Distribution and Transactions

Scale out: Sharding (partition data), replication.

ETH: Support ACID? Sharding? Many do partial.

Neo4j: Causal clustering for reads/writes.

O’Reilly: Transactions ensure consistency.

Student: Balance with query needs.

Challenge: Global queries on distributed graphs.

• Sharding: Partition by hash or custom.
• Replication: Copies for failover/reads.
• Transactions: ACID ensures atomic commits.

Explaining Sharding in Depth

Divides data across nodes, e.g., hash node IDs.

Why: Horizontal scale.

Code Sample (Conceptual):

// Query may route to shard
MATCH (n) WHERE id(n) % 3 = 0 RETURN n  // Sim hash

flowchart LR
    Data["Data set"] --> Shard1["Shard 1"]
    Data --> Shard2["Shard 2"]
    Data --> Shard3["Shard 3"]

Explaining Replication in Depth

Copies data for reads, failover.

Why: High availability.

Code Sample:

// Read from replica
MATCH (n) RETURN n

flowchart LR
    Master["Master"] --> Replica1["Replica 1"]
    Master --> Replica2["Replica 2"]
    Master --> ReplicaRead["Read replica"]

Explaining Transactions in Depth

ACID: Atomic, Consistent, Isolated, Durable.

Why: Reliable ops.

Code Sample:

with session.begin_transaction() as tx:
    tx.run("CREATE (n)")
    tx.success = True

flowchart LR
    Start["Begin Tx"] --> Ops["Operations"]
    Ops --> Commit["Commit (ACID)"]
    Ops --> Rollback["Rollback"]