From 8f1036b7ef79e80304d101d7eca2a50e4950af67 Mon Sep 17 00:00:00 2001 From: Shreya Shankar Date: Wed, 8 Jan 2025 15:20:10 -0800 Subject: [PATCH] fix: equijoin is out of date (#269) * fix: equijoin is out of date * fix: equijoin is out of date and there are runtime errors * chore: bump up version * fix: equijoin is out of date and there are runtime errors * fix: equijoin is out of date and there are runtime errors * fix: equijoin is out of date and there are runtime errors * fix: equijoin is out of date and there are runtime errors * fix: equijoin is out of date and there are runtime errors --- docetl/__init__.py | 2 +- docetl/builder.py | 27 +- docetl/cli.py | 1 - docetl/operations/equijoin.py | 160 ++++++-- docetl/operations/map.py | 2 +- docetl/optimizers/join_optimizer.py | 2 +- docetl/runner.py | 4 +- poetry.lock | 583 ++++++++++++---------------- pyproject.toml | 4 +- 9 files changed, 405 insertions(+), 380 deletions(-) diff --git a/docetl/__init__.py b/docetl/__init__.py index 9f4ef485..348a16a7 100644 --- a/docetl/__init__.py +++ b/docetl/__init__.py @@ -1,4 +1,4 @@ -__version__ = "0.2" +__version__ = "0.21" from docetl.runner import DSLRunner from docetl.builder import Optimizer diff --git a/docetl/builder.py b/docetl/builder.py index f973b2a8..d0307c7b 100644 --- a/docetl/builder.py +++ b/docetl/builder.py @@ -645,12 +645,13 @@ def optimize(self) -> float: for op in optimized_step["operations"]: changed_op = False + op_name = list(op.keys())[0] if isinstance(op, dict) else op for i, op_config in enumerate(self.optimized_config["operations"]): - if op_config["name"] == op: - self.optimized_config["operations"][i] = step_operations[op] + if op_config["name"] == op_name: + self.optimized_config["operations"][i] = step_operations[op_name] changed_op = True if not changed_op: - self.optimized_config["operations"].append(step_operations[op]) + self.optimized_config["operations"].append(step_operations[op_name]) self.optimized_config["pipeline"]["steps"] = [ step @@ -670,7 +671,7 @@ def optimize(self) -> float: if s["name"] == step_name ][0], "operations": [ - self.find_operation(op, self.optimized_config) + self.find_operation(list(op.keys())[0] if isinstance(op, dict) else op, self.optimized_config) for op in optimized_step["operations"] ], } @@ -830,8 +831,10 @@ def _optimize_step( # Run the pipeline step_ops = [] for step_op in step.get("operations"): - if step_op in replacement_operations: - step_ops.extend(replacement_operations[step_op]) + step_op_name = list(step_op.keys())[0] if isinstance(step_op, dict) else step_op + + if step_op_name in replacement_operations: + step_ops.extend(replacement_operations[step_op_name]) else: step_ops.append(step_op) @@ -852,7 +855,10 @@ def _optimize_step( # If optimize is False or operation type is not supported, just use the operation without optimization output_data = self._run_operation(op_object, input_data) optimized_operations[operation_name] = op_object - optimized_operation_names.append(operation_name) + if op_object.get("type") == "equijoin": + optimized_operation_names.append(operation) + else: + optimized_operation_names.append(operation_name) selectivity = len(output_data) / len(input_data) self.selectivities[step.get("name")][operation_name] = selectivity @@ -934,8 +940,8 @@ def _optimize_step( new_right_name, ) = self._optimize_equijoin( op_object, - operation["left"], - operation["right"], + next(iter(operation.values()))["left"], + next(iter(operation.values()))["right"], input_data["left"], input_data["right"], status, @@ -1033,6 +1039,7 @@ def _optimize_step( output_data = input_data optimized_step = step.copy() + optimized_step["operations"] = optimized_operation_names return optimized_step, optimized_operations, output_data @@ -1075,7 +1082,7 @@ def _get_sample_data( { "step": step, "operations": [ - self.find_operation(op) for op in step["operations"] + self.find_operation(list(op.keys())[0] if isinstance(op, dict) else op, self.optimized_config) for op in step["operations"] ], } ).encode() diff --git a/docetl/cli.py b/docetl/cli.py index dbbe09fe..f5dcac92 100644 --- a/docetl/cli.py +++ b/docetl/cli.py @@ -11,7 +11,6 @@ app = typer.Typer() - @app.command() def build( yaml_file: Path = typer.Argument( diff --git a/docetl/operations/equijoin.py b/docetl/operations/equijoin.py index 23967fab..44f128ed 100644 --- a/docetl/operations/equijoin.py +++ b/docetl/operations/equijoin.py @@ -9,18 +9,17 @@ from multiprocessing import Pool, cpu_count from typing import Any, Dict, List, Tuple, Optional +from docetl import console from docetl.operations.utils import strict_render +from docetl.operations.utils.progress import RichLoopBar import numpy as np from jinja2 import Template from litellm import model_cost from rich.prompt import Confirm +from rich.console import Console from docetl.operations.base import BaseOperation -from docetl.operations.utils import ( - rich_as_completed, -) from docetl.utils import completion_cost -from pydantic import Field # Global variables to store shared data @@ -40,6 +39,10 @@ def is_match(left_item: Dict[str, Any], right_item: Dict[str, Any]) -> bool: for condition in _blocking_conditions ) +# LLM-based comparison for blocked pairs +def get_hashable_key(item: Dict) -> str: + return json.dumps(item, sort_keys=True) + def process_left_item( left_item: Dict[str, Any] @@ -69,7 +72,7 @@ class schema(BaseOperation.schema): limit_comparisons: Optional[int] = None blocking_keys: Optional[Dict[str, List[str]]] = None timeout: Optional[int] = None - litellm_completion_kwargs: Dict[str, Any] = Field(default_factory=dict) + litellm_completion_kwargs: Dict[str, Any] = {} def compare_pair( self, @@ -95,8 +98,11 @@ def compare_pair( Tuple[bool, float]: A tuple containing a boolean indicating whether the items match and the cost of the comparison. """ - - prompt = strict_render(comparison_prompt, {"left": item1, "right": item2}) + try: + prompt = strict_render(comparison_prompt, {"left": item1, "right": item2}) + except Exception as e: + self.console.print(f"[red]Error rendering prompt: {e}[/red]") + return False, 0 response = self.runner.api.call_llm( model, "compare", @@ -107,10 +113,16 @@ def compare_pair( bypass_cache=self.config.get("bypass_cache", False), litellm_completion_kwargs=self.config.get("litellm_completion_kwargs", {}), ) - output = self.runner.api.parse_llm_response( - response.response, {"is_match": "bool"} - )[0] - return output["is_match"], response.total_cost + cost = 0 + try: + cost = response.total_cost + output = self.runner.api.parse_llm_response( + response.response, {"is_match": "bool"} + )[0] + except Exception as e: + self.console.print(f"[red]Error parsing LLM response: {e}[/red]") + return False, cost + return output["is_match"], cost def syntax_check(self) -> None: """ @@ -215,10 +227,6 @@ def execute( limit_comparisons = self.config.get("limit_comparisons") total_cost = 0 - # LLM-based comparison for blocked pairs - def get_hashable_key(item: Dict) -> str: - return json.dumps(item, sort_keys=True) - if len(left_data) == 0 or len(right_data) == 0: return [], 0 @@ -244,8 +252,13 @@ def get_hashable_key(item: Dict) -> str: # Check if we have exceeded the pairwise comparison limit if limit_comparisons is not None and len(blocked_pairs) > limit_comparisons: - # Sample pairs randomly - sampled_pairs = random.sample(blocked_pairs, limit_comparisons) + # Sample pairs based on cardinality and length + sampled_pairs = stratified_length_sample( + blocked_pairs, + limit_comparisons, + sample_size=1000, + console=self.console + ) # Calculate number of dropped pairs dropped_pairs = len(blocked_pairs) - limit_comparisons @@ -257,7 +270,7 @@ def get_hashable_key(item: Dict) -> str: f"[yellow]Warning: {dropped_pairs} pairs will be dropped due to the comparison limit. " f"Proceeding with {limit_comparisons} randomly sampled pairs. " f"Do you want to continue?[/yellow]", - self.console, + console=self.console, ): raise ValueError("Operation cancelled by user due to pair limit.") @@ -410,6 +423,9 @@ def get_embeddings( results = [] comparison_costs = 0 + if self.status: + self.status.stop() + with ThreadPoolExecutor(max_workers=self.max_threads) as executor: future_to_pair = { executor.submit( @@ -424,12 +440,14 @@ def get_embeddings( for left, right in blocked_pairs } - for future in rich_as_completed( - future_to_pair, - total=len(future_to_pair), - desc="Comparing pairs", + pbar = RichLoopBar( + range(len(future_to_pair)), + desc=f"Comparing pairs", console=self.console, - ): + ) + + for i in pbar: + future = list(future_to_pair.keys())[i] pair = future_to_pair[future] is_match, cost = future.result() comparison_costs += cost @@ -460,6 +478,9 @@ def get_embeddings( total_cost += comparison_costs + if self.status: + self.status.start() + # Calculate and print the join selectivity join_selectivity = ( len(results) / (len(left_data) * len(right_data)) @@ -472,3 +493,96 @@ def get_embeddings( self.status.start() return results, total_cost + + +def estimate_length(items: List[Dict], sample_size: int = 1000) -> float: + """ + Estimates average document length in the relation. + Returns a normalized score (0-1) representing relative document size. + + Args: + items: List of dictionary items to analyze + sample_size: Number of items to sample for estimation + + Returns: + float: Normalized score based on average document length + """ + if not items: + return 0.0 + + sample_size = min(len(items), sample_size) + sample = random.sample(items, sample_size) + + def get_doc_length(doc: Dict) -> int: + """Calculate total length of all string values in document""" + total_len = 0 + for value in doc.values(): + if isinstance(value, str): + total_len += len(value) + elif isinstance(value, (list, dict)): + # For nested structures, use their string representation + total_len += len(str(value)) + return total_len + + lengths = [get_doc_length(item) for item in sample] + if not lengths: + return 0.0 + + avg_length = sum(lengths) / len(lengths) + return avg_length + + +def stratified_length_sample( + blocked_pairs: List[Tuple[Dict, Dict]], + limit_comparisons: int, + sample_size: int = 1000, + console: Console = None +) -> List[Tuple[Dict, Dict]]: + """ + Samples pairs stratified by the smaller cardinality relation, + prioritizing longer matches within each stratum. + """ + # Extract left and right items + left_items = [left for left, _ in blocked_pairs] + right_items = [right for _, right in blocked_pairs] + + # Estimate length for both relations + left_length = estimate_length(left_items, sample_size) + right_length = estimate_length(right_items, sample_size) + + # Group by the relation with estimated lower length + use_left_as_key = left_length > right_length + if console: + longer_length = max(left_length, right_length) + longer_side = "left" if left_length > right_length else "right" + console.log(f"Longer length is {longer_length:.2f} ({longer_side} side). Using {longer_side} to sample matches.") + groups = defaultdict(list) + + for left, right in blocked_pairs: + key = get_hashable_key(left if use_left_as_key else right) + value = (left, right) + groups[key].append(value) + + # Sort each group by length of the other relation's item + for key in groups: + groups[key].sort( + key=lambda x: len(x[1 if use_left_as_key else 0]), + reverse=True # Prioritize longer matches + ) + + # Calculate samples per group + n_groups = len(groups) + base_samples_per_group = limit_comparisons // n_groups + extra_samples = limit_comparisons % n_groups + + # Sample from each group + sampled_pairs = [] + for i, (key, pairs) in enumerate(groups.items()): + # Add one extra sample to early groups if we have remainder + group_sample_size = min( + len(pairs), + base_samples_per_group + (1 if i < extra_samples else 0) + ) + sampled_pairs.extend(pairs[:group_sample_size]) + + return sampled_pairs diff --git a/docetl/operations/map.py b/docetl/operations/map.py index f6c7a67b..afb1256d 100644 --- a/docetl/operations/map.py +++ b/docetl/operations/map.py @@ -40,7 +40,7 @@ class schema(BaseOperation.schema): batch_size: Optional[int] = None clustering_method: Optional[str] = None batch_prompt: Optional[str] = None - litellm_completion_kwargs: Dict[str, Any] = Field(default_factory=dict) + litellm_completion_kwargs: Dict[str, Any] = {} @field_validator("drop_keys") def validate_drop_keys(cls, v): if isinstance(v, str): diff --git a/docetl/optimizers/join_optimizer.py b/docetl/optimizers/join_optimizer.py index 42d51871..8da92c5d 100644 --- a/docetl/optimizers/join_optimizer.py +++ b/docetl/optimizers/join_optimizer.py @@ -711,7 +711,7 @@ def optimize_equijoin( if self.status: self.status.stop() # Use Rich's Confirm for input - if Confirm.ask("Use this rule?", self.console): + if Confirm.ask("Use this rule?", console=self.console): selected_containment_rules.append(rule) # Restart the status if self.status: diff --git a/docetl/runner.py b/docetl/runner.py index 1d16101d..e3bb8ed3 100644 --- a/docetl/runner.py +++ b/docetl/runner.py @@ -369,8 +369,8 @@ def execute_step( self.status, ) if op_object["type"] == "equijoin": - left_data = self.datasets[op_object["left"]].load() - right_data = self.datasets[op_object["right"]].load() + left_data = self.datasets[next(iter(operation.values()))["left"]].load() + right_data = self.datasets[next(iter(operation.values()))["right"]].load() input_data, cost = operation_instance.execute(left_data, right_data) else: input_data, cost = operation_instance.execute(input_data) diff --git a/poetry.lock b/poetry.lock index cb64b1bd..9681062b 100644 --- a/poetry.lock +++ b/poetry.lock @@ -136,13 +136,13 @@ files = [ [[package]] name = "anyio" -version = "4.7.0" +version = "4.8.0" description = "High level compatibility layer for multiple asynchronous event loop implementations" optional = false python-versions = ">=3.9" files = [ - {file = "anyio-4.7.0-py3-none-any.whl", hash = "sha256:ea60c3723ab42ba6fff7e8ccb0488c898ec538ff4df1f1d5e642c3601d07e352"}, - {file = "anyio-4.7.0.tar.gz", hash = "sha256:2f834749c602966b7d456a7567cafcb309f96482b5081d14ac93ccd457f9dd48"}, + {file = "anyio-4.8.0-py3-none-any.whl", hash = "sha256:b5011f270ab5eb0abf13385f851315585cc37ef330dd88e27ec3d34d651fd47a"}, + {file = "anyio-4.8.0.tar.gz", hash = "sha256:1d9fe889df5212298c0c0723fa20479d1b94883a2df44bd3897aa91083316f7a"}, ] [package.dependencies] @@ -153,7 +153,7 @@ typing_extensions = {version = ">=4.5", markers = "python_version < \"3.13\""} [package.extras] doc = ["Sphinx (>=7.4,<8.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx_rtd_theme"] -test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21)"] +test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21)"] trio = ["trio (>=0.26.1)"] [[package]] @@ -351,116 +351,103 @@ files = [ [[package]] name = "charset-normalizer" -version = "3.4.0" +version = "3.4.1" description = "The Real First Universal Charset Detector. 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