map-with-tree 0.0.13

A Python library for reading and writing compressed, sorted key-value stores with efficient lookup using a B-tree-like structure.

Map With Tree

A Python library for reading and writing compressed, sorted key-value stores with efficient lookup using a B-tree-like structure.

Features

• Efficient Storage: Data is compressed using zstandard (zstd) with configurable compression levels
• Fast Lookups: Built-in B-tree index for O(log n) key lookups
• Fast Iteration: Sequential block iteration without tree traversal for O(n) scanning
• Sorted Keys: Keys are automatically sorted during finalization for efficient range queries
• Data Integrity: MD5 hash of all entries is computed and stored for verification
• Flexible Types: Support for multiple key and value types including bytes, strings, integers, floats, and JSON
• Memory Efficient: Block-based compression and caching minimize memory usage
• Simple API: Pythonic interface with context managers and dict-like operations

Installation

pip install map_with_tree

Requires Python >= 3.8 and zstd >= 1.5.

Quick Start

Writing Data

import map_with_tree

# Create a new map file with string values
with map_with_tree.open("data.mwt", "w", types=("bytes", "string")) as writer:
    writer.set(b"key_1", "value_1")
    writer.set(b"key_2", "value_2")
    writer.set(b"key_3", "value_3")

Reading Data

import map_with_tree

# Open and read from a map file
with map_with_tree.open("data.mwt") as reader:
    # Get a value by key
    value = reader[b"key_1"]
    
    # Check if key exists
    if b"key_2" in reader:
        print("Key exists!")
    
    # Get with default value
    value = reader.get(b"key_99", default="not found")
    
    # Iterate over all entries
    for key, value in reader:
        print(f"{key}: {value}")
    
    # Get file metadata
    print(f"Total entries: {len(reader)}")
    print(f"Header: {reader.header}")

API Reference

Opening Files

map_with_tree.open(path, mode="r", **kwargs)

• path: File path for the map file
• mode: "r" for reading, "w" for writing
• **kwargs: Additional options for writing (see Writer Options)

Writer Options

MapWithTreeWriter(
    path,
    header=None,              # Custom header metadata (dict)
    types=("bytes", "bytes"), # Tuple of (key_type, value_type)
    keys_per_node=128,        # Number of keys per B-tree node
    block_size=64*1024,       # Block size for compression (64KB default)
    compression_level=3       # zstd compression level (0-22)
)

Supported Types

• bytes: Raw bytes (default)
• string or str: UTF-8 encoded strings
• int or i64: 64-bit signed integer
• uint or u64: 64-bit unsigned integer
• i8, i16, i32: Signed integers (8, 16, 32 bit)
• u8, u16, u32: Unsigned integers (8, 16, 32 bit)
• float or f64: 64-bit float
• f32: 32-bit float
• json: JSON-serializable objects
• struct:format: Custom struct format (e.g., "struct:<IIf" for two unsigned ints and a float)

Writer Methods

writer.set(key, value)        # Add a key-value pair
writer.finalize()             # Finalize the file (called automatically on context exit)
writer.close()                # Close file handles

Reader Methods

reader[key]                   # Get value by key (raises KeyError if not found)
reader.get(key, default=None) # Get value with default
key in reader                 # Check if key exists
len(reader)                   # Get number of entries
iter(reader)                  # Iterate over (key, value) pairs in insertion order
reader.sorted_keys()          # Iterate over keys in sorted order (tree traversal)
reader.header                 # Access header metadata
reader.close()                # Close file handle

File Format

Map With Tree (.mwt) files consist of:

1. Magic Header: 8-byte signature (mwt\0\0\0\0\1)
2. Header Offset: 8-byte pointer to compressed header
3. Data Blocks: Compressed blocks containing key-value pairs with zstd
4. B-tree Index: Tree structure pointing into blocks for efficient key lookups
5. Compressed Header: JSON metadata with file statistics

Dual Access Modes

The format supports two efficient access patterns:

• Random Access: Use the B-tree index for O(log n) lookup of specific keys
• Sequential Iteration: Read blocks sequentially for O(n) full scans without tree traversal

This design enables:

• Sequential writes for optimal I/O performance during creation
• Minimal memory usage during both reading and writing
• Fast random access through the B-tree index
• Fast sequential iteration by reading blocks in order
• Efficient compression with block-level granularity

Examples

Large Dataset

import map_with_tree
import uuid

# Write 100,000 entries
with map_with_tree.open("large.mwt", "w", types=("bytes", "string")) as writer:
    for i in range(100000):
        key = f"key_{i:06d}".encode()
        value = uuid.uuid4().hex
        writer.set(key, value)

# Read and check compression
with map_with_tree.open("large.mwt") as reader:
    print(f"Entries: {len(reader)}")
    uncompressed = reader.header["uncompressed_size"]
    compressed = reader.header["compressed_size"]
    print(f"Compression ratio: {compressed / uncompressed:.2%}")

Structured Data with JSON

import map_with_tree

with map_with_tree.open("users.mwt", "w", types=("string", "json")) as writer:
    writer.set("user_1", {"name": "Alice", "age": 30, "city": "NYC"})
    writer.set("user_2", {"name": "Bob", "age": 25, "city": "SF"})

with map_with_tree.open("users.mwt") as reader:
    user = reader[b"user_1"]
    print(f"{user['name']} is {user['age']} years old")

Custom Struct Types

import map_with_tree

# Values are tuples of (unsigned int, unsigned int, float)
with map_with_tree.open("metrics.mwt", "w", types=("bytes", "struct:<IIf")) as writer:
    writer.set(b"metric_1", (100, 200, 3.14))
    writer.set(b"metric_2", (150, 250, 2.71))

with map_with_tree.open("metrics.mwt") as reader:
    count1, count2, ratio = reader[b"metric_1"]
    print(f"Counts: {count1}, {count2}, Ratio: {ratio}")

Efficient Iteration

The format stores both keys and values in the data blocks, enabling fast sequential iteration without traversing the B-tree:

import map_with_tree

# Iterate through all entries efficiently
with map_with_tree.open("data.mwt") as reader:
    # Sequential iteration reads blocks in order
    for key, value in reader:
        process(key, value)
    
    # This is faster than tree traversal for full scans
    # because it only decompresses blocks without tree lookups

The iteration order is the insertion order (before sorting), which allows you to:

• Process all entries efficiently in O(n) time
• Scan through data without random access overhead
• Stream process large datasets with minimal memory

Sorted Key Iteration

If you need to iterate over keys in sorted order, use the sorted_keys() method which traverses the B-tree:

import map_with_tree

with map_with_tree.open("data.mwt") as reader:
    # Get keys in sorted order via tree traversal
    for key in reader.sorted_keys():
        value = reader[key]
        process(key, value)
    
    # Or just get the sorted keys
    all_sorted_keys = list(reader.sorted_keys())

Key differences between iteration modes:

• iter(reader): Returns (key, value) pairs in insertion order, reads blocks sequentially
• sorted_keys(): Returns keys only in sorted order, traverses the B-tree structure
• Use regular iteration for full scans, use sorted_keys() for sorted access or range queries

Performance Tips

1. Adjust block size: Larger blocks (e.g., 256KB) improve compression but use more memory
2. Tune compression level: Lower levels (1-3) for speed, higher (10-22) for size
3. Choose appropriate types: Use native types (int, float) instead of strings when possible
4. Batch writes: Add all entries before finalizing to ensure optimal tree structure
5. Keys per node: Increase for larger datasets to reduce tree height