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Document in Elasticsearch

What is an Elasticsearch document?

While an SQL database has rows of data stored in tables, Elasticsearch stores data as multiple documents inside an index. This is where the analogy must end however, since the way that Elasticsearch treats documents and indices differs significantly from a relational database.

For example, documents could be:

• Products in an e-commerce index
• Log lines in a data logging application
• Invoice lines in an invoicing system

Document fields

Each document is essentially a JSON structure, which is ultimately considered to be a series of key:value pairs. These pairs are then indexed in a way that is determined by the document mapping. The mapping defines the field data type as text, keyword, float, time, geo point or various other data types.

Elasticsearch documents are described as schema-less because Elasticsearch does not require us to pre-define the index field structure, nor does it require all documents in an index to have the same structure. However, once a field is mapped to a given data type, then all documents in the index must maintain that same mapping type.

Each field can also be mapped in more than one way in the index. This can be useful because we may want a keyword structure for aggregations, and at the same time be able to keep an analysed data structure which enables us to carry out full text searches for individual words in the field.

For a full discussion on mapping please see here.

Document source

An Elasticsearch document _source consists of the original JSON source data before it is indexed. This data is retrieved when fetched by a search query.

Document metadata

Each document is also associated with metadata, the most important items being:

_index – The index where the document is stored

_id – The unique ID which identifies the document in the index

Documents and index architecture

Note that different applications could consider  a “document” to be a different thing.  For example, in an invoicing system, we could have an architecture which stores invoices as documents (1 document per invoice),  or we could have an index structure which stores multiple documents as “invoice lines” for each invoice. The choice would depend on how we want to store, map and query the data.

Examples:

Creating a document in the user’s index:

POST  /users/_doc 
{
    "name" : "Petey",
    "lastname" : "Cruiser",
    "email" : "petey@gmail.com"
}

In the above request, we haven’t mentioned an ID for the document so the index operation generates a unique ID for the document. Here _doc is the type of document.

POST  /users/_doc/1
{
    "name" : "Petey",
    "lastname" : "Cruiser",
    "email" : "petey@gmail.com"
}

In the above query, the document will be created with ID 1.

You can use the below ‘GET’ query to get a document from the index using ID:

GET  /users/_doc/1

Below is the result, which contains the document (in _source field) as metadata:

{
    "_index": "users",
    "_type": "_doc",
    "_id": "1",
    "_version": 1,    "_seq_no": 1,    "_primary_term": 1,
    "found": true,
    "_source": {
        "name": "Petey",
        "lastname": "Cruiser",
        "email": "petey@gmail.com"
    }
}

Notes

Starting version 7.0 types are deprecated, so for backward compatibility on version 7.x all docs are under type ‘_doc’, starting 8.x type will be completely removed from ES APIs.

Settings in Elasticsearch

In Elasticsearch, you can configure cluster-level settings, node-level settings and index level settings. Here is a quick rundown of each level.

A. Cluster settings

These settings can either be:

1. Persistent, meaning they apply across restarts, or
2. Transient, meaning they won’t survive a full cluster restart.

If a transient setting is reset, the first one of these values that is defined is applied:

• The persistent setting
• The setting in the configuration file
• The default value

The order of precedence for cluster settings is:

1. Transient cluster settings
2. Persistent cluster settings
3. Settings in the elasticsearch.yml configuration file

Examples

An example of persistent cluster settings update:

PUT /_cluster/settings
{
    "persistent" : {
        "indices.recovery.max_bytes_per_sec" : "500mb"
    }
}

An example of a transient update:

PUT /_cluster/settings
{
    "transient" : {
        "indices.recovery.max_bytes_per_sec" : "40mb"
    }
}

B. Index settings

These are the settings that are applied to individual indices. There is an API to update index level settings.

Examples

The following API call will set the number of replica shards to 5 for my_index index.

PUT /my_index/_settings
{
    "index" : {
        "number_of_replicas" : 5    
     }
}

To revert a setting to the default value, use null.

PUT /my_index/_settings
{
    "index" : {
        "refresh_interval" : null
    }
}

C. Node settings

These settings apply to nodes. Nodes can fulfill different roles. These include the master, data, and coordination roles. Node settings are set through the elasticsearch.yml file for each node. 

Examples

Setting a node to be a data node (in the elasticsearch.yml file):

node.data: true

Disabling the ingest role for the node (which is enabled by default):

node.ingest: false

For production clusters, you will need to run each type of node on a dedicated machine with two or more instances of each, for HA (minimum three for master nodes).

Notes and good things to know

• Learning more about the cluster settings and index settings is important – it can spare you a lot of trouble. For example, if you are going to ingest huge amounts of data into an index and the number of replica shards is set to say, 5, the indexing process will be super slow because the data will be replicated at the same time it is indexed. What you can do to speed up indexing is to set the replica shards to 0 by updating the settings, and set it back to the original number when indexing is done, using the settings API.
• Another useful example of using cluster-level settings is when a node has just joined the cluster and the cluster is not assigning any shards to the node. Although shard allocation is enabled by default on all nodes, someone may have disabled shard allocation at some point (for example, in order to perform a rolling restart), and forgot to re-enable it later. To enable shard allocation, you can update the Cluster Settings API:

PUT /_cluster/settings{"transient":{"cluster.routing.allocation.enable":"all"}}

• It’s better to set cluster-wide settings with Settings API instead of with the elasticsearch.yml file and to use the file only for local changes. This will keep the same setting on all nodes. However, if you define different settings on different nodes by accident using the elasticsearch.yml configuration file, it is hard to notice these discrepancies.
• See also: Recovery