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Design patterns for implementing Hive Metastore for Amazon EMR on EKS
- Hive Metastore (HMS) serves as a central metadata store for data lake table formats, providing clients access to metadata via the Metastore Service API.
- HMS architecture patterns include implementing HMS as a sidecar container, cluster dedicated HMS, and external HMS.
- The sidecar pattern co-locates HMS with the data processing framework, suitable for small-scale deployments with simplicity.
- Cluster dedicated HMS pattern involves running HMS in pods managed by Kubernetes, offering moderate isolation and resource efficiency.
- External HMS pattern deploys HMS in a separate EKS cluster, ideal for scenarios needing a centralized metastore service.
- Implementing these patterns with Spark demonstrates various approaches for managing metadata efficiently.
- Each pattern has distinct advantages based on needs, like simplicity, resource isolation, and scalability.
- The article provides detailed steps for configuring and testing the HMS patterns in AWS EMR on EKS using Spark Operator.
- Cleanup steps after testing are included to avoid incurring charges for resources created during setup.
- Experimenting with these design patterns can optimize Hive Metastore deployments for performance and security in EMR on EKS environments.
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