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Introduction to Logstash Cluster

The Logstash cluster is one of the features, and it will be worked to the specific Logstash hosts and used inside with some beats configurations made during the load balancer setup; the TCP and other protocols are called during the Logstash front endpoint will be managed to each cluster setup that behind on the load balancer for latency fit with specified nodes in the cluster.

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What is Logstash Cluster?

Logstash is the server-side data processing pipeline that can be used to allow the data collected from several sources and delivered to the destination. It’s one of the open-source analytics and the search engine. It is frequently utilized with the data pipeline for popular choices loading the elasticsearch data because of the close interaction with powerful log features pre-defined with the open-source plugins. In addition, it may be used to fetch the values by using the data index.

How to Setup Logstash Cluster?

The logstash cluster will be working on the ELK stack-based Elastic Search, Logstash, and Kibana called the ELK cluster. We can send the Logstash to Elasticsearch API via Logstash and Beats etc. The node’s target is the Elasticsearch ELK stack, described as the type of technology comprising the three products together. All these three are open source and stack application API integration which will be handled plenty of projects to find the solutions to the log management console. Additionally, we used Beats as one of the products like Logstash, but Beast is the type of agent that can be configured in the number of servers and containers, and it helps to collect the type of datas and forward it to the ELK stack processing.

We already know that when we make a cluster, the nodes have to be played the main part of the servers. So in these n number of nodes are to be joined and to form a cluster, and we can call it the instance of each node cluster. So the cluster is the group of nodes with the same cluster name across all the attributes. The nodes are joined or left as the cluster; then, it automatically recognizes itself to check and validate the data processing on every distributed node cluster.

The above diagram explains the group of nodes in the node master, which formed the cluster.

Logstash Cluster Module

This logstash cluster folder contains mainly the Terraform module to deploy the codes in the Logstash cluster in the AWS, Azure, or any other hosting technology. On top of the Auto Scaling Group, the main idea is to create the hosting machine-like AMI(Amazon Machine Image), etc. Then, the Logstash will be installed using the install-logstash modules.

Terraform Module:

module "logstash_cluster" { source = URL ami_id = "" user_data = <<-EOF #!/bin/bash /usr/share/logstash/bin/run-logstash EOF }

The above code is the basic format, and we can pass the values in the source, ami_id, and user_data parameters. Mainly this module contains three different features,

Auto Scaling Group,

Load Balancer

Security Group

Auto Scaling Group is the main module part that helps run the Logstash on top of the Auto Scaling Group(ASG), which will run with different instances and spread worldwide. Each instance will be running with the AMI machine, which is already installed Logstash on the machine.

Regarding Load, a balancer is generally used in web-based scenarios; we can call and send the application request to multiple servers using the Load balancer URL. It will be splitted accordingly so that it’s called the Network Load balancer for performing the health check on the cluster, Logstash node, and even Filebeat can be accessed to the Logstash cluster.

Final Security Group will be called and perform the AWS or any other hosting environment instance to all the incoming and outbound requests. Even it has the set of security group id for exporting the logstash security group modules to open all the ports, which is necessary for the Security Sections.

The logstash-cluster module mainly used the Gruntwork server-group modules, while running the terraform module will apply the zero-downtime for rolling the deployment. We used AWS and other instances like EC2 to pass the Load balancer health checkups and replicate the other parameters from the same as the old cluster.

Logstash Cluster Configuring

Initially, we used a git clone to reproduce this on our machine.

We need to Terraform for deploying the clusters Automatically based on the configuration.

Creating terraform instance files like the below,

module "ec2_instance" { source = "terraform-aws-modules/ec2-instance/aws" name = "single-instance" ami = "ami-ebd02392" instance_type = "t2.micro" key_name = "user1" monitoring = true vpc_security_group_ids = ["sg-12345678"] subnet_id = "subnet-eddcdzz4" tags = { Terraform = "true" Environment = "dev" } }

The above code is the basic config for creating the Single cluster setup using Terraform modules. We can save the instance file as chúng tôi format. We required an AWS login for the AMI setup in the terraform module for performing the Logstash cluster setup.

Conclusion

Logstash is the server-side feature and technology for accessing the user datas like logs, and it can be coordinated with the Kibana and Elasticsearch modules. Elasticsearch is the centralized component for using the cluster setup and performing the Logstash operations, so we can call it the ELK stack module with the cluster setup.

How To Use Logstash Stdout With Commands?

Introduction to Logstash stdout

stdout is the standard output data plugin of Logstash, which has its latest version launched of 3.1.4, which was made in April 2023. This plugin helps display and print the output, which is in the format of STDOUT as the resultant of the shell that executes in the Logstash pipelines’ background. After the data is passed through the processes of ingestion and filtering, it makes the provision and allowance of accessing the events instantly, making the process of debugging the configurations of plugins easier and more convenient.

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In this article, we will look at what logstash stdout is and study its subtopics, including What logstash stdout is and how to use it and Logstash stdout and Logstash stdout command, Logstash stdout plugins, and a Conclusion about the same.

What is Logstash stdout?

To print the output, which is in the format of STDOUT, to the shell of the Logstash, we can use the simple plugin available in Logstash named stdout. Whenever there is a need to debug the working and execution of the plugin and its configurations, the stdout plugin’s output is of great help. Moreover, after the event data passes through various filters and the inputs, enabling access to the event data is done by Logstash, which makes it helpful in the debugging process.

How to use Logstash stdout?

Let us consider one example to understand how the configuration of the output plugin to stdout proves helpful. Viewing the output results of pipelines inside the event that are used while performing quick iteration uses the following output configuration and the -e command option available in Logstash.

}

When the above output configuration is used in ruby-debug, which is done by default when using codec in the standard output plugin of Logstash along with the library name awesome_print of ruby gives the result as the output of event data.

When the stdout mentions the codec as json, the output results in the structured event data format in JSON format. The configuration of stdout in the case of json should be set as below to get the event data in JSON structured format –

}

Logstash stdout command

The logstash-stdout command is used for printing the event and its data o the standard output, and the complete details can be acquired in the git repository of Logstash-output-stdout, which can be found on this link.

}

Now, to verify whether the configurations are set as expected, we can execute the following command –

bin/Logstash -f chúng tôi --config.test_and_exit

Execution of the above command gives the following output –

Logstash stdout plugins

We can use Logstash stdout by specifying the same in the pipeline configuration chúng tôi Along with that, we can even go for specifying various options related to configurations. The below table contains the list of all the supported configuration options for the stdout plugin –

Configuration option setting Type of input data Optional/ Required

Id String value Optional

Enable_metric Boolean Optional

Codec Codec Optional

Id – This configuration setting option accepts the string value and, by default, when not specified, does not contain any value. When specified, it should be a unique identifier for the plugin. When we do not specify any value for this configuration setting, it will result in internal automated value generation by Logstash. It is always a good practice to mention the value of id in our configurations. If there are two or more multiple plugins of the same type, it will be useful for identifying each one individually. Let us consider an example in case we have two outputs of stdout. Using the specification of the id value for both plugins will be useful for Logstash in the monitoring process when we use API of monitoring. The sample of specification of id for stdout plugin is as shown below-

}

Please note that when going for the variable substitution of the configuration setting of id, it will only provide the support for the environment variables and not the personal store values.

Enable_metric – It accepts the Boolean value and, when not specified, contains the default value of true. When we specify this configuration setting option, we can enable or disable metrics logging corresponding to a particular plugin. For example, the default behavior of the Logstash is such that it keeps track and records all the metrics; however, we can disable this behavior for the particular plugin(s) if we want.

Conclusion

Logstash stdout plugin is used for printing the event’s data to the standard output. We can even use configuration setting options such as id, codec, or enable_metric, which help define the details of the plugin. We can even print the event data in standard output in the format of JSON structured data when we set the codec to json.

Numpy Refresher For Beginners (With Anaconda Setup)

This article was published as a part of the Data Science Blogathon

“Machine Learning can change the future”, this line you might have heard thousands of times if you belong to the technology, indeed it can, we all know the answer, but how will it? You might have heard or seen Autonomous Vehicles, Smart Chips for the brain, Image Regeneration, etc. but what if you want to try out all these things, you would have to start from somewhere right? don’t worry I got you, you are at the right place as starting from this article I would be writing a series of Deep Learning Articles that would help you get started in the Deep Learning Industry.

These articles would be purely based on Python language and its libraries. I would be giving both theoretical and practical examples wherever needed. So stay tuned and I would suggest reading it at your pace.

Every war that has been fought needs weapons and tools similarly if you want to enter any field you need to have some set of tools that would help you go ahead in the field. Here the tool is Anaconda (don’t worry it’s not the snake).

This article is divided into two sections. The first section is about setting up the Anaconda Environment Where we would be discussing the following:

what is Anaconda?

Anaconda Installation

What are Python Packages?

Managing python packages

Managing Environments

While the second one would be a refresher for your matrix concepts that are the soul of Neural Network calculations. In this section we would be discussing the following:

Data Dimensions

Numpy Data

Elementwise data operations

Matrix Multiplication

Matrix Transpose

So let’s start with understanding the tools that are required to set foot in Deep Learning.

Anaconda Setup

In this section let’s set up the Anaconda Environment.

What is Anaconda?

Anaconda is a program that helps in managing python packages, environments, editors, and notebooks for python language. It’s a UI-based program where users can simply search for packages and editors and create environments to work on.

If you already have the python language installed in your system it’s no problem you can still install Anaconda and use it or else you can just install Anaconda which would come up with the required python version. Anaconda already comes up with a bunch of python packages, a package management system (PIP), and environmental management system (Conda) which makes it easy to use.

The typical size of this program is around 500MB since it already comes up with some python packages, and comes up with the following things:

1. Python: A specific version of python language based on anaconda’s version.

2. Conda: A command-line utility for environments and packages management that works the same way in both Unix and Windows environments.

3. Anaconda Navigator: it’s a UI with which users can interact, check and install the packages and start multiple python related applications.

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Anaconda Installation

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You can verify the installations by starting “Anaconda Prompt” in your system. As Anaconda is now installed let’s jump to understanding the python packages.

What is a Python package?

A Python package is a collection of modules/functions where each module is designed to solve a specific task. You can simply import the modules using the word “import” and specifying the module or submodule name eg. import numpy (Numpy is a module used for scientific computations).

If you want to create your own package you can create a Python file with some modules implemented using OOPS and publish it on chúng tôi and then everyone would be able to access it.

Anaconda already comes up with a bunch of Python packages that may be useful for you if not you can delete them if needed.

Managing Python Packages:

Python package management can be done using two utilities PIP or Conda. These are called Python Package Managers that can help in the installation, deletion, and management of the Python packages. The only difference between these two is Conda manages the packages that are available from Anaconda distribution while PIP is the default package management system for Python. You can download your required package just by specifying the following command:

$ pip install package_name $ conda install package_name

Which one should you prefer PIP or Conda?

For deleting any package you can delete it using PIP only even if it was downloaded from Conda:

$ pip uninstall package_name Managing Environments:

A Python environment is the collection of the following entities:

Python Interpreter

Python Packages &

Python Package Management Utilities like PIP and Conda

Anaconda comes up with a base environment that has all the preinstalled packages. You can have an environment inside that base environment or you can create a whole new one. These environments are called “Virtual Environments”.

Why do we need multiple environments?

If we want to create multiple Machine Learning based algorithms which use different package versions you can not have the same package with multiple versions in the same environment so you need to have multiple environments, so the basic purpose of having multiple environments is to keep the development isolated.

This often happens when you work on projects that have Python2 and Python3 based dependencies since both of these versions have different sets of libraries compatibility.

How to create a virtual environment?

To create a virtual environment you need to install the python package named “virtualenv”. You can download it using the following command:

$ pip install virtualenv

Once the library is installed you can create the environment using the following command:

$ virtualenv my_env

this command would create a virtualenv with the name “my_env”, and to activate this you can use the following command:

Windows:

$ my_envScriptsactivate

Unix:

$ source my_env/bin/activate

This would activate the environment now you can go ahead and install the required packages in the environment.

Numpy Refresher

Now that you know how to set up Anaconda it’s time for you to have a refresher about the matrix and various matrix manipulation techniques. Deep learning calculations are built on top of matrix math so you need to have a thorough knowledge of it to start building your own Neural Networks. One of the beauties of Python language is that it comes up with the ability to process these matrices through a library named Numpy.

Data Dimensions using Numpy

Neural Networks do a lot of maths in the backend to predict a value or to classify the data. One important thing we must know is how that data is represented ? or What is the shape of the data? For example, a number can represent a single entity while a list of numbers can tell a lot of other information, or for instance, an image has a set of pixel values that are also represented in some order.

Data that we use for NN calculations are classified into three different categories based on their dimension. These categories are as follows:

1. Scalar Values: This is just a single numerical value like 1, 3, 7.6, etc. Scaler values have no dimensions at all or are often called zero-dimensional data.

2. Vectors: These are a list of scalar values that are typical of two types, Row Vector and Column Vector. The basic difference between these two is they store data in a horizontal and vertical manner respectively.

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Vectors have only one dimension and are of various lengths depending on the number of elements they have.

3. Matrics: These are the collection of values arranged in rows and columns order. Matrices are two-dimensional data types represented using mxn, where m is the number of rows and n is the number of columns.

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In the above image, you can clearly see the matrix of dimension 2×3 that has 2 rows and 3 columns.

4. Tensors: These are n-dimensional values, just to be specific anything above a two-dimensional matrix is called a tensor. Normally these are hard to visualize so we consider them as the collection of vectors depending on their dimensions.

Numpy data

Each and every data dimension that we have seen can be represented in python using numerical values or lists, but the only issue is Python can be a bit slow while doing data manipulation. normally we see tensors of dimension 10×20 or 5×10 but in real-world data, dimensions can be much higher so doing computations would be even slower. So is there any way we can fasten these manipulations? Yes, Python provides one such library called NumPy whose computation speed is must faster than list comprehension. Now let’s start exploring Numpy for a bit.

Numpy Installation

NumPy is available on PyPI so you can directly install it using PIP.

$ pip install numpy Importing Numpy

To import NumPy in your code you can write the following code:

import numpy as np

The most common Numpy objects are ndarray, which are similar to python lists but can have n number of dimensions and are faster than list manipulation. Now let’s discuss each data dimension in NumPy.

Scalars:

NumPy scalers are a bit different than Python scalar values, they allow users to specify signed and unsigned types along with their data types like uint8, uint16, etc. There is no specific function in NumPy to define a scalar so we have to use the array function only while providing the scalar value.

s = np.array(5)

To check the shape of the above scalar you can write the following code:

s.shape

this gives the output (), indicating it has zero dimensions.

Vectors:

Creating vectors in NumPy is easy, you just need to pass the list for which you need to create the vector.

v = np.array([1,2,3]) v.shape

The shape of the above vector is (3,) as it has 3 rows and dimensionality of 1. You can access various elements of this vector by just passing their index like this:

v[0]

this would return element “1” as default indexing for NumPy starts with 0. To get a series of elements starting from an index you can write the following:

v[1:]

This would return all the elements starting from index 1. To get the list of elements to a particular index you can use the following:

v[:2]

This would return all the elements starting from index 0 to 1 as arrays have defaulted from 0 and the upper bound of the array would always be “upper bound – 1” i.e. 2-1 = 1, and finally if you want to slice the vector you can use the following:

v[0:2]

This would return elements from index 0 to 1 as the last number is not included in the bound.

Matrices:

Matrices are multi-dimensional lists, so NumPy uses these multidimensional lists to obtain the NumPy matrices.

m = np.array([[1,2,3], [4,5,6], [7,8,9]]) m.shape

Here we have created a matrix of shape (3,3) i.e. three rows and three columns respectively. To access the index-wise elements we need to define the row and column number of the element (again indexing for both rows and columns starts from 0).

m[1][2]

This returns the element belonging to row 1 and column 2 i.e. 2. and for Slicing the matrix you can again use “:” for both rows and columns.

Tensors:

It’s the matrix with a higher number of dimensions. and can be defined as follows:

t = np.array([[[[1],[2]],[[3],[4]],[[5],[6]]],[[[7],[8]], [[9],[10]],[[11],[12]]],[[[13],[14]],[[15],[16]],[[17],[17]]]]) t.shape

The shape of the given tensor is (3, 3, 2, 1), and you can access each and every element of the tensor in the same fashion as matrix eg. t[1][1][1][0].

Element wise Data Operations using NumPy

Applying mathematics operations on scaler values is easy right? but when it comes to lists, vectors, matrices, and tensors we need to apply the same operation for each number of elements. Here scalers are defined as one category while all other forms of data are considered the same as they all have a list of data.

Scaler and Matrix operation:

To perform mathematical operations between scaler value and the matrix we just need to write them together and perform the operation to each element of the matrix, for example:

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As you can see in the above image scaler value 2 is added to each and every element of the matrix resulting in a new matrix. All other mathematical operations like subtraction, division, and multiplication can be applied between scaler and matrix values in the same way.

Now let’s check the code part for scaler and matrix operations. The traditional way of doing it is to iterate over each value in the matrix and do the mathematical operation with the given scaler value.

values = [1,2,3,4,5] for i in range(len(values)): values[i] += 5 print('addition of five:', values)

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The only issue here is it increases the time complexity as we are iterating over each element and performing the operation. The only solution to this is using NumPy which can accelerate the computation. To do the same operation in the NumPy way you would have to do the following:

import numpy as np values = [1,2,3,4,5] values = np.array(values) + 5 print('addition of five using numpy:', values)

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Here you can see that just converting the list to NumPy array and then adding scaler value to the vector is very similar to adding two scaler values. Other mathematical operations are applied in the same way for scaler to matrix operation.

Matrix and Matrix operation in NumPy

For a matrix to matrix operation, all the matrices should have the same shape to perform the mathematical operation, for example:

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As you see both the matrix are of the same shape (2×2) and addition operations are performed for each respective element. a[0] [0] is added to the b[0][0] and so on. Other mathematical operations like subtraction and division can be applied in the same fashion as the addition, only the multiplication operation is different from all these operations.

For matrix to matrix operation again you can go ahead with iterating over each value in the matrices and perform the mathematical operations but that is again time-consuming so you would be using the same NumPy solution.

import numpy as np a = np.array([[1,3],[5,7]]) print('a =', a, 'n') b = np.array([[2,4],[6,8]]) print('b =', b, 'n') print('a+b = ', a + b)

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Here you can see both the matrix are of the same shape and produced the desired output as applied operation. Similarly, subtraction and division operations can be formed in the same way.

Let’s check what happens when you apply any operation between matrices of two different shapes.

import numpy as np a = np.array([[1,3],[5,7]]) print('a =', a, 'n') c = np.array([[2,3,6],[4,5,9],[1,8,7]]) print('c =', c, 'n') print('a+c = ', a + c)

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You can see in the given image that the code has thrown an error that both the matrices are of different shapes.

Matrix Multiplication in NumPy

When we talk about matrix multiplication the operation is the same as any other operation.

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As you can see in the image that elements from one matrix are being multiplied by the respective element in the other matrix.

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Now let’s check how NumPy does this operation.

There are two ways of calculating the multiplication of two matrices, the first is by using “*” and the second by using a function named multiply() and passing matrices to it as parameters.

import numpy m = np.array([[1,2,3],[4,5,6]]) print('m = ', m, 'n') n = m * 0.25 print('n = ', n, 'n') print('mxn = ',m * n, 'n') print('mxn = ',np.multiply(m, n))

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For calculating the dot product of two matrices a function named matmul() is used where we pass the matrices as parameters.

import numpy as np a = np.array([[1,2,3,4],[5,6,7,8]]) print('a = ', a, 'n') b = np.array([[1,2,3],[4,5,6],[7,8,9],[10,11,12]]) print('b = ', b, 'n') c = np.matmul(a, b) print('m.n = ', c, 'n')

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Keep in mind that matrices shapes should be compatible otherwise you would face the shape error.

Matrix Transpose using NumPy

Now there is only one thing left to discuss which is calculating the transpose of a matrix, this is the most used operation while working on the Neural Networks math. Transpose is an operation of converting a matrix into another matrix such that rows from the original matrix are columns in another and columns in the original one are rows in the resultant matrix.

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To calculate the transpose of any matrix you can use the transpose function of the NumPy or you can use “.T” to do the same.

import numpy m = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]]) print('m = ', m, 'n') n = m.T print('transpose = ', n, 'n') o = m.transpose() print('transpose = ', o, 'n')

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Conclusion

The first step towards learning the Deep Learning technology is to set up the python and virtual environment which I hope you can do now on your own. Setting up an Anaconda environment and creating a virtual environment would give you the confidence and excitement to move further in Deep Learning.

Also, you now have a deeper understanding of what kind of operations are used in Neural Network math. Only going through these concepts would not be enough you would have to practice them with different input shapes and check what outputs or errors you would get. This would help you debug the errors in the future when you would be training your own NN models.

In the next series of lectures, I would be diving deeper into more conceptual and technical concepts so stay tuned to learn something new and exciting.

References:

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How To Play Monopoly: Setup, Rules, And Gameplay

This article was co-authored by wikiHow staff writer, Eric McClure . Eric McClure is an editing fellow at wikiHow where he has been editing, researching, and creating content since 2023. A former educator and poet, his work has appeared in Carcinogenic Poetry, Shot Glass Journal, Prairie Margins, and The Rusty Nail. His digital chapbook, The Internet, was also published in TL;DR Magazine. He was the winner of the Paul Carroll award for outstanding achievement in creative writing in 2014, and he was a featured reader at the Poetry Foundation’s Open Door Reading Series in 2023. Eric holds a BA in English from the University of Illinois at Chicago, and an MEd in secondary education from DePaul University. This article has been viewed 2,092,862 times.

Article Summary

Monopoly is a 2-8 player board game where players buy properties and try to get the other players to go bankrupt. To start the game, choose one player to be the banker. The banker is responsible for changing out money, collecting bank fees, and distributing money for passing Go. The banker gives $1,500 to each player that’s made up of two $500’s, two $100’s, 2 $50’s, 6 $20’s, 5 $10’s, 5 $5’s, and 5 $1’s. Place the Chance and Community chest cards face-down on their spots in the middle of the board. Then, each player selects a token and places it on the Go space. Each player rolls a pair of dice and the player with the highest roll goes first. On a player’s turn, they roll the dice and move their token that number of spaces. If the player lands on a utility, property, or railroad, they may purchase the deed from the bank and collect the card for that property. If they land on a Chance or Community Chest space, they draw a card from the corresponding pile and follow the instructions on the card. If a player can’t afford or doesn’t want a property, utility, or railroad, the property goes up for auction. During an auction, each player can bid to buy the property. Whoever bids the highest amount wins and gets the property. If a player lands on a property, utility, or railroad that’s already owned by another player, they owe that player the rent price listed on the deed. Players can increase the rent owed on their properties by owning a complete set of one color, called a monopoly, and buying houses and hotels on those properties. Whenever a player passes Go, they collect $200 from the bank. Once a player is done with their turn, the player to their left goes next. If a player rolls doubles, they roll again after their first turn is over. If a player rolls three doubles in a row, they go directly to the Jail space on the board. Players may also be sent to Jail by Chance or Community Chest cards or by landing on the Go to Jail space on the board. If a player ends up in Jail, they can either pay $50 to get out at the beginning of their next turn, or they can try rolling doubles on their next turn to get out for free. If they don’t get doubles, they have to wait until their next turn to try again. If 3 turns go by and they still don’t get doubles, that player pays $50 and leaves Jail. Players are allowed to trade properties with other players during their turn to try to build monopolies. If a player can’t afford to buy a property or pay another player rent, they can mortgage their properties and collect the mortgage value from the bank. Players don’t collect rent on mortgaged properties. If a player runs out of money at any point in the game, they lose. If they ran out of money by landing on another player’s space, all of their property and remaining money goes to that player. The game continues until only one person is left in the game and wins! For more strategies and ways to adapt the rules to your preferences, read on!

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How To Setup An Ftp Server In Windows Using Iis

Earlier, I had written a post on how to turn your computer into a Windows file sharing server using a couple of different programs. If you’re looking for a quick way to share the files on your local computer with friends or family, this is an easy way to do so.

However, if you’re looking to setup your own FTP server in Windows using IIS, you can do so, but it requires more technical knowledge. Of course, it also gives your more granular control over sharing and security, so it’s better for anyone who has a little computer know-how. Plus, IIS is all about running websites, so if you want to run a couple of websites along with an FTP server, then IIS is the best choice.

Table of Contents

It’s also worth noting that different versions of IIS come with each flavor of Windows and they all have slightly different feature sets. IIS 5.0 came with Windows 2000 and 5.1 came with Windows XP Professional. IIS 6 was for Windows Server 2003 and Windows XP Professional 64-bit. IIS 7 was a complete rewrite of IIS and was included with Windows Server 2008 and Windows Vista.

IIS 7.5 was released along with Windows 7, IIS 8 released with Windows 8 and IIS 8.5 released with Windows 8.1. It’s best to use IIS 7.5 or higher if possible as they support the most features and have better performance.

Setup and Configure an FTP Server in IIS

The first thing you’ll need to setup your own FTP server in Windows is to make sure you have Internet Information Services (IIS) installed. Remember, IIS only comes with Pro, Professional, Ultimate or Enterprise versions of Windows.

Setup and configure IIS for FTP

For Windows 7 and higher, you’ll see a different look to IIS. Firstly, there is no play button or anything like that. Also, you’ll see a bunch of configuration options right on the home screen for authentication, SSL settings, directory browsing, etc.

This opens the FTP wizard where you start by giving your FTP site a name and choosing the physical location for the files.

Next, you have to configure the bindings and SSL. Bindings are basically what IP addresses you want the FTP site to use. You can leave it at All Unassigned if you don’t plan on running any other website. Keep the Start FTP site automatically box checked and choose No SSL unless you understand certificates.

Lastly, you have to setup authentication and authorization. You have to choose whether you want Anonymous or Basic authentication or both. For authorization, you choose from All Users, Anonymous users or specific users.

You can actually access the FTP server locally by opening Explorer and typing in ftp://localhost. If all worked well, you should see the folder load with no errors.

If you have an FTP program, you can do the same thing. Open the FTP client software and type in localhost as the host name and choose Anonymous for the login. Connect and you should now see the folder.

Ok, so now we got the site up and running! Now where do you drop the data you want to share? In IIS, the default FTP site is actually located in C:Inetpubftproot. You can dump data in there, but what if you already have data located somewhere else and don’t want to move it to inetpub?

In Windows 7 and higher, you can pick any location you want via the wizard, but it’s still only one folder. If you want to add more folders to the FTP site, you have to add virtual directories. For now, just open the ftproot directory and dump some files into it.

Now refresh your FTP client and you should now see your files listed! So you now have an up and running FTP server on your local computer. So how would you connect from another computer on the local network?

In your FTP client on the other computer, type in the IP Address you just wrote down and connect anonymously. You should now be able to see all of your files just like you did on the FTP client that was on the local computer. Again, you can also go to Explorer and just type in FTP:\ipaddress to connect.

Now that the FTP site is working, you can add as many folders as you like for FTP purposes. In this way, when a user connects, they specify a path that will connect to one specific folder.

When you create a virtual directory in IIS, you’re basically going to create an alias that points to a folder on the local hard drive. So in the wizard, the first thing you’ll be asked is for a alias name. Make is something simple and useful like “WordDocs” or “FreeMovies”, etc.

You can connect to you using your FTP client by putting in the Path field “/Test” or “/NameOfFolder”. In Explorer, you would just type in ftp://ipaddress/aliasname.

Now you’ll only see the files that are in the folder that we created the alias for.

Finally, you’ll need to forward the FTP port on your router to your local computer that is hosting the FTP server. Port Forward is a great site to show you how to forward ports on your router to computers on your home network. You should also read my other posts on port forwarding and dynamic DNS:

What is Port Forwarding?

Setup Dynamic DNS for Remote Access

How To Setup A Web Server In Mac Os X Mountain Lion

In the recent version of Mac OS X, the web server is one of the component that is built-in by default. Prior to Mountain Lion, users can easily turn on the web server via the “Web Sharing” option in the Sharing Preference pane. That component was removed in Mountain Lion. In this tutorial, we will show you how to activate the web server in Mountain Lion, as well as setting up PHP, MySQL and PhpMyAdmin. At the end of this tutorial, you will have a MAMP (Mac, Apache, MySQL, Php) server running on your Mac.

Starting the Apache server

Apache server is pre-installed in Mac OS X, so there is no need to install it. However, to start the Apache server, we will have to use command line in the Terminal.

2. Type the following command:

To restart the Apache server, use the command:

sudo

apachectl restart

To stop the Apache server, use the command:

sudo

apachectl stop

Activating the PHP module

The Apache server is only good enough for you to run static HTML files. If you want to run a more complicated setup, like installing WordPress, you will need to activate the PHP module.

PHP is pre-installed in Mac OS X as well, but it is not included by default.

1. In the terminal, type:

2. Remove down the list until you see the line:

LoadModule php5_module libexec

apache2

libphp5.so

Remove the “#” in front of the line, so it becomes:

3. Save the changes (using shortcut key “Ctrl + o”) and exit (using shortcut key “Ctrl + x”). Restart Apache.

sudo

apachectl restart

The PHP module is now activated.

Configuring Sites folder

1. Open the Finder and go to your Home folder (the folder with a Home icon and your username). Create a new folder “Sites” if it is not available.

2. Back to the Terminal, type the command:

sudo

nano

etc

apache2

users

username.conf

Replace the “username” with your login username. In my case, it will be “sudo nano /etc/apache2/users/damienoh.conf“.

3. Copy and paste the following code to the conf file.

Options Indexes MultiViews AllowOverride All Order allow,deny Allow from all

4. Next, type the command:

nano

Users

username

Sites

phpinfo.php

and paste the line:

Restart Apache server

Setting up MySQL

MySQL is not included in Mountain Lion, so you will need to download and install it manually.

1. Go to MySQL Download site and download the MySQL installer for Mac. For easier installation, you might want to grab the .DMG image than the one in chúng tôi format.

2. Once the download is completed, open up the installer, you should see two .pkg files and one .prefPane file. Install all three of them.

Setting upi MySQL root password

In the Terminal, type the command:

usr

local

mysql

bin

mysqladmin

-u

root password

'yourpasswordhere'

Replace the “yourpasswordhere” with your own password.

Note: Do not confuse this password with your Mac login account. They are not the same. This is the password for the script to access your database.

Note: Removing MySQL is not as straightforward. Run the commands, line by line, in the terminal:

sudo

usr

local

mysql

sudo

-rf

usr

local

mysql

sudo

-rf

Library

StartupItems

MySQLCOM

sudo

-rf

Library

PreferencePanes

-rf

Library

PreferencePanes

sudo

-rf

Library

Receipts

mysql

sudo

-rf

Library

Receipts

MySQL

sudo

-rf

private

var

receipts

mysql

Open the file “hostconfig” with the command “sudo nano /etc/hostconfig” and remove the line MYSQLCOM=-YES-.

Installing PhpMyAdmin

PhpMyAdmin is basically a bunch of PHP files, so installing them is a breeze.

1. Download PhpMyAdmin from its website.

2. Extract the compressed file to your Sites folder and rename it as “phpmyadmin”.

3. Open the “phpmyadmin” folder and create a new folder call “config”. Change its permission with the command:

6. Go to the “Authentication” tab and enter MySQL root password in the “Password for config auth” field.

7. Lastly, enter the following commands in the terminal:

sudo

mkdir

var

mysql

Conclusion

It will probably be easier if you install a third party tool like MAMP, but that will add duplicate features to what is already available in your Mac. With a little tinkering, you can easily get your Mac to be a web server for all your web hosting needs.

Damien

Damien Oh started writing tech articles since 2007 and has over 10 years of experience in the tech industry. He is proficient in Windows, Linux, Mac, Android and iOS, and worked as a part time WordPress Developer. He is currently the owner and Editor-in-Chief of Make Tech Easier.

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