Elasticsearch vs relational databases - Trade-offs & Expert Analysis

Overview - Elasticsearch vs relational databases

What is it?

Elasticsearch and relational databases are two types of systems used to store and search data. Relational databases organize data in tables with rows and columns, using structured query language (SQL) to manage it. Elasticsearch is a search engine that stores data in a flexible, document-based way, optimized for fast searching and analyzing large amounts of text. Both help find information, but they work differently under the hood.

Why it matters

Without these systems, finding specific information in large collections of data would be slow and difficult. Relational databases solve the problem of organizing structured data with clear relationships, while Elasticsearch solves the problem of quickly searching through huge amounts of text or complex data. Understanding their differences helps choose the right tool for tasks like building websites, apps, or data analysis.

Where it fits

Before learning this, you should understand basic data storage concepts and what databases do. After this, you can explore advanced data modeling, indexing strategies, and how to integrate search engines with applications.

Mental Model

Core Idea

Relational databases organize data in fixed tables for precise queries, while Elasticsearch stores flexible documents optimized for fast, full-text search.

Think of it like...

Think of a relational database like a well-organized library with books sorted by categories and shelves, where you look up exact titles or authors. Elasticsearch is like a powerful search assistant who quickly scans all books for any word or phrase, even if you don’t know exactly where to look.

┌───────────────────────────────┐       ┌───────────────────────────────┐
│       Relational Database      │       │         Elasticsearch         │
├───────────────┬───────────────┤       ├───────────────┬───────────────┤
│ Tables        │ Rows & Columns│       │ Index         │ Documents     │
│ Structured    │ Fixed Schema  │       │ Flexible      │ JSON-like     │
│ SQL Queries   │ Relationships │       │ Full-text     │ Search Engine │
└───────────────┴───────────────┘       └───────────────┴───────────────┘

Build-Up - 7 Steps

1

Foundation

Understanding relational databases basics

Concept: Relational databases store data in tables with rows and columns and use SQL to query data.

Imagine a spreadsheet where each row is a record and each column is a type of information, like name or age. Relational databases organize data this way and allow you to ask questions like 'Find all people older than 30'. The data is structured and relationships between tables can be defined, like linking customers to orders.

Result

You can store structured data and retrieve it using precise queries.

Understanding the table-based structure and SQL querying is key to grasping how relational databases organize and access data.

2

Foundation

Basics of Elasticsearch and documents

Concept: Elasticsearch stores data as flexible documents in indexes, optimized for fast searching.

Instead of tables, Elasticsearch stores data as documents, like JSON objects, which can have different fields. These documents are grouped in indexes. Elasticsearch is designed to quickly search through large amounts of text and data, even if the structure varies. You can search for words, phrases, or patterns inside these documents.

Result

You can quickly find information inside large, varied data sets using full-text search.

Knowing that Elasticsearch uses documents and indexes helps understand its flexibility and speed for search tasks.

3

Intermediate

Comparing data structure and schema

🤔Before reading on: Do you think Elasticsearch requires a fixed schema like relational databases? Commit to your answer.

Concept: Relational databases require a fixed schema, while Elasticsearch allows flexible, dynamic schemas.

Relational databases need you to define the structure of tables and columns before adding data. This means every row must follow the same format. Elasticsearch, however, can accept documents with different fields and structures in the same index. This flexibility lets you store varied data without redesigning the schema.

Result

Relational databases enforce strict data formats; Elasticsearch adapts to changing data structures.

Understanding schema rigidity versus flexibility explains why Elasticsearch is better for evolving or unstructured data.

4

Intermediate

Querying differences: SQL vs full-text search

🤔Before reading on: Do you think relational databases can perform fast full-text searches like Elasticsearch? Commit to your answer.

Concept: Relational databases use SQL for precise queries, while Elasticsearch uses full-text search optimized for speed and relevance.

SQL queries in relational databases are great for exact matches, filtering, and joining tables. But searching for words inside large text fields can be slow. Elasticsearch uses inverted indexes and scoring algorithms to quickly find documents matching search terms, ranking results by relevance. This makes it ideal for search engines and analytics.

Result

Relational databases excel at structured queries; Elasticsearch excels at fast, relevant text search.

Knowing the strengths of each query type helps choose the right tool for your data retrieval needs.

5

Intermediate

Handling relationships and joins

🤔Before reading on: Can Elasticsearch perform complex joins like relational databases? Commit to your answer.

Concept: Relational databases support complex joins; Elasticsearch has limited join capabilities and uses denormalization.

Relational databases can link tables with joins to combine related data easily. Elasticsearch does not support traditional joins well because it is designed for speed and scalability. Instead, it encourages denormalizing data—storing related information together in one document—to avoid joins and speed up searches.

Result

Relational databases handle complex relationships; Elasticsearch favors denormalized data for performance.

Understanding join limitations in Elasticsearch clarifies why data modeling differs between these systems.

6

Advanced

Performance and scaling differences

🤔Before reading on: Do you think relational databases or Elasticsearch scale better for large search workloads? Commit to your answer.

Concept: Elasticsearch is built to scale horizontally for fast search across big data; relational databases scale vertically and can struggle with large search loads.

Relational databases often run on a single powerful server and can become slow with huge data or complex searches. Elasticsearch is designed to run on many servers (nodes) that share data and search work, making it fast and scalable for large datasets and real-time search. This distributed nature helps handle big data and high query volumes.

Result

Elasticsearch scales out easily for search; relational databases scale up for structured data.

Knowing scaling strategies helps pick the right system for growing data and user demands.

7

Expert

Trade-offs and hybrid usage patterns

🤔Before reading on: Would combining Elasticsearch and relational databases in one system be beneficial? Commit to your answer.

Concept: Experts often combine both systems to leverage strengths: relational databases for transactions and Elasticsearch for search.

In real-world applications, relational databases handle data integrity and complex transactions, while Elasticsearch provides fast search and analytics. Data is often synchronized from the database to Elasticsearch. This hybrid approach balances consistency and search performance but requires careful data syncing and design.

Result

Combining both systems yields powerful, flexible applications with reliable data and fast search.

Understanding hybrid architectures reveals practical solutions beyond choosing one system.

Under the Hood

Relational databases store data in tables with fixed schemas and use B-tree indexes to quickly find rows. They execute SQL queries by parsing, optimizing, and running operations like joins and filters. Elasticsearch stores data as JSON documents in shards distributed across nodes. It builds inverted indexes mapping terms to documents, enabling fast full-text search and relevance scoring. Queries are parsed into search requests that run across shards and aggregate results.

Why designed this way?

Relational databases were designed in the 1970s to manage structured business data with strong consistency and complex relationships. Elasticsearch was built later to handle large volumes of text data with fast, scalable search, sacrificing some relational features for speed and flexibility. The design tradeoff favors different use cases: precise data management vs. rapid search and analytics.

Relational DB:
┌─────────────┐
│   Client    │
└─────┬───────┘
      │ SQL Query
┌─────▼───────┐
│ Query Engine│
└─────┬───────┘
      │ Access
┌─────▼───────┐
│  Storage    │
│ Tables &    │
│ Indexes     │
└─────────────┘

Elasticsearch:
┌─────────────┐
│   Client    │
└─────┬───────┘
      │ Search Request
┌─────▼───────┐
│  Coordinator│
│  Node       │
└─────┬───────┘
      │ Distribute
┌─────▼───────┐
│ Data Nodes  │
│ Shards &   │
│ Inverted   │
│ Indexes    │
└─────────────┘

Myth Busters - 4 Common Misconceptions

Quick: Do you think Elasticsearch can replace relational databases for all data needs? Commit yes or no.

Common Belief: Elasticsearch can be used as a full replacement for relational databases.

Reality: Elasticsearch is optimized for search and analytics but lacks strong transactional support and complex relational features of relational databases.

Why it matters: Using Elasticsearch as a primary database can lead to data inconsistency and difficulties managing complex relationships.

Quick: Do you think relational databases are always slower than Elasticsearch? Commit yes or no.

Common Belief: Relational databases are always slower than Elasticsearch for any query.

Reality: Relational databases are faster for structured queries and transactions, while Elasticsearch is faster for full-text search and analytics.

Why it matters: Choosing the wrong system for the task can cause poor performance and user experience.

Quick: Do you think Elasticsearch supports joins like relational databases? Commit yes or no.

Common Belief: Elasticsearch supports complex joins just like relational databases.

Reality: Elasticsearch has limited join capabilities and encourages denormalization instead of joins.

Why it matters: Expecting joins in Elasticsearch can lead to inefficient designs and slow queries.

Quick: Do you think schema flexibility in Elasticsearch means no planning is needed? Commit yes or no.

Common Belief: Because Elasticsearch is schema-less, you don't need to plan data structure.

Reality: Elasticsearch has dynamic mapping but requires careful planning to avoid mapping conflicts and performance issues.

Why it matters: Ignoring schema design can cause errors and slow searches in production.

Expert Zone

1

Elasticsearch’s inverted index structure means that adding or updating documents is slower than reading, influencing update strategies.

2

Relational databases use ACID properties to guarantee data consistency, which Elasticsearch does not fully support, affecting use in critical systems.

3

Elasticsearch scoring algorithms can be customized to fine-tune search relevance, a powerful but complex feature often overlooked.

When NOT to use

Avoid using Elasticsearch as the sole system for transactional data or where strong consistency and complex joins are required. Use relational databases or NewSQL systems instead. Conversely, avoid relational databases for large-scale full-text search or real-time analytics; use Elasticsearch or specialized search engines.

Production Patterns

Commonly, systems use relational databases for core data storage and Elasticsearch for search features. Data is synchronized via ETL processes or change data capture. Elasticsearch clusters are tuned for shard count and replication to balance performance and reliability. Query DSL in Elasticsearch is used to build complex search queries combining filters, full-text search, and aggregations.

Connections

NoSQL databases

Elasticsearch is a type of NoSQL database focusing on document storage and search, while relational databases are SQL-based.

Understanding Elasticsearch as NoSQL helps grasp its schema flexibility and scaling compared to structured relational models.

Information retrieval

Elasticsearch applies principles of information retrieval like inverted indexes and relevance scoring.

Knowing information retrieval concepts explains why Elasticsearch excels at text search and ranking results.

Library cataloging systems

Both Elasticsearch and relational databases organize and retrieve information, similar to how library catalogs index books by different methods.

Seeing database systems as cataloging tools clarifies their purpose: organizing data for easy, fast access.

Common Pitfalls

#1 Trying to perform complex joins in Elasticsearch like in relational databases.

Wrong approach:

GET /my_index/_search
{
"query": {
"join": {
"from": "parent",
"to": "child",
"query": { "match_all": {} }
}
}
}

Correct approach:

Denormalize data by embedding related information inside documents or perform joins in application code after separate queries.

Root cause:

Misunderstanding Elasticsearch’s limited join support and expecting relational database features.

#2 Using Elasticsearch as the primary system for transactional data.

Wrong approach:

Storing all user account data and performing updates only in Elasticsearch without a relational database.

Correct approach:

Use a relational database for transactional data and sync relevant data to Elasticsearch for search.

Root cause:

Confusing search engine capabilities with full database transactional requirements.

#3 Ignoring schema design in Elasticsearch leading to mapping conflicts.

Wrong approach:

Indexing documents with the same field name but different data types without defining mappings.

Correct approach:

Define explicit mappings for fields to ensure consistent data types and avoid conflicts.

Root cause:

Assuming Elasticsearch is completely schema-less and requires no planning.

Key Takeaways

Relational databases store structured data in tables with fixed schemas and excel at precise queries and transactions.

Elasticsearch stores flexible documents optimized for fast, full-text search and analytics using inverted indexes.

Choosing between them depends on your data type, query needs, and scaling requirements.

Many real-world systems combine both, using relational databases for data integrity and Elasticsearch for search performance.

Understanding their differences and trade-offs helps build efficient, reliable, and scalable applications.