import scrapy
from scrapy.crawler import CrawlerProcess
class MySpider1(scrapy.Spider):
# Your first spider definition
...
class MySpider2(scrapy.Spider):
# Your second spider definition
...
process = CrawlerProcess()
process.crawl(MySpider1)
process.crawl(MySpider2)
process.start()
As here we are running our Spider in 2 parallel.
So is there any number of limit of running the parallel job in scrapy. As when I have run with 10 process, in some result i don't get any record.
As I have run with different number of process, but not sure what is the limit number for running the parallel and if there any example of running the parallel process, please share.
Related
These days I've been stucked in problem of speeding up groupby&apply,Here is code:
dat = dat.groupby(['glass_id','label','step'])['equip'].apply(lambda x:'_'.join(sorted(list(x)))).reset_index()
which cost large time when data size grows.
I've try to change the groupby&apply to for type which didn't work;
then I tried to use unique() but still fail to speed up the running time.
I wanna a update code for less run-time,and gonna be very appreciate if there is a solvement to this problem
I think you can consider to use multiprocessing
Check the following example
import multiprocessing
import numpy as np
# The function which you use in conjunction with multiprocessing
def loop_many(sub_df):
grouped_by_KEY_SEQ_and_count=sub_df.groupby(['KEY_SEQ']).agg('count')
return grouped_by_KEY_SEQ_and_count
# You will use 6 processes (which is configurable) to process dataframe in parallel
NUMBER_OF_PROCESSES=6
pool = multiprocessing.Pool(processes=NUMBER_OF_PROCESSES)
# Split dataframe into 6 sub-dataframes
df_split = np.array_split(pre_sale, NUMBER_OF_PROCESSES)
# Process split sub-dataframes by loop_many() on multiple processes
processed_sub_dataframes=pool.map(loop_many,df_split)
# Close multiprocessing pool
pool.close()
pool.join()
concatenated_sub_dataframes=pd.concat(processed_sub_dataframes).reset_index()
I try to run multiple functions at the same time in Jupiter Notebook.
I have two web scraping functions that use Selenium and run for an infinite amount of time, both always creating an updated DataFrame. Another function merges the two DataFrames and does some calculations.
As the data always changes and the calculations from the different DataFrames need to be calculated within the same second (The two DataFrames update every 5 seconds), I wonder how I can run all functions at the same time.
As my code is mainly WebScraping I used this more to describe my goal and hopefully make it more readable. I already tried using 'multiprocessing' but it just does not do anything in the notebook.
def FirstWebScraping():
while True:
time.sleep(5).
#getting all data for DataFrame
def SecondtWebScraping():
while True:
time.sleep(5).
#getting all data for DataFrame
def Calculations():
while True:
#merging DataFrame from First- and SecondWebScraping
#doing calculations
#running this function infinite and looking for specific values
#Goal
def run_all_at_the_same_time()
FirstWebScraping()
SecondWebScraping()
Calculations()
Even though threading does not show the same benefits as multiprocessing it worked for me and with selenium. I put a waiting time at the beginning for the Calculations function and from there they were all looped infinitely.
from threading import Thread
if __name__ == '__main__':
Thread(target = FirstWebScraping).start()
Thread(target = SecondWebscraping).start()
Thread(target = Calculations).start()
You can run multiprocessing in Jupyter, if you follow two rules:
Put the worker functions in a separate module.
Protect the main process-only code with if __name__ == '__main__':
Assuming your three functions are moved to worker.py:
import multiprocessing as mp
import worker
if __name__ == '__main__':
mp.Process(target=worker.FirstWebScraping).start()
mp.Process(target=worker.SecondWebscraping).start()
mp.Process(target=worker.Calculations).start()
import time
from multiprocessing import Pool, RawArray, sharedctypes
from ctypes import c_int
def init_worker(X):
print(f"{X}")
def worker_func(i):
print(f"{X}")
time.sleep(i) # Some heavy computations
return
# We need this check for Windows to prevent infinitely spawning new child
# processes.
if __name__ == '__main__':
X =sharedctypes.RawValue(c_int)
X=3
with Pool(processes=4, initializer=init_worker, initargs=(X)) as pool:
pool.map(worker_func, [1,2,3,4])
print(X)
--- I am simply trying to print the value of X in each subprocess. This is a toy program in order to check whether I can share a value
and update it by using multiple processes.
This program spawns infinite number of processes because it there is no ,(comma) after the X in initargs=(X) ; it should be initargs=(X,) . That is because, if the comma is left-out that causes an error.
I have developed a machine learning python script (let's call it classify_obj written with python 3.6) that imports TensorFlow. It was developed initially for bulk analysis but now I find the need to run this script repeatedly on smaller datasets to cater for more real time usage. I am doing this on Linux RH7.
Process Flow:
Master tool (written in Java) call classify_obj with object input to categorize.
classify_obj generates the classification result as a csv (takes about 7-10s)
Master tool reads the result from #2
Master tool proceeds to do other logic
Repeat #1 with next object input
To breakdown the time taken, I switched off the main logic and just do the modules import without performing any other action. I found that the import takes about 4-5s out of the 7-10s run time on the small dataset. The classification takes about 2s. I am also looking at other ways to reduce the run time for other areas but the bulk seems to be from the import.
Import time: 4-6s
Classify time: 1s
Read, write and other logic time: 0.2s
I am thinking what options are there to reduce the import time?
One idea I had was to modify the classify_obj into a "stay alive" process. The master tool after completing all its activity will stop this process/service. The intent (not sure if this would be the case) is that all the required libraries are already loaded during the process start and when the master tool calls that process/service, it will only incur the classification time instead of needing to import the libraries repeated.
What do you think about this? Also how can I set this up on Linux RHEL 7.4? Some reference links would be greatly appreciated.
Other suggestion would be greatly appreciated.
Thanks and have a great day!
This is the solution I designed to achieve the above.
Reference: https://realpython.com/python-sockets/
I have to create 2 scripts.
1. client python script: Used to pass the raw data to be classified to the server python script using socket programming.
server python script: Loads the keras (tensorflow) lib and model at launch. Continues to stay alive until a 'stop' request from client (to exit the while loop). When the client script sends the data to the server script, server script will process the incoming data and return a ok/not ok output back to the client script.
In the end, the classification time is reduced to 0.1 - 0.3s.
Client Script
import socket
import argparse
from argparse import ArgumentParser
def main():
parser = ArgumentParser(description='XXXXX')
parser.add_argument('-i','--input', default='NA', help='Input txt file path')
parser.add_argument('-o','--output', default='NA', help='Output csv path with class')
parser.add_argument('-stop','--stop', default='no', help='Stop the server script')
args = parser.parse_args()
str = args.input + ',' + args.output + ',' + args.stop
HOST = '127.0.0.1' # The server's hostname or IP address
PORT = 65432 # The port used by the server
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
sock.connect((HOST, PORT))
bytedata = str.encode()
sock.send(bytedata)
data = sock.recv(1024)
print('Received', data)
if __name__== "__main__":
main()
Server Script
def main():
HOST = '127.0.0.1' # Standard loopback interface address (localhost)
PORT = 65432 # Port to listen on (non-privileged ports are > 1023)
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
sock.bind((HOST,PORT))
sock.listen(5)
stop_process = 'no'
while (stop_process == 'no'):
# print('Waiting for connection')
conn, addr = sock.accept()
data = ''
try:
# print('Connected by', addr)
while True:
data = conn.recv(1024)
if data:
stop_process = process_input(data) # process_input function processes incoming data. If client sends 'yes' for the stop argument, the stop_process variable will be set to 'yes' by the function.
byte_reply = stop_process.encode()
conn.sendall(byte_reply) # send reply back to client
else:
break
conn.close()
# print('Closing connection',addr)
finally:
conn.close()
if __name__== "__main__":
main()
I am doing scrapy project I want run multiple spiders at a time
This is code for run spiders from script. I getting error .. how to do
from spiders.DmozSpider import DmozSpider
from spiders.CraigslistSpider import CraigslistSpider
from scrapy import signals, log
from twisted.internet import reactor
from scrapy.crawler import Crawler
from scrapy.settings import Settings
TO_CRAWL = [DmozSpider, CraigslistSpider]
RUNNING_CRAWLERS = []
def spider_closing(spider):
"""Activates on spider closed signal"""
log.msg("Spider closed: %s" % spider, level=log.INFO)
RUNNING_CRAWLERS.remove(spider)
if not RUNNING_CRAWLERS:
reactor.stop()
log.start(loglevel=log.DEBUG)
for spider in TO_CRAWL:
settings = Settings()
# crawl responsibly
settings.set("USER_AGENT", "Kiran Koduru (+http://kirankoduru.github.io)")
crawler = Crawler(settings)
crawler_obj = spider()
RUNNING_CRAWLERS.append(crawler_obj)
# stop reactor when spider closes
crawler.signals.connect(spider_closing, signal=signals.spider_closed)
crawler.configure()
crawler.crawl(crawler_obj)
crawler.start()
blocks process so always keep as the last statement
reactor.run()
Sorry to not answer the question itself but just bringing into your attention scrapyd and scrapinghub (at least for a quick test). reactor.run() (when you make it) will run any number of Scrapy instances on a single CPU. Do you want this side effect? Even if you have a look on scrapyd's code, they don't run multiple instances with a single thread but they do fork/spawn subprocesses.
You need something like the code below. You can easily find it from Scrapy docs :)
First utility you can use to run your spiders is
scrapy.crawler.CrawlerProcess. This class will start a Twisted reactor
for you, configuring the logging and setting shutdown handlers. This
class is the one used by all Scrapy commands.
# -*- coding: utf-8 -*-
import sys
import logging
import traceback
from scrapy.crawler import CrawlerProcess
from scrapy.conf import settings
from scrapy.utils.project import get_project_settings
from spiders.DmozSpider import DmozSpider
from spiders.CraigslistSpider import CraigslistSpider
SPIDER_LIST = [
DmozSpider, CraigslistSpider
]
if __name__ == "__main__":
try:
## set up the crawler and start to crawl one spider at a time
process = CrawlerProcess(get_project_settings())
for spider in SPIDER_LIST:
process.crawl(spider)
process.start()
except Exception, e:
exc_type, exc_obj, exc_tb = sys.exc_info()
logging.info('Error on line {}'.format(sys.exc_info()[-1].tb_lineno))
logging.info("Exception: %s" % str(traceback.format_exc()))
References:
http://doc.scrapy.org/en/latest/topics/practices.html