Elasticsearch vs relational databases - Quick Revision & Key Differences

Recall & Review

beginner

What is Elasticsearch primarily designed for?

Elasticsearch is designed for fast, full-text search and analytics on large volumes of data, using a distributed, document-oriented approach.

beginner

How do relational databases store data compared to Elasticsearch?

Relational databases store data in tables with rows and columns, using a fixed schema, while Elasticsearch stores data as JSON documents in an index without a fixed schema.

intermediate

Which system is better for complex transactions and why?

Relational databases are better for complex transactions because they support ACID properties (Atomicity, Consistency, Isolation, Durability) ensuring reliable and consistent data changes.

intermediate

What is a key advantage of Elasticsearch over relational databases in search use cases?

Elasticsearch provides near real-time search with powerful full-text search capabilities, including relevance scoring and complex queries, which relational databases typically do not optimize for.

beginner

Can Elasticsearch replace relational databases for all applications?

No, Elasticsearch is not a replacement for relational databases. It excels at search and analytics but lacks strong transactional support and relational integrity needed for many applications.

What type of data structure does Elasticsearch use to store data?

A JSON documents

B Tables with rows and columns

C Key-value pairs only

D Flat files

Elasticsearch stores data as JSON documents in indexes, allowing flexible and schema-less storage.

Which database type supports ACID transactions?

A Relational databases

B NoSQL document stores

C Elasticsearch

D Flat file systems

Relational databases support ACID transactions to ensure data consistency and reliability.

Which system is optimized for full-text search and analytics?

A Key-value stores

B Relational databases

C Spreadsheet software

D Elasticsearch

Elasticsearch is built for fast full-text search and analytics on large datasets.

Which of the following is a limitation of Elasticsearch compared to relational databases?

A Lack of schema flexibility

B Poor full-text search

C Limited transactional support

D Slow data retrieval

Elasticsearch does not provide strong transactional guarantees like relational databases.

For which scenario would you choose a relational database over Elasticsearch?

A Real-time log search

B Complex financial transactions

C Full-text search on documents

D Analytics on large text data

Relational databases are better suited for complex transactions requiring data integrity.

Explain the main differences between Elasticsearch and relational databases in terms of data storage and use cases.

Think about how each stores data and what they are best at.

Describe why Elasticsearch is preferred for search applications but not for transactional systems.

Consider what makes search fast and what makes transactions reliable.

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