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Architect fault-tolerant applications with instance fleets on Amazon EMR on EC2
- Amazon EMR on EC2 clusters help process large-scale data workloads using frameworks like Apache Spark, Hive, and Trino, but effective capacity planning is essential for managing sudden demand spikes.
- Using consistent EC2 instance types for daily Spark jobs on Amazon EMR can lead to capacity constraints during spikes, necessitating the need for auto scaling and flexible strategies.
- Instance fleets in Amazon EMR offer a flexible way to manage EC2 instances and support Amazon EC2 On-Demand Capacity Reservations for predictable workloads.
- Stable workloads with predictable resource usage benefit from reserving baseline capacity using ODCRs and configuring EMR clusters accordingly.
- Spiky workloads with fluctuating demands require flexibility through instance fleet strategies, intelligent subnet selection, and managed scaling for optimal resource allocation.
- Creating Capacity Reservations and Resource Groups, associating them, and implementing them in EMR clusters with targeted ODCRs helps optimize capacity while ensuring reliability.
- Using Amazon CloudWatch for monitoring ODCR usage and creating resource groups like EMRSparkSteadyStateGroup with proper tagging enhances capacity reservation management.
- For spiky workloads, incorporating EC2 instance flexibility, prioritized allocation strategies, multi-AZ deployment, and managed scaling in EMR clusters improves availability and cost-effectiveness.
- Prioritized allocation strategies and combining instance types in instance fleets enhance resource provisioning and cost optimization for varying workload demands.
- Using diverse instance types, subnets across AZs, and managed scaling in Amazon EMR clusters help balance cost, availability, and performance for optimal resource utilization.
- Implementing a hybrid approach with ODCRs for baseline capacity and strategic instance fleet configurations can effectively manage both predictable and unpredictable workload patterns on Amazon EMR.
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