Elasticsearch 7 and Elastic Stack - In Depth and Hands On! - How Elasticsearch Scales?

The video tutorial explains how Elasticsearch scales using shards, which are distributed across a cluster of computers. It covers the concepts of primary and replica shards, which ensure resiliency against node failures. The tutorial also discusses how Elasticsearch handles data writing and reading, emphasizing the importance of planning shard configuration to accommodate future growth. The video highlights the fault tolerance of Elasticsearch and provides insights into the mathematical configuration of shards.

What is the main scaling technique used by Elasticsearch?

• Using a single powerful server

• Increasing the RAM of the server

• Reducing the number of documents

• Splitting an index into shards

How does Elasticsearch ensure resiliency against node failures?

• By using RAID configurations

• By storing data in the cloud

• By distributing primary and replica shards across nodes

• By using a single backup server

What happens when a node containing a primary shard fails?

• The cluster stops accepting new data

• Data is lost permanently

• A replica shard is promoted to primary

• The entire cluster goes down

What is the role of a primary shard during a write request?

• It handles read requests

• It deletes old data

• It stores the initial write data

• It balances the load

How can you increase the read capacity of an Elasticsearch cluster?

• By increasing the CPU power

• By adding more replica shards

• By adding more primary shards

• By reducing the number of nodes

Why is it important to define the number of primary shards upfront?

• Because it affects the read capacity

• Because it cannot be changed later

• Because it reduces the cost

• Because it improves security

If you configure an index with 3 primary shards and 2 replicas, how many total shards will you have?

• 9

• 12

• 6

• 3