How do graph databases handle scalability?
An example of an AI chatbot conversation about scalability in graph databases.
How do graph databases handle scalability?
Graph databases are designed to scale horizontally, allowing them to handle large volumes of data and complex queries efficiently.
What are the advantages of using graph databases for large datasets?
Graph databases excel in managing relationships and connections, making them ideal for large datasets with intricate interdependencies.
Can you explain how sharding works in graph databases?
Sharding in graph databases involves partitioning the graph into smaller, manageable pieces, which can be distributed across multiple servers to enhance performance.
Are there any limitations to scalability in graph databases?
Yes, while graph databases are scalable, they can face challenges with very large graphs, such as increased complexity in query performance.
What are some popular graph databases that support scalability?
Popular graph databases that support scalability include Neo4j, Amazon Neptune, and ArangoDB, each offering unique features.
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