Elasticsearch 101: Key Concepts, Benefits & Use Cases

As businesses continue to generate more and more data, the need for efficient data management and a scalable search solution has become increasingly important.

Enter Elasticsearch, a powerful search engine designed to handle massive amounts of data with ease. In this article, we'll provide an overview of Elasticsearch and explore how it can benefit businesses.

What is Elasticsearch?

Elasticsearch (ES) is a powerful open-source software that combines a distributed, highly scalable data store with the search engine capabilities of Apache Lucene. It is designed to provide fast full-text search functionality using simple REST APIs, with complex internal mechanisms hidden from users. Written in Java, Elasticsearch is built on top of Lucene and uses its search and indexing capabilities to deliver lightning-fast search results.

Why Use Elasticsearch?

Elasticsearch is particularly well-suited for use cases involving text-based searching and analysis, such as log analysis, e-commerce search, and website search. It supports full-text search, faceted search, and various types of aggregations to help users find the information they need quickly and easily.

Overall, Elasticsearch is a powerful tool for managing and searching large amounts of data, and it can help businesses gain valuable insights from their data quickly and efficiently. It's important to understand the capabilities and benefits of Elasticsearch in order to make informed decisions about using it within your organization.

How Does Elasticsearch Work?

Elasticsearch is a distributed search and analytics engine that allows for quick and efficient searching of large amounts of data. It works by organizing data into indices, which are containers for related documents and dedicated data nodes that are stored across multiple nodes in a cluster.

Elasticsearch is designed to be highly scalable and fault-tolerant, which means it can handle large volumes of data and continue to function even if some nodes in the Elasticsearch cluster fail. It uses sharding to distribute data across multiple nodes and replicas to ensure data availability in case of node failures.

If the inner workings of Elasticsearch are new to you, many of these concepts will seem confusing. Keep in mind that if you're mainly interested in whether Elasticsearch is right for your business, rather than learning how to manage your own Elasticsearch cluster, we'd advise you to skip ahead to the upcoming section. Jump

Next, we'll cover the general concepts of Elasticsearch, and how the components, including the cluster, node, index, shard, and replica, work together to provide reliable and fault-tolerant data storage and search capabilities.

Understanding the Basic Concepts 

To work effectively with Elasticsearch, it's important to understand some of its basic concepts. This provides a foundation for working with Elasticsearch, and they are essential for understanding how data is stored, the data type, and how a user's search request is retrieved when using Elasticsearch. Here are some of the key concepts:

Cluster

A cluster is a collection of one or more nodes that work together to store data and perform search and other indexing and search capabilities and operations. Elasticsearch is designed to be highly scalable, and clusters can contain hundreds or even thousands of nodes.

Data Node

A data node is a single server that is part of the cluster. Each data node stores only a subset of the data in the form of one or more indices, and it participates in search and indexing operations.

Index

An index is a collection of documents that share similar characteristics, such as data type or source. Each document is stored as a JSON object, and it includes fields that contain the data.

Master Node

The master node has a specific role for managing administrative operations. Data nodes monitor availability or failures. The master nodes create and delete the index. Consequently, master nodes are critical in Elasticsearch clusters. A single master node in a cluster will fail. Elasticsearch allows the creation of a number of master-approved node sets in the database.

Document

A document is a single instance of data stored in an index. Each document in the inverted index is uniquely identified by a document ID, and it includes one or more fields that contain the data.

Field

A field is a named piece of data that is stored within a document or data structure. Fields can be of different data types, such as text, numeric, or date.

Mapping

Mapping is used to define the structure of the data stored in an index. It includes field types, analyzers, and other properties of the same index that help Elasticsearch understand the data and how to search it.

Query

A search query is a request for data from Elasticsearch. Search queries can be simple, such as searching for a specific term, or they can be complex, such as a complex data type object combining multiple search criteria.

Analyzer

An analyzer is used to break down the text into tokens, which can be used for search and indexing. Elasticsearch includes several built-in analyzers for different languages and use cases, and users can also create custom analyzers to meet their specific needs.

Aggregation

An aggregation is an operation that groups and summarizes data in various ways. Aggregations can be used to calculate metrics such as counts, averages, and sums, and they can be used to build complex visualizations and dashboards.

Shards and Replicas

Elasticsearch uses sharding to distribute data across multiple nodes for better performance and scalability. Each shard is a subset of the data in an index, and it is hosted on a separate node in the cluster. Replicas are copies of the data node or primary shard that provide redundancy and ensure data availability in the event of a node failure.

Can Elasticsearch be Your Primary Data Store?

It is important to note that Elasticsearch is not intended to replace traditional relational databases and database management systems (RDBMS) for transactional data needs. Instead, it is best suited for handling search and analytics use cases. Therefore, it is common practice to use a relational database as the primary data store and inject only the necessary data into Elasticsearch.

Compared to RDBMS, Elasticsearch stores data as JSON documents, which are denormalized and do not support transactions, referential integrity, joins, or subqueries. However, since Elasticsearch is a relational database, it can handle structured, semi-structured, and unstructured data, making it a versatile tool for handling a wide range of data types.

Benefits: Why Use Elasticsearch?

Many business owners find Elasticsearch to be a valuable tool for their organizations. Elasticsearch is designed to handle large volumes of data and is highly scalable, making it an ideal solution for businesses that need to store and search large amounts of information.

Real-Time Functionality

A large benefit of Elasticsearch is its real-time data analysis capabilities. Elasticsearch can perform real-time analysis on data as it's generated, allowing businesses to gain up-to-date insights into their operations.

Flexibility & Customization

Elasticsearch is also highly flexible and can be used with a variety of data sources, including structured, semi-structured, and unstructured data. This makes it a customizable solution that can be tailored to meet the specific needs of your organization.

Powers Data Driven Decision Making

Elasticsearch is a powerful tool that can help businesses gain valuable insights from their data quickly and efficiently. Whether you're analyzing website traffic, monitoring logs, or tracking customer data, Elasticsearch can provide the functionality you need to make informed decisions and stay ahead of the competition.

Support for Multiple Engine Types

One of the key strengths of Elasticsearch is its ability to support multiple types of engines, each designed to handle different use cases and workloads. Whether you need to search and analyze log data, build real-time search applications, or perform complex data analysis, Elasticsearch has an engine that can meet your needs.

Below, we will explore the different types of engines that Elasticsearch offers and how they can be used to solve various problems.

Full Text Search Engine

Elasticsearch is one of the most popularly used Full Text Search Engine. This is a type of search technology that allows users to search for specific words and phrases within documents, as opposed to just searching for document titles or metadata.

This technology is particularly useful for businesses that deal with large amounts of unstructured data, such as emails, social media posts, or user-generated content.

It allows Elasticsearch to quickly search for words or phrases within documents, even when the documents are stored in different formats or multiple languages, including non-Latin scripts. It also provides fuzzy matching, stemming algorithms, highlighting of search terms, and the ability to define synonyms for more accurate search results.

Analytical Engine

Elasticsearch's Analytical Engine offers businesses a powerful tool for performing complex data analysis on their search data, with advanced features including aggregations, statistical analysis, and machine learning. Aggregations allow for easy grouping and summarization of data based on various criteria, with sorting and aggregation queries making it easy to identify patterns and trends in the data and to perform complex data analysis.

Statistical analysis provides the ability to calculate key metrics, which is useful for businesses that need to perform a detailed analysis of their search data, such as e-commerce companies that need to track sales trends over time.

Machine learning algorithms can identify patterns and anomalies in data, enabling predictive analytics and other applications. This is especially helpful for businesses that need to perform predictive analytics, such as identifying which products are likely to sell well based on historical sales data.

Aggregation Engine

The Aggregation Engine is used to summarize and group search results, allowing businesses to gain insights into their data. It supports a wide range of aggregation functions, such as sum, average, min, max, and percentile, and can be used to perform complex data analysis tasks.

Query Engine

The Query Engine is responsible for processing search queries and returning relevant search results. It supports a wide range of query types, including simple text queries, Boolean queries, and more advanced search features such as fuzzy matching, phrase matching, and proximity searches.

Geo Engine

The Geo Engine is used to perform geospatial searches, allowing businesses to search for data based on geographic location. It supports a wide range of geospatial queries, such as searching for data within a specific radius or within a specific boundary.

Machine Learning Engine

The Machine Learning Engine is used to perform advanced data analysis tasks, such as anomaly detection and predictive modeling. It supports a wide range of machine learning algorithms and can be used to analyze large volumes of data to uncover insights and trends.

Ingestion Engine

This engine is responsible for processing and transforming incoming data before indexing it into Elasticsearch. It provides a flexible and scalable way to preprocess data and enrich it with additional metadata.

Search Relevance Engine

Elasticsearch's search relevance engine is responsible for ranking search results based on relevance to the query. It uses a combination of factors such as term frequency, document popularity, and recency to determine the relevance of each search result.

Indexing Engine

The indexing engine in Elasticsearch is responsible for storing and retrieving data from the index. It uses a combination of memory and disk storage to provide fast and efficient indexing and retrieval of data.

Security and Access Control

Elasticsearch provides a robust security framework that allows businesses to secure their data and control access to it. It supports authentication and authorization mechanisms such as role-based access control (RBAC) and LDAP integration.

Challenges of Elasticsearch

Elasticsearch is a powerful tool for managing and searching all the data a business has, but it also presents a number of challenges for businesses.

Data Management

One key challenge is data management. When a business stores data in an organized way, it makes it easy to search and retrieve using Elasticsearch. Without proper data management, businesses may experience difficulties in data retrieval, resulting in inaccurate or incomplete search results.

Scalability

Another challenge businesses may face is scalability. While Elasticsearch is designed to be scalable and handle lots of data, businesses must ensure that their infrastructure can support Elasticsearch and that they have the necessary resources to handle data growth. Failure to properly plan for scalability can result in slow search speeds or system crashes.

Security

Security is also a concern when implementing Elasticsearch. Businesses must ensure that Elasticsearch is properly secured to adhere to security best practices and that access to the system is restricted to authorized personnel. Failure to properly secure Elasticsearch can result in data breaches and other security incidents.

Maintenance & Updates

Maintenance is yet another challenge that businesses must address. Elasticsearch, like any other technology solution, requires regular maintenance to ensure optimal performance.

Businesses must ensure that their Elasticsearch cluster and system are regularly maintained, updated, and monitored for issues. Failure to properly maintain Elasticsearch can result in performance issues, downtime, and other issues that can impact productivity.

Avoiding Obstacles

It can pay off enormously to have a trusted software partner to address the challenges that may arise with Elasticsearch. They bring expertise in data management, infrastructure planning, security best practices, and maintenance and monitoring strategies.

By partnering with a software consultant or development team, businesses can ensure that their Elasticsearch system is properly implemented and maintained, minimizing the risk of issues and maximizing the benefits of the platform.

Use Cases for Elasticsearch

Elasticsearch is a versatile search and analytics engine that can be used in a variety of industries and applications. It's particularly useful for businesses that need to search and analyze large volumes of data quickly and efficiently. Here are some use cases for Elasticsearch which Softjourn has extensive experience:

Finance: Financial services applications take advantage of Elasticsearch to store and search transactional data, as well as to perform fraud detection and risk analysis.

1. Fraud Detection: Elasticsearch can be used to analyze and identify patterns in financial transactions, which helps detect fraudulent activities. Financial institutions can proactively prevent fraud and mitigate risks by quickly searching and analyzing datasets.
2. Risk Management: Elasticsearch can process and analyze risk-related data, such as credit scores, historical transactions, and market trends, allowing financial institutions to make informed decisions and manage risks more effectively.
3. Portfolio Analysis: Elasticsearch can analyze and visualize data of financial institutions, enabling portfolio managers to make better investment decisions based on trends, correlations, and other insights.
4. Real-time Trading Analytics: Elasticsearch can process real-time market data and provide insights into trading patterns, helping traders and investment managers optimize their trading strategies.

We heard from Pedro Braz, the Co-Founder of Investing in the Web, a comparison tool for online investment platforms, about how he uses Elasticsearch in Fintech:

"We decided to use Elasticsearch for our business as we needed a powerful search engine to help our readers easily navigate and find the relevant financial information they're looking for."

About the Benefits of Elasticsearch:

One of the main benefits of using Elasticsearch is its scalability and speed. It allows us to index a large volume of data in real-time, which means our readers can find the information they need quickly and efficiently. Additionally, Elasticsearch's flexible and powerful search capabilities enable us to provide more accurate and relevant search results.

About the Challenges of Elasticsearch:

As a relatively new business, we faced some challenges during the implementation process, including optimizing our Elasticsearch queries and configuring the search engine to suit our specific needs. However, the benefits we have reaped from using Elasticsearch have far outweighed any challenges we faced.

Event Ticketing: Event ticketing companies can use Elasticsearch to provide their users with a fast and efficient search experience, allowing customers to easily find and purchase tickets for events based on various parameters.

1. Personalized Recommendations: Elasticsearch can analyze user preferences and behavior to provide customized event recommendations, improving the user experience and increasing ticket sales.
2. Real-time Inventory Management: Elasticsearch can handle large volumes of data in real-time, allowing event organizers to monitor ticket sales, manage inventory, and optimize pricing strategies.
3. Search and Filtering: Elasticsearch can help users quickly search and filter through many events and tickets, allowing them to find the most relevant events and purchase tickets more efficiently.
4. Data Analysis for Event Performance: Elasticsearch can analyze event data, such as attendance, ticket sales, and social media engagement, enabling event organizers to optimize their marketing efforts and improve future events.
5. Fraud detection: Elasticsearch can be used to detect fraudulent activities such as fake tickets or multiple purchases from the same user. By analyzing patterns in user behavior, Elasticsearch can flag suspicious activities for further investigation.

Media and Entertainment: Elasticsearch is used in media and entertainment applications to provide fast search and recommendation engines for content, such as movies and music. It can also be used to track user behavior and preferences to provide personalized recommendations.

1. Content Search and Discovery: Elasticsearch can index and search through vast amounts of media content, such as articles, videos, and audio files, allowing users to find and discover relevant content quickly.
2. Sentiment Analysis: Elasticsearch can analyze social media data, reviews, and comments to gauge audience sentiment and engagement, helping content creators and marketers better understand their target audience.
3. Personalized Recommendations: Elasticsearch can analyze user behavior and preferences to provide personalized content recommendations, improving user engagement and increasing content consumption.
4. Real-time Analytics: Elasticsearch can process and analyze large volumes of user data in real time, allowing media and entertainment companies to monitor content performance, engagement, and advertising effectiveness.

We spoke with Khamani Harrison, the CEO of Key Bookstore, an interactive online bookstore, about how she uses Elasticsearch in the Media industry:

"I have used Elasticsearch in a variety of projects, including creating digital experiences for Oprah's Magazine, Afrotech, Buzzfeed, and PBS."

About the Benefits of Elasticsearch:

Elasticsearch has been a great tool for me, as it provides a powerful search engine that can quickly and easily search large datasets. It also allows for scalability and flexibility, which is important for projects that require large amounts of data. Additionally, it is easy to set up and maintain, which makes it a great choice for projects that require quick turnaround times.
About the Challenges of Elasticsearch:

The main challenge I have encountered with Elasticsearch is that it can be difficult to debug and troubleshoot when something goes wrong. Additionally, it can be difficult to optimize the search engine for specific use cases. However, with the right expertise and experience, these challenges can be overcome.

Business Travel & Expense Management: Business travel and expense management companies use Elasticsearch to improve their services and increase efficiency.

1. Improve Efficiency: Elasticsearch can help business travel and expense management companies provide a fast and efficient search experience for their users.
2. Fast & Accurate Search Results: Elasticsearch can assist in quickly processing large amounts of data, leading to faster and more accurate search results and report generation.
3. Deep Analysis: The analytical capabilities can provide valuable insights into travel patterns and expense trends, enabling companies to optimize their services, reduce costs, and make informed decisions about their policies.
4. Detect Fraud: It can be used to detect fraudulent expense claims by analyzing patterns in data such as expense amounts, frequency of expenses, and locations of expenses. It can also help detect duplicate expenses and identify unusual or suspicious behavior such as multiple expenses submitted for the same purchase.

Other Use Cases

E-commerce: In e-commerce applications, Elasticsearch is widely used to quickly index and search product data, as well as to provide real-time inventory and pricing information.

Healthcare: Elasticsearch can be used in healthcare applications to store and search electronic health records (EHRs), medical images, and other patient data.

Government: Used for storing and searching data related to citizen services, such as tax and license information, Elasticsearch can be utilized in government applications.

Cybersecurity: Elasticsearch is used in cybersecurity applications to store, search, and identify patterns in log data for security events, such as network traffic and access attempts.

We talked to Maksym Lushpenko, the CEO of Brokee, an innovative DevOps evaluation platform, about how he has utilized Elasticsearch in Cybersecurity:

"I’ve leveraged Elasticsearch to store vast amounts of security incident data at past companies."

About the Benefits:

Our goal was to enable end users and security analysts to easily retrieve information about past incidents and their underlying causes in order to prevent future occurrences. Elasticsearch's full- text search capabilities were highly beneficial in handling large volumes of data.

Challenges:

We encountered challenges with Elasticsearch as our dataset continued to grow. The interconnectedness of the data made it difficult to maintain stability, and as a result, we had to continually optimize our queries and cluster performance. 

Despite working with Elasticsearch consultants, we still faced limitations that were challenging to overcome. Running Elasticsearch on Kubernetes across multiple zones added another layer of complexity in terms of managing memory, storage, and data movement. Ultimately, ensuring our Elasticsearch cluster remained stable required extensive testing and proactive monitoring to mitigate the risks associated with infrastructure failures.

Elasticsearch is a powerful tool that can be used in a wide range of applications to provide fast and accurate search and analytics capabilities. Its flexibility and scalability make it an ideal solution for businesses and organizations of all sizes.

Elasticsearch Case Study

We helped a global leader in expense management upgrade their Elasticsearch from version 2.3.4 to version 7.9. Upgrading Elasticsearch is essential as every new version offers users new features, fixes, and enhancements.

However, upgrading from an older version can be challenging and time-consuming, and holding off on updates can lead to obstacles in the future. Softjourn helped the client by developing a comparison tool to ensure the accuracy of the new search before launching the product to their users.

The tool gave the client confidence in launching the upgrade, and their users experienced less than a 1% difference between the old and new versions. The client's Elasticsearch is now hosted on AWS infrastructure, and their users will have the same experience - or even faster - when using the search functionalities.

By partnering with Softjourn, the client had access to a partner with knowledge of Elasticsearch upgrades and expertise, facilitating a difficult Elasticsearch migration.

Softjourn Helps You Get the Most Out of Your Elasticsearch

Softjourn is a leader in providing end-to-end Elasticsearch advising for clients all around the world. We use the best industry practices to create customized solutions and deliver insightful recommendations to our clients. 

Elasticsearch is the most popular and widely deployed open-source search engine for all types of structured and unstructured data, including textual, numerical, and geospatial. We offer a full range of consulting and implementation services, including Elasticsearch and Elastic Stack development, upgrades, migrations, scaling existing deployments, and ongoing support services. 

For nearly two decades, Softjourn has been building brilliant tech solutions for clients in Finance, Ticketing, and Media & Entertainment, so we understand exactly what is needed to have an edge over the competition in these industries. Leverage Softjourn’s extensive expertise to harness the full power of Elasticsearch and provide your users with a seamless experience.