savov-compact-tree 0.2.0

Compact, read-only nested dictionary backed by succinct data structures

compact-tree

Compact, read-only nested dictionary backed by succinct data structures.

CompactTree stores a nested Python dict using a LOUDS-encoded trie with DAWG-style key/value deduplication, enabling low-memory random access and efficient serialization.

Features

• Memory-efficient: Uses succinct data structures (LOUDS trie + DAWG-style deduplication)
• Fast lookups: O(1) rank/select operations via Poppy bit vectors
• Serializable: Save and load from disk with efficient binary format
• Read-only: Optimized for lookup-heavy workloads
• Storage-agnostic: Works with local files and remote storage via fsspec

Installation

pip install compact-tree

Or install from source:

git clone https://github.com/andrey-savov/compact-tree.git
cd compact-tree
pip install -e .

Quick Start

from compact_tree import CompactTree

# Build from a nested dict
tree = CompactTree.from_dict({
    "a": {
        "x": "1",
        "y": "2"
    },
    "b": "3"
})

# Access like a normal dict
print(tree["a"]["x"])   # "1"
print(tree["b"])        # "3"
print("a" in tree)      # True

# Serialize to file
tree.serialize("tree.ctree")

# Load from file
loaded_tree = CompactTree("tree.ctree")

# Convert back to plain dict
plain_dict = loaded_tree.to_dict()

How It Works

LOUDS (Level-Order Unary Degree Sequence)

LOUDS is a succinct tree representation that encodes an ordered tree into a single bit string using roughly 2n bits for n nodes (close to the information-theoretic minimum).

Encoding rule: Traverse the tree in breadth-first (level) order. For each node, write d 1-bits (where d is the node's number of children) followed by a single 0-bit.

Example for a root with children A (2 kids) and B (0 kids):

root  ->  1 1 0      (2 children, then 0-terminator)
A     ->  1 1 0      (2 children)
B     ->  0          (leaf)

Navigation relies on rank and select queries:

Operation	Description
first_child(v)	Find the (v-1)-th 0, check next position
next_sibling(v)	Find the 1-bit for node v, check next position

DAWG-Style Deduplication

• Keys are collected, sorted, and deduplicated into a global vocabulary
• Values (leaves) are similarly deduplicated into a value table
• Edge labels store integer IDs rather than raw strings
• Same key/value appearing multiple times is stored only once

Architecture

CompactTree
  |
  +-- louds    : _LOUDS        bit-vector tree topology (Poppy rank/select)
  +-- elbl     : memoryview    edge labels  (uint32 key ids, 4 bytes per node)
  +-- vcol     : memoryview    value column (uint32: value id or 0xFFFFFFFF for internal nodes)
  +-- _keys_buf: memoryview    length-prefixed UTF-8 key strings
  +-- val      : memoryview    length-prefixed UTF-8 value strings

Binary Format (v2)

Magic   : 5 bytes   "CTree"
Version : 8 bytes   uint64 LE (always 2)
Header  : 5 x 8 bytes  lengths of: keys, values, louds, vcol, elbl
Payload : keys_bytes | val_bytes | louds_bytes | vcol_bytes | elbl_bytes

Dependencies

• bitarray — Mutable bit arrays
• succinct (Poppy) — Rank/select in O(1)
• fsspec — Filesystem abstraction for local and remote storage

Testing

pytest test_compact_tree.py

Contributing

Contributions are welcome! Please see CONTRIBUTING.md for details.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Based on LOUDS (Level-Order Unary Degree Sequence) tree representation
• Uses Poppy rank/select implementation from the succinct library
• Inspired by DAWG (Directed Acyclic Word Graph) compression techniques