It is pretty apparent that our future looks more and more digital in all aspects of our lives. To accomplish this digital wave, not only will large amounts of power be needed, but things like network bandwidth and copious amounts of space for disk storage will also be needed. This is where string compression algorithms come into play.
This blog explains how we can improve the efficiency of the aforementioned resources, using “compression” with the help of .NET and C#. First, let us see some theoretical parts of the “compression” algorithm and, later, the live example.
We have an option to compress and decompress using System.IO.Compression. As a result, the compressed values will result in a decent reduction in bytes.
What is the String Compression Algorithm?
As the name specifies, a string compression algorithm is a reduction in size due to applying force on an object. When it comes to the data part, compressing data into smaller formats without loss of data is paramount.
How to choose a Compression algorithm?
Different string compression algorithms have different levels of effectiveness. As a developer, we should be aware of the following factors while choosing the required algorithm:
- Time taken to compress a data type
- Amount of space saved after compressing, and;
- Amount of loss after compressing
Also Read, The Curious Case of Rails Association
Compression Algorithms using GZip & Brotli
To include the string compression algorithm, we need .NET 5 framework and above. Utilizing this framework, we can access two compression algorithms: GZip and Brotli. GZip is a lossless algorithm used for data compression that includes various checks for detecting any issues in the data. Creators optimized GZip for uncompressed data.
The Brotli algorithm is also a lossless data compression algorithm that operates on HTML, JavaScript, and CSS. Brotli is considered to contain more advanced logic than GZip, and fortunately, most browsers support it. It also offers a better result in data compression than the GZip algorithm. Let’s see some live examples.
The first method is GZipStream and the output of this logic in a real-time example.
The above image shows where the number of bytes got reduced to nearly half the original size.
- Input String: hello-world
- Input Byte: 22 (Before Compression)
- Output Byte: 10 (After Compression)
Adding to that, the above is just a bit of sample code to show how it works. We can also write extra codes and extend them to various methods based on our requirement, where they must compress data to a large scale.
Let’s see another method of compression using “BrotliStream.”
Like GzipStream logic, BrotliStream can also be expanded by writing some extra code and extending it to various methods based on our requirements, where the data is to be compressed very efficiently.
Theoretically, we get a more efficient result by using GzipStream versus BrotliEncoder.
We can also test the same logic using a file reader and based on our input requirements, this could help reduce a large amount of power, network bandwidth, and high disk space as mentioned earlier.
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