Elasticsearch stands as a powerful search engine enriched with analytical capabilities, all rooted in Lucene. This versatile platform seamlessly integrates three key solutions: Observability, Security, and Enterprise Search. Moreover, it offers the flexibility for users to craft ad hoc applications leveraging its robust search, machine learning, and analytics functionalities. Whether deployed on-premises or through the convenient  Elastic Cloud service in the cloud, Elasticsearch empowers businesses with unparalleled search capabilities and data insights.

Key Features of Elasticsearch:

• Full Text Search: Elasticsearch offers robust full-text search capabilities, including customizable analyzers tailored to suit specific use cases. 
• Distributed Architecture and Scalability: Its distributed architecture allows Elasticsearch to scale horizontally, facilitating efficient data management and lifecycle processes. This scalability ensures high availability, making data resilient to major outages. 
• Fast Response Times: Elasticsearch boosts impressively fast response times, making it ideal for customer-facing search applications. This attribute has led to its widespread adoption by online retailers worldwide.
• Machine Learning Capabilities: Elasticsearch features dedicated machine learning nodes, providing access to pre-built models and the ability to upload and execute custom models. This opens up avenues for advanced natural language processing (NLP), clustering, and other machine-learning applications.

Main Concepts

1. Kibana: Kibana serves as a vital component within the Elastic ecosystem, offering a web interface for Elasticsearch. Positioned as the visualization and UI layer of the stack, Kibana empowers users with dashboards, maps, and a monitoring interface, facilitating the overall usability of the stack.

2. Elasticsearch Node: An Elasticsearch node represents an individual instance within the Elasticsearch infrastructure. Each node may fulfill one or more roles, such as data storage, master management, or machine learning capabilities.

2.1 Cluster: A cluster comprises one or more Elasticsearch nodes, with a minimum of three recommended to achieve high availability. Within an Elasticsearch cluster, data, processing, and management are shared, ensuring robustness and high availability.

3. Index: An index serves as a mechanism for organizing documents with similar characteristics within Elasticsearch. Each index has settings and mappings that dictate how data is stored and retrieved.

4. Shard: Shards are subdivisions of an index designed to be distributed on data nodes, thereby facilitating scalability and fault tolerance. Replicas are shards maintained on different nodes to ensure data availability in the event of node failures. Additionally, having replicas facilitate distributed query processing, leading to faster response times.

Basic Architecture for an Elastic Deployment

The simplest architecture ensuring high availability and stability typically consists of three data nodes, each fulfilling both data and master roles. Among these nodes, one is designated as the master node. With this configuration, up to two replicas can be maintained, distributing data across all nodes for redundancy.

Access is facilitated through a dedicated Kibana node, establishing a connection to the Elasticsearch nodes. Via Kibana, users can execute queries, construct visualizations, and manage the cluster, including configuration adjustments within Elasticsearch.

Alternatively, data access can be achieved by sending requests to the RESTful API provided by  Elasticsearch. This approach enables performing tasks similar to those accomplished through Kibana programmatically. A common scenario involves generating a search request based on user input, forwarding it to Elasticsearch, and presenting the results on the frontend.

Going further we can have much more complex architectures, with multiple Kibana nodes, dedicated Coordinating, Master and machine learning Elasticsearch nodes and even with data tiers. 

Elasticsearch emerges as an invaluable tool catering to a spectrum of real-time use cases, ranging from its comprehensive full-text search functionality to leveraging machine learning-powered forecasting. Having a robust architecture that ensures high availability and the option to use it as a service, Elasticsearch can be used in production environments with confidence. In my experience, Elasticsearch is a very useful tool that enables a wide range of use cases and adapts very well to any of the client’s needs. It is useful to build search engines, recommendation systems, observability, and security platforms alike.

Written by:

Alexander Dávila
Software Engineer – Elastic Certified Engineer & Elastic Certified Analyst
Country: Ecuador