kitedb 0.2.3

High-performance embedded graph database
Documentation 
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
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510

#!/usr/bin/env python3
"""
KiteDB Python Fluent API Benchmark

Compares the new fluent API against the low-level API for common operations.
This helps measure the overhead of the Python wrapper layer.

Usage:
    python benchmark_fluent.py [options]

Options:
    --nodes N         Number of nodes (default: 1000)
    --edges M         Number of edges (default: 5000)
    --iterations I    Iterations for latency benchmarks (default: 1000)
    --no-output       Disable file output
"""

import argparse
import os
import random
import shutil
import sys
import tempfile
import time
from dataclasses import dataclass
from datetime import datetime
from pathlib import Path
from typing import Dict, List, Optional

# Add parent directory to path for kitedb import
sys.path.insert(0, str(Path(__file__).parent.parent))

try:
    # Low-level API
    from kitedb import Database, PropValue
    # Fluent API
    from kitedb import ray, define_node, define_edge, prop, optional
except ImportError as e:
    print(f"Error: kitedb module not found ({e}). Make sure to build the Python bindings first:")
    print("  cd ray-rs && maturin develop --features python")
    sys.exit(1)


# =============================================================================
# Schema Definition (for fluent API)
# =============================================================================

user = define_node("user",
    key=lambda id: f"user:{id}",
    props={
        "name": prop.string("name"),
        "email": prop.string("email"),
        "age": optional(prop.int("age")),
    }
)

knows = define_edge("knows", {
    "since": prop.int("since"),
})


# =============================================================================
# Configuration
# =============================================================================

@dataclass
class BenchConfig:
    nodes: int = 1000
    edges: int = 5000
    iterations: int = 1000
    no_output: bool = False


def parse_args() -> BenchConfig:
    parser = argparse.ArgumentParser(description="KiteDB Python Fluent API Benchmark")
    parser.add_argument("--nodes", type=int, default=1000, help="Number of nodes")
    parser.add_argument("--edges", type=int, default=5000, help="Number of edges")
    parser.add_argument("--iterations", type=int, default=1000, help="Iterations for latency benchmarks")
    parser.add_argument("--no-output", action="store_true", help="Disable file output")
    
    args = parser.parse_args()
    return BenchConfig(
        nodes=args.nodes,
        edges=args.edges,
        iterations=args.iterations,
        no_output=args.no_output,
    )


# =============================================================================
# Latency Tracking
# =============================================================================

@dataclass
class LatencyStats:
    count: int
    min_ns: float
    max_ns: float
    sum_ns: float
    p50: float
    p95: float
    p99: float
    
    @property
    def avg_ns(self) -> float:
        return self.sum_ns / self.count if self.count > 0 else 0
    
    @property
    def ops_per_sec(self) -> float:
        return self.count / (self.sum_ns / 1_000_000_000) if self.sum_ns > 0 else 0


class LatencyTracker:
    def __init__(self):
        self.samples: List[float] = []
    
    def record(self, latency_ns: float):
        self.samples.append(latency_ns)
    
    def get_stats(self) -> LatencyStats:
        if not self.samples:
            return LatencyStats(0, 0, 0, 0, 0, 0, 0)
        
        sorted_samples = sorted(self.samples)
        count = len(sorted_samples)
        
        return LatencyStats(
            count=count,
            min_ns=sorted_samples[0],
            max_ns=sorted_samples[-1],
            sum_ns=sum(sorted_samples),
            p50=sorted_samples[int(count * 0.5)],
            p95=sorted_samples[int(count * 0.95)],
            p99=sorted_samples[int(count * 0.99)],
        )


def format_latency(ns: float) -> str:
    if ns < 1000:
        return f"{ns:.0f}ns"
    if ns < 1_000_000:
        return f"{ns / 1000:.2f}us"
    return f"{ns / 1_000_000:.2f}ms"


def format_number(n: int) -> str:
    return f"{n:,}"


def print_comparison(name: str, low_level: LatencyStats, fluent: LatencyStats):
    overhead = fluent.p50 / low_level.p50 if low_level.p50 > 0 else 0
    print(
        f"{name:<35} "
        f"low-level p50={format_latency(low_level.p50):>10}  "
        f"fluent p50={format_latency(fluent.p50):>10}  "
        f"overhead={overhead:.2f}x"
    )


# =============================================================================
# Benchmark Functions
# =============================================================================

def benchmark_low_level_inserts(db: Database, config: BenchConfig) -> LatencyStats:
    """Benchmark low-level insert operations."""
    tracker = LatencyTracker()
    name_key = db.get_or_create_propkey("name")
    email_key = db.get_or_create_propkey("email")
    age_key = db.get_or_create_propkey("age")
    
    for i in range(config.iterations):
        start = time.perf_counter_ns()
        db.begin()
        node_id = db.create_node(f"bench:low:{i}")
        db.set_node_prop(node_id, name_key, PropValue.string(f"User {i}"))
        db.set_node_prop(node_id, email_key, PropValue.string(f"user{i}@example.com"))
        db.set_node_prop(node_id, age_key, PropValue.int(20 + (i % 50)))
        db.commit()
        tracker.record(time.perf_counter_ns() - start)
    
    return tracker.get_stats()


def benchmark_fluent_inserts(db, config: BenchConfig) -> LatencyStats:
    """Benchmark fluent API insert operations."""
    tracker = LatencyTracker()
    
    for i in range(config.iterations):
        start = time.perf_counter_ns()
        db.insert(user).values(
            key=f"bench:fluent:{i}",
            name=f"User {i}",
            email=f"user{i}@example.com",
            age=20 + (i % 50),
        ).returning()
        tracker.record(time.perf_counter_ns() - start)
    
    return tracker.get_stats()


def benchmark_low_level_key_lookup(db: Database, keys: List[str], iterations: int) -> LatencyStats:
    """Benchmark low-level key lookups."""
    tracker = LatencyTracker()
    
    for _ in range(iterations):
        key = random.choice(keys)
        start = time.perf_counter_ns()
        db.get_node_by_key(key)
        tracker.record(time.perf_counter_ns() - start)
    
    return tracker.get_stats()


def benchmark_fluent_key_lookup(db, key_args: List[str], iterations: int) -> LatencyStats:
    """Benchmark fluent API key lookups (with property loading)."""
    tracker = LatencyTracker()
    
    for _ in range(iterations):
        key_arg = random.choice(key_args)
        start = time.perf_counter_ns()
        db.get(user, key_arg)
        tracker.record(time.perf_counter_ns() - start)
    
    return tracker.get_stats()


def benchmark_fluent_get_ref(db, key_args: List[str], iterations: int) -> LatencyStats:
    """Benchmark fluent API getRef (without property loading)."""
    tracker = LatencyTracker()
    
    for _ in range(iterations):
        key_arg = random.choice(key_args)
        start = time.perf_counter_ns()
        db.get_ref(user, key_arg)
        tracker.record(time.perf_counter_ns() - start)
    
    return tracker.get_stats()


def benchmark_low_level_traversal(db: Database, node_ids: List[int], etype_id: int, iterations: int) -> LatencyStats:
    """Benchmark low-level traversal."""
    tracker = LatencyTracker()
    
    for _ in range(iterations):
        node_id = random.choice(node_ids)
        start = time.perf_counter_ns()
        neighbors = db.traverse_out(node_id, etype_id)
        tracker.record(time.perf_counter_ns() - start)
    
    return tracker.get_stats()


def benchmark_fluent_traversal(db, user_refs: List, iterations: int) -> LatencyStats:
    """Benchmark fluent API traversal (count - no props loaded)."""
    tracker = LatencyTracker()
    
    for _ in range(iterations):
        user_ref = random.choice(user_refs)
        start = time.perf_counter_ns()
        db.from_(user_ref).out(knows).count()
        tracker.record(time.perf_counter_ns() - start)
    
    return tracker.get_stats()


def benchmark_fluent_traversal_ids(db, user_refs: List, iterations: int) -> LatencyStats:
    """Benchmark fluent API traversal - ids() (fastest)."""
    tracker = LatencyTracker()
    
    for _ in range(iterations):
        user_ref = random.choice(user_refs)
        start = time.perf_counter_ns()
        db.from_(user_ref).out(knows).ids()
        tracker.record(time.perf_counter_ns() - start)
    
    return tracker.get_stats()


def benchmark_fluent_traversal_to_list(db, user_refs: List, iterations: int) -> LatencyStats:
    """Benchmark fluent API traversal - to_list() (no props by default)."""
    tracker = LatencyTracker()
    
    for _ in range(iterations):
        user_ref = random.choice(user_refs)
        start = time.perf_counter_ns()
        db.from_(user_ref).out(knows).to_list()
        tracker.record(time.perf_counter_ns() - start)
    
    return tracker.get_stats()


def benchmark_fluent_traversal_with_props(db, user_refs: List, iterations: int) -> LatencyStats:
    """Benchmark fluent API traversal - with_props().to_list() (full loading)."""
    tracker = LatencyTracker()
    
    for _ in range(iterations):
        user_ref = random.choice(user_refs)
        start = time.perf_counter_ns()
        db.from_(user_ref).out(knows).with_props().to_list()
        tracker.record(time.perf_counter_ns() - start)
    
    return tracker.get_stats()


def benchmark_fluent_traversal_load_props(db, user_refs: List, iterations: int) -> LatencyStats:
    """Benchmark fluent API traversal - load_props("name").to_list() (selective)."""
    tracker = LatencyTracker()
    
    for _ in range(iterations):
        user_ref = random.choice(user_refs)
        start = time.perf_counter_ns()
        db.from_(user_ref).out(knows).load_props("name").to_list()
        tracker.record(time.perf_counter_ns() - start)
    
    return tracker.get_stats()


# =============================================================================
# Main
# =============================================================================

def run_benchmarks(config: BenchConfig):
    now = datetime.now()
    print("=" * 100)
    print("KiteDB Python Fluent API vs Low-Level Benchmark")
    print("=" * 100)
    print(f"Date: {now.isoformat()}")
    print(f"Nodes: {format_number(config.nodes)}")
    print(f"Edges: {format_number(config.edges)}")
    print(f"Iterations: {format_number(config.iterations)}")
    print("=" * 100)
    
    # Create temporary directories for both databases
    low_level_dir = tempfile.mkdtemp(prefix="ray-low-level-")
    fluent_dir = tempfile.mkdtemp(prefix="ray-fluent-")
    
    try:
        # =================================================================
        # Setup Phase
        # =================================================================
        print("\n[1/6] Setting up databases...")
        
        # Low-level database setup
        low_level_db = Database(os.path.join(low_level_dir, "test.kitedb"))
        knows_etype = low_level_db.get_or_create_etype("knows")
        name_key = low_level_db.get_or_create_propkey("name")
        email_key = low_level_db.get_or_create_propkey("email")
        age_key = low_level_db.get_or_create_propkey("age")
        
        # Fluent database setup
        fluent_db = ray(os.path.join(fluent_dir, "test.kitedb"), 
                       nodes=[user], edges=[knows])
        
        # =================================================================
        # Build Test Data
        # =================================================================
        print("\n[2/6] Building test data...")
        
        # Low-level: create nodes
        low_level_node_ids: List[int] = []
        low_level_keys: List[str] = []
        
        low_level_db.begin()
        for i in range(config.nodes):
            key = f"user:{i}"
            node_id = low_level_db.create_node(key)
            low_level_db.set_node_prop(node_id, name_key, PropValue.string(f"User {i}"))
            low_level_db.set_node_prop(node_id, email_key, PropValue.string(f"user{i}@example.com"))
            low_level_db.set_node_prop(node_id, age_key, PropValue.int(20 + (i % 50)))
            low_level_node_ids.append(node_id)
            low_level_keys.append(key)
        low_level_db.commit()
        
        # Low-level: create edges
        low_level_db.begin()
        for _ in range(config.edges):
            src = random.choice(low_level_node_ids)
            dst = random.choice(low_level_node_ids)
            if src != dst:
                low_level_db.add_edge(src, knows_etype, dst)
        low_level_db.commit()
        
        print(f"  Low-level: {len(low_level_node_ids)} nodes, {config.edges} edges")
        
        # Fluent: create nodes
        fluent_user_refs = []
        fluent_key_args: List[str] = []
        
        for i in range(config.nodes):
            key_arg = str(i)
            user_ref = fluent_db.insert(user).values(
                key=key_arg,
                name=f"User {i}",
                email=f"user{i}@example.com",
                age=20 + (i % 50),
            ).returning()
            fluent_user_refs.append(user_ref)
            fluent_key_args.append(key_arg)
        
        # Fluent: create edges
        for _ in range(config.edges):
            src = random.choice(fluent_user_refs)
            dst = random.choice(fluent_user_refs)
            if src.id != dst.id:
                fluent_db.link(src, knows, dst, since=2020)
        
        print(f"  Fluent: {len(fluent_user_refs)} nodes, {config.edges} edges")
        
        # Optimize both databases
        low_level_db.optimize()
        fluent_db.optimize()
        
        # =================================================================
        # Benchmark: Insert Operations
        # =================================================================
        print("\n[3/6] Benchmarking insert operations...")
        
        low_level_insert_stats = benchmark_low_level_inserts(low_level_db, config)
        fluent_insert_stats = benchmark_fluent_inserts(fluent_db, config)
        
        # =================================================================
        # Benchmark: Key Lookups
        # =================================================================
        print("\n[4/6] Benchmarking key lookups...")
        
        low_level_lookup_stats = benchmark_low_level_key_lookup(
            low_level_db, low_level_keys, config.iterations
        )
        fluent_get_stats = benchmark_fluent_key_lookup(
            fluent_db, fluent_key_args, config.iterations
        )
        fluent_get_ref_stats = benchmark_fluent_get_ref(
            fluent_db, fluent_key_args, config.iterations
        )
        
        # =================================================================
        # Benchmark: Traversals
        # =================================================================
        print("\n[5/6] Benchmarking traversals...")
        
        low_level_traversal_stats = benchmark_low_level_traversal(
            low_level_db, low_level_node_ids, knows_etype, config.iterations
        )
        fluent_traversal_stats = benchmark_fluent_traversal(
            fluent_db, fluent_user_refs, config.iterations
        )
        fluent_traversal_ids_stats = benchmark_fluent_traversal_ids(
            fluent_db, fluent_user_refs, config.iterations
        )
        fluent_traversal_to_list_stats = benchmark_fluent_traversal_to_list(
            fluent_db, fluent_user_refs, config.iterations
        )
        fluent_traversal_with_props_stats = benchmark_fluent_traversal_with_props(
            fluent_db, fluent_user_refs, config.iterations
        )
        fluent_traversal_load_props_stats = benchmark_fluent_traversal_load_props(
            fluent_db, fluent_user_refs, config.iterations
        )
        
        # =================================================================
        # Results
        # =================================================================
        print("\n[6/6] Results")
        print("=" * 100)
        print("\n=== Comparison (lower latency is better, overhead closer to 1.0x is better) ===\n")
        
        print_comparison("Insert (single node + props)", low_level_insert_stats, fluent_insert_stats)
        print_comparison("Key lookup (raw)", low_level_lookup_stats, fluent_get_stats)
        print_comparison("Key lookup (getRef, no props)", low_level_lookup_stats, fluent_get_ref_stats)
        print_comparison("1-hop traversal (count)", low_level_traversal_stats, fluent_traversal_stats)
        print_comparison("1-hop traversal (ids)", low_level_traversal_stats, fluent_traversal_ids_stats)
        print_comparison("1-hop traversal (to_list)", low_level_traversal_stats, fluent_traversal_to_list_stats)
        print_comparison("1-hop traversal (with_props)", low_level_traversal_stats, fluent_traversal_with_props_stats)
        print_comparison("1-hop traversal (load_props)", low_level_traversal_stats, fluent_traversal_load_props_stats)
        
        print("\n--- Detailed Statistics ---\n")
        
        print("Insert Operations:")
        print(f"  Low-level:  p50={format_latency(low_level_insert_stats.p50):>10}  p95={format_latency(low_level_insert_stats.p95):>10}  ({format_number(int(low_level_insert_stats.ops_per_sec))} ops/sec)")
        print(f"  Fluent:     p50={format_latency(fluent_insert_stats.p50):>10}  p95={format_latency(fluent_insert_stats.p95):>10}  ({format_number(int(fluent_insert_stats.ops_per_sec))} ops/sec)")
        
        print("\nKey Lookups:")
        print(f"  Low-level:  p50={format_latency(low_level_lookup_stats.p50):>10}  p95={format_latency(low_level_lookup_stats.p95):>10}  ({format_number(int(low_level_lookup_stats.ops_per_sec))} ops/sec)")
        print(f"  Fluent get: p50={format_latency(fluent_get_stats.p50):>10}  p95={format_latency(fluent_get_stats.p95):>10}  ({format_number(int(fluent_get_stats.ops_per_sec))} ops/sec)")
        print(f"  Fluent ref: p50={format_latency(fluent_get_ref_stats.p50):>10}  p95={format_latency(fluent_get_ref_stats.p95):>10}  ({format_number(int(fluent_get_ref_stats.ops_per_sec))} ops/sec)")
        
        print("\nTraversals (1-hop):")
        print(f"  Low-level:      p50={format_latency(low_level_traversal_stats.p50):>10}  p95={format_latency(low_level_traversal_stats.p95):>10}  ({format_number(int(low_level_traversal_stats.ops_per_sec))} ops/sec)")
        print(f"  Fluent count:   p50={format_latency(fluent_traversal_stats.p50):>10}  p95={format_latency(fluent_traversal_stats.p95):>10}  ({format_number(int(fluent_traversal_stats.ops_per_sec))} ops/sec)")
        print(f"  Fluent ids:     p50={format_latency(fluent_traversal_ids_stats.p50):>10}  p95={format_latency(fluent_traversal_ids_stats.p95):>10}  ({format_number(int(fluent_traversal_ids_stats.ops_per_sec))} ops/sec)")
        print(f"  Fluent to_list: p50={format_latency(fluent_traversal_to_list_stats.p50):>10}  p95={format_latency(fluent_traversal_to_list_stats.p95):>10}  ({format_number(int(fluent_traversal_to_list_stats.ops_per_sec))} ops/sec)")
        print(f"  Fluent w/props: p50={format_latency(fluent_traversal_with_props_stats.p50):>10}  p95={format_latency(fluent_traversal_with_props_stats.p95):>10}  ({format_number(int(fluent_traversal_with_props_stats.ops_per_sec))} ops/sec)")
        print(f"  Fluent load_p:  p50={format_latency(fluent_traversal_load_props_stats.p50):>10}  p95={format_latency(fluent_traversal_load_props_stats.p95):>10}  ({format_number(int(fluent_traversal_load_props_stats.ops_per_sec))} ops/sec)")
        
        # Cleanup
        low_level_db.close()
        fluent_db.close()
        
    finally:
        shutil.rmtree(low_level_dir, ignore_errors=True)
        shutil.rmtree(fluent_dir, ignore_errors=True)
    
    print("\n" + "=" * 100)
    print("Benchmark complete.")
    print("=" * 100)


if __name__ == "__main__":
    config = parse_args()
    run_benchmarks(config)