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Introduction to PySpark Join on Multiple Columns

PySpark Join on multiple columns contains join operation, which combines the fields from two or more data frames. We are doing PySpark join of various conditions by applying the condition on different or same columns. We can eliminate the duplicate column from the data frame result using it. Join on multiple columns contains a lot of shuffling.

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Overview

Using the join function, we can merge or join the column of two data frames into the PySpark. Different types of arguments in join will allow us to perform the different types of joins. We can use the outer join, inner join, left join, right join, left semi join, full join, anti join, and left anti join. In analytics, PySpark is a very important term; this open-source framework ensures that data is processed at high speed. It will be supported in different types of languages.

PySpark is a very important python library that analyzes data with exploration on a huge scale. It is used to design the ML pipeline for creating the ETL platform. Pyspark is used to join the multiple columns and will join the function the same as in SQL. The join function includes multiple columns depending on the situation.

How to use Join Multiple Columns in PySpark?

We must follow the steps below to use the PySpark Join multiple columns. First, we are installing the PySpark in our system.

In the below example, we are installing the PySpark in the windows system by using the pip command as follows.

pip install pyspark

Installing the module of PySpark in this step, we login into the shell of python as follows.

python

After logging into the python shell, we import the required packages we need to join the multiple columns.

import pyspark from chúng tôi import SparkSession

After importing the modules in this step, we create the first data frame.

Code:

spark_join = SparkSession.builder.appName ('sparkdf').getOrCreate() data_join = [(13, "ABC"), (15, "PQR"), (17, "XYZ")] columns_join = ['stud_id', 'stud_name'] dataframe_join = spark_join.createDataFrame (data_join, columns_join) dataframe_join.show()

Output:

After creating the first data frame now in this step we are creating the second data frame as follows.

Code:

spark_join1 = SparkSession.builder.appName ('sparkdf').getOrCreate() data_join1 = [(13, "ABC"), (15, "PQR"), (17, "XYZ")] columns_join1 = ['stud_id', 'stud_name'] dataframe_join1 = spark_join1.createDataFrame (data_join1, columns_join1) dataframe_join1.show ()

Output:

After creating the data frame, we are joining two columns from two different datasets.

Code:

import pyspark from chúng tôi import SparkSession spark_join = SparkSession.builder.appName ('sparkdf').getOrCreate() data_join = [(13, "ABC"), (15, "PQR"), (17, "XYZ")] columns_join = ['stud_id', 'NAME1'] dataframe_join = spark_join.createDataFrame (data_join, columns_join) data_join = [(13, "ABC"), (15, "PQR"), (17, "XYZ")] columns_join = ['stud_id', 'stud_name'] dataframe_join1 = spark_join.createDataFrame (data_join, columns_join) dataframe_join.join (dataframe_join1, (dataframe_join.stud_id == dataframe_join1.stud_id) & (dataframe_join.NAME1 == dataframe_join1.stud_name)).show()

Output:

How Multiple Columns work in PySpark?

Below are the different types of joins available in PySpark. As per join, we are working on the dataset.

Inner join

Left outer join

Right outer join

Full outer join

Cross join

Left semi join

Left anti-join.

The inner join is a general kind of join that was used to link various tables. It will be returning the records of one row, the below example shows how inner join will work as follows.

Code:

import pyspark from chúng tôi import SparkSession spark_join = SparkSession.builder.appName('sparkdf').getOrCreate() data_join = [( )] columns_join = ['stud_id', 'NAME1'] dataframe_join = spark_join.createDataFrame(data_join, columns_join) data_join = [( )] columns_join = ['stud_id', 'stud_name'] dataframe_join1 = spark_join.createDataFrame (data_join, columns_join) join = dataframe_join.join(dataframe_join1, on=['stud_id'], how='inner') join.show ()

Output:

The outer join into the PySpark will combine the result of the left and right outer join. The below example shows how outer join will work in PySpark as follows.

Code:

import pyspark from chúng tôi import SparkSession spark_join = SparkSession.builder.appName ('sparkdf').getOrCreate() data_join = [( )] columns_join = ['stud_id', 'NAME1'] dataframe_join = spark_join.createDataFrame(data_join, columns_join) data_join = [( )] columns_join = ['stud_id', 'stud_name'] dataframe_join1 = spark_join.createDataFrame (data_join, columns_join) join = dataframe_join.join(dataframe_join1, on=['stud_id'], how='outer') join.show()

Pyspark Join on Multiple Columns Dataframes

Pyspark join on multiple column data frames is used to join data frames. The below syntax shows how we can join multiple columns by using a data frame as follows:

Syntax:

join(right, joinExprs, joinType) join(right)

In the above first syntax right, joinExprs, joinType as an argument and we are using joinExprs to provide the condition of join. In a second syntax dataset of right is considered as the default join.

In the below example, we are creating the first dataset, which is the emp dataset, as follows.

Code:

import pyspark from chúng tôi import SparkSession spark_join1 = SparkSession.builder.appName('sparkdf').getOrCreate() data_join1 = [(21, "BC"), (23, "QR"), (25, "YZ")] columns_join1 = ['emp_id', 'emp_name'] dataframe_join1 = spark_join1.createDataFrame(data_join, columns_join) dataframe_join1.show()

Output:

In the below example, we are creating the second dataset for PySpark as follows. Here we are defining the emp set.

Code:

import pyspark from chúng tôi import SparkSession spark_join2 = SparkSession.builder.appName ('sparkdf').getOrCreate() data_join2 = [(31, "AC"), (33, "PR"), (35, "XZ")] columns_join2 = ['emp_id', 'emp_name'] dataframe_join2 = spark_join2.createDataFrame (data_join, columns_join) dataframe_join2.show()

Output:

Examples

Below are the different examples:

Example #1

Code:

import pyspark from chúng tôi import SparkSession spark_join1 = SparkSession.builder.appName('sparkdf').getOrCreate() data_join1 = [(21, "BC"), (23, "QR"), (25, "YZ")] columns_join1 = ['emp_id', 'emp_name'] dataframe_join1 = spark_join.createDataFrame(data_join, columns_join1) data_join1 = [(31, "AC"), (33, "PR"), (35, "XZ")] columns_join1 = ['emp_id', 'stud_name'] dataframe_join2 = spark_join.createDataFrame(data_join1, columns_join1) dataframe_join1.join(dataframe_join2, (dataframe_join1.emp_id == dataframe_join2.emp_id) & (dataframe_join1.emp_name == dataframe_join2.stud_name)).show()

Output:

Example #2

In the below example, we are using the inner left join.

Code:

import pyspark from chúng tôi import SparkSession spark_join1 = SparkSession.builder.appName ('sparkdf').getOrCreate() data_join1 = [(21, "BC"), (23, "QR"), (25, "YZ")] columns_join1 = ['emp_id', 'emp_name'] dataframe_join1 = spark_join.createDataFrame (data_join, columns_join1) data_join1 = [(31, "AC"), (33, "PR"), (35, "XZ")] columns_join1 = ['emp_id', 'stud_name'] dataframe_join2 = spark_join.createDataFrame (data_join, columns_join) join = dataframe_join1.join (dataframe_join2, on=['emp_name'], how='left') join.show()

Output:

Key Takeaways

In PySpark join on multiple columns, we can join multiple columns by using the function name as join also, we are using a conditional operator to join multiple columns.

We also join the PySpark multiple columns by using OR operator. We need to specify the condition while joining.

FAQ

Given below are the FAQs mentioned:

Q1. What is the use of multiple columns join in PySpark?

Answer: It is used to join the two or multiple columns. We join the column as per the condition that we have used.

Q2. Which operator is used to join the multiple columns in PySpark?

Answer: We can use the OR operator to join the multiple columns in PySpark. We are using a data frame for joining the multiple columns.

Q3. What are the join types used in PySpark?

Answer: We are using inner, left, right outer, left outer, cross join, anti, and semi-left join in PySpark.

Conclusion

There are different types of arguments in join that will allow us to perform different types of joins in PySpark. Pyspark joins on multiple columns contains join operation which was used to combine the fields from two or more frames of data.

Recommended Articles

This is a guide to PySpark Join on Multiple Columns. Here we discuss the introduction and how to join multiple columns in PySpark along with working and examples. You may also have a look at the following articles to learn more –

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How To Join The Metaverse

The metaverse is getting a lot of hype at the moment, driven by companies like Facebook and its founder Mark Zuckerberg. The idea is appealing, but it’s not at all clear how you’re supposed to join the metaverse!

The problem is that all this talk of the metaverse makes it sound like a single place you access or subscribe to, but the truth is that there are a whole bunch of metaverses out there, and even more are coming.

Table of Contents

The Metaverse in a Nutshell

This article assumes that you already more or less know what the metaverse is. If you don’t and you have some time on your hands, head over to our What Is the Metaverse explainer for an in-depth discussion. If you’re in a hurry, here’s the gist of it.

Metaverse Hardware Requirements Paying for (and Owning) Things in the Metaverse

One of the main features of a metaverse is that you can buy and own things within it. That includes virtual property, objects, and anything else that has utility within the metaverse. You’re also likely to have access to real-world products as well. Don’t be surprised if online retailers like Amazon eventually set up shop within a metaverse as well!

In general, there are two ways you can pay for something in a metaverse. The first is to simply use real-world currencies with a credit card, PayPal, or any of the other common digital payment platforms we already use. The second is to use cryptocurrency

Cryptocurrency and the Metaverse

Cryptocurrency and blockchain technology are especially interesting when it comes to the metaverse. That’s because the blockchain has a level of permanence that digital items bought from a central server can’t match. If it’s in the blockchain, then evidence that you own a specific digital asset only disappears when the last copy of the blockchain is destroyed.

NFTs (Non-fungible tokens) make much more sense in a metaverse context than they do in the real world because they can act as title deeds for your virtual property. Of course, if the content hosting and computing power for a given metaverse aren’t also decentralized, then NFTs don’t mean much. 

There are already a few video games that make use of crypto and NFTs, such as Cryptokitties and Axie Infinity (AXS). AXS is a trading and battling game that also allows for the purchase of virtual property, and it lets users cash out their cryptocurrency every 14 days for use in the real world. This means it’s shaping up to be a kind of metaverse itself.

Going ahead, that’s likely to change. Especially since companies like Nike are getting ready to sell you virtual products! So perhaps it’s time to open a crypto wallet on the Bitcoin or Ethereum blockchain and load it with a little digital cash.

The Best Metaverse Platforms You Can Join Today

The metaverse isn’t one place, although one day all metaverses may be linked to each other using common standards and practices. For now, you’ll have to pick the one or two metaverse platforms that offer the sort of experience you want. Each digital world represented here has its own unique charms, and most are built to let third-party creators (including users) add their own content.

Horizon Worlds (Rift S & Quest 2)

Horizon Worlds can be accessed using a Quest 2 or Rift S (connected to a PC). It supports full motion tracking in 3D space and has an integrated game creation system. From a central plaza, users can enter portals to visit user-created worlds. The sky’s the limit with Horizon Worlds, and since it was only released to the public early in December of 2023, you can bet it’s still got plenty of new stuff coming.

On a technical level, Decentraland has some serious work ahead. It’s a little janky to be honest, but it’s also a fascinating collection of ideas. More than half a million people have already signed up to be part of this virtual world.

Having a crypto wallet is optional, but obviously, if you want to be a creator and have a place to keep your NFTs, you’ll need one. During the peak of the NFT craze, plots of land were selling for as much as $100,000!

Roblox (Windows, macOS, iOS, Android, Xbox One)

Roblox started out as an innovative game that coasted under the radar for quite some time. After exploding in popularity, it’s a smash-hit akin to Minecraft today and it also happens to be a metaverse.

Roblox is a free-to-play game, so it’s always been ready to host a vibrant economy. What really elevates it to metaverse status is Roblox Studio. Users can use Studio to create entire games, which players of Roblox can then play. 

Individual digital items are also bought and sold on the platform, and every now and then Roblox hosts virtual events. So far, at least, Roblox is free of any sort of cryptocurrency, token, or blockchain technology, sticking with its own traditional non-crypto “Robux” currency.

The Sandbox Metaverse (iOS, Android, Windows and macOS)

The Sandbox is a blockchain-based game with its own token named SAND. Users can buy land, create their own content, create entire games, buy, sell, and explore everything within the Sandbox Metaverse.

At the time of writing The Sandbox Metaverse is in the Alpha stage, but there’s major hype around it with companies like Square Enix and Softbank investing millions of dollars in the company. While everything is still in an unfinished state on a technical level, the concepts are solid, and getting in early is probably a good idea!

VR Chat (Oculus VR, Oculus Quest, SteamVR, Windows Desktop Mode)

VRChat is a VR-centric virtual world that does have a desktop mode for use with flat screens but really demands VR hardware to get the most out of it.

In VRChat, users can create their own instanced worlds. That means it’s not an open persistent virtual world, but one that exists for the player and their friends.

VRChat has been popular for a large part of its existence, but the pandemic turbo-charged the number of users who were looking for a way to spend time with people, without physically being in their presence. As a new user, you don’t rate high enough on the trust system to start making your own content, but that’s only temporary. Stick around and stick to the rules, and you’ll get the keys to your own kingdom soon.

Second Life (Windows and macOS)

As the name suggests, Second Life is a place where people can live, hang out, experience things, buy property, customize their spaces, and much more. There are even official business premises in Second Life that you can visit for customer service or buy products.

As one of the oldest metaverses that’s still around, Second Life is due for some upgrades, and the people behind its creation are working on an evolution of this virtual world, although for now that still doesn’t include VR.

Fortnite (Windows, Switch, PlayStation 4, PlayStation 5, Xbox One, Xbox Series)

Fortnite started out as a video game and became one of the most popular free-to-play titles in history. Since then it’s grown into something more than a game, with people hanging out with each other rather than just shooting one another 24/7.

Fortnite has successfully dabbled in hosting non-game events such as concerts as well, becoming more like a social platform as time went by. Now Fortnite has launched Party Worlds, which is an expansion to the game that formally gives people a place to hang out, create their own party worlds, and generally qualifies the game as a full-fledged metaverse.

Fortnite is available on virtually every platform, but for the foreseeable future, iOS and macOS users are out of luck thanks to a massive legal battle with Apple.

Is This the Real Life?

Our digital screens have been a way to escape from the boredom or stresses of real life for decades. People already spend thousands of hours in-game worlds and on social platforms. They make friends there, they have fun there, and sometimes they have bad experiences.

How To Drop In On Multiple Echo Devices At Once

When Amazon introduced Alexa’s Drop In feature, it gave Echo devices a new purpose as an intercom system. While it’s useful being able to ask Alexa to drop in on a specific device or room, you may not want to repeat the process for every room in your home. That’s why Amazon made it possible to drop in on multiple Echo devices at once in addition to one at a time, calling the feature Group Chat.

What Makes this Different

Before, you could only use the Drop In feature on the device you named. For example, if you have three kids, each with their own Echo device, you needed to drop in on each device individually.

Now, if you want to reach all three kids, you can drop in on all the devices in your home with a single command. If you already have Drop In enabled, it’s even easier to set up. It’s also easy to set up if you don’t have Drop In enabled.

Enabling Drop In

You’ll need the Alexa app and to connect to the devices in your home. If your app is already connected to your Echo devices, enabling Drop In is as easy as opening the Alexa app and tapping Communicate at the bottom of the screen. If you’ve never been asked about Drop In before, you’ll receive a pop-up asking if you’d like to “Try Home Drop In.”

Tap Enable to enable Drop In.

Allowing Communications on Devices

You’ll also need to allow each device to accept communications. Tap Devices in the bottom right corner of your screen to see your devices.

Existing devices are listed under Echo & Alexa and All Devices (if you want to see all your connected devices, including those that can’t act as an intercom). Tap the device you want to enable communications and Drop In on. Select Communication.

Toggle Communication to “On” and tap Drop In to turn it to On if it’s not already enabled.

Repeat this process for every device.

Drop In on Multiple Echo Devices

Once Drop In is enabled, you can say “Alexa, drop in on [Name of device].” Alexa then allows you to speak through the device.

You just have one final setting to enable before you can ask Alexa to drop in on all your devices. You can say “Alexa, drop in on all devices” to be prompted to change the setting, or you can do it manually beforehand.

In order to use a group chat Drop In, you have to allow Amazon to temporarily decrypt group chats in order to combine the multiple audio streams. If this isn’t something you want to do, you won’t be able to use Drop In on multiple Echo devices at once. It’s a privacy compromise, but if you don’t mind Alexa hearing you say “dinner’s ready” or something else non-personal, then you’re fine.

Open Settings and tap Communication in the Alexa app.

Tap “Enhanced Features.”

Toggle the setting to Enabled. It’s disabled by default.

Now you’re ready to say “Alexa, drop in on all devices.”

Other Benefits

In addition to having a whole home intercom without having to say individual device names, you can also drop in from your Alexa app even if you’re not home. For instance, you may just want to check in on your kids at home while you’re still at work or ask the pet sitter how things are going. Since you don’t always know what room someone will be in, you can drop in on all your devices to ensure you get an answer.

You can also set up reminders to use all your devices. When you set up a reminder, choose “All Devices” in “Announces From.” Now, you’ll get your reminders wherever you are in your home. You can do this with individual reminders or all of them.

If the above is not enough for you, find out more ways to make Alexa even smarter.

Crystal Crowder

Crystal Crowder has spent over 15 years working in the tech industry, first as an IT technician and then as a writer. She works to help teach others how to get the most from their devices, systems, and apps. She stays on top of the latest trends and is always finding solutions to common tech problems.

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Comparing Calculated Columns And Dax Measures In Power Bi

In this tutorial, I will cover the two places where you can write your DAX formulas. These two places are the calculated columns and measures. You may watch the full video of this tutorial at the bottom of this blog.

I will go over each one at a time and I’ll start with calculated columns.

A calculated column is an additional column that doesn’t exist in your raw data source.

This means that we need to add it physically to your data table.

To do this, you put some DAX formula logic into a column to create that additional column. This is very similar to working in Excel and you want to add another column with a formula.

In this example, we will use this fact table that contains all the sales that we’re making in our retail stores and we will add the price of the product.

The price actually already exists in the Products Table here, where we have the Original Sales Price and Current Price.

But to show you how to create a calculated column, I’m also going to add this to the Sales Table.

In a lot of these examples, especially with calculated columns, you don’t actually need to create these columns.

If you’re coming from an Excel background, then you might think you have to, but you don’t have to in Power BI. I’m only doing this to show you what a calculated column is.

But later on, I’m going to show you how you can actually use measures to run these calculations versus adding a physical column inside the data table.

So let’s add the Price here just as our first example.

To create a calculated column, open the Modeling ribbon and select New Column.

I’m going to write some pretty simple logic here to get the Price into this column. I’ll call it Sales Price and then use the RELATED function to reference a column name.

In this case, I’m going to reference the Current Price column. That’s going to give me a sales price for every single item that was sold.

The RELATED function is bringing in the price of each individual product.

And then we could write a new column here and call it as Total Revenue. We use the formula:

Total Revenue = Sales[Quantity] * Sales[Sales Price]

These are just some examples of how you can create a calculated column.

You can also create calculated columns in any table in your data model. It doesn’t have to be just the fact table or the sales table. It can be inside of your LOOKUP Tables as well.

For example, we jump to this detailed Dates Table. Think of these columns as the columns that are going to be filters of your DAX measures.

As I look at this table, I see that there is a dimension here that does not exist currently, which I might want to put into some of my visualisations.

To showcase another way of creating a calculated column, I will use the MonthName column.

The MonthName here is the full month, but I only want the first three letters of each month.

So I’m gonna go to New Column in the Modeling ribbon and call this column as Short Month.

I’m gonna use some logic that you might be familiar with from Excel. I’m going to use LEFT, then find my month name, and I’m going to only use the first three letters of that month name.

Now if we go across to the side, we will see the Short Month column, where we only have the first three letters of each month.

I like to call this adding additional dimensions to analysis because we essentially created another filter that we can use throughout any of our analysis that we do from here on out.

If we go back to the data model, you will see that the Short Month column now exists in our Dates Table and it can filter anything that we do down inside this Sales table.

So if we will run a calculation and count up the quantity, we can now filter it by the Short Month.

I would like to reiterate that it is not recommended that you create these columns in here because we can actually create all of these calculations in memory. 

Through creating measures, we can do these internal calculations without having to put them physically inside the table.

That’s a key thing to remember as you learn how to write DAX formula on top of your data tables.

Now let’s talk about measures.

Think about measure as a virtual calculation. It doesn’t actually sit inside your model, but it sits on top of your model.

When you use a measure, it only goes and does a calculation at the time that you use it.

In Excel, every time you run a calculation in the column or in any cell, it recalculates all the time. But in Power BI, a measure only calculates itself when it needs to.

A measure is like a stored calculation procedure that only gets enabled if you use it in a visualization.

So let’s create a simple measure to highlight that point. First, I’m going to select the Sales Table and then select any column in there.

I’ll put in Total Quantity Sold to get the sum of the Quantity column in the Sales table.

Now we have this really simple measure, and it is virtually completing its calculation.

It is also calculating everything in memory. In other words, this is calculating the total items that we have sold throughout the time.

The key thing to remember here is that this measure is just stored inside our model, but it doesn’t actually go and run any calculation, unless we drag it on our report page. Then it will go and run the calculation virtually.

So in this case, this measure is virtually going to the Sales table, going to the Quantity column in that table, and then doing a sum over that entire column.

This is actually called an aggregation measure, which we’ll be going over shortly.

Now I’m going to create a new measure and I’m going to call this as Total Sales. Then I’ll use the iterating function called SUMX, which I’ll explain in another model shortly.

I referenced the Sales table, and come up with this formula:

Total Sales = SUMX( Sales, Sales[Quantity] * RELATED( Products[Current Price] )

If you remember, we didn’t physically put this RELATED current price inside the data table. But in this case, I’m virtually putting it inside the data table by incorporating it in this measure.

Then the iterating function SUMX goes to the Sales table and picks every single row in the table multiplied by the quantity by the related current price.

This Total Sales will now give me a result.

You can also do the formatting in the Modeling tab, where your Data type is at the top.

We went over calculated columns and measures, where you can write your DAX formula.

The key thing with calculated columns is that you are physically putting a column of data into your model. If you do that sometimes on some of your larger tables, those can be very large columns.

It is important to recognize that these calculated columns can take up a lot of memory in your model.

They can make your file size larger, and they can sometimes impact performance depending on how big the table is.

But you can counteract this by using measures effectively to run a lot of these calculations virtually. You will still get the same results that you would get by writing these calculated columns.

I hope that this tutorial makes it a lot clearer for you the two places where you can write your DAX formula in and the considerations when writing DAX formulas.

This will also help you understand how to incorporate DAX into your analysis within Power BI.

Enjoy reviewing this one.

Sam

Multiple Ports On Your Pc: What Do They Do For You?

While the examples here focus on desktop PCs, most of these connections are available on various laptop PCs as well.

For an examination of common and not-so-common connections that you may find on your system’s motherboard, see “Motherboard Port Guide: Solving Your Connector Mystery.”

Schizophrenic USB

USB used to be simple. You had USB 2.0 and… that was it. USB 1.0 connections existed for a brief time, but once USB 2.0 came along, with substantially better throughput, it became widely adopted. As with any widely adopted standard, variants appeared. Let’s look at some flavors of USB you might find, and how they might vary.

USB 2.0 is the standard port type. These days, mice, keyboards, hard drives, optical drives, printers, and just about anything else can be found in a USB 2.0 flavor to plug into one of these ports. Even with the emergence of USB 3.0 (SuperSpeed USB), USB 2.0 is still the most versatile connection.

This particular type of USB port ships on certain recent Asus-manufactured motherboards. This is a standard USB 2.0 port, and can be used as a normal connection to USB 2.0 devices. However, it’s also able to auto-install a BIOS. You need to copy a special BIOS flash program to a USB flash memory key, as well as the BIOS you want to install. Then press a button next to the port when the system is powered up, and the BIOS auto-installs. This is a pretty geeky feature, tailor-made for hard-core enthusiasts who may have gotten themselves into trouble with severe overclocking or other tweaks.

These types of ports are available on motherboards made by Asus, Gigabyte and possibly other manufacturers. It increases the available trickle current out of the USB port to charge up mobile devices like smartphones and tablets. This is in response to devices like Apple’s iPad, which requires more current to charge than the normal USB 2.0 port might supply to charge in a reasonable amount of time.

USB 3.0 is the latest version of USB, and is also known as SuperSpeed USB. It increases maximum throughput to 5 gigabits per second (625MB per second.) Most PCs implement USB 3.0 through a discrete chip built onto the motherboard, but some AMD chipsets have USB 3.0 built into the PC’s core logic. Intel will be building USB 3.0 into its core logic in its next-generation Ivy Bridge chipsets.

USB 3.0 is backward-compatible, so you can plug in USB 2.0 devices, but you’ll get only USB 2.0 speeds. Also, USB 3.0 cables are different than earlier USB cables, so be sure to get the right cable type for your USB 3.0 device, if your spiffy SuperSpeed USB gizmo didn’t include a cable in the box.

Next: eSATA, Audio, and Networking

eSATA: Redundant, but Useful

Enter eSATA or external SATA. The latest eSATA connections can handle 6gbps SATA drives and connections, which is a little faster than USB 3.0. A variety of external SATA enclosures exist that support various RAID formats if you’re looking for redundant storage.

Networking Connections

The most obvious networking connection on desktop PCs is the ubiquitous ethernet jack.

Gigabit ethernet is built into most systems today, and if you have a wired house, there’s currently no faster network connection available, though that may change if 802.11ac wireless networking becomes a reality.

Some motherboards support wireless connectivity. Those that do now ship with 802.11n Wi-Fi on board, which does allow for throughput up to 600 megabits per second. And you can even occasionally find Bluetooth on board, as this image indicates. This allows easier integration with Bluetooth-capable devices, such as smartphones.

The Sound and the Fury: Audio Connections

The most commonly used audio connections are the analog minijacks on the back of the PC. If you’re one of those rare folk with a multichannel PC speaker setup, you’ll use three or four output connections–typically green, black, orange, and gray–to your speaker setup for multichannel audio. The pink one is the microphone input and the blue jack is the line input.

Next: Display Connectors

Display Connectors: Past, Present, and Future

Of all the types of connectors, monitor connections seem to have the longest lifespan. It always surprises me when I unpack a Dell monitor to find the VGA cable pre-attached. What century is this again?

Systems supporting integrated graphics often still have VGA connectors. Most monitors shipping today still offer VGA as a connection as well, though we’re finally seeing some displays without that ancient analog connector. I see very few discrete graphics cards with VGA any longer, though most still ship with a DVI-to-VGA dongle should you need it.

DVI (digital visual interface) first appeared in 1999, while VGA emerged over a decade earlier, in 1987. However, both DVI and VGA will ride off into the sunset together in 2024. DVI was the first widely adopted digital connection for PC monitors, and will be superseded by DisplayPort.

DisplayPort seems redundant, given the existence of HDMI. But DisplayPort brings a few wrinkles to the table for PC monitors, wrinkles not available with HDMI. Licensing is one aspect–DisplayPort is licensed through the industry standards body VESA, and is royalty-free. With DisplayPort 1.2, you can daisy-chain up to two high-bandwidth monitors, and the standard will support DisplayPort hubs for connecting even more monitors. DisplayPort also supports bit rates up to twice the throughput of HDMI, enabling support for very-high-resolution displays.

DisplayPort can also carry audio signals, up to eight channels total, with an aggregate bandwidth of 49 megabits per second.

Mini-DisplayPort was originally popularized by Apple, but is included as part of DisplayPort 1.2. It’s common on current-generation graphics cards built with AMD Radeon HD 6000 and HD 7000 series graphics cards.

Next: The Past and the Future

Showing Their Age

A number of connectors still show up in a few systems, even though they’re rarely used by most home PC users. Some of these are more useful for businesses, which may need them to support older hardware still used to help run some applications.

FireWire, or IEEE 1394, is also still fairly common, though rarer on the newest motherboards. It’s useful if you have older camcorders or pro audio gear.

The nine-pin serial port is almost impossible to find any longer; this picture is from an old Pentium 4-based motherboard. Despite that scarcity, a number of laboratory instruments, point-of-sale devices, and other hardware in some businesses require serial connections. In fact, you’ll find serial-port pinouts available on many recent-generation motherboards, but no way to actually connect them. You can buy PCI bracket adapters with serial ports that plug into these motherboard connectors, and a few boards do ship with those adapters.

Future Connections

We haven’t shown some connections you’ll likely see in PCs this coming year, or may already exist on some Apple Mac OS systems. One example is Thunderbolt, the new high-speed serial interface that has appeared on some Apple systems. It’s likely that we’ll see Thunderbolt ports on upcoming Ivy Bridge-based systems running Windows later this year.

So what type of oddball connector is on the back of your system? Drop by and tell us what’s on the back of your PC that we haven’t mentioned.

Midjourney Ai Login: How To Join And Access The Ai

Join Midjourney AI Login and access their AI-based image generator through Discord. Create stunning art pieces with faster and more efficient image generation, accurate and consistent results, and greater flexibility and customization options. Expand your imagination and explore new creative possibilities with Midjourney.

If you’re interested in exploring the world of AI-generated art, then Midjourney AI Login is the perfect platform for you. Midjourney is a research lab that aims to expand the imaginative powers of the human species through artificial intelligence. They have developed an AI-based image generator that is accessible through Discord, making it available to millions of people. In this article, we will guide you on how to join and access the Midjourney AI Login image generator. Read on to learn more about how you can create stunning art pieces using Midjourney AI Login and how it compares to other AI image generators.

Midjourney is a research lab that explores new mediums of thought and expands the imaginative powers of the human species. The lab aims to create new forms of art and expression using artificial intelligence (AI).

Midjourney has developed an AI-based image generator that is accessible through Discord. The image generator uses a machine learning algorithm to create unique and beautiful images that can be used for a variety of purposes.

See Also: How to use Midjourney AI on Discord

To use Midjourney, you need a Discord account, which is a popular social messaging and video call software. If you do not have a Discord account, you can create one for free by downloading and using the dedicated app or by using your web browser.

The very first time you use Midjourney, the Midjourney Bot will ask you to accept the terms of service. You must agree to the terms of service to access the AI-based image generator.

After accepting the terms, you can start using the Midjourney AI image generator. The image generator has a range of options and settings that you can customize to generate unique and beautiful images.

Check More: Best AI Like Midjourney But Free

At present, Midjourney is not available for free. The basic plan costs $10, while the premium tier can be accessed for $60. Despite this, there are numerous other online tools that can produce similar results, providing alternatives for those who cannot afford Midjourney’s fees.

Also Useful: How To Use Midjourney For Free?

First, ensure that you have a verified Discord login.

If you don’t already have a Discord account, visit the Discord website and register for a new account.

Once you have successfully joined the Midjourney Discord channel, select any “newbies-#” channel visible in the left sidebar.

On the Midjourney Official Server, use the /imagine command to interact with the Midjourney Bot on Discord. This will allow you to create images, change default settings, monitor user info, and perform other helpful tasks.

After successfully verifying your Discord account and accessing the Midjourney AI, you can begin creating stunning art pieces using the tool.

Faster and More Efficient Image Generation: Midjourney uses AI technology to quickly generate high-resolution images based on text prompts. This can save artists and designers a significant amount of time and effort, as they no longer need to manually create each image.

More Accurate and Consistent Results: With Midjourney, users can expect more accurate and consistent results than with traditional image creation methods. The AI algorithms used by Midjourney are designed to generate images that closely match the input text, ensuring that the resulting images meet the user’s expectations.

More Flexibility and Customization Options: Midjourney allows users to customize and tweak the generated images to their liking, providing greater flexibility than traditional image creation methods. Users can experiment with different text prompts and settings to create unique and personalized images.

Useful for Artists and Professionals: Midjourney is a powerful tool for artists and professionals who need to generate high-quality images for their projects. It can be used to create concept art, illustrations, marketing materials, and more.

Expands Human Imagination Limits: By using Midjourney, users can expand their imagination and explore new creative possibilities. The tool can generate images that users may not have thought of otherwise, inspiring new ideas and concepts. Additionally, Midjourney can be used to transform the user’s perception of what is possible, opening up new artistic avenues.

Midjourney’s AI-based image generator is a powerful tool for creating unique and beautiful images using artificial intelligence. With the easy accessibility through Discord, it is available to anyone with a Discord account. Join Midjourney today to explore the world of AI-generated art!

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