Your Home Is Full of Energy Vampires. This $12 Meter Helped Me Find the Biggest Ones


Most of us assume that switching something off means it stops using power. It doesn’t. Appliances, devices and home electronics continue using electricity in standby mode — and according to the US Department of Energy, that sneaky power draw costs the average household roughly $100 a year.

Your home is harboring energy vampires in nearly every room — and they’re quietly draining your wallet. To find the worst in my home, I ordered a $12 power meter from Amazon and tested the passive power draw of nearly every device and appliance I could — 18 in total.

The goal? Find which of them wastes the most energy when off and whether it’s worth unplugging the worst offenders when not in use. The winner (worst offender) shocked me; an unassuming piece of TV tech that nearly every home has.  

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How I tested to find energy vampires

There were a few limitations to the project, the most notable being that I couldn’t test my washer, dryer or oven. Those appliances use large 240-volt outlets and the power meter I bought only works with standard NEMA 5-15R outlets. I also wasn’t able to properly test my fridge because there’s no way to power it down while it’s plugged in.

That said, I did test virtually every other device in my house that could be turned off or put into a sleep or standby mode. I went through my house, testing every appliance and device, including the exterior LED light strips I recently installed.

Common household energy vampires

I decided to catalog my results room by room. All told, I ended up testing tech in my home office, living room, kitchen, bedroom and the light strips outside. This is what I found.

Energy meter with nothing plugged in

The energy meter only works with AC outlets so I wasn’t able to test 240-volt appliances, like washers and dryers. 

Alan Bradley/CNET

Home office

Let’s start with the lair of some of the most prominent suspects on my list: my home office. I do the vast majority of my work and spend a fair amount of my downtime there, and I have a pretty energy-intensive setup that includes my desktop PC, laptop, a 60-inch TCL television and an 18-inch monitor. 

As I suspected, there were some power-hungry devices throughout my setup. Some of the highest-consuming devices in my home were in my home office, including my custom-built desktop PC, which, while fully powered down, siphoned off between 1.8 and 2 watts. When left idling in sleep mode, this spiked to 3.1 watts.

The laptop was also a chief offender. The 2025 version of the Framework 16 laptop drew between 0.5 and 1.3 watts when off and 1.9 watts in sleep mode. While these were some of the highest passive siphons, bear in mind that those figures are still quite low. For context, my fridge, while running at a medium cooling setting, gulped down 509 watts. 

Energy meter doing a reading for a laptop

My Framework 16 laptop ended up having some passive energy draw. 

Alan Bradley/CNET

I was surprised by the efficiency of the giant 4K 60-inch TCL TV, which showed a 0-watt power draw when plugged into the meter. Interestingly, the much smaller, 18-inch, 1080p HP Omen monitor did pull down a trickle of energy, though only 0.1 watts. 

There’s also an Echo Dot on my desk, Amazon’s portal to its Alexa smart assistant, which is always passively listening for voice prompts (and to everything else, if you listen to the conspiracy theorists). I wasn’t surprised to find that the Dot drew a 1.7-watt phantom load even while not in active use.

I also keep my Nintendo Switch in my office, and it pulled down a consistent 0.8 watts when off and 1.3 watts while in sleep mode. Those numbers remained the same whether the Switch was physically docked or in handheld mode. I also tested my Canon printer, which showed a draw of 0.2 watts. 

Energy meter with a Nintendo Switch in front of it

The Nintendo Switch had some modest power draw, whether physically docked or in handheld mode. 

Alan Bradley/CNET

I was pleasantly surprised to discover that none of the chargers in my office, whether for my cordless vacuum or for USB-C and USB-A devices, drew any passive power whatsoever.

I also tested my router in a couple of configurations. While fully off but plugged in, it showed zero watts of draw. When I powered it on and ensured there were no live connections from any devices, it spiked to 4.3 watts. This represents the router’s active idle power, not a passive phantom load, so it’s not included in the final “vampire energy” ranking. However, it gives you a sense of how much power the router uses even when not connected. 

Living room

Second on my hit list was the living room, which also hosts a number of electronics I suspected might be power-hungry. I have another TV there, so I started with it to see if it could match the TCL’s zero-watt power draw in my office. Sadly, it did not. Despite being smaller (a slightly older 50-inch 4K RCA TV), it showed a passive power draw of 0.3 watts. Not massive, but slightly disappointing after the TCL’s showing.

A PlayStation 5

The PlayStation 5 actually proved to be fairly energy efficient in sleep mode.

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I moved on to the PlayStation 5 console and tested it in sleep mode and while powered off. When off, it registered a teensy 0.1 watts, although it jumped to 1.5 watts in rest mode. 

The real surprise here was the cable box. While you can never fully turn it off, as there’s always a digital clock display and it includes a DVR to record scheduled shows/films, there is a distinct on and off mode. Although I didn’t expect it to draw significantly more than other electronics in rest mode, like the game consoles or my PCs, it showed a (relatively) massive 19.9-watt draw while powered off. 

A cable box

This isn’t my DirecTV cable box but it’s similar. This device ended up being the biggest energy vampire in my house. 

David Katzmaier/CNET

Aside from the aforementioned DVR capability, the significant phantom load is likely caused by being kept in a relatively high-power state to ensure instant-on. Because we expect our TVs to start displaying a channel almost immediately after we switch on the cable box, many boxes are kept in a higher-power state than other devices. 

At the other end of the spectrum, I also tested a number of lamps in the living room (and throughout the house). I tried standard table lamps with on/off switches, a larger floor lamp, and a lamp with three brightness settings and touch activation. Every single lamp, regardless of size or activation type, showed a zero-watt power draw.

Kitchen

The kitchen is also somewhat of a target-rich environment, given how many appliances I have plugged in at any given time. While there was a fair range of results, none of the appliances I tested showed a particularly high drain.

The thirstiest beast in my kitchen was the microwave, which clocked in at 0.5 watts. Below that were my drip coffee maker, at 0.3 watts, and my large air fryer, at a fairly scant 0.2 watts. The electric kettle, which I tend to leave plugged in for convenience even though I don’t use it that regularly, showed no passive draw at all. 

Energy meter doing a reading for a microwave

My microwave had the biggest power draw in the kitchen but it wasn’t that bad compared to some other devices.

Alan Bradley/CNET

Most surprising was the full-size, mobile dishwasher I have, which plugs into an outlet and attaches directly to the sink, but can be rolled around the kitchen on four wheels. Purely based on its size and capacity (and the cacophony it emits while running), I expected a high phantom load but it impressed with a 0-watt draw. 

Bedroom

Last were the bedrooms, which don’t host a ton of electronics (sleep hygiene is important and blue light can ruin your rest). There are some phone chargers and lamps, none of which showed any passive draw. 

I also have a humidifier in my room, which I tested despite not typically leaving it plugged in; it also showed no passive draw. The spare room is home to an alarm clock that showed 0 watts of draw, as well as an essential oil diffuser that drew no power when switched off.

Before I wrapped up, I ducked out to test one of the 50-foot LED light strips I have installed outside. They showed a relatively high passive draw of 1.2 watts.

LED light strips outside on the porch to a house

The LED light strips I put up outside my house had a fairly high passive draw.

Alan Bradley/CNET

Ranking my home’s worst offenders

After some fairly exhaustive testing, the surprise “winner” among the energy suckers was the DirecTV cable box. At a passive 19.9 watts, it surpassed every other device by a wide margin, because, as mentioned above, it has DVR and instant-on capabilities (though to be clear, it wasn’t recording anything during my test). At 19.9 watts, this means the box is drawing about 477.6 Watt-hours every day, and 174.3 Kilowatt-hours each year. 

So, roughly, how much is it costing me annually? I pay around 16.4 cents per kWh, so if I were to leave the cable box turned off (without recording), it would cost me a base of $27.89 per year for those 174.3 kWh.

Less surprising was the runner-up, the desktop PC, coming in at 3.1 watts in sleep mode (and 1.8-2 watts fully powered off). What was surprising were some of the zero-watt appliances, especially the big mobile dishwasher, and my 60-inch TCL flatscreen TV.

Here’s the complete list, from highest to lowest draw:

Energy vampire power draw

Device/appliance Power draw (watts)
Cable box (passive mode) 19.9 W
Desktop PC (off/sleep mode) 1.8 – 2 W/3.1 W
Framework 16 laptop (off/sleep mode) 0.5 – 1.3 W/1.9 W
Echo Dot (passive mode) 1.7 W
PlayStation 5 (off/sleep mode) 0.1 W/1.5 W
Nintendo Switch (off/sleep mode) 0.8 W/1.3 W
LED light strip 1.2 W
Microwave 0.5 W
TV (50-inch 4K RCA) 0.3 W
Coffee maker 0.3 W
Air fryer 0.2 W
Printer 0.2 W
Monitor (18-inch, 1080p HP Omen) 0.1 W
TV (60-inch 4K TCL) 0 W
Mobile dishwasher 0 W
Table/floor/touch lamps 0 W
Electric kettle 0 W
Device chargers (unattached) 0 W

Which energy vampires are actually worth slaying?

The unfortunate reality is that a lot of devices need to stay plugged in even when not in active use, at least if you value convenience over some relatively modest savings. 

That includes my worst offender, the cable box. Unplugging it means it can’t record scheduled shows or movies, and it also has to go through a lengthy, annoying boot cycle every time. That results in a 5- to 10-minute delay — not something I’m willing to sit through whenever I want to watch TV in the living room, though I can likely unplug it when I leave for vacation or an extended work trip. Things like fridges, many smart appliances and routers also need to stay connected, for better or for worse.

However, there are several ways you can save on energy bills without seriously disrupting your daily routine. 

For one, you may want to consider fully powering off things like game consoles and PCs rather than leaving them in a state of eternal slumber. This is especially true of older consoles or ones that you don’t use frequently, or if you have a desktop and laptop but find you don’t use one or the other very frequently, consider powering them all the way down.

A cord plugged into a Kill A Watt meter.

Plug the Kill A Watt into the wall, then plug your device into the Kill A Watt and discover its energy use.

Eric Mack/CNET

Other big vampires that I don’t personally own are older AV receivers and antiquated printers, which notoriously aren’t great at regulating power use. Older devices in general should be high on your list, and you may also want to consider unplugging any kitchen appliances that don’t really need to be constantly feeding off the grid. Do you really need your microwave to tell you what time it is?

You can also shave off some use by turning off instant-on features on consoles and TVs where possible, or use smart plugs with scheduling for entertainment centers. Power strips with remote on/off functionality are also a great choice, and replacing older appliances with newer, more efficient models can lead to significant savings over time. 

As my experiment shows, energy vampires are real, but not all of them drain power to the same degree.





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What is Hadoop? 

As data generation grew over time, higher volumes and more formats appeared. To save time, multiple processors were needed to process data. However, due to the network overhead caused, a single storage unit became the bottleneck. As a result, each processor now has a distributed storage facility, which makes data access much simpler. Parallel processing with distributed storage is the term for this system, in which multiple computers run processes on different storages.

This article provides a comprehensive overview of Big Data problems, as well as what Hadoop is, what its components are, and how it can be used. Next, we’ll look at the components of Hadoop to get a better understanding of what it is.

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Why Hadoop?

It’s quick to get Hadoop contagious. Its adoption in one organization may contribute to the adoption of similar practices in other organizations. Handling massive data seems to be much simpler today, thanks to this piece of technology’s robustness and cost-effectiveness. Another great function is the ability to incorporate HIVE into an EMR workflow. It’s extremely easy to start a cluster, install HIVE, and begin running basic SQL analytics in no time. Let’s take a closer look at why Hadoop is so strong.

Key features of Hadoop

1. Flexible:

Since only 20% of data in enterprises is organized and the remaining is unstructured, managing unstructured data that goes unattended is critical. Hadoop is a software platform that handles various kinds of Big Data, whether structured or unstructured, encoded or formatted, or some other kind of data, and makes it usable for decision-making. Hadoop is also easy, appropriate, and schema-free! Though Hadoop is better known for supporting Java programming, the MapReduce technique allows any programming language to be used in Hadoop. Hadoop is better suited for Windows and Linux, but it can also run on BSD and OS X.

2.  Scalable

Hadoop is a flexible framework in the sense that new nodes can be introduced to the system as required without having to change data formats, data loading practices, program writing methods, or even current applications. Hadoop is free and open-source software that runs on commodity hardware. Hadoop is also fault resistant, which ensures that if a node fails or goes out of operation, the machine will simply reallocate work to another place in the data and resume processing as if nothing has happened!

3. Building a more efficient data economy:

Hadoop has revolutionized big data mining and analysis all over the world. Until now, businesses have been concerned with how to handle the constant inflow of data into their applications. Hadoop is more akin to a “dam,” collecting an infinite number of data and generating a great deal of power in the form of related data. Hadoop has fully altered the economics of data storage and analysis!

4. Robust Ecosystem:

Hadoop provides a rather versatile and rich environment that is well-tailored to developers, web start-ups, and other organizations’ computational needs. The Ecosystem is made up of several similar initiatives, including MapReduce, Hive, HBase, Zookeeper, HCatalog, and Apache Pig, which make it capable of delivering a wide range of services.

5. Hadoop is getting more “Real-Time”!

Have you ever wondered how to feed data into a cluster and test it in real-time? It’s a problem for which Hadoop has a solution. Yes, skills are becoming more real-time. It also offers a standardized approach to a diverse range of big data analytics APIs, such as MapReduce, query languages, and database access, among others.

6. Cost-Effective:

With so many wonderful features, the icing on the cake is that Hadoop saves money by adding massively parallel processing to commodity servers, resulting in a significant decrease in the cost per terabyte of storage, making it possible to model all of your files. The basic concept here is to do cost-effective data analysis through the internet!

7.  Upcoming Technologies using Hadoop:

Hadoop is contributing to phenomenal technological advances by bolstering its capability. HBase, for example, is quickly becoming a critical platform for Blob Stores (Binary Large Objects) and Lightweight OLTP (Online Transaction Processing). It’s also been a stable basis for new-school graph and NoSQL databases, as well as enhanced relational databases.

8.  Hadoop is getting cloudy!

Hadoop is becoming hazier! In reality, many companies are synchronizing with cloud storage to handle Big Data. Hadoop is going to be one of the most important cloud computing apps. The number of clusters provided by cloud providers in different industries shows this. As a result, it will soon be in the cloud!

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Components of Hadoop

Enterprise data is generating at an accelerated pace these days, and how we use it for a company’s growth is critical.  With its tremendous support for big data storage and analytics, Hadoop is hitting new heights. Companies all over the world began moving their data to Hadoop to join the early adopters of the technology and get the best out of their data.

Hadoop is a Big Data storage and management system that makes use of distributed storage and parallel processing. It is the most widely used program for dealing with large amounts of data. Hadoop is made up of three components.

  • Hadoop HDFS – Hadoop’s storage unit is the Hadoop Distributed File System (HDFS).
  • Hadoop MapReduce – Hadoop MapReduce is the Hadoop processing unit.
  • Hadoop YARN – Hadoop YARN is a Hadoop resource management unit.

Hadoop Common

As it functions as a channel or a SharePoint for all other Hadoop components, it is regarded as one of the Hadoop core components. Hadoop Common is a set of libraries and utilities that help other Hadoop modules work together. Consider the following scenario: To access HDFS, HBase or Hive must first use the Hadoop Common’s Java archives (JAR files).

Hadoop HDFS

HDFS is Hadoop’s default data storage, and data is saved there before it’s required for processing. The data in HDFS is divided into several units called blocks and distributed throughout the cluster. It generates several replicas of data blocks and distributes them through clusters for consistent and convenient access.

Namenode, Data Node, and Secondary Name Node are the other three key components of HDFS. It employs a Master-Slave architecture paradigm. In this architecture, the Namenode serves as a master node to control the storage system, while the Data node serves as a slave node to manage the Hadoop cluster’s various structures.

HDFS is a file system designed specifically for storing large datasets on commodity hardware. For the full processor, an enterprise version of a server costs about $10,000 per terabyte. If you need to purchase 100 of these enterprise-level servers, the cost would exceed a million dollars. Data nodes in Hadoop can be commodity devices. You won’t have to spend millions on data nodes this way. The word node, on the other hand, has always been an enterprise server.

Features of HDFS

  • Distributed storage is provided.
  • It is possible to implement it on product hardware.
  • Provides data protection.
  • Highly fault-tolerant – if one system breaks down, the data from that machine is transferred to the next.

Master and Slave Nodes

HDFS is composed of master and slave nodes. The master is the name node, while the slaves are the data nodes.

Master and Slave Nodes

The name node is in charge of the data nodes’ operations. It also keeps track of metadata.

The data nodes are responsible for reading, writing, processing, and replicating information. They often relay signals to the name node known as heartbeats. The data node’s status is indicated by these heartbeats.

data nodes

Consider the fact that the name node contains 30TB of data. This data is replicated among the data notes by the name node, which delivers it across the data nodes. The blue, grey and red data are replicated among the three data nodes, as seen in the image above.

By default, data replication takes place three times. This is achieved so that if a commodity machine breaks down, a new machine with the same data can be used to replace it.

In the next section of the What is Hadoop post, we’ll concentrate on Hadoop MapReduce.

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Hadoop MapReduce

Hadoop MapReduce is the Hadoop processing unit. The processing takes place on the slave nodes, and the final output is sent to the master node in the MapReduce approach.

To handle all of the data, a data containing code is used. Concerning the raw data, this coded data is normally very small. To run a heavy-duty operation on computers, you only need to submit a few kilobytes of code.

Apache Hadoop includes MapReduce as a key feature. It allows programmers to handle massive amounts of data while writing programs. MapReduce is a Java program that can process vast volumes of data. Its main function is to divide the data into small, separate bits that can be processed in parallel.

The MapReduce algorithm is made up of two main parts: Map and Reduce. When the Map function completes its mission, the Reduce function begins. The map takes a set of data and converts it into tuples. The Reduce function takes the Map function’s output and combines it with another set of tuples to generate a new set of tuples. Hadoop relies heavily on MapReduce’s parallel processing functionality. It enables big data processing to be performed on several computers in the same cluster.

Hadoop mapreduce

Let’s take a closer look at each feature.

Map Stage:

The input data is converted using the mapper tool. The data can be stored in HDFS in a variety of formats, such as folders or directories. The entire data set is sequentially transferred through the Map Function, which transforms it into tuples. 

Reduce stage:

The data is shuffled and reduced to some extent at this point. It uses the Map function’s output to perform the data processing function. It generates a new output after the reduced operation is completed, which is automatically stored in the Hadoop Distributed File System.

In this article, we’ll focus on Hadoop YARN, which is the next concept we’ll look at.

Hadoop YARN

The YARN’s key concept is to separate the resource control and work scheduling functions into various daemons. YARN is responsible for allocating resources to the Hadoop cluster’s various applications.

Resource manager and Node manager are the two key components of YARN. The data computation system is made up of these two components. The resource manager is in charge of delegating work to all applications in the system, while the node manager is in charge of containers and tracks their resource usage (CPU, disk, memory, and network) and sends the same information to the Resource manager.

Hadoop’s YARN acronym stands for Yet Another Resource Negotiator. It is Hadoop’s resource management unit, and it is used in Hadoop version 2 as a component. 

Hadoop YARN serves as an operating system for Hadoop. It’s a file system that uses HDFS as a foundation.
It’s in charge of handling cluster resources to prevent overloading a single server.
It manages work schedules to ensure that jobs are planned in the right places.

Hadoop YARN

Assume a client computer requires the execution of a query or the retrieval of code for data processing. The resource manager (Hadoop Yarn), who is responsible for the resource allocation and management, receives this job request.

Each node has its node manager in the node section. These node managers are responsible for the nodes and keep track of their resource usage. Physical resources such as RAM, CPU, and hard drives are contained within the containers. The app master requests the container from the node manager whenever a job request is received. The resource is returned to the Resource Manager until the node manager has received it.

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YARN components : (Yet Another Resource Negotiator) 

Hadoop YARN distributes work among its components and keeps them accountable for completing the task at hand. The tasks assigned to the various Core components of YARN are described below.

  • A global Resource manager is in charge of accepting user work submissions and scheduling them by allocating resources.
  • To the Resource manager, a Node manager is a Reporter. Each Node has a node manager who reports back to the Resource Manager on the functionality of each node.
  • Each framework has its Application Master, which aids the Node Manager in executing and monitoring tasks and smoothing out the resource allocation process.
  • The Resource container, which is operated by Node managers and distributed with the system resources allocated to individual applications, is another aspect of YARN.

Conclusion: 

So far, we have focused on what Hadoop is, why Hadoop is necessary, and what are the various Hadoop components that make it up. Thus you have now learned the essential knowledge to understand different components of Hadoop that will assist you when you start working on Hadoop.

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