AWS Certified Data Analytics Specialty 2021 - Hands-On! - Amazon Elasticsearch Service Performance

The video tutorial discusses the challenges of memory pressure in clusters, particularly focusing on JVM memory issues caused by unbalanced shard allocations and excessive shards. It emphasizes the importance of managing shards effectively to prevent performance degradation. Solutions include reducing the number of shards by deleting old or unused indices and offloading data to archives like Amazon Glacier. The tutorial provides practical advice for avoiding JVM memory pressure errors, especially in Amazon Elasticsearch environments.

What can cause memory pressure in a cluster due to shard allocation?

• Increasing the number of nodes

• Reducing the number of nodes

• Unbalanced shard allocation

• Balanced shard allocation

What is a fundamental truth about distributed computing mentioned in the video?

• More nodes reduce memory pressure

• Distributing processing requires less memory

• Less can be more in terms of resource management

• More shards always lead to better performance

What is the impact of having too many shards in a cluster?

• Increased memory management complexity

• Simplified data processing

• Reduced memory usage

• Improved performance

How can you reduce JVM memory pressure according to the video?

• By adding more nodes to the cluster

• By deleting old or unused indices

• By increasing the number of shards

• By increasing the JVM heap size

What is one suggested method for offloading data from a cluster?

• Storing data in a local database

• Using cloud storage like Amazon Glacier

• Reducing the number of nodes

• Increasing the cluster's memory