clemfromspace

from billiard.context import Process
from scrapy.crawler import Crawler
from scrapy import signals
from scrapy.utils.project import get_project_settings
from twisted.internet import reactor

from celery_app import app


class CrawlerProcess(Process):

miticojo

apiVersion: v1
kind: ServiceAccount
metadata:
  name: elasticsearch-logging
  namespace: kube-system
  labels:
    k8s-app: elasticsearch-logging
    version: v1
    kubernetes.io/cluster-service: "true"
    addonmanager.kubernetes.io/mode: Reconcile

luckydev

# maximum capability of system
user@ubuntu:~$ cat /proc/sys/fs/file-max
708444

# available limit
user@ubuntu:~$ ulimit -n
1024

# To increase the available limit to say 200000
user@ubuntu:~$ sudo vim /etc/sysctl.conf

coingraham

import boto3
import json

# update these with your actual settings
maxreceivecount = 3
queueurl = "https://sqs.region.amazonaws.com/accountnumber/queuename"
deadlettertargetarn = "arn:aws:sqs:region:accountnumber:deadletterqueuename"

# you could just create a json string
policy = {"maxReceiveCount" : maxreceivecount, "deadLetterTargetArn": deadlettertargetarn}

drmalex07

# This is a quickstart! In the real world use a real broker (message queue)
# such as Redis or RabbitMQ !!
BROKER_URL = 'sqlalchemy+sqlite:///tasks.db'

CELERY_RESULT_BACKEND = "db+sqlite:///results.db"

CELERY_IMPORTS = ['tasks']

CELERY_TASK_SERIALIZER = 'json'
CELERY_RESULT_SERIALIZER = 'json'

javierarilos

'use strict';
/**
 * Implementing main examples from Martin Fowler article
 * on Collection Pipeline programming pattern:
 * http://martinfowler.com/articles/collection-pipeline/
 * with #javascript and underscore.js: http://underscorejs.org/
 *
 * tested with nodejs v0.10.32
 *
 * underscore.js is required:

somandubey

How to increase ulimit in Linux:

• Step 1 (ulimit): open the sysctl.conf and add this line fs.file-max = 65536

    vi /etc/sysctl.conf   
  

  add following at end of file in above file:

    fs.file-max = 65536
  

save and exit.

prschmid

"""An example of how to perform a multi-threaded unit test of a web service.

The particulars of this example make use of some conventions used when
developing a web service when using the Flask microframework, but can be
modified to fit most needs.
"""

import json
import threading
import time

ibeex

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