Elasticsearch is built on top of the Java Virtual Machine (JVM). The most critical part of memory allocation for Elasticsearch is the JVM heap memory. The heap memory is where Elasticsearch stores data structures, indexes, and caches.

The JVM is the runtime environment for Elasticsearch, and it's responsible for executing Elasticsearch's code and managing its memory. Allocating memory to the JVM is crucial because it affects Elasticsearch's performance and stability.

This means that Elasticsearch is implemented in the Java programming language and relies on the JVM for its execution.

When you start Elasticsearch, it launches as a Java process that runs within the JVM. The JVM acts as the runtime environment for Elasticsearch

Elasticsearch is built on top of the Java Virtual Machine (JVM). The most critical part of memory allocation for Elasticsearch is the JVM heap memory. The heap memory is where Elasticsearch stores data structures, indexes, and caches.

fielddata=true: When you set fielddata=true in your mapping for a field, you enable Elasticsearch to load that field's values into memory for fast access. This can be useful for certain operations but should be used with caution, especially for high-cardinality fields.

High Cardinality: High cardinality fields, such as fields containing user IDs, IP addresses, or unique product names, can consume a significant amount of memory if fielddata is enabled, as each unique term needs to be loaded into memory.

If the client's version of the document is newer (i.e., it has a higher version number) than the version currently in the index, the update is allowed to proceed. However, if the client's version is older (i.e., it has a lower version number), the update is rejected to prevent overwriting a newer version of the document with an older one.

Each request typically involves several steps, including establishing a network connection, sending the request data, processing the request on the server, and returning the response to the client.