diff --git a/docetl/checkpoint_manager.py b/docetl/checkpoint_manager.py new file mode 100644 index 00000000..eae204c1 --- /dev/null +++ b/docetl/checkpoint_manager.py @@ -0,0 +1,720 @@ +""" +Flexible checkpoint manager for DocETL pipelines. + +This module provides storage and retrieval of intermediate datasets +using either JSON or PyArrow format. PyArrow offers better compression +and faster I/O for large datasets, while JSON provides human-readable +checkpoints and simpler debugging. +""" + +import json +import os +import shutil +from typing import Any, Dict, List, Optional, Tuple + +import pandas as pd +import pyarrow as pa +import pyarrow.parquet as pq + + +class CheckpointManager: + """ + Manages checkpoints for DocETL pipeline operations using JSON or PyArrow format. + + This class provides flexible storage and retrieval of intermediate datasets, + supporting both JSON (human-readable, default) and PyArrow (efficient, compressed) + storage formats. Users can choose the format based on their needs. + """ + + def __init__(self, intermediate_dir: str, console=None, storage_type: str = "json"): + """ + Initialize the checkpoint manager. + + Args: + intermediate_dir: Directory to store checkpoint files + console: Rich console for logging (optional) + storage_type: Storage format - "json" (default) or "arrow" + """ + self.intermediate_dir = intermediate_dir + self.console = console + self.storage_type = storage_type.lower() + + if self.storage_type not in ["json", "arrow"]: + raise ValueError( + f"Invalid storage_type '{storage_type}'. Must be 'json' or 'arrow'" + ) + + self.config_path = ( + os.path.join(intermediate_dir, ".docetl_intermediate_config.json") + if intermediate_dir + else None + ) + + # Ensure the intermediate directory exists + if intermediate_dir: + os.makedirs(intermediate_dir, exist_ok=True) + + @classmethod + def from_intermediate_dir( + cls, intermediate_dir: str, console=None, storage_type: Optional[str] = None + ): + """ + Create a CheckpointManager from an intermediate directory path. + + If storage_type is not specified, automatically detects the most common format + in the directory (prefers arrow if both formats exist equally). + + Args: + intermediate_dir: Path to the intermediate directory containing checkpoints + console: Rich console for logging (optional) + storage_type: Storage format - "json", "arrow", or None for auto-detection + + Returns: + CheckpointManager instance + """ + if storage_type is None: + storage_type = cls._detect_storage_type(intermediate_dir) + + return cls(intermediate_dir, console=console, storage_type=storage_type) + + @staticmethod + def _detect_storage_type(intermediate_dir: str) -> str: + """ + Detect the primary storage type used in an intermediate directory. + + Args: + intermediate_dir: Path to the intermediate directory + + Returns: + Detected storage type ("json" or "arrow"), defaults to "json" if unclear + """ + if not os.path.exists(intermediate_dir): + return "json" # Default for new directories + + json_count = 0 + parquet_count = 0 + + # Count checkpoint files of each type + for root, dirs, files in os.walk(intermediate_dir): + for file in files: + if file.endswith(".json") and not file.startswith("."): + json_count += 1 + elif file.endswith(".parquet"): + parquet_count += 1 + + # Prefer arrow if more parquet files, or if equal and both exist + if parquet_count > json_count or ( + parquet_count > 0 and parquet_count == json_count + ): + return "arrow" + else: + return "json" + + def _get_checkpoint_path( + self, step_name: str, operation_name: str, storage_type: Optional[str] = None + ) -> Optional[str]: + """Get the file path for a checkpoint.""" + if not self.intermediate_dir: + return None + + storage = storage_type or self.storage_type + extension = "parquet" if storage == "arrow" else "json" + + return os.path.join( + self.intermediate_dir, step_name, f"{operation_name}.{extension}" + ) + + def _find_existing_checkpoint( + self, step_name: str, operation_name: str + ) -> Optional[Tuple[str, str]]: + """Find existing checkpoint, checking both JSON and Parquet formats. + + Returns: + Tuple of (file_path, format) if found, None otherwise + """ + # Check current storage type first + current_path = self._get_checkpoint_path(step_name, operation_name) + if current_path and os.path.exists(current_path): + return current_path, self.storage_type + + # Check the other format for backward compatibility + other_type = "json" if self.storage_type == "arrow" else "arrow" + other_path = self._get_checkpoint_path(step_name, operation_name, other_type) + if other_path and os.path.exists(other_path): + return other_path, other_type + + return None + + def _log(self, message: str) -> None: + """Log a message if console is available.""" + if self.console: + self.console.log(message) + + def save_checkpoint( + self, step_name: str, operation_name: str, data: List[Dict], operation_hash: str + ) -> None: + """ + Save a checkpoint using the configured storage format. + + Args: + step_name: Name of the pipeline step + operation_name: Name of the operation + data: Data to checkpoint + operation_hash: Hash of the operation configuration + """ + if not self.intermediate_dir: + return + + checkpoint_path = self._get_checkpoint_path(step_name, operation_name) + + # Create directory if it doesn't exist + os.makedirs(os.path.dirname(checkpoint_path), exist_ok=True) + + # Save based on storage type + if self.storage_type == "arrow": + self._save_as_parquet(checkpoint_path, data) + else: # json + self._save_as_json(checkpoint_path, data) + + # Update the configuration file with the hash + self._update_config(step_name, operation_name, operation_hash) + + format_name = "PyArrow" if self.storage_type == "arrow" else "JSON" + self._log( + f"[green]✓ [italic]Checkpoint saved ({format_name}) for operation '{operation_name}' " + f"in step '{step_name}' at {checkpoint_path}[/italic][/green]" + ) + + def _save_as_json(self, checkpoint_path: str, data: List[Dict]) -> None: + """Save checkpoint data as JSON.""" + with open(checkpoint_path, "w") as f: + json.dump(data, f) + + def _sanitize_for_parquet(self, data: List[Dict]) -> List[Dict]: + """Sanitize data to make it compatible with PyArrow/Parquet serialization.""" + import json + + def sanitize_value(value): + """Recursively sanitize a value for PyArrow compatibility.""" + if isinstance(value, dict): + if not value: # Empty dict + return {"__empty_dict__": True} + return {k: sanitize_value(v) for k, v in value.items()} + elif isinstance(value, list): + if not value: # Empty list + return ["__empty_list__"] + # Check if list has mixed types or contains None - serialize as JSON string if so + has_none = any(item is None for item in value) + if len(value) > 1: + types = set( + type(item).__name__ for item in value if item is not None + ) + if len(types) > 1 or ( + has_none and len(types) >= 1 + ): # Mixed types or has None with other types + return f"__mixed_list_json__:{json.dumps(value)}" + return [sanitize_value(item) for item in value] + elif value is None: + return "__null__" + else: + return value + + def sanitize_record(record): + """Sanitize a single record.""" + if not isinstance(record, dict): + return record + return {k: sanitize_value(v) for k, v in record.items()} + + return [sanitize_record(record) for record in data] + + def _desanitize_from_parquet(self, data: List[Dict]) -> List[Dict]: + """Restore original data structure from sanitized Parquet data.""" + import json + + import numpy as np + + def desanitize_value(value): + """Recursively restore original value structure.""" + # Handle numpy arrays (from pandas conversion) + if isinstance(value, np.ndarray): + # Convert to list first, then check for empty list markers + value_list = value.tolist() + if value_list == ["__empty_list__"]: + return [] + return [desanitize_value(item) for item in value_list] + elif isinstance(value, str) and value.startswith("__mixed_list_json__:"): + # Restore mixed-type list from JSON + json_str = value[len("__mixed_list_json__:") :] + return json.loads(json_str) + elif isinstance(value, dict): + if value == {"__empty_dict__": True}: + return {} + return {k: desanitize_value(v) for k, v in value.items()} + elif isinstance(value, list): + if value == ["__empty_list__"]: + return [] + return [desanitize_value(item) for item in value] + elif value == "__null__": + return None + elif pd.isna(value): # Handle pandas NaN values + return None + else: + return value + + def desanitize_record(record): + """Desanitize a single record.""" + if not isinstance(record, dict): + return record + return {k: desanitize_value(v) for k, v in record.items()} + + return [desanitize_record(record) for record in data] + + def _save_as_parquet(self, checkpoint_path: str, data: List[Dict]) -> None: + """Save checkpoint data as Parquet with data sanitization.""" + if not data: + # Handle empty data case + empty_table = pa.Table.from_arrays([], names=[]) + pq.write_table(empty_table, checkpoint_path, compression="snappy") + return + + # Sanitize data to make it PyArrow-compatible + sanitized_data = self._sanitize_for_parquet(data) + + try: + df = pd.DataFrame(sanitized_data) + table = pa.Table.from_pandas(df) + pq.write_table(table, checkpoint_path, compression="snappy") + except Exception as e: + # If sanitization still doesn't work, raise the error + raise RuntimeError( + f"Failed to serialize data to Parquet format even after sanitization. " + f"This indicates a more fundamental incompatibility. Original error: {str(e)}" + ) + + def load_checkpoint( + self, step_name: str, operation_name: str, operation_hash: str + ) -> Optional[List[Dict]]: + """ + Load a checkpoint if it exists and is valid. + + Args: + step_name: Name of the pipeline step + operation_name: Name of the operation + operation_hash: Expected hash of the operation configuration + + Returns: + List of dictionaries if checkpoint exists and is valid, None otherwise + """ + if not self.intermediate_dir: + return None + + # Check if config file exists + if not self.config_path or not os.path.exists(self.config_path): + return None + + # Load and validate configuration + try: + with open(self.config_path, "r") as f: + config = json.load(f) + except (json.JSONDecodeError, IOError): + return None + + # Check if the hash matches + if config.get(step_name, {}).get(operation_name) != operation_hash: + return None + + # Find existing checkpoint (checks both formats) + checkpoint_info = self._find_existing_checkpoint(step_name, operation_name) + if not checkpoint_info: + return None + + checkpoint_path, format_type = checkpoint_info + + try: + # Load based on the format of the existing file + if format_type == "arrow": + data = self._load_from_parquet(checkpoint_path) + else: # json + data = self._load_from_json(checkpoint_path) + + format_name = "PyArrow" if format_type == "arrow" else "JSON" + self._log( + f"[green]✓[/green] [italic]Loaded checkpoint ({format_name}) for operation '{operation_name}' " + f"in step '{step_name}' from {checkpoint_path}[/italic]" + ) + + return data + + except Exception as e: + self._log(f"[red]Failed to load checkpoint: {e}[/red]") + return None + + def _load_from_json(self, checkpoint_path: str) -> List[Dict]: + """Load checkpoint data from JSON.""" + with open(checkpoint_path, "r") as f: + return json.load(f) + + def _load_from_parquet(self, checkpoint_path: str) -> List[Dict]: + """Load checkpoint data from Parquet and desanitize.""" + table = pq.read_table(checkpoint_path) + df = table.to_pandas() + data = df.to_dict("records") + # Restore original data structure from sanitized data + return self._desanitize_from_parquet(data) + + def _update_config( + self, step_name: str, operation_name: str, operation_hash: str + ) -> None: + """Update the checkpoint configuration file.""" + if not self.config_path: + return + + # Load existing config or create new one + if os.path.exists(self.config_path): + try: + with open(self.config_path, "r") as f: + config = json.load(f) + except (json.JSONDecodeError, IOError): + config = {} + else: + config = {} + + # Ensure nested structure exists + if step_name not in config: + config[step_name] = {} + + # Update the hash + config[step_name][operation_name] = operation_hash + + # Save updated config + with open(self.config_path, "w") as f: + json.dump(config, f, indent=2) + + def load_output_by_step_and_op( + self, step_name: str, operation_name: str + ) -> Optional[List[Dict]]: + """ + Load output data for a specific step and operation. + + Args: + step_name: Name of the pipeline step + operation_name: Name of the operation + + Returns: + List of dictionaries if data exists, None otherwise + """ + # Find existing checkpoint (checks both formats) + checkpoint_info = self._find_existing_checkpoint(step_name, operation_name) + if not checkpoint_info: + return None + + checkpoint_path, format_type = checkpoint_info + + try: + # Load based on the format of the existing file + if format_type == "arrow": + return self._load_from_parquet(checkpoint_path) + else: # json + return self._load_from_json(checkpoint_path) + except Exception as e: + self._log(f"[red]Failed to load output: {e}[/red]") + return None + + def load_output_as_dataframe( + self, step_name: str, operation_name: str + ) -> Optional[pd.DataFrame]: + """ + Load output data as a pandas DataFrame. + + Args: + step_name: Name of the pipeline step + operation_name: Name of the operation + + Returns: + DataFrame if data exists, None otherwise + """ + # Find existing checkpoint (checks both formats) + checkpoint_info = self._find_existing_checkpoint(step_name, operation_name) + if not checkpoint_info: + return None + + checkpoint_path, format_type = checkpoint_info + + try: + # Load based on the format of the existing file + if format_type == "arrow": + table = pq.read_table(checkpoint_path) + return table.to_pandas() + else: # json + data = self._load_from_json(checkpoint_path) + return pd.DataFrame(data) if data else pd.DataFrame() + except Exception as e: + self._log(f"[red]Failed to load output as DataFrame: {e}[/red]") + return None + + def list_outputs(self) -> List[Tuple[str, str]]: + """ + List all available outputs (step_name, operation_name pairs). + + Returns: + List of tuples containing (step_name, operation_name) + """ + outputs = [] + + if not self.intermediate_dir or not os.path.exists(self.intermediate_dir): + return outputs + + # Walk through the directory structure + for step_name in os.listdir(self.intermediate_dir): + step_path = os.path.join(self.intermediate_dir, step_name) + + # Skip files and hidden directories + if not os.path.isdir(step_path) or step_name.startswith("."): + continue + + # Look for checkpoint files in the step directory (both formats) + for filename in os.listdir(step_path): + if filename.endswith(".parquet"): + operation_name = filename[:-8] # Remove .parquet extension + outputs.append((step_name, operation_name)) + elif filename.endswith(".json") and not filename.startswith("."): + operation_name = filename[:-5] # Remove .json extension + # Avoid duplicates if both formats exist + if (step_name, operation_name) not in outputs: + outputs.append((step_name, operation_name)) + + return sorted(outputs) + + def clear_all_checkpoints(self) -> None: + """Clear all checkpoints and configuration.""" + if self.intermediate_dir and os.path.exists(self.intermediate_dir): + shutil.rmtree(self.intermediate_dir) + os.makedirs(self.intermediate_dir, exist_ok=True) + self._log("[green]✓ All checkpoints cleared[/green]") + + def clear_step_checkpoints(self, step_name: str) -> None: + """ + Clear all checkpoints for a specific step. + + Args: + step_name: Name of the step to clear + """ + step_path = os.path.join(self.intermediate_dir, step_name) + if os.path.exists(step_path): + shutil.rmtree(step_path) + + # Remove from config + if self.config_path and os.path.exists(self.config_path): + try: + with open(self.config_path, "r") as f: + config = json.load(f) + + if step_name in config: + del config[step_name] + + with open(self.config_path, "w") as f: + json.dump(config, f, indent=2) + + except (json.JSONDecodeError, IOError): + pass + + self._log(f"[green]✓ Cleared checkpoints for step '{step_name}'[/green]") + + def get_checkpoint_size(self, step_name: str, operation_name: str) -> Optional[int]: + """ + Get the size of a checkpoint file in bytes. + + Args: + step_name: Name of the pipeline step + operation_name: Name of the operation + + Returns: + Size in bytes if file exists, None otherwise + """ + # Find existing checkpoint (checks both formats) + checkpoint_info = self._find_existing_checkpoint(step_name, operation_name) + if not checkpoint_info: + return None + + checkpoint_path, _ = checkpoint_info + return os.path.getsize(checkpoint_path) + + def get_total_checkpoint_size(self) -> int: + """ + Get the total size of all checkpoints in bytes. + + Returns: + Total size in bytes + """ + total_size = 0 + + if not self.intermediate_dir or not os.path.exists(self.intermediate_dir): + return total_size + + for root, dirs, files in os.walk(self.intermediate_dir): + for file in files: + if file.endswith((".parquet", ".json")) and not file.startswith("."): + file_path = os.path.join(root, file) + total_size += os.path.getsize(file_path) + + return total_size + + def save_incremental_checkpoint( + self, + step_name: str, + operation_name: str, + data: List[Dict], + operation_hash: str, + input_hashes: Optional[List[str]] = None, + ) -> None: + """ + Save checkpoint with incremental processing capabilities. + + This method can detect which records have changed based on input hashes + and potentially avoid reprocessing unchanged records in future runs. + + Args: + step_name: Name of the pipeline step + operation_name: Name of the operation + data: Data to checkpoint + operation_hash: Hash of the operation configuration + input_hashes: Optional list of hashes for input records to enable change detection + """ + # For now, delegate to regular save_checkpoint + # Future enhancement: store input_hashes for change detection + self.save_checkpoint(step_name, operation_name, data, operation_hash) + + # Store input hashes for future incremental processing + if input_hashes and self.intermediate_dir: + hash_path = self._get_hash_tracking_path(step_name, operation_name) + if hash_path: + try: + hash_data = { + "operation_hash": operation_hash, + "input_hashes": input_hashes, + "record_count": len(data), + } + os.makedirs(os.path.dirname(hash_path), exist_ok=True) + with open(hash_path, "w") as f: + json.dump(hash_data, f) + except Exception as e: + self._log( + f"[yellow]Warning: Could not save hash tracking data: {e}[/yellow]" + ) + + def _get_hash_tracking_path( + self, step_name: str, operation_name: str + ) -> Optional[str]: + """Get the path for storing input hash tracking data.""" + if not self.intermediate_dir: + return None + return os.path.join( + self.intermediate_dir, step_name, f"{operation_name}_input_hashes.json" + ) + + def get_incremental_processing_info( + self, step_name: str, operation_name: str, current_input_hashes: List[str] + ) -> Dict[str, Any]: + """ + Get information about what records need reprocessing for incremental updates. + + Args: + step_name: Name of the pipeline step + operation_name: Name of the operation + current_input_hashes: Hashes of current input records + + Returns: + Dictionary with incremental processing information: + - 'needs_full_reprocess': Boolean indicating if full reprocessing is needed + - 'changed_indices': List of indices that have changed + - 'unchanged_indices': List of indices that haven't changed + - 'new_indices': List of indices for new records + - 'removed_count': Number of records that were removed + """ + if not self.intermediate_dir: + return {"needs_full_reprocess": True, "reason": "No intermediate directory"} + + hash_path = self._get_hash_tracking_path(step_name, operation_name) + if not hash_path or not os.path.exists(hash_path): + return { + "needs_full_reprocess": True, + "reason": "No previous hash tracking data", + } + + try: + with open(hash_path, "r") as f: + previous_data = json.load(f) + + previous_hashes = previous_data.get("input_hashes", []) + + # Compare current vs previous hashes + changed_indices = [] + unchanged_indices = [] + new_indices = [] + + min_len = min(len(current_input_hashes), len(previous_hashes)) + + # Check existing records for changes + for i in range(min_len): + if current_input_hashes[i] != previous_hashes[i]: + changed_indices.append(i) + else: + unchanged_indices.append(i) + + # Check for new records + if len(current_input_hashes) > len(previous_hashes): + new_indices = list( + range(len(previous_hashes), len(current_input_hashes)) + ) + + removed_count = max(0, len(previous_hashes) - len(current_input_hashes)) + + return { + "needs_full_reprocess": False, + "changed_indices": changed_indices, + "unchanged_indices": unchanged_indices, + "new_indices": new_indices, + "removed_count": removed_count, + "total_changes": len(changed_indices) + + len(new_indices) + + removed_count, + } + + except (json.JSONDecodeError, IOError, KeyError) as e: + return { + "needs_full_reprocess": True, + "reason": f"Error reading hash data: {e}", + } + + def load_incremental_checkpoint( + self, + step_name: str, + operation_name: str, + operation_hash: str, + unchanged_indices: Optional[List[int]] = None, + ) -> Optional[List[Dict]]: + """ + Load checkpoint data, optionally filtered to unchanged records only. + + Args: + step_name: Name of the pipeline step + operation_name: Name of the operation + operation_hash: Expected hash of the operation configuration + unchanged_indices: Optional list of indices to load (for incremental processing) + + Returns: + List of dictionaries if checkpoint exists, None otherwise + """ + data = self.load_checkpoint(step_name, operation_name, operation_hash) + + if data is None or unchanged_indices is None: + return data + + # Filter to only unchanged records + try: + return [data[i] for i in unchanged_indices if i < len(data)] + except (IndexError, TypeError): + self._log( + "[yellow]Warning: Could not filter incremental data, returning full dataset[/yellow]" + ) + return data diff --git a/docetl/operations/equijoin.py b/docetl/operations/equijoin.py index fc573087..3b620508 100644 --- a/docetl/operations/equijoin.py +++ b/docetl/operations/equijoin.py @@ -118,6 +118,9 @@ def compare_pair( output = self.runner.api.parse_llm_response( response.response, {"is_match": "bool"} )[0] + # Convert to bool if it's a string + if isinstance(output["is_match"], str): + output["is_match"] = output["is_match"].lower() == "true" except Exception as e: self.console.log(f"[red]Error parsing LLM response: {e}[/red]") return False, cost diff --git a/docetl/operations/resolve.py b/docetl/operations/resolve.py index cb17c1a7..06029368 100644 --- a/docetl/operations/resolve.py +++ b/docetl/operations/resolve.py @@ -92,6 +92,10 @@ def compare_pair( {"is_match": "bool"}, )[0] + # Convert to bool if it's a string + if isinstance(output["is_match"], str): + output["is_match"] = output["is_match"].lower() == "true" + return output["is_match"], response.total_cost, prompt def syntax_check(self) -> None: diff --git a/docetl/runner.py b/docetl/runner.py index 260feaa9..3882b580 100644 --- a/docetl/runner.py +++ b/docetl/runner.py @@ -27,7 +27,6 @@ import hashlib import json import os -import shutil import time from collections import defaultdict from typing import Any, Dict, List, Optional, Tuple, Union @@ -37,6 +36,7 @@ from rich.markup import escape from rich.panel import Panel +from docetl.checkpoint_manager import CheckpointManager from docetl.config_wrapper import ConfigWrapper from docetl.containers import OpContainer, StepBoundary from docetl.dataset import Dataset, create_parsing_tool_map @@ -128,8 +128,17 @@ def __init__(self, config: Dict, max_threads: int = None, **kwargs): def _initialize_state(self) -> None: """Initialize basic runner state and datasets""" self.datasets = {} - self.intermediate_dir = ( - self.config.get("pipeline", {}).get("output", {}).get("intermediate_dir") + output_config = self.config.get("pipeline", {}).get("output", {}) + self.intermediate_dir = output_config.get("intermediate_dir") + storage_type = output_config.get("storage_type", "json") # default to json + + # Initialize checkpoint manager + self.checkpoint_manager = ( + CheckpointManager( + self.intermediate_dir, console=self.console, storage_type=storage_type + ) + if self.intermediate_dir + else None ) def _setup_parsing_tools(self) -> None: @@ -544,14 +553,7 @@ def save(self, data: List[Dict]) -> None: def _load_from_checkpoint_if_exists( self, step_name: str, operation_name: str ) -> Optional[List[Dict]]: - if self.intermediate_dir is None: - return None - - intermediate_config_path = os.path.join( - self.intermediate_dir, ".docetl_intermediate_config.json" - ) - - if not os.path.exists(intermediate_config_path): + if not self.checkpoint_manager: return None # Make sure the step and op name is in the checkpoint config path @@ -561,40 +563,18 @@ def _load_from_checkpoint_if_exists( ): return None - # See if the checkpoint config is the same as the current step op hash - with open(intermediate_config_path, "r") as f: - intermediate_config = json.load(f) - - if ( - intermediate_config.get(step_name, {}).get(operation_name, "") - != self.step_op_hashes[step_name][operation_name] - ): - return None - - checkpoint_path = os.path.join( - self.intermediate_dir, step_name, f"{operation_name}.json" + # Use the checkpoint manager to load the checkpoint + operation_hash = self.step_op_hashes[step_name][operation_name] + return self.checkpoint_manager.load_checkpoint( + step_name, operation_name, operation_hash ) - # check if checkpoint exists - if os.path.exists(checkpoint_path): - if f"{step_name}_{operation_name}" not in self.datasets: - self.datasets[f"{step_name}_{operation_name}"] = Dataset( - self, "file", checkpoint_path, "local" - ) - - self.console.log( - f"[green]✓[/green] [italic]Loaded checkpoint for operation '{operation_name}' in step '{step_name}' from {checkpoint_path}[/italic]" - ) - - return self.datasets[f"{step_name}_{operation_name}"].load() - return None def clear_intermediate(self) -> None: """ Clear the intermediate directory. """ - # Remove the intermediate directory - if self.intermediate_dir: - shutil.rmtree(self.intermediate_dir) + if self.checkpoint_manager: + self.checkpoint_manager.clear_all_checkpoints() return raise ValueError("Intermediate directory not set. Cannot clear intermediate.") @@ -605,7 +585,7 @@ def _save_checkpoint( """ Save a checkpoint of the current data after an operation. - This method creates a JSON file containing the current state of the data + This method saves the current state of the data using PyArrow format after an operation has been executed. The checkpoint is saved in a directory structure that reflects the step and operation names. @@ -618,44 +598,15 @@ def _save_checkpoint( The checkpoint is saved only if a checkpoint directory has been specified when initializing the DSLRunner. """ - checkpoint_path = os.path.join( - self.intermediate_dir, step_name, f"{operation_name}.json" - ) - if os.path.dirname(checkpoint_path): - os.makedirs(os.path.dirname(checkpoint_path), exist_ok=True) - with open(checkpoint_path, "w") as f: - json.dump(data, f) - - # Update the intermediate config file with the hash for this step/operation - # so that future runs can validate and reuse this checkpoint. - if self.intermediate_dir: - intermediate_config_path = os.path.join( - self.intermediate_dir, ".docetl_intermediate_config.json" - ) - - # Initialize or load existing intermediate configuration - if os.path.exists(intermediate_config_path): - try: - with open(intermediate_config_path, "r") as cfg_file: - intermediate_config: Dict[str, Dict[str, str]] = json.load(cfg_file) - except json.JSONDecodeError: - # If the file is corrupted, start fresh to avoid crashes - intermediate_config = {} - else: - intermediate_config = {} - - # Ensure nested dict structure exists - step_dict = intermediate_config.setdefault(step_name, {}) - - # Write (or overwrite) the hash for the current operation - step_dict[operation_name] = self.step_op_hashes[step_name][operation_name] + if not self.checkpoint_manager: + return - # Persist the updated configuration - with open(intermediate_config_path, "w") as cfg_file: - json.dump(intermediate_config, cfg_file, indent=2) + # Get the operation hash for validation + operation_hash = self.step_op_hashes[step_name][operation_name] - self.console.log( - f"[green]✓ [italic]Intermediate saved for operation '{operation_name}' in step '{step_name}' at {checkpoint_path}[/italic][/green]" + # Use the checkpoint manager to save the checkpoint + self.checkpoint_manager.save_checkpoint( + step_name, operation_name, data, operation_hash ) def should_optimize( diff --git a/docs/execution/running-pipelines.md b/docs/execution/running-pipelines.md index 5f25ff81..e298111b 100644 --- a/docs/execution/running-pipelines.md +++ b/docs/execution/running-pipelines.md @@ -33,4 +33,59 @@ Here are some additional notes to help you get the most out of your pipeline: type: file path: ... intermediate_dir: intermediate_results - ``` \ No newline at end of file + storage_type: json # Optional: "json" (default) or "arrow" + ``` + +- **Storage Format**: You can choose the storage format for intermediate checkpoints using the `storage_type` parameter in your pipeline's output configuration: + + - **JSON Format** (`storage_type: json`): Human-readable format that's easy to inspect and debug. This is the default format for backward compatibility. + - **PyArrow Format** (`storage_type: arrow`): Compressed binary format using Parquet files. Offers better performance and smaller file sizes for large datasets. Complex nested data structures are automatically sanitized for PyArrow compatibility while preserving the original data structure when loaded. + + Example configurations: + + ```yaml + # Use JSON format (default) + pipeline: + output: + type: file + path: results.json + intermediate_dir: checkpoints + storage_type: json + ``` + + ```yaml + # Use PyArrow format for better performance + pipeline: + output: + type: file + path: results.json + intermediate_dir: checkpoints + storage_type: arrow + ``` + + The checkpoint system is fully backward compatible - you can read existing JSON checkpoints even when using `storage_type: arrow`, and vice versa. This allows for seamless migration between formats. + +- **Standalone CheckpointManager Usage**: You can use the CheckpointManager independently from DocETL pipelines to load and analyze checkpoint data programmatically: + + ```python + from docetl.checkpoint_manager import CheckpointManager + + # Create from existing intermediate directory (auto-detects storage format) + cm = CheckpointManager.from_intermediate_dir("/path/to/intermediate") + + # List all available checkpoints + outputs = cm.list_outputs() + print(f"Available checkpoints: {outputs}") + + # Load specific checkpoint data + data = cm.load_output_by_step_and_op("step_name", "operation_name") + + # Load as pandas DataFrame for analysis + df = cm.load_output_as_dataframe("step_name", "operation_name") + + # Check checkpoint file sizes + size = cm.get_checkpoint_size("step_name", "operation_name") + total_size = cm.get_total_checkpoint_size() + ``` + + This is useful for post-pipeline analysis, debugging, or building custom tools that work with DocETL checkpoint data. \ No newline at end of file diff --git a/poetry.lock b/poetry.lock index 2fa3906c..e47cadd9 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 2.1.2 and should not be changed by hand. +# This file is automatically @generated by Poetry 2.1.3 and should not be changed by hand. 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"1.3.0.post6" @@ -5919,4 +5974,4 @@ server = ["azure-ai-documentintelligence", "azure-ai-formrecognizer", "docling", [metadata] lock-version = "2.1" python-versions = "^3.10" -content-hash = "a9db121f66d62d5070b40eb6d89a42bfc9f3731d472a8c7960dc8ae7507d92b6" +content-hash = "ae40ca3a72d0723c5fa274e50b0c3b953755ef502cb0ec858e0bc282edda569b" diff --git a/pyproject.toml b/pyproject.toml index ac1564d2..9347f28e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -43,6 +43,7 @@ boto3 = "^1.37.27" pandas = "^2.3.0" python-multipart = "^0.0.20" fastapi = { version = "^0.115.4", optional = true } +pyarrow = "^18.0.0" [tool.poetry.extras] parsing = ["python-docx", "openpyxl", "pydub", "python-pptx", "azure-ai-documentintelligence", "paddlepaddle", "pymupdf"] diff --git a/tests/test_checkpoint_manager.py b/tests/test_checkpoint_manager.py new file mode 100644 index 00000000..0260f75a --- /dev/null +++ b/tests/test_checkpoint_manager.py @@ -0,0 +1,1760 @@ +import pytest +import tempfile +import os +import json +import pandas as pd +from unittest.mock import Mock +from docetl.checkpoint_manager import CheckpointManager + + +@pytest.fixture +def temp_dir(): + """Create a temporary directory for testing.""" + with tempfile.TemporaryDirectory() as tmpdirname: + yield tmpdirname + + +@pytest.fixture +def sample_data(): + """Sample data for testing.""" + return [ + {"id": 1, "text": "First document", "category": "A"}, + {"id": 2, "text": "Second document", "category": "B"}, + {"id": 3, "text": "Third document", "category": "A"}, + ] + + +@pytest.fixture +def empty_data(): + """Empty data for testing.""" + return [] + + +@pytest.fixture +def mock_console(): + """Mock console for testing.""" + return Mock() + + +@pytest.fixture +def checkpoint_manager(temp_dir, mock_console): + """Create a checkpoint manager instance.""" + return CheckpointManager(temp_dir, console=mock_console) + + +def test_checkpoint_manager_init(temp_dir, mock_console): + """Test checkpoint manager initialization.""" + cm = CheckpointManager(temp_dir, console=mock_console) + assert cm.intermediate_dir == temp_dir + assert cm.console == mock_console + assert cm.config_path == os.path.join(temp_dir, ".docetl_intermediate_config.json") + assert os.path.exists(temp_dir) + + +def test_checkpoint_manager_init_no_console(temp_dir): + """Test checkpoint manager initialization without console.""" + cm = CheckpointManager(temp_dir) + assert cm.console is None + + +def test_save_and_load_checkpoint(checkpoint_manager, sample_data): + """Test saving and loading a checkpoint.""" + step_name = "test_step" + operation_name = "test_operation" + operation_hash = "test_hash_123" + + # Save checkpoint + checkpoint_manager.save_checkpoint(step_name, operation_name, sample_data, operation_hash) + + # Verify checkpoint file exists + checkpoint_path = checkpoint_manager._get_checkpoint_path(step_name, operation_name) + assert os.path.exists(checkpoint_path) + + # Load checkpoint + loaded_data = checkpoint_manager.load_checkpoint(step_name, operation_name, operation_hash) + + # Verify data integrity + assert loaded_data == sample_data + assert len(loaded_data) == 3 + assert loaded_data[0]["id"] == 1 + assert loaded_data[1]["text"] == "Second document" + + +def test_save_and_load_empty_checkpoint(checkpoint_manager, empty_data): + """Test saving and loading an empty checkpoint.""" + step_name = "test_step" + operation_name = "test_operation" + operation_hash = "test_hash_empty" + + # Save empty checkpoint + checkpoint_manager.save_checkpoint(step_name, operation_name, empty_data, operation_hash) + + # Load checkpoint + loaded_data = checkpoint_manager.load_checkpoint(step_name, operation_name, operation_hash) + + # Verify empty data + assert loaded_data == [] + + +def test_load_nonexistent_checkpoint(checkpoint_manager): + """Test loading a checkpoint that doesn't exist.""" + loaded_data = checkpoint_manager.load_checkpoint("nonexistent_step", "nonexistent_op", "fake_hash") + assert loaded_data is None + + +def test_load_checkpoint_wrong_hash(checkpoint_manager, sample_data): + """Test loading a checkpoint with wrong hash.""" + step_name = "test_step" + operation_name = "test_operation" + correct_hash = "correct_hash" + wrong_hash = "wrong_hash" + + # Save checkpoint with correct hash + checkpoint_manager.save_checkpoint(step_name, operation_name, sample_data, correct_hash) + + # Try to load with wrong hash + loaded_data = checkpoint_manager.load_checkpoint(step_name, operation_name, wrong_hash) + assert loaded_data is None + + # Load with correct hash should work + loaded_data = checkpoint_manager.load_checkpoint(step_name, operation_name, correct_hash) + assert loaded_data == sample_data + + +def test_load_output_by_step_and_op(checkpoint_manager, sample_data): + """Test loading output by step and operation name.""" + step_name = "test_step" + operation_name = "test_operation" + operation_hash = "test_hash" + + # Save checkpoint + checkpoint_manager.save_checkpoint(step_name, operation_name, sample_data, operation_hash) + + # Load output directly + loaded_data = checkpoint_manager.load_output_by_step_and_op(step_name, operation_name) + assert loaded_data == sample_data + + +def test_load_output_as_dataframe(checkpoint_manager, sample_data): + """Test loading output as pandas DataFrame.""" + step_name = "test_step" + operation_name = "test_operation" + operation_hash = "test_hash" + + # Save checkpoint + checkpoint_manager.save_checkpoint(step_name, operation_name, sample_data, operation_hash) + + # Load as DataFrame + df = checkpoint_manager.load_output_as_dataframe(step_name, operation_name) + + # Verify DataFrame + assert isinstance(df, pd.DataFrame) + assert len(df) == 3 + assert list(df.columns) == ["id", "text", "category"] + assert df.iloc[0]["id"] == 1 + assert df.iloc[1]["text"] == "Second document" + + +def test_load_output_nonexistent(checkpoint_manager): + """Test loading output for nonexistent step/operation.""" + loaded_data = checkpoint_manager.load_output_by_step_and_op("nonexistent", "nonexistent") + assert loaded_data is None + + df = checkpoint_manager.load_output_as_dataframe("nonexistent", "nonexistent") + assert df is None + + +def test_list_outputs(checkpoint_manager, sample_data): + """Test listing all outputs.""" + # Initially no outputs + outputs = checkpoint_manager.list_outputs() + assert outputs == [] + + # Save multiple checkpoints + checkpoint_manager.save_checkpoint("step1", "op1", sample_data, "hash1") + checkpoint_manager.save_checkpoint("step1", "op2", sample_data, "hash2") + checkpoint_manager.save_checkpoint("step2", "op1", sample_data, "hash3") + + # List outputs + outputs = checkpoint_manager.list_outputs() + expected = [("step1", "op1"), ("step1", "op2"), ("step2", "op1")] + assert sorted(outputs) == sorted(expected) + + +def test_get_checkpoint_size(checkpoint_manager, sample_data): + """Test getting checkpoint file size.""" + step_name = "test_step" + operation_name = "test_operation" + operation_hash = "test_hash" + + # Size should be None for nonexistent checkpoint + size = checkpoint_manager.get_checkpoint_size(step_name, operation_name) + assert size is None + + # Save checkpoint + checkpoint_manager.save_checkpoint(step_name, operation_name, sample_data, operation_hash) + + # Size should be positive + size = checkpoint_manager.get_checkpoint_size(step_name, operation_name) + assert size > 0 + + +def test_get_total_checkpoint_size(checkpoint_manager, sample_data): + """Test getting total size of all checkpoints.""" + # Initially zero + total_size = checkpoint_manager.get_total_checkpoint_size() + assert total_size == 0 + + # Save multiple checkpoints + checkpoint_manager.save_checkpoint("step1", "op1", sample_data, "hash1") + checkpoint_manager.save_checkpoint("step1", "op2", sample_data, "hash2") + + # Total size should be positive + total_size = checkpoint_manager.get_total_checkpoint_size() + assert total_size > 0 + + +def test_clear_all_checkpoints(checkpoint_manager, sample_data): + """Test clearing all checkpoints.""" + # Save some checkpoints + checkpoint_manager.save_checkpoint("step1", "op1", sample_data, "hash1") + checkpoint_manager.save_checkpoint("step2", "op2", sample_data, "hash2") + + # Verify they exist + assert len(checkpoint_manager.list_outputs()) == 2 + + # Clear all + checkpoint_manager.clear_all_checkpoints() + + # Verify they're gone + assert len(checkpoint_manager.list_outputs()) == 0 + assert checkpoint_manager.get_total_checkpoint_size() == 0 + + +def test_clear_step_checkpoints(checkpoint_manager, sample_data): + """Test clearing checkpoints for a specific step.""" + # Save checkpoints for multiple steps + checkpoint_manager.save_checkpoint("step1", "op1", sample_data, "hash1") + checkpoint_manager.save_checkpoint("step1", "op2", sample_data, "hash2") + checkpoint_manager.save_checkpoint("step2", "op1", sample_data, "hash3") + + # Verify all exist + assert len(checkpoint_manager.list_outputs()) == 3 + + # Clear step1 + checkpoint_manager.clear_step_checkpoints("step1") + + # Verify only step2 remains + outputs = checkpoint_manager.list_outputs() + assert len(outputs) == 1 + assert outputs[0] == ("step2", "op1") + + +def test_config_file_management(checkpoint_manager, sample_data): + """Test that config file is properly managed.""" + step_name = "test_step" + operation_name = "test_operation" + operation_hash = "test_hash" + + # Config file shouldn't exist initially + assert not os.path.exists(checkpoint_manager.config_path) + + # Save checkpoint + checkpoint_manager.save_checkpoint(step_name, operation_name, sample_data, operation_hash) + + # Config file should exist + assert os.path.exists(checkpoint_manager.config_path) + + # Verify config content + with open(checkpoint_manager.config_path, 'r') as f: + config = json.load(f) + + assert config[step_name][operation_name] == operation_hash + + +def test_corrupted_config_file(checkpoint_manager, sample_data): + """Test handling of corrupted config file.""" + step_name = "test_step" + operation_name = "test_operation" + operation_hash = "test_hash" + + # Create corrupted config file + with open(checkpoint_manager.config_path, 'w') as f: + f.write("corrupted json content") + + # Load should return None + loaded_data = checkpoint_manager.load_checkpoint(step_name, operation_name, operation_hash) + assert loaded_data is None + + +def test_directory_structure(checkpoint_manager, sample_data): + """Test that directory structure is created correctly.""" + step_name = "test_step" + operation_name = "test_operation" + operation_hash = "test_hash" + + # Save checkpoint + checkpoint_manager.save_checkpoint(step_name, operation_name, sample_data, operation_hash) + + # Verify directory structure + step_dir = os.path.join(checkpoint_manager.intermediate_dir, step_name) + assert os.path.exists(step_dir) + assert os.path.isdir(step_dir) + + # Check for the correct file extension based on storage type + extension = "parquet" if checkpoint_manager.storage_type == "arrow" else "json" + checkpoint_file = os.path.join(step_dir, f"{operation_name}.{extension}") + assert os.path.exists(checkpoint_file) + + +def test_checkpoint_manager_without_intermediate_dir(): + """Test checkpoint manager without intermediate directory.""" + cm = CheckpointManager(None) + + # All operations should be no-ops + cm.save_checkpoint("step", "op", [], "hash") + assert cm.load_checkpoint("step", "op", "hash") is None + assert cm.load_output_by_step_and_op("step", "op") is None + assert cm.load_output_as_dataframe("step", "op") is None + assert cm.list_outputs() == [] + assert cm.get_checkpoint_size("step", "op") is None + assert cm.get_total_checkpoint_size() == 0 + + +def test_data_types_preservation(checkpoint_manager): + """Test that different data types are preserved correctly.""" + data = [ + {"string": "text", "integer": 42, "float": 3.14, "boolean": True}, + {"string": "more text", "integer": 0, "float": -1.5, "boolean": False}, + ] + + step_name = "test_step" + operation_name = "test_operation" + operation_hash = "test_hash" + + # Save and load + checkpoint_manager.save_checkpoint(step_name, operation_name, data, operation_hash) + loaded_data = checkpoint_manager.load_checkpoint(step_name, operation_name, operation_hash) + + # Verify data types are preserved + assert loaded_data[0]["string"] == "text" + assert loaded_data[0]["integer"] == 42 + assert loaded_data[0]["float"] == 3.14 + assert loaded_data[0]["boolean"] is True + assert loaded_data[1]["boolean"] is False + + +def test_space_efficiency_vs_json(temp_dir): + """Test that PyArrow storage is more space efficient than JSON.""" + # Create larger, more realistic test data + large_data = [] + for i in range(1000): + large_data.append({ + "id": i, + "text": f"This is a longer text document with some repetitive content that would benefit from compression. Document number {i}. " * 3, + "category": "A" if i % 2 == 0 else "B", + "score": i * 0.1, + "tags": ["tag1", "tag2", "tag3"] if i % 3 == 0 else ["tag4", "tag5"], + "metadata": {"source": "test", "processed": True, "version": 1} + }) + + # Test with CheckpointManager (PyArrow) + checkpoint_manager = CheckpointManager(temp_dir, storage_type="arrow") + step_name = "test_step" + operation_name = "test_operation" + operation_hash = "test_hash" + + # Save with PyArrow + checkpoint_manager.save_checkpoint(step_name, operation_name, large_data, operation_hash) + + # Get PyArrow file size + parquet_size = checkpoint_manager.get_checkpoint_size(step_name, operation_name) + + # Save same data as JSON for comparison + json_path = os.path.join(temp_dir, "test_data.json") + with open(json_path, 'w') as f: + json.dump(large_data, f) + + json_size = os.path.getsize(json_path) + + # PyArrow should be more space efficient + assert parquet_size < json_size, f"PyArrow ({parquet_size} bytes) should be smaller than JSON ({json_size} bytes)" + + # Calculate compression ratio + compression_ratio = parquet_size / json_size + print(f"PyArrow size: {parquet_size} bytes") + print(f"JSON size: {json_size} bytes") + print(f"Compression ratio: {compression_ratio:.2f} (smaller is better)") + + # Verify we get at least some compression benefit + assert compression_ratio < 0.8, "Expected at least 20% space savings" + + +def test_storage_efficiency_with_repetitive_data(temp_dir): + """Test storage efficiency with highly repetitive data that should compress well.""" + # Create data with lots of repetition (common in ETL pipelines) + repetitive_data = [] + base_text = "This is a base document that will be repeated many times with slight variations. " * 5 + + for i in range(500): + repetitive_data.append({ + "id": i, + "text": base_text + f"Variation {i % 10}", # Only 10 unique variations + "category": "Category " + str(i % 5), # Only 5 categories + "status": "processed" if i % 2 == 0 else "pending", + "tags": ["common", "tag"] + ([f"special_{i % 3}"] if i % 3 == 0 else []), + "metadata": {"type": "document", "version": 1, "processed_by": "system"} + }) + + checkpoint_manager = CheckpointManager(temp_dir, storage_type="arrow") + step_name = "repetitive_step" + operation_name = "repetitive_operation" + operation_hash = "repetitive_hash" + + # Save with PyArrow + checkpoint_manager.save_checkpoint(step_name, operation_name, repetitive_data, operation_hash) + parquet_size = checkpoint_manager.get_checkpoint_size(step_name, operation_name) + + # Save as JSON + json_path = os.path.join(temp_dir, "repetitive_data.json") + with open(json_path, 'w') as f: + json.dump(repetitive_data, f) + json_size = os.path.getsize(json_path) + + # With repetitive data, compression should be even better + compression_ratio = parquet_size / json_size + print(f"Repetitive data - PyArrow size: {parquet_size} bytes") + print(f"Repetitive data - JSON size: {json_size} bytes") + print(f"Repetitive data - Compression ratio: {compression_ratio:.2f}") + + # Should get significant compression with repetitive data + assert compression_ratio < 0.6, "Expected at least 40% space savings with repetitive data" + + +@pytest.fixture +def large_sample_data(): + """Generate larger sample data for performance testing.""" + return [ + { + "id": i, + "text": f"Document {i}: " + "This is sample text content that simulates real document processing. " * 10, + "category": f"Category_{i % 10}", + "score": i * 0.01, + "tags": [f"tag_{i % 5}", f"tag_{(i+1) % 5}"], + "metadata": {"source": f"source_{i % 3}", "processed": True} + } + for i in range(100) + ] + + +def test_performance_comparison(temp_dir, large_sample_data): + """Compare load/save performance between PyArrow and JSON.""" + import time + + checkpoint_manager = CheckpointManager(temp_dir) + step_name = "perf_step" + operation_name = "perf_operation" + operation_hash = "perf_hash" + + # Time PyArrow save + start_time = time.time() + checkpoint_manager.save_checkpoint(step_name, operation_name, large_sample_data, operation_hash) + parquet_save_time = time.time() - start_time + + # Time PyArrow load + start_time = time.time() + loaded_parquet = checkpoint_manager.load_checkpoint(step_name, operation_name, operation_hash) + parquet_load_time = time.time() - start_time + + # Time JSON save + json_path = os.path.join(temp_dir, "perf_test.json") + start_time = time.time() + with open(json_path, 'w') as f: + json.dump(large_sample_data, f) + json_save_time = time.time() - start_time + + # Time JSON load + start_time = time.time() + with open(json_path, 'r') as f: + loaded_json = json.load(f) + json_load_time = time.time() - start_time + + # Verify data integrity + assert len(loaded_parquet) == len(loaded_json) == len(large_sample_data) + # Check first few records to verify data integrity + assert loaded_parquet[0]["id"] == large_sample_data[0]["id"] + assert loaded_json[0]["id"] == large_sample_data[0]["id"] + + print(f"PyArrow save time: {parquet_save_time:.4f}s") + print(f"PyArrow load time: {parquet_load_time:.4f}s") + print(f"JSON save time: {json_save_time:.4f}s") + print(f"JSON load time: {json_load_time:.4f}s") + + # Performance will vary, but let's at least verify operations complete + assert parquet_save_time > 0 and parquet_load_time > 0 + assert json_save_time > 0 and json_load_time > 0 + + +def test_incremental_checkpoint_potential(temp_dir): + """Test the potential space savings from incremental checkpoint storage.""" + # Simulate a typical ETL pipeline: input -> map -> filter -> map + + # Original dataset (e.g., from data loading) + original_data = [] + for i in range(1000): + original_data.append({ + "id": i, + "text": f"Document {i}: " + "Original content that will be preserved through transformations. " * 10, + "category": f"category_{i % 5}", + "metadata": {"source": "original", "timestamp": f"2024-01-{i%30+1:02d}"} + }) + + # After map operation (adds analysis fields but preserves original data) + after_map_data = [] + for record in original_data: + new_record = record.copy() + new_record.update({ + "sentiment": "positive" if record["id"] % 2 == 0 else "negative", + "analyzed": True, + "summary": f"Summary of document {record['id']}" + }) + after_map_data.append(new_record) + + # After filter operation (removes some records but preserves all fields) + after_filter_data = [r for r in after_map_data if r["id"] % 3 != 0] # Remove 1/3 of records + + # After second map operation (adds more analysis) + after_second_map_data = [] + for record in after_filter_data: + new_record = record.copy() + new_record.update({ + "enriched": True, + "score": record["id"] * 0.1, + "tags": ["tag1", "tag2"] + }) + after_second_map_data.append(new_record) + + # Test current approach (storing full datasets) + checkpoint_manager = CheckpointManager(temp_dir) + + # Save each stage + checkpoint_manager.save_checkpoint("pipeline", "load", original_data, "hash1") + checkpoint_manager.save_checkpoint("pipeline", "map1", after_map_data, "hash2") + checkpoint_manager.save_checkpoint("pipeline", "filter", after_filter_data, "hash3") + checkpoint_manager.save_checkpoint("pipeline", "map2", after_second_map_data, "hash4") + + # Get sizes + original_size = checkpoint_manager.get_checkpoint_size("pipeline", "load") + map1_size = checkpoint_manager.get_checkpoint_size("pipeline", "map1") + filter_size = checkpoint_manager.get_checkpoint_size("pipeline", "filter") + map2_size = checkpoint_manager.get_checkpoint_size("pipeline", "map2") + + total_current_size = original_size + map1_size + filter_size + map2_size + + print(f"Current checkpoint sizes:") + print(f" Original: {original_size} bytes") + print(f" After map1: {map1_size} bytes") + print(f" After filter: {filter_size} bytes") + print(f" After map2: {map2_size} bytes") + print(f" Total: {total_current_size} bytes") + + # Calculate potential savings if we stored deltas + # This is a rough estimate - actual implementation would be more sophisticated + + # Map1 delta: just the new fields (sentiment, analyzed, summary) + map1_delta_estimate = len(after_map_data) * 100 # Rough estimate for new fields + + # Filter delta: just record IDs that were removed + filter_delta_estimate = (len(after_map_data) - len(after_filter_data)) * 20 # Just IDs + + # Map2 delta: just the new fields (enriched, score, tags) + map2_delta_estimate = len(after_second_map_data) * 80 # Rough estimate + + estimated_incremental_size = original_size + map1_delta_estimate + filter_delta_estimate + map2_delta_estimate + + print(f"\nEstimated incremental checkpoint sizes:") + print(f" Original: {original_size} bytes") + print(f" Map1 delta: {map1_delta_estimate} bytes") + print(f" Filter delta: {filter_delta_estimate} bytes") + print(f" Map2 delta: {map2_delta_estimate} bytes") + print(f" Total estimated: {estimated_incremental_size} bytes") + + potential_savings = (total_current_size - estimated_incremental_size) / total_current_size + print(f"\nPotential space savings: {potential_savings:.1%}") + + # This test shows PyArrow compression is already very effective + # The real benefit of incremental processing is avoiding recomputation, not storage + print("Note: PyArrow compression makes storage deltas less beneficial") + print("Real value is in incremental reprocessing to avoid expensive operations") + + +def test_incremental_processing_workflow(temp_dir): + """Test the incremental processing workflow for change detection.""" + import hashlib + + def compute_record_hash(record): + """Compute hash of a record for change detection.""" + record_str = json.dumps(record, sort_keys=True) + return hashlib.md5(record_str.encode()).hexdigest() + + checkpoint_manager = CheckpointManager(temp_dir) + + # Initial dataset + initial_data = [ + {"id": 1, "text": "Document 1", "category": "A"}, + {"id": 2, "text": "Document 2", "category": "B"}, + {"id": 3, "text": "Document 3", "category": "A"} + ] + + # Compute hashes for initial data + initial_hashes = [compute_record_hash(record) for record in initial_data] + + # Save initial checkpoint with hash tracking + checkpoint_manager.save_incremental_checkpoint( + "test", "process", initial_data, "hash1", initial_hashes + ) + + # Simulate processing the data (e.g., adding analysis results) + processed_data = [] + for record in initial_data: + new_record = record.copy() + new_record["processed"] = True + new_record["score"] = record["id"] * 10 + processed_data.append(new_record) + + # Save processed results + checkpoint_manager.save_incremental_checkpoint( + "test", "analyzed", processed_data, "hash2", initial_hashes + ) + + # Now simulate a data update scenario + # - Record 1 unchanged + # - Record 2 modified + # - Record 3 unchanged + # - Record 4 added + updated_data = [ + {"id": 1, "text": "Document 1", "category": "A"}, # unchanged + {"id": 2, "text": "Document 2 UPDATED", "category": "B"}, # changed + {"id": 3, "text": "Document 3", "category": "A"}, # unchanged + {"id": 4, "text": "Document 4", "category": "C"} # new + ] + + updated_hashes = [compute_record_hash(record) for record in updated_data] + + # Get incremental processing info + incremental_info = checkpoint_manager.get_incremental_processing_info( + "test", "analyzed", updated_hashes + ) + + print(f"Incremental processing info: {incremental_info}") + + # Verify change detection + assert not incremental_info["needs_full_reprocess"] + assert incremental_info["changed_indices"] == [1] # Record 2 changed + assert incremental_info["unchanged_indices"] == [0, 2] # Records 1 and 3 unchanged + assert incremental_info["new_indices"] == [3] # Record 4 is new + assert incremental_info["total_changes"] == 2 # 1 changed + 1 new + + # Load unchanged records from previous processing + unchanged_processed = checkpoint_manager.load_incremental_checkpoint( + "test", "analyzed", "hash2", incremental_info["unchanged_indices"] + ) + + print(f"Unchanged records: {len(unchanged_processed)} out of {len(processed_data)}") + + # Verify we got the right unchanged records + assert len(unchanged_processed) == 2 + assert unchanged_processed[0]["id"] == 1 + assert unchanged_processed[1]["id"] == 3 + assert all(r["processed"] for r in unchanged_processed) + + # In a real scenario, you would: + # 1. Process only changed records (indices [1]) and new records (indices [3]) + # 2. Merge with unchanged_processed to get complete result + # 3. Save the new complete result with updated hashes + + print("✓ Incremental processing successfully detected changes and preserved unchanged results") + + +def test_incremental_processing_edge_cases(temp_dir): + """Test edge cases for incremental processing.""" + checkpoint_manager = CheckpointManager(temp_dir) + + # Test with no previous data + info = checkpoint_manager.get_incremental_processing_info("new", "op", ["hash1"]) + assert info["needs_full_reprocess"] + assert "No previous hash tracking data" in info["reason"] + + # Test loading incremental checkpoint with no previous data + result = checkpoint_manager.load_incremental_checkpoint("new", "op", "hash", [0, 1]) + assert result is None + + print("✓ Edge cases handled correctly") + + +def test_incremental_processing_realistic_pipeline(temp_dir): + """Test incremental processing with realistic text processing pipeline.""" + import hashlib + import random + import string + + def generate_large_text(): + """Generate realistic large text documents.""" + # Common words to create realistic text + words = [ + "the", "quick", "brown", "fox", "jumps", "over", "lazy", "dog", + "python", "programming", "language", "machine", "learning", "artificial", + "intelligence", "data", "science", "analysis", "processing", "algorithm", + "computer", "software", "development", "application", "framework", + "database", "query", "optimization", "performance", "scalability" + ] + + # Generate sentences with 15-30 words each + sentences = [] + for _ in range(random.randint(10, 25)): # 10-25 sentences per document + sentence_words = random.choices(words, k=random.randint(15, 30)) + sentences.append(" ".join(sentence_words).capitalize() + ".") + + return " ".join(sentences) + + def compute_record_hash(record): + """Compute hash of input record for change detection.""" + # Only hash the input fields, not processed results + input_content = {"id": record["id"], "content": record["content"]} + record_str = json.dumps(input_content, sort_keys=True) + return hashlib.md5(record_str.encode()).hexdigest() + + def extract_first_letters(text): + """Extract first letter of every word for first 15 words.""" + words = text.split()[:15] # Take first 15 words + first_letters = [word[0].upper() for word in words if word] + return "".join(first_letters) + + def analyze_text_length(text): + """Analyze text characteristics.""" + words = text.split() + return { + "word_count": len(words), + "char_count": len(text), + "avg_word_length": sum(len(word) for word in words) / len(words) if words else 0 + } + + checkpoint_manager = CheckpointManager(temp_dir) + + # Generate initial large dataset (1000 documents with substantial text) + print("Generating initial dataset with large text documents...") + initial_data = [] + for i in range(1000): + content = generate_large_text() + initial_data.append({ + "id": i, + "content": content, + "source": f"source_{i % 10}", + "timestamp": f"2024-01-{(i % 30) + 1:02d}" + }) + + # Compute hashes for change detection + initial_hashes = [compute_record_hash(record) for record in initial_data] + + print(f"Generated {len(initial_data)} documents, avg size: {sum(len(d['content']) for d in initial_data) / len(initial_data):.0f} chars") + + # Save initial checkpoint + checkpoint_manager.save_incremental_checkpoint( + "pipeline", "raw_data", initial_data, "hash1", initial_hashes + ) + + # Stage 1: Extract first letters (simulate expensive text processing) + print("Stage 1: Extracting first letters from each document...") + stage1_data = [] + for record in initial_data: + new_record = record.copy() + new_record["first_letters"] = extract_first_letters(record["content"]) + stage1_data.append(new_record) + + checkpoint_manager.save_incremental_checkpoint( + "pipeline", "first_letters", stage1_data, "hash2", initial_hashes + ) + + # Stage 2: Analyze text characteristics + print("Stage 2: Analyzing text characteristics...") + stage2_data = [] + for record in stage1_data: + new_record = record.copy() + new_record["analysis"] = analyze_text_length(record["content"]) + stage2_data.append(new_record) + + checkpoint_manager.save_incremental_checkpoint( + "pipeline", "analyzed", stage2_data, "hash3", initial_hashes + ) + + # Now simulate data updates - modify 5% of documents, add 2% new ones + print("\nSimulating data updates (5% modified, 2% new)...") + num_changed = int(len(initial_data) * 0.05) # 5% changed + num_new = int(len(initial_data) * 0.02) # 2% new + + # Create updated dataset + updated_data = initial_data.copy() + changed_indices = random.sample(range(len(initial_data)), num_changed) + + # Modify some existing documents + for idx in changed_indices: + updated_data[idx] = updated_data[idx].copy() + updated_data[idx]["content"] = generate_large_text() # New content + updated_data[idx]["timestamp"] = "2024-02-01" # Updated timestamp + + # Add new documents + for i in range(num_new): + new_id = len(initial_data) + i + updated_data.append({ + "id": new_id, + "content": generate_large_text(), + "source": f"source_{new_id % 10}", + "timestamp": "2024-02-01" + }) + + # Compute new hashes + updated_hashes = [compute_record_hash(record) for record in updated_data] + + # Test incremental processing for each stage + print("\nTesting incremental processing...") + + # Check what needs reprocessing for stage 1 (first letters) + incremental_info = checkpoint_manager.get_incremental_processing_info( + "pipeline", "first_letters", updated_hashes + ) + + print(f"Stage 1 incremental analysis:") + print(f" Total records: {len(updated_data)}") + print(f" Changed: {len(incremental_info['changed_indices'])}") + print(f" New: {len(incremental_info['new_indices'])}") + print(f" Unchanged: {len(incremental_info['unchanged_indices'])}") + print(f" Total changes: {incremental_info['total_changes']}") + + # Verify the change detection is accurate + expected_changes = num_changed + num_new + actual_changes = incremental_info['total_changes'] + print(f" Expected changes: {expected_changes}, Detected: {actual_changes}") + + # Load unchanged results from stage 1 + unchanged_stage1 = checkpoint_manager.load_incremental_checkpoint( + "pipeline", "first_letters", "hash2", incremental_info["unchanged_indices"] + ) + + print(f" Reusing {len(unchanged_stage1)} unchanged results from stage 1") + + # Simulate processing only changed/new records + print(" Processing only changed and new records...") + records_to_process = ( + incremental_info["changed_indices"] + incremental_info["new_indices"] + ) + + newly_processed = [] + for idx in records_to_process: + if idx < len(updated_data): + record = updated_data[idx] + new_record = record.copy() + new_record["first_letters"] = extract_first_letters(record["content"]) + newly_processed.append(new_record) + + print(f" Processed {len(newly_processed)} records (vs {len(updated_data)} total)") + processing_reduction = (len(updated_data) - len(newly_processed)) / len(updated_data) + print(f" Processing reduction: {processing_reduction:.1%}") + + # Test with stage 2 as well + incremental_info_stage2 = checkpoint_manager.get_incremental_processing_info( + "pipeline", "analyzed", updated_hashes + ) + + unchanged_stage2 = checkpoint_manager.load_incremental_checkpoint( + "pipeline", "analyzed", "hash3", incremental_info_stage2["unchanged_indices"] + ) + + print(f"\nStage 2 incremental analysis:") + print(f" Reusing {len(unchanged_stage2)} unchanged results from stage 2") + print(f" Processing reduction: {(len(updated_data) - incremental_info_stage2['total_changes']) / len(updated_data):.1%}") + + # Verify incremental processing achieved significant savings + assert processing_reduction > 0.90, f"Expected >90% processing reduction, got {processing_reduction:.1%}" + assert len(unchanged_stage1) > 0, "Should have some unchanged records to reuse" + assert len(unchanged_stage2) > 0, "Should have some unchanged records to reuse" + + print(f"\n✓ Incremental processing test successful!") + print(f"✓ Achieved {processing_reduction:.1%} reduction in processing work") + print(f"✓ Successfully reused cached results for unchanged records") + + # Show some actual examples of the text processing + print(f"\nExample processed results:") + example_record = stage2_data[0] + print(f" Document ID: {example_record['id']}") + print(f" First 100 chars: {example_record['content'][:100]}...") + print(f" First letters: {example_record['first_letters']}") + print(f" Analysis: {example_record['analysis']}") + + # Verify the first letter extraction is working correctly + test_text = "The quick brown fox jumps over the lazy dog and then something else happens here today" + extracted = extract_first_letters(test_text) + # Count: The(1) quick(2) brown(3) fox(4) jumps(5) over(6) the(7) lazy(8) dog(9) and(10) then(11) something(12) else(13) happens(14) here(15) + expected = "TQBFJOTLDATSEHH" # First letters of first 15 words + assert extracted == expected, f"Expected {expected}, got {extracted}" + print(f" ✓ First letter extraction verified: '{test_text}' -> '{extracted}'") + + +def test_incremental_checkpointing_with_real_docetl_pipeline(temp_dir): + """Test incremental checkpointing with an actual DocETL pipeline using DSLRunner.""" + import tempfile + import os + from docetl.runner import DSLRunner + import json + import hashlib + + def compute_record_hash(record): + """Compute hash for change detection.""" + content = {"title": record["title"], "content": record["content"]} + return hashlib.md5(json.dumps(content, sort_keys=True).encode()).hexdigest() + + # Create input data files + input_file_v1 = os.path.join(temp_dir, "input_v1.json") + input_file_v2 = os.path.join(temp_dir, "input_v2.json") + output_file = os.path.join(temp_dir, "output.json") + + # Initial dataset + initial_documents = [ + {"title": "AI Research", "content": "Artificial intelligence is advancing rapidly in natural language processing."}, + {"title": "Machine Learning", "content": "Deep learning models are becoming more sophisticated and accurate."}, + {"title": "Data Science", "content": "Big data analytics helps organizations make better decisions."}, + {"title": "Cloud Computing", "content": "Distributed systems enable scalable computing infrastructure."}, + {"title": "Cybersecurity", "content": "Protecting digital assets requires comprehensive security strategies."} + ] + + # Save initial dataset + with open(input_file_v1, 'w') as f: + json.dump(initial_documents, f) + + print(f"Created initial dataset with {len(initial_documents)} documents") + + # DocETL pipeline configuration + pipeline_config = { + "default_model": "gpt-4o-mini", + "operations": [ + { + "name": "extract_keywords", + "type": "map", + "prompt": "Extract 3 key topics from this text: '{{ input.content }}'. Return as a comma-separated list.", + "output": {"schema": {"keywords": "string"}}, + "model": "gpt-4o-mini" + }, + { + "name": "categorize", + "type": "map", + "prompt": "Categorize this document based on its title '{{ input.title }}' and keywords '{{ input.keywords }}'. Choose from: Technology, Business, Science, Education.", + "output": {"schema": {"category": "string"}}, + "model": "gpt-4o-mini" + } + ], + "datasets": { + "input_docs": { + "type": "file", + "path": input_file_v1 + } + }, + "pipeline": { + "steps": [ + { + "name": "step1_extract", + "input": "input_docs", + "operations": ["extract_keywords"] + }, + { + "name": "step2_categorize", + "input": "step1_extract", + "operations": ["categorize"] + } + ], + "output": { + "type": "file", + "path": output_file, + "intermediate_dir": temp_dir + } + } + } + + # Create runner with checkpoint directory + runner = DSLRunner(pipeline_config, max_threads=4) + + print("\\nRunning initial pipeline...") + + # Run initial pipeline + cost_v1 = runner.load_run_save() + + # Load the result + with open(output_file, 'r') as f: + result_v1 = json.load(f) + + print(f"Initial pipeline completed. Output size: {len(result_v1)}") + + # Check checkpoints were created + checkpoint_sizes = {} + for step_name in ["step1_extract", "step2_categorize"]: + for op_name in ["extract_keywords", "categorize"]: + size = runner.get_checkpoint_size(step_name, op_name) + if size: + checkpoint_sizes[f"{step_name}_{op_name}"] = size + print(f"Checkpoint {step_name}/{op_name}: {size} bytes") + + total_checkpoint_size = runner.get_total_checkpoint_size() + print(f"Total checkpoint size: {total_checkpoint_size} bytes") + + # Now simulate data changes - modify 2 docs, add 1 new doc + print("\\nSimulating data changes...") + modified_documents = initial_documents.copy() + + # Modify 2nd document + modified_documents[1] = { + "title": "Advanced Machine Learning", # Changed title + "content": "Deep learning and neural networks are revolutionizing AI applications across industries." # Changed content + } + + # Modify 4th document + modified_documents[3] = { + "title": "Cloud Computing", # Same title + "content": "Modern cloud platforms provide elastic, scalable computing resources with global reach." # Changed content + } + + # Add new document + modified_documents.append({ + "title": "Quantum Computing", + "content": "Quantum algorithms promise exponential speedups for certain computational problems." + }) + + # Save modified dataset + with open(input_file_v2, 'w') as f: + json.dump(modified_documents, f) + + print(f"Created modified dataset with {len(modified_documents)} documents") + print("Changes: 2 documents modified, 1 document added") + + # Analyze what changed using our incremental functionality + initial_hashes = [compute_record_hash(doc) for doc in initial_documents] + modified_hashes = [compute_record_hash(doc) for doc in modified_documents] + + # Check what incremental processing would detect + if runner.checkpoint_manager: + incremental_info = runner.checkpoint_manager.get_incremental_processing_info( + "step1_extract", "extract_keywords", modified_hashes + ) + else: + incremental_info = {"needs_full_reprocess": True, "reason": "No checkpoint manager"} + + print(f"\\nIncremental analysis (if we had tracked hashes):") + if incremental_info.get("needs_full_reprocess"): + print(f" Would need full reprocess: {incremental_info.get('reason', 'Unknown')}") + else: + print(f" Changed documents: {len(incremental_info.get('changed_indices', []))}") + print(f" New documents: {len(incremental_info.get('new_indices', []))}") + print(f" Unchanged documents: {len(incremental_info.get('unchanged_indices', []))}") + print(f" Total changes: {incremental_info.get('total_changes', 0)}") + + potential_reuse = len(incremental_info.get('unchanged_indices', [])) + total_docs = len(modified_documents) + if total_docs > 0: + efficiency = potential_reuse / total_docs * 100 + print(f" Potential processing efficiency: {efficiency:.1f}% reuse") + + # Update pipeline config to use new input file + pipeline_config["datasets"]["input_docs"]["path"] = input_file_v2 + output_file_v2 = os.path.join(temp_dir, "output_v2.json") + pipeline_config["pipeline"]["output"]["path"] = output_file_v2 + + # Create new runner for modified pipeline + runner_v2 = DSLRunner(pipeline_config, max_threads=4) + + print("\\nRunning pipeline with modified data...") + + # This will reuse existing checkpoints where possible + cost_v2 = runner_v2.load_run_save() + + # Load the result + with open(output_file_v2, 'r') as f: + result_v2 = json.load(f) + + print(f"Modified pipeline completed. Output size: {len(result_v2)}") + + # Compare results + print(f"\\nResults comparison:") + print(f" Initial run: {len(result_v1)} documents processed") + print(f" Modified run: {len(result_v2)} documents processed") + print(f" New total checkpoint size: {runner_v2.get_total_checkpoint_size()} bytes") + + # Show actual processing results + if result_v2: + print(f"\\nExample processed document:") + example = result_v2[0] + print(f" Title: {example.get('title', 'N/A')}") + print(f" Keywords: {example.get('keywords', 'N/A')}") + print(f" Category: {example.get('category', 'N/A')}") + + # Verify pipeline actually ran + assert len(result_v1) > 0, "Initial pipeline should produce results" + assert len(result_v2) > 0, "Modified pipeline should produce results" + # Note: Second run might reuse checkpoints, so result count may differ + # This actually demonstrates checkpoint reuse working! + + print("\\n✓ Real DocETL pipeline with checkpointing test completed!") + print("✓ Pipeline successfully processed documents and used checkpoint system") + + return { + "initial_docs": len(initial_documents), + "modified_docs": len(modified_documents), + "initial_checkpoints": total_checkpoint_size, + "modified_checkpoints": runner_v2.get_total_checkpoint_size(), + "incremental_info": incremental_info + } + + +def test_docetl_pipeline_large_dataset_space_efficiency(temp_dir): + """Test real DocETL pipeline with large synthetic dataset to measure true space efficiency.""" + import random + import os + import json + from docetl.runner import DSLRunner + + def generate_realistic_document(doc_id): + """Generate realistic document content.""" + # Base content pools for realistic variety + topics = [ + "artificial intelligence", "machine learning", "deep learning", "neural networks", + "natural language processing", "computer vision", "robotics", "automation", + "data science", "big data", "analytics", "business intelligence", "statistics", + "cloud computing", "distributed systems", "microservices", "containers", "kubernetes", + "cybersecurity", "encryption", "privacy", "data protection", "compliance", + "software engineering", "agile development", "devops", "continuous integration", + "blockchain", "cryptocurrency", "fintech", "digital transformation" + ] + + companies = [ + "TechCorp", "DataSoft", "AI Innovations", "CloudFirst", "SecureNet", "AnalyticsPro", + "NextGen Systems", "Digital Solutions", "SmartTech", "FutureLabs", "CyberGuard", + "DataFlow", "InnovateTech", "CloudScale", "TechAdvance", "DigitalEdge" + ] + + actions = [ + "revolutionizing", "transforming", "advancing", "improving", "optimizing", + "streamlining", "enhancing", "accelerating", "modernizing", "innovating", + "disrupting", "empowering", "enabling", "facilitating", "delivering" + ] + + outcomes = [ + "business operations", "customer experience", "market efficiency", "operational costs", + "productivity levels", "competitive advantage", "innovation cycles", "decision making", + "process automation", "data insights", "system performance", "user engagement", + "revenue growth", "market reach", "service delivery", "operational excellence" + ] + + # Generate varied, realistic content + topic = random.choice(topics) + company = random.choice(companies) + action = random.choice(actions) + outcome = random.choice(outcomes) + + # Create realistic document with varied length + base_content = f"{company} is {action} {topic} to improve {outcome}." + + # Add varied additional content + additional_sentences = [ + f"This technology represents a significant advancement in the field.", + f"Industry experts predict widespread adoption within the next few years.", + f"The implementation has shown promising results in initial testing phases.", + f"Cost savings and efficiency gains are expected to be substantial.", + f"Integration with existing systems has been seamless and effective.", + f"Customer feedback has been overwhelmingly positive and encouraging.", + f"The solution addresses key challenges faced by organizations today.", + f"Scalability and performance metrics exceed industry benchmarks." + ] + + # Randomly add 2-6 additional sentences + num_additional = random.randint(2, 6) + selected_additional = random.sample(additional_sentences, num_additional) + + full_content = base_content + " " + " ".join(selected_additional) + + # Generate varied titles + title_templates = [ + f"{topic.title()} Innovation at {company}", + f"How {company} Uses {topic.title()}", + f"{company}: {action.title()} with {topic.title()}", + f"The Future of {topic.title()} at {company}", + f"{company} {topic.title()} Case Study" + ] + + title = random.choice(title_templates) + + return { + "id": doc_id, + "title": title, + "content": full_content, + "source": f"source_{doc_id % 20}", # 20 different sources + "department": random.choice(["Engineering", "Product", "Research", "Operations", "Marketing"]), + "priority": random.choice(["High", "Medium", "Low"]), + "timestamp": f"2024-{random.randint(1,12):02d}-{random.randint(1,28):02d}" + } + + # Generate large dataset (100 documents - manageable for real LLM calls) + print("Generating large synthetic dataset (100 documents)...") + large_dataset = [generate_realistic_document(i) for i in range(100)] + + # Save dataset + input_file = os.path.join(temp_dir, "large_input.json") + output_file = os.path.join(temp_dir, "large_output.json") + + with open(input_file, 'w') as f: + json.dump(large_dataset, f) + + # Calculate input data size + input_size = os.path.getsize(input_file) + print(f"Input dataset size: {input_size:,} bytes ({input_size/1024:.1f} KB)") + + # DocETL pipeline configuration + pipeline_config = { + "default_model": "gpt-4o-mini", + "operations": [ + { + "name": "extract_keywords", + "type": "map", + "prompt": "Extract 3-5 key technical topics from this content: '{{ input.content }}'. Return as comma-separated list.", + "output": {"schema": {"keywords": "string"}}, + "model": "gpt-4o-mini" + }, + { + "name": "categorize_domain", + "type": "map", + "prompt": "Based on the title '{{ input.title }}' and keywords '{{ input.keywords }}', categorize this into one domain: AI/ML, Cloud/Infrastructure, Security, Data/Analytics, or Software Development.", + "output": {"schema": {"domain": "string"}}, + "model": "gpt-4o-mini" + }, + { + "name": "assess_priority", + "type": "map", + "prompt": "Rate the business impact of this '{{ input.domain }}' initiative: '{{ input.title }}'. Return: Critical, High, Medium, or Low.", + "output": {"schema": {"business_impact": "string"}}, + "model": "gpt-4o-mini" + } + ], + "datasets": { + "large_docs": { + "type": "file", + "path": input_file + } + }, + "pipeline": { + "steps": [ + { + "name": "step1_keywords", + "input": "large_docs", + "operations": ["extract_keywords"] + }, + { + "name": "step2_domain", + "input": "step1_keywords", + "operations": ["categorize_domain"] + }, + { + "name": "step3_priority", + "input": "step2_domain", + "operations": ["assess_priority"] + } + ], + "output": { + "type": "file", + "path": output_file, + "intermediate_dir": temp_dir + } + } + } + + print("\\nRunning large dataset pipeline...") + + # Run pipeline + runner = DSLRunner(pipeline_config, max_threads=4) + + import time + start_time = time.time() + cost = runner.load_run_save() + execution_time = time.time() - start_time + + # Load results + with open(output_file, 'r') as f: + results = json.load(f) + + output_size = os.path.getsize(output_file) + + print(f"\\nPipeline completed in {execution_time:.2f} seconds") + print(f"Processed {len(results)} documents") + print(f"Output size: {output_size:,} bytes ({output_size/1024:.1f} KB)") + + # Get checkpoint sizes + checkpoint_sizes = {} + total_checkpoint_size = 0 + + operations = [ + ("step1_keywords", "extract_keywords"), + ("step2_domain", "categorize_domain"), + ("step3_priority", "assess_priority") + ] + + for step_name, op_name in operations: + size = runner.get_checkpoint_size(step_name, op_name) + if size: + checkpoint_sizes[f"{step_name}/{op_name}"] = size + total_checkpoint_size += size + print(f"Checkpoint {step_name}/{op_name}: {size:,} bytes") + + print(f"Total checkpoint size: {total_checkpoint_size:,} bytes ({total_checkpoint_size/1024:.1f} KB)") + + # Calculate space efficiency vs JSON + # The output file is already JSON, so compare checkpoint size to output size + if total_checkpoint_size > 0 and output_size > 0: + efficiency_ratio = total_checkpoint_size / output_size + print(f"\\nSpace efficiency analysis:") + print(f" Output JSON size: {output_size:,} bytes") + print(f" PyArrow checkpoints: {total_checkpoint_size:,} bytes") + print(f" Ratio: {efficiency_ratio:.3f}") + + if efficiency_ratio < 1: + savings = (1 - efficiency_ratio) * 100 + print(f" Space savings: {savings:.1f}% (PyArrow more efficient)") + else: + overhead = (efficiency_ratio - 1) * 100 + print(f" Space overhead: {overhead:.1f}% (JSON more efficient)") + + # Show sample results + print(f"\\nSample processed documents:") + for i, doc in enumerate(results[:3]): + print(f" {i+1}. {doc.get('title', 'N/A')}") + print(f" Keywords: {doc.get('keywords', 'N/A')}") + print(f" Domain: {doc.get('domain', 'N/A')}") + print(f" Impact: {doc.get('business_impact', 'N/A')}") + + # Test checkpoint reuse with modified dataset + print(f"\\nTesting checkpoint reuse...") + + # Modify 5% of documents (5 docs) + modified_dataset = large_dataset.copy() + num_to_modify = 5 + + for i in range(num_to_modify): + idx = random.randint(0, len(modified_dataset) - 1) + # Modify content to trigger reprocessing + modified_dataset[idx] = generate_realistic_document(len(modified_dataset) + i) + + # Save modified dataset + input_file_v2 = os.path.join(temp_dir, "large_input_v2.json") + output_file_v2 = os.path.join(temp_dir, "large_output_v2.json") + + with open(input_file_v2, 'w') as f: + json.dump(modified_dataset, f) + + # Update config for second run + pipeline_config["datasets"]["large_docs"]["path"] = input_file_v2 + pipeline_config["pipeline"]["output"]["path"] = output_file_v2 + + # Run modified pipeline (should reuse some checkpoints) + runner_v2 = DSLRunner(pipeline_config, max_threads=4) + + start_time_v2 = time.time() + cost_v2 = runner_v2.load_run_save() + execution_time_v2 = time.time() - start_time_v2 + + print(f"Second run completed in {execution_time_v2:.2f} seconds") + print(f"Performance improvement: {((execution_time - execution_time_v2) / execution_time * 100):.1f}% faster") + + # Final assertions + assert len(results) == 100, "Should process all 100 documents" + assert total_checkpoint_size > 0, "Should create checkpoints" + assert execution_time_v2 < execution_time, "Second run should be faster due to checkpoint reuse" + + print(f"\\n✓ Large dataset pipeline test completed successfully!") + print(f"✓ Demonstrated real space efficiency and performance benefits") + + return { + "documents_processed": len(results), + "input_size": input_size, + "output_size": output_size, + "checkpoint_size": total_checkpoint_size, + "initial_time": execution_time, + "rerun_time": execution_time_v2, + "efficiency_ratio": total_checkpoint_size / output_size if output_size > 0 else 0 + } + + +def test_large_data_handling(checkpoint_manager): + """Test handling of larger datasets.""" + # Create larger dataset + large_data = [ + {"id": i, "text": f"Document {i}", "value": i * 1.5} + for i in range(1000) + ] + + step_name = "test_step" + operation_name = "test_operation" + operation_hash = "test_hash" + + # Save and load + checkpoint_manager.save_checkpoint(step_name, operation_name, large_data, operation_hash) + loaded_data = checkpoint_manager.load_checkpoint(step_name, operation_name, operation_hash) + + # Verify data integrity + assert len(loaded_data) == 1000 + assert loaded_data[0]["id"] == 0 + assert loaded_data[999]["id"] == 999 + assert loaded_data[500]["text"] == "Document 500" + assert loaded_data[100]["value"] == 150.0 + + +def test_special_characters_in_names(checkpoint_manager, sample_data): + """Test handling of special characters in step and operation names.""" + step_name = "test-step_with.special@chars" + operation_name = "test-operation_with.special@chars" + operation_hash = "test_hash" + + # This should work without issues + checkpoint_manager.save_checkpoint(step_name, operation_name, sample_data, operation_hash) + loaded_data = checkpoint_manager.load_checkpoint(step_name, operation_name, operation_hash) + + assert loaded_data == sample_data + + +def test_console_logging(mock_console, temp_dir, sample_data): + """Test that console logging works correctly.""" + cm = CheckpointManager(temp_dir, console=mock_console) + + # Save checkpoint + cm.save_checkpoint("step", "op", sample_data, "hash") + + # Verify console.log was called + mock_console.log.assert_called() + + # Check that log message contains expected content + log_calls = mock_console.log.call_args_list + assert any("Checkpoint saved" in str(call) for call in log_calls) + + +# New tests for storage type compatibility + +@pytest.fixture +def json_checkpoint_manager(temp_dir, mock_console): + """Create a JSON checkpoint manager instance.""" + return CheckpointManager(temp_dir, console=mock_console, storage_type="json") + + +@pytest.fixture +def arrow_checkpoint_manager(temp_dir, mock_console): + """Create a PyArrow checkpoint manager instance.""" + return CheckpointManager(temp_dir, console=mock_console, storage_type="arrow") + + +def test_storage_type_validation(temp_dir): + """Test that invalid storage types are rejected.""" + with pytest.raises(ValueError, match="Invalid storage_type 'invalid'"): + CheckpointManager(temp_dir, storage_type="invalid") + + +def test_json_storage_format(json_checkpoint_manager, sample_data): + """Test JSON storage format specifically.""" + step_name = "test_step" + operation_name = "test_operation" + operation_hash = "test_hash" + + # Save checkpoint + json_checkpoint_manager.save_checkpoint(step_name, operation_name, sample_data, operation_hash) + + # Verify file extension is .json + checkpoint_path = json_checkpoint_manager._get_checkpoint_path(step_name, operation_name) + assert checkpoint_path.endswith(".json") + assert os.path.exists(checkpoint_path) + + # Verify content is valid JSON + with open(checkpoint_path, 'r') as f: + loaded_json = json.load(f) + assert loaded_json == sample_data + + # Load checkpoint and verify + loaded_data = json_checkpoint_manager.load_checkpoint(step_name, operation_name, operation_hash) + assert loaded_data == sample_data + + +def test_arrow_storage_format(arrow_checkpoint_manager, sample_data): + """Test PyArrow storage format specifically.""" + step_name = "test_step" + operation_name = "test_operation" + operation_hash = "test_hash" + + # Save checkpoint + arrow_checkpoint_manager.save_checkpoint(step_name, operation_name, sample_data, operation_hash) + + # Verify file extension is .parquet + checkpoint_path = arrow_checkpoint_manager._get_checkpoint_path(step_name, operation_name) + assert checkpoint_path.endswith(".parquet") + assert os.path.exists(checkpoint_path) + + # Load checkpoint and verify + loaded_data = arrow_checkpoint_manager.load_checkpoint(step_name, operation_name, operation_hash) + assert loaded_data == sample_data + + +def test_backward_compatibility_json_to_arrow(temp_dir, sample_data): + """Test that Arrow manager can read existing JSON checkpoints.""" + step_name = "test_step" + operation_name = "test_operation" + operation_hash = "test_hash" + + # Create JSON checkpoint first + json_manager = CheckpointManager(temp_dir, storage_type="json") + json_manager.save_checkpoint(step_name, operation_name, sample_data, operation_hash) + + # Create Arrow manager and try to read JSON checkpoint + arrow_manager = CheckpointManager(temp_dir, storage_type="arrow") + loaded_data = arrow_manager.load_checkpoint(step_name, operation_name, operation_hash) + + assert loaded_data == sample_data + + +def test_backward_compatibility_arrow_to_json(temp_dir, sample_data): + """Test that JSON manager can read existing Arrow checkpoints.""" + step_name = "test_step" + operation_name = "test_operation" + operation_hash = "test_hash" + + # Create Arrow checkpoint first + arrow_manager = CheckpointManager(temp_dir, storage_type="arrow") + arrow_manager.save_checkpoint(step_name, operation_name, sample_data, operation_hash) + + # Create JSON manager and try to read Arrow checkpoint + json_manager = CheckpointManager(temp_dir, storage_type="json") + loaded_data = json_manager.load_checkpoint(step_name, operation_name, operation_hash) + + assert loaded_data == sample_data + + +def test_mixed_storage_list_outputs(temp_dir, sample_data): + """Test list_outputs with mixed JSON and Arrow checkpoints.""" + # Create checkpoints with different storage types + json_manager = CheckpointManager(temp_dir, storage_type="json") + arrow_manager = CheckpointManager(temp_dir, storage_type="arrow") + + json_manager.save_checkpoint("step1", "op1", sample_data, "hash1") + arrow_manager.save_checkpoint("step1", "op2", sample_data, "hash2") + json_manager.save_checkpoint("step2", "op1", sample_data, "hash3") + arrow_manager.save_checkpoint("step2", "op2", sample_data, "hash4") + + # Both managers should see all outputs + json_outputs = json_manager.list_outputs() + arrow_outputs = arrow_manager.list_outputs() + + expected_outputs = [("step1", "op1"), ("step1", "op2"), ("step2", "op1"), ("step2", "op2")] + assert sorted(json_outputs) == sorted(expected_outputs) + assert sorted(arrow_outputs) == sorted(expected_outputs) + + +def test_storage_format_preference(temp_dir, sample_data): + """Test that managers prefer their own format but can read others.""" + step_name = "test_step" + operation_name = "test_operation" + operation_hash = "test_hash" + + # Create both JSON and Arrow checkpoints for same operation + json_manager = CheckpointManager(temp_dir, storage_type="json") + arrow_manager = CheckpointManager(temp_dir, storage_type="arrow") + + json_data = sample_data.copy() + arrow_data = [{"id": 999, "text": "Arrow data", "category": "Z"}] + + json_manager.save_checkpoint(step_name, operation_name, json_data, operation_hash) + arrow_manager.save_checkpoint(step_name, operation_name, arrow_data, operation_hash) + + # Each manager should prefer its own format + json_loaded = json_manager.load_checkpoint(step_name, operation_name, operation_hash) + arrow_loaded = arrow_manager.load_checkpoint(step_name, operation_name, operation_hash) + + assert json_loaded == json_data # JSON manager gets JSON data + assert arrow_loaded == arrow_data # Arrow manager gets Arrow data + + +def test_load_output_as_dataframe_both_formats(temp_dir, sample_data): + """Test loading as DataFrame works for both storage formats.""" + step_name = "test_step" + operation_name = "test_operation" + operation_hash = "test_hash" + + # Test JSON format + json_manager = CheckpointManager(temp_dir, storage_type="json") + json_manager.save_checkpoint(step_name, "json_op", sample_data, operation_hash) + json_df = json_manager.load_output_as_dataframe(step_name, "json_op") + + assert isinstance(json_df, pd.DataFrame) + assert len(json_df) == len(sample_data) + assert list(json_df.columns) == ["id", "text", "category"] + + # Test Arrow format + arrow_manager = CheckpointManager(temp_dir, storage_type="arrow") + arrow_manager.save_checkpoint(step_name, "arrow_op", sample_data, operation_hash) + arrow_df = arrow_manager.load_output_as_dataframe(step_name, "arrow_op") + + assert isinstance(arrow_df, pd.DataFrame) + assert len(arrow_df) == len(sample_data) + assert list(arrow_df.columns) == ["id", "text", "category"] + + # DataFrames should be equivalent + pd.testing.assert_frame_equal(json_df, arrow_df) + + +def test_checkpoint_size_both_formats(temp_dir, sample_data): + """Test checkpoint size calculation for both formats.""" + step_name = "test_step" + operation_name = "test_operation" + operation_hash = "test_hash" + + # Create checkpoints in both formats + json_manager = CheckpointManager(temp_dir, storage_type="json") + arrow_manager = CheckpointManager(temp_dir, storage_type="arrow") + + json_manager.save_checkpoint(step_name, "json_op", sample_data, operation_hash) + arrow_manager.save_checkpoint(step_name, "arrow_op", sample_data, operation_hash) + + # Both should return valid sizes + json_size = json_manager.get_checkpoint_size(step_name, "json_op") + arrow_size = arrow_manager.get_checkpoint_size(step_name, "arrow_op") + + assert json_size > 0 + assert arrow_size > 0 + + # Total size should include both + total_json = json_manager.get_total_checkpoint_size() + total_arrow = arrow_manager.get_total_checkpoint_size() + + # Both managers should see both files in total + assert total_json == json_size + arrow_size + assert total_arrow == json_size + arrow_size + + +def test_default_storage_type(): + """Test that default storage type is JSON.""" + with tempfile.TemporaryDirectory() as temp_dir: + cm = CheckpointManager(temp_dir) + assert cm.storage_type == "json" + + +def test_case_insensitive_storage_type(temp_dir): + """Test that storage type is case insensitive.""" + cm_upper = CheckpointManager(temp_dir, storage_type="JSON") + assert cm_upper.storage_type == "json" + + cm_mixed = CheckpointManager(temp_dir, storage_type="Arrow") + assert cm_mixed.storage_type == "arrow" + + +def test_pyarrow_sanitization_handling(temp_dir): + """Test that PyArrow sanitizes and desanitizes problematic data structures.""" + arrow_manager = CheckpointManager(temp_dir, storage_type="arrow") + + # Create data that PyArrow has trouble with (empty nested structures) + problematic_data = [ + { + "id": 1, + "text": "Normal text", + "empty_struct": {}, # Empty dict + "nested_empty": {"level1": {"level2": {}}}, # Deeply nested empty + "mixed_list": [1, "text", {}], # Mixed types with empty dict + "null_value": None, # None values + "empty_list": [], # Empty list + }, + { + "id": 2, + "text": "Another document", + "complex_nested": { + "_kv_pairs_preresolve_name_email_resolver": {}, # Known problematic structure + "normal_field": "value" + } + } + ] + + step_name = "test_step" + operation_name = "test_operation" + operation_hash = "test_hash" + + # This should not fail, even with problematic data + arrow_manager.save_checkpoint(step_name, operation_name, problematic_data, operation_hash) + + # Should be able to load the data back exactly as it was + loaded_data = arrow_manager.load_checkpoint(step_name, operation_name, operation_hash) + assert loaded_data == problematic_data + + # Verify that a .parquet file was created (not JSON fallback) + checkpoint_path = arrow_manager._get_checkpoint_path(step_name, operation_name) + assert checkpoint_path.endswith('.parquet') + assert os.path.exists(checkpoint_path) + + +def test_pyarrow_sanitization_methods(temp_dir): + """Test the sanitization and desanitization methods directly.""" + arrow_manager = CheckpointManager(temp_dir, storage_type="arrow") + + # Test data with various edge cases + original_data = [ + { + "empty_dict": {}, + "empty_list": [], + "null_value": None, + "nested_empty": {"level1": {"empty": {}, "list": []}}, + "normal": "value" + } + ] + + # Test sanitization + sanitized = arrow_manager._sanitize_for_parquet(original_data) + + # Verify sanitized structure + assert sanitized[0]["empty_dict"] == {"__empty_dict__": True} + assert sanitized[0]["empty_list"] == ["__empty_list__"] + assert sanitized[0]["null_value"] == "__null__" + assert sanitized[0]["normal"] == "value" + + # Test desanitization + desanitized = arrow_manager._desanitize_from_parquet(sanitized) + assert desanitized == original_data + + +def test_complex_data_structures(temp_dir): + """Test various complex data structures that might cause PyArrow issues.""" + arrow_manager = CheckpointManager(temp_dir, storage_type="arrow") + + complex_data = [ + { + "id": 1, + "simple_list": [1, 2, 3], + "mixed_list": [1, "text", True, None], + "nested_dict": {"a": {"b": {"c": "deep_value"}}}, + "empty_collections": { + "empty_list": [], + "empty_dict": {}, + "empty_string": "" + }, + "none_values": None, + "boolean_values": [True, False, None] + }, + { + "id": 2, + "unicode_text": "Unicode: 🚀 émojis and spëcial chars", + "large_number": 12345678901234567890, + "float_precision": 3.141592653589793, + "date_string": "2023-01-01T12:00:00Z" + } + ] + + # Should handle these without failing + arrow_manager.save_checkpoint("complex", "structures", complex_data, "hash") + loaded = arrow_manager.load_checkpoint("complex", "structures", "hash") + + # Data should round-trip correctly + assert len(loaded) == len(complex_data) + assert loaded[0]["id"] == 1 + assert loaded[1]["unicode_text"] == "Unicode: 🚀 émojis and spëcial chars" \ No newline at end of file