Future Challenges

ETH outlines:

• Better distribution for massive graphs.
• Hybrid with other NoSQL.
• Streaming graphs (real-time changes).
• AI integration.

Explaining Better Distribution in Depth

For trillion edges, advanced sharding needed.

Why: Handle web-scale.

Code Sample (Future conceptual):

MATCH (n) DISTRIBUTED RETURN n

Explaining Hybrid with Other NoSQL in Depth

Combine graph with document/column.

Why: Best of breeds.

Code Sample:

MATCH (n) CALL document.lookup(n.id) RETURN n

Explaining Streaming Graphs in Depth

Real-time updates, like Kafka integration.

Why: Dynamic data.

Code Sample:

SUBSCRIBE TO changes CREATE (n)

flowchart LR
    Massive["Trillion edges"] --> Advanced["Advanced sharding"]
    HybridGraph["Graph engine"] --> HybridDoc["Document store"]
    HybridGraph --> HybridColumn["Column store"]
    StreamIn["Real-time stream"] --> GraphUpdate["Graph updates applied"]
    GraphUpdate --> Consumers["Apps use updates"]
    DataCore["Graph data"] --> GNN["Graph neural nets"]
    GNN --> Insights["Predictions"]

Explaining AI Integration in Depth

Graph neural nets for predictions.

Why: Enhanced analytics.

Code Sample (PyTorch sim):

import torch
# Graph NN model

Neo4j: Evolving queries.

O’Reilly: More algorithms.

Student: Scalability tweaks.

Graphs are rising; tackle these for dominance.