS in msg")
|
||||
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
|
||||
soup = BeautifulSoup(msg["msg"], "html.parser")
|
||||
msg_str = soup.get_text(separator="\n")
|
||||
msg["msg"] = msg_str
|
||||
# Annotate sentiment/NLP
|
||||
if "msg" in msg:
|
||||
# Language
|
||||
text = Text(msg["msg"])
|
||||
try:
|
||||
lang_code = text.language.code
|
||||
lang_name = text.language.name
|
||||
msg["lang_code"] = lang_code
|
||||
msg["lang_name"] = lang_name
|
||||
except cld2_error as e:
|
||||
log.error(f"Error detecting language: {e}")
|
||||
# So below block doesn't fail
|
||||
lang_code = None
|
||||
|
||||
# Blatant discrimination
|
||||
if lang_code == "en":
|
||||
|
||||
# Sentiment
|
||||
vs = analyzer.polarity_scores(str(msg["msg"]))
|
||||
addendum = vs["compound"]
|
||||
msg["sentiment"] = addendum
|
||||
|
||||
# Tokens
|
||||
n = nlp(msg["msg"])
|
||||
for tag in TAGS:
|
||||
tag_name = tag.lower()
|
||||
tags_flag = [token.lemma_ for token in n if token.pos_ == tag]
|
||||
msg[f"words_{tag_name}"] = tags_flag
|
||||
|
||||
# Add the mutated message to the return buffer
|
||||
to_store.append(msg)
|
||||
return to_store
|
||||
|
|
|
|||
|
|
@ -5,8 +5,18 @@ redis
|
|||
siphashc
|
||||
aiohttp[speedups]
|
||||
python-dotenv
|
||||
manticoresearch
|
||||
#manticoresearch
|
||||
numpy
|
||||
ujson
|
||||
aioredis[hiredis]
|
||||
aiokafka
|
||||
vaderSentiment
|
||||
polyglot
|
||||
pyicu
|
||||
pycld2
|
||||
morfessor
|
||||
six
|
||||
nltk
|
||||
spacy
|
||||
python-Levenshtein
|
||||
orjson
|
||||
|
|
|
|||
|
|
@ -2,19 +2,13 @@
|
|||
import asyncio
|
||||
import random
|
||||
import string
|
||||
from concurrent.futures import ProcessPoolExecutor
|
||||
from datetime import datetime
|
||||
from math import ceil
|
||||
|
||||
import aiohttp
|
||||
import ujson
|
||||
from bs4 import BeautifulSoup
|
||||
from numpy import array_split
|
||||
from siphashc import siphash
|
||||
|
||||
import db
|
||||
import util
|
||||
from schemas.ch4_s import ATTRMAP
|
||||
|
||||
# CONFIGURATION #
|
||||
|
||||
|
|
@ -30,13 +24,8 @@ CRAWL_DELAY = 5
|
|||
# Semaphore value ?
|
||||
THREADS_SEMAPHORE = 1000
|
||||
|
||||
# Maximum number of CPU threads to use for post processing
|
||||
CPU_THREADS = 8
|
||||
|
||||
# CONFIGURATION END #
|
||||
|
||||
p = ProcessPoolExecutor(CPU_THREADS)
|
||||
|
||||
|
||||
class Chan4(object):
|
||||
"""
|
||||
|
|
@ -83,10 +72,12 @@ class Chan4(object):
|
|||
self.log.debug(f"Got boards: {self.boards}")
|
||||
|
||||
async def get_thread_lists(self, boards):
|
||||
self.log.debug(f"Getting thread list for {boards}")
|
||||
# self.log.debug(f"Getting thread list for {boards}")
|
||||
board_urls = {board: f"{board}/catalog.json" for board in boards}
|
||||
responses = await self.api_call(board_urls)
|
||||
to_get = []
|
||||
flat_map = [board for board, thread in responses]
|
||||
self.log.debug(f"Got thread list for {flat_map}: {len(responses)}")
|
||||
for mapped, response in responses:
|
||||
if not response:
|
||||
continue
|
||||
|
|
@ -95,7 +86,6 @@ class Chan4(object):
|
|||
no = threads["no"]
|
||||
to_get.append((mapped, no))
|
||||
|
||||
self.log.debug(f"Got thread list for {mapped}: {len(response)}")
|
||||
if not to_get:
|
||||
return
|
||||
split_threads = array_split(to_get, ceil(len(to_get) / THREADS_CONCURRENT))
|
||||
|
|
@ -122,46 +112,20 @@ class Chan4(object):
|
|||
(board, thread): f"{board}/thread/{thread}.json"
|
||||
for board, thread in thread_list
|
||||
}
|
||||
self.log.debug(f"Getting information for threads: {thread_urls}")
|
||||
# self.log.debug(f"Getting information for threads: {thread_urls}")
|
||||
responses = await self.api_call(thread_urls)
|
||||
self.log.debug(f"Got information for threads: {thread_urls}")
|
||||
self.log.debug(f"Got information for {len(responses)} threads")
|
||||
|
||||
all_posts = {}
|
||||
for mapped, response in responses:
|
||||
if not response:
|
||||
continue
|
||||
board, thread = mapped
|
||||
self.log.debug(f"Got thread content for thread {thread} on board {board}")
|
||||
all_posts[mapped] = response["posts"]
|
||||
|
||||
# Split into 10,000 chunks
|
||||
if not all_posts:
|
||||
return
|
||||
await self.handle_posts(all_posts)
|
||||
# threads_per_core = int(len(all_posts) / CPU_THREADS)
|
||||
# for i in range(CPU_THREADS):
|
||||
# new_dict = {}
|
||||
# pulled_posts = self.take_items(all_posts, threads_per_core)
|
||||
# for k, v in pulled_posts:
|
||||
# if k in new_dict:
|
||||
# new_dict[k].append(v)
|
||||
# else:
|
||||
# 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))
|
||||
# # print("SPLIT CHUNK", len(split_posts))
|
||||
# for posts in split_posts:
|
||||
# print("SPAWNED THREAD TO PROCESS", len(posts), "POSTS")
|
||||
# await self.handle_posts_thread(posts)
|
||||
|
||||
# await self.handle_posts_thread(all_posts)
|
||||
|
||||
@asyncio.coroutine
|
||||
def handle_posts_thread(self, posts):
|
||||
loop = asyncio.get_event_loop()
|
||||
yield from loop.run_in_executor(p, self.handle_posts, posts)
|
||||
|
||||
async def handle_posts(self, posts):
|
||||
to_store = []
|
||||
|
|
@ -170,50 +134,13 @@ class Chan4(object):
|
|||
for index, post in enumerate(post_list):
|
||||
posts[key][index]["type"] = "msg"
|
||||
|
||||
# # Calculate hash for post
|
||||
# post_normalised = ujson.dumps(post, sort_keys=True)
|
||||
# hash = siphash(self.hash_key, post_normalised)
|
||||
# hash = str(hash)
|
||||
# redis_key = f"cache.{board}.{thread}.{post['no']}"
|
||||
# key_content = db.r.get(redis_key)
|
||||
# if key_content:
|
||||
# key_content = key_content.decode("ascii")
|
||||
# if key_content == hash:
|
||||
# continue
|
||||
# else:
|
||||
# posts[key][index]["type"] = "update"
|
||||
# #db.r.set(redis_key, hash)
|
||||
|
||||
# for key2, value in list(post.items()):
|
||||
# if key2 in ATTRMAP:
|
||||
# post[ATTRMAP[key2]] = posts[key][index][key2]
|
||||
# del posts[key][index][key2]
|
||||
# if "ts" in post:
|
||||
# old_time = posts[key][index]["ts"]
|
||||
# # '08/30/22(Tue)02:25:37'
|
||||
# time_spl = old_time.split(":")
|
||||
# if len(time_spl) == 3:
|
||||
# old_ts = datetime.strptime(old_time, "%m/%d/%y(%a)%H:%M:%S")
|
||||
# else:
|
||||
# old_ts = datetime.strptime(old_time, "%m/%d/%y(%a)%H:%M")
|
||||
# # new_ts = old_ts.isoformat()
|
||||
# new_ts = int(old_ts.timestamp())
|
||||
# posts[key][index]["ts"] = new_ts
|
||||
# if "msg" in post:
|
||||
# soup = BeautifulSoup(posts[key][index]["msg"], "html.parser")
|
||||
# msg = soup.get_text(separator="\n")
|
||||
# posts[key][index]["msg"] = msg
|
||||
|
||||
posts[key][index]["src"] = "4ch"
|
||||
posts[key][index]["net"] = board
|
||||
posts[key][index]["channel"] = thread
|
||||
|
||||
to_store.append(posts[key][index])
|
||||
|
||||
# print({name_map[name]: val for name, val in post.items()})
|
||||
# print(f"Got posts: {len(posts)}")
|
||||
if to_store:
|
||||
print("STORING", len(to_store))
|
||||
await db.queue_message_bulk(to_store)
|
||||
|
||||
async def fetch(self, url, session, mapped):
|
||||
|
|
@ -238,7 +165,7 @@ class Chan4(object):
|
|||
async with aiohttp.ClientSession(connector=connector) as session:
|
||||
for mapped, method in methods.items():
|
||||
url = f"{self.api_endpoint}/{method}"
|
||||
self.log.debug(f"GET {url}")
|
||||
# self.log.debug(f"GET {url}")
|
||||
task = asyncio.create_task(self.bound_fetch(sem, url, session, mapped))
|
||||
# task = asyncio.ensure_future(self.bound_fetch(sem, url, session))
|
||||
tasks.append(task)
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
import asyncio
|
||||
|
||||
import ujson
|
||||
import orjson
|
||||
|
||||
import db
|
||||
import util
|
||||
|
|
@ -8,9 +8,13 @@ from processing import process
|
|||
|
||||
SOURCES = ["4ch", "irc", "dis"]
|
||||
KEYPREFIX = "queue."
|
||||
CHUNK_SIZE = 90000
|
||||
|
||||
# Chunk size per source (divide by len(SOURCES) for total)
|
||||
CHUNK_SIZE = 9000
|
||||
ITER_DELAY = 0.5
|
||||
|
||||
log = util.get_logger("ingest")
|
||||
|
||||
|
||||
class Ingest(object):
|
||||
def __init__(self):
|
||||
|
|
@ -18,8 +22,6 @@ class Ingest(object):
|
|||
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)
|
||||
|
|
@ -31,11 +33,8 @@ class Ingest(object):
|
|||
chunk = await db.ar.spop(key, CHUNK_SIZE)
|
||||
if not chunk:
|
||||
continue
|
||||
# self.log.info(f"Got chunk: {chunk}")
|
||||
for item in chunk:
|
||||
item = ujson.loads(item)
|
||||
# self.log.info(f"Got item: {item}")
|
||||
item = orjson.loads(item)
|
||||
items.append(item)
|
||||
if items:
|
||||
print("PROCESSING", len(items))
|
||||
await process.spawn_processing_threads(items)
|
||||
|
|
|
|||
Loading…
Reference in New Issue