Elasticsearch Elasticsearch Data Ingestion: Advanced Techniques and Best Practices

By Opster Team

Updated: Jul 23, 2023

| 2 min read

Introduction

Data ingestion is a critical aspect of Elasticsearch, as it involves the process of importing, processing, and storing data into the Elasticsearch cluster. This article will discuss advanced techniques and best practices for data ingestion in Elasticsearch, focusing on the following topics:

1. Ingestion methods
2. Data preprocessing with ingest nodes
3. Bulk indexing for improved performance
4. Monitoring and optimizing data ingestion

Let’s now handle each topic in turn.

1. Ingestion methods

There are several methods for ingesting data into Elasticsearch, including:

• Logstash: A popular open-source data processing pipeline that can ingest data from various sources, transform it, and send it to Elasticsearch.
• Beats: Lightweight data shippers that can collect and send data directly to Elasticsearch or Logstash.
• Elasticsearch Ingest Node: A node type in Elasticsearch that can preprocess documents before indexing.
• Elasticsearch API: The RESTful API provided by Elasticsearch for indexing and querying data.

When choosing an ingestion method, consider factors such as data volume, data format, and the required preprocessing steps.

2. Data Preprocessing with Ingest Nodes

Ingest nodes are Elasticsearch nodes that have the ingest role enabled. They can preprocess documents before indexing by applying a series of processors to the data. Processors can perform various operations, such as:

• Extracting fields from text
• Converting data types
• Enriching documents with additional data
• Removing or renaming fields

To use an ingest node, create an ingest pipeline by defining a series of processors and their configurations. Here’s an example of creating an ingest pipeline with two processors:

PUT _ingest/pipeline/my_pipeline
{
  "description": "My custom pipeline",
  "processors": [
    {
      "grok": {
        "field": "message",
        "patterns": ["%{IP:client_ip} %{WORD:method} %{URIPATHPARAM:request}"]
      }
    },
    {
      "geoip": {
        "field": "client_ip",
        "target_field": "geo"
      }
    }
  ]
}

To index documents using this pipeline, include the `pipeline` parameter when indexing:

PUT my_index/_doc/1?pipeline=my_pipeline
{
  "message": "23.23.11.10 GET /search?q=elasticsearch"
}

After going through the ingest pipeline, the enriched document will look like this:

{
  "client_ip": "23.23.11.10",
  "message": "23.23.11.10 GET /search?q=elasticsearch",
  "method": "GET",
  "request": "/search?q=elasticsearch",
  "geo": {
    "continent_name": "North America",
    "region_iso_code": "US-VA",
    "city_name": "Ashburn",
    "country_iso_code": "US",
    "country_name": "United States",
    "region_name": "Virginia",
    "location": {
      "lon": -77.4903,
      "lat": 39.0469
    }
  }
}

3. Bulk Indexing for Improved Performance

When ingesting large volumes of data, using the bulk API can significantly improve indexing performance. The bulk API allows you to perform multiple indexing, updating, or deleting operations in a single request. Here’s an example of using the bulk API to index two documents:

POST _bulk
{ "index": { "_index": "my_index", "_id": "1" } }
{ "field1": "value1", "field2": "value2" }
{ "index": { "_index": "my_index", "_id": "2" } }
{ "field1": "value3", "field2": "value4" }

When using the bulk API, consider the following best practices:

• Keep the bulk request size reasonable, typically between 5-15 MB.
• Monitor the indexing performance and adjust the bulk request size accordingly.
• Use multiple threads or processes to send bulk requests concurrently.

4. Monitoring and Optimizing Data Ingestion

To ensure optimal data ingestion performance, monitor key metrics such as indexing rate, indexing latency, and node resource usage. Elasticsearch provides various APIs and tools for monitoring, including:

• Index Management API: Provides information about index settings, mappings, and statistics.
• Nodes Stats API: Provides statistics about nodes, including indexing performance and resource usage.
• Cluster Health API: Provides an overview of the cluster’s health and status.

Based on the monitoring data, you can optimize data ingestion by:

• Adjusting index settings, such as the number of shards and replicas.
• Tuning ingest pipeline configurations to minimize preprocessing overhead.
• Scaling the Elasticsearch cluster by adding more nodes or increasing resources.

Conclusion

In conclusion, Elasticsearch offers various methods and features for efficient data ingestion. By understanding and applying advanced techniques such as ingest nodes, bulk indexing, and monitoring, you can ensure optimal performance and reliability for your Elasticsearch cluster.