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import os
import json
import hashlib
from typing import Tuple, Optional, Dict, Any
from pathlib import Path
from tqdm import tqdm
class ChunkManager:
"""
Manages chunked reading of large JSONL files.
This class handles:
- File scanning to count total lines without loading all text
- Estimating chunk boundaries based on file size
- Tracking which line ranges belong to each chunk
"""
def __init__(self, jsonl_path: str, chunk_size_gb: float = 5.0,
samples_per_chunk: Optional[int] = None,
enable_metadata_cache: bool = True, chunk_cache_dir: str = ".cache/chunks",
max_samples: Optional[int] = None):
"""
Initialize ChunkManager.
Args:
jsonl_path: Path to JSONL file
chunk_size_gb: Approximate chunk size in GB (ignored if samples_per_chunk is set)
samples_per_chunk: Number of samples per chunk (takes precedence over chunk_size_gb)
enable_metadata_cache: Enable caching of file scan metadata
chunk_cache_dir: Directory to store cache files
max_samples: Limit total samples to at most this many (if total_lines > max_samples)
Raises:
FileNotFoundError: If JSONL file doesn't exist
ValueError: If file is empty
"""
self.jsonl_path = Path(jsonl_path)
self.chunk_size_bytes = int(chunk_size_gb * 1024 ** 3) # Convert GB to bytes
self.max_samples = max_samples # Limit total samples if specified
print (f"Initializing ChunkManager for {self.jsonl_path} with target chunk size {chunk_size_gb} GB")
if samples_per_chunk is not None:
print(f" Overriding chunk size with {samples_per_chunk} samples per chunk")
if max_samples is not None:
print(f" Limiting dataset to {max_samples} samples")
self.samples_per_chunk = samples_per_chunk # If set, overrides GB-based chunking
self.enable_metadata_cache = enable_metadata_cache
self.chunk_cache_dir = Path(chunk_cache_dir)
if not self.jsonl_path.exists():
raise FileNotFoundError(f"JSONL file not found: {self.jsonl_path}")
self.file_size_bytes = os.path.getsize(self.jsonl_path)
self.file_mtime = os.path.getmtime(self.jsonl_path)
if self.file_size_bytes == 0:
raise ValueError("JSONL file is empty")
# Will be populated by _scan_file()
self.total_lines = 0
self.effective_lines = 0
self.line_sizes = [] # bytes per line
self.valid_line_offsets = [] # byte offset of each VALID JSON line (for seeking)
self.chunk_line_ranges = [] # [(start_line, end_line), ...]
# Try to load from cache first
cache_loaded = False
if self.enable_metadata_cache:
cache_loaded = self._load_metadata_cache()
# If cache not used, scan the file
if not cache_loaded:
self._scan_file()
self._compute_chunk_ranges()
# Save metadata cache for future runs
if self.enable_metadata_cache:
self._save_metadata_cache()
else:
# Cache stores file scan metadata. Recompute chunk ranges for the
# current training config so samples_per_chunk/max_samples changes
# are honored without rescanning the large JSONL file.
self._compute_chunk_ranges()
def _get_cache_path(self) -> Path:
"""Get the metadata cache file path for this JSONL file."""
# Create a hash of the file path to use as cache filename
file_hash = hashlib.md5(str(self.jsonl_path.absolute()).encode()).hexdigest()[:8]
cache_file = self.chunk_cache_dir / f"{file_hash}.metadata.json"
return cache_file
def _load_metadata_cache(self) -> bool:
"""
Load metadata from cache if it exists and is valid.
Returns:
True if cache was loaded successfully, False otherwise
"""
cache_file = self._get_cache_path()
if not cache_file.exists():
return False
try:
with open(cache_file, 'r', encoding='utf-8') as f:
cache_data = json.load(f)
# Validate cache: check file hasn't changed
if (cache_data.get('file_size') != self.file_size_bytes or
cache_data.get('file_mtime') != self.file_mtime or
cache_data.get('jsonl_path') != str(self.jsonl_path.absolute())):
return False
# Load cached data
self.total_lines = cache_data.get('total_lines', 0)
self.line_sizes = cache_data.get('line_sizes', [])
self.valid_line_offsets = cache_data.get('valid_line_offsets', [])
# Convert loaded lists back to tuples for chunk_line_ranges
chunk_ranges = cache_data.get('chunk_line_ranges', [])
self.chunk_line_ranges = [tuple(r) for r in chunk_ranges]
self.chunk_size_bytes = cache_data.get('chunk_size_bytes', self.chunk_size_bytes)
print(f"✓ Loaded scan metadata from cache: {cache_file.name}")
print(f" Found {self.total_lines:,} valid JSON lines in {len(self.chunk_line_ranges)} chunks")
return True
except Exception as e:
# If cache loading fails, fall back to scanning
return False
def _save_metadata_cache(self) -> None:
"""Save metadata cache to file."""
cache_file = self._get_cache_path()
cache_file.parent.mkdir(parents=True, exist_ok=True)
cache_data = {
'jsonl_path': str(self.jsonl_path.absolute()),
'file_size': self.file_size_bytes,
'file_mtime': self.file_mtime,
'total_lines': self.total_lines,
'line_sizes': self.line_sizes,
'valid_line_offsets': self.valid_line_offsets,
'chunk_line_ranges': self.chunk_line_ranges,
'chunk_size_bytes': self.chunk_size_bytes,
}
try:
# Write atomically using a temp file + rename
temp_file = cache_file.with_suffix('.tmp')
with open(temp_file, 'w', encoding='utf-8') as f:
json.dump(cache_data, f, indent=2)
temp_file.replace(cache_file)
print(f" Saved scan metadata to cache: {cache_file.name}")
except Exception as e:
print(f" ⚠ Warning: failed to save cache: {e}")
def _get_chunk_cache_dir(self) -> Path:
"""Get the directory for storing cached chunk data for this JSONL file."""
file_hash = hashlib.md5(str(self.jsonl_path.absolute()).encode()).hexdigest()[:8]
chunk_dir = self.chunk_cache_dir / "chunks" / file_hash
return chunk_dir
def _get_chunk_cache_file(self, chunk_num: int) -> Path:
"""Get the cache file path for a specific chunk."""
chunk_dir = self._get_chunk_cache_dir()
return chunk_dir / f"chunk_{chunk_num:06d}.jsonl"
def _get_chunk_index_file(self) -> Path:
"""Get the index file that lists all cached chunks."""
chunk_dir = self._get_chunk_cache_dir()
return chunk_dir / "index.json"
def extract_and_cache_chunks(self) -> Dict[str, Any]:
"""
Extract chunks from the original JSONL file and save them as separate cached files.
This is optional and should be called manually if you want to pre-cache chunks
for faster repeated access. It can significantly speed up training but uses more disk space.
Returns:
Dictionary with cache information:
- 'cache_dir': path to cache directory
- 'num_chunks': number of chunks cached
- 'total_size_gb': total size of cached chunks
"""
chunk_dir = self._get_chunk_cache_dir()
chunk_dir.mkdir(parents=True, exist_ok=True)
print(f"💾 Extracting {len(self.chunk_line_ranges)} chunks to cache...")
total_size = 0
for chunk_num in range(len(self.chunk_line_ranges)):
cache_file = self._get_chunk_cache_file(chunk_num)
# Skip if already cached
if cache_file.exists():
total_size += os.path.getsize(cache_file)
continue
# Read chunk and save to cache file
chunk_examples = self.read_chunk(chunk_num, _from_cache=False)
with open(cache_file, 'w', encoding='utf-8') as f:
for obj in chunk_examples:
f.write(json.dumps(obj) + '\n')
total_size += os.path.getsize(cache_file)
if (chunk_num + 1) % max(1, len(self.chunk_line_ranges) // 10) == 0:
print(f" - Cached {chunk_num + 1}/{len(self.chunk_line_ranges)} chunks...")
# Write index file
index_data = {
'jsonl_path': str(self.jsonl_path.absolute()),
'num_chunks': len(self.chunk_line_ranges),
'chunk_ranges': self.chunk_line_ranges,
}
with open(self._get_chunk_index_file(), 'w', encoding='utf-8') as f:
json.dump(index_data, f, indent=2)
print(f"✓ Cached {len(self.chunk_line_ranges)} chunks ({total_size / (1024**3):.2f} GB)")
return {
'cache_dir': str(chunk_dir),
'num_chunks': len(self.chunk_line_ranges),
'total_size_gb': total_size / (1024**3),
}
def clear_chunk_cache(self, keep_metadata: bool = False) -> None:
"""
Clear cached chunk data.
Args:
keep_metadata: If True, only remove chunk files, keep the metadata cache
"""
chunk_dir = self._get_chunk_cache_dir()
if chunk_dir.exists():
import shutil
shutil.rmtree(chunk_dir)
print(f"✓ Cleared chunk cache: {chunk_dir}")
if not keep_metadata:
cache_file = self._get_cache_path()
if cache_file.exists():
cache_file.unlink()
print(f"✓ Cleared metadata cache: {cache_file}")
def _scan_file(self) -> None:
"""
Scan JSONL file to count lines and track offsets.
This reads the file once to:
- Count total valid JSON lines
- Record byte offset of each VALID line for seeking
- Estimate size per line
"""
print(f"📖 Scanning JSONL file: {self.jsonl_path}")
print(f" File size: {self.file_size_bytes / (1024**3):.2f} GB")
self.valid_line_offsets = []
current_offset = 0
valid_lines = 0
try:
with open(self.jsonl_path, 'r', encoding='utf-8') as f:
for line in tqdm(f, desc="Scanning JSONL", unit=" lines"):
# Skip empty lines - don't count toward line numbers
if not line.strip():
current_offset += len(line.encode('utf-8'))
continue
try:
json.loads(line)
# Valid JSON line - record its starting byte offset
self.valid_line_offsets.append(current_offset)
valid_lines += 1
line_bytes = len(line.encode('utf-8'))
self.line_sizes.append(line_bytes)
except json.JSONDecodeError:
# Skip invalid JSON lines - don't count toward line numbers
pass
current_offset += len(line.encode('utf-8'))
except Exception as e:
raise ValueError(f"Error scanning JSONL file: {e}")
self.total_lines = valid_lines
if self.total_lines == 0:
raise ValueError("No valid JSON lines found in JSONL file")
print(f"✓ Found {self.total_lines:,} valid JSON lines")
# Calculate average line size
avg_line_size = sum(self.line_sizes) / len(self.line_sizes) if self.line_sizes else 0
print(f" Average line size: {avg_line_size:.2f} bytes")
print(f" Chunk size target: {self.chunk_size_bytes / (1024**3):.2f} GB")
def _compute_chunk_ranges(self) -> None:
"""
Compute line ranges for each chunk based on target chunk size.
If samples_per_chunk is set, uses that. Otherwise, divides file
based on chunk_size_bytes. If max_samples is set, limits chunks to cover
at most max_samples lines.
"""
if self.total_lines == 0:
self.chunk_line_ranges = []
return
# Apply max_samples limit to effective line count
self.effective_lines = self.total_lines
if self.max_samples is not None:
self.effective_lines = min(self.total_lines, self.max_samples)
# Determine lines per chunk
if self.samples_per_chunk is not None:
# Use explicit sample count
lines_per_chunk = self.samples_per_chunk
else:
# Use GB-based calculation
avg_line_size = sum(self.line_sizes) / len(self.line_sizes) if self.line_sizes else 1
lines_per_chunk = max(1, int(self.chunk_size_bytes / avg_line_size))
chunk_ranges = []
start_line = 0
# Create chunks up to self.effective_lines (honors max_samples)
while start_line < self.effective_lines:
end_line = min(start_line + lines_per_chunk, self.effective_lines)
chunk_ranges.append((start_line, end_line))
start_line = end_line
self.chunk_line_ranges = chunk_ranges
self.num_chunks = len(chunk_ranges)
print(f" Divided into {self.num_chunks} chunks (covering {self.effective_lines:,} lines)")
def get_chunk_indices(self, chunk_num: int) -> Tuple[int, int]:
"""
Get (start_line, end_line) for a given chunk number.
Args:
chunk_num: Chunk number (0-indexed)
Returns:
Tuple of (start_line, end_line) where end_line is exclusive
Raises:
IndexError: If chunk_num is out of range
"""
if chunk_num < 0 or chunk_num >= len(self.chunk_line_ranges):
raise IndexError(f"Chunk {chunk_num} out of range [0, {len(self.chunk_line_ranges)-1}]")
return self.chunk_line_ranges[chunk_num]
def read_chunk(self, chunk_num: int, _from_cache: bool = True) -> list[dict]:
"""
Read a specific chunk and return parsed JSON objects.
If chunk cache is available, reads from cache. Otherwise reads from original JSONL
using file.seek() for O(1) lookup instead of O(n) scanning.
Args:
chunk_num: Chunk number (0-indexed)
_from_cache: Internal parameter to force reading from original (used during cache extraction)
Returns:
List of parsed JSON objects from that chunk
Raises:
IndexError: If chunk_num is out of range
ValueError: If JSON parsing fails
"""
# Try to read from cache first (if it exists)
if _from_cache:
cache_file = self._get_chunk_cache_file(chunk_num)
if cache_file.exists():
examples = []
try:
with open(cache_file, 'r', encoding='utf-8') as f:
for line in f:
if line.strip():
try:
obj = json.loads(line)
examples.append(obj)
except json.JSONDecodeError:
pass
return examples
except Exception as e:
print(f" ⚠ Warning: failed to read chunk from cache, falling back to original: {e}")
# Read from original JSONL file using seek optimization
start_line, end_line = self.get_chunk_indices(chunk_num)
examples = []
with open(self.jsonl_path, 'r', encoding='utf-8') as f:
# Seek to the byte offset of the start line
# This is O(1) instead of O(start_line) iteration
if start_line < len(self.valid_line_offsets):
f.seek(self.valid_line_offsets[start_line])
else:
# Fallback if valid_line_offsets not available (shouldn't happen)
f.seek(0)
current_line = start_line
# Read lines from start_line to end_line
for line in f:
# Skip empty lines
if not line.strip():
continue
# Stop when we've read enough lines
if current_line >= end_line:
break
try:
obj = json.loads(line)
examples.append(obj)
current_line += 1
except json.JSONDecodeError:
# Skip invalid JSON lines, but don't increment line counter
# This maintains alignment with line numbering from scan
pass
return examples
@property
def num_chunks(self) -> int:
"""Return number of chunks."""
return len(self.chunk_line_ranges)
@num_chunks.setter
def num_chunks(self, value: int) -> None:
"""Set number of chunks (internal use)."""
self._num_chunks = value
def __repr__(self) -> str:
"""String representation."""
return (
f"ChunkManager(file={self.jsonl_path.name}, "
f"size={self.file_size_bytes/(1024**3):.2f}GB, "
f"lines={self.effective_lines:,}, "
f"chunks={self.num_chunks})"
)
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