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Brotli

Compresses web content with dictionary references and entropy coding.

Quick Facts

Compact metadata for implementation and discovery.

Brotli combines LZ-style matching, a built-in dictionary, context modeling, and entropy coding for strong text compression.

dictionary referenceprobability modellossless compressionsymbol representationlossless decodingstream encoding

Quick facts

Runtime and memory behavior at a glance.

Best

O(n)

Average

O(n)

Worst

O(nw)

Space

O(w)

Use Brotli when you need to use a sliding window and static dictionary.
Use it for web asset compression with inputs like byte stream.
Use it when the data has repetition, predictable structure, or symbol frequency imbalance.
Use it when the Lossless Compression tradeoff is acceptable for the problem size.

Avoid it when small inputs or already compressed data would make metadata larger than the savings.
Do not treat the O(n) average case as a guarantee; check the O(nw) worst case.
Avoid it when the required output is not brotli compressed stream.

Input: text = 'AAAABBBCCDAA'
Output: compressed representation with repeated runs or shorter codes
Explanation: Brotli: The algorithm replaces predictable or repeated structure with a more compact representation.

Keep the input contract explicit: Byte stream; Compression level.
Use this step as the implementation spine: Search for repeated sequences.
Then preserve the next state change: Use dictionary entries when helpful.
Keep encoder and decoder tests paired so every compressed sample round-trips exactly.

Higher Brotli quality settings can be much slower to encode but often compress better.
Forgetting that the decoder needs the same model, dictionary, or metadata used by the encoder.
Returning brotli compressed stream without handling empty, invalid, or boundary inputs.