Use of ElasticSearch plug-ins and examples of implementation

Elasticsearch Features and Plugins

Elasticsearch is an open source distributed search engine that provides many features to enable fast text search and data analysis as shown below.

<Main features of Elasticsearch>

• Indexing and Searching: Elasticsearch provides fast indexing and efficient searching of large amounts of text data and documents.
• Text Search: Full text search capabilities allow text searches based on words and phrases. Full-text search indexing and query execution are available.
• Analysis and Aggregation: Elasticsearch provides data analysis functions such as aggregation, aggregation, and grouping. Aggregate results can be retrieved in real time.
• Scalability: Elasticsearch uses a distributed architecture to provide scalability for processing large data sets.
• RESTful API: Elasticsearch operations are performed through a RESTful API. This allows for easy operation regardless of programming language or tools.
• Multilingual support: Elasticsearch provides the ability to perform appropriate analysis and search on multilingual text data.

Various plug-ins are also available to extend the functionality of Elasticsearch, such as

Elasticsearch Ingest Node Plugin

Elasticsearch Ingest Node Plugin is a plugin for pre-processing and transforming data that is built into Elasticsearch. transformations and filtering can be applied to the data before it is indexed. This allows for efficient data quality improvement and preparation work for analysis.

<Ingest Node Plugin Key Features>

• Data preprocessing: Before indexing data, custom preprocessing can be performed, such as adding, removing, updating, splitting, or merging fields. Examples include date reformatting, text data normalization, etc.
• Data Transformation: Transformations can be applied to convert data to another format, such as CSV to JSON.
• Filtering based on criteria: only data that meet specific criteria can be selected for indexing. For example, it is possible to process only data in which a particular field has a particular value.
• Chainable processors: Multiple processors can be chained together to process data sequentially. This allows complex data conversion and preprocessing to be performed flexibly.

<Example of Implementation>

The following example shows how to implement Elasticsearch’s Ingest Node Plugin, a mechanism for adding custom preprocessing steps to Elasticsearch’s data processing pipeline. The following example illustrates how to create a simple text preprocessing pipeline.

 1. Setting Up a Plugin Project

First, set up a project for the Elasticsearch plugin: create the project using Gradle or Maven and add the necessary dependencies.

 2. Implementing the Ingest Processor

Next, implement the Ingest Processor. This is the main component of the Ingest Node Plugin and provides custom logic to transform, convert, or process data.

As an example, let’s implement a custom processor that performs text preprocessing. The following is an example of a processor that performs simple HTML tag removal.

import org.elasticsearch.ingest.AbstractProcessor;
import org.elasticsearch.ingest.IngestDocument;
import org.elasticsearch.ingest.Processor;

import java.util.Map;

public class HtmlTagRemovalProcessor extends AbstractProcessor {

    public HtmlTagRemovalProcessor(String tag) {
        super(tag);
    }

    @Override
    public IngestDocument execute(IngestDocument ingestDocument) throws Exception {
        Map<String, Object> source = ingestDocument.getSourceAndMetadata();
        if (source.containsKey(field)) {
            String fieldValue = source.get(field).toString();
            // Remove HTML tags using regex or any other method you prefer
            String cleanValue = fieldValue.replaceAll("<[^>]*>", "");
            source.put(field, cleanValue);
        }
        return ingestDocument;
    }

    public static final String TYPE = "html_tag_removal";

    public static Processor.Factory factory() {
        return new Processor.Factory() {
            @Override
            public Processor create(Map<String, Processor.Factory> factories, String tag, Map<String, Object> config) throws Exception {
                return new HtmlTagRemovalProcessor(tag);
            }
        };
    }
}

3. Registering plug-ins

Create a file that provides meta-information to register the plugin with Elasticsearch, named META-INF/services/org.elasticsearch.ingest.Processor.Factory and containing a custom processor Specify the factory class.

org.example.plugin.HtmlTagRemovalProcessor$HtmlTagRemovalProcessorFactory

4. Build and install the plugin

Build the plugin and place it in the plugins directory of Elasticsearch. After that, Elasticsearch must be restarted to load the plugin.

This completes the Ingest Node Plugin with a simple HTML tag removal custom Ingest Processor implemented. Based on this example, you can customize the plugin to perform various preprocessing tasks.

Elasticsearch Machine Learning Plugin

The Elasticsearch Machine Learning Plugin is a plug-in offered as part of Elasticsearch that provides functionality for anomaly detection, predictive analysis, and pattern recognition of data using machine learning algorithms. This allows users to extract valuable information from data using Elasticsearch and gain insights in real time.

<Main features of the Elasticsearch Machine Learning Plugin>

• Anomaly Detection: The Elasticsearch Machine Learning Plugin provides algorithms to automatically detect anomalous behavior in data. It can model patterns in time-series and numerical data and identify anomalous behavior. This is used for security monitoring and detection of anomalous system behavior.
• Forecasting: The Elasticsearch Machine Learning Plugin provides algorithms that use time-series data to predict future trends and patterns. This is used for demand forecasting and resource optimization.
• Clustering and Segmentation: The Elasticsearch Machine Learning Plugin provides functionality for automatically clustering and partitioning data into different groups. This allows for customer segmentation and market segmentation analysis.
• Identifying Influencing Factors: Functionality is also provided to identify key influencing factors within a data set. This allows the user to understand how specific variables affect the results.
• Automatic Feature Generation: Functionality is also included to automatically generate features for use in training machine learning models. This simplifies the model training process.

When using the Elasticsearch Machine Learning Plugin, jobs are created via Kibana, data is configured and algorithms are selected, and information provided within the plugin, such as anomaly scores and prediction results, are stored in Elasticsearch’s index and can be visualized and analyzed through Kibana. With this plugin, Elasticsearch will be able to extract more advanced insights from the data and provide value for applications such as anomaly detection and predictive analysis.

Machine learning with Elastic Search is described in detail in “Elasticsearch and Machine Learning.

Elasticsearch SQL Plugin

The Elasticsearch SQL Plugin will be a plug-in that provides an SQL-style query language for Elasticsearch. This allows users to access and query Elasticsearch data using traditional SQL syntax, and the SQL Plugin makes it possible for users familiar with writing SQL queries and existing SQL tools to work with Elasticsearch data. Elasticsearch data can be manipulated by users who are familiar with writing SQL queries or by using existing SQL tools.

<Main features of the Elasticsearch SQL Plugin>

• SQL query support: The Elasticsearch SQL Plugin can query data using SQL-style query syntax. This allows users with traditional SQL query experience to easily manipulate Elasticsearch data.
• Data Extraction and Filtering: Data can be extracted and filtered using SQL queries; SELECT statements can be used to retrieve data based on specific fields or conditions.
• Aggregation and Grouping: Able to use SQL GROUP BY and aggregate functions to aggregate and group data. This allows for the creation of aggregate reports of data.
• Sorting and Ordering: SQL’s ORDER BY clause can be used to sort data by specific fields or to display data in ascending or descending order.
• Subqueries and Joins: Subqueries and JOINs can be used to combine multiple data sets or perform complex queries.
• Integration with Kibana: The Elasticsearch SQL Plugin is integrated with Kibana’s Discover and Visualize features, allowing you to run SQL queries and visualize the results through Kibana.

When using the SQL Plugin, it is necessary to specify the connection information to Elasticsearch and the SQL query. The results of the query execution are returned in JSON format. the SQL Plugin allows users familiar with SQL to easily access Elasticsearch data for analysis and reporting.

<Example Implementation>

Elasticsearch has an SQL plugin that allows you to access Elasticsearch data using SQL queries. Below is an example implementation using the Elasticsearch SQL plugin. However,

1. Installing Plug-ins

First, install the SQL plugin for Elasticsearch. Plug-ins vary depending on the version of Elasticsearch, so it is necessary to select and install the correct version of the plug-in according to the official documentation.

2. Execute SQL query

To execute an SQL query, submit the query using the REST API. The following is a Python example that uses Elasticsearch’s REST API to execute an SQL query.

import requests

url = "http://localhost:9200/_sql?format=txt"
query = "SELECT field1, field2 FROM index_name WHERE field3 = 'value'"

response = requests.post(url, data=query)

if response.status_code == 200:
    result = response.text
    print(result)
else:
    print("Query failed with status code:", response.status_code)
    print(response.text)

3. SQL Plugin Notes

• To use the SQL plugin, it is important to understand Elasticsearch’s index and field schemas, and SQL queries may behave a bit differently than Elasticsearch queries.
• While SQL plugins are useful for performing advanced queries and aggregations, Elasticsearch’s native query and aggregation capabilities are also worth considering.

Elasticsearch Graph Plugin

The Elasticsearch Graph Plugin is a plug-in built into Elasticsearch that provides functionality for visualizing and analyzing data relationships and networks. By using this plug-in, it is possible to reveal potential relationships and patterns that exist in the data, and to visually understand the connections between different elements.

<Main features of the Elasticsearch Graph Plugin>

• Relevance Extraction: The Elasticsearch Graph Plugin extracts relevance from indexed data. Relevance refers to the connection between related elements in the data.
• Relevance Visualization: Generates a network graph to visualize the extracted relevance, using nodes and edges to visually represent the elements and their relationships.
• Identify patterns: Identify clustering and patterns in the graph to understand the characteristics of different groups and clusters, thereby identifying similarities and differences in the data.
• Discover unknown associations: Elasticsearch Graph Plugin also provides functionality for discovering new associations and patterns that are different from known associations, thereby enabling unknown insights to be gained.
• Integration with queries: You can run queries against nodes and edges in the graph to search for specific associations and patterns, and this functionality allows you to focus your analysis on specific associations.

The Elasticsearch Graph Plugin is provided as part of Kibana and can be used in conjunction with Kibana’s Discover and Visualize features. When using the plugin, graphs are configured and visualized via Kibana. The Elasticsearch Graph Plugin can be a very useful tool when you need to understand data relationships and networks.

<Example Implementation>

Elasticsearch has a Graph Plugin that can be used to visualize data relationships and patterns. This makes it easier to visually understand connections and correlations among data. Below is a basic implementation example using the Graph Plugin of Elasticsearch.

1. Installing Plug-ins

First, install the Graph Plugin in Elasticsearch.

2. Data Indexing

To use the Graph Plugin, you must first index the appropriate data in the index. For example, prepare data with relationships between people and organizations.

3. Running Graph Exploration

To explore data relationships using the Graph Plugin, use the following query

POST /index_name/_graph/explore
{
  "query": {
    "query_string": {
      "query": "field_name:value"
    }
  }
}

The above query would be a search for data relationships based on the values of specific fields.

4. View Graph Visualization

To visualize the results of the Graph Plugin, use a tool such as Kibana; when using Kibana, the results of the Graph Plugin can be retrieved and the relationships between the data visualized using Kibana’s visualization capabilities.

Elasticsearch Vector Scoring Plugin

Elasticsearch Vector Scoring Plugin will be a plug-in for performing similarity and multimodal searches on vector data using vector scoring. By using this plug-in, data of different modalities and vector data can be integrated to search for similar items.

<Main features of the Elasticsearch Vector Scoring Plugin>

• Vector data indexing: Vector data (e.g. feature vectors, embedded vectors, etc.) can be indexed into Elasticsearch, thereby managing vector data in a searchable format.
• Vector scoring: A vector scoring function is provided to calculate the similarity between vector data, allowing the user to calculate the similarity between a query vector and the vector data in the index and search for relevant items.
• Similarity Search: Based on the query vector provided by the user, the similarity between the query vector and the vector data in the index can be calculated to retrieve the most similar items. This can be used for multimodal searches such as image and voice searches.
• Custom Scoring: Users can define custom similarity scoring functions, which allows them to implement scoring that is optimal for their particular application.

The Elasticsearch Vector Scoring Plugin can be used in combination with standard Elasticsearch queries. In order to index vector data and perform similarity searches, the corresponding scoring functions and similarity calculation algorithms must be selected and configured. Especially in cases such as multimodal searches, this plug-in can be used to search data of different modalities in an integrated manner.

<Example Implementation>

The Elasticsearch Vector Scoring Plugin can be used to score search queries using vector data. Vector data can be an embedded feature representation for various data types such as text, images, and audio. Below is a basic example implementation of the Elasticsearch Vector Scoring Plugin.

1. Installing Plug-ins

First, install the Vector Scoring Plugin in Elasticsearch.

2. Indexing of vector data

In order to perform scoring using vector data, the vector data must be properly indexed in the index. For example, to index a document’s embedded vectors, index the document as follows

POST /index_name/_doc/1
{
  "text": "This is an example document",
  "embedding": [0.1, 0.2, 0.3, ...]  // Embedding vector
}

3. Vector scoring queries

Vector scoring queries are used to score search queries using vector data. The following is an example of a vector scoring query.

POST /index_name/_search
{
  "query": {
    "script_score": {
      "query": {
        "match": {
          "text": "example"
        }
      },
      "script": {
        "source": "cosineSimilarity(params.query_vector, 'embedding') + 1.0",
        "params": {
          "query_vector": [0.2, 0.4, 0.1, ...]  // Query embedding vector
        }
      }
    }
  }
}

The above query searches for documents matching “example” in the text field, computes the vector score, and returns a score. cosineSimilarity function computes the cosine similarity between the two vectors and adjusts the score by adding 1.0 to the scoring result.

Reference Information and Reference Books

For more information on search information, including Elastic Search, see “About Search Technology.

For reference books, please refer to “Search Technology” “User Interfaces for Information Retrieval” and “Search tool Elastic Search -reference books“.