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c1feb60 e4a41fa c1feb60 e4a41fa c1feb60 e4a41fa c1feb60 e4a41fa c1feb60 e4a41fa c1feb60 e4a41fa c1feb60 e4a41fa c1feb60 e4a41fa c1feb60 e4a41fa c1feb60 e4a41fa c1feb60 e4a41fa c1feb60 e4a41fa c1feb60 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 | """Tests for SmartCrusher crushability analysis.
These tests verify that SmartCrusher correctly identifies when it's SAFE
to crush data vs when it should SKIP crushing.
The key insight: High variability + No importance signal = DON'T CRUSH.
Test scenarios:
1. DB results (unique entities, no signal) → SKIP
2. Search results (has score field) → CRUSH using score
3. Log entries (has errors) → CRUSH keeping errors
4. Time series (has anomalies) → CRUSH keeping anomalies
5. Repetitive data (low uniqueness) → CRUSH with sampling
"""
import json
import pytest
from headroom.transforms.smart_crusher import (
CompressionStrategy,
SmartAnalyzer,
SmartCrusherConfig,
smart_crush_tool_output,
)
class TestCrushabilityDetection:
"""Test the crushability analysis logic."""
@pytest.fixture
def analyzer(self):
"""Create a SmartAnalyzer instance."""
return SmartAnalyzer(SmartCrusherConfig())
def test_db_results_not_crushable(self, analyzer):
"""DB query results with unique IDs and no signal should NOT be crushed."""
# Simulate: SELECT * FROM users LIMIT 50
items = [
{
"id": i,
"name": f"User {i}",
"email": f"user{i}@example.com",
"department": "Engineering",
}
for i in range(50)
]
analysis = analyzer.analyze_array(items)
# Should detect unique entities with no importance signal
assert analysis.crushability is not None
assert not analysis.crushability.crushable, (
f"DB results should NOT be crushable. "
f"Reason: {analysis.crushability.reason}, "
f"Signals: {analysis.crushability.signals_present}"
)
assert analysis.recommended_strategy == CompressionStrategy.SKIP
assert "unique" in analysis.crushability.reason.lower()
def test_db_results_with_unique_uuid(self, analyzer):
"""DB results with UUID field should NOT be crushed."""
items = [
{
"uuid": f"550e8400-e29b-41d4-a716-44665544{i:04d}",
"name": f"Record {i}",
"value": i * 10,
}
for i in range(50)
]
analysis = analyzer.analyze_array(items)
assert analysis.crushability is not None
assert not analysis.crushability.crushable
assert analysis.crushability.has_id_field
def test_search_results_crushable(self, analyzer):
"""Search results with score field SHOULD be crushed."""
items = [
{
"id": i,
"title": f"Document {i}",
"snippet": f"This is document {i} content...",
"score": 1.0 - (i * 0.01), # Decreasing relevance
}
for i in range(100)
]
analysis = analyzer.analyze_array(items)
# Should detect score field as importance signal
assert analysis.crushability is not None
assert analysis.crushability.crushable, (
f"Search results should be crushable. Reason: {analysis.crushability.reason}"
)
assert analysis.crushability.has_score_field
assert any("score" in s for s in analysis.crushability.signals_present)
def test_log_entries_with_errors_crushable(self, analyzer):
"""Log entries containing structural outliers SHOULD be crushed (outliers preserved)."""
items = []
for i in range(100):
item = {
"id": i,
"timestamp": f"2024-01-15T10:{i:02d}:00Z",
"message": f"Request processed successfully - {i}",
"level": "INFO",
}
# Add some errors - these are STRUCTURAL OUTLIERS (have extra "error" field)
if i % 20 == 0:
item["level"] = "ERROR"
item["message"] = f"Connection failed: timeout at {i}"
item["error"] = "TimeoutError" # Extra field that most items don't have
items.append(item)
analysis = analyzer.analyze_array(items)
# Should detect structural outliers (items with rare fields like "error")
assert analysis.crushability is not None
assert analysis.crushability.crushable
# Now uses structural_outliers instead of keyword-based error count
assert any(
"structural_outliers" in s or "outlier" in s.lower()
for s in analysis.crushability.signals_present
)
def test_time_series_with_anomalies_crushable(self, analyzer):
"""Time series with numeric anomalies SHOULD be crushed."""
items = []
for i in range(100):
value = 100.0 # Normal value
if i in [25, 50, 75]: # Anomaly points
value = 999.0
items.append(
{
"id": i,
"timestamp": i,
"cpu_usage": value,
}
)
analysis = analyzer.analyze_array(items)
# Should detect anomalies as importance signal
assert analysis.crushability is not None
assert analysis.crushability.crushable
assert analysis.crushability.anomaly_count > 0
def test_repetitive_data_crushable(self, analyzer):
"""Repetitive data (low uniqueness) SHOULD be crushable."""
# Same status repeated many times
items = [
{
"id": i,
"status": "success", # Same for all
"code": 200, # Same for all
"message": "OK", # Same for all
}
for i in range(100)
]
analysis = analyzer.analyze_array(items)
# Should detect low uniqueness - safe to sample
assert analysis.crushability is not None
assert analysis.crushability.crushable
# Can be "low_uniqueness" or "repetitive_content_with_ids"
assert (
"low_uniqueness" in analysis.crushability.reason
or "repetitive" in analysis.crushability.reason
)
def test_file_listing_not_crushable(self, analyzer):
"""File listing with unique paths should NOT be crushed."""
items = [
{
"id": i,
"path": f"/home/user/project/src/module{i}/file{i}.py",
"size": 1000 + i,
"modified": f"2024-01-{(i % 28) + 1:02d}",
}
for i in range(50)
]
analysis = analyzer.analyze_array(items)
# Paths are highly unique, no importance signal
assert analysis.crushability is not None
# Should NOT crush file listings
assert not analysis.crushability.crushable or analysis.crushability.confidence < 0.7
def test_order_list_not_crushable(self, analyzer):
"""Order list with unique order IDs should NOT be crushed."""
items = [
{
"order_id": f"ORD-2024-{i:05d}",
"customer": f"Customer {i}",
"total": 50.0 + i,
"status": "completed",
}
for i in range(50)
]
analysis = analyzer.analyze_array(items)
# Each order is a unique entity
assert analysis.crushability is not None
# order_id contains 'id' pattern
assert not analysis.crushability.crushable
class TestCrushabilityEndToEnd:
"""End-to-end tests for crushability-aware crushing."""
def test_db_results_preserved_completely(self):
"""DB results should be returned unchanged when not crushable."""
items = [{"id": i, "name": f"User {i}", "email": f"user{i}@test.com"} for i in range(30)]
content = json.dumps(items)
config = SmartCrusherConfig(max_items_after_crush=10)
crushed, was_modified, info = smart_crush_tool_output(content, config)
# Should NOT be modified (skip crushing)
if was_modified:
result = json.loads(crushed)
# If it was modified, all items should still be there
assert len(result) == 30, (
f"DB results should not lose items! Had 30, got {len(result)}. Info: {info}"
)
def test_search_results_crushed_by_score(self):
"""Search results should be crushed using score field."""
items = [
{
"id": i,
"title": f"Result {i}",
"score": 100 - i, # Higher score = more relevant
}
for i in range(100)
]
content = json.dumps(items)
config = SmartCrusherConfig(max_items_after_crush=15)
crushed, was_modified, info = smart_crush_tool_output(content, config)
assert was_modified
result = json.loads(crushed)
assert len(result) < 100
# Top scores should be preserved
scores = [item.get("score", 0) for item in result]
assert max(scores) >= 90 # Top items preserved
def test_mixed_data_with_errors_preserves_errors(self):
"""Data with errors should crush but preserve ALL errors."""
items = []
error_ids = [5, 25, 45, 65, 85]
for i in range(100):
item = {"id": i, "data": f"value_{i}"}
if i in error_ids:
item["status"] = "failed"
item["error"] = f"Error at {i}"
items.append(item)
content = json.dumps(items)
config = SmartCrusherConfig(max_items_after_crush=20)
crushed, was_modified, info = smart_crush_tool_output(content, config)
result = json.loads(crushed)
# All errors must be preserved
error_count = sum(1 for item in result if item.get("error"))
assert error_count == len(error_ids), (
f"All {len(error_ids)} errors should be preserved, got {error_count}"
)
class TestCrushabilitySignals:
"""Test individual signal detection."""
@pytest.fixture
def analyzer(self):
return SmartAnalyzer(SmartCrusherConfig())
def test_detects_id_field_variations(self, analyzer):
"""Should detect various ID field naming patterns."""
test_cases = [
("id", [{"id": i} for i in range(20)]),
("uuid", [{"uuid": f"uuid-{i}"} for i in range(20)]),
("_id", [{"_id": f"mongo-{i}"} for i in range(20)]),
("pk", [{"pk": i} for i in range(20)]),
("key", [{"key": f"key-{i}"} for i in range(20)]),
("user_id", [{"user_id": i} for i in range(20)]),
]
for field_name, items in test_cases:
analysis = analyzer.analyze_array(items)
assert analysis.crushability is not None
assert analysis.crushability.has_id_field, f"Should detect '{field_name}' as ID field"
def test_detects_score_field_variations(self, analyzer):
"""Should detect various score field naming patterns."""
test_cases = [
"score",
"rank",
"relevance",
"confidence",
"_score",
"rating",
]
for field_name in test_cases:
items = [{field_name: i * 0.1, "data": f"item_{i}"} for i in range(20)]
analysis = analyzer.analyze_array(items)
assert analysis.crushability is not None
assert analysis.crushability.has_score_field, (
f"Should detect '{field_name}' as score field"
)
def test_detects_error_keywords(self, analyzer):
"""Should detect various error keyword patterns."""
error_keywords = ["error", "exception", "failed", "failure", "critical", "fatal"]
for keyword in error_keywords:
items = [{"id": i, "msg": "OK"} for i in range(20)]
items[10]["msg"] = f"Something {keyword} happened"
analysis = analyzer.analyze_array(items)
assert analysis.crushability is not None
assert analysis.crushability.error_item_count >= 1, (
f"Should detect '{keyword}' as error indicator"
)
class TestCrushabilityEdgeCases:
"""Test edge cases in crushability analysis."""
@pytest.fixture
def analyzer(self):
return SmartAnalyzer(SmartCrusherConfig())
def test_empty_array(self, analyzer):
"""Empty array should not crash."""
analysis = analyzer.analyze_array([])
assert analysis.recommended_strategy == CompressionStrategy.NONE
def test_small_array_skipped(self, analyzer):
"""Arrays below min_items_to_analyze should be skipped."""
items = [{"id": i} for i in range(3)]
analysis = analyzer.analyze_array(items)
assert analysis.recommended_strategy == CompressionStrategy.NONE
def test_mixed_signals(self, analyzer):
"""Data with multiple signals should still be crushable."""
items = []
for i in range(100):
item = {
"id": i,
"score": 100 - i, # Score signal
"value": 50.0,
}
if i == 50:
item["error"] = "Test error" # Error signal
item["value"] = 999.0 # Anomaly signal
items.append(item)
analysis = analyzer.analyze_array(items)
assert analysis.crushability is not None
assert analysis.crushability.crushable
assert len(analysis.crushability.signals_present) >= 2
def test_all_items_are_errors(self, analyzer):
"""When all items are errors, keyword detection finds them as a signal.
With keyword-based error detection (for the preservation guarantee),
when ALL items have error keywords, we detect error_keywords:50 as a
signal. This makes the data technically crushable.
However, since ALL items are errors, they will ALL be preserved due to
the preservation guarantee. The end result is the same - no data loss.
"""
items = [{"id": i, "error": f"Error {i}", "status": "failed"} for i in range(50)]
analysis = analyzer.analyze_array(items)
assert analysis.crushability is not None
# With keyword-based error detection, all 50 items contain error keywords
# This IS a signal (error_keywords:50), making the data crushable.
# However, all 50 items will be preserved due to the preservation guarantee.
assert analysis.crushability.crushable
assert "error_keywords:50" in analysis.crushability.signals_present
class TestCrushabilityConfidence:
"""Test confidence scoring in crushability analysis."""
@pytest.fixture
def analyzer(self):
return SmartAnalyzer(SmartCrusherConfig())
def test_high_confidence_for_clear_cases(self, analyzer):
"""Clear-cut cases should have high confidence."""
# Low uniqueness - clearly safe
items = [{"status": "ok", "code": 200} for _ in range(100)]
analysis = analyzer.analyze_array(items)
assert analysis.crushability is not None
assert analysis.crushability.confidence >= 0.8
def test_lower_confidence_for_ambiguous_cases(self, analyzer):
"""Ambiguous cases should have lower confidence."""
# Medium uniqueness with weak signal
items = [
{"id": i, "value": i % 10, "status": "active" if i % 2 == 0 else "inactive"}
for i in range(100)
]
# Add one error to provide weak signal
items[50]["error"] = "minor issue"
analysis = analyzer.analyze_array(items)
assert analysis.crushability is not None
# Should be lower confidence due to ambiguity
assert analysis.crushability.confidence <= 0.7
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