AWS Certified Data Analytics Specialty 2021 – Hands-On - Amazon OpenSearch Service Performance

The video tutorial discusses managing memory pressure in Amazon OpenSearch clusters, focusing on JVM memory issues caused by unbalanced shard allocations and excessive shard counts. It suggests solutions like reducing shard numbers by deleting unused indices and offloading data to archives, thereby improving performance and avoiding memory errors.

What can cause memory pressure in Amazon Open Search clusters?

• Reducing the number of shards

• Increasing the number of nodes

• Balanced shard allocations

• Unbalanced shard allocations

What is the effect of running out of memory in a cluster?

• Increased memory availability

• Swapping memory to disk

• No impact on performance

• Improved performance

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

• Memory usage is not a concern

• More shards always improve performance

• Less can be more sometimes

• All nodes should have equal memory

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

• By increasing the number of shards

• By adding more nodes

• By deleting older unused indices

• By increasing JVM heap size

What is a suggested method to manage old data in the cluster?

• Use more shards for old data

• Increase the cluster size

• Offload data to an archive

• Keep all data in the cluster