How In-Memory Computing Works

In-memory computer science is about two things : making computing faster and scaling it to potentially support petabytes of in-memory data. In-memory computing leverages two key technologies : random-access memory ( RAM ) storehouse and parallelization .

Speed: RAM Storage

The foremost key is that in-memory computing takes the data from your magnetic disk drives and moves it into RAM. The hard drive is by far the slowest share of your waiter. A typical arduous drive is literally a spin harrow, like an old- fashion turntable. It has many moving parts, and it spins in a vacuum where the arm of the turntable physically scans across the phonograph record to read your data. In addition, moving data from your harrow to RAM for serve is time consume, which adds more delays to the travel rapidly at which you can process data. meanwhile, RAM is the second-fastest component in your server. merely the processor is faster .
With RAM, there are no moving parts. Memory is precisely a nick. In physical terms, an electrical sign reads the information stored in RAM. It works at the accelerate of electricity, which is the speed of light up. When you move data from a disk to RAM storage, your computer runs anywhere from five thousand to a million times faster .
The human beware has a hard time grasping that kind of rush. We are talking about nanoseconds, milliseconds, and microseconds. A effective doctrine of analogy is that traditional calculation is like a banana slug crawling through your garden at 0.007 miles per hour while in-memory calculate is like an F-18 champion k traveling at 1,190 miles per hour, or twice the accelerate of sound. In other words, disk drives are truly, truly behind. And when you copy all of your data from disk and put it into RAM, computing becomes very, in truth flying.

You can look at it like a chef in a restaurant. The chef needs ingredients to cook his meals : that ‘s your data. The ingredients might be in the chef ‘s refrigerator or they might be ten miles down the road at the grocery store. The refrigerator is like RAM storehouse : The chef can immediately access the ingredients he needs. When he ‘s done with the ingredients and the meal is finished, he puts the leftovers back in the refrigerator, all at the lapp time. The grocery store storehouse is like harrow storehouse. The chef has to drive to the shop to get the ingredients he needs. Worse, he has to pick them up one at a clock time. If he needs cheese, garlic, and pasta, he has to make one trip to the grocery store memory for the cheese, bring it back, and use it. then he has to go through the hale process again for the garlic and the pasta. If that is n’t enough, he has to drive the leftover ingredients back to the grocery store again, one by one, right after he ‘s done using each of them .
But that ‘s not all. Assume you could make a phonograph record drive that was adenine fast as RAM, alike to flash drives. The system that traditional calculate uses to look for the data on a hard harrow – processor to RAM to controller to disk – would silent make it much slower than in-memory calculation .
To return to our exemplar, let ‘s say there are two chefs : one representing in-memory calculate and the other traditional calculate. The chef representing in-memory calculate has his refrigerator right following to him and he besides knows precisely where everything is on the shelves. interim, the chef representing traditional calculate does n’t know where any of the ingredients are in the grocery store shop. He has to walk down all of the aisles until he finds the tall mallow. then he has to walk down the same aisles again for the garlic, then the pasta, and indeed on. That ‘s the difference in efficiency between RAM and disk storage .

RAM versus Flash

Flash storage was created to replace a harrow drive. When it ‘s used for that purpose, it is besides called a solid-state device, or SSD. SSDs are made of silicon and are five to ten times faster than phonograph record drives. however, both flaunt memory and harrow drives are attached to the same control in your calculator. even when you use news bulletin, you hush have to go through the lapp process of take and writing from a disk. The central processing unit goes to RAM, RAM goes to the restrainer, and the control retrieves the data from the magnetic disk .
Flash accesses the data faster than disk, but it still uses the lapp decelerate process to get the datum to the processor. furthermore, because of the implicit in limitation in flash ‘s forcible design, it has a finite number of reads and writes before it needs to be replaced. Modern RAM, on the other hand, has unlimited life and takes up less space than flash. Flash may be five to ten times faster than a standard harrow drive but RAM is up to a million times faster than the harrow. Combined with the early benefits, there ‘s no comparison .

Scale: Parallelization

RAM handles the accelerate of in-memory computer science. But the scalability of the technology comes from parallelization. Parallelization came about in the early 2000s to solve a different problem : the insufficiency of 32-bit processors. By 2012, most servers had switched to 64-bit processors which can handle a distribute more data. But in 2003, 32-bit processors were coarse and they were very limited. They could n’t manage more than four gigabytes of RAM memory at a time. tied if you put more RAM on the computer, the 32-bit processor could n’t see it. But the demand for more ram repositing was growing anyhow.

The solution was to put data into RAM across a fortune of different computers. Once it was broken down like this, a central processing unit could address it. The cluster of computers looked like it was one application running on one calculator with lots of RAM. You split up the data and the tasks, you use the collective RAM for storehouse, and you use all the computers for work. That was how you handled a fleshy load in the 32-bit world and it was called parallelization or massively parallel action ( MPP ) .
When 64-bit processors were released, they could handle more or less an inexhaustible total of RAM. Parallelization was no long necessity for its original use. But in-memory calculate saw a different way to take advantage of it : scalability .
flush though 64-bit processors could handle a lot more data, it was still impossible for a unmarried computer to support a billion users. But when you distributed the work load across many computers, that kind of corroborate was possible. Better, if the number of users increased, all you had to do was add a few more computers to grow with them .
painting a row of six computers. You could have thousands of computers but we ‘ll use six for this exercise. These computers are connected through a network, so we call them a bunch. now imagine you have an application that will draw a lot of traffic, excessively much traffic to store all of the data on one computer. With parallelization, you take your application and break its data into pieces. then you put one piece of it in calculator 1, another slice in calculator 2, and so on until the data is distributed optimally across the bunch. Your single application runs on the whole bunch of computers. When the cluster gets a request for data, it knows where that data is and processes the information in RAM from there. The datum does n’t move around the room it does in traditional calculate .
even better, you can replicate specific parts of your data on different computers in the same bunch. In our exercise, let ‘s say the data on computer 6 is in high demand. You can add another calculator to the bunch that carries the same datum. That way, not only can you handle things faster, but if calculator 6 goes down, the extra one just takes over and carries on as usual .
If you tried to scale up like this with a single calculator, it would get more and more expensive. At the end of the day, it would placid slow you down. With parallelization, in-memory calculate allows you to scale to demand linearly and without limits.

Let ‘s return to the chef doctrine of analogy, where a computer processor is a chef and memory storage is the chef ‘s stove. A customer comes in and orders an appetizer. The chef cooks the appetizer on his one stave right field off and the customer is glad .
now what happens when 20 customers order appetizers ? The one chef with his one stave ca n’t handle it. That 20th customer is going to wait three hours to get her appetizer. The solution is to bring in more chefs with more stoves, all of them trained to cook the appetizer the lapp manner. The more customers you get, the more chefs and stoves you bring into the picture so that no one has to wait. And if one stave breaks, it ‘s no big bargain : plenty of other stoves in the kitchen can take its place .
The Internet has created a flush of scale that would have been unheard of just 15 or 20 years ago. Parallelization gives in-memory computing the power to scale to fit the world .

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