Title

AWS re:Invent 2023 - Amazon Neptune architectures for scale, availability, and insight (DAT406)

Summary

• Ian Robinson, a principal graph architect at AWS, discusses scaling Amazon Neptune applications for success, addressing the increase in users, queries, and complex workloads.
• Identifying scaling needs involves monitoring worker thread utilization and buffer cache churn, with solutions including scaling up instances or scaling out with read replicas.
• Neptune Serverless is introduced as a solution for dynamic workloads, scaling with Neptune Capacity Units (NCUs).
• The talk covers the use of custom endpoints and a new package called dynamic custom endpoints to efficiently distribute work across instances for multiple workloads or tenants.
• Ian discusses Neptune Blue-Green Deployment to minimize downtime during engine updates, leveraging fast database cloning and Neptune streams.
• He introduces Neptune notebooks, a fully managed IDE combining query authoring, application development, and no-code visualization.
• The session concludes with a discussion on the intersection of graphs and generative AI, including using large language models (LLMs) for query authoring and data model design, and retrieval augmented generation for enriching LLM responses with external data sources like Neptune.

Insights

• Scaling Amazon Neptune:

  • Scaling needs can be identified by monitoring the main request queue pending requests CloudWatch metric and CPU utilization.
  • Solutions for scaling include larger instances for more worker threads and buffer cache, adding read replicas, and using serverless instances for variable workloads.
  • Neptune Serverless is suitable for workloads with fluctuating demands, scaling dynamically with NCUs.

• Efficient Resource Utilization:

  • Custom endpoints can be used to route specific workload traffic to dedicated replicas, reducing cache churn and allowing for workload-specific tuning.
  • Dynamic custom endpoints, a new package, can adapt to cluster topology changes and manage membership sets using tags.

• Minimizing Downtime:

  • Neptune Blue-Green Deployment is a new feature that allows cloning a production cluster and upgrading it with minimal downtime.
  • It is essential to enable Neptune streams and have a DynamoDB VPC endpoint in the Neptune VPC before using blue-green deployment.

• Neptune Notebooks:

  • Neptune notebooks provide a managed environment with tools like the Graph Notebook, Graph Explorer, AWS SDK for Python, and AWS SDK for Pandas.
  • These notebooks facilitate query authoring, application development, and visualization, supporting various query languages and data models.

• Graphs and Generative AI:

  • Generative AI can assist in authoring graph queries and designing graph data models using natural language descriptions.
  • Retrieval augmented generation can enrich LLM responses by querying external data sources like Neptune.
  • The use of Amazon Bedrock and Langchain demonstrates how to integrate Neptune with LLMs for query generation and natural language responses.