Elasticsearch vs relational databases - Hands-On Comparison

Comparing Elasticsearch and Relational Databases

📖 Scenario: You work in a company that stores customer data. You want to see how Elasticsearch and relational databases handle searching and storing data differently.

🎯 Goal: Build a simple example to store and search customer data using Elasticsearch queries and compare it with relational database style queries.

📋 What You'll Learn

Create a sample dataset of customers with name and age

Set a minimum age filter value

Write an Elasticsearch query to find customers older than the minimum age

Print the query JSON to see the output

💡 Why This Matters

🌍 Real World

Elasticsearch is used for fast, flexible searching in big data, while relational databases are used for structured data storage and complex transactions.

💼 Career

Understanding both helps in roles like data engineering, backend development, and search engine optimization.

1

Create the customer data

Create a variable called customers that holds a list of dictionaries with these exact entries: {"name": "Alice", "age": 30}, {"name": "Bob", "age": 25}, and {"name": "Charlie", "age": 35}.

Elasticsearch

# Create the customers list with exact entries
# Your code here

Hint

Use a list with dictionaries for each customer.

2

Set the minimum age filter

Create a variable called min_age and set it to 30.

Elasticsearch

customers = [
    {"name": "Alice", "age": 30},
    {"name": "Bob", "age": 25},
    {"name": "Charlie", "age": 35}
]
# Set the minimum age filter
# Your code here

Hint

Just assign the number 30 to min_age.

3

Write the Elasticsearch query

Create a variable called es_query that holds a dictionary representing an Elasticsearch query to find customers with age greater than or equal to min_age. Use the range query on the age field with gte set to min_age.

Elasticsearch

customers = [
    {"name": "Alice", "age": 30},
    {"name": "Bob", "age": 25},
    {"name": "Charlie", "age": 35}
]
min_age = 30
# Create the Elasticsearch query dictionary
# Your code here

Hint

Use nested dictionaries to build the query structure.

4

Print the Elasticsearch query

Write a print statement to display the es_query variable.

Elasticsearch

customers = [
    {"name": "Alice", "age": 30},
    {"name": "Bob", "age": 25},
    {"name": "Charlie", "age": 35}
]
min_age = 30
es_query = {
    "query": {
        "range": {
            "age": {
                "gte": min_age
            }
        }
    }
}
# Print the Elasticsearch query
# Your code here

Hint

Use print(es_query) to show the query.

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