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b339b93 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 | """Three-tier validation suite for Voteview parquet conversions."""
from __future__ import annotations
import csv
import math
import random
from dataclasses import dataclass, field
from pathlib import Path
from typing import TYPE_CHECKING, Any
import pyarrow.compute as pc
import pyarrow.parquet as pq
from .exceptions import (
ChecksumMismatchError,
RowCountMismatchError,
SampleMismatchError,
)
if TYPE_CHECKING:
from .converter import StreamingStats
@dataclass
class ValidationResult:
"""Results from validation suite."""
row_count_valid: bool = False
row_count_expected: int = 0
row_count_actual: int = 0
checksum_valid: bool = False
sum_column_name: str | None = None
sum_column_expected: float = 0.0
sum_column_actual: float = 0.0
non_null_counts: dict[str, tuple[int, int]] = field(default_factory=dict)
sample_valid: bool = False
sample_size: int = 0
@property
def all_valid(self) -> bool:
"""Check if all validation tiers passed."""
return self.row_count_valid and self.checksum_valid and self.sample_valid
def validate_row_count(
source_path: Path,
output_path: Path,
expected_count: int,
) -> ValidationResult:
"""
Tier 1: Verify row counts match using parquet metadata.
This is the fastest validation - reads only metadata, no actual data.
"""
meta = pq.read_metadata(output_path)
actual_count = meta.num_rows
result = ValidationResult(
row_count_expected=expected_count,
row_count_actual=actual_count,
)
if actual_count != expected_count:
raise RowCountMismatchError(
source_path=source_path,
message="Row count validation failed",
expected_rows=expected_count,
actual_rows=actual_count,
)
result.row_count_valid = True
return result
def validate_checksums(
source_path: Path,
output_path: Path,
source_stats: StreamingStats,
result: ValidationResult,
sum_column: str | None,
key_columns: list[str] | None = None,
) -> ValidationResult:
"""
Tier 2: Verify column checksums.
Validates:
- Sum of configurable numeric column (detects truncation/conversion errors)
- Non-null counts for key columns (detects dropped data)
"""
if key_columns is None:
key_columns = []
result.sum_column_name = sum_column
# Checksum 1: Sum validation
if sum_column:
source_sum = source_stats.sum_column_value
sum_table = pq.read_table(output_path, columns=[sum_column])
parquet_sum = pc.sum(sum_table.column(sum_column)).as_py() or 0.0
result.sum_column_expected = source_sum
result.sum_column_actual = parquet_sum
# Allow tiny floating point tolerance
if abs(source_sum - parquet_sum) > 0.01:
raise ChecksumMismatchError(
source_path=source_path,
message=f"{sum_column} sum mismatch",
column_name=sum_column,
expected_value=source_sum,
actual_value=parquet_sum,
)
# Checksum 2: Non-null count validation
for col in key_columns:
source_count = source_stats.non_null_counts.get(col, 0)
col_table = pq.read_table(output_path, columns=[col])
parquet_count = pc.count(col_table.column(col)).as_py()
result.non_null_counts[col] = (source_count, parquet_count)
if source_count != parquet_count:
raise ChecksumMismatchError(
source_path=source_path,
message=f"Non-null count mismatch for {col}",
column_name=col,
expected_value=source_count,
actual_value=parquet_count,
)
result.checksum_valid = True
return result
def validate_sample_rows(
source_path: Path,
output_path: Path,
sample_size: int,
result: ValidationResult,
) -> ValidationResult:
"""
Tier 3: Compare random sample of rows field-by-field.
Most thorough validation - catches subtle conversion errors.
Memory-efficient: reads parquet in batches, captures only sample rows.
"""
meta = pq.read_metadata(output_path)
total_rows = meta.num_rows
# Adjust sample size if file is smaller
actual_sample_size = min(sample_size, total_rows)
sample_indices = sorted(random.sample(range(total_rows), actual_sample_size))
# Read source CSV rows at sample indices
source_rows = _read_csv_rows_at_indices(source_path, sample_indices)
# Read parquet in batches, capturing sample rows
parquet_file = pq.ParquetFile(output_path)
schema_names = parquet_file.schema_arrow.names
index_to_position = {idx: pos for pos, idx in enumerate(sample_indices)}
parquet_sample_rows: list[dict[str, Any] | None] = [None] * len(sample_indices)
current_row = 0
for batch in parquet_file.iter_batches():
batch_end = current_row + batch.num_rows
for sample_idx in sample_indices:
if current_row <= sample_idx < batch_end:
local_idx = sample_idx - current_row
row_dict: dict[str, Any] = {}
for col_name in schema_names:
row_dict[col_name] = batch.column(col_name)[local_idx].as_py()
parquet_sample_rows[index_to_position[sample_idx]] = row_dict
current_row = batch_end
# Early exit if we've captured all sample rows
if all(r is not None for r in parquet_sample_rows):
break
# Compare rows field by field
result.sample_size = len(sample_indices)
for i, row_idx in enumerate(sample_indices):
source_row = source_rows[i]
parquet_row = parquet_sample_rows[i]
for col_name in schema_names:
source_val = source_row.get(col_name) if source_row else None
parquet_val = parquet_row.get(col_name) if parquet_row else None
source_normalized = _normalize_value(source_val)
parquet_normalized = _normalize_value(parquet_val)
if not _values_equal(source_normalized, parquet_normalized):
raise SampleMismatchError(
source_path=source_path,
message="Sample row mismatch",
row_index=row_idx,
column_name=col_name,
expected_value=str(source_normalized),
actual_value=str(parquet_normalized),
)
result.sample_valid = True
return result
def _read_csv_rows_at_indices(path: Path, indices: list[int]) -> list[dict[str, str] | None]:
"""Read specific rows from plain CSV by index."""
indices_set = set(indices)
index_to_position = {idx: pos for pos, idx in enumerate(sorted(indices))}
rows: list[dict[str, str] | None] = [None] * len(indices)
with path.open(encoding="utf-8") as f:
reader = csv.DictReader(f, doublequote=True)
for i, row in enumerate(reader):
if i in indices_set:
rows[index_to_position[i]] = row
if all(r is not None for r in rows):
break
return rows
def _normalize_value(val: Any) -> Any:
"""Normalize values for comparison."""
if val is None or val == "":
return None
if isinstance(val, float):
if math.isnan(val):
return None
return round(val, 6)
if isinstance(val, str):
stripped = val.strip()
# Treat various null representations as None
if stripped.lower() in ("nan", "n/a", "na", "null"):
return None
return stripped
return val
def _values_equal(a: Any, b: Any) -> bool:
"""Compare two normalized values with tolerance for floats."""
if a is None and b is None:
return True
if a is None or b is None:
return False
# Try numeric comparison with tolerance
try:
a_float = float(a) if isinstance(a, str) else a
b_float = float(b) if isinstance(b, str) else b
if isinstance(a_float, (int, float)) and isinstance(b_float, (int, float)):
return abs(float(a_float) - float(b_float)) < 0.000001
except (ValueError, TypeError):
pass
# String comparison
return str(a) == str(b)
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