Introduction
Elasticsearch is a widely used search and analytics engine that allows you to store, search, and analyze large volumes of data in near real-time. One of the core operations in Elasticsearch is indexing documents, which involves adding or updating documents in an index.
In this article, we will discuss advanced techniques and best practices for using the Elasticsearch Put Document API to index documents efficiently and effectively. If you want to learn about Elasticsearch find document by field value, check out this guide. You should also take a look at this guide, which contains a detailed explanation on Elasticsearch document.
1. Bulk Indexing for Improved Performance
When indexing a large number of documents, it is more efficient to use the Bulk API instead of the Put Document API. The Bulk API allows you to perform multiple index, update, and delete operations in a single request, reducing the overhead of individual requests and improving indexing performance.
Example:
POST _bulk
{ "index" : { "_index" : "test", "_id" : "1" } }
{ "field1" : "value1" }
{ "index" : { "_index" : "test", "_id" : "2" } }
{ "field1" : "value2" }2. Auto-Generated Document IDs
When indexing a document without specifying an ID, Elasticsearch will automatically generate a unique ID for the document. This can be useful when you don’t have a natural identifier for your documents or when you want to avoid potential ID collisions.
Example:
POST test/_doc
{
"field1": "value1"
}3. Optimizing Index Settings for Bulk Indexing
Before performing bulk indexing, it is recommended to optimize your index settings for better performance. Some of the settings to consider are:
- Increase the refresh interval: Set the `index.refresh_interval` to a higher value or disable it temporarily (i.e. set it to -1) during bulk indexing to reduce the frequency of refreshes and improve indexing performance.
- Disable replicas: Set the `index.number_of_replicas` to 0 during bulk indexing to avoid the overhead of replicating data across nodes. After indexing is complete, you can increase the number of replicas as needed.
Example:
PUT test/_settings
{
"index": {
"refresh_interval": "-1",
"number_of_replicas": 0
}
}4. Using the Update API with Upserts
When you want to update a document or insert it if it doesn’t exist, you can use the Update API with the `upsert` option. This allows you to perform an update or insert operation in a single request, reducing the need for additional requests to check for document existence.
Example:
POST test/_update/1
{
"script": {
"source": "ctx._source.field1 += params.count",
"params": {
"count": 1
}
},
"upsert": {
"field1": 1
}
}The above command will either create a new document with ID 1 containing `field1: 1` if the document does not already exists or update the existing document and increment `field1` by 1 (i.e. the value in `params.count`).
5. Handling Partial Updates
Elasticsearch does not support partial updates natively. When updating a document, the entire document is reindexed. To minimize the impact of reindexing, you can use the Update API with a script to modify only specific fields in the document.
Example:
POST test/_update/1
{
"script": {
"source": "ctx._source.field1 = params.new_value",
"params": {
"new_value": "updated_value"
}
}
}6. Versioning and Optimistic Concurrency Control
Elasticsearch supports versioning and optimistic concurrency control to prevent conflicting updates to the same document. When indexing a document, you can use the `if_seq_no` and `if_primary_term` parameters to specify the expected version of the document. If the current version does not match the expected version, the operation will fail.
Example:
PUT test/_doc/1?if_seq_no=10&if_primary_term=1
{
"field1": "value1"
}Conclusion
In conclusion, the Elasticsearch Put Document API is a powerful tool for indexing documents, but it is essential to follow best practices and advanced techniques to ensure efficient and effective indexing. By using bulk indexing, optimizing index settings, handling partial updates, and leveraging versioning and optimistic concurrency control, you can improve the performance and reliability of your Elasticsearch operations.
