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How-To Tutorials

7019 Articles
article-image-tree-test-and-surveys
Packt
21 Feb 2018
13 min read
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Tree Test and Surveys

Packt
21 Feb 2018
13 min read
In this article by Pablo Perea and  Pau Giner, authors of the book UX Design for Mobile, we will cover how to use different techniques that can be applied according to the needs of the project and the how to obtain the information that we want. (For more resources related to this topic, see here.) Tree Test This is also called reverse card sorting. This is a method where the participants try to find elements in a given structure. The objective of this method is to discover findability problems and improve the organization and labeling system. The organization structure used should represent a realistic navigation for the application or web page you are evaluating. If you don’t have one by the time the experiment is taking place, try to create a real scenario, as using a fake organization will not lead to really valuable results. There are some platforms available to perform this type of experiment with a computer. One example is https://www.optimalworkshop.com. This can have several advantages: the experiment can be carried out without requiring a physical displacement by the participant, and you can also study the participant steps and not just analyze whether the participant succeeded or not. It can be that the participants found the objectives but had to make many attempts to achieve them. The method steps Create the structure: Type or create the navigation structure with all the different levels you want to evaluate. Create a set of findability tasks: Think about different items that the participant should find or give a location in the given structure. Test with participants: The participant will receive a set of tasks to do. The following are some examples of possible tasks: Find some different products to buy Contact the customer support Get the shipping rates The results: At the end, we should have a success rate for each of the tasks. Tasks such as finding products in a store must be done several times with products located in different sections. This will help us classify our assortment and show us how to organize the first levels of the structure better. How to improve the organization Once we find the main weak points and workarounds we have in our structure, we can create alternative structures to retest and try to find better results. We can repeat this process several times until we get the desired results. The Information Architecture is the science field of organizing and labeling content in a web page to support usability and findability. There's a growing community of Information architecture specialists that supports the Information architecture Institute--https://en.wikipedia.org/wiki/Information_architecture. There are some general lines of work in which we have to invest time in order to improve our Information architecture. Content organization The content can be ordered by following different schemes, in the same way that a supermarket orders products according to different criteria. We should try to find the one that better fits our user needs. We can order the content, dividing it into groups according to nature, goals, audience, chronological entry, and so on. Each of these approaches will lead to different results and each will work better with different kinds of users. In the case of mobile applications, it is common to have certain sections where they mix contents of a different nature, for instance, integrating messages for the user in the contents of the activity view. However, an abuse of these types of techniques can lead to turning the section into a confusing area for the user. Areas naming There are words that have a completely different meaning for one person to another, especially if those people are thinking in different fields when they use our solution. Understanding our user needs, and how the think and speak, will help us provide clear names for sections and subsections. For example, the word pool will represent a different set of products for a person looking for summer products than for a person looking for games. In the case of applications, we will have to find a balance between simplicity and clarity. If space permits, adding a label along with the icon will clarify and reduce the possible ambiguities that may be encountered in recognizing the meaning of these graphic representations. In the case of mobiles, where space is really small, we can find some universal icons, but we must test with users to ensure that they interpret them properly. In the following examples, you can find two different approaches. In the Gmail app, attachment and send are known icons and can work without a label. We find a very different scenario in the Samsung Clock app, where it would be really difficult to differentiate between the Alarm, the Stopwatch, and the Timer without labels:      Samsung system and Google Gmail App screenshots (source: Screenshot from Google Gmail App, source: Screenshot from Gmail App) The working memory limit The way the information is displayed to the user can drastically change the ease with which it is understood. When we talk about mobiles, where space is very limited, limiting the number of options and providing a navigation adapted to small spaces can help our user have a more satisfactory experience. As you probably know, the human working memory is not limitless, and it is commonly supposed to be limited to remembering a maximum of seven elements (https://en.wikipedia.org/wiki/The_Magical_Number_Seven,_Plus_or_Minus_Two). Some authors such as Nelson Cowan suggested that the number of elements an adult can remember while performing a task is even lower, and gives the number of reference as four (https://en.wikipedia.org/wiki/Working_memory). This means that your users will understand the information you give them better if you block it into groups according to their limitations. Once we create a new structure, we can evaluate the efficiency of this new structure versus the last version. With small improvements, we will be able to increase the user engagement. Another way to learn about how the user understands the organization of our app or web is by testing a competitor product. This is one of the cheapest ways to have a quick prototype. Evaluate as many versions as you can; in each review, you will find new ideas to organize and show the content of your application or web app better. Surveys Surveys allow us to gather information from lots of participants without too much effort. Sometimes, we need information from a big group of people, and interviewing them one by one will not be affordable. Instead of that, surveys can quickly provide answers from lots of participants and analyze the results with bulk methods. It is not the purpose of this book to deal in depth with the field of questionnaires since there are books devoted entirely to this subject. Nevertheless, we will give some brushstrokes on the subject since they are commonly used to gather information in both web pages and mobile applications. Creating proper questions is a key part of the process that will reduce the noise and help the participants provide useful answers. Some questions will require more effort to analyze, but they will give us answers with deeper level of detail. Questions with pre-established answers are usually easier to automatize, and we can get results in less time. What we want to discover? The first thing to do is to define the objective for which we are making a survey. Working with a clear objective will help the process be focused and will get better results. Plan carefully and determine the information that you really need at that moment. We should avoid surveys with lots of questions that do not have a clear purpose. They will produce poor outcome and result in meaningless exercises for the participants. On the contrary, if we have a general leitmotiv for the questionnaire, it will also help the participants understand how the effort of completing the survey will help the company, and therefore it will give clear value to the time expended. You can plan your survey according to different planning approaches, your questions can be focussed on short and long term goals: Long-term planning: Understanding your users expectations and their view about your product in the long term will help plan the evolution of your application and help create new features that match their needs. For example, imagine that you are designing a music application, and you are unsure about focusing on mass majority music or maybe giving more visibility to amateur groups. Creating a long-term survey can help you understand what your users want to find in your platform and plan changes that match the conclusions extracted from the survey analysis. Short-term planning: This is usually related to operational actions. The objective with these kind of surveys is to gather information for taking actions later with a defined mission. These kind of surveys are useful when we need to choose between two options, that is, whether we are deciding to make a change in our platform or not. For example, it can help to decide what type of information is most important for the user when choosing between one group and another, so we can make that information more visible. We will take better decisions by understanding the main aspects our users will expect to find in our platform. Find the participants Depending on the goal of the survey, we can use a wider range of participants or reduce their number, filtering by their demographics, experience, or the relationship with our brand or products. If the goal is to expand our number of users, it may be interesting to expand the search range to participants outside our current set of users. Looking for new niches and interesting features for our potential users can make new users try out our application. If, on the contrary, our objective is to keep our current users loyal, it can be a great source of improvement to consult them about their preferences and their opinions about the things that work properly in our application. This data, along with the data of use and navigation, will let us see areas for improvement, and we will be able to solve problems of navigation and usability. Determining the questions We can ask different types of questions; depending on the type, we will get more or less detailed answers. If we choose the right type, we can save analysis effort, or we can reduce the number of participants when we require a deep analysis of each response. It is common to include questions at the beginning of the questionnaires in order to classify the results. They are usually called filtering or screening questions, and they will allow us to analyze the answers based on data such as age, gender, or technical skills. These questions have the objective of knowing the person answering the survey. If we know the person solving the questionnaire, we will be able to determine whether the answers given by this user are useful for our goals or not. We can add questions about the experience the participant has with general technology, or with our app, and about the relation with the brand. We can create two kinds of questions based on the type of answers the participant can provide; each of them, therefore, will lead to different results. Open-answer questions The objective of this type of questions is to know more about the participant without guiding the answers. We will try to ask objectively for a subject without providing possible answers. The participant will answer these type of questions with open-ended answers, so it will be easier to know more about how that participant thinks and which aspects are proving more or less satisfactory. While the advantage of this kind of questions is that you will gain a lot of insights and new ideas, the con is the cost of managing big amounts of data. So, these type of questions will be more useful when the number of participants is reduced. Here are some examples of open-answer questions: How often have you been using our customer service? How was your last purchase experience on our platform? Questions with pre-established answers These type of questions facilitate the analysis when the number of participants is high. We will create questions with a clear objective and give different options to respond. Participants will be able to choose one of the options in response. The analysis of these types of questions can be automated and therefore is faster, but it will not give us as detailed information as an open question, in which the participant can expose all his ideas about the matter in the question. The following is an example of a question with pre-established answers: Questions: How many times have you used our application in the last week? Answers: 1) More than five times 2) Two to Five 3) Once 4) None Another great advantage is the facility to answer these types of questions when the participant does not have much time or interest to respond. In environments such as mobile phones, typing long answers can be costly and frustrating. With these types of questions, we can offer answers that the user can select with a single click. This can help increase the number of participants completing the form. It is common to mix both the types of questions. Open-answer questions where the user can respond in more detail can be included as optional questions. The participants willing to share more information can use these fields to introduce more detailed answers. This way, we can make a quicker analysis on the questions with pre-established answers and analyze the questions that require more precise revision later. Humanize the forms When we create a form, we must think about the person who will answer it. Almost no one likes to fill in questionnaires, especially if they are long and complex. To make our participants feel comfortable filling out all the answers on our form, we have to try to treat the process as a human relationship: The first thing we should do is to explain the reason of our form. If our participants understand how their answers will be used in the project, and how they can help us achieve the goal, they will feel more encouraged to answer the questions and take their role seriously. Ask only what is strictly necessary for the purpose you have set for it. We must prevent different departments from introducing questions without a common goal. If the form will answer concerns of different departments, all of them should have the same goal. This way, the form will have more cohesion. The tone used on the form should be friendly and clear. We should not go beyond the limits of indiscretion with our questions, or the participant may feel overwhelmed, especially if the participants of our study are not users of our application or our services, we must treat them as unknown. Being respectful and kind is a key point in getting high participation. Summary In the article we saw how to apply Tree Test and how to conduct surveys to gain information that you want. Resources for Article:   Further resources on this subject: Trends UX Design [article] Building Mobile Apps [article] Auditing Mobile Applications [article]
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Savia Lobo
21 Feb 2018
5 min read
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How to create a conversational assistant or chatbot using Python

Savia Lobo
21 Feb 2018
5 min read
[box type="note" align="" class="" width=""]This article is an excerpt taken from a book Natural Language Processing with Python Cookbook written by Krishna Bhavsar, Naresh Kumar, and Pratap Dangeti. This book includes unique recipes to teach various aspects of performing Natural Language Processing with NLTK—the leading Python platform for the task.[/box] Today we will learn to create a conversational assistant or chatbot using Python programming language. Conversational assistants or chatbots are not very new. One of the foremost of this kind is ELIZA, which was created in the early 1960s and is worth exploring. In order to successfully build a conversational engine, it should take care of the following things: 1. Understand the target audience 2. Understand the natural language in which communication happens.  3. Understand the intent of the user 4. Come up with responses that can answer the user and give further clues NLTK has a module, nltk.chat, which simplifies building these engines by providing a generic framework. Let's see the available engines in NLTK: Engines Modules Eliza nltk.chat.eliza Python module Iesha nltk.chat.iesha Python module Rude nltk.chat.rudep ython module Suntsu Suntsu nltk.chat.suntsu module Zen nltk.chat.zen module In order to interact with these engines we can just load these modules in our Python program and invoke the demo() function. This recipe will show us how to use built-in engines and also write our own simple conversational engine using the framework provided by the nltk.chat module. Getting ready You should have Python installed, along with the nltk library. Having an understanding of regular expressions also helps. How to do it...    Open atom editor (or your favorite programming editor).    Create a new file called Conversational.py.    Type the following source code:    Save the file.    Run the program using the Python interpreter.    You will see the following output: How it works... Let's try to understand what we are trying to achieve here. import nltk This instruction imports the nltk library into the current program. def builtinEngines(whichOne): This instruction defines a new function called builtinEngines that takes a string parameter, whichOne: if whichOne == 'eliza': nltk.chat.eliza.demo() elif whichOne == 'iesha': nltk.chat.iesha.demo() elif whichOne == 'rude': nltk.chat.rude.demo() elif whichOne == 'suntsu': nltk.chat.suntsu.demo() elif whichOne == 'zen': nltk.chat.zen.demo() else: print("unknown built-in chat engine {}".format(whichOne)) These if, elif, else instructions are typical branching instructions that decide which chat engine's demo() function is to be invoked depending on the argument that is present in the whichOne variable. When the user passes an unknown engine name, it displays a message to the user that it's not aware of this engine. It's a good practice to handle all known and unknown cases also; it makes our programs more robust in handling unknown situations def myEngine():. This instruction defines a new function called myEngine(); this function does not take any parameters. chatpairs = ( (r"(.*?)Stock price(.*)", ("Today stock price is 100", "I am unable to find out the stock price.")), (r"(.*?)not well(.*)", ("Oh, take care. May be you should visit a doctor", "Did you take some medicine ?")), (r"(.*?)raining(.*)", ("Its monsoon season, what more do you expect ?", "Yes, its good for farmers")), (r"How(.*?)health(.*)", ("I am always healthy.", "I am a program, super healthy!")), (r".*", ("I am good. How are you today ?", "What brings you here ?")) ) This is a single instruction where we are defining a nested tuple data structure and assigning it to chat pairs. Let's pay close attention to the data structure: We are defining a tuple of tuples Each subtuple consists of two elements: The first member is a regular expression (this is the user's question in regex format) The second member of the tuple is another set of tuples (these are the answers) def chat(): print("!"*80) print(" >> my Engine << ") print("Talk to the program using normal english") print("="*80) print("Enter 'quit' when done") chatbot = nltk.chat.util.Chat(chatpairs, nltk.chat.util.reflections) chatbot.converse() We are defining a subfunction called chat()inside the myEngine() function. This is permitted in Python. This chat() function displays some information to the user on the screen and calls the nltk built-in nltk.chat.util.Chat() class with the chatpairs variable. It passes nltk.chat.util.reflections as the second argument. Finally we call the chatbot.converse() function on the object that's created using the chat() class. chat() This instruction calls the chat() function, which shows a prompt on the screen and accepts the user's requests. It shows responses according to the regular expressions that we have built before: if   name    == '  main  ': for engine in ['eliza', 'iesha', 'rude', 'suntsu', 'zen']: print("=== demo of {} ===".format(engine)) builtinEngines(engine) print() myEngine() These instructions will be called when the program is invoked as a standalone program (not using import). They do these two things: Invoke the built-in engines one after another (so that we can experience them) Once all the five built-in engines are excited, they call our myEngine(), where our customer engine comes into play We have learned to create a chatbot of our own using the easiest programming language ‘Python’. To know more about how to efficiently use NLTK and implement text classification, identify parts of speech, tag words, etc check out Natural Language Processing with Python Cookbook.
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article-image-ipv6-unix-domain-sockets-and-network-interfaces
Packt
21 Feb 2018
38 min read
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IPv6, Unix Domain Sockets, and Network Interfaces

Packt
21 Feb 2018
38 min read
 In this article, given by Pradeeban Kathiravelu, author of the book Python Network Programming Cookbook - Second Edition, we will cover the following topics: Forwarding a local port to a remote host Pinging hosts on the network with ICMP Waiting for a remote network service Enumerating interfaces on your machine Finding the IP address for a specific interface on your machine Finding whether an interface is up on your machine Detecting inactive machines on your network Performing a basic IPC using connected sockets (socketpair) Performing IPC using Unix domain sockets Finding out if your Python supports IPv6 sockets Extracting an IPv6 prefix from an IPv6 address Writing an IPv6 echo client/server (For more resources related to this topic, see here.) This article extends the use of Python's socket library with a few third-party libraries. It also discusses some advanced techniques, for example, the asynchronous asyncore module from the Python standard library. This article also touches upon various protocols, ranging from an ICMP ping to an IPv6 client/server. In this article, a few useful Python third-party modules have been introduced by some example recipes. For example, the network packet capture library, Scapy, is well known among Python network programmers. A few recipes have been dedicated to explore the IPv6 utilities in Python including an IPv6 client/server. Some other recipes cover Unix domain sockets. Forwarding a local port to a remote host Sometimes, you may need to create a local port forwarder that will redirect all traffic from a local port to a particular remote host. This might be useful to enable proxy users to browse a certain site while preventing them from browsing some others. How to do it... Let us create a local port forwarding script that will redirect all traffic received at port 8800 to the Google home page (http://www.google.com). We can pass the local and remote host as well as port number to this script. For the sake of simplicity, let's only specify the local port number as we are aware that the web server runs on port 80. Listing 3.1 shows a port forwarding example, as follows: #!/usr/bin/env python # This program is optimized for Python 2.7.12 and Python 3.5.2. # It may run on any other version with/without modifications. import argparse LOCAL_SERVER_HOST = 'localhost' REMOTE_SERVER_HOST = 'www.google.com' BUFSIZE = 4096 import asyncore import socket class PortForwarder(asyncore.dispatcher): def __init__(self, ip, port, remoteip,remoteport,backlog=5): asyncore.dispatcher.__init__(self) self.remoteip=remoteip self.remoteport=remoteport self.create_socket(socket.AF_INET,socket.SOCK_STREAM) self.set_reuse_addr() self.bind((ip,port)) self.listen(backlog) def handle_accept(self): conn, addr = self.accept() print ("Connected to:",addr) Sender(Receiver(conn),self.remoteip,self.remoteport) class Receiver(asyncore.dispatcher): def __init__(self,conn): asyncore.dispatcher.__init__(self,conn) self.from_remote_buffer='' self.to_remote_buffer='' self.sender=None def handle_connect(self): pass def handle_read(self): read = self.recv(BUFSIZE) self.from_remote_buffer += read def writable(self): return (len(self.to_remote_buffer) > 0) def handle_write(self): sent = self.send(self.to_remote_buffer) self.to_remote_buffer = self.to_remote_buffer[sent:] def handle_close(self): self.close() if self.sender: self.sender.close() class Sender(asyncore.dispatcher): def __init__(self, receiver, remoteaddr,remoteport): asyncore.dispatcher.__init__(self) self.receiver=receiver receiver.sender=self self.create_socket(socket.AF_INET, socket.SOCK_STREAM) self.connect((remoteaddr, remoteport)) def handle_connect(self): pass def handle_read(self): read = self.recv(BUFSIZE) self.receiver.to_remote_buffer += read def writable(self): return (len(self.receiver.from_remote_buffer) > 0) def handle_write(self): sent = self.send(self.receiver.from_remote_buffer) self.receiver.from_remote_buffer = self.receiver.from_remote_buffer[sent:] def handle_close(self): self.close() self.receiver.close() if __name__ == "__main__": parser = argparse.ArgumentParser(description='Stackless Socket Server Example') parser.add_argument('--local-host', action="store", dest="local_host", default=LOCAL_SERVER_HOST) parser.add_argument('--local-port', action="store", dest="local_port", type=int, required=True) parser.add_argument('--remote-host', action="store", dest="remote_host", default=REMOTE_SERVER_HOST) parser.add_argument('--remote-port', action="store", dest="remote_port", type=int, default=80) given_args = parser.parse_args() local_host, remote_host = given_args.local_host, given_args.remote_host local_port, remote_port = given_args.local_port, given_args.remote_port print ("Starting port forwarding local %s:%s => remote %s:%s" % (local_host, local_port, remote_host, remote_port)) PortForwarder(local_host, local_port, remote_host, remote_port) asyncore.loop() If you run this script, it will show the following output: $ python 3_1_port_forwarding.py --local-port=8800 Starting port forwarding local localhost:8800 => remote www.google.com:80 Now, open your browser and visit http://localhost:8800. This will take you to the Google home page and the script will print something similar to the following command: ('Connected to:', ('127.0.0.1', 37236)) The following screenshot shows the forwarding a local port to a remote host: How it works... We created a port forwarding class, PortForwarder subclassed, from asyncore.dispatcher, which wraps around the socket object. It provides a few additional helpful functions when certain events occur, for example, when the connection is successful or a client is connected to a server socket. You have the choice of overriding the set of methods defined in this class. In our case, we only override the handle_accept() method. Two other classes have been derived from asyncore.dispatcher. The receiver class handles the incoming client requests and the sender class takes this receiver instance and processes the sent data to the clients. As you can see, these two classes override the handle_read(), handle_write(), and writeable() methods to facilitate the bi-directional communication between the remote host and local client. In summary, the PortForwarder class takes the incoming client request in a local socket and passes this to the sender class instance, which in turn uses the receiver class instance to initiate a bi-directional communication with a remote server in the specified port. Pinging hosts on the network with ICMP An ICMP ping is the most common type of network scanning you have ever encountered. It is very easy to open a command-line prompt or terminal and type ping www.google.com. How difficult is that from inside a Python program? This recipe shows you an example of a Python ping. Getting ready You need the superuser or administrator privilege to run this recipe on your machine. How to do it... You can lazily write a Python script that calls the system ping command-line tool, as follows: import subprocess import shlex command_line = "ping -c 1 www.google.com" args = shlex.split(command_line) try: subprocess.check_call(args,stdout=subprocess.PIPE, stderr=subprocess.PIPE) print ("Google web server is up!") except subprocess.CalledProcessError: print ("Failed to get ping.") However, in many circumstances, the system's ping executable may not be available or may be inaccessible. In this case, we need a pure Python script to do that ping. Note that this script needs to be run as a superuser or administrator. Listing 3.2 shows the ICMP ping, as follows: #!/usr/bin/env python # This program is optimized for Python 3.5.2. # Instructions to make it run with Python 2.7.x is as follows. # It may run on any other version with/without modifications. import os import argparse import socket import struct import select import time ICMP_ECHO_REQUEST = 8 # Platform specific DEFAULT_TIMEOUT = 2 DEFAULT_COUNT = 4 class Pinger(object): """ Pings to a host -- the Pythonic way""" def __init__(self, target_host, count=DEFAULT_COUNT, timeout=DEFAULT_TIMEOUT): self.target_host = target_host self.count = count self.timeout = timeout def do_checksum(self, source_string): """ Verify the packet integritity """ sum = 0 max_count = (len(source_string)/2)*2 count = 0 while count < max_count: # To make this program run with Python 2.7.x: # val = ord(source_string[count + 1])*256 + ord(source_string[count]) # ### uncomment the preceding line, and comment out the following line. val = source_string[count + 1]*256 + source_string[count] # In Python 3, indexing a bytes object returns an integer. # Hence, ord() is redundant. sum = sum + val sum = sum & 0xffffffff count = count + 2 if max_count<len(source_string): sum = sum + ord(source_string[len(source_string) - 1]) sum = sum & 0xffffffff sum = (sum >> 16) + (sum & 0xffff) sum = sum + (sum >> 16) answer = ~sum answer = answer & 0xffff answer = answer >> 8 | (answer << 8 & 0xff00) return answer def receive_pong(self, sock, ID, timeout): """ Receive ping from the socket. """ time_remaining = timeout while True: start_time = time.time() readable = select.select([sock], [], [], time_remaining) time_spent = (time.time() - start_time) if readable[0] == []: # Timeout return time_received = time.time() recv_packet, addr = sock.recvfrom(1024) icmp_header = recv_packet[20:28] type, code, checksum, packet_ID, sequence = struct.unpack( "bbHHh", icmp_header ) if packet_ID == ID: bytes_In_double = struct.calcsize("d") time_sent = struct.unpack("d", recv_packet[28:28 + bytes_In_double])[0] return time_received - time_sent time_remaining = time_remaining - time_spent if time_remaining <= 0: return We need a send_ping() method that will send the data of a ping request to the target host. Also, this will call the do_checksum() method for checking the integrity of the ping data, as follows: def send_ping(self, sock, ID): """ Send ping to the target host """ target_addr = socket.gethostbyname(self.target_host) my_checksum = 0 # Create a dummy heder with a 0 checksum. header = struct.pack("bbHHh", ICMP_ECHO_REQUEST, 0, my_checksum, ID, 1) bytes_In_double = struct.calcsize("d") data = (192 - bytes_In_double) * "Q" data = struct.pack("d", time.time()) + bytes(data.encode('utf-8')) # Get the checksum on the data and the dummy header. my_checksum = self.do_checksum(header + data) header = struct.pack( "bbHHh", ICMP_ECHO_REQUEST, 0, socket.htons(my_checksum), ID, 1 ) packet = header + data sock.sendto(packet, (target_addr, 1)) Let us define another method called ping_once() that makes a single ping call to the target host. It creates a raw ICMP socket by passing the ICMP protocol to socket(). The exception handling code takes care if the script is not run by a superuser or if any other socket error occurs. Let's take a look at the following code: def ping_once(self): """ Returns the delay (in seconds) or none on timeout. """ icmp = socket.getprotobyname("icmp") try: sock = socket.socket(socket.AF_INET, socket.SOCK_RAW, icmp) except socket.error as e: if e.errno == 1: # Not superuser, so operation not permitted e.msg += "ICMP messages can only be sent from root user processes" raise socket.error(e.msg) except Exception as e: print ("Exception: %s" %(e)) my_ID = os.getpid() & 0xFFFF self.send_ping(sock, my_ID) delay = self.receive_pong(sock, my_ID, self.timeout) sock.close() return delay The main executive method of this class is ping(). It runs a for loop inside which the ping_once() method is called count times and receives a delay in the ping response in seconds. If no delay is returned, that means the ping has failed. Let's take a look at the following code: def ping(self): """ Run the ping process """ for i in range(self.count): print ("Ping to %s..." % self.target_host,) try: delay = self.ping_once() except socket.gaierror as e: print ("Ping failed. (socket error: '%s')" % e[1]) break if delay == None: print ("Ping failed. (timeout within %ssec.)" % self.timeout) else: delay = delay * 1000 print ("Get pong in %0.4fms" % delay) if __name__ == '__main__': parser = argparse.ArgumentParser(description='Python ping') parser.add_argument('--target-host', action="store", dest="target_host", required=True) given_args = parser.parse_args() target_host = given_args.target_host pinger = Pinger(target_host=target_host) pinger.ping() This script shows the following output. This has been run with the superuser privilege: $ sudo python 3_2_ping_remote_host.py --target-host=www.google.com Ping to www.google.com... Get pong in 27.0808ms Ping to www.google.com... Get pong in 17.3445ms Ping to www.google.com... Get pong in 33.3586ms Ping to www.google.com... Get pong in 32.3212ms How it works... A Pinger class has been constructed to define a few useful methods. The class initializes with a few user-defined or default inputs, which are as follows: target_host: This is the target host to ping count: This is how many times to do the ping timeout: This is the value that determines when to end an unfinished ping operation The send_ping() method gets the DNS hostname of the target host and creates an ICMP_ECHO_REQUEST packet using the struct module. It is necessary to check the data integrity of the method using the do_checksum() method. It takes the source string and manipulates it to produce a proper checksum. On the receiving end, the receive_pong() method waits for a response until the timeout occurs or receives the response. It captures the ICMP response header and then compares the packet ID and calculates the delay in the request and response cycle. Waiting for a remote network service Sometimes, during the recovery of a network service, it might be useful to run a script to check when the server is online again. How to do it... We can write a client that will wait for a particular network service forever or for a timeout. In this example, by default, we would like to check when a web server is up in localhost. If you specified some other remote host or port, that information will be used instead. Listing 3.3 shows waiting for a remote network service, as follows: #!/usr/bin/env python # This program is optimized for Python 2.7.12 and Python 3.5.2. # It may run on any other version with/without modifications. import argparse import socket import errno from time import time as now DEFAULT_TIMEOUT = 120 DEFAULT_SERVER_HOST = 'localhost' DEFAULT_SERVER_PORT = 80 class NetServiceChecker(object): """ Wait for a network service to come online""" def __init__(self, host, port, timeout=DEFAULT_TIMEOUT): self.host = host self.port = port self.timeout = timeout self.sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) def end_wait(self): self.sock.close() def check(self): """ Check the service """ if self.timeout: end_time = now() + self.timeout while True: try: if self.timeout: next_timeout = end_time - now() if next_timeout < 0: return False else: print ("setting socket next timeout %ss" %round(next_timeout)) self.sock.settimeout(next_timeout) self.sock.connect((self.host, self.port)) # handle exceptions except socket.timeout as err: if self.timeout: return False except socket.error as err: print ("Exception: %s" %err) else: # if all goes well self.end_wait() return True if __name__ == '__main__': parser = argparse.ArgumentParser(description='Wait for Network Service') parser.add_argument('--host', action="store", dest="host", default=DEFAULT_SERVER_HOST) parser.add_argument('--port', action="store", dest="port", type=int, default=DEFAULT_SERVER_PORT) parser.add_argument('--timeout', action="store", dest="timeout", type=int, default=DEFAULT_TIMEOUT) given_args = parser.parse_args() host, port, timeout = given_args.host, given_args.port, given_args.timeout service_checker = NetServiceChecker(host, port, timeout=timeout) print ("Checking for network service %s:%s ..." %(host, port)) if service_checker.check(): print ("Service is available again!") If a web server is running on your machine, this script will show the following output: $ python 3_3_wait_for_remote_service.py Waiting for network service localhost:80 ... setting socket next timeout 120.0s Service is available again! If you do not have a web server already running in your computer, make sure to install one such as Apache 2 Web Server: $ sudo apt install apache2 Now, stop the Apache process: $ sudo /etc/init.d/apache2 stop It will print the following message while stopping the service. [ ok ] Stopping apache2 (via systemctl): apache2.service. Run this script, and start Apache again. $ sudo /etc/init.d/apache2 start[ ok ] Starting apache2 (via systemctl): apache2.service. The output pattern will be different. On my machine, the following output pattern was found: Exception: [Errno 103] Software caused connection abort setting socket next timeout 119.0s Exception: [Errno 111] Connection refused setting socket next timeout 119.0s Exception: [Errno 103] Software caused connection abort setting socket next timeout 119.0s Exception: [Errno 111] Connection refused setting socket next timeout 119.0s And finally when Apache2 is up again, the following log is printed: Service is available again! The following screenshot shows the waiting for an active Apache web server process: How it works... The preceding script uses the argparse module to take the user input and process the hostname, port, and timeout, that is how long our script will wait for the desired network service. It launches an instance of the NetServiceChecker class and calls the check() method. This method calculates the final end time of waiting and uses the socket's settimeout() method to control each round's end time, that is next_timeout. It then uses the socket's connect() method to test if the desired network service is available until the socket timeout occurs. This method also catches the socket timeout error and checks the socket timeout against the timeout values given by the user. Enumerating interfaces on your machine If you need to list the network interfaces present on your machine, it is not very complicated in Python. There are a couple of third-party libraries out there that can do this job in a few lines. However, let's see how this is done using a pure socket call. Getting ready You need to run this recipe on a Linux box. To get the list of available interfaces, you can execute the following command: $ /sbin/ifconfig How to do it... Listing 3.4 shows how to list the networking interfaces, as follows: #!/usr/bin/env python # Python Network Programming Cookbook, Second Edition -- Article - 3 # This program is optimized for Python 2.7.12 and Python 3.5.2. # It may run on any other version with/without modifications. import sys import socket import fcntl import struct import array SIOCGIFCONF = 0x8912 #from C library sockios.h STUCT_SIZE_32 = 32 STUCT_SIZE_64 = 40 PLATFORM_32_MAX_NUMBER = 2**32 DEFAULT_INTERFACES = 8 def list_interfaces(): interfaces = [] max_interfaces = DEFAULT_INTERFACES is_64bits = sys.maxsize > PLATFORM_32_MAX_NUMBER struct_size = STUCT_SIZE_64 if is_64bits else STUCT_SIZE_32 sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) while True: bytes = max_interfaces * struct_size interface_names = array.array('B', b' ' * bytes) sock_info = fcntl.ioctl( sock.fileno(), SIOCGIFCONF, struct.pack('iL', bytes, interface_names.buffer_info()[0]) ) outbytes = struct.unpack('iL', sock_info)[0] if outbytes == bytes: max_interfaces *= 2 else: break namestr = interface_names.tostring() for i in range(0, outbytes, struct_size): interfaces.append((namestr[i:i+16].split(b' ', 1)[0]).decode('ascii', 'ignore')) return interfaces if __name__ == '__main__': interfaces = list_interfaces() print ("This machine has %s network interfaces: %s." %(len(interfaces), interfaces)) The preceding script will list the network interfaces, as shown in the following output: $ python 3_4_list_network_interfaces.py This machine has 2 network interfaces: ['lo', 'wlo1']. How it works... This recipe code uses a low-level socket feature to find out the interfaces present on the system. The single list_interfaces()method creates a socket object and finds the network interface information from manipulating this object. It does so by making a call to the fnctl module's ioctl() method. The fnctl module interfaces with some Unix routines, for example, fnctl(). This interface performs an I/O control operation on the underlying file descriptor socket, which is obtained by calling the fileno() method of the socket object. The additional parameter of the ioctl() method includes the SIOCGIFADDR constant defined in the C socket library and a data structure produced by the struct module's pack() function. The memory address specified by a data structure is modified as a result of the ioctl() call. In this case, the interface_names variable holds this information. After unpacking the sock_info return value of the ioctl() call, the number of network interfaces is increased twice if the size of the data suggests it. This is done in a while loop to discover all interfaces if our initial interface count assumption is not correct. The names of interfaces are extracted from the string format of the interface_names variable. It reads specific fields of that variable and appends the values in the interfaces' list. At the end of the list_interfaces() function, this is returned. Finding the IP address for a specific interface on your machine Finding the IP address of a particular network interface may be needed from your Python network application. Getting ready This recipe is prepared exclusively for a Linux box. There are some Python modules specially designed to bring similar functionalities on Windows and Mac platforms. For example, see http://sourceforge.net/projects/pywin32/ for Windows-specific implementation. How to do it... You can use the fnctl module to query the IP address on your machine. Listing 3.5 shows us how to find the IP address for a specific interface on your machine, as follows: #!/usr/bin/env python # This program is optimized for Python 2.7.12 and Python 3.5.2. # It may run on any other version with/without modifications. import argparse import sys import socket import fcntl import struct import array def get_ip_address(ifname): s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) return socket.inet_ntoa(fcntl.ioctl( s.fileno(), 0x8915, # SIOCGIFADDR struct.pack(b'256s', bytes(ifname[:15], 'utf-8')) )[20:24]) if __name__ == '__main__': parser = argparse.ArgumentParser(description='Python networking utils') parser.add_argument('--ifname', action="store", dest="ifname", required=True) given_args = parser.parse_args() ifname = given_args.ifname print ("Interface [%s] --> IP: %s" %(ifname, get_ip_address(ifname))) The output of this script is shown in one line, as follows: $ python 3_5_get_interface_ip_address.py --ifname=lo Interface [lo] --> IP: 127.0.0.1 In the preceding execution, make sure to use an existing interface, as printed in the previous recipe. In my computer, I got the output previously for3_4_list_network_interfaces.py: This machine has 2 network interfaces: ['lo', 'wlo1']. If you use a non-existing interface, an error will be printed. For example, I do not have eth0 interface right now.So the output is, $ python3 3_5_get_interface_ip_address.py --ifname=eth0 Traceback (most recent call last): File "3_5_get_interface_ip_address.py", line 27, in <module> print ("Interface [%s] --> IP: %s" %(ifname, get_ip_address(ifname))) File "3_5_get_interface_ip_address.py", line 19, in get_ip_address struct.pack(b'256s', bytes(ifname[:15], 'utf-8')) OSError: [Errno 19] No such device How it works... This recipe is similar to the previous one. The preceding script takes a command-line argument: the name of the network interface whose IP address is to be known. The get_ip_address() function creates a socket object and calls the fnctl.ioctl() function to query on that object about IP information. Note that the socket.inet_ntoa() function converts the binary data to a human-readable string in a dotted format as we are familiar with it. Finding whether an interface is up on your machine If you have multiple network interfaces on your machine, before doing any work on a particular interface, you would like to know the status of that network interface, for example, if the interface is actually up. This makes sure that you route your command to active interfaces. Getting ready This recipe is written for a Linux machine. So, this script will not run on a Windows or Mac host. In this recipe, we use nmap, a famous network scanning tool. You can find more about nmap from its website http://nmap.org/. Install nmap in your computer. For Debian-based system, the command is: $ sudo apt-get install nmap You also need the python-nmap module to run this recipe. This can be installed by pip,  as follows: $ pip install python-nmap How to do it... We can create a socket object and get the IP address of that interface. Then, we can use any of the scanning techniques to probe the interface status. Listing 3.6 shows the detect network interface status, as follows: #!/usr/bin/env python # This program is optimized for Python 2.7.12 and Python 3.5.2. # It may run on any other version with/without modifications. import argparse import socket import struct import fcntl import nmap SAMPLE_PORTS = '21-23' def get_interface_status(ifname): sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) ip_address = socket.inet_ntoa(fcntl.ioctl( sock.fileno(), 0x8915, #SIOCGIFADDR, C socket library sockios.h struct.pack(b'256s', bytes(ifname[:15], 'utf-8')) )[20:24]) nm = nmap.PortScanner() nm.scan(ip_address, SAMPLE_PORTS) return nm[ip_address].state() if __name__ == '__main__': parser = argparse.ArgumentParser(description='Python networking utils') parser.add_argument('--ifname', action="store", dest="ifname", required=True) given_args = parser.parse_args() ifname = given_args.ifname print ("Interface [%s] is: %s" %(ifname, get_interface_status(ifname))) If you run this script to inquire the status of the eth0 status, it will show something similar to the following output: $ python 3_6_find_network_interface_status.py --ifname=lo Interface [lo] is: up How it works... The recipe takes the interface's name from the command line and passes it to the get_interface_status() function. This function finds the IP address of that interface by manipulating a UDP socket object. This recipe needs the nmap third-party module. We can install that PyPI using the pip install command. The nmap scanning instance, nm, has been created by calling PortScanner(). An initial scan to a local IP address gives us the status of the associated network interface. Detecting inactive machines on your network If you have been given a list of IP addresses of a few machines on your network and you are asked to write a script to find out which hosts are inactive periodically, you would want to create a network scanner type program without installing anything on the target host computers. Getting ready This recipe requires installing the Scapy library (> 2.2), which can be obtained at http://www.secdev.org/projects/scapy/files/scapy-latest.zip. At the time of writing, the default Scapy release works with Python 2, and does not support Python 3. You may download the Scapy for Python 3 from https://pypi.python.org/pypi/scapy-python3/0.20 How to do it... We can use Scapy, a mature network-analyzing, third-party library, to launch an ICMP scan. Since we would like to do it periodically, we need Python's sched module to schedule the scanning tasks. Listing 3.7 shows us how to detect inactive machines, as follows: #!/usr/bin/env python # This program is optimized for Python 2.7.12 and Python 3.5.2. # It may run on any other version with/without modifications. # Requires scapy-2.2.0 or higher for Python 2.7. # Visit: http://www.secdev.org/projects/scapy/files/scapy-latest.zip # As of now, requires a separate bundle for Python 3.x. # Download it from: https://pypi.python.org/pypi/scapy-python3/0.20 import argparse import time import sched from scapy.all import sr, srp, IP, UDP, ICMP, TCP, ARP, Ether RUN_FREQUENCY = 10 scheduler = sched.scheduler(time.time, time.sleep) def detect_inactive_hosts(scan_hosts): """ Scans the network to find scan_hosts are live or dead scan_hosts can be like 10.0.2.2-4 to cover range. See Scapy docs for specifying targets. """ global scheduler scheduler.enter(RUN_FREQUENCY, 1, detect_inactive_hosts, (scan_hosts, )) inactive_hosts = [] try: ans, unans = sr(IP(dst=scan_hosts)/ICMP(), retry=0, timeout=1) ans.summary(lambda r : r.sprintf("%IP.src% is alive")) for inactive in unans: print ("%s is inactive" %inactive.dst) inactive_hosts.append(inactive.dst) print ("Total %d hosts are inactive" %(len(inactive_hosts))) except KeyboardInterrupt: exit(0) if __name__ == "__main__": parser = argparse.ArgumentParser(description='Python networking utils') parser.add_argument('--scan-hosts', action="store", dest="scan_hosts", required=True) given_args = parser.parse_args() scan_hosts = given_args.scan_hosts scheduler.enter(1, 1, detect_inactive_hosts, (scan_hosts, )) scheduler.run() The output of this script will be something like the following command: $ sudo python 3_7_detect_inactive_machines.py --scan-hosts=10.0.2.2-4 Begin emission: *.Finished to send 3 packets. . Received 6 packets, got 1 answers, remaining 2 packets 10.0.2.2 is alive 10.0.2.4 is inactive 10.0.2.3 is inactive Total 2 hosts are inactive Begin emission: *.Finished to send 3 packets. Received 3 packets, got 1 answers, remaining 2 packets 10.0.2.2 is alive 10.0.2.4 is inactive 10.0.2.3 is inactive Total 2 hosts are inactive How it works... The preceding script first takes a list of network hosts, scan_hosts, from the command line. It then creates a schedule to launch the detect_inactive_hosts() function after a one-second delay. The target function takes the scan_hosts argument and calls Scapy's sr() function. This function schedules itself to rerun after every 10 seconds by calling the schedule.enter() function once again. This way, we run this scanning task periodically. Scapy's sr() scanning function takes an IP, protocol and some scan-control information. In this case, the IP() method passes scan_hosts as the destination hosts to scan, and the protocol is specified as ICMP. This can also be TCP or UDP. We do not specify a retry and one-second timeout to run this script faster. However, you can experiment with the options that suit you. The scanning sr()function returns the hosts that answer and those that don't as a tuple. We check the hosts that don't answer, build a list, and print that information. Performing a basic IPC using connected sockets (socketpair) Sometimes, two scripts need to communicate some information between themselves via two processes. In Unix/Linux, there's a concept of connected socket, of socketpair. We can experiment with this here. Getting ready This recipe is designed for a Unix/Linux host. Windows/Mac is not suitable for running this one. How to do it... We use a test_socketpair() function to wrap a few lines that test the socket's socketpair() function. List 3.8 shows an example of socketpair, as follows: #!/usr/bin/env python # This program is optimized for Python 3.5.2. # It may run on any other version with/without modifications. # To make it run on Python 2.7.x, needs some changes due to API differences. # Follow the comments inline to make the program work with Python 2. import socket import os BUFSIZE = 1024 def test_socketpair(): """ Test Unix socketpair""" parent, child = socket.socketpair() pid = os.fork() try: if pid: print ("@Parent, sending message...") child.close() parent.sendall(bytes("Hello from parent!", 'utf-8')) # Comment out the preceding line and uncomment the following line for Python 2.7. # parent.sendall("Hello from parent!") response = parent.recv(BUFSIZE) print ("Response from child:", response) parent.close() else: print ("@Child, waiting for message from parent") parent.close() message = child.recv(BUFSIZE) print ("Message from parent:", message) child.sendall(bytes("Hello from child!!", 'utf-8')) # Comment out the preceding line and uncomment the following line for Python 2.7. # child.sendall("Hello from child!!") child.close() except Exception as err: print ("Error: %s" %err) if __name__ == '__main__': test_socketpair() The output from the preceding script is as follows: $ python 3_8_ipc_using_socketpairs.py @Parent, sending message... @Child, waiting for message from parent Message from parent: b'Hello from parent!' Response from child: b'Hello from child!!' How it works... The socket.socketpair() function simply returns two connected socket objects. In our case, we can say that one is a parent and another is a child. We fork another process via a os.fork() call. This returns the process ID of the parent. In each process, the other process' socket is closed first and then a message is exchanged via a sendall() method call on the process's socket. The try-except block prints any error in case of any kind of exception. Performing IPC using Unix domain sockets Unix domain sockets (UDS) are sometimes used as a convenient way to communicate between two processes. As in Unix, everything is conceptually a file. If you need an example of such an IPC action, this can be useful. How to do it... We launch a UDS server that binds to a filesystem path, and a UDS client uses the same path to communicate with the server. Listing 3.9a shows a Unix domain socket server, as follows: #!/usr/bin/env python # This program is optimized for Python 2.7.12 and Python 3.5.2. # It may run on any other version with/without modifications. import socket import os import time SERVER_PATH = "/tmp/python_unix_socket_server" def run_unix_domain_socket_server(): if os.path.exists(SERVER_PATH): os.remove( SERVER_PATH ) print ("starting unix domain socket server.") server = socket.socket( socket.AF_UNIX, socket.SOCK_DGRAM ) server.bind(SERVER_PATH) print ("Listening on path: %s" %SERVER_PATH) while True: datagram = server.recv( 1024 ) if not datagram: break else: print ("-" * 20) print (datagram) if "DONE" == datagram: break print ("-" * 20) print ("Server is shutting down now...") server.close() os.remove(SERVER_PATH) print ("Server shutdown and path removed.") if __name__ == '__main__': run_unix_domain_socket_server() Listing 3.9b shows a UDS client, as follows: #!/usr/bin/env python # Python Network Programming Cookbook, Second Edition -- Article - 3 # This program is optimized for Python 3.5.2. # It may run on any other version with/without modifications. # To make it run on Python 2.7.x, needs some changes due to API differences. # Follow the comments inline to make the program work with Python 2. import socket import sys SERVER_PATH = "/tmp/python_unix_socket_server" def run_unix_domain_socket_client(): """ Run "a Unix domain socket client """ sock = socket.socket(socket.AF_UNIX, socket.SOCK_DGRAM) # Connect the socket to the path where the server is listening server_address = SERVER_PATH print ("connecting to %s" % server_address) try: sock.connect(server_address) except socket.error as msg: print (msg) sys.exit(1) try: message = "This is the message. This will be echoed back!" print ("Sending [%s]" %message) sock.sendall(bytes(message, 'utf-8')) # Comment out the preceding line and uncomment the bfollowing line for Python 2.7. # sock.sendall(message) amount_received = 0 amount_expected = len(message) while amount_received < amount_expected: data = sock.recv(16) amount_received += len(data) print ("Received [%s]" % data) finally: print ("Closing client") sock.close() if __name__ == '__main__': run_unix_domain_socket_client() The server output is as follows: $ python 3_9a_unix_domain_socket_server.py starting unix domain socket server. Listening on path: /tmp/python_unix_socket_server -------------------- This is the message. This will be echoed back! The client output is as follows: $ python 3_9b_unix_domain_socket_client.py connecting to /tmp/python_unix_socket_server Sending [This is the message. This will be echoed back!] How it works... A common path is defined for a UDS client/server to interact. Both the client and server use the same path to connect and listen to. In a server code, we remove the path if it exists from the previous run of this script. It then creates a Unix datagram socket and binds it to the specified path. It then listens for incoming connections. In the data processing loop, it uses the recv() method to get data from the client and prints that information on screen. The client-side code simply opens a Unix datagram socket and connects to the shared server address. It sends a message to the server using sendall(). It then waits for the message to be echoed back to itself and prints that message. Finding out if your Python supports IPv6 sockets IP version 6 or IPv6 is increasingly adopted by the industry to build newer applications. In case you would like to write an IPv6 application, the first thing you'd like to know is if your machine supports IPv6. This can be done from the Linux/Unix command line, as follows: $ cat /proc/net/if_inet6 00000000000000000000000000000001 01 80 10 80 lo fe80000000000000642a57c2e51932a2 03 40 20 80 wlo1 From your Python script, you can also check if the IPv6 support is present on your machine, and Python is installed with that support. Getting ready For this recipe, use pip to install a Python third-party library, netifaces, as follows: $ pip install netifaces How to do it... We can use a third-party library, netifaces, to find out if there is IPv6 support on your machine. We can call the interfaces() function from this library to list all interfaces present in the system. Listing 3.10 shows the Python IPv6 support checker, as follows: #!/usr/bin/env python # This program is optimized for Python 2.7.12 and Python 3.5.2. # It may run on any other version with/without modifications. # This program depends on Python module netifaces => 0.8 import socket import argparse import netifaces as ni def inspect_ipv6_support(): """ Find the ipv6 address""" print ("IPV6 support built into Python: %s" %socket.has_ipv6) ipv6_addr = {} for interface in ni.interfaces(): all_addresses = ni.ifaddresses(interface) print ("Interface %s:" %interface) for family,addrs in all_addresses.items(): fam_name = ni.address_families[family] print (' Address family: %s' % fam_name) for addr in addrs: if fam_name == 'AF_INET6': ipv6_addr[interface] = addr['addr'] print (' Address : %s' % addr['addr']) nmask = addr.get('netmask', None) if nmask: print (' Netmask : %s' % nmask) bcast = addr.get('broadcast', None) if bcast: print (' Broadcast: %s' % bcast) if ipv6_addr: print ("Found IPv6 address: %s" %ipv6_addr) else: print ("No IPv6 interface found!") if __name__ == '__main__': inspect_ipv6_support() The output from this script will be as follows: $ python 3_10_check_ipv6_support.py IPV6 support built into Python: True Interface lo: Address family: AF_PACKET Address : 00:00:00:00:00:00 Address family: AF_INET Address : 127.0.0.1 Netmask : 255.0.0.0 Address family: AF_INET6 Address : ::1 Netmask : ffff:ffff:ffff:ffff:ffff:ffff:ffff:ffff/128 Interface enp2s0: Address family: AF_PACKET Address : 9c:5c:8e:26:a2:48 Broadcast: ff:ff:ff:ff:ff:ff Address family: AF_INET Address : 130.104.228.90 Netmask : 255.255.255.128 Broadcast: 130.104.228.127 Address family: AF_INET6 Address : 2001:6a8:308f:2:88bc:e3ec:ace4:3afb Netmask : ffff:ffff:ffff:ffff::/64 Address : 2001:6a8:308f:2:5bef:e3e6:82f8:8cca Netmask : ffff:ffff:ffff:ffff::/64 Address : fe80::66a0:7a3f:f8e9:8c03%enp2s0 Netmask : ffff:ffff:ffff:ffff::/64 Interface wlp1s0: Address family: AF_PACKET Address : c8:ff:28:90:17:d1 Broadcast: ff:ff:ff:ff:ff:ff Found IPv6 address: {'lo': '::1', 'enp2s0': 'fe80::66a0:7a3f:f8e9:8c03%enp2s0'} How it works... The IPv6 support checker function, inspect_ipv6_support(), first checks if Python is built with IPv6 using socket.has_ipv6. Next, we call the interfaces() function from the netifaces module. This gives us the list of all interfaces. If we call the ifaddresses() method by passing a network interface to it, we can get all the IP addresses of this interface. We then extract various IP-related information, such as protocol family, address, netmask, and broadcast address. Then, the address of a network interface has been added to the IPv6_address dictionary if its protocol family matches AF_INET6. Extracting an IPv6 prefix from an IPv6 address In your IPv6 application, you need to dig out the IPv6 address for getting the prefix information. Note that the upper 64-bits of an IPv6 address are represented from a global routing prefix plus a subnet ID, as defined in RFC 3513. A general prefix (for example, /48) holds a short prefix based on which a number of longer, more specific prefixes (for example, /64) can be defined. A Python script can be very helpful in generating the prefix information. How to do it... We can use the netifaces and netaddr third-party libraries to find out the IPv6 prefix information for a given IPv6 address. Make sure to have netifaces and netaddr installed in your system. $ pip install netaddr The program is as follows: #!/usr/bin/env python # This program is optimized for Python 2.7.12 and Python 3.5.2. # It may run on any other version with/without modifications. # This program depends on Python modules netifaces and netaddr. import socket import netifaces as ni import netaddr as na def extract_ipv6_info(): """ Extracts IPv6 information""" print ("IPv6 support built into Python: %s" %socket.has_ipv6) for interface in ni.interfaces(): all_addresses = ni.ifaddresses(interface) print ("Interface %s:" %interface) for family,addrs in all_addresses.items(): fam_name = ni.address_families[family] for addr in addrs: if fam_name == 'AF_INET6': addr = addr['addr'] has_eth_string = addr.split("%eth") if has_eth_string: addr = addr.split("%eth")[0] try: print (" IP Address: %s" %na.IPNetwork(addr)) print (" IP Version: %s" %na.IPNetwork(addr).version) print (" IP Prefix length: %s" %na.IPNetwork(addr).prefixlen) print (" Network: %s" %na.IPNetwork(addr).network) print (" Broadcast: %s" %na.IPNetwork(addr).broadcast) except Exception as e: print ("Skip Non-IPv6 Interface") if __name__ == '__main__': extract_ipv6_info() The output from this script is as follows: $ python 3_11_extract_ipv6_prefix.py IPv6 support built into Python: True Interface lo: IP Address: ::1/128 IP Version: 6 IP Prefix length: 128 Network: ::1 Broadcast: ::1 Interface enp2s0: IP Address: 2001:6a8:308f:2:88bc:e3ec:ace4:3afb/128 IP Version: 6 IP Prefix length: 128 Network: 2001:6a8:308f:2:88bc:e3ec:ace4:3afb Broadcast: 2001:6a8:308f:2:88bc:e3ec:ace4:3afb IP Address: 2001:6a8:308f:2:5bef:e3e6:82f8:8cca/128 IP Version: 6 IP Prefix length: 128 Network: 2001:6a8:308f:2:5bef:e3e6:82f8:8cca Broadcast: 2001:6a8:308f:2:5bef:e3e6:82f8:8cca Skip Non-IPv6 Interface Interface wlp1s0: How it works... Python's netifaces module gives us the network interface IPv6 address. It uses the interfaces() and ifaddresses() functions for doing this. The netaddr module is particularly helpful to manipulate a network address. It has a IPNetwork() class that provides us with an address, IPv4 or IPv6, and computes the prefix, network, and broadcast addresses. Here, we find this information class instance's version, prefixlen, and network and broadcast attributes. Writing an IPv6 echo client/server You need to write an IPv6 compliant server or client and wonder what could be the differences between an IPv6 compliant server or client and its IPv4 counterpart. How to do it... We use the same approach as writing an echo client/server using IPv6. The only major difference is how the socket is created using IPv6 information. Listing 12a shows an IPv6 echo server, as follows: #!/usr/bin/env python # This program is optimized for Python 2.7.12 and Python 3.5.2. # It may run on any other version with/without modifications. import argparse import socket import sys HOST = 'localhost' def echo_server(port, host=HOST): """Echo server using IPv6 """ for result in socket.getaddrinfo(host, port, socket.AF_UNSPEC, socket.SOCK_STREAM, 0, socket.AI_PASSIVE): af, socktype, proto, canonname, sa = result try: sock = socket.socket(af, socktype, proto) except socket.error as err: print ("Error: %s" %err) try: sock.bind(sa) sock.listen(1) print ("Server lisenting on %s:%s" %(host, port)) except socket.error as msg: sock.close() continue break sys.exit(1) conn, addr = sock.accept() print ('Connected to', addr) while True: data = conn.recv(1024) print ("Received data from the client: [%s]" %data) if not data: break conn.send(data) print ("Sent data echoed back to the client: [%s]" %data) conn.close() if __name__ == '__main__': parser = argparse.ArgumentParser(description='IPv6 Socket Server Example') parser.add_argument('--port', action="store", dest="port", type=int, required=True) given_args = parser.parse_args() port = given_args.port echo_server(port) Listing 12b shows an IPv6 echo client, as follows: #!/usr/bin/env python # Python Network Programming Cookbook, Second Edition -- Article - 3 # This program is optimized for Python 2.7.12 and Python 3.5.2. # It may run on any other version with/without modifications. import argparse import socket import sys HOST = 'localhost' BUFSIZE = 1024 def ipv6_echo_client(port, host=HOST): for res in socket.getaddrinfo(host, port, socket.AF_UNSPEC, socket.SOCK_STREAM): af, socktype, proto, canonname, sa = res try: sock = socket.socket(af, socktype, proto) except socket.error as err: print ("Error:%s" %err) try: sock.connect(sa) except socket.error as msg: sock.close() continue if sock is None: print ('Failed to open socket!') sys.exit(1) msg = "Hello from ipv6 client" print ("Send data to server: %s" %msg) sock.send(bytes(msg.encode('utf-8'))) while True: data = sock.recv(BUFSIZE) print ('Received from server', repr(data)) if not data: break sock.close() if __name__ == '__main__': parser = argparse.ArgumentParser(description='IPv6 socket client example') parser.add_argument('--port', action="store", dest="port", type=int, required=True) given_args = parser.parse_args() port = given_args.port ipv6_echo_client(port) The server output is as follows: $ python 3_12a_ipv6_echo_server.py --port=8800 Server lisenting on localhost:8800 ('Connected to', ('127.0.0.1', 56958)) Received data from the client: [Hello from ipv6 client] Sent data echoed back to the client: [Hello from ipv6 client] The client output is as follows: $ python 3_12b_ipv6_echo_client.py --port=8800 Send data to server: Hello from ipv6 client ('Received from server', "'Hello from ipv6 client'") The following screenshot indicates the server and client output: How it works... The IPv6 echo server first determines its IPv6 information by calling socket.getaddrinfo(). Notice that we passed the AF_UNSPEC protocol for creating a TCP socket. The resulting information is a tuple of five values. We use three of them, address family, socket type, and protocol, to create a server socket. Then, this socket is bound with the socket address from the previous tuple. It then listens to the incoming connections and accepts them. After a connection is made, it receives data from the client and echoes it back. On the client-side code, we create an IPv6-compliant client socket instance and send the data using the send() method of that instance. When the data is echoed back, the recv() method is used to get it back. Summary In this article, the author has tried to explain certain recipes that explains the various IPv6 utilities in Python including an IPv6 client/server. Also some other protocols like ICMP ping and their working is touched upon throroughly. Scapy is explained so as to give a even better understanding about its popularity amongst the network Python programmers. Resources for Article: Further resources on this subject: Introduction to Network Security [article] Getting Started with Cisco UCS and Virtual Networking [article] Revisiting Linux Network Basics [article]
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21 Feb 2018
9 min read
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API Gateway and its Need

Packt
21 Feb 2018
9 min read
 In this article by Umesh R Sharma, author of the book Practical Microservices, we will cover API Gateway and its need with simple and short examples. (For more resources related to this topic, see here.) Dynamic websites show a lot on a single page, and there is a lot of information that needs to be shown on the page. The common success order summary page shows the cart detail and customer address. For this, frontend has to fire a different query to the customer detail service and order detail service. This is a very simple example of having multiple services on a single page. As a single microservice has to deal with only one concern, in result of that to show much information on page, there are many API calls on the same page. So, a website or mobile page can be very chatty in terms of displaying data on the same page. Another problem is that, sometimes, microservice talks on another protocol, then HTTP only, such as thrift call and so on. Outer consumers can't directly deal with microservice in that protocol. As a mobile screen is smaller than a web page, the result of the data required by the mobile or desktop API call is different. A developer would want to give less data to the mobile API or have different versions of the API calls for mobile and desktop. So, you could face a problem such as this: each client is calling different web services and keeping track of their web service and developers have to give backward compatibility because API URLs are embedded in clients like in mobile app. Why do we need the API Gateway? All these preceding problems can be addressed with the API Gateway in place. The API Gateway acts as a proxy between the API consumer and the API servers. To address the first problem in that scenario, there will only be one call, such as /successOrderSummary, to the API Gateway. The API Gateway, on behalf of the consumer, calls the order and user detail, then combines the result and serves to the client. So basically, it acts as a facade or API call, which may internally call many APIs. The API Gateway solves many purposes, some of which are as follows. Authentication API Gateways can take the overhead of authenticating an API call from outside. After that, all the internal calls remove security check. If the request comes from inside the VPC, it can remove the check of security, decrease the network latency a bit, and make the developer focus more on business logic than concerning about security. Different protocol Sometimes, microservice can internally use different protocols to talk to each other; it can be thrift call, TCP, UDP, RMI, SOAP, and so on. For clients, there can be only one rest-based HTTP call. Clients hit the API Gateway with the HTTP protocol and the API Gateway can make the internal call in required protocol and combine the results in the end from all web service. It can respond to the client in required protocol; in most of the cases, that protocol will be HTTP. Load-balancing The API Gateway can work as a load balancer to handle requests in the most efficient manner. It can keep a track of the request load it has sent to different nodes of a particular service. Gateway should be intelligent enough to load balances between different nodes of a particular service. With NGINX Plus coming into the picture, NGINX can be a good candidate for the API Gateway. It has many of the features to address the problem that is usually handled by the API Gateway. Request dispatching (including service discovery) One main feature of the gateway is to make less communication between client and microservcies. So, it initiates the parallel microservices if that is required by the client. From the client side, there will only be one hit. Gateway hits all the required services and waits for the results from all services. After obtaining the response from all the services, it combines the result and sends it back to the client. Reactive microservice designs can help you achieve this. Working with service discovery can give many extra features. It can mention which is the master node of service and which is the slave. Same goes for DB in case any write request can go to the master or read request can go to the slave. This is the basic rule, but users can apply so many rules on the basis of meta information provided by the API Gateway. Gateway can record the basic response time from each node of service instance. For higher priority API calls, it can be routed to the fastest responding node. Again, rules can be defined on the basis of the API Gateway you are using and how it will be implemented. Response transformation Being a first and single point of entry for all API calls, the API Gateway knows which type of client is calling a mobile, web client, or other external consumer; it can make the internal call to the client and give the data to different clients as per needs and configuration. Circuit breaker To handle the partial failure, the API Gateway uses a technique called circuit breaker pattern. A service failure in one service can cause the cascading failure in the flow to all the service calls in stack. The API Gateway can keep an eye on some threshold for any microservice. If any service passes that threshold, it marks that API as open circuit and decides not to make the call for a configured time. Hystrix (by Netflix) served this purpose efficiently. Default value in this is failing of 20 requests in 5 seconds. Developers can also mention the fall back for this open circuit. This fall back can be of dummy service. Once API starts giving results as expected, then gateway marks it as a closed service again. Pros and cons of API Gateway Using the API Gateway itself has its own pros and cons. In the previous section, we have described the advantages of using the API Gateway already. I will still try to make them in points as the pros of the API Gateway. Pros Microservice can focus on business logic Clients can get all the data in a single hit Authentication, logging, and monitoring can be handled by the API Gateway Gives flexibility to use completely independent protocols in which clients and microservice can talk It can give tailor-made results, as per the clients needs It can handle partial failure Addition to the preceding mentioned pros, some of the trade-offs are also to use this pattern. Cons It can cause performance degrade due to lots of happenings on the API Gateway With this, discovery service should be implemented Sometimes, it becomes the single point of failure Managing routing is an overhead of the pattern Adding additional network hope in the call Overall. it increases the complexity of the system Too much logic implementation in this gateway will lead to another dependency problem So, before using the API Gateway, both of the aspects should be considered. Decision of including the API Gateway in the system increases the cost as well. Before putting effort, cost, and management in this pattern, it is recommended to analysis how much you can gain from it. Example of API Gateway In this example, we will try to show only sample product pages that will fetch the data from service product detail to give information about the product. This example can be increased in many aspects. Our focus of this example is to only show how the API Gateway pattern works; so we will try to keep this example simple and small. This example will be using Zuul from Netflix as an API Gateway. Spring also had an implementation of Zuul in it, so we are creating this example with Spring Boot. For a sample API Gateway implementation, we will be using http://start.spring.io/ to generate an initial template of our code. Spring initializer is the project from Spring to help beginners generate basic Spring Boot code. A user has to set a minimum configuration and can hit the Generate Project button. If any user wants to set more specific details regarding the project, then they can see all the configuration settings by clicking on the Switch to the full version button, as shown in the following screenshot: Let's create a controller in the same package of main application class and put the following code in the file: @SpringBootApplication @RestController public class ProductDetailConrtoller { @Resource ProductDetailService pdService; @RequestMapping(value = "/product/{id}") public ProductDetail getAllProduct( @PathParam("id") String id) { return pdService.getProductDetailById(id); } }   In the preceding code, there is an assumption of the pdService bean that will interact with Spring data repository for product detail and get the result for the required product ID. Another assumption is that this service is running on port 10000. Just to make sure everything is running, a hit on a URL such as http://localhost:10000/product/1 should give some JSON as response. For the API Gateway, we will create another Spring Boot application with Zuul support. Zuul can be activated by just adding a simple @EnableZuulProxy annotation. The following is a simple code to start the simple Zuul proxy: @SpringBootApplication @EnableZuulProxy public class ApiGatewayExampleInSpring { public static void main(String[] args) { SpringApplication.run(ApiGatewayExampleInSpring.class, args); } }   Rest all the things are managed in configuration. In the application.properties file of the API Gateway, the content will be something as follows: zuul.routes.product.path=/product/** zuul.routes.produc.url=http://localhost:10000 ribbon.eureka.enabled=false server.port=8080  With this configuration, we are defining rules such as this: for any request for a URL such as /product/xxx, pass this request to http://localhost:10000. For outer world, it will be like http://localhost:8080/product/1, which will internally be transferred to the 10000 port. If we defined a spring.application.name variable as product in product detail microservice, then we don't need to define the URL path property here (zuul.routes.product.path=/product/** ), as Zuul, by default, will make it a URL/product. The example taken here for an API Gateway is not very intelligent, but this is a very capable API Gateway. Depending on the routes, filter, and caching defined in the Zuul's property, one can make a very powerful API Gateway. Summary In this article, you learned about the API Gateway, its need, and its pros and cons with the code example. Resources for Article:   Further resources on this subject: What are Microservices? [article] Microservices and Service Oriented Architecture [article] Breaking into Microservices Architecture [article]
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Packt
21 Feb 2018
29 min read
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Open and Proprietary Next Generation Networks

Packt
21 Feb 2018
29 min read
In this article by Steven Noble, the author of the book Building Modern Networks, we will discuss networking concepts such as hyper-scale networking, software-defined networking, network hardware and software design along with a litany of network design ideas utilized in NGN. (For more resources related to this topic, see here.) The term Next Generation Network (NGN) has been around for over 20 years and refers to the current state of the art network equipment, protocols and features. A big driver in NGN is the constant newer, better, faster forwarding ASICs coming out of companies like Barefoot, Broadcom, Cavium, Nephos (MediaTek) and others. The advent of commodity networking chips has shortened the development time for generic switches, allowing hyper scale networking end users to build equipment upgrades into their network designs. At the time of writing, multiple companies have announced 6.4 Tbps switching chips. In layman terms, a 6.4 Tbps switching chip can handle 64x100GbE of evenly distributed network traffic without losing any packets. To put the number in perspective, the entire internet in 2004 was about 4 Tbps, so all of the internet traffic in 2004 could have crossed this one switching chip without issue. (Internet Traffic 1.3 EB/month http://blogs.cisco.com/sp/the-history-and-future-of-internet-traffic) A hyper-scale network is one that is operated by companies such as Facebook, Google, Twitter and other companies that add hundreds if not thousands of new systems a month to keep up with demand. Examples of next generation networking At the start of the commercial internet age (1994), software routers running on minicomputers such as BBNs PDP-11 based IP routers designed in the 1970's were still in use and hubs were simply dumb hardware devices that broadcast traffic everywhere. At that time, the state of the art in networking was the Cisco 7000 series router, introduced in 1993. The next generation router was the Cisco 7500 (1995), while the Cisco 12000 series (gigabit) routers and the Juniper M40 were only concepts. When we say next generation, we are speaking of the current state of the art and the near future of networking equipment and software. For example, 100 GB Ethernet is the current state of the art, while 400 GB Ethernet is in the pipeline. The definition of a modern network is a network that contains one or more of the following concepts: Software-defined Networking (SDN) Network design concepts Next generation hardware Hyper scale networking Open networking hardware and software Network Function Virtualization (NFV) Highly configurable traffic management Both Open and Closed network hardware vendors have been innovating at a high rate of speed with the help of and due to hyper-scale companies like Google, Facebook and others who have the need for next generation high speed network devices. This provides the network architect with a reasonable pipeline of equipment to be used in designs. Google and Facebook are both companies with hyper scale networks. A hyper scale network is one where the data stored, transferred, and updated on the network grows exponentially. Hyper scale companies deploy new equipment, software, and configurations weekly or even daily to support the needs of their customers. These companies have needs that are outside of the normal networking equipment available, so they must innovate by building their own next generation network devices, designing multi-tiered networks (like a three stage Clos network) and automating the installation and configuration of the next generation networking devices. The need of hyper scalers is well summed up by Google's Amin Vahdat in a 2014 Wired article "We couldn't buy the hardware we needed to build a network of the size and speed we needed to build". Terms and concepts in networking Here you will find the definition of some terms that are important in networking. They have been broken into groups of similar concepts. Routing and switching concepts In network devices and network designs there are many important concepts to understand. Here we begin with the way data is handled. The easiest way to discuss networking is to look at the OSI layer and point out where each device sits. OSI Layer with respect to routers and switches: Layer 1 (Physical): Layer 1 includes cables, hub, and switch ports. This is how all of the devices connect to each other including copper cables (CatX), fiber optics and Direct Attach Cables (DAC) which connect SFP ports without fiber. Layer 2 (Data link Layer): Layer 2 includes the raw data sent over the links and manages the Media Access Control (MAC) addresses for Ethernet Layer 3 (Network layer): Layer 3 includes packets that have more than just layer 2 data, such as IP, IPX (Novell Networks protocol), AFP (Apple's protocol) Routers and switches In a network you will have equipment that switches and/or routes traffic. A switch is a networking device that connects multiple devices such as servers, provides local connectivity and provides an uplink to the core network. A router is a network device that computes paths to remote and local devices, providing connectivity to devices across a network. Both switches and routers can use copper and fiber connections to interconnect. There are a few parts to a networking device, the forwarding chip, the TCAM, and the network processor. Some newer switches have Baseboard Management Controllers (BMCs) which manage the power, fans and other hardware, lessening the burden on the NOS to manage these devices. Currently routers and switches are very similar as there are many Layer 3 forwarding capable switches and some Layer 2 forwarding capable routers. Making a switch Layer 3 capable is less of an issue than making a router Layer 2 forwarding as the switch already is doing Layer 2 and adding Layer 3 is not an issue. A router does not do Layer 2 forwarding in general, so it has to be modified to allow for ports to switch rather than route. Control plane The control plane is where all of the information about how packets should be handled is kept. Routing protocols live in the control plane and are constantly scanning information received to determine the best path for traffic to flow. This data is then packed into a simple table and pushed down to the data plane. Data plane The data plane is where forwarding happens. In a software router, this would be done in the devices CPU, in a hardware router, this would be done using the forwarding chip and associated memories. VLAN/VXLAN A Virtual Local Area Network (VLAN) is a way of creating separate logical networks within a physical network. VLANs are generally used to separate/combine different users, or network elements such as phones, servers, workstations, and so on. You can have up to 4,096 VLANs on a network segment. A Virtual Extensible LAN (VXLAN) was created to all for large, dynamic isolated logical networks for virtualized and multiple tenant networks. You can have up to 16 million VXLANs on a network segment. A VXLAN Tunnel Endpoint (VTEP) is a set of two logical interfaces inbound which encapsulates incoming traffic into VXLANs and outbound which removes the encapsulation of outgoing traffic from VXLAN back to its original state.  Network design concepts Network design requires the knowledge of the physical structure of the network so that the proper design choices are made. For example, in data center you would have a local area network, if you have multiple data centers near each other, they would be considered a metro area network. LAN A Local Area Network (LAN), generally considered to be within the same building. These networks can be bridged (switched) or routed. In general LANs are segmented into areas to avoid large broadcast domains. MAN A Metro Area Network (MAN), generally defined as multiple sites in the same geographic area or city, that is, metropolitan area. A MAN generally runs at the same speed as a LAN but is able to cover larger distances. WAN A Wide Area Network (WAN), essentially everything that is not a LAN or MAN is a WAN. WANs generally use fiber optic cables to transmit data from one location to another. WAN circuits can be provided via multiple connections and data encapsulations including MPLS, ATM, and Ethernet. Most large network providers utilize Dense Wavelength Division Multiplexing (DWDM) to put more bits on their fiber networks. DWDM puts multiple colors of light onto the fiber, allowing up to 128 different wavelengths to be sent down a single fiber. DWDM has just entered open networking with the introduction of Facebook's Voyager system. Leaf-Spine design In a Leaf-Spine network design, there are Leaf switches (the switches that connect to the servers) sometimes called Top of Rack (ToR) switches connected to a set of Spine (switches that connect leafs together) sometimes called End of Rack (EoR) switches. Clos network A Clos network is one of the ways to design a multi-stage network. Based on the switching network design by Charles Clos in 1952, a three stage Clos is the smallest version of a Clos network. It has an ingress, a middle, and an egress stage. Some hyper scale networks are using five stage Clos where the middle is replaced with another three stage Clos. In a five stage Clos there is an ingress, a middle ingress, a middle, a middle egress and an egress stage. All stages are connected to their neighbor, so in the example shown, Ingress 1 is connected to all four of the middle stages just as Egress 1 is connected to all four of the middle stages. A Clos network can be built in odd numbers starting with 3, so a 5, 7, and so on stage Clos is possible. For even numbered designs, Benes designs are usable. Benes network A Benes design is a non-blocking Clos design where the middle stage is 2x2 instead of NxN. A Benes network can have even numbers of stages. Here is a four stage Benes network. Network controller concepts Here we will discuss the concepts of network controllers. Every networking device has a controller, whether built in or external to manage the forwarding of the system. Controller A controller is a computer that sits on the network and manages one or more network devices. A controller can be built into a device, like the Cisco Supervisor module or standalone like an OpenFlow controller. The controller is responsible for managing all of the control plane data and deciding what should be sent down to the data plane. Generally, a controller will have a Command-line Interface (CLI) and more recently a web configuration interface. Some controllers will even have an Application Programming Interface (API). OpenFlow controller An OpenFlow controller, as it sounds is a controller that uses the OpenFlow protocol to communicate with network devices. The most common OpenFlow controllers that people hear about are OpenDaylight and ONOS. People who are working with OpenFlow would also know of Floodlight and RYU. Supervisor module A route processor is a computer that sits inside of the chassis of the network device you are managing. Sometimes the route processor is built in to the system, while other times it is a module that can be replaced/upgraded. Many vendor multi-slot systems have multiple route processors for redundancy. An example of a removable route processor is the Cisco 9500 series Supervisor module. There are multiple versions available including revision A, with a 4 core processor and 16 GB of RAM and revision B with a 6 core processor and 24 GB of RAM. Previous systems such as the Cisco Catalyst 7600 had options such as the SUP720 (Supervisor Module 720) of which they offered multiple versions. The standard SUP720 had a limited number of routes that it could support (256k) versus the SUP720 XL which could support up to 1M routes. Juniper Route Engine In Juniper terminology, the controller is called a Route Engine. They are similar to the Cisco Route Processor/Supervisor modules. Unlike Cisco Supervisor modules which utilize special CPUS, Juniper's REs generally use common x86 CPUs. Like Cisco, Juniper multi-slot systems can have redundant processors. Juniper has recently released the information about the NG-REs or Next Generation Route Engines. One example is the new RE-S-X6-64G, a 6-core x86 CPU based routing engine with 64 GB DRAM and 2x 64 GB SSD storage available for the MX240/MX480/MX960. These NG-REs allow for containers and other virtual machines to be run directly. Built in processor When looking at single rack unit (1 RU) or pizza box design switches there are some important design considerations. Most 1 RU switches do not have redundant processors, or field replaceable route processors. In general the field replaceable units (FRUs) that the customer can replace are power supplies and fans. If the failure is outside of the available FRUs, the entire switch must be replaced in the event of a failure. With white box switches this can be a simple process as white box switches can be used in multiple locations of your network including the customer edge, provider edge and core. Sparing (keeping a spare switch) is easy when you have the same hardware in multiple parts of the network. Recently commodity switch fabric chips have come with built-in low power ARM CPUs that can be used to manage the entire system, leading to cheaper and less power hungry designs. Facebook Wedge microserver The Facebook Wedge is different from most white box switches as it has its controller as an add in module, the same board that is used in some of the OCP servers. By separating the controller board from the switch, different boards can be put in place, such as higher memory, faster CPUs, different CPU types, and so on. Routing protocols A routing protocol is a daemon that runs on a controller and communicates with other network devices to exchange route information. For this section we will use common words to demonstrate the way the routing protocol is working, these should not be construed as the actual way that the protocols talk. BGP Border Gateway Protocol (BGP) is a path vector based External Gateway Protocol (EGP) protocol that makes routing decisions based on paths, network policies, or rules (route-maps on Cisco). Though designed as a EGP, BGP can be used as both an interior (iboga) and exterior (eBGP) routing protocol. BGP uses keep alive packets (are you there?) to confirm that neighbors are still accessible. BGP is the protocol that is utilized to route traffic across the internet, exchanging routing information between different Autonomous Systems (AS). An AS is all of the connected networks under the control of a single entity such as Level 3 (AS1) or Sprint (AS1239). When two different ASes interconnect, BGP peering sessions are setup between two or more network devices that have direct connections to each other. In an eBGP scenario, AS1 and AS1239 would setup BGP peering sessions that would allow traffic to route between their AS. In an iBGP scenario, the same AS would peer with other routers with the same AS and transfer the routes that are defined on the system. While iBGP is used internally in most networks, iBGP is used in large corporate networks because other Interior Gateway Protocols (IGPs) may not scale. Examples: iBGP next hop self In this scenario AS1 and AS2 are peered with each other and exchanging one prefix each. AS1 advertises 192.168.1.0/24 and AS2 advertises 192.168.2.0/24. Each network has two routers, one border router, which connects to other ASes and one internal router which gets its routes from the border router. The routes are advertised internally with the next-hop set as the border router. This is a standard scenario when you are not running an IGP inside to distribute the routes for the border router external interfaces. The conversation goes like this: AS1 -> AS2: Hi AS2, I am AS1 AS2 -> AS1: Hi AS1, I am AS2 AS1 -> AS2: I have the following route, 192.168.1.0/24 AS2 - AS1: I have received the route, I have 192.168.2.0/24 AS1 - AS2: I have received the route AS1 -> Internal Router AS1: I have this route, 192.168.2.0/24, you can reach it through me at 10.1.1.1 AS2 -> Internal Router AS2: I have this route, 192.168.1.0/24, you can reach it through me at 10.1.1.1 iBGP next-hop unmodified In the next scenario the border routers are the same, but the internal routers are given a next-hop of the external (Other AS) border router. The last scenario is where you peer with a router server, a system that handles peering, filtering the routes based on what you have specified you send. The routes are then forwarded onto your peers with your IP as the next hop. OSPF Open Shortest Path First (OSPF) is a relatively simple protocol. Different links on the same router are put into the same or different areas. For example, you would use area 1 for the interconnects between campuses but you would use another area, such as area 10 for the campus itself. By separating areas, you can reduce the amount of cross talk that happens between devices. There are two versions of OSPF, v2 and v3. The main difference between v2 and v3 is that v2 is for IPv4 networks and v3 is for IPv6 networks. When there are multiple paths that can be taken, the cost of the links must be taken into account. Below you can see where there are two paths, one has a total cost of 20 (5+5+10), the other 16 (8+8) so the traffic will take the lowest cost link. IS-IS IS-IS is a link-state routing protocol, operating by flooding link state information throughout a network of routers using NETs (Network Entity Title). Each IS-IS router has its own database of the network topology, built by aggregating the flooded network information. IS-IS is used by companies who are looking for Fast convergence, scalability and Rapid flooding of new information. IS-IS uses the concept of levels instead of areas as in OSPF. There are two levels in IS-IS, Level 1 - area and Level2 - backbone. A Level 1 Intermediate System (IS), keeps track of the destinations within its area, while a Level 2 IS keep track of paths to the Level 1 areas. EIGRP Enhanced Interior Gateway Routing Protocol (EIGRP) is Cisco's proprietary routing protocol. It is hardly ever seen in current networks but if you see it in yours, then you need to plan accordingly. Replacing EIGRP with OSPF is suggested so that you can interoperate with non-cisco devices. RIP If Routing Information Protocol (RIP) is being used in your network, it must be replaced during the design. Most newer routing stacks do not support RIP. RIP is one of the original routing protocols, using the number of hops (routed ports) between the device and remote location to determine the optimal path. RIP sends its entire routing database out every 30 seconds. When routing tables were small, many years ago, RIP worked fine. With larger tables, the traffic bursts and resulting re-computing by other routers in the network causes routers to run at almost 100 percent CPU all the time. Cables Here we will review the major types of cables. Copper Copper cables have been around for a very long time, originally network devices were connected together using coax cable (the same cable used for television antennas and cable).  These days there are a few standard cables that are used. RJ45 Cables Cat5 - A 100Mb capable cable, used for both 10Mb and 100Mb connections  Cat5E - 1GbE capable cable but not suggested for 1GbE networks (Cat6 is better and the price difference is nominal). Cat6 - A 1GbE capable cable, can be used for any speed at or below 1GbE including 100Mb and 10Mb. SFPs SFP - Small Form-factor Pluggable port. Capable of up to 1GbE connections SFP+ - Same size as the SFP, capable of up to 10Gb connections SFP28 - Same size as the SFP, capable of up to 25Gb connections QSFP - Quad Small Form-factor Pluggable - A bit wider than the SFP but capable of multiple GbE connections QSFP+ - Same size as the QSFP - capable of 40GbE as 4x10GbE on the same cable QSFP28 - Same size as the QSFP - capable of 100GbE DAC - A direct attach cable that fits into a SFP or QSFP port Fiber/Hot pluggable Breakout Cables As routers and switches continue to become more dense, where the number of ports on the front of the device can no longer fit in the space, manufacturers have moved to what we call breakout cables. For example, if you have a switch that can handle 3.2Tb/s of traffic, you need to provide 3200Gbp/s of port capacity. The easiest way to do that is to use 32 100Gb ports which will fit on the front of a 1U device.  You cannot fit 128 10Gb ports without using either a breakout patch panel (which will then use another few rack units (RUs), or a breakout cable. For a period of time in the 1990's, Cisco used RJ21 connectors to provide up to 96 ethernet ports per slot Network engineers would then create breakout cables to go from the RJ21 to RJ45. These days, we have both DAC (Direct Attach Cable) and Fiber breakout cables. For example, here you can see a 1x4 breakout cable, providing 4 10g or 25G ports from a single 40G or 100G port. If you build a LAN network that only includes switches that provide layer 2 connectivity, any devices you want to connect together need to be in the same IP block. If you have a router in your network, it can route traffic between IP blocks. Part 1: What defines a modern network There is a litany of concepts that define a modern network, from simple principles to full feature sets. In general, a next-generation data center design enables you to move to a widely distributed non-blocking fabric with uniform chipset, bandwidth, and buffering characteristics in a simple architecture. In one example, to support these requirements, you would begin with a true three-tier Clos switching architecture with Top of Rack (ToR), spine, and fabric layers to build a data center network. Each ToR would have access to multiple fabrics and have the ability to select a desired path based on application requirement or network availability. Following the definition of a modern network from the introduction, here we layout the general definition of the parts. Modern network pieces Here we will discuss the concepts that build a Next Generation Network (NGN). Software Defined Networks Software defined networks can be defined in multiple ways. The general definition of a Software defined network is one which can be controlled as a singular unit instead of at a system by system basis. The control-plane which would normally be in the device and using routing protocols is replaced with a controller. Software defined networks can be built using many different technologies including OpenFlow, overlay networks and automation tools. Next generation networking and hyper scale networks As we mention in the introduction, twenty years ago NGN hardware would have been the Cisco GSR (officially introduced in 1997) or the Juniper M40 (officially released in 1998). Large Cisco and Juniper customers would have been working with the companies to help come up with the specifications and determining how to deploy the devices (possibly Alpha or Beta versions) in their networks. Today we can look at the hyper scale networking companies to see what a modern network looks like. A hyper scale network is one where the data stored, transferred and updated on the network grows exponentially. Technology such as 100Gb Ethernet, software defined networking, Open networking equipment and software are being deployed by hyper scale companies. Open networking hardware overview Open Hardware has been around for about 10 years, first in the consumer space and more recently in the enterprise space. Enterprise open networking hardware companies such as Quanta and Accton provide a significant amount of the hardware currently utilized in networks today. Companies such as Google and Facebook have been building their own hardware for many years. Facebook's routers such as the Wedge 100 and Backpack are available publicly for end users to utilize. Some examples of Open Networking hardware are: The Dell S6000-ON - a 32x40G switch with 32 QSFP ports on the front. The Quanta LY8 - a 48x10G + 6x40G switch with 48 SFP+ ports and 6 QSFP ports. The Facebook Wedge 100 - a 32x100G switch with 32 QSFP28 ports on the front. Open networking software overview To use open networking hardware, you need an operating system. The operating system manages the system devices such as fans, power, LEDs and temperature. On top of the operating system you will run a forwarding agent, examples of forwarding agents are Indigo, the open source OpenFlow daemon and Quagga, an open source routing agent. Closed networking hardware overview Cisco and Juniper are the leaders in the Closed Hardware and Software space. Cisco produces switches like the Nexus series (3000, 7000, 9000) with the 9000 programmable by ACI. Juniper provides the MX series (480, 960, 2020) with the 2020 being the highest end forwarding system they sell. Closed networking software overview Cisco has multiple network operating systems including IOS, NX-OS, IOS-XR. All Cisco NOSs are closed source and proprietary to the system that they run on. Cisco has what the industry calls a "industry standard CLI" which is emulated by many other companies. Juniper ships a single NOS, JunOS which can install on multiple different systems. JunOS is a closed source BSD based NOS. The JunOS CLI is significantly different from IOS and is more focused on engineers who program. Network Virtualization Not to be confused with Network Function Virtualization (NFV), Network virtualization is the concept of re-creating the hardware interfaces that exist in a traditional network in software. By creating a software counterpart to the hardware interfaces, you decouple the network forwarding from the hardware. There are a few companies and software projects that allow the end user to enable network virtualization. The first one is NSX which comes from the same team that developed OvS (Open Virtual Switch) Nicira, which was acquired by VMWare in 2012. Another project is Big Switch Networks Big Cloud Fabric, which utilizes a heavily modified version of Indigo, an OpenFlow controller. Network Function Virtualization Network Function Virtualization can be summed up by the statement that: "Due to recent network focused advancements in PC hardware, any service able to be delivered on proprietary, application specific hardware should be able to be done on a virtual machine". Essentially: routers, firewalls, load balancers and other network devices all running virtualized on commodity hardware. Traffic Engineering Traffic engineering is a method of optimizing the performance of a telecommunications network by dynamically analyzing, predicting and regulating the behavior of data transmitted over that network. Part 2: Next generation networking examples In my 25 or so years of networking, I have dealt with a lot of different networking technologies, each iteration (supposedly) better than the last. Starting with Thin Net (10BASE2), moving through ArcNet, 10BASE-T, Token Ring, ATM to the Desktop, FDDI and onwards. Generally, the technology improved for each system until it was swapped out. A good example is the change from a literal ring for token ring to a switching design where devices hung off of a hub (as in 10BASE-T). ATM to the desktop was a novel idea, providing up to 25Mbps to connected devices, but the complexity of configuring and managing it was not worth the gain. Today almost everything is Ethernet as shown by the Facebook Voyager DWDM system, which uses Ethernet over both traditional SFP ports and the DWDM interfaces.  Ethernet is simple, well supported and easy to manage. Example 1 - Migration from FDDI to 100Base-T In late 1996, early 1997, the Exodus network used FDDI rings (Fiber Distributed Data Interface) to connect the main routers together at 100Mbps. As the network grew we had to decide between two competing technologies, FDDI switches and Fast Ethernet (100Base-T) both providing 100Mbp/s. FDDI switches from companies like DEC (FDDI Gigaswitch) were used in most of the Internet Exchange Points (IXPs) and worked reasonably well with one minor issue, head of line blocking (HOLB), which also impacted other technologies. Head of line blocking occurs when a packet is destined for an interface that is already full, so a queue is built, if the interface continues to be full, eventually the queue will be dropped. While we were testing the DEC FDDI Gigaswitches, we were also in deep discussions with Cisco about the availability of Fast Ethernet (FE) and working on designs. Because FE was new, there were concerns about how it would perform and how we would be able to build a redundant network design. In the end, we decided to use FE, connect the main routers in a full mesh and use routing protocols to manage fail-over. Example 2 - NGN Failure - LANE (LAN Emulation) During the high growth period at Exodus communications, there was a request to connect a new data center to the original one and allow customers to put servers in both locations using the same address space. To do this, we chose LAN Emulation or LANE which allows a ATM network to be used like a LAN. On paper, LANE looked like a great idea, the ability to extend the LAN so that customers could use the same IP space in two different locations. In reality, it was very different. For hardware, we were using Cisco 5513 switches which provided a combination of Ethernet and ATM ports. There were multiple issues with this design: First, the customer is provided with an ethernet interface, which runs over an ATM optical interface.  Any error on the physical connection between switches or the ATM layer would cause errors on the Ethernet layer. Second, monitoring was very hard, when there were network issues, you had to look in multiple locations to determine where the errors were happening. After a few weeks, we did a midnight swap putting Cisco 7500 routers in to replace the 5500 switches and moving customers onto new blocks for the new data center. Part 3: Designing a modern network When designing a new network, some of the following might be important to you: Simple, focused yet non-blocking IP fabric Multistage parallel fabrics based on Clos network concept Simple merchant silicon Distributed control plane with some centralized controls Wide multi-path (ECMP) Uniform chipset, bandwidth, and buffering 1:1 oversubscribed (non-blocking fabric) Minimize the hardware necessary to carry east–west traffic Ability to support a large number of bare metal servers without adding an additional layer Limit fabric to a 5 stage Clos within the data center to minimize lookups and switching latency. Support host attachment at 10G, 25G, 50G and 100G Ethernet Traffic management In a modern network one of the first decisions is whether you will use a centralized controller or not. If you use a centralized controller, you will be able to see and control the entire network from one location. If you do not use a centralized controller, you will need to either manage each system directly or via automation. There is a middle space where you can use some software defined network pieces to manage parts of the network, such as an OpenFlow controller for the WAN or VMware NSX for your virtualized workloads. Once you know what the general management goal is, the next decision is whether to use open, proprietary, or a combination of both open and proprietary networking equipment. Open networking equipment is a concept that has been around less than a decade and started when very large network operators decided that they wanted a better control of the cost and features of the equipment in their networks. Google is a good example. In the following figure, you can see how Facebook used both their own hardware, 6-Pack/Backpack and legacy vendor hardware for their interoperability and performance testing. Google wanted to build a high-speed backbone, but was not looking to pay the prices that the incumbent proprietary vendors such as Cisco and Juniper wanted. Google set a price per port (1G/10G/40G) that they wanted to hit and designed equipment around that. Later companies like Facebook decided to go the same direction and contracted with commodity manufacturers to build network switches that met their needs. Proprietary vendors can offer the same level of performance or better using their massive teams of engineers to design and optimize hardware. This distinction even applies on the software side where companies like VMware and Cisco have created software defined networking tools such as NSX and ACI. With the large amount of networking gear available, designing and building a modern network can appear to be a complex concept. Designing a modern network requires research and a good understanding of networking equipment. While complex, the task is not hard if you follow the guidelines. These are a few of the stages of planning that need to be followed before the modern network design is started: The first step is to understand the scope of the project (single site, multi-site, multi-continent, multi-planet). The second step is to determine if the project is a green field (new) or brown field deployment (how many of the sites already exist and will/will not be upgraded). The third step is to determine if there will be any software defined networking (SDN), next generation networking (NGN) or Open Networking pieces. Finally, it is key that the equipment to be used is assembled and tested to determine if the equipment meets the needs of the network. Summary In this article, we have discussed many different concepts that tie NGN together. The term NGN refers to the latest and near-term networking equipment and designs. We looked at networking concepts such as local, metro and wide area networks, network controllers, routers and switches. Routing protocols such as BGP, IS-IS, OSPF and RIP. Then we discussed many pieces that are used either singularly or together that create a modern network. In the end, we also learned some guidelines that should be followed while designing a network. Resources for Article:   Further resources on this subject: Analyzing Social Networks with Facebook [article] Social Networks [article] Point-to-Point Networks [article]
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Packt
21 Feb 2018
11 min read
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Is React Native is really Native framework?

Packt
21 Feb 2018
11 min read
This article by Vladimir Novick, author of the book React Native - Building Mobile Apps with JavaScript, introduces the concept of how the the React Native is really a Native framework, it's working, information flow, architecture, and benefits. (For more resources related to this topic, see here.) Introduction So how React Native is different? Well it doesn’t fall under hybrid category because the approach is different. When hybrid apps are trying to make platform specific features reusable between platforms, React Native have platform independent features, but also have lots of specific platform implementations. Meaning that on iOS and on Android code will look different, but somewhere between 70-90 percent of code will be reused. Also React Native does not depend on HTML or CSS. You write in JavaScript, but this JavaScript is compiled to platform specific Native code using React Native bridge. It happens all the time, but it’s optimize to a way, that application will run smoothly in 60fps. So to summarize React Native is not really a Native framework, but It’s much closer to Native code, than hybrid apps. And now let’s dive a bit deeper and understand how JavaScript gets converted into a Native code. How React Native bridge from JavaScript to Native world works? Let’s dive a bit deeper and understand how React Native works under the hood, which will help us understand how JavaScript is compiled to a Native code and how the whole process works. It’s crucial to know how the whole process works, so if you will have performance issues one day, you will understand where it originates. Information flow So we’ve talked about React concepts that power up React Native and one of them is that UI is a function of data. You change the state and React knows what to update. Let’s visualize now how information flows through common React app. Check out the diagram:  We have React component, which passes data to three child components Under the hood what is happening is, Virtual DOM tree is created representing these component hierarchy When state of the parent component is updated, React knows how to pass information to the children Since children are basically representation of UI, React figures out how to batch Browser DOM updates and executes them So now let’s remove Browser DOM and think that instead of batching Browser DOM updates, React Native does the same with calls to Native modules. So what about passing information down to Native modules? It can be done in two ways: Shared mutable data Serializable messages exchanged between JavaScript and Native modules React Native is going with the second approach. Instead of mutating data on shareable objects it passes asynchronous serialized batched messages to React Native Bridge. Bridge is the layer that is responsible for glueing together Native and JavaScript environments. Architecture Let’s take a look at the following diagram, which explains how React Native Architecture is structured and walk through the diagram: In diagram, pictured three layers: Native, Bridge and JavaScript. Native layer is pictured at the last in picture, because the layer that is closer to device itself. Bridge is the layer that connects between JavaScript and Native modules and basically is a transport layer that transport asynchronous serialized batched response messages from JavaScript to Native modules. When event is executed on Native layer. It can be touch, timer, network request. Basically any event involving device Native modules, It’s data is collected and is sent to the Bridge as a serialized message. Bridge pass this message to JavaScript layer. JavaScript layer is an event loop. Once Bridge passes Serialized payload to JavaScript, Event is processed and your application logic comes into play. If you update state, triggering your UI to re-render for example, React Native will batch Update UI and send them to the Bridge. Bridge will pass this Serialized batched response to Native layer, which will process all commands, that it can distinguish from serialized batched response and will Update UI accordingly. Threading model Up till now we’ve seen that there are lots of stuff going on under the hood of React Native. It’s important to know that everything is done on three main threads: UI (application main thread) Native modules JavaScript Runtime UI thread is the main Native thread where Native level rendering occurs. It is here, where your platform of choice, iOS or Android, does measures, layouting, drawing. If your application accesses any Native APIs, it’s done on a separate Native modules thread. For example, if you want to access the camera, Geo location, photos, and any other Native API. Panning and gestures in general are also done on this thread. JavaScript Runtime thread is the thread where all your JavaScript code will run. It’s slower than UI thread since it’s based on JavaScript event loop, so if you do complex calculations in your application, that leads to lots of UI changes, these can lead to bad performance. The rule of thumb is that if your UI will change slower than 16.67ms, then UI will appear sluggish. What are benefits of React Native? React Native brings with it lots of advantages for mobile development. We covered some of them briefly before, but let’s go over now in more detail. These advantages are what made React Native so popular and trending nowadays. And most of all it give web developers to start developing Native apps with relatively short learning curve compared to overhead learning Objective-C and Java. Developer experience One of the amazing changes React Native brings to mobile development world is enhancing developer experience. If we check developer experience from the point of view of web developer, it’s awesome. For mobile developer it’s something that every mobile developer have dreamt of. Let’s go over some of the features React Native brings for us out of the box. Chrome DevTools debugging Every web developer is familiar with Chrome Developer tools. These tools give us amazing experience debugging web applications. In mobile development debugging mobile applications can be hard. Also it’s really dependent on your target platform. None of mobile application debugging techniques does not even come near web development experience. In React Native, we already know, that JavaScript event loop is running on a separate thread and it can be connected to Chrome DevTools. By clicking Ctrl/Cmd + D in application simulator, we can attach our JavaScript code to Chrome DevTools and bring web debugging to a mobile world. Let’s take a look at the following screenshot: Here you see a React Native debug tools. By clicking on Debug JS Remotely, a separate Google Chrome window is opened where you can debug your applications by setting breakpoints, profiling CPU and memory usage and much more. Elements tab in Chrome Developer tools won’t be relevant though. For that we have a different option. Let’s take a look at what we will get with Chrome Developer tools Remote debugger. Currently Chrome developer tools are focused on Sources tab. You can notice that JavaScript is written in ECMAScript 2015 syntax. For those of you who are not familiar with React JSX, you see weird XML like syntax. Don’t worry, this syntax will be also covered in the book in the context of React Native.  If you put debugger inside your JavaScript code, or a breakpoint in your Chrome development tools, the app will pause on this breakpoint or debugger and you will be able to debug your application while it’s running. Live reload As you can see in React Native debugging menu, the third row says Live Reload. If you enable this option, whenever you change your code and save, the application will be automatically reloaded. This ability to Live reload is something mobile developers only dreamt of. No need to recompile application after each minor code change. Just save and the application will reload itself in simulator. This greatly speed up application development and make it much more fun and easy than conventional mobile development. The workflow for every platform is different while in React Native the experience is the same. Does not matter for which platform you develop. Hot reload Sometimes you develop part of the application which requires several user interactions to get to. Think of, for example logging in, opening menu and choosing some option. When we change our code and save, while live reload is enabled, our application is reloaded and we need to once again do these steps. But it does not have to be like that. React Native gives us amazing experience of hot reloading. If you enable this option in React Native development tools and if you change your React Native component, only the component will be reloaded while you stay on the same screen you were before. This speeds up the development process even more. Component hierarchy inspections I’ve said before, that we cannot use elements panel in Chrome development tools, but how you inspect your component structure in React Native apps? React Native gives us built in option in development tools called Show Inspector. When clicking it, you will get the following window: After inspector is opened, you can select any component on the screen and inspect it. You will get the full hierarchy of your components as well as their styling: In this example I’ve selected Welcome to React Native! text. In the opened pane I can see it’s dimensions, padding margin as well as component hierarchy. As you can see it’s IntroApp/Text/RCTText. RCTText is not a React Native JavaScript component, but a Native text component, connected to React Native bridge. In that way you also can see that component is connected to a Native text component. There are even more dev tools available in React Native, that I will cover later on, but we all can agree, that development experience is outstanding. Web inspired layout techniques Styling for Native mobile apps can be really painful sometimes. Also it’s really different between iOS and Android. React Native brings another solution. As you may’ve seen before the whole concept of React Native is bringing web development experience to mobile app development. That’s also the case for creating layouts. Modern way of creating layout for the web is by using flexbox. React Native decided to adopt this modern technique for web and bring it also to the mobile world with small differences. In addition to layouting, all styling in React Native is very similar to using inline styles in HTML. Let’s take a look at example: const styles = StyleSheet.create({ container: { flex: 1, justifyContent: 'center', alignItems: 'center', backgroundColor: '#F5FCFF', }); As you can see in this example, there are several properties of flexbox used as well as background color. This really reminds CSS properties, however instead of using background-color, justify-content and align-items, CSS properties are named in a camel case manner. In order to apply these styles to text component for example. It’s enough to pass them as following: <Text styles={styles.container}>Welcome to React Native </Text> Styling will be discussed in the book, however as you can see from example before , styling techniques are similar to web. They are not dependant on any platform and the same for both iOS and Android Code reusability across applications In terms of code reuse, if an application is properly architectured (something we will also learn in this book), around 80% to 90% of code can be reused between iOS and Android. This means that in terms of development speed React Native beats mobile development. Sometimes even code used for the web can be reused in React Native environment with small changes. This really brings React Native to top of the list of the best frameworks to develop Native mobile apps. Summary In this article, we learned about the concept of how the React Native is really a Native framework, working, information flow, architecture, and it's benefits briefly. Resources for Article:   Further resources on this subject: Building Mobile Apps [article] Web Development with React and Bootstrap [article] Introduction to JavaScript [article]
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Packt
21 Feb 2018
9 min read
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VMware vSphere storage, datastores, snapshots

Packt
21 Feb 2018
9 min read
VMware vSphere storage, datastores, snapshotsIn this article, byAbhilash G B, author of the book,VMware vSphere 6.5 CookBook - Third Edition, we will cover the following:Managing VMFS volumes detected as snapshotsCreating NFSv4.1 datastores with Kerberos authenticationEnabling storage I/O control (For more resources related to this topic, see here.) IntroductionStorage is an integral part of any infrastructure. It is used to store the files backing your virtual machines. The most common way to refer to a type of storage presented to a VMware environment is based on the protocol used and the connection type. NFS are storage solutions that can leverage the existing TCP/IP network infrastructure. Hence, they are referred to as IP-based storage. Storage IO Control (SIOC) is one of the mechanisms to use ensure a fair share of storage bandwidth allocation to all Virtual Machines running on shared storage, regardless of the ESXi host the Virtual Machines are running on. Managing VMFS volumes detected as snapshotsSome environments maintain copies of the production LUNs as a backup, by replicating them. These replicas are exact copies of the LUNs that were already presented to the ESXi hosts. If for any reason a replicated LUN is presented to an ESXi host, then the host will not mount the VMFS volume on the LUN. This is a precaution to prevent data corruption. ESXi identifies each VMFS volume using its signature denoted by aUniversally Unique Identifier (UUID). The UUID is generated when the volume is first created or resignatured and is stored in the LVM header of the VMFS volume. When an ESXi host scans for new LUN ;devices and VMFS volumes on it, it compares the physical device ID (NAA ID) of the LUN with the device ID (NAA ID) value stored in the VMFS volumes LVM header. If it finds a mismatch, then it flags the volume as a snapshot volume.Volumes detected as snapshots are not mounted by default. There are two ways to mount such volumes/datastore:Mount by Keeping the Existing Signature Intact - This is used when you are attempting to temporarily mount the snapshot volume on an ESXi that doesn't see the original volume. If you were to attempt mounting the VMFS volume by keeping the existing signature and if the host sees the original volume, then you will not be allowed to mount the volume and will be warned about the presence of another VMFS volume with the same UUID:Mount by generating a new VMFS Signature - This has to be used if you are mounting a clone or a snapshot of an existing VMFS datastore to the same host/s. The process of assigning a new signature will not only update the LVM header with the newly generated UUID, but all the Physical Device ID (NAA ID) of the snapshot LUN. Here, the VMFS volume/datastore will be renamed by prefixing the wordsnap followed by a random number and the name of the original datastore: Getting readyMake sure that the original datastore and its LUN is no longer seen by the ESXi host the snapshot is being mounted to. How to do it...The following procedure will help mount a VMFS volume from a LUN detected as a snapshot:Log in to the vCenter Server using the vSphere Web Client and use the key combination Ctrl+Alt+2 to switch to the Host and Clusters view.Right click on the ESXi host the snapshot LUN is mapped to and go to Storage | New Datastore.On the New Datastore wizard, select VMFS as the filesystem type and click Next to continue.On the Name and Device selection screen, select the LUN detected as a snaphsot and click Next to continue:On the Mount Option screen, choose to either mount by assigning a new signature or by keeping the existing signature, and click Next to continue:On the Ready to Complete screen, review the setting and click Finish to initiate the operation. Creating NFSv4.1 datastores with Kerberos authenticationVMware introduced support for NFS 4.1 with vSphere 6.0. The vSphere 6.5 added several enhancements:It now supports AES encryptionSupport for IP version 6Support Kerberos's integrity checking mechanismHere, we will learn how to create NFS 4.1 datastores. Although the procedure is similar to NFSv3, there are a few additional steps that needs to be performed. Getting readyFor Kerberos authentication to work, you need to make sure that the ESXi hosts and the NFS Server is joined to the Active Directory domainCreate a new or select an existing AD user for NFS Kerberos authenticationConfigure the NFS Server/Share to Allow access to the AD user chosen for NFS Kerberos authentication How to do it...The following procedure will help you mount an NFS datasture using the NFSv4.1 client with Kerberos authentication enabled:Log in to the vCenter Server using the vSphere Web Client and use the key combination Ctrl+Alt+2 to switch to the Host and Clusters view, select the desired ESXi host and navigate to it  Configure | System | Authentication Services section and supply the credentials of the Active Directory user that was chosen for NFS Kerberon Authentication:Right-click on the desired ESXi host and go to Storage | New Datastore to bring-up the Add Storage wizard.On the New Datastore wizard, select the Type as NFS and click Next to continue.On the Select NFS version screen, select NFS 4.1 and click Next to continue. Keep in mind that it is not recommended to mount an NFS Export using both NFS3 and NFS4.1 client. On the Name and Configuration screen, supply a Name for the Datastore, the NFS export's folder path and NFS Server's IP Address or FQDN. You can also choose to mount the share as ready-only if desired:On the Configure Kerberos Authentication screen, check the Enable Kerberos-based authentication box and choose the type of authentication required and click Next to continue:On the Ready to Complete screen review the settings and click Finish to mount the NFS export. Enabling storage I/O controlThe use of disk shares will work just fine as long as the datastore is seen by a single ESXi host. Unfortunately, that is not a common case. Datastores are often shared among multiple ESXi hosts. When datastores are shared, you bring in more than one local host scheduler into the process of balancing the I/O among the virtual machines. However, these lost host schedules cannot talk to each other and their visibility is limited to the ESXi hosts they are running on. This easily contributes to a serious problem called thenoisy neighbor situation. The job of SIOC is to enable some form of communication between local host schedulers so that I/O can be balanced between virtual machines running on separate hosts.  How to do it...The following procedure will help you enable SIOC on a datastore:Connect to the vCenter Server using the Web Client and switch to the Storage view using the key combination Ctrl+Alt+4.Right-click on the desired datastore and go to Configure Storage I/O Control:On the Configure Storage I/O Control window, select the checkbox Enable Storage I/O Control, set a custom congestion threshold (only if needed) and click OK to confirm the settings: With the Virtual Machine selected from the inventory, navigate to its Configure | General tab and review its datastore capability settings to ensure that SIOC is enabled:  How it works...As mentioned earlier, SIOC enables communication between these local host schedulers so that I/O can be balanced between virtual machines running on separate hosts. It does so by maintaining a shared file in the datastore that all hosts can read/write/update. When SIOC is enabled on a datastore, it starts monitoring the device latency on the LUN backing the datastore. If the latency crosses the threshold, it throttles the LUN's queue depth on each of the ESXi hosts in an attempt to distribute a fair share of access to the LUN for all the Virtual Machines issuing the I/O.The local scheduler on each of the ESXi hosts maintains an iostats file to keep its companion hosts aware of the device I/O statistics observed on the LUN. The file is placed in a directory (naa.xxxxxxxxx) on the same datastore.For example, if there are six virtual machines running on three different ESXi hosts, accessing a shared LUN. Among the six VMs, four of them have a normal share value of 1000 and the remaining two have high (2000) disk share value sets on them. These virtual machines have only a single VMDK attached to them. VM-C on host ESX-02 is issuing a large number of I/O operations. Since that is the only VM accessing the shared LUN from that host, it gets the entire queue's bandwidth. This can induce latency on the I/O operations performed by the other VMs: ESX-01 and ESX-03. If the SIOC detects the latency value to be greater than the dynamic threshold, then it will start throttling the queue depth: The throttled DQLEN for a VM is calculated as follows:DQLEN for the VM = (VM's Percent of Shares) of (Queue Depth)Example: 12.5 % of 64 → (12.5 * 64)/100 = 8The throttled DQLEN per host is calculated as follows:DQLEN of the Host = Sum of the DQLEN of the VMs on itExample: VM-A (8) + VM-B(16) = 24The following diagram shows the effect of SIOC throttling the queue depth: SummaryIn this article we learnt, how to mount a VMFS volume from a LUN detected as a snapshot, how to mount an NFS datasture using the NFSv4.1 client with Kerberos authentication enabled, and how to enable SIOC on a datastore. Resources for Article:   Further resources on this subject: Essentials of VMware vSphere [article] Working with VMware Infrastructure [article] Network Virtualization and vSphere [article]
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Amarabha Banerjee
21 Feb 2018
7 min read
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Installing TensorFlow in Windows, Ubuntu and Mac OS

Amarabha Banerjee
21 Feb 2018
7 min read
[box type="note" align="" class="" width=""]This article is taken from the book Machine Learning with Tensorflow 1.x, written by Quan Hua, Shams Ul Azeem and Saif Ahmed. This book will help tackle common commercial machine learning problems with Google’s TensorFlow 1.x library.[/box] Today, we shall explore the basics of getting started with TensorFlow, its installation and configuration process. The proliferation of large public datasets, inexpensive GPUs, and open-minded developer culture has revolutionized machine learning efforts in recent years. Training data, the lifeblood of machine learning, has become widely available and easily consumable in recent years. Computing power has made the required horsepower available to small businesses and even individuals. The current decade is incredibly exciting for data scientists. Some of the top platforms used in the industry include Caffe, Theano, and Torch. While the underlying platforms are actively developed and openly shared, usage is limited largely to machine learning practitioners due to difficult installations, non-obvious configurations, and difficulty with productionizing solutions. TensorFlow has one of the easiest installations of any platform, bringing machine learning capabilities squarely into the realm of casual tinkerers and novice programmers. Meanwhile, high-performance features, such as—multiGPU support, make the platform exciting for experienced data scientists and industrial use as well. TensorFlow also provides a reimagined process and multiple user-friendly utilities, such as TensorBoard, to manage machine learning efforts. Finally, the platform has significant backing and community support from the world's largest machine learning powerhouse--Google. All this is before even considering the compelling underlying technical advantages, which we'll dive into later. Installing TensorFlow TensorFlow conveniently offers several types of installation and operates on multiple operating systems. The basic installation is CPU-only, while more advanced installations unleash serious horsepower by pushing calculations onto the graphics card, or even to multiple graphics cards. We recommend starting with a basic CPU installation at first. More complex GPU and CUDA installations will be discussed in Appendix, Advanced Installation. Even with just a basic CPU installation, TensorFlow offers multiple options, which are as follows: A basic Python pip installation A segregated Python installation via Virtualenv A fully segregated container-based installation via Docker Ubuntu installation Ubuntu is one of the best Linux distributions for working with Tensorflow. We highly recommend that you use an Ubuntu machine, especially if you want to work with GPU. We will do most of our work on the Ubuntu terminal. We will begin with installing pythonpip and python-dev via the following command: sudo apt-get install python-pip python-dev A successful installation will appear as follows: If you find missing packages, you can correct them via the following command: sudo apt-get update --fix-missing Then, you can continue the python and pip installation. We are now ready to install TensorFlow. The CPU installation is initiated via the following command: sudo pip install tensorflow A successful installation will appear as follows: macOS installation If you use Python, you will probably already have the Python package installer, pip. However, if not, you can easily install it using the easy_install pip command. You'll note that we actually executed sudo easy_install pip—the sudo prefix was required because the installation requires administrative rights. We will make the fair assumption that you already have the basic package installer, easy_install, available; if not, you can install it from https://pypi.python.org/pypi/setuptools. A successful installation will appear as shown in the following screenshot: Next, we will install the six package: sudo easy_install --upgrade six A successful installation will appear as shown in the following screenshot: Surprisingly, those are the only two prerequisites for TensorFlow, and we can now install the core platform. We will use the pip package installer mentioned earlier and install TensorFlow directly from Google's site. The most recent version at the time of writing this book is v1.3, but you should change this to the latest version you wish to use: sudo pip install tensorflow The pip installer will automatically gather all the other required dependencies. You will see each individual download and installation until the software is fully installed. A successful installation will appear as shown in the following screenshot: That's it! If you were able to get to this point, you can start to train and run your first model. Skip to Chapter 2, Your First Classifier, to train your first model. macOS X users wishing to completely segregate their installation can use a VM instead, as described in the Windows installation. Windows installation As we mentioned earlier, TensorFlow with Python 2.7 does not function natively on Windows. In this section, we will guide you through installing TensorFlow with Python 3.5 and set up a VM with Linux if you want to use TensorFlow with Python 2.7. First, we need to install Python 3.5.x or 3.6.x 64-bit from the following links: https://www.python.org/downloads/release/python-352/ https://www.python.org/downloads/release/python-362/ Make sure that you download the 64-bit version of Python where the name of the installation has amd64, such as python-3.6.2-amd64.exe. The Python 3.6.2 installation looks like this: We will select Add Python 3.6 to PATH and click Install Now. The installation process will complete with the following screen: We will click the Disable path length limit and then click Close to finish the Python installation. Now, let's open the Windows PowerShell application under the Windows menu. We will install the CPU-only version of Tensorflow with the following command: pip3 install tensorflow. The result of the installation will look like this: Congratulations, you can now use TensorFlow on Windows with Python 3.5.x or 3.6.x support. In the next section, we will show you how to set up a VM to use TensorFlow with Python 2.7. However, you can skip to the Test installation section of Chapter 2, Your First Classifier, if you don't need Python 2.7. Now, we will show you how to set up a VM with Linux to use TensorFlow with Python 2.7. We recommend the free VirtualBox system available at https://www.virtualbox.org/wiki/Downloads. The latest stable version at the time of writing is v5.0.14, available at the following URL: http:/ / download. virtualbox. org/ virtualbox/ 5. 1. 28/ VirtualBox- 5. 1. 28- 117968- Win. exe A successful installation will allow you to run the Oracle VM VirtualBox Manager dashboard, which looks like this: Testing the installation In this section, we will use TensorFlow to compute a simple math operation. First, open your terminal on Linux/macOS or Windows PowerShell in Windows. Now, we need to run python to use TensorFlow with the following command: python Enter the following program in the Python shell: import tensorflow as tf a = tf.constant(1.0) b = tf.constant(2.0) c = a + b sess = tf.Session() print(sess.run(c)) The result will look like the following screen where 3.0 is printed at the end: We covered TensorFlow installation on the three major operating systems, so that you are up and running with the platform. Windows users faced an extra challenge, as TensorFlow on Windows only supports Python 3.5.x or Python 3.6.x 64-bit version. However, even Windows users should now be up and running. Further get a detailed understanding of implementing Tensorflow with contextual examples in this post. If you liked this article, be sure to check out Machine Learning with Tensorflow 1.x which will help you take up any challenge you may face while implementing TensorFlow 1.x in your machine learning environment.  
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Packt
21 Feb 2018
13 min read
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Some Basic Concepts of Theano

Packt
21 Feb 2018
13 min read
 In this article by Christopher Bourez, the author of the book Deep Learning with Theano, presents Theano as a compute engine, and the basics for symbolic computing with Theano. Symbolic computing consists in building graphs of operations that will be optimized later on for a specific architecture, using the computation libraries available for this architecture. (For more resources related to this topic, see here.) Although this article might sound far from practical application. Theano may be defined as a library for scientific computing; it has been available since 2007 and is particularly suited for deep learning. Two important features are at the core of any deep learning library: tensor operations, and the capability to run the code on CPU or GPU indifferently. These two features enable us to work with massive amount of multi-dimensional data. Moreover, Theano proposes automatic differentiation, a very useful feature to solve a wider range of numeric optimizations than deep learning problems. The content of the article covers the following points: Theano install and loading Tensors and algebra Symbolic programming Need for tensor Usually, input data is represented with multi-dimensional arrays: Images have three dimensions: The number of channels, the width and height of the image Sounds and times series have one dimension: The time length Natural language sequences can be represented by two dimensional arrays: The time length and the alphabet length or the vocabulary length In Theano, multi-dimensional arrays are implemented with an abstraction class, named tensor, with many more transformations available than traditional arrays in a computer language like Python. At each stage of a neural net, computations such as matrix multiplications involve multiple operations on these multi-dimensional arrays. Classical arrays in programming languages do not have enough built-in functionalities to address well and fastly multi-dimensional computations and manipulations. Computations on multi-dimensional arrays have known a long history of optimizations, with tons of libraries and hardwares. One of the most important gains in speed has been permitted by the massive parallel architecture of the Graphical Computation Unit (GPU), with computation ability on a large number of cores, from a few hundreds to a few thousands. Compared to the traditional CPU, for example a quadricore, 12-core or 32-core engine, the gain with GPU can range from a 5x to a 100x times speedup, even if part of the code is still being executed on the CPU (data loading, GPU piloting, result outputing). The main bottleneck with the use of GPU is usually the transfer of data between the memory of the CPU and the memory of the GPU, but still, when well programmed, the use of GPU helps bring a significant increase in speed of an order of magnitude. Getting results in days rather than months, or hours rather than days, is an undeniable benefit for experimentation. Theano engine has been designed to address these two challenges of multi-dimensional array and architecture abstraction from the beginning. There is another undeniable benefit of Theano for scientific computation: the automatic differentiation of functions of multi-dimensional arrays, a well-suited feature for model parameter inference via objective function minimization. Such a feature facilitates the experimentation by releasing the pain to compute derivatives, which might not be so complicated, but prone to many errors. Installing and loading Theano Conda package and environment manager The easiest way to install Theano is to use conda, a cross-platform package and environment manager. If conda is not already installed on your operating system, the fastest way to install conda is to download the miniconda installer from https://conda.io/miniconda.html. For example, for conda under Linux 64 bit and Python 2.7: wget https://repo.continuum.io/miniconda/Miniconda2-latest-Linux-x86_64.sh chmod +x Miniconda2-latest-Linux-x86_64.sh bash ./Miniconda2-latest-Linux-x86_64.sh   Conda enables to create new environments in which versions of Python (2 or 3) and the installed packages may differ. The conda root environment uses the same version of Python as the version installed on your system on which you installed conda. Install and run Theano on CPU Last, let’s install Theano: conda install theano Run a python session and try the following commands to check your configuration: >>> from theano import theano   >>> theano.config.device   'cpu'   >>> theano.config.floatX   'float64'   >>> print(theano.config) The last command prints all the configuration of Theano. The theano.config object contains keys to many configuration options. To infer the configuration options, Theano looks first at ~/.theanorc file, then at any environment variables available, which override the former options, last at the variable set in the code, that are first in the order of precedence: >>> theano.config.floatX='float32' Some of the properties might be read-only and cannot be changed in the code, but floatX property, that sets the default floating point precision for floats, is among properties that can be changed directly in the code. It is advised to use float32 since GPU have a long history without float64, float64 execution speed on GPU is slower, sometimes much slower (2x to 32x on latest generation Pascal hardware), and that float32 precision is enough in practice. GPU drivers and libraries Theano enables the use of GPU (graphic computation units), the units usually used to compute the graphics to display on the computer screen. To have Theano work on the GPU as well, a GPU backend library is required on your system. CUDA library (for NVIDIA GPU cards only) is the main choice for GPU computations. There exists also the OpenCL standard, which is opensource, but far less developed, and much more experimental and rudimentary on Theano. Most of the scientific computations still occur on NVIDIA cards today. If you have a NVIDIA GPU card, download CUDA from the NVIDIA website at https://developer.nvidia.com/cuda-downloads and install it. The installer will install the lastest version of the gpu drivers first if they are not already installed. It will install the CUDA library in /usr/local/cuda directory. Install the cuDNN library, a library by NVIDIA also, that offers faster implementations of some operations for the GPU To install it, I usually copy /usr/local/cuda directory to a new directory /usr/local/cuda-{CUDA_VERSION}-cudnn-{CUDNN_VERSION} so that I can choose the version of CUDA and cuDNN, depending on the deep learning technology I use, and its compatibility. In your .bashrc profile, add the following line to set $PATH and $LD_LIBRARY_PATH variables: export PATH=/usr/local/cuda-8.0-cudnn-5.1/bin:$PATH export LD_LIBRARY_PATH=/usr/local/cuda-8.0-cudnn-5.1/lib64::/usr/local/cuda-8.0-cudnn-5.1/lib:$LD_LIBRARY_PATH Install and run Theano on GPU N-dimensional GPU arrays have been implemented in Python under 6 different GPU library (Theano/CudaNdarray,PyCUDA/ GPUArray,CUDAMAT/ CUDAMatrix, PYOPENCL/GPUArray, Clyther, Copperhead), are a subset of NumPy.ndarray. Libgpuarray is a backend library to have them in a common interface with the same property. To install libgpuarray with conda: conda install pygpu To run Theano in GPU mode, you need to configure the config.device variable before execution since it is a read-only variable once the code is run. With the environment variable THEANO_FLAGS: THEANO_FLAGS="device=cuda,floatX=float32" python >>> import theano Using cuDNN version 5110 on context None Mapped name None to device cuda: Tesla K80 (0000:83:00.0) >>> theano.config.device 'gpu' >>> theano.config.floatX 'float32' The first return shows that GPU device has been correctly detected, and specifies which GPU it uses. By default, Theano activates CNMeM, a faster CUDA memory allocator, an initial preallocation can be specified with gpuarra.preallocate option. At the end, my launch command will be: THEANO_FLAGS="device=cuda,floatX=float32,gpuarray.preallocate=0.8" python >>> from theano import theano Using cuDNN version 5110 on context None Preallocating 9151/11439 Mb (0.800000) on cuda Mapped name None to device cuda: Tesla K80 (0000:83:00.0)   The first line confirms that cuDNN is active, the second confirms memory preallocation. The third line gives the default context name (that is None when the flag device=cuda is set) and the model of the GPU used, while the default context name for the CPU will always be cpu. It is possible to specify a different GPU than the first one, setting the device to cuda0, cuda1,... for multi-GPU computers. It is also possible to run a program on multiple GPU in parallel or in sequence (when the memory of one GPU is not sufficient), in particular when training very deep neural nets. In this case, the context flag contexts=dev0->cuda0;dev1->cuda1;dev2->cuda2;dev3->cuda3 activates multiple GPU instead of one, and designate the context name to each GPU device to be used in the code. For example, on a 4-GPU instance: THEANO_FLAGS="contexts=dev0->cuda0;dev1->cuda1;dev2->cuda2;dev3->cuda3,floatX=float32,gpuarray.preallocate=0.8" python >>> import theano Using cuDNN version 5110 on context None Preallocating 9177/11471 Mb (0.800000) on cuda0 Mapped name dev0 to device cuda0: Tesla K80 (0000:83:00.0) Using cuDNN version 5110 on context dev1 Preallocating 9177/11471 Mb (0.800000) on cuda1 Mapped name dev1 to device cuda1: Tesla K80 (0000:84:00.0) Using cuDNN version 5110 on context dev2 Preallocating 9177/11471 Mb (0.800000) on cuda2 Mapped name dev2 to device cuda2: Tesla K80 (0000:87:00.0) Using cuDNN version 5110 on context dev3 Preallocating 9177/11471 Mb (0.800000) on cuda3 Mapped name dev3 to device cuda3: Tesla K80 (0000:88:00.0)   To assign computations to a specific GPU in this multi-GPU setting, the names we choose dev0, dev1, dev2, and dev3 have been mapped to each device (cuda0, cuda1, cuda2, cuda3). This name mapping enables to write codes that are independent of the underlying GPU assignments and libraries (CUDA or other). To keep the current configuration flags active at every Python session or execution without using environment variables, save your configuration in the ~/.theanorc file as: [global] floatX = float32 device = cuda0 [gpuarray] preallocate = 1 Now, you can simply run python command. You are now all set. Tensors In Python, some scientific libraries such as NumPy provide multi-dimensional arrays. Theano doesn't replace Numpy but works in concert with it. In particular, NumPy is used for the initialization of tensors. To perform the computation on CPU and GPU indifferently, variables are symbolic and represented by the tensor class, an abstraction, and writing numerical expressions consists in building a computation graph of Variable nodes and Apply nodes. Depending on the platform on which the computation graph will be compiled, tensors are replaced either: By a TensorType variable, which data has to be on CPU By a GpuArrayType variable, which data has to be on GPU That way, the code can be written indifferently of the platform where it will be executed. Here are a few tensor objects: Object class Number of dimensions Example theano.tensor.scalar 0-dimensional array 1, 2.5 theano.tensor.vector 1-dimensional array [0,3,20] theano.tensor.matrix 2-dimensional array [[2,3][1,5]] theano.tensor.tensor3 3-dimensional array [[[2,3][1,5]],[[1,2],[3,4]]] Playing with these Theano objects in the Python shell gives a better idea: >>> import theano.tensor as T   >>> T.scalar() <TensorType(float32, scalar)>   >>> T.iscalar() <TensorType(int32, scalar)>   >>> T.fscalar() <TensorType(float32, scalar)>   >>> T.dscalar() <TensorType(float64, scalar)> With a i, l, f, d letter in front of the object name, you initiate a tensor of a given type, integer32, integer64, floats32 or float64. For real-valued (floating point) data, it is advised to use the direct form T.scalar() instead of the f or d variants since the direct form will use your current configuration for floats: >>> theano.config.floatX = 'float64'   >>> T.scalar() <TensorType(float64, scalar)>   >>> T.fscalar() <TensorType(float32, scalar)>   >>> theano.config.floatX = 'float32'   >>> T.scalar() <TensorType(float32, scalar)> Symbolic variables either: Play the role of placeholders, as a starting point to build your graph of numerical operations (such as addition, multiplication): they receive the flow of the incoming data during the evaluation, once the graph has been compiled Represent intermediate or output results Symbolic variables and operations are both part of a computation graph that will be compiled either towards CPU or GPU for fast execution. Let's write a first computation graph consisting in a simple addition: >>> x = T.matrix('x')   >>> y = T.matrix('y')   >>> z = x + y   >>> theano.pp(z) '(x + y)'   >>> z.eval({x: [[1, 2], [1, 3]], y: [[1, 0], [3, 4]]}) array([[ 2., 2.],        [ 4., 7.]], dtype=float32) At first place, two symbolic variables, or Variable nodes are created, with names x and y, and an addition operation, an Apply node, is applied between both of them, to create a new symbolic variable, z, in the computation graph. The pretty print function pp prints the expression represented by Theano symbolic variables. Eval evaluates the value of the output variable z, when the first two variables x and y are initialized with two numerical 2-dimensional arrays. The following example explicit the difference between the variables x and y, and their names x and y: >>> a = T.matrix()   >>> b = T.matrix()   >>> theano.pp(a + b) '(<TensorType(float32, matrix)> + <TensorType(float32, matrix)>)' Without names, it is more complicated to trace the nodes in a large graph. When printing the computation graph, names significantly helps diagnose problems, while variables are only used to handle the objects in the graph: >>> x = T.matrix('x')   >>> x = x + x   >>> theano.pp(x) '(x + x)' Here the original symbolic variable, named x, does not change and stays part of the computation graph. x + x creates a new symbolic variable we assign to the Python variable x. Note also, that with names, the plural form initializes multiple tensors at the same time: >>> x, y, z = T.matrices('x', 'y', 'z') Now, let's have a look at the different functions to display the graph. Summary Thus, this article helps us to give a brief idea on how to download and install Theano on various platforms along with the packages such as NumPy and SciPy. Resources for Article:   Further resources on this subject: Introduction to Deep Learning [article] Getting Started with Deep Learning [article] Practical Applications of Deep Learning [article]
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Sunith Shetty
20 Feb 2018
9 min read
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Getting to know Generative Models and their types

Sunith Shetty
20 Feb 2018
9 min read
[box type="note" align="" class="" width=""]This article is an excerpt from a book written by Rajdeep Dua and Manpreet Singh Ghotra titled Neural Network Programming with Tensorflow. In this book, you will use TensorFlow to build and train neural networks of varying complexities, without any hassle.[/box] In today’s tutorial, we will learn about generative models, and their types. We will also look into how discriminative models differs from generative models. Introduction to Generative models Generative models are the family of machine learning models that are used to describe how data is generated. To train a generative model we first accumulate a vast amount of data in any domain and later train a model to create or generate data like it. In other words, these are the models that can learn to create data that is similar to data that we give them. One such approach is using Generative Adversarial Networks (GANs). There are two kinds of machine learning models: generative models and discriminative models. Let's examine the following list of classifiers: decision trees, neural networks, random forests, generalized boosted models, logistic regression, naive bayes, and Support Vector Machine (SVM). Most of these are classifiers and ensemble models. The odd one out here is Naive Bayes. It's the only generative model in the list. The others are examples of discriminative models. The fundamental difference between generative and discriminative models lies in the underlying probability inference structure. Let's go through some of the key differences between generative and discriminative models. Discriminative versus generative models Discriminative models learn P(Y|X), which is the conditional relationship between the target variable Y and features X. This is how least squares regression works, and it is the kind of inference pattern that gets used. It is an approach to sort out the relationship among variables. Generative models aim for a complete probabilistic description of the dataset. With generative models, the goal is to develop the joint probability distribution P(X, Y), either directly or by computing P(Y | X) and P(X) and then inferring the conditional probabilities required to classify newer data. This method requires more solid probabilistic thought than regression demands, but it provides a complete model of the probabilistic structure of the data. Knowing the joint distribution enables you to generate the data; hence, Naive Bayes is a generative model. Suppose we have a supervised learning task, where xi is the given features of the data points and yi is the corresponding labels. One way to predict y on future x is to learn a function f() from (xi,yi) that takes in x and outputs the most likely y. Such models fall in the category of discriminative models, as you are learning how to discriminate between x's from different classes. Methods like SVMs and neural networks fall into this category. Even if you're able to classify the data very accurately, you have no notion of how the data might have been generated. The second approach is to model how the data might have been generated and learn a function f(x,y) that gives a score to the configuration determined by x and y together. Then you can predict y for a new x by finding the y for which the score f(x,y) is maximum. A canonical example of this is Gaussian mixture models. Another example of this is: you can imagine x to be an image and y to be a kind of object like a dog, namely in the image. The probability written as p(y|x) tells us how much the model believes that there is a dog, given an input image compared to all possibilities it knows about. Algorithms that try to model this probability map directly are called discriminative models. Generative models, on the other hand, try to learn a function called the joint probability p(y, x). We can read this as how much the model believes that x is an image and there is a dog y in it at the same time. These two probabilities are related and that could be written as p(y, x) = p(x) p(y|x), with p(x) being how likely it is that the input x is an image. The p(x) probability is usually called a density function in literature. The main reason to call these models generative ultimately connects to the fact that the model has access to the probability of both input and output at the same time. Using this, we can generate images of animals by sampling animal kinds y and new images x from p(y, x). We can mainly learn the density function p(x) which only depends on the input space. Both models are useful; however, comparatively, generative models have an interesting advantage over discriminative models, namely, they have the potential to understand and explain the underlying structure of the input data even when there are no labels available. This is very desirable when working in the real world. Types of generative models Discriminative models have been at the forefront of the recent success in the field of machine learning. Models make predictions that depend on a given input, although they are not able to generate new samples or data. The idea behind the recent progress of generative modeling is to convert the generation problem to a prediction one and use deep learning algorithms to learn such a problem. Autoencoders One way to convert a generative to a discriminative problem can be by learning the mapping from the input space itself. For example, we want to learn an identity map that, for each image x, would ideally predict the same image, namely, x = f(x), where f is the predictive model. This model may not be of use in its current form, but from this, we can create a generative model. Here, we create a model formed of two main components: an encoder model q(h|x) that maps the input to another space, which is referred to as hidden or the latent space represented by h, and a decoder model q(x|h) that learns the opposite mapping from the hidden input space. These components--encoder and decoder--are connected together to create an end-to-end trainable model. Both the encoder and decoder models are neural networks of different architectures, for example, RNNs and Attention Nets, to get desired outcomes. As the model is learned, we can remove the decoder from the encoder and then use them separately. To generate a new data sample, we can first generate a sample from the latent space and then feed that to the decoder to create a new sample from the output space. GAN As seen with autoencoders, we can think of a general concept to create networks that will work together in a relationship, and training them will help us learn the latent spaces that allow us to generate new data samples. Another type of generative network is GAN, where we have a generator model q(x|h) to map the small dimensional latent space of h (which is usually represented as noise samples from a simple distribution) to the input space of x. This is quite similar to the role of decoders in autoencoders. The deal is now to introduce a discriminative model p(y| x), which tries to associate an input instance x to a yes/no binary answer y, about whether the generator model generated the input or was a genuine sample from the dataset we were training on. Let's use the image example done previously. Assume that the generator model creates a new image, and we also have the real image from our actual dataset. If the generator model was right, the discriminator model would not be able to distinguish between the two images easily. If the generator model was poor, it would be very simple to tell which one was a fake or fraud and which one was real. When both these models are coupled, we can train them end to end by assuring that the generator model is getting better over time to fool the discriminator model, while the discriminator model is trained to work on the harder problem of detecting frauds. Finally, we desire a generator model with outputs that are indistinguishable from the real data that we used for the training. Through the initial parts of the training, the discriminator model can easily detect the samples coming from the actual dataset versus the ones generated synthetically by the generator model, which is just beginning to learn. As the generator gets better at modeling the dataset, we begin to see more and more generated samples that look similar to the dataset. The following example depicts the generated images of a GAN model learning over time: Sequence models If the data is temporal in nature, then we can use specialized algorithms called Sequence Models. These models can learn the probability of the form p(y|x_n, x_1), where i is an index signifying the location in the sequence and x_i is the ith  input sample. As an example, we can consider each word as a series of characters, each sentence as a series of words, and each paragraph as a series of sentences. Output y could be the sentiment of the sentence. Using a similar trick from autoencoders, we can replace y with the next item in the series or sequence, namely y = x_n + 1, allowing the model to learn. To summarize, we learned generative models are a fast advancing area of study and research. As we proceed to advance these models and grow the training and datasets, we can expect to generate data examples that depict completely believable images. This can be used in several applications such as image denoising, painting, structured prediction, and exploration in reinforcement learning. To know more about how to build and optimize neural networks using TensorFlow, do checkout this book Neural Network Programming with Tensorflow.    
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Dave Maclean
20 Feb 2018
4 min read
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Stack Wars: The epic struggle for control of the tech stack

Dave Maclean
20 Feb 2018
4 min read
The choice of tech stack for a project, team or organisation is an ongoing struggle between competing forces. Each of the players has their own logic, beliefs and drivers. Where you stand and what side you are on totally determines the way you see the struggle. Packt is on the developer team. This is how we see the struggle we’re all part of: Technology vendors are the Empire Any organisation that is selling tools, technologies or platform services is either already behaving like the Empire, or will, eventually, become the Empire. Vendors want the stack to include their tech, and if the vendor has a full stack like IBM, MS, or Oracle then they want you to live in their world. To be completely Blue or Red Stack. The economics driving this are relentless. The biggest cost for large software vendors is acquiring customers. Once you have a customer, it makes sense to keep expanding your product portfolio to sell more to each customer. The end game is when the Empire captures whole planets from the Alliance and enslaves the occupants in a move called Large Outsourcing Deals. Businesses and IT departments are the Rebel Alliance Companies and organisations build systems to try and serve their users and customers. Their underlying intentions are good. They are trying to do the right thing. They do the best they can. They have to manage within a structured organisation, co-ordinating different groups and teams. They sometimes have some cool new stuff, but often they are struggling with outdated kit, against overwhelming odds. Companies sometimes achieve great things in specific battles with heroic individuals and teams, but they also have to keep the whole show on the road. The Empire Vendors are constantly trying to bring them into their captive stack-universe, to make life “easier" with the comforting myth of the one-stop-shop. The Alliance gets new weapons and allies in the form of insurgent vendors who start out fighting the Empire, like GitHhub, Jira and AWS. However, these can be dangerous alliances. The iron law of the costs of customer acquisition will drive even the insurgent vendors to continually expand their product offer and then - BAM! – another empire wanting to lock you in. They call this the ‘Land and Expand’ strategy and every vendor has it, overtly or secretly. Even the currently much-beloved Slack will eventually try and turn itself into the Facebook of the office, and will gobble up the app ecosystem just like Facebook.  They all cross over to the dark side eventually. Developers are the Jedi Devs have a deep understanding of how technologies really work in action because they have to actually build things. This knowledge can appear mystical to outsiders. It is hard to express and articulate the intuitive skills gained from actual development experience. The very best devs are 10, 100, 1000 times more productive than the implementation teams from the vendors. Devs know what vendor tools are really like under the hood, when the action starts. They know that even the Death Star has hidden yet fatal vulnerabilities, no matter how great it looks from a distance. Over the years devs have evolved their own special ways of working that is hard for outsiders to understand. These go by the names of Agile and Open Source. Agile is a semi-mysterious Way, trusting the process to migrate towards success, without being really able to say what that is before we realise we get there. Open Source is the shared network that binds developers together into a powerful network of shared power on platforms like GitHub. Devs have two forces driving them. The first is to get the very best tech stack for each project, based on their unique technical insight into how it really works. Devs always want to choose best of breed, for this problem, here and now. But devs also have personal weapons of choice, over which they have mastery, and will try and use these wherever possible. Laser swords can do a lot more than you think, but there are other, better weapons in certain circumstances. Stack Wars are never going to end. There will be more and more episodes of this eternal struggle. The Empire can never be completely defeated, any more than the Jedi can die out. The story needs all three, and ebbs and flows over time in a pattern that repeats itself but in new and different ways.
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Packt
20 Feb 2018
4 min read
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Your First Swift Program

Packt
20 Feb 2018
4 min read
 In this article, by Keith Moon author of the book Swift 4 Programming Cookbook, we will learn how to write your first swift program. (For more resources related to this topic, see here.) Your first Swift program In this first recipe will be get up and running with Swift using a Swift Playground, and run our first piece of Swift code. Getting ready To run our first Swift program, we first need to download and install our IDE. During the beta of Apple's Xcode 9, it is available as a direct download from Apple's developer website at http://developer.apple.com/download, access to this beta will require a free Apple developer account. Once the beta has ended and Xcode 9 is publically available, it will also be available from the Mac App Store. By obtaining it from the Mac App Store, you will automatically be informed of updates, so this is the preferred route, once Xcode 9 is out of beta. Xcode from the Mac App Store Open up the Mac App Store, either from the dock or via Spotlight: Search for xcode: Click Install: Xcode is a large download (over 4 GB). So, depending on your internet connection, this could take a while! Progress can be monitored from Launchpad: Xcode as a direct download Go to the Apple Developer download page at http://developer.apple.com/download  Click the Download button to download Xcode within a .xip file.  Double click on the downloaded file to unpack the Xcode application. Drag the Xcode application into your Applications folder How to do it... With Xcode downloaded, let create our first Swift playground: Launch Xcode from the icon in your dock. From the welcome screen, choose Get started with a playground. From the template chooser, select the blank template from the iOS tab: Choose a name for your playground and a location to save it: Xcode Playgrounds can be based on one of three different Apple platforms, iOS, tvOS and macOS (the operating system formerly known as OSX). Playgrounds provide full access to the frameworks available to either iOS, tvOS or macOS, depending on which you choose. An iOS playground will be assumed for the entirety of this chapter, chiefly because this is the platform of choice of the author. Where recipes do have UI components, the iOS platform will be used until otherwise stated. You are now presented with a view that looks like this: Let's replace the word playground with Swift!. Press the blue play button in the bottom left-hand corner of the window to execute the code in the playground: Congratulations! You have just run some Swift code. On the right-hand side of the window, you will see the output of each line of code in the playground. We can see our line of code has output "Hello, Swift!": There's more... If you put your cursor over the output on the left-hand side, you will see two buttons, one that looks like an eye, another that is a circle: Click on the eye button and you get a Quick Look box of the output. This isn't that useful for just a string, but can be useful for more visual output like colors and views. Click on the square button, and a box will be added in-line, under your code, showing the output of the code. This can be really useful if you want to see how the output changes as you change the code. Summary In this article, we learnt how to run your first swift program. Resources for Article: Further resources on this subject: Your First Swift App [article] Exploring Swift [article] Functions in Swift [article]
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Pravin Dhandre
20 Feb 2018
6 min read
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Installing and Configuring X-pack on Elasticsearch and Kibana

Pravin Dhandre
20 Feb 2018
6 min read
[box type="note" align="" class="" width=""]This article is an excerpt from a book written by Pranav Shukla and Sharath Kumar M N titled Learning Elastic Stack 6.0. This book provides detailed coverage on fundamentals of Elastic Stack, making it easy to search, analyze and visualize data across different sources in real-time.[/box] In this short tutorial, we will show step-by-step installation and configuration of X-pack components in Elastic Stack to extend the functionalities of Elasticsearch and Kibana. As X-Pack is an extension of Elastic Stack, prior to installing X-Pack, you need to have both Elasticsearch and Kibana installed. You must run the version of X-Pack that matches the version of Elasticsearch and Kibana. Installing X-Pack on Elasticsearch X-Pack is installed just like any plugin to extend Elasticsearch. These are the steps to install X-Pack in Elasticsearch: Navigate to the ES_HOME folder. Install X-Pack using the following command: $ ES_HOME> bin/elasticsearch-plugin install x-pack During installation, it will ask you to grant extra permissions to X-Pack, which are required by Watcher to send email alerts and also to enable Elasticsearch to launch the machine learning analytical engine. Specify y to continue the installation or N to abort the installation. You should get the following logs/prompts during installation: -> Downloading x-pack from elastic [=================================================] 100% @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ @ WARNING: plugin requires additional permissions @ @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ * java.io.FilePermission .pipe* read,write * java.lang.RuntimePermissionaccessClassInPackage.com.sun.activation.registries * java.lang.RuntimePermission getClassLoader * java.lang.RuntimePermission setContextClassLoader * java.lang.RuntimePermission setFactory * java.net.SocketPermission * connect,accept,resolve * java.security.SecurityPermission createPolicy.JavaPolicy * java.security.SecurityPermission getPolicy * java.security.SecurityPermission putProviderProperty.BC * java.security.SecurityPermission setPolicy * java.util.PropertyPermission * read,write * java.util.PropertyPermission sun.nio.ch.bugLevel write See http://docs.oracle.com/javase/8/docs/technotes/guides/security/permissions.html for descriptions of what these permissions allow and the associated Risks. Continue with installation? [y/N]y @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ @ WARNING: plugin forks a native controller @ @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ This plugin launches a native controller that is not subject to the Java security manager nor to system call filters. Continue with installation? [y/N]y Elasticsearch keystore is required by plugin [x-pack], creating... -> Installed x-pack Restart Elasticsearch: $ ES_HOME> bin/elasticsearch Generate the passwords for the default/reserved users—elastic, kibana, and logstash_system—by executing this command: $ ES_HOME>bin/x-pack/setup-passwords interactive You should get the following logs/prompts to enter the password for the reserved/default users: Initiating the setup of reserved user elastic,kibana,logstash_system passwords. You will be prompted to enter passwords as the process progresses. Please confirm that you would like to continue [y/N]y Enter password for [elastic]: elastic Reenter password for [elastic]: elastic Enter password for [kibana]: kibana Reenter password for [kibana]:kibana Enter password for [logstash_system]: logstash Reenter password for [logstash_system]: logstash Changed password for user [kibana] Changed password for user [logstash_system] Changed password for user [elastic] Please make a note of the passwords set for the reserved/default users. You can choose any password of your liking. We have chosen the passwords as elastic, kibana, and logstash for elastic, kibana, and logstash_system users, respectively, and we will be using them throughout this chapter. To verify the X-Pack installation and enforcement of security, point your web browser to http://localhost:9200/ to open Elasticsearch. You should be prompted to log in to Elasticsearch. To log in, you can use the built-in elastic user and the password elastic. Upon a successful log in, you should see the following response: { name: "fwDdHSI", cluster_name: "elasticsearch", cluster_uuid: "08wSPsjSQCmeRaxF4iHizw", version: { number: "6.0.0", build_hash: "8f0685b", build_date: "2017-11-10T18:41:22.859Z", build_snapshot: false, lucene_version: "7.0.1", minimum_wire_compatibility_version: "5.6.0", minimum_index_compatibility_version: "5.0.0" }, tagline: "You Know, for Search" } A typical cluster in Elasticsearch is made up of multiple nodes, and X-Pack needs to be installed on each node belonging to the cluster. To skip the install prompt, use the—batch parameters during installation: $ES_HOME>bin/elasticsearch-plugin install x-pack --batch. Your installation of X-Pack will have created folders named x-pack in bin, config, and plugins found under ES_HOME. We shall explore these in later sections of the chapter. Installing X-Pack on Kibana X-Pack is installed just like any plugins to extend Kibana. The following are the steps to install X-Pack in Kibana: Navigate to the KIBANA_HOME folder. Install X-Pack using the following command: $KIBANA_HOME>bin/kibana-plugin install x-pack You should get the following logs/prompts during installation: Attempting to transfer from x-pack Attempting to transfer from https://artifacts.elastic.co/downloads/kibana-plugins/x-pack/x-pack -6.0.0.zip Transferring 120307264 bytes.................... Transfer complete Retrieving metadata from plugin archive Extracting plugin archive Extraction complete Optimizing and caching browser bundles... Plugin installation complete Add the following credentials in the kibana.yml file found under $KIBANA_HOME/config and save it: elasticsearch.username: "kibana" elasticsearch.password: "kibana" If you have chosen a different password for the kibana user during password setup, use that value for the elasticsearch.password property. Start Kibana: $KIBANA_HOME>bin/kibana To verify the X-Pack installation, go to http://localhost:5601/ to open Kibana. You should be prompted to log in to Kibana. To log in, you can use the built-in elastic user and the password elastic. Your installation of X-Pack will have created a folder named x-pack in the plugins folder found under KIBANA_HOME. You can also optionally install X-Pack on Logstash. However, X-Pack currently supports only monitoring of Logstash. Uninstalling X-Pack To uninstall X-Pack: Stop Elasticsearch. Remove X-Pack from Elasticsearch: $ES_HOME>bin/elasticsearch-plugin remove x-pack Restart Elasticsearch and stop Kibana 2. Remove X-Pack from Kibana: $KIBANA_HOME>bin/kibana-plugin remove x-pack Restart Kibana. Configuring X-Pack X-Pack comes bundled with security, alerting, monitoring, reporting, machine learning, and graph capabilities. By default, all of these features are enabled. However, one might not be interested in all the features it provides. One can selectively enable and disable the features that they are interested in from the elasticsearch.yml and kibana.yml configuration files. Elasticsearch supports the following features and settings in the elasticsearch.yml file: Kibana supports these features and settings in the kibana.yml file: If X-Pack is installed on Logstash, you can disable the monitoring by setting the xpack.monitoring.enabled property to false in the logstash.yml configuration file.   With this, we successfully explored how to install and configure the X-Pack components in order to bundle different capabilities of X-pack into one package of Elasticsearch and Kibana. If you found this tutorial useful, do check out the book Learning Elastic Stack 6.0 to examine the fundamentals of Elastic Stack in detail and start developing solutions for problems like logging, site search, app search, metrics and more.    
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article-image-decision-trees
Packt
20 Feb 2018
17 min read
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Decision Trees

Packt
20 Feb 2018
17 min read
In this article by David Toth, the author of the book Data Science Algorithms in a Week, we will cover the following topics: Concepts Analysis Concepts A decision tree is the arrangement of the data in a tree structure where at each node data is separated to different branches according to the value of the attribute at the node. Analysis To construct a decision tree, we will use a standard ID3 learning algorithm that chooses an attribute that classifies the data samples in the best possible way to maximize the information gain – a measure based on information entropy. Information entropy Information entropy of the given data measures the least amount of the information necessary to represent a data item from the given data. The unit of the information entropy is a familiar unit – a bit and a byte, a kilobyte, and so on. The lower information entropy, the more regular, data is, the more pattern occurs in the data and thus less amount of the information is necessary to represent it. That is why compression tools on the computer can take large text files and compress them to a much smaller size, as words and word expressions keep reoccurring, forming a pattern. Coin flipping Imagine we flip and unbiased coin. We would like to know if the result is head or tail. How much information do we need to represent the result? Both words head and tail consists of 4 characters and if we represent one character with one byte (8 bits) as it is standard in ASCII table, then we would need 4 bytes or 32 bits to represent the result. But the information entropy is the least amount of the data necessary to represent the result. We know that there are only two possible results – head or tail. If we agree to represent head with 0 and tail with 1, then 1 bit would be sufficient to communicate the result efficiently. Here the data is the space of the possibilities of the result of the coin throw. It is the set {head,tail} which can represented as a set {0,1}. The actual result is a data item from this set. It turns out that the entropy of the set is 1. This is owing to that the probability of head and tail are both 50%. Now imagine that the coin is biased and throws head 25% of time and tails 75% of time. What would be the entropy of the probability space {0,1} this time? We could certainly represent the result with 1 bit of the information. But can we do better? 1 bit is of course indivisible, but maybe we could generalize the concept of the information to indiscrete amounts. In the previous example, we know nothing about the previous result of the coin flip unless we look at the coin. But in the example with the biased coin, we know that the result tail is more likely to happen. If we recorded n results of coin flips in a file representing heads with 0 and tails with 1, then about 75% of the bits there would have the value 1 and 25% of them would have the value 0. The size of such file would be n bits. But since it is more regular (the pattern of 1s prevails in it) a good compression tool should be able to compress it to less than n bits. To learn the theoretical bound to the compression and the amount of the information necessary to represent a data item we define information entropy precisely. Definition of Information Entropy Suppose that we are given a probability space S with the elements 1, 2, …, n. The probability an element i would be chosen from the probability space is pi. Then the information entropy of the probability space is defined as: E(S)=-p1 *log2(p1) - … - pn *log2(pn) where log2 is a binary logarithm. So for the information entropy of the probability space of unbiased coin throws is E = -0.5 * log2(0.5) – 0.5*log2(0.5)=0.5+0.5=1. When the coin is based with 25% chance of a head and 75% change of a tail, then the information entropy of such space is: E = -0.25 * log2(0.25) – 0.75*log2(0.75) = 0.81127812445 which is less than 1. Thus for example if we had a large file with about 25% of 0 bits and 75% of 1 bits, a good compression tool should be able to compress it down to about 81.12% of its size. Information gain The information gain is the amount of the information entropy gained as a result of a certain procedure. For example, if we would like to know the results of 3 fair coins, then its information entropy is 3. But if we could look at the 3rd coin, then information entropy of the result for the remaining 2 coins would be 2. Thus by looking at the 3rd coin we gained 1 bit information, so the information gain was 1. We may also gain the information entropy by dividing the whole set S into sets grouping them by similar pattern. If we group elements by their value of an attribute A, then we define the information gain as IG(S, A) = E(S) – Sumv in values(A)[(|Sv|/|S|) * E(Sv)] where Sv is a set with the elements of S that have the value v for the attribute A. For example let us calculate the information gain for the 6 rows in the swimming example by taking swimming suit as an attribute. Because we are interested whether a given row of data is classified as no or yes for the question whether one should swim, we will use swim preference to calculate the entropy and information gain. We partition the set S by the attribute swimming suit: Snone={(none,cold,no),(none,warm,no)} Ssmall={(small,cold,no),(small,warm,no)} Sgood= {(good,cold,no),(good,warm,yes)} The information entropy of S is E(S)=-(1/6)*log2(1/6)-(5/6)*log2(5/6)~0.65002242164 The information entropy of the partitions is: E(Snone)=-(2/2)*log2(2/2)=-log2(1)=0 since all instances have the class no. E(Ssmall)=0 for a similar reason E(Sgood)=-(1/2)*log2(1/2)=1 Therefore the information gain is IG(S,swimming suit)=E(S)-[(2/6)*E(Snone)+(2/6)*E(Ssmall)+(2/6)*E(Sgood)] =0.65002242164-(1/3)=0.3166890883 If we chose the attribute water temperature to partition the set S, what would be the information gain IG(S,water temperature)? The water temperature partitions the set S into the following sets: Scold={(none,cold,no),(small,cold,no),(good,cold,no)} Swarm={(none,warm,no),(small,warm,no),(good,warm,yes)} Their entropies are: E(Scold)=0 as all instances are classified as no. E(Swarm)=-(2/3)*log2(2/3)-(1/3)*log2(1/3)~0.91829583405 which is less than IG(S,swimming suit). Therefore, we can gain more information about the set S (the classification of its instances) by partitioning it per the attribute swimming suit instead of the attribute water temperature. This finding will be the basis of the ID3 algorithm constructing a decision tree in the next paragraph. ID3 algorithm ID3 algorithm constructs a decision tree from the data based on the information gain. In the beginning, we start with the set S. The data items in the set S have various properties according to which we can partition the set S. If an attribute A has the values {v1, …, vn}, then we partition the set S into the sets Sv1, …, Svn. Where the set Svi is a subset of the set S where the elements have the value vi for the attribute A. If each element in the set S has attributes A1, …, Am, then we can partition the set S according to any of the possible attributes. ID3 algorithm partitions the set S according to the attribute that yields the highest information gain. Now suppose that it was an attribute A1. Then for the set S we have the partitions Sv1, …, Svn where A1 has the possible values {v1,…, vn}. Since we have not constructed any tree yet, we first place a root node into the tree. For every partition of S we place a new branch from the root. Every branch represents one value of the selected attributes. A branch has data samples with the same value for that attribute. For every new branch we can define a new node that will have data samples from its ancestor branch. Once we have defined a new node, we choose another of the remaining attributes with the highest information gain for the data at that node to partition the data at that node further, then define new branches and nodes. This process can be repeated until we run out of all the attributes for the nodes or even earlier until all the data at the node have the same class of our interest. In the case of a swimming example there are only two possible classes for swimming preference: class no and class yes. The last node is called a leaf node and decides the class of a data item from the data. Tree construction by ID3 algorithm Here we describe step by step how an ID3 algorithm would construct a decision tree from the given data samples in the swimming example. The initial set consists of 6 data samples: S={(none,cold,no),(small,cold,no),(good,cold,no),(none,warm,no),(small,warm,no),(good,warm,yes)} In the previous sections we calculated the information gains for both and the only non- classifying attributes swimming suit and water temperature: IG(S,swimming suit)=0.3166890883 IG(S,water temperature)=0.19087450461 Hence we would choose the attribute swimming suit as it has a higher information gain. There is no tree drawn yet, so we start from the root node. As the attribute swimming suit has 3 possible values {none, small, good}, we draw 3 possible branches out of it for each. Each branch will have one partition from the partitioned set S: Snone, Ssmall, Sgood. We add nodes to the ends of the branches. Snone data samples have the same class swimming preference = no, so we do not need to branch that node by a further attribute and partition set. Thus the node with the data Snone is already a leaf node. The same is true for the node with the data Ssmall. But the node with the data Sgood has two possible classes for swimming preference. Therefore, we will branch the node further. There is only one non- classifying attribute left – water temperature. So there is no need to calculate the information gain for that attribute with the data Sgood. From the node Sgood we will have 2 branches each with the partition from the set Sgood. One branch will have the set of the data sample Sgood, cold={(good,cold,no)}, the other branch will have the partition Sgood, warm={(good,warm,yes)}. Each of these 2 branches will end with a node. Each node will be a leaf node because each node has the data samples of the same value for the classifying attribute swimming preference. The resulting decision tree has 4 leaf nodes and is the tree in the picture decision tree for the swimming preference example. Deciding with a decision tree Once we have constructed a decision tree from the data with the attributes A1, …, Am and the classes {c1, …, ck}; we can use this decision tree to classify a new data item with the attributes A1, …, Am into one of the classes {c1, …, ck}. Given a new data item that we would like to classify, we can think of each node including the root as a question for data sample: What value does that data sample for the selected attribute Aihave? Then based on the answer we select the branch of a decision tree and move further to the next node. Then another question is answered about the data sample and another until the data sample reaches the leaf node. A leaf node has an associated one of the classes {c1, …, ck} with it, e.g. ci. Then the decision tree algorithm would classify the data sample into the class ci. Deciding a data sample with the swimming preference decision tree Let us construct a decision tree for the swimming preference example with the ID3 algorithm. Consider a data sample (good,cold,?) and we would like to use the constructed decision tree to decide into which class it should belong. Start with a data sample at the root of the tree. The first attribute that branches from the root is swimming suit, so we ask for the value for the attribute swimming suit of the sample (good,cold,?). We learn that the value of the attribute is swimming suit=good, therefore move down the rightmost branch with that value for its data samples. We arrive at the node with the attribute water temperature and ask the question: what is the value of the attribute water temperature for the data sample (good,cold,?). We learn that for that data sample we have water temperature=cold, therefore we move down the left branch into the leaf node. This leaf is associated with the class swimming preference=no. Therefore the decision tree would classify the data sample (good,cold,?) to be in that class swimming preference, i.e. to complete it to the data sample (good,cold,no). Therefore, the decision tree says that if one has a good swimming suit, but the water temperature is cold, then one would still not want to swim based on the data collected in the table. Implementation decision_tree.py import math import imp import sys #anytree module is used to visualize the decision tree constructed by this ID3 algorithm. from anytree import Node, RenderTree import common #Node for the construction of a decision tree. class TreeNode: definit(self,var=None,val=None): self.children=[] self.var=varself.val=val defadd_child(self,child): self.children.append(child) defget_children(self): return self.children defget_var(self): return self.var defis_root(self): return self.var==None and self.val==None defis_leaf(self): return len(self.children)==0 def name(self): if self.is_root(): return “[root]” return “[“+self.var+”=“+self.val+”]” #Constructs a decision tree where heading is the heading of the table with the data, i.e. the names of the attributes. #complete_data are data samples with a known value for every attribute. #enquired_column is the index of the column (starting from zero) which holds the classifying attribute. defconstuct_decision_tree(heading,complete_data,enquired_column): available_columns=[] for col in range(0,len(heading)): if col!=enquired_column: available_columns.append(col) tree=TreeNode() add_children_to_node(tree,heading,complete_data,available_columns,enquired_ column) return tree #Splits the data samples into the groups with each having a different value for the attribute at the column col. defsplit_data_by_col(data,col): data_groups={} for data_item in data: if data_groups.get(data_item[col])==None: data_groups[data_item[col]]=[] data_groups[data_item[col]].append(data_item) return data_groups #Adds a leaf node to node. defadd_leaf(node,heading,complete_data,enquired_column): node.add_child(TreeNode(heading[enquired_column],complete_data[0][enquired_ column])) #Adds all the descendants to the node. def add_children_to_node(node,heading,complete_data,available_columns,enquired_ column): if len(available_columns)==0: add_leaf(node,heading,complete_data,enquired_column) return -1 selected_col=select_col(complete_data,available_columns,enquired_column) for i inrange(0,len(available_columns)): if available_columns[i]==selected_col: available_columns.pop(i) break data_groups=split_data_by_col(complete_data,selected_col) if(len(data_groups.items())==1): add_leaf(node,heading,complete_data,enquired_column) return -1 for child_group, child_data in data_groups.items(): child=TreeNode(heading[selected_col],child_group) add_children_to_node(child,heading,child_data,list(available_columns),enquired_column) node.add_child(child) #Selects an available column/attribute with the highest information gain. defselect_col(complete_data,available_columns,enquired_column): selected_col=-1 selected_col_information_gain=-1 for col in available_columns: current_information_gain=col_information_gain(complete_data,col,enquired_column) if current_information_gain>selected_col_information_gain: selected_col=col selected_col_information_gain=current_information_gainreturn selected_col #Calculates the information gain when partitioning complete_dataaccording to the attribute at the column col and classifying by the attribute at enquired_column. defcol_information_gain(complete_data,col,enquired_column): data_groups=split_data_by_col(complete_data,col) information_gain=entropy(complete_data,enquired_column) for _,data_group in data_groups.items(): information_gain- =(float(len(data_group))/len(complete_data))*entropy(data_group,enquired_column) return information_gain #Calculates the entropy of the data classified by the attribute at the enquired_column. def entropy(data,enquired_column): value_counts={} for data_item in data: if value_counts.get(data_item[enquired_column])==None: value_counts[data_item[enquired_column]]=0 value_counts[data_item[enquired_column]]+=1 entropy=0 for _,count in value_counts.items(): probability=float(count)/len(data) entropy-=probability*math.log(probability,2) return entropy #A visual output of a tree using the text characters. defdisplay_tree(tree): anytree=convert_tree_to_anytree(tree) for pre, fill, node in RenderTree(anytree): pre=pre.encode(encoding=‘UTF-8’,errors=‘strict’) print(“%s%s” % (pre, node.name)) #A simple textual output of a tree without the visualization. defdisplay_tree_simple(tree): print(‘***Tree structure***’) display_node(tree) sys.stdout.flush() #A simple textual output of a node in a tree. defdisplay_node(node): if node.is_leaf(): print(‘The node ‘+node.name()+’ is a leaf node.’) return sys.stdout.write(‘The node ‘+node.name()+’ has children: ‘) for child in node.get_children(): sys.stdout.write(child.name()+’‘) print(‘‘) for child in node.get_children(): display_node(child) #Convert a decision tree into the anytree module tree format to make it ready for rendering. defconvert_tree_to_anytree(tree): anytree=Node(“Root”) attach_children(tree,anytree) return anytree#Attach the children from the decision tree into the anytree tree format. defattach_children(parent_node, parent_anytree_node): for child_node in parent_node.get_children(): child_anytree_node=Node(child_node.name(),parent=parent_anytree_node) attach_children(child_node,child_anytree_node) ###PROGRAM START### if len(sys.argv)<2: sys.exit(‘Please, input as an argument the name of the CSV file.’) csv_file_name=sys.argv[1] (heading,complete_data,incomplete_data,enquired_column)=common.csv_file_to_ ordered_data(csv_file_name) tree=constuct_decision_tree(heading,complete_data,enquired_column) display_tree(tree) common.py #Reads the csv file into the table and then separates the table into heading, complete data, incomplete data and then produces also the index number for the column that is not complete, i.e. contains a question mark. defcsv_file_to_ordered_data(csv_file_name): with open(csv_file_name, ‘rb’) as f: reader = csv.reader(f) data = list(reader) return order_csv_data(data) deforder_csv_data(csv_data): #The first row in the CSV file is the heading of the data table. heading=csv_data.pop(0) complete_data=[] incomplete_data=[] #Let enquired_column be the column of the variable which conditional probability should be calculated. Here set that column to be the last one. enquired_column=len(heading)-1 #Divide the data into the complete and the incomplete data. An incomplete row is the one that has a question mark in the enquired_column. The question mark will be replaced by the calculated Baysian probabilities from the complete data. for data_item in csv_data: if is_complete(data_item,enquired_column): complete_data.append(data_item) else: incomplete_data.append(data_item) return (heading,complete_data,incomplete_data,enquired_column) Program input swim.csv swimming_suit,water_temperature,swimNone,Cold,No None,Warm,NoSmall,Cold,NoSmall,Warm,NoGood,Cold,NoGood,Warm,Yes Program output $ python decision_tree.py swim.csv Root ├── [swimming_suit=Small] │├──[water_temperature=Cold] ││└──[swim=No] │└──[water_temperature=Warm] │└──[swim=No] ├── [swimming_suit=None] │├──[water_temperature=Cold] ││└──[swim=No] │└──[water_temperature=Warm] │└──[swim=No] └── [swimming_suit=Good] ├── [water_temperature=Cold] │└──[swim=No] └── [water_temperature=Warm] └── [swim=Yes] Summary In this article we have learned the concept of decision tree, analysis using ID3 algorithm, and implementation. Resources for Article: Further resources on this subject: Working with Data – Exploratory Data Analysis [article] Introduction to Data Analysis and Libraries [article] Data Analysis Using R [article]
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Packt
20 Feb 2018
10 min read
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Introduction with Device Management

Packt
20 Feb 2018
10 min read
In this article by Yatish Patil, the author of the book Microsoft Azure IOT Development Cookbook, we will look at device management using different techniques with Azure IoT Hub. We will see the following recipes: Device registry operations Device twins Device direct methods Device jobs (For more resources related to this topic, see here.) Azure IoT Hub has the capabilities that can be used by a developer to build a robust device management. There could be different use cases or scenarios across multiple industries but these device management capabilities, their patterns and the SDK code remains same, saving the significant time in developing and managing as well as maintaining the millions of devices. Device management will be the central part of any IoT solution. The IoT solution is going to help the users to manage the devices remotely, take actions from the cloud based application like disable, update data, run any command, and firmware update. In this article, we are going to perform all these tasks for device management and will start with creating the device. Device registry operations This sample application is focused on device registry operations and how it works, we will create a console application as our first IoT solution and look at the various device management techniques. Getting ready Let’s create a console application to start with IoT: Create a new project in Visual Studio: Create a Console Application Add IoT Hub connectivity extension in Visual Studio: Add the extension for IoT Hub connectivity Now right click on the Solution and go to Add a Connected Services. Select Azure IoT Hub and click Add. Now select Azure subscription and the IoT Hub created: Select IoT Hub for our application Next it will ask you to add device or you can skip this step and click Complete the configuration. How to do it... Create device identity: initialize the Azure IoT Hub registry connection: registryManager = RegistryManager.CreateFromConnectionString(connectionString); Device device = new Device(); try { device = await registryManager.AddDeviceAsync(new Device(deviceId)); success = true; } catch (DeviceAlreadyExistsException) { success = false; } Retrieve device identity by ID: Device device = new Device(); try { device = await registryManager.GetDeviceAsync(deviceId); } catch (DeviceAlreadyExistsException) { return device; } Delete device identity: Device device = new Device(); try { device = GetDevice(deviceId); await registryManager.RemoveDeviceAsync(device); success = true; } catch (Exception ex) { success = false; } List up to 1000 identities: try { var devicelist = registryManager.GetDevicesAsync(1000); return devicelist.Result; } catch (Exception ex) { // Export all identities to Azure blob storage: var blobClient = storageAccount.CreateCloudBlobClient(); string Containername = "iothubdevices"; //Get a reference to a container var container = blobClient.GetContainerReference(Containername); container.CreateIfNotExists(); //Generate a SAS token var storageUri = GetContainerSasUri(container); await registryManager.ExportDevicesAsync(storageUri, "devices1.txt", false); } Import all identities to Azure blob storage: await registryManager.ImportDevicesAsync(storageUri, OutputStorageUri); How it works... Let’s now understand the steps we performed. We initiated by creating a console application and configured it for the Azure IoT Hub solution. The idea behind this is to see the simple operation for device management. In this article, we started with simple operation for provision of the device by adding it to IoT Hub. We need to create connection to the IoT Hub followed by the created object of registry manager which is a part of devices namespace. Once we are connected we can perform operations like, add device, delete device, get device, these methods are asynchronous ones. IoT Hub also provides a way where in it connects with Azure storage blob for bulk operations like export all devices or import all devices, this works on JSON format only, the entire set of IoT devices gets exported in this way. There's more... Device identities are represented as JSON documents. It consists of properties like: deviceId: It represents the unique identification or the IoT device. ETag: A string representing a weak ETag for the device identity. symkey: A composite object containing a primary and a secondary key, stored in base64 format. status: If enabled, the device can connect. If disabled, this device cannot access any device-facing Endpoint. statusReason: A string that can be used to store the reason for the status changes. connectionState: It can be connected or disconnected. Device twins First we need to understand what device twin is and what is the purpose where we can use the device twin in any IoT solution. The device twin is a JSON formatted document that describes the metadata, properties of any device created within IoT Hub. It describes the individual device specific information. The device twin is made up of: tags, desired properties, and the reported properties. The operation that can be done by a IoT solution are basically update this the data, query for any IoT device. Tags hold the device metadata that can be accessed from IoT solution only. Desired properties are set from IoT solution and can be accessed on the device. Whereas the reported properties are set on the device and retrieved at IoT solution end. How to do it... Store device metadata: var patch = new { properties = new { desired = new { deviceConfig = new { configId = Guid.NewGuid().ToString(), DeviceOwner = "yatish", latitude = "17.5122560", longitude = "70.7760470" } }, reported = new { deviceConfig = new { configId = Guid.NewGuid().ToString(), DeviceOwner = "yatish", latitude = "17.5122560", longitude = "70.7760470" } } }, tags = new { location = new { region = "US", plant = "Redmond43" } } }; await registryManager.UpdateTwinAsync(deviceTwin.DeviceId, JsonConvert.SerializeObject(patch), deviceTwin.ETag); Query device metadata: var query = registryManager.CreateQuery("SELECT * FROM devices WHERE deviceId = '" + deviceTwin.DeviceId + "'"); Report current state of device: var results = await query.GetNextAsTwinAsync(); How it works... In this sample, we retrieved the current information of the device twin and updated the desired properties, which will be accessible on the device side. In the code, we will set the co-ordinates of the device with latitude and longitude values, also the device owner name and so on. This same value will be accessible on the device side. In the similar manner, we can set some properties on the device side which will be a part of the reported properties. While using the device twin we must always consider: Tags can be set, read, and accessed only by backend . Reported properties are set by device and can be read by backend. Desired properties are set by backend and can be read by backend. Use version and last updated properties to detect updates when necessary. Each device twin size is limited to 8 KB by default per device by IoT Hub There's more... Device twin metadata always maintains the last updated time stamp for any modifications. This is UTC time stamp maintained in the metadata. Device twin format is JSON format in which the tags, desired, and reported properties are stored, here is sample JSON with different nodes showing how it is stored: "tags": { "$etag": "1234321", "location": { "country": "India" "city": "Mumbai", "zipCode": "400001" } }, "properties": { "desired": { "latitude": 18.75, "longitude": -75.75, "status": 1, "$version": 4 }, "reported": { "latitude": 18.75, "longitude": -75.75, "status": 1, "$version": 4 } } Device direct methods Azure IoT Hub provides a fully managed bi-directional communication between the IoT solution on the backend and the IoT devices in the fields. When there is need for an immediate communication result, a direct method best suites the scenarios. Lets take example in home automation system, one needs to control the AC temperature or on/off the faucet showers. Invoke method from application: public async Task<CloudToDeviceMethodResult> InvokeDirectMethodOnDevice(string deviceId, ServiceClient serviceClient) { var methodInvocation = new CloudToDeviceMethod("WriteToMessage") { ResponseTimeout = TimeSpan.FromSeconds(300) }; methodInvocation.SetPayloadJson("'1234567890'"); var response = await serviceClient.InvokeDeviceMethodAsync(deviceId, methodInvocation); return response; } Method execution on device: deviceClient = DeviceClient.CreateFromConnectionString("", TransportType.Mqtt); deviceClient.SetMethodHandlerAsync("WriteToMessage", new DeviceSimulator().WriteToMessage, null).Wait(); deviceClient.SetMethodHandlerAsync("GetDeviceName", new DeviceSimulator().GetDeviceName, new DeviceData("DeviceClientMethodMqttSample")).Wait(); How it works... Direct method works on request-response interaction with the IoT device and backend solution. It works on timeout basis if no reply within that, it fails. These synchronous requests have by default 30 seconds of timeout, one can modify the timeout and increase up to 3600 depending on the IoT scenarios they have.  The device needs to connect using the MQTT protocol whereas the backend solution can be using HTTP. The JSON data size direct method can work up to 8 KB Device jobs In a typical scenario, device administrator or operators are required to manage the devices in bulk. We look at the device twin which maintains the properties and tags. Conceptually the job is nothing but a wrapper on the possible actions which can be done in bulk. Suppose we have a scenario in which we need to update the properties for multiple devices, in that case one can schedule the job and track the progress of that job. I would like to set the frequency to send the data at every 1 hour instead of every 30 min for 1000 IoT devices. Another example could be to reboot the multiple devices at the same time. Device administrators can perform device registration in bulk using the export and import methods. How to do it... Job to update twin properties. var twin = new Twin(); twin.Properties.Desired["HighTemperature"] = "44"; twin.Properties.Desired["City"] = "Mumbai"; twin.ETag = "*"; return await jobClient.ScheduleTwinUpdateAsync(jobId, "deviceId='"+ deviceId + "'", twin, DateTime.Now, 10); Job status. var twin = new Twin(); twin.Properties.Desired["HighTemperature"] = "44"; twin.Properties.Desired["City"] = "Mumbai"; twin.ETag = "*"; return await jobClient.ScheduleTwinUpdateAsync(jobId, "deviceId='"+ deviceId + "'", twin, DateTime.Now, 10); How it works... In this example, we looked at a job updating the device twin information and we can follow up the job for its status to find out if the job was completed or failed. In this case, instead of having single API calls, a job can be created to execute on multiple IoT devices. The job client object provides the jobs available with the IoT Hub using the connection to it. Once we locate the job using its unique ID we can retrieve the status for it. The code snippet mentioned in the How to do it... preceding recipe, uses the temperature properties and updates the data. The job is scheduled to start execution immediately with 10 seconds of execution timeout set. There's more... For a job, the life cycle begins with initiation from the IoT solution. If any job is in execution, we can query to it and see the status of execution. Another most common scenario where this could be useful is the firmware update, reboot, configuration updates, and so on, apart from the device property read or write. Each device job has properties that helps us working with them. The useful properties are start and end date time, status, and lastly device job statistics which gives the job execution statistics. Summary We have learned the device management using different techniques with Azure IoT Hub in detail. We have explained, how the IoT solution is going to help the users to manage the devices remotely, take actions from the cloud based application like disable, update data, run any command, and firmware update. We also performed different tasks for device management. Resources for Article: Further resources on this subject: Device Management in Zenoss Core Network and System Monitoring: Part 1 [article] Device Management in Zenoss Core Network and System Monitoring: Part 2 [article] Managing Network Devices [article]
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