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import math
 
def analyze_fanout_impact(record_count, fanout_values):
    """
    Analyze the impact of different fanout values on tree characteristics.
    """
    print(f"Analysis for {record_count:,} records")
    print("=" * 70)
    print(f"{'Fanout':<10} {'Height':<8} {'Leaves':<15} {'Internal':<12} {'Total Size':<12}")
    print("-" * 70)
    
    for f in fanout_values:
        # Calculate tree structure
        leaves = math.ceil(record_count / (f - 1))  # leaf capacity = f-1
        height = 1
        level_nodes = leaves
        internal_nodes = 0
        
        while level_nodes > 1:
            parent_nodes = math.ceil(level_nodes / f)
            internal_nodes += parent_nodes
            level_nodes = parent_nodes
            height += 1
        
        total_nodes = leaves + internal_nodes
        # Assume 8KB pages
        total_size_gb = total_nodes * 8 / (1024 * 1024)
        
        print(f"{f:<10} {height:<8} {leaves:<15,} {internal_nodes:<12,} {total_size_gb:<12.2f} GB")
 
# Analyze for different fanout values
record_count = 1_000_000_000  # 1 billion
fanout_values = [100, 200, 300, 500, 750, 1000]
analyze_fanout_impact(record_count, fanout_values)
 
# Impact of 20% fanout improvement
print("\n" + "=" * 70)
print("Impact of 20% fanout improvement:")
base_fanout = 400
improved_fanout = 480
 
for f, label in [(base_fanout, "Baseline"), (improved_fanout, "Optimized")]:
    leaves = math.ceil(record_count / (f - 1))
    height = math.ceil(math.log(leaves, f)) + 1
    print(f"{label}: Fanout {f} → Height {height}, ~{leaves//1000}K leaf pages")

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def find_common_prefix(keys):
    """Find the longest common prefix among a list of keys."""
    if not keys:
        return ""
    
    prefix = keys[0]
    for key in keys[1:]:
        while not key.startswith(prefix):
            prefix = prefix[:-1]
            if not prefix:
                return ""
    return prefix
 
def prefix_compress_page(keys):
    """
    Simulate prefix compression on a B+-tree page.
    Returns compression statistics.
    """
    if not keys:
        return {"original_size": 0, "compressed_size": 0, "ratio": 0}
    
    # Original size
    original_size = sum(len(k) for k in keys)
    
    # Find common prefix
    prefix = find_common_prefix(keys)
    prefix_len = len(prefix)
    
    # Compressed storage:
    # - 2 bytes for prefix length
    # - prefix itself
    # - For each key: 1 byte suffix length + suffix
    compressed_size = 2 + prefix_len
    for key in keys:
        suffix = key[prefix_len:]
        compressed_size += 1 + len(suffix)
    
    ratio = (original_size - compressed_size) / original_size * 100
    
    return {
        "original_size": original_size,
        "compressed_size": compressed_size,
        "savings": original_size - compressed_size,
        "ratio": ratio,
        "prefix": prefix,
        "prefix_len": prefix_len
    }
 
# Example: URL index
urls = [
    "https://example.com/products/category/electronics/item/12345",
    "https://example.com/products/category/electronics/item/12346",
    "https://example.com/products/category/electronics/item/12347",
    "https://example.com/products/category/electronics/item/12348",
    "https://example.com/products/category/electronics/item/12349",
]
 
result = prefix_compress_page(urls)
print("URL Index Prefix Compression")
print(f"Original size: {result['original_size']} bytes")
print(f"Compressed size: {result['compressed_size']} bytes")
print(f"Savings: {result['savings']} bytes ({result['ratio']:.1f}%)")
print(f"Common prefix: '{result['prefix']}'")
 
# Example: UUID index (worst case - random, no common prefix)
import uuid
uuids = [str(uuid.uuid4()) for _ in range(5)]
uuid_result = prefix_compress_page(uuids)
print(f"\nUUID Compression: {uuid_result['ratio']:.1f}% (minimal savings expected)")

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def compute_minimal_separator(left_max, right_min):
    """
    Compute the minimal separator key between two adjacent child ranges.
    
    The separator must be:
    - > left_max (strictly greater)
    - <= right_min (less than or equal)
    
    We find the shortest prefix of right_min that satisfies these conditions.
    """
    # Find the first character position where they differ
    min_len = min(len(left_max), len(right_min))
    
    for i in range(min_len):
        if left_max[i] < right_min[i]:
            # We can truncate here - prefix up to i (inclusive) is sufficient
            return right_min[:i+1]
        elif left_max[i] > right_min[i]:
            # This shouldn't happen in a valid B+-tree (left_max < right_min)
            raise ValueError("Invalid ordering: left_max >= right_min")
    
    # If we get here, one is a prefix of the other
    # right_min must be longer (since left_max < right_min)
    return right_min[:min_len + 1]
 
def analyze_suffix_truncation(separators):
    """Analyze savings from suffix truncation on separator keys."""
    total_full = 0
    total_truncated = 0
    
    for left_max, right_min in separators:
        minimal = compute_minimal_separator(left_max, right_min)
        full_len = len(right_min)
        truncated_len = len(minimal)
        
        total_full += full_len
        total_truncated += truncated_len
        
        savings = (full_len - truncated_len) / full_len * 100
        print(f"Full: '{right_min[:30]}...' ({full_len})")
        print(f"Minimal: '{minimal}' ({truncated_len})")
        print(f"Savings: {savings:.1f}%\n")
    
    overall_savings = (total_full - total_truncated) / total_full * 100
    print(f"Overall: {total_full} → {total_truncated} bytes ({overall_savings:.1f}% reduction)")
 
# Example: Log entry keys (timestamp + ID)
separators = [
    ("2024-01-15T23:59:59.999Z_id_99999", "2024-01-16T00:00:00.001Z_id_00001"),
    ("2024-01-16T00:59:59.999Z_id_99999", "2024-01-16T01:00:00.001Z_id_00001"),
    ("2024-01-16T01:59:59.999Z_id_99999", "2024-01-16T02:00:00.001Z_id_00001"),
]
 
print("Suffix Truncation Analysis: Time-Series Index")
print("=" * 60)
analyze_suffix_truncation(separators)

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import struct
 
def normalize_integer(value, signed=True, width=8):
    """
    Normalize an integer for ordered byte comparison.
    
    For memcmp to work correctly:
    - Flip sign bit for signed integers (makes negative < positive)
    - Use big-endian byte order (MSB first for proper ordering)
    """
    if signed:
        # Flip the sign bit so memcmp gives correct ordering
        # For 8-byte signed: XOR with 0x8000000000000000
        flip_mask = 1 << (width * 8 - 1)
        adjusted = value ^ flip_mask
        # Handle negative wraparound
        if adjusted < 0:
            adjusted += (1 << (width * 8))
    else:
        adjusted = value
    
    return adjusted.to_bytes(width, byteorder='big')
 
def normalize_string(s, max_len=8):
    """
    Normalize a string for ordered byte comparison.
    Simple approach: truncate/pad to fixed length.
    Production systems use more sophisticated encoding.
    """
    # Encode as UTF-8 and truncate/pad
    encoded = s.encode('utf-8')[:max_len]
    return encoded.ljust(max_len, b'\x00')
 
def normalize_composite_key(timestamp, user_id, txn_id):
    """
    Normalize a composite key: (timestamp, user_id, txn_id)
    All components concatenated in order for single memcmp comparison.
    """
    ts_bytes = normalize_integer(int(timestamp * 1000000), signed=False, width=8)
    user_bytes = normalize_integer(user_id, signed=False, width=4)
    txn_bytes = normalize_integer(txn_id, signed=False, width=8)
    
    return ts_bytes + user_bytes + txn_bytes
 
# Demonstrate that normalized comparison matches semantic comparison
test_integers = [-100, -1, 0, 1, 100]
print("Integer Normalization Test")
print("=" * 50)
normalized = [(n, normalize_integer(n)) for n in test_integers]
 
# Sort by normalized bytes
sorted_by_bytes = sorted(normalized, key=lambda x: x[1])
sorted_values = [x[0] for x in sorted_by_bytes]
print(f"Original order: {test_integers}")
print(f"After byte-sort: {sorted_values}")
print(f"Correct: {sorted_values == sorted(test_integers)}")

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class DictionaryEncoder:
    """
    Simple dictionary encoder for B+-tree keys.
    Used for low-cardinality column values.
    """
    
    def __init__(self):
        self.value_to_code = {}
        self.code_to_value = {}
        self.next_code = 1
    
    def encode(self, value):
        """Encode a value to its dictionary code."""
        if value not in self.value_to_code:
            self.value_to_code[value] = self.next_code
            self.code_to_value[self.next_code] = value
            self.next_code += 1
        return self.value_to_code[value]
    
    def decode(self, code):
        """Decode a dictionary code to its value."""
        return self.code_to_value.get(code)
    
    def bytes_per_code(self):
        """Number of bytes needed for codes."""
        if self.next_code <= 256:
            return 1
        elif self.next_code <= 65536:
            return 2
        else:
            return 4
 
def analyze_dictionary_compression(values):
    """Analyze space savings from dictionary compression."""
    encoder = DictionaryEncoder()
    
    # Original size
    original_size = sum(len(str(v).encode('utf-8')) for v in values)
    
    # Encode all values
    codes = [encoder.encode(v) for v in values]
    
    # Dictionary size
    dict_size = sum(len(str(v).encode('utf-8')) + 4 for v in encoder.value_to_code.keys())
    
    # Compressed size
    bytes_per = encoder.bytes_per_code()
    compressed_size = len(codes) * bytes_per + dict_size
    
    cardinality = len(encoder.value_to_code)
    
    return {
        "original_size": original_size,
        "compressed_size": compressed_size,
        "dict_size": dict_size,
        "cardinality": cardinality,
        "bytes_per_code": bytes_per,
        "ratio": (original_size - compressed_size) / original_size * 100
    }
 
# Example: Country column (low cardinality)
import random
countries = ["United States", "United Kingdom", "Germany", "France", 
             "Japan", "Canada", "Australia", "Brazil", "India", "China"]
 
# 1 million rows with random countries
values = [random.choice(countries) for _ in range(1_000_000)]
 
result = analyze_dictionary_compression(values)
print("Dictionary Compression: Country Column (1M rows)")
print(f"Cardinality: {result['cardinality']} distinct values")
print(f"Original: {result['original_size']:,} bytes")
print(f"Compressed: {result['compressed_size']:,} bytes")
print(f"Dictionary overhead: {result['dict_size']:,} bytes")
print(f"Savings: {result['ratio']:.1f}%")

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/* Fixed-format internal node (traditional) */
struct FixedInternalNode {
    uint16_t num_keys;
    uint32_t child_pointers[MAX_FANOUT];
    char     keys[MAX_FANOUT - 1][MAX_KEY_SIZE];  // Wastes space!
};
 
/* Variable-format internal node (optimized) */
struct VarInternalNode {
    uint16_t num_keys;
    uint16_t free_space_offset;
    
    /* Slot array: offsets to actual key data */
    uint16_t key_offsets[1];  // Dynamic size
    
    /* After key_offsets array: */
    /* - Child pointers (packed) */
    /* - Key data (variable length, grows from end of page) */
};
 
/* Benefits:
 * - Short keys use less space
 * - More entries fit per page
 * - Suffix-truncated separators pack efficiently
 * 
 * Trade-offs:
 * - More complex slot management
 * - Insertion requires shifting
 * - Must track free space
 */

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-- PostgreSQL: Estimate index fanout from statistics
SELECT
    indexrelname as index_name,
    pg_relation_size(indexrelid) as index_bytes,
    idx_scan as index_scans,
    idx_tup_read as tuples_via_index,
    idx_tup_fetch as tuples_fetched,
    -- Estimate pages and entries
    (pg_relation_size(indexrelid) / 8192)::int as index_pages,
    (SELECT reltuples FROM pg_class WHERE oid = indexrelid)::bigint as estimated_entries
FROM pg_stat_user_indexes
WHERE indexrelname LIKE 'idx_%'
ORDER BY pg_relation_size(indexrelid) DESC;
 
-- PostgreSQL: Detailed B-tree statistics (requires pageinspect extension)
CREATE EXTENSION IF NOT EXISTS pageinspect;
 
-- Get B-tree meta page info
SELECT * FROM bt_metap('idx_users_email');
 
-- Get statistics for internal pages (level > 0)
SELECT 
    blkno,
    type,
    live_items,
    dead_items,
    avg_item_size,
    free_size
FROM bt_page_stats('idx_users_email', 1);  -- Page 1 (root)
 
-- MySQL InnoDB: Index statistics
SELECT 
    table_name,
    index_name,
    stat_name,
    stat_value,
    stat_description
FROM mysql.innodb_index_stats
WHERE database_name = 'mydb'
  AND table_name = 'users'
  AND index_name = 'idx_email';
 
-- Oracle: Index statistics
SELECT
    index_name,
    blevel,         -- B-tree level (height - 1)
    leaf_blocks,    -- Number of leaf pages
    distinct_keys,
    avg_leaf_blocks_per_key,
    avg_data_blocks_per_key
FROM user_indexes
WHERE index_name = 'IDX_USERS_EMAIL';