Elasticsearch vs MySQL
A detailed comparison
Compare Elasticsearch and MySQL for time series and OLAP workloads
Learn About Time Series DatabasesChoosing the right database is a critical choice when building any software application. All databases have different strengths and weaknesses when it comes to performance, so deciding which database has the most benefits and the most minor downsides for your specific use case and data model is an important decision. Below you will find an overview of the key concepts, architecture, features, use cases, and pricing models of Elasticsearch and MySQL so you can quickly see how they compare against each other.
The primary purpose of this article is to compare how Elasticsearch and MySQL perform for workloads involving time series data, not for all possible use cases. Time series data typically presents a unique challenge in terms of database performance. This is due to the high volume of data being written and the query patterns to access that data. This article doesn’t intend to make the case for which database is better; it simply provides an overview of each database so you can make an informed decision.
Elasticsearch vs MySQL Breakdown
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| Database Model | Distributed search and analytics engine, document-oriented |
Relational database |
| Architecture | Elasticsearch is built on top of Apache Lucene and uses a RESTful API for communication. It stores data in a flexible JSON document format, and the data is automatically indexed for fast search and retrieval. Elasticsearch can be deployed as a single node, in a cluster configuration, or as a managed cloud service (Elastic Cloud) |
MySQL uses a client-server model with a multi-layered server design. It supports the SQL query language and offers various storage engines, such as InnoDB and MyISAM, for different use cases. MySQL can be deployed on-premises, in the cloud, or as a managed service. |
| License | Elastic License |
GNU General Public License v2 (for the open-source Community Edition) |
| Use Cases | Full-text search, log and event data analysis, real-time application monitoring, analytics |
Web applications, e-commerce, data warehousing, content management systems, business applications |
| Scalability | Horizontally scalable with support for data sharding, replication, and distributed querying |
Supports vertical scaling by adding more resources to a single node; horizontal scaling can be achieved through replication, sharding, and third-party tools |
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Elasticsearch Overview
Elasticsearch is an open-source distributed search and analytics engine built on top of Apache Lucene. It was first released in 2010 and has since become popular for its scalability, near real-time search capabilities, and ease of use. Elasticsearch is designed to handle a wide variety of data types, including structured, unstructured, and time-based data. It is often used in conjunction with other tools from the Elastic Stack, such as Logstash for data ingestion and Kibana for data visualization.
MySQL Overview
MySQL is an open source relational database management system that was first released in 1995. It is one of the most popular databases worldwide due to its ease of use, reliability, and performance. MySQL is widely used for web applications, online transaction processing, and data warehousing. Oracle Corporation acquired MySQL in 2010, but it remains open source software with an active community of contributors.
Elasticsearch for Time Series Data
Elasticsearch can be used for time series data storage and analysis, thanks to its distributed architecture, near real-time search capabilities, and support for aggregations. However, it might not be as optimized for time series data as dedicated time series databases. Despite this, Elasticsearch is widely used for log and event data storage and analysis which can be considered time series data.
MySQL for Time Series Data
MySQL can be used for storing and analyzing time series data, but it will not be as efficient as a dedicated time series databases. MySQL’s flexibility and support for various indexing techniques can make it a suitable choice for small to medium sized time series datasets. For large-scale time series data workloads, with high write throughput or use cases where low latency queries are required, MySQL will tend to struggle unless highly customized.
Elasticsearch Key Concepts
- Inverted Index: A data structure used by Elasticsearch to enable fast and efficient full-text searches.
- Cluster: A group of Elasticsearch nodes that work together to distribute data and processing tasks.
- Shard: A partition of an Elasticsearch index that allows data to be distributed across multiple nodes for improved performance and fault tolerance.
MySQL Key Concepts
- Table: A collection of related data organized in rows and columns, which is the primary structure for storing data in MySQL.
- Primary Key: A unique identifier for each row in a table, used to enforce data integrity and enable efficient querying.
- Foreign Key: A column or set of columns in a table that refers to the primary key in another table, used to establish relationships between tables.
Elasticsearch Architecture
Elasticsearch is a distributed, RESTful search and analytics engine that uses a schema-free JSON document data model. It is built on top of Apache Lucene and provides a high-level API for indexing, searching, and analyzing data. Elasticsearch’s architecture is designed to be horizontally scalable, with data distributed across multiple nodes in a cluster. Data is indexed using inverted indices, which enable fast and efficient full-text searches.
MySQL Architecture
MySQL is a relational database management system that uses SQL for defining and manipulating data. It follows the client-server model, where a MySQL server accepts connections from multiple clients and processes their queries. MySQL’s architecture includes a storage engine framework that allows users to choose from different storage engines, such as InnoDB, MyISAM, or Memory, to optimize the database for specific use cases.
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Elasticsearch Features
Full-Text Search
Elasticsearch provides powerful full-text search capabilities with support for complex queries, scoring, and relevance ranking.
Scalability
Elasticsearch’s distributed architecture enables horizontal scalability, allowing it to handle large volumes of data and high query loads.
Aggregations
Elasticsearch supports various aggregation operations, such as sum, average, and percentiles, which are useful for analyzing and summarizing data.
MySQL Features
ACID compliance
MySQL supports transactions and adheres to the ACID (Atomicity, Consistency, Isolation, Durability) properties, ensuring data integrity and consistency.
Scalability
MySQL can scale both vertically and horizontally, depending on the storage engine and configuration.
Replication and high availability
MySQL supports various replication techniques, including master-slave and master-master replication, to provide high availability and fault tolerance.
Elasticsearch Use Cases
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 Use Cases
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 is suitable for OLTP systems that require high concurrency, fast response times, and support for transactions.
Data warehousing
While not specifically 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.
Elasticsearch Pricing Model
Elasticsearch is open-source software and can be self-hosted without any licensing fees. However, operational costs, such as hardware, hosting, and maintenance, should be considered. Elasticsearch also offers a managed cloud service called Elastic Cloud, which provides various pricing tiers based on factors like storage, computing resources, and support. Elastic Cloud includes additional features and tools, such as Kibana, machine learning, and security features.
MySQL Pricing Model
MySQL is available in multiple editions with different feature sets and pricing models. The MySQL Community Edition is open source and free to use, while the MySQL Enterprise Edition includes additional features, such as advanced security, monitoring, and management tools, and requires a subscription. Pricing for the Enterprise Edition depends on the number of server instances and the level of support required.
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