duydo

# Use this script to test that your Telegram bot works.
#
# Install the dependency
#
#   $ gem install telegram_bot
#
# Run the bot
#
#   $ ruby bot.rb
#

duydo

Decoding the data in /proc/net/tcp:

Linux 5.x  /proc/net/tcp
Linux 6.x  /proc/PID/net/tcp

Given a socket:

$ ls -l  /proc/24784/fd/11
lrwx------ 1 jkstill dba 64 Dec  4 16:22 /proc/24784/fd/11 -> socket:[15907701]

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##################################################################
# /etc/elasticsearch/elasticsearch.yml
#
# Base configuration for a write heavy cluster
#

# Cluster / Node Basics
cluster.name: logng

# Node can have abritrary attributes we can use for routing

duydo

import operator
from pprint import pprint

def is_dict(d):
    return isinstance(d, dict)
    
def get(c, k, default=None):
    try:
        return c[k]
    except (IndexError, KeyError, TypeError):

duydo

nginx/
!nginx/.gitkeep
!nginx/logs/.gitkeep

src/

tmp/

duydo

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duydo

#System Design Cheatsheet

Picking the right architecture = Picking the right battles + Managing trade-offs

##Basic Steps

1. Clarify and agree on the scope of the system

• User cases (description of sequences of events that, taken together, lead to a system doing something useful)
  • Who is going to use it?
  • How are they going to use it?

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1. General Background and Overview

• Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
• Models and Issues in Data Stream Systems
• Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
• Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
• [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep

duydo

import sys
from gevent import server
from gevent.baseserver import _tcp_listener
from gevent.monkey import patch_all; patch_all()
from multiprocessing import Process, current_process, cpu_count


def note(format, *args):
    sys.stderr.write('[%s]\t%s\n' % (current_process().name, format%args))

duydo

I've been hacking away recently at a JVM framework for doing asynchronous, non-blocking applications using a variation of the venerable Reactor pattern. The core of the framework is currently in Java. I started with Scala then went with Java and am now considering Scala again for the core. What can I say: I'm a grass-is-greener waffler! :) But it understands how to invoke Groovy Closures, Scala anonymous functions, and Clojure functions, so you can use the framework directly without needing wrappers.

I've been continually micro-benchmarking this framework because I feel that the JVM is a better foundation on which to build highly-concurrent, highly-scalable, C100K applications than V8 or Ruby. The problem has been, so far, no good tools exist for JVM developers to leverage the excellent performance and manageability of the JVM. This yet-to-be-publicly-released framework is an effort to give Java, Groovy, Scala, [X JVM language] developers access to an easy-to-use programming model that removes the necessity