Making the right database choice is pivotal for businesses when there are many options. Elasticsearch is known for its distributed search and analytics powers, and MySQL is a stalwart in relational databases. This blog post aims to dissect the features of Elasticsearch and MySQL, draw comparisons, and outline the pros and cons, helping you make informed decisions.

What is Elasticsearch?

Elasticsearch, developed by Elastic, is an open-source distributed search and analytics engine designed to handle large volumes of unstructured or semi-structured data. Its core strength lies in real-time search capabilities, making it a popular choice for applications like log analytics, full-text search, and business intelligence.

Here are some of the key features of Elasticsearch:

1. Real-time Search: Elasticsearch excels in providing fast and real-time search capabilities, making it ideal for applications demanding instant access to data.

2. Distributed and Scalable: Elasticsearch’s architecture is built for distribution, ensuring scalability across multiple nodes to handle large datasets and high query loads.

3. Full-text Search: Elasticsearch’s robust text analysis features and relevance scoring make it a leader in full-text search capabilities.

4. JSON-based Documents: Data in Elasticsearch is stored in JSON format, offering flexibility in handling complex and diverse data structures.

5. Support for Geospatial Data: Elasticsearch accommodates geospatial queries, enhancing its suitability for applications dealing with location-based data.

What is MySQL?

MySQL, an open-source relational database management system (RDBMS), has been a mainstay in the database world for decades. Renowned for its reliability, scalability, and ACID compliance, MySQL is preferred for applications requiring structured data storage and transactional support.

Here are some of the key features of MySQL:

1. Structured Data: MySQL excels in managing structured data with predefined schemas, ensuring data integrity and consistency.

2. ACID Compliance: MySQL adheres to the ACID properties, ensuring the reliability of transactions by guaranteeing Atomicity, Consistency, Isolation, and Durability.

3. Joins: MySQL supports using joins to establish relationships between tables, facilitating complex queries involving data from multiple tables.

4. Transactions: MySQL’s transaction support suits applications requiring strong consistency and data integrity.

5. Data Integrity Constraints: MySQL enforces constraints such as unique keys, foreign keys, and check constraints to maintain data integrity.

Feature comparison of Elaticsearch and MySQL

Let’s compare the features now to understand better.

Database Model

Elasticsearch is a distributed, RESTful search and analytics engine built on top of Apache Lucene and provides a high-level API for indexing, searching, and analyzing data. It is a NoSQL database and falls into the category of a document store. It uses a JSON-based document-oriented data model. Documents, which are JSON objects, store data, and each document is associated with a unique identifier.

MySQL is a relational database management system (RDBMS) that uses a structured schema with tables, rows, and columns. It supports SQL (Structured Query Language) for defining and manipulating the data. It follows the client-server model, where a MySQL server accepts connections from multiple clients and processes their queries. MySQL requires a predefined schema with a fixed structure. Changes to the schema, such as adding or removing columns, can be challenging and may require downtime for the database.

Query Language

The query languages for Elasticsearch and MySQL differ due to their distinct data models and purposes. Let’s compare the query languages for both databases.

Elasticsearch:

Elasticsearch uses a RESTful API for communication, and its query language is based on JSON. The queries are sent to Elasticsearch as JSON-formatted requests. Some key aspects of Elasticsearch’s query language include the following.

1. Match Query

  The match query performs a full-text search on text fields.

  Example:

{
  "query": {
    "match": {
      "field_name": "search_text"
    }
  }
}

2. Term Query

   The term query is used for exact matching on fields.

   Example:

{
  "query": {
    "term": {
      "field_name": "exact_value"
    }
  }
}

3. Filtering

  Filtering can be achieved using the `filter` clause in queries, allowing for efficient data retrieval without affecting scoring.

 Example:

{
  "query": {
    "bool": {
      "filter": {
        "range": {
          "numeric_field": {"gte": 10, "lte": 20}
        }
      }
    }
  }
}

4. Aggregations

   Elasticsearch supports aggregations for analytics and summarization.

   Example:

{
  "aggs": {
    "avg_price": {
      "avg": {
        "field": "price"
      }
    }
  }
}

5. Nested Queries

   Elasticsearch allows nested queries for dealing with nested documents or arrays.

   Example:

{
  "query": {
    "nested": {
      "path": "nested_field",
      "query": {
        "match": {
          "nested_field.value": "search_text"
        }
      }
    }
  }
}

MySQL uses SQL for querying, a declarative language focused on structured data. In contrast, Elasticsearch uses a JSON-based query language designed for working with unstructured or semi-structured documents and is particularly powerful for full-text search and complex data retrieval tasks.

MySQL:

MySQL, an open-source relational database management system (RDBMS), has remained a mainstay in the database world for decades. Renowned for its reliability, scalability, and ACID compliance, many prefer MySQL for applications requiring structured data storage and transactional support.

1. SELECT Statement

• The `SELECT` statement retrieves data from one or more tables.
• SQL supports various clauses like `WHERE` for filtering, `JOIN` for combining data from multiple tables, `GROUP BY` for grouping results, and `ORDER BY` for sorting

2. Data Manipulation   

• SQL provides statements like `INSERT`, `UPDATE`, and `DELETE` for manipulating data.

3. Joins

• MySQL supports different types of joins, such as INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN, to combine data from multiple tables based on specified conditions.

Scalability

Elasticsearch: Elasticsearch excels at horizontal scalability, making it well-suited for large datasets and high query loads. The automatic sharding and data distribution across nodes make it relatively easy to scale out by adding more nodes to the cluster.

MySQL: MySQL can scale vertically and supports read scalability through replication. Sharding is possible but often requires manual effort and careful planning. Write scalability may face limitations depending on the capacity of the master server.

In conclusion, if you anticipate the need for significant scalability, especially in handling large volumes of data and queries, Elasticsearch’s horizontal scaling capabilities provide a more straightforward and efficient solution than MySQL’s traditional vertical scaling and manual sharding approaches.

Use cases

Elasticsearch:

• Log and Event Data Analysis – Elasticsearch is widely used for storing and analyzing log and event data, such as web server logs, application logs, and network events, to help identify patterns, troubleshoot issues, and monitor system performance.
• Full-Text Search – Elasticsearch is a popular choice for implementing full-text search functionality in applications, websites, and content management systems due to its powerful search capabilities and flexible data model.
• Security Analytics – Elasticsearch, in combination with other Elastic Stack components, can be used for security analytics, such as monitoring network traffic, detecting anomalies, and identifying potential threats.

MySQL:

• Web applications – MySQL is a popular choice for powering web applications, content management systems, and e-commerce platforms due to its flexibility, ease of use, and performance.
• Online transaction processing (OLTP) – MySQL suits OLTP systems requiring high concurrency, fast response times, and transaction support.
• Data warehousing – While not explicitly designed for data warehousing, MySQL can be used for small to medium-sized data warehouses, leveraging its support for indexing, partitioning, and other optimization techniques.

License

Elasticsearch was initially released under the Apache 2.0 open-source license. However, there have been changes in the licensing strategy introduced by Elastic, the company behind Elasticsearch, as of version 7.11.0 (February 2021). Elasticsearch and Kibana are now under the Server Side Public License (SSPL), which the Open Source Initiative (OSI) does not recognize as an open-source license.

The SSPL includes terms that require companies offering Elasticsearch or Kibana as a cloud service to open-source not only their changes to the software but also the entire set of software they use to provide the service. This move has sparked discussions and debates within the open-source community about the compatibility of SSPL with traditional open-source principles.

MySQL has traditionally been released under the GNU General Public License (GPL). There are different editions of MySQL with varying licensing terms:

• MySQL Community Edition: This edition is freely available under the GPL. Users can modify and distribute the source code but must adhere to the GPL conditions, such as providing access to the modified source code when distributing the software.
• MySQL Enterprise Edition: This is a commercially licensed edition of MySQL provided by Oracle. It includes additional features and support services. Users of the Enterprise Edition need to comply with Oracle’s commercial licensing terms.

Summary: Elasticsearch vs. MySQL

Let’s look at the pros and cons of Elasticsearch and MySQL:

At Knowi

Knowi has firmly established itself as an Elasticsearch analytics and MySQL data Visualization leader. It integrates with Elasticsearch and allows you the flexibility to integrate natively with NoSQL databases or RDBMS and NLP queries. With MySQL visualization, you can turn your data into actionable insights.

As the business landscape continues evolving, so does Knowi, ensuring our offerings remain cutting-edge and relevant. If you want to elevate your data-driven decision-making, there’s no better time to explore what Knowi offers. 

Conclusion

Choosing between Elasticsearch and MySQL hinges on the specific needs of your project. If you focus on real-time search and handling unstructured or semi-structured data, Elasticsearch is a compelling choice. On the other hand, if your application demands robust transactional support, structured data management, and reliability, MySQL remains a tried-and-tested solution. Understanding your project’s nuances and data characteristics is vital to making an informed decision that aligns with your business objectives. Still confused? Book a demo today with Knowi and embark on a transformative analytics journey.