Elasticsearch Elasticsearch Analytics: Advanced Techniques for Data Analysis

By Opster Team

Updated: Jun 22, 2023

| 2 min read


Elasticsearch is a widely used search and analytics engine that enables organizations to analyze large volumes of data in real-time. In this article, we will discuss advanced techniques for data analysis using Elasticsearch, focusing on aggregations, machine learning, and custom scoring. If you want to learn how to leverage data frame analytics in Elasticsearch, check out this guide.

Advanced techniques for data analysis using Elasticsearch

1. Aggregations

Aggregations are a powerful way to analyze and summarize data in Elasticsearch. They allow you to group and extract statistics from your data based on specific criteria. There are several types of aggregations available, including:

  • Bucket Aggregations: These group documents into buckets based on certain criteria, such as terms, ranges, or filters.
  • Metric Aggregations: These calculate metrics, such as the sum, average, or count, for each bucket.
  • Pipeline Aggregations: These perform additional calculations on the results of other aggregations.

Here’s an example of using a terms aggregation to find the top 10 most common categories in a set of documents:

GET /_search
  "size": 0,
  "aggs": {
    "top_categories": {
      "terms": {
        "field": "categories.keyword",
        "size": 10

2. Machine Learning

Elasticsearch offers machine learning capabilities, which can help you detect anomalies, forecast trends, and classify data. Some of the key machine learning features include:

  • Anomaly Detection: Identify unusual patterns in your data using unsupervised machine learning algorithms. This can be useful for detecting fraud, monitoring system performance, or identifying outliers in your data.
  • Data Frame Analytics: Perform supervised machine learning tasks, such as classification and regression, to predict outcomes or categorize data based on historical examples.
  • Model Inference: Use pre-trained machine learning models to make predictions on new data.

To get started with anomaly detection, you can create a machine learning job using the following API request:

POST _ml/anomaly_detectors/my_job
  "analysis_config": {
    "bucket_span": "15m",
    "detectors": [
        "function": "high_mean",
        "field_name": "response_time"
  "data_description": {
    "time_field": "@timestamp"

3. Custom Scoring

Elasticsearch uses a relevance score to rank search results based on how well they match the query. However, you may want to customize the scoring to better suit your specific use case. There are several ways to achieve this, including:

  • Function Score Query: Modify the relevance score using functions, such as field value factor, decay functions, or custom scripts. This allows you to boost or penalize documents based on specific criteria.
  • Scripted Similarity: Define a custom similarity algorithm using Painless, Elasticsearch’s scripting language. This can be useful for implementing domain-specific ranking strategies.

Here’s an example of using a function score query to boost documents with a higher view count:

GET /_search
  "query": {
    "function_score": {
      "query": {
        "match": {
          "title": "Elasticsearch"
      "field_value_factor": {
        "field": "view_count",
        "modifier": "log1p",
        "factor": 1.5


In conclusion, Elasticsearch offers a wide range of advanced analytics capabilities that can help you gain valuable insights from your data. By leveraging aggregations, machine learning, and custom scoring, you can perform complex data analysis tasks and improve the relevance of your search results. As you continue to work with Elasticsearch, consider exploring these advanced techniques to unlock the full potential of your data.

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