Index in Elasticsearch

What it is

In Elasticsearch, an index (indices in plural) can be thought of as a table inside a database that has a schema and can have one or more shards and replicas. An Elasticsearch index is divided into shards and each shard is an instance of a Lucene index.

Indices are used to store the documents in dedicated data structures corresponding to the data type of fields. For example, text fields are stored inside an inverted index whereas numeric and geo fields are stored inside BKD trees.

Examples

Create Index

The following example is based on Elasticsearch version 5.x onwards. An index with two shards, each having one replica will be created with the name test_index1

PUT /test_index1?pretty
{
    "settings" : {
        "number_of_shards" : 2,
        "number_of_replicas" : 1
    },
    "mappings" : {
        "properties" : {
            "tags" : { "type" : "keyword" },
            "updated_at" : { "type" : "date" }
        }
    }
}

List Indices

All the index names and their basic information can be retrieved using the following command:

GET _cat/indices?v

Index a document

Let’s add a document in the index with below command:

PUT test_index1/_doc/1
{
  "tags": [
    "opster",
    "elasticsearch"
  ],
  "date": "01-01-2020"
}

Query an index

GET test_index1/_search
{
  "query": {
    "match_all": {}
  }
}

Query Multiple Indices

It is possible to search multiple indices with a single request. If it is a raw HTTP request, Index names should be sent in comma-separated format, as shown in the example below, and in the case of a query via a programming language client such as python or Java, index names are to be sent in a list format.

GET test_index1,test_index2/_search

Delete Indices

DELETE test_index1

Common Problems

DELETE /*

action.destructive_requires_name: true

Memory in Elasticsearch

What is it

Memory is one of the most critical resources to monitor in Elasticsearch. Elasticsearch runs on JVM and uses heap memory areas for query cache, request cache, accessing lucene segments and storing fielddata for aggregations and sorting.

Commos problems and important points

• The most common error that arises in Elasticsearch is OutOfMemory error. This error comes when the node is not able to cope up with the required heap size space. To avoid this, you need to closely monitor the heap utilization and garbage collector performance.
• As per the most up-to-date best practices you should not allocate more than 50 percent of total RAM to JVM heap size. Starting from Elasticsearch version 5.x onward this can be set using -Xms and -Xmx parameters inside jvm.options configuration file. The defaults are set to 1 GB for both minimum and maximum heap size.
• The heap size should not set more than 31 GB in any case to avoid the poor garbage collection.