For example, the word “the” would be compressed even when it appears in words like “there,” “their,” and “then.” With repeated text, you can get some crazy compression ratios. In reality, most text is compressed with keys as small as just a few characters. If you’re interested in a more detailed explanation, this video from Computerphile is pretty helpful. So now, when it gets to (h), it will look back to “howtogeek” and read that instead. In reality, LZ77 doesn’t use a list of keys, but instead replaces the second and third occurrence with a link back in memory: We call compression like this “lossless”-the data you put in is the same as the data you get out.

Then, when it wants to read the text back, it would replace every instance of (h) with “howtogeek,” bringing us back to the original phrase. The LZ77 algorithm would look at this text, realize that it repeats “howtogeek” three times, and change it to this: Take this short piece of text for example: It seeks to remove duplicate words and replace them with a smaller “key” that represents the word. LZ77, on the other hand, is relatively simple and is what we’ll be talking about here. If you want to learn more about it, check out this article on how the code works, or this explainer by Computerphile. Primarily, it uses some fancy math to assign shorter binary codes to individual letters, shrinking file sizes in the process. These two algorithms are “LZ77” and “Huffman coding.” Huffman coding is quite complicated, and we won’t be going into detail on that one here. The most popular libraries for compressing text rely on two compression algorithms, using both at the same time to achieve very high compression ratios. To answer that question would involve explaining some very complicated math, certainly more than we can cover in this article, but you don’t need to understand precisely how it works mathematically to understand the basics.

How do zip files work