Run Complex Queries on Massive Amounts of Data Stored on Your Amazon Documentdb Clusters Using Apache Spark Running on Amazon EMR
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Optimizing Spark Dynamic Allocation: Limitations and Efficiency
By: Pepperdata
Type: White Paper
This white paper examines the limitations of Spark's Dynamic Allocation feature and why it isn't a complete solution for optimizing Apache Spark applications. Key points include:
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Spark Dynamic Allocation can't prevent low resource utilization inside executors, leading to waste.
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Dynamic Allocation can't ensure fair resource allocation in multi-tenant environments, where resource-hungry applications could starve others.
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While Dynamic Allocation offers benefits, it can't solve the issue of Spark applications underutilizing resources, especially during non-peak times.
The paper debunks myths around observability, autoscaling, rightsizing, and manual tuning.
Read the full white paper for more.
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Optimizing Apache Spark to Eliminate Application Waste
By: Pepperdata
Type: White Paper
Cloud computing enables rapid innovation but introduces challenges in optimizing infrastructure and application performance. Solutions like observability tools and autoscaling minimize infrastructure waste but often overlook application waste.
This white paper explores how Pepperdata's Capacity Optimizer addresses application-level waste, accounting for up to 40% of optimization potential. It autonomously detects and remediates unused resources in Apache Spark applications, allowing more workloads on existing nodes. This results in 30-47% savings on instance hours and costs, without changes to application code or configurations.
Read the full white paper to learn more.
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Exploring the Limitations of Instance Rightsizing for Apache Spark
By: Pepperdata
Type: White Paper
Instance rightsizing can reduce cloud costs but has limitations in optimizing Apache Spark applications. This white paper explores the challenges of relying solely on instance rightsizing:
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Tools like Karpenter don't address inefficiencies within applications, such as overprovisioning or poorly written jobs, which are hard to fix at scale.
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Real-time changes in application performance can make the initially chosen instance type suboptimal, leading to resource waste.
The paper argues that instance rightsizing is a partial solution, and additional strategies are needed to fully optimize Apache Spark in the cloud.
Read the full white paper to explore comprehensive approaches.
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