Zstandard is an open source file compression library and file format designed to be a fast, safe, transparent compression format suitable for data compression in memory, on disk, or in the transmission of data between computers. Zstandard is a zlib-compatible, deflate-based compression library. It is suitable for data compression in memory, on disk, or in the transmission of data between computers. Zstandard encodes data in a linear sequence of bytes and can be used with the gzip, deflate or zlib file formats.
A Step by Step and Detailed Zstandard Help for the Beginners
Zstandard 1.0 is a compression algorithm that has been used by many startups for managing data. One of the leading organizations that use the compression algorithm is Facebook. It is recognized as one of the leading compression algorithms that deliver best-in-kind performance. The system keeps updating to provide better services.
In the recent updates, the system commits to deliver faster decompression speed and a stronger compression ratio to the users. So, how Zstandard can provide benefits to the data management process? How Facebook is benefiting through Zstandard? You can find the answers to these questions and Zstandard help in the following section.
A Short and Precise Zstandard Help
Zstandard, also known as zstd in the shorter format, is a super-speedy lossless algorithm for compression. The algorithm aims to provide real-time compression at a faster speed and higher compression ratio.
The algorithm comes with two rich libraries for the programmers, and they are FSE and Huff0. For the programmers, the biggest challenge is to set a proper ratio between compression speed and compression trade-off.
Developers can also change the decompression speed with Zstandard. However, decompression speed cannot be changed to a great extent. It remains more or less the same in all settings. For handling small data, there is a special mode offered by Zstandard. The mode has been termed dictionary compression.
The reason behind such naming is the facility available to create dictionaries from any sample set. If you want to learn Zstandard Python, you should learn about the compression achieved by the system by providing faster decompression and compression speeds.
Steps for Installing Zstandard
On a Linux distribution, you can easily install Zstandard. One should compile the system from the sources, though a few tools are needed to be installed before installing Zstandard. Make sure that the necessary development tools are present on your system. When they have been installed, you can follow the steps provided below.
$ sudo apt update && sudo apt install build-essential #Ubuntu/Debian
# yum group install “Development Tools” #CentOS/REHL
# dnf groupinstall “C Development Tools and Libraries” #Fedora 22+
After installing all the required tools, you need to start the process of downloading the source package. For downloading the source package, you should move to the local repo directory. After visiting the directory, you have to develop a binary. Finally, you need to install the system as instructed below.
$ cd ~/Downloads
$ git clone https://github.com/facebook/zstd.git
$ cd zstd
$ sudo make install
Learning a Few Zstandard Commands
For Zstandard decompress, you need to learn a few crucial commands. The beginners should focus on learning a few basic commands to understand the algorithm for compression in a more accurate manner. In the following section, you can find some top examples for learning the Zstandard commands.
1. Removing the Source File
After conducting an operation, users may need to remove the source file. For removing the default source file after compression or decompression, you can use the –rm option. In the following section, you can find the command for the operation.
$ ls etcher-1.3.1-x86_64.AppImage
$ zstd –rm etcher-1.3.1-x86_64.AppImage
$ ls etcher-1.3.1-x86_64.AppImage
2. Creating a Compression File
Since Zstandard is a compression algorithm, you should know the process to compress with the algorithm. What is the code for compression with Zstandard? You can provide the filename to compress it. The alternative method is using the –z flag for the compression process. The latter one is the default process, and thus many programmers prefer the second process.
$ zstd etcher-1.3.1-x86_64.AppImage
$ zstd -z etcher-1.3.1-x86_64.AppImage
3. Decompression of the Files
In the above section of this article, you have observed Zstd compression example. Along with compression, you should also know the decompression process using Zstandard. For decompression simple method is used. In compression, programmers use –z flag, while decompression can be done using the –d flag. In the following section, you can find the code.
$ zstd -d etcher-1.3.1-x86_64.AppImage.zst
$ unzstd etcher-1.3.1-x86_64.AppImage.zst
4. Altering the Compression Speed
Zstandard is a reputed compression algorithm for offering excellent compression speed. It provides a better speed for compression than other similar algorithms. You can find a compression speed ratio at 1:10 with Zstandard. However, programmers should know that the default compression speed for the system is 1.
You have to enhance the speed through codes. Using –fast option, you can alter the compression speed. When you select a high number between 1 and 10, you will get a higher speed for compression. The code for monitoring compression speed is provided below.
$ zstd –fast=10 etcher-1.3.1-x86_64.AppImage
5. Testing Integrity of the Compressed Files
Using ZSTD, you can also monitor the integrity of the compressed files. For monitoring the integrity, you need to use –t flag. With the following code, you can perform this simple operation.
$ zstd -t etcher-1.3.1-x86_64.AppImage.zst
6. Displaying Information of Compressed Files
You can also check the information that a compressed file contains. With simple coding, you can check the information of a compressed file. It helps the users to decide on decompressing a compressed file. For checking information on a compressed file, you should use -1 flag. Alternatively, you should use the following coding for the process.
$ zstd -l etcher-1.3.1-x86_64.AppImage.zst
Steps for Comparing Compression
You have learned the process to install Zstandard above. Now, you need to know the steps for comparing compression. There are different compression algorithms. So, it is important to compare their performances for better implementation of the compression algorithms.
Typically, programmers and experts believe that Zstandard is the faster compression algorithm. Compressing large data takes a massive time. So, it becomes challenging for the developers to cope with the time. Using Zstandard will ease your worry about wasting precious time. It can speed up the compression process dramatically. For comparing the compression algorithms, you have to consider the following factors.
- Compression Speed: Speed is measured by time. Zstandard compression algorithm offers ten speed limits for compressing files. The default speed starts at 1, while the highest speed is 10.
- Compression Ratio: So, what is compression ratio? How can you determine the ratio? The original size compared to the compressed size of the file has been known as the compression ratio. If the compression ratio is 1 or more, the compression is regarded as satisfactory.
- Decompression Speed: Using Zstandard, you can enhance compression speed. It also provides faster decompression speed. However, you cannot regulate the decompression. The speed of decompression will remain more or less identical for all files.
Scalability of Zstandard
Zstd decompresses and compress processes are faster than other compression algorithms. Thus, this algorithm has been more frequently used by users today. Large organizations prefer using the algorithm for file compression and decompression.
As stated above, Facebook is one of the leading organizations that have adopted Zstd. However, faster compression is not the only reason for deploying Zstandard. There are some other benefits too. Scalability is one of the most highlighting benefits of Zstd.
Since Zstandard is a scalable algorithm, it comes with the ability to adapt arrays of requirements with precision. Most algorithms come with levels that are based on the time – space trade-off. With a higher level, better compression can be achieved. But, a higher-level also leads to loss of the compression speed.
You get more compressed files with a high amount of time invested in the process. This is where Zstandard is different from others. It comes with speed levels one to ten. You can choose the higher speed level, and it would have a minimal impact on the size of the compressed files. In terms of scalability, Zstandard gives you the following features. For Zstandard help, analyzing the following data is crucial.
- With the same compression ratio, zstd offers three to five times better compression speed.
- At the same compression speed, you can get a smaller compressed file from Zstandard.
- The decompression process is also faster than compression algorithms. On average, decompression is two times faster than others.
- At a high compression ratio, it can scale easily. At the same time, it does not harm the faster decompression speed.
Comparison of Memory
Before using Zstd java, you should know the difference between Zstandard and zlib in trms of memory. When you consider Zlib, you will find that the algorithm is limited to a 32KB window. In 90’s, people used to prefer 32KB systems.
Now, they prefer using advanced systems with higher memory. Zstandard does not feature any inherent limit. It can even recognize terabytes, though it happens rarely. Zstandard is more compatible with advanced systems. Thus, developers prefer using zstd over zlib.
Another major feature of Zstandard is the branchless design. You will find that people use advanced CPUs today. They are powerful computing machines with high frequency. Many compression algorithms find difficulty in working with such systems. But, Zstandard comes with a branchless design. It is compatible with different kinds of advanced CPUs. Within a critical loop, the branchless design model works brilliantly.
In this post, we will be learning about Z scores and standardization. By learning about both of these topics, you will learn how to calculate exact proportions using the standard normal distribution.
Standard Normal Distribution
The standard normal distribution is a special type of normal distribution with a mean of zero and a standard deviation of one. Because of this, the standard normal distribution is always centered at zero and has intervals that increase by one. Each number on the horizontal axis corresponds to a Z score. A Z score tells us how many standard deviations and observations are from the mean(μ). For example, a Z score of negative 2 tells me that I am 2 standard deviations to the left of the mean, and a z score of 1.5 tells me that I am one and a half standard deviations to the right of the mean. Most importantly, a Z score allows us to calculate how much area that specific Z score is associated with. We can find out that exact area using a Z score table, also known as the standard normal table.
This table tells us the total amount of area contained to the left side of any value of Z for this table; the top row and the first column correspond to Z values, and all the numbers in the middle correspond to areas.
According to the table, a Z score of negative 1.95 has an area of 0.0256 to its left.
To say this more formally, we can say that the proportion of Z less than negative 1.95 is equal to 0.0256. We can also use the standard normal table to determine the area to the right of any Z value. All we have to do is take one minus the area that corresponds to the Z value.
To determine the area to the right of a Z score of 0.57. We have to find the area that corresponds to this set value and then subtracted from one. According to the table, the Z score of 0.57 has an area of 0.57 to the left of it, so one minus 0.7157 gives us an area of 0.2843, and that is our answer.
We can do this because we have to remember that the normal distribution is a density curve, and it always has a total area equal to 1 or 100 percent.
You can also use the Z score table to do a reverse lookup, which means you can use the table to see what Z score is associated with a specific area. So if I wanted to know what value of Z corresponds to an area of 0.8461 to the left of it, all we have to do is find 0.8461 on the table and see what Z’s value corresponds to. We see that it corresponds to a Z value of 1.02.
The special thing about the standard normal distribution is that any normal distribution can be transformed into it. In other words, any normal distribution with any value of μ and Sigma can be transformed into the standard normal distribution where you have a mean of zero and a standard deviation of one. This conversion process is called standardization. The benefit of standardization is that it allows us to use the Z score table to calculate exact areas for any given normally distributed population with any value of MU or Sigma. Standardization involves using this formula.
This formula says that the Z score is equal to an observation X minus the population mean (μ) divided by the population standard deviation sigma. So suppose that we gather data from last year’s final chemistry exam and found that it followed a normal distribution with a mean of 60 and a standard deviation of 10; if we were to draw the normal distribution, we would have 60 located at the center of the distribution because it is the value of the mean and each interval would increase by ten since that is the value of the standard deviation.
To convert this distribution to the standard normal distribution, we will use the formula. The value of μ is equal to 60, and the value of Sigma is equal to 10. We can then take each value of X and plug it into the equation. If I plug in 60, I will get a value of zero. If I plug in 50, I will get a value of negative one. If I plug in 40, I will get a value of negative two. If we do this for each value, you can see that we end up with the same values as a standard normal distribution. When doing this conversion process, the mean of the normal distribution will always be converted to zero, and the standard deviation will always correspond to a value of one. It’s important to remember that this will happen with any normal distribution, no matter what value the μ and Sigma are.
Now, if I asked you what proportion of students score less than 49 on the exam, it is this area that we are interested in.
However, the proportion of X less than 49 is unknown until we use the standardization formula. After plugging in 49 into this formula, we end up with a value of negative 1.1. As a result, we will be looking for the proportion of Z less than negative 1.1. And finally, we can use the Z score table to determine how much area is associated with a Z score.
According to the table, there’s an area of 0.1357 to the left of this Z value. This means that the proportion of Z less than negative 1.1 is 0.1357. This value is, in fact, the same proportion of individuals that scored less than 49 on the exam; as a result, this is the answer. Let’s do one more example.
When measuring all students’ heights at a local university, it was found that it was normally distributed with a mean of 5.5 feet and a standard deviation of 0.5 feet. What proportion of students is between 5.81 feet and 6.3 feet tall?
Before we solve this question, it’s always a good habit to first-rate down important information. So we have a μ of 5.5 feet and a sigma of 0.5 feet. We are also looking for the proportion of individuals between 5.81 feet and 6.3 feet tall. This corresponds to this highlighted area.
To determine this area, we need to standardize the distribution, so we will use the standardization formula.
z=x – μσ
Plugging in 5.81 to this formula gives us a Z score of 0.62, and plugging in 6.3 into the formula gives us a Z score of 1.6. According to the standard normal table, the Z score, 0.62, corresponds to an area of 0.734, and there’s a score of 1.6 corresponds to an area of 0.9452; to find the proportion of values between 0.62 and 1.6, we must subtract the smaller area from the bigger area. So 0.9452 minus 0.7324 gives us 0.2128. As a result, the proportion of students between 5.81 feet and 6.3 feet tall is 0.2128.