Elasticsearch vs relational databases - When to Use Which

The Big Idea

What if your search could be as fast and smart as your thoughts?

The Scenario

Imagine you have a huge library of books and you want to find all books about "space travel" published after 2010. Using a traditional relational database, you write complex queries joining many tables and scanning large amounts of data.

The Problem

This manual approach is slow and complicated because relational databases are designed for structured data and exact matches. Searching through large text fields or handling fuzzy searches is painful and often too slow for real-time needs.

The Solution

Elasticsearch is built for fast, full-text search and analytics. It indexes data in a way that makes searching through large text fields lightning fast and supports flexible queries like fuzzy matching, relevance scoring, and aggregations.

Before vs After

✗ Before

SELECT * FROM books WHERE title LIKE '%space travel%' AND year > 2010;

✓ After

GET /books/_search
{
  "query": {
    "bool": {
      "must": [
        { "match": { "title": "space travel" } },
        { "range": { "year": { "gt": 2010 } } }
      ]
    }
  }
}

What It Enables

It enables lightning-fast, flexible search and analytics on huge volumes of data, making complex queries simple and interactive.

Real Life Example

Online stores use Elasticsearch to let customers quickly find products by keywords, filter by price or rating, and get instant suggestions as they type.

Key Takeaways

Relational databases excel at structured data and exact matches.

Elasticsearch shines at full-text search and fast, flexible queries.

Choosing the right tool depends on your data and search needs.

Practice

1. Which of the following best describes the main use case for Elasticsearch compared to relational databases?

easy

A. Elasticsearch is optimized for fast full-text search and analytics.

B. Elasticsearch is mainly used for storing structured transactional data.

C. Elasticsearch uses SQL to manage complex relationships between tables.

D. Elasticsearch is designed for batch processing of large datasets only.

Solution

1. Step 1: Understand Elasticsearch's purpose

   Elasticsearch is built for fast full-text search and analytics on large volumes of data.

2. Step 2: Compare with relational databases

   Relational databases focus on structured data and transactional integrity, not optimized for text search.

3. Final Answer:

   Elasticsearch is optimized for fast full-text search and analytics. -> Option A

4. Quick Check:

   Elasticsearch = fast text search [OK]

Hint: Elasticsearch = search and analytics, relational = structured data [OK]

Common Mistakes:

• Confusing Elasticsearch with relational database transactional features
• Thinking Elasticsearch uses SQL for queries
• Assuming Elasticsearch is only for batch processing

2. Which query language is primarily used to interact with relational databases?

easy

A. XPath

B. JSON Query DSL

C. SQL

D. GraphQL

Solution

1. Step 1: Identify relational database query language

   Relational databases use SQL (Structured Query Language) to manage and query data.

2. Step 2: Compare other options

   JSON Query DSL is for Elasticsearch, XPath is for XML, GraphQL is a query language for APIs.

3. Final Answer:

   SQL -> Option C

4. Quick Check:

   Relational DBs use SQL [OK]

Hint: Relational DBs use SQL, Elasticsearch uses JSON DSL [OK]

Common Mistakes:

• Confusing JSON Query DSL with SQL
• Thinking XPath or GraphQL are used for relational databases
• Mixing query languages between technologies

3. Given the following Elasticsearch query snippet, what is the main purpose of this query?

{"query": {"match": {"title": "Elasticsearch"}}}

medium

A. Update documents with the title 'Elasticsearch'.

B. Create a new document with title 'Elasticsearch'.

C. Delete documents where the title is exactly 'Elasticsearch'.

D. Retrieve documents where the title field contains the word 'Elasticsearch'.

Solution

1. Step 1: Analyze the query structure

   The query uses "match" on the "title" field with the term "Elasticsearch" which searches for documents containing that word.

2. Step 2: Understand Elasticsearch query actions

   Queries retrieve data; update, delete, and create require different API calls.

3. Final Answer:

   Retrieve documents where the title field contains the word 'Elasticsearch'. -> Option D

4. Quick Check:

   Match query = search documents [OK]

Hint: "match" queries search text fields in Elasticsearch [OK]

Common Mistakes:

• Thinking the query modifies data instead of searching
• Confusing match with exact match or update operations
• Assuming it deletes or creates documents

4. Identify the error in this SQL query compared to Elasticsearch query style:

SELECT * FROM products WHERE name = 'laptop'

medium

A. SQL uses JSON format, so this query is invalid.

B. Elasticsearch does not use SQL syntax; it uses JSON-based queries.

C. The query is missing a JOIN clause required in Elasticsearch.

D. The query should use double quotes around 'laptop' in SQL.

Solution

1. Step 1: Understand query languages

   SQL uses its own syntax; Elasticsearch uses JSON Query DSL, not SQL syntax.

2. Step 2: Identify the mismatch

   The given query is valid SQL but invalid for Elasticsearch which requires JSON format queries.

3. Final Answer:

   Elasticsearch does not use SQL syntax; it uses JSON-based queries. -> Option B

4. Quick Check:

   Elasticsearch queries = JSON, not SQL [OK]

Hint: Elasticsearch queries are JSON, not SQL [OK]

Common Mistakes:

• Assuming Elasticsearch supports SQL syntax natively
• Thinking JOINs are mandatory in Elasticsearch queries
• Confusing string quoting rules between SQL and JSON

5. You need to build a system that supports complex transactions with multiple table joins and strict data integrity. Which database type should you choose and why?

hard

A. Relational database, because it supports ACID transactions and joins.

B. Relational database, because it is optimized for full-text search.

C. Elasticsearch, because it stores data in JSON and is schema-less.

D. Elasticsearch, because it handles complex joins efficiently.

Solution

1. Step 1: Identify requirements

   The system needs complex transactions, multiple joins, and strict data integrity.

2. Step 2: Match requirements to database features

   Relational databases provide ACID transactions and support joins, ensuring data integrity.

3. Step 3: Compare Elasticsearch capabilities

   Elasticsearch is optimized for search and analytics, not transactional integrity or joins.

4. Final Answer:

   Relational database, because it supports ACID transactions and joins. -> Option A

5. Quick Check:

   Transactions + joins = Relational DB [OK]

Hint: Use relational DB for transactions and joins, Elasticsearch for search [OK]

Common Mistakes:

• Choosing Elasticsearch for transactional systems
• Confusing JSON storage with transactional support
• Assuming Elasticsearch supports complex joins