Learning DEFLATE and Huffman Coding by Building Your Own Compressor

Compression is a feature used daily in the world of computing, yet its inner workings are rarely considered. Compress a file and its size shrinks to a fraction; decompress it and it returns exactly, without a single bit of error. An attempt to understand this mechanism by implementing it from first principles has been published.

As reported by wofo on Lobsters via ochagavia.nl, a series that teaches the principles of compression step by step through a custom implementation has been introduced. The article, titled “Let’s build a compressor from scratch” and published on September 2, 2026, reframes compression not as magic but as a re-representation of information, illustrating how specific encoding choices affect file size.

Compression is one of those wonderful things we have grown accustomed to in the computer world. You wave a magic wand and —poof!— a file suddenly shrinks to a fraction of its size!

The article focuses on DEFLATE, the quintessential general-purpose compression method, and its component, Huffman coding. It aims to translate abstract theory into concrete code through the process of readers building a compressor with their own hands.

The Basics of Compression:

Representing Information with Fewer Bytes

The essence of compression lies in rewriting the same information with fewer bytes. The article compares two storage formats using an array of eight boolean values as an example. One is serialization as JSON, written as follows.

[true, false, false, true, false, true, true, false]

This format requires 52 bytes. The other represents true as 1 and false as 0 in a bit string, 10010110 — 8 bits, or 1 byte. While both hold equivalent information, there is a 52-fold difference in size. The latter can be seen as a conversion of the former into a more efficient representation.

This relationship is exactly that of a compressor and decompressor specialized for a particular data format. A program that converts the JSON representation to the binary representation corresponds to a compressor, and the reverse conversion corresponds to a decompressor. Specialized compression is easy to understand but has the limitation of a narrow scope of application.

This idea also applies to the choice of data representation in development workflows. For example, when building container images, how a Dockerfile is written and how layers are handled affects the final size and reproducibility. As shown in Cloud Native Buildpacks Graduates from CNCF, Building Containers Without Dockerfiles, abstracting the build process and ensuring reproducibility overlaps with the thinking behind compression — how to make a representation more efficient.

The Two Pillars of General-Purpose

Compression: DEFLATE

General-purpose compression, which does not depend on a specific format, targets arbitrary byte sequences. The behavior of gzip cited in the article succinctly illustrates this characteristic. A 622KB book file shrinks to 234KB, and a 90MB compiled binary of a Rust program shrinks to 30MB. In contrast, a 54MB MP3 file — already compressed — remains 54MB even after gzip is applied. That already-compressed data cannot be compressed further is a natural consequence of information theory.

The algorithm used by gzip is called DEFLATE. According to the article, DEFLATE combines two main techniques.

The first is the detection and replacement of repeated byte sequences. When a byte sequence that has appeared before appears again, it is replaced with a reference marker indicating its position and length. If the marker is shorter than the original sequence, the difference is the amount saved. For example, an instruction such as “duplicate 15 bytes from position 2397” can represent a long repetition in just a few bytes.

The second is changing code lengths based on the frequency of each byte. By assigning short bit sequences to frequent bytes and long bit sequences to rare bytes, the average code length overall is shortened. This technique is Huffman coding.

These two pillars form the basic structure of general-purpose compression: duplicate elimination and entropy coding. While the former captures redundancy in data structurally, the latter exploits statistical bias. By combining the two, stable compression ratios can be achieved for diverse inputs such as text and binary.

The handling of data duplication and references is also important in a security context. How data is duplicated externally and retained is debated in areas beyond compression. The zero-data-retention design covered in OpenAI Announces Private Safety Processing, Achieving Zero Data Retention offers another perspective on the representation and retention of information, in terms of how to process data without retaining it.

Huffman Coding: Varying Length by Frequency

Of the two elements of DEFLATE, the article describes Huffman coding as “magically interesting.” The series provides a “Huffman playground” where readers can actually build this encoding and observe how it works.

The principle of Huffman coding is straightforward. It counts the occurrences of each byte in the entire input and builds a binary tree based on those frequencies. High-frequency symbols are placed shallow in the tree, low-frequency symbols deep. As a result, frequent symbols are encoded with bit sequences shorter than one byte, and rare symbols with sequences longer than one byte. In many cases, the total number of bits is smaller than with a fixed-length 8-bit code.

Importantly, this code is reversible. With the code table, a bit sequence can be uniquely decoded back to the original byte sequence. The compressor and decompressor must share the same tree, and the code table itself is stored as part of the compressed data. The net compression effect is determined by the balance between the additional size required to describe the code table and the savings from shorter code lengths.

The article’s example of converting boolean values into a bit string can also be seen as an extreme form of Huffman coding. When there are only two possible values, assigning one bit to each is optimal. General-purpose Huffman coding extends this idea to 256 possible byte values.

Why Building It Yourself Matters:

Implementation Deepens Understanding

The “build from scratch” approach provides an understanding that cannot be obtained by simply using existing libraries. In the process of building a compressor yourself, you must directly handle bit-level I/O, construction of code tables, and management of reference markers — processes normally hidden behind abstractions.

The article does not stop at explaining theory; it is structured so readers can get hands-on and verify for themselves. Starting with the contrast between JSON and bit strings, moving to the two stages of DEFLATE, and finally implementing and testing Huffman coding, the flow shows a gradual increase in abstraction. By narrowing the focus from format-specific compression to general-purpose compression and then to the internal encoding, readers can understand the role of each layer separately.

This learning method is also effective for deeply understanding existing development tools and frameworks. By recreating the internal structure yourself, the reasons for performance characteristics and constraints become clear. Why compression ratios depend on the nature of the input data, why MP3s cannot be recompressed, and why text and binary have different compression ratios all become naturally clear by following the implementation.

Applications in Development and Future Challenges

Knowledge of compression is directly relevant to everyday development work. In situations that involve byte sequences — such as log storage, object storage configuration, network transfer, and container registry capacity management — there is always a trade-off between size and speed. If you understand DEFLATE’s two techniques, you can judge in advance which types of data are suitable for compression and which are not.

Compression is also not unrelated to security and legal proceedings. When tracing the route of data duplication and distribution, the presence of compression or encoding complicates verification of identity. As in the case of Take-Two Subpoenas Discord and Microsoft to Identify GTA VI Leaker, where information flow across platforms is tracked, the format in which data was stored and transferred can be an important clue. Grasping the fundamentals of compression is also meaningful as prerequisite knowledge for such digital forensics.

While the series starts with the implementation of Huffman coding, it suggests that it will proceed to reconstruct DEFLATE as a whole. There are many implementation challenges, such as the efficiency of searching for reference markers, optimization of code tables, and handling of bitstream boundaries. Solving these one by one will bring the familiar technology of compression into sharper focus.

Editorial Opinion

In the short term, we expect content that teaches fundamental implementations like building a compressor from scratch to be re-evaluated as material for developer education. As AI-generated code becomes widespread, understanding of low-level topics such as bit manipulation and encoding tends to fade. The experience of tracing DEFLATE and Huffman coding by building them yourself can be seen as building fundamental strength that supports the ability to verify the correctness of generated code and to make design decisions about data formats. Use in internal study groups and new-hire training may expand over the next 3–6 months.

In the long term, we expect the importance of compression to grow further against the backdrop of increasing data volumes and rising transfer costs. Highly textual data such as logs, telemetry, and datasets for model training will continue to increase. Whether engineers who understand the principles of general-purpose compression can choose the appropriate encoding when selecting storage formats and transfer protocols will directly affect infrastructure costs and performance. Over a span of 1–3 years, data design premised on compression is likely to become a standard skill.

As a question from the editorial team, there are points we would like readers to consider.

References

• “Let’s build a compressor from scratch”, by ochagavia.nl by wofo — Lobsters, 2026-09-02T19:54:21.000Z (ARR)
• Source URL: https://ochagavia.nl/blog/lets-build-a-compressor-from-scratch/