compact-tree
Compact, read-only nested dictionary backed by a DAWG-style radix trie.
CompactTree stores a nested Python dict using a path-compressed radix trie with DAWG-style key/value deduplication, enabling low-memory random access and efficient serialization.
Features
- Memory-efficient: DAWG-style deduplication via two
MarisaTrieinstances (one for keys, one for values) - Flat variant:
CompactTreeFlatstores all leaf paths as flattuple[str, …] → val_idmappings — no intermediate nodes,get_path()only, ~38% faster thanCompactTree.get_path()for full-path lookups - Fast lookups: Plain list-indexing over parallel arrays — no rank/select overhead
- C-accelerated lookups: Optional
_marisa_extC extension providesTrieIndexandTreeIndexfor ~5–10× faster uncached queries - Multi-level traversal:
get_path(*keys)descends multiple levels in a single C call, eliminating intermediate_Nodeallocations - Shared trie:
shared_trie=Trueuses a singleMarisaTriefor both keys and values, saving memory when the vocabularies overlap - High-performance builds: 7.3s for a 6.2M-leaf, 173K-key tree (v2.0.0)
- Fast serialization: 14/s at 173K keys, 77 MiB files
- Serializable: Save and load from disk with efficient binary format
- Gzip compression: Optional gzip compression for smaller files on disk
- Pickle support: Fully serializable via Python's
picklemodule - Read-only: Optimized for lookup-heavy workloads
- Storage-agnostic: Works with local files and remote storage via
fsspec - Dict-like interface: Supports
[],in,len(), iteration,repr(), andstr()
Installation
pip install savov-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
print(len(tree)) # 2
print(list(tree)) # ["a", "b"]
# String representations
print(str(tree)) # {'a': {'x': '1', 'y': '2'}, 'b': '3'}
print(repr(tree)) # CompactTree.from_dict({'a': {'x': '1', ...}, 'b': '3'})
# Serialize to file
tree.serialize("tree.ctree")
# Load from file
loaded_tree = CompactTree("tree.ctree")
# Serialize with gzip compression
tree.serialize("tree.ctree.gz", compression="gzip")
loaded_gz = CompactTree("tree.ctree.gz", compression="gzip")
# Shared trie: keys and values share one MarisaTrie (saves memory when vocabularies overlap)
tree_shared = CompactTree.from_dict({"a": "b", "b": "a"}, shared_trie=True)
# Pickle support
import pickle
data = pickle.dumps(tree)
tree2 = pickle.loads(data)
# Convert back to plain dict
plain_dict = loaded_tree.to_dict()
# Multi-level lookup in a single C call (when C extension is compiled)
result = tree.get_path("a", "x") # equivalent to tree["a"]["x"] but faster
CompactTreeFlat — flat full-path lookup
When you always know the full path depth at lookup time and don't need intermediate
navigation, use CompactTreeFlat for ~38% faster get_path() calls:
from compact_tree_flat import CompactTreeFlat
d = {"a": {"x": "1"}, "b": {"x": "2", "y": "3"}}
tree = CompactTreeFlat.from_dict(d)
tree.get_path("a", "x") # "1" — single dict lookup + restore_key, no trie traversal
tree.get_path("b", "y") # "3"
("b", "x") in tree # True — __contains__ takes a tuple
len(tree) # 3 (total leaf paths)
tree.to_dict() # {"a": {"x": "1"}, "b": {"x": "2", "y": "3"}}
# Serialize / deserialize (CTFlt v1 binary format)
tree.serialize("tree.ctflat")
tree2 = CompactTreeFlat("tree.ctflat")
tree.serialize("tree.ctflat.gz", compression="gzip")
tree3 = CompactTreeFlat("tree.ctflat.gz", compression="gzip")
import pickle
tree4 = pickle.loads(pickle.dumps(tree))
How It Works
MarisaTrie
MarisaTrie is a compact word-to-index mapping backed by a path-compressed radix trie with subtree word counts for minimal perfect hashing (MPH). CompactTree uses two MarisaTrie instances — one for keys and one for values — to provide DAWG-style deduplication.
- Path compression: single-child edges are merged for compactness
- Dense indexing: every unique word gets an index in
[0, N) - Reverse lookup: recover the original word from its index
- Bulk enumeration:
to_dict()returns{word: index}for all words in O(N); the first call after construction returns a pre-built mapping (zero trie traversals) and frees it immediately - Per-instance LRU cache:
functools.lru_cacheonindex()lookups, automatically sized to the vocabulary; cache size preserved through serialization
At query time, navigation uses plain Python parallel lists (_node_labels, _node_children, _node_counts, _node_terminal) — no rank/select overhead.
DAWG-Style Deduplication
- Keys are collected, sorted, and deduplicated via a
MarisaTrie - Values (leaves) are similarly deduplicated via a second
MarisaTrie - Edge labels store integer IDs rather than raw strings
- The same key or value appearing at multiple levels is stored only once
Architecture
CompactTree
|
+-- _child_start : array.array('I') child start offsets (CSR), one per node
+-- _child_count : array.array('I') child counts (CSR), one per node
+-- elbl : array.array('I') edge labels (uint32 key ids, one per node)
+-- vcol : array.array('I') value column (uint32: value id or 0xFFFFFFFF for internal nodes)
+-- _key_trie : MarisaTrie key vocabulary (word <-> dense index)
+-- _val_trie : MarisaTrie value vocabulary (word <-> dense index)
+-- _shared_trie : bool True when a single MarisaTrie covers keys and values
+-- _c_tree : TreeIndex | None optional C-level traversal helper (None if extension not built)
CompactTreeFlat
|
+-- _key_dict : dict[tuple[str, ...], int] full path → val_id
+-- _val_trie : MarisaTrie value vocabulary
+-- _val_vocab_size: int lru_cache size hint
MarisaTrie additionally exposes:
_c_index(TrieIndex) — C-level lookup helper, set by_build_c_index()when the extension is available._first_char_maps/_prefix_counts/_node_label_lens— navigation tables built by_build_navigation_tables()for O(1) child dispatch and MPH index accumulation.
Each non-root node v (0-indexed) occupies a slot in both elbl (its edge label / key id) and vcol (its value id, or the sentinel 0xFFFFFFFF for internal nodes).
Child navigation uses CSR (Compressed Sparse Row) arrays: _child_start[v] is the start offset and _child_count[v] is the count of children of node v.
Binary Format
CompactTree — format v6
Magic : 5 bytes "CTree"
Version : 8 bytes uint64 LE (always 6)
Header : 8 × 8 bytes shared_flag, keys_trie_len, val_trie_len, child_count_len,
vcol_len, elbl_len, key_vocab_size, val_vocab_size
Payload : keys_trie_bytes | val_trie_bytes (empty when shared)
| child_count_bytes | vcol_bytes | elbl_bytes
When shared_flag=1 (built with shared_trie=True), val_trie_len=0 and no
value-trie blob is written; on load both _key_trie and _val_trie point to the
same deserialized MarisaTrie.
Files written in v5 or earlier are not supported.
CompactTreeFlat — format CTFlt v1
Magic : 5 bytes "CTFlt"
Version : 8 bytes uint64 LE (always 1)
Header : 4 × 8 bytes n_paths, val_trie_len, paths_buf_len, val_vocab_size
Payload : val_trie_bytes | paths_buf
Dependencies
bitarray— Bit-packed boolean arrays (used for terminal flags in MarisaTrie serialization)fsspec— Filesystem abstraction for local and remote storage
Testing
pytest test_compact_tree.py test_marisa_trie.py
Benchmarks
Run performance benchmarks with pytest-benchmark:
pytest test_compact_tree.py::TestLoadPerformance --benchmark-only -v
See BENCHMARK_RESULTS.md for detailed results and OPTIMIZATIONS.md for optimization history.
Performance
Benchmark: 3-level nested dict, shape {L0=9, L1=4, L2=173,000}, 6.2M leaf entries.
Windows 11, Intel Core Ultra 9 285H, Python 3.14.3, 10 s timed loops.
| Target | Lookups / s | µs / lookup | vs dict |
|---|---|---|---|
dict[k0][k1][k2] |
228,998 | 4.4 | 1.0× (baseline) |
CompactTreeFlat.get_path() |
194,220 | 5.1 | 0.85× |
CompactTree.get_path() (C ext) |
141,224 | 7.1 | 0.62× |
CompactTree[k0][k1][k2] (no cache) |
124,384 | 8.0 | 0.54× |
See PERFORMANCE.md for full comparisons including PyArrow.
CompactTreeFlat.get_path() is ~38% faster than CompactTree.get_path() because a
full-path lookup reduces to one Python dict.__getitem__ + one restore_key call.
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.
Metadata
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