1
# Safe but painfully slow
2
def load_relationships_sequential(relationships, session):
3
    for source, target, rel_type in relationships:
4
        session.run("""
5
            MATCH (s {id: $source})
6
            MATCH (t {id: $target})
7
            CREATE (s)-[r:$type]->(t)
8
        """, source=source, target=target, type=rel_type)

1
def create_relationship_with_retry(source, target, rel_type, session, max_retries=5):
2
    for attempt in range(max_retries):
3
        try:
4
            session.run("""
5
                MATCH (s {id: $source})
6
                MATCH (t {id: $target})
7
                CREATE (s)-[r:$type]->(t)
8
            """, source=source, target=target, type=rel_type)
9
            return True
10
        except DeadlockException:
11
            time.sleep(2 ** attempt)  # Exponential backoff
12
    return False

1
def batch_create_relationships(relationships, batch_size=1000):
2
    for i in range(0, len(relationships), batch_size):
3
        batch = relationships[i:i+batch_size]
4
        # This can still deadlock with other batches!
5
        create_batch(batch)

1
def partition_nodes(relationships, num_partitions=10):
2
    """
3
    Assign each node to a partition using a deterministic function.
4
    """
5
    node_partitions = {}
6

7
    # Extract all unique nodes
8
    nodes = set()
9
    for source, target, _ in relationships:
10
        nodes.add(source)
11
        nodes.add(target)
12

13
    # Assign partitions
14
    for node_id in nodes:
15
        # Use modulo for numeric IDs, hash for strings
16
        if isinstance(node_id, (int, float)):
17
            partition = int(node_id) % num_partitions
18
        else:
19
            partition = hash(str(node_id)) % num_partitions
20

21
        node_partitions[node_id] = partition
22

23
    return node_partitions

1
def create_partition_codes(relationships, node_partitions):
2
    """
3
    Assign a partition code to each relationship.
4
    """
5
    partition_codes = {}
6

7
    for idx, (source, target, _) in enumerate(relationships):
8
        source_partition = node_partitions[source]
9
        target_partition = node_partitions[target]
10

11
        # Create partition code
12
        partition_code = f"{source_partition}-{target_partition}"
13
        partition_codes[idx] = partition_code
14

15
    return partition_codes

1
def organize_batches(partition_codes, num_partitions=10):
2
    """
3
    Organize relationships into non-conflicting batches.
4
    """
5
    # Group relationships by partition code
6
    code_to_indices = defaultdict(list)
7
    for idx, code in partition_codes.items():
8
        code_to_indices[code].append(idx)
9

10
    batches = []
11

12
    # Create batches using diagonal pattern
13
    for offset in range(num_partitions):
14
        batch = []
15
        for i in range(num_partitions):
16
            j = (i + offset) % num_partitions
17
            code = f"{i}-{j}"
18
            if code in code_to_indices:
19
                batch.extend(code_to_indices[code])
20

21
        if batch:
22
            batches.append(batch)
23

24
    return batches

1
def process_batches(batches, relationships, neo4j_driver, num_workers=8):
2
    """
3
    Process batches with guaranteed deadlock-free parallelism.
4
    """
5
    total_created = 0
6

7
    for batch_num, batch in enumerate(batches):
8
        print(f"Processing batch {batch_num + 1}/{len(batches)}")
9

10
        # Within this batch, we can parallelize safely!
11
        with ThreadPoolExecutor(max_workers=num_workers) as executor:
12
            futures = []
13

14
            # Split batch into chunks for workers
15
            chunk_size = max(1, len(batch) // num_workers)
16
            for i in range(0, len(batch), chunk_size):
17
                chunk = batch[i:i + chunk_size]
18
                chunk_rels = [relationships[idx] for idx in chunk]
19

20
                future = executor.submit(create_relationships_chunk,
21
                                       chunk_rels, neo4j_driver)
22
                futures.append(future)
23

24
            # Collect results
25
            for future in as_completed(futures):
26
                total_created += future.result()
27

28
    return total_created

1
# Initialize Neo4j driver
2
from neo4j import GraphDatabase
3

4
driver = GraphDatabase.driver("bolt://localhost:7687",
5
                            auth=("neo4j", "password"))
6

7
# Prepare your relationships
8
relationships = [
9
    ("user_1", "product_100", "PURCHASED", {"date": "2024-01-01"}),
10
    ("user_2", "product_101", "VIEWED", {"timestamp": 1234567890}),
11
    # ... millions more
12
]
13

14
# Create loader
15
loader = MixAndBatchLoader(driver, num_partitions=10, concurrency=8)
16

17
# Load relationships
18
created, metrics = loader.load_relationships(relationships)
19

20
print(f"Created {created} relationships")
21
print(f"Performance: {metrics['relationships_per_second']:.0f} rel/s")
22
print(f"Partitioning: {metrics['partitioning_time']:.2f}s")
23
print(f"Batching: {metrics['batching_time']:.2f}s")
24
print(f"Execution: {metrics['execution_time']:.2f}s")

1
def organize_bipartite_batches(self, partition_codes, set_a_partitions, set_b_partitions):
2
    """
3
    Optimized batching for bipartite graphs.
4
    """
5
    # We know relationships only go from Set A to Set B
6
    # This allows for more efficient batching
7

8
    batches = []
9
    num_a = len(set_a_partitions)
10
    num_b = len(set_b_partitions)
11

12
    # Create batches that maximize parallelism
13
    for offset in range(max(num_a, num_b)):
14
        batch = []
15

16
        for i in range(num_a):
17
            j = (i + offset) % num_b
18
            code = f"A{i}-B{j}"
19

20
            if code in code_to_indices:
21
                batch.extend(code_to_indices[code])
22

23
        if batch:
24
            batches.append(batch)
25

26
    return batches

1
def organize_monopartite_batches(self, partition_codes, num_partitions):
2
    """
3
    Optimized batching for monopartite graphs with bidirectional relationships.
4
    """
5
    # Group relationships by normalized partition codes
6
    normalized_codes = defaultdict(list)
7

8
    for idx, code in partition_codes.items():
9
        parts = code.split('-')
10
        source_p, target_p = int(parts[0]), int(parts[1])
11

12
        # Normalize code to handle bidirectional relationships
13
        normalized = f"{min(source_p, target_p)}-{max(source_p, target_p)}"
14
        normalized_codes[normalized].append(idx)
15

16
    # Create batches ensuring no conflicts
17
    batches = []
18
    for k in range(num_partitions):
19
        batch = []
20

21
        for i in range(num_partitions):
22
            j = (i + k) % num_partitions
23
            code = f"{min(i, j)}-{max(i, j)}"
24

25
            if code in normalized_codes:
26
                batch.extend(normalized_codes[code])
27

28
        if batch:
29
            batches.append(batch)
30

31
    return batches

1
def handle_supernodes(self, relationships, threshold=1000):
2
    """
3
    Special handling for highly connected nodes.
4
    """
5
    # Count connections per node
6
    node_degree = defaultdict(int)
7
    for source, target, _, _ in relationships:
8
        node_degree[source] += 1
9
        node_degree[target] += 1
10

11
    # Identify supernodes
12
    supernodes = {node for node, degree in node_degree.items()
13
                  if degree > threshold}
14

15
    # Separate supernode relationships
16
    supernode_rels = []
17
    regular_rels = []
18

19
    for rel in relationships:
20
        if rel[0] in supernodes or rel[1] in supernodes:
21
            supernode_rels.append(rel)
22
        else:
23
            regular_rels.append(rel)
24

25
    # Process with different strategies
26
    return regular_rels, supernode_rels

1
def calculate_optimal_partitions(self, relationships):
2
    """
3
    Dynamically determine optimal partition count.
4
    """
5
    num_nodes = len(self._extract_node_ids(relationships))
6
    num_relationships = len(relationships)
7

8
    # Estimate relationship density
9
    density = num_relationships / (num_nodes ** 2) if num_nodes > 0 else 0
10

11
    # More partitions for denser graphs
12
    if density > 0.1:
13
        return min(32, max(16, int(num_nodes ** 0.25)))
14
    elif density > 0.01:
15
        return min(16, max(8, int(num_nodes ** 0.25)))
16
    else:
17
        return min(10, max(4, int(num_nodes ** 0.25)))

1
def process_relationships_streaming(self, relationship_iterator, batch_size=100000):
2
    """
3
    Process relationships in a streaming fashion for memory efficiency.
4
    """
5
    buffer = []
6
    total_created = 0
7

8
    for rel in relationship_iterator:
9
        buffer.append(rel)
10

11
        if len(buffer) >= batch_size:
12
            # Process this chunk
13
            created, _ = self.load_relationships(buffer)
14
            total_created += created
15
            buffer = []
16

17
    # Don't forget the last chunk
18
    if buffer:
19
        created, _ = self.load_relationships(buffer)
20
        total_created += created
21

22
    return total_created

1
def get_diagnostics(self):
2
    """
3
    Provide detailed diagnostics for performance tuning.
4
    """
5
    return {
6
        "partition_distribution": self._analyze_partition_distribution(),
7
        "batch_efficiency": self._calculate_batch_efficiency(),
8
        "deadlock_count": 0,  # Always zero with Mix and Batch!
9
        "average_batch_size": sum(len(b) for b in self.batches) / len(self.batches),
10
        "parallelism_factor": self.concurrency * len(self.batches),
11
        "theoretical_speedup": self._calculate_theoretical_speedup()
12
    }