Databases

MySQL

Use Cases

  • Web applications: MySQL is often used as the backend database for web applications due to its reliability and ease of integration with web technologies.
  • E-commerce: It supports complex transactions and inventory management systems.
  • Content Management Systems (CMS) like WordPress, Joomla, and Drupal.

Pros

  • Widely Used and Supported: MySQL is one of the most popular relational database management systems (RDBMS), ensuring a vast community and numerous resources.
  • Reliable: It is known for its reliability and robustness.
  • Compatibility: MySQL is compatible with all major hosting platforms, making it easy to deploy.

Cons

  • Scalability: While MySQL can handle a lot of concurrent connections, it might struggle with extremely large databases or high write-load applications compared to some NoSQL databases.
  • Complexity in High Availability: Setting up a highly available MySQL cluster can be more complex compared to some NoSQL counterparts.

Best Practices

Regularly backup your databases and test the restore procedure to ensure data integrity. Use InnoDB storage engine for transactions and data integrity. Optimize your queries and indexes for better performance.

MongoDB

Use Cases

Big Data Applications: MongoDB handles large volumes of data and is suitable for big data applications. Mobile and Social Networking Applications: It's ideal for applications that require rapid development, as well as flexible and agile sprints. Document Storage: Suited for applications that require a flexible schema and the ability to store nested data structures like JSON documents.

Pros

  • Schema-less: MongoDB is a NoSQL database that allows you to store - documents without a predefined schema.
  • Scalability: It offers horizontal scalability through sharding.
  • Flexibility: The flexible schema allows for easy modifications of data structure.

Cons

  • Data Size: The size of the data can grow significantly due to its document-based structure.
  • Transactions: While MongoDB supports transactions, they might not be as mature or straightforward as in traditional RDBMS like MySQL.

Best Practices

  • Design your schema according to your application's access patterns.
  • Use indexes effectively to speed up query performance.
  • Monitor your MongoDB clusters and optimize them regularly.

Elasticsearch

Use Cases

  • Full-Text Search: Ideal for applications requiring complex search capabilities, like e-commerce sites or content repositories.
  • Log and Event Data Analysis: Commonly used for storing, searching, and analyzing large volumes of log data.
  • Real-Time Analytics: Suitable for applications requiring real-time analysis of data.

Pros

  • Fast Search: Built on Lucene, Elasticsearch provides fast full-text search capabilities.
  • Scalable: It can easily scale horizontally to handle large volumes of data.
  • Flexible: Supports complex query types and analytics.

Cons

  • Complexity: Can be complex to set up and maintain, especially in larger clusters.
  • Resource Intensive: Can be resource-intensive, requiring significant memory and CPU.

Best Practices

  • Plan your index strategy carefully (e.g., sharding and replication strategies).
  • Monitor cluster health and performance actively.
  • Secure your Elasticsearch cluster, especially if it's exposed to the internet.

InfluxDB

Use Cases

  • Time-Series Data: InfluxDB is specifically designed for time-series data, making it suitable for IoT applications, monitoring systems, and real-time analytics.
  • Metrics Collection: Commonly used for storing application performance monitoring (APM) data or network performance data.

Pros

  • Optimized for Time-Series: High performance and efficiency for time-series data.
  • Simple Query Language: Uses a SQL-like query language that's easy to learn.
  • Built-in HTTP API: Facilitates easy integration with various data sources and applications.

Cons

  • Limited to Time-Series: Not suitable for general-purpose data storage.
  • Scalability: Clustering and high availability setups can be more complex compared to some other databases.

Best Practices

  • Structure your data to leverage InfluxDB's strengths in time-series data storage.
  • Use continuous queries for downsampling data to optimize storage.
  • Regularly monitor and tune your InfluxDB for optimal performance.

Prometheus

Use Cases

  • Monitoring and Alerting: Prometheus is primarily used for IT infrastructure monitoring and alerting based on time-series data.
  • System Metrics Collection: Collects and stores metrics as time-series data, making it suitable for observing the health of IT infrastructure.

Pros

  • Multidimensional Data Model: Uses a powerful data model and a query language that can handle complex queries efficiently.
  • Active Community: Supported by a large community and a part of the Cloud Native Computing Foundation (CNCF).
  • Integrated Alerting: Comes with an in-built alerting mechanism, Alertmanager.

Cons

  • Data Retention: By default, it does not handle long-term metric storage.
  • Complexity in High Availability Setup: Setting up a highly available Prometheus can be complex.

Best Practices

  • Use exporters and integrations wisely to gather metrics from your systems and services.
  • Regularly review and optimize your alerting rules to reduce noise.
  • Consider integrating with long-term storage solutions if needed for your use case.

Time-Series Databases (General)

Use Cases

  • IoT and Sensor Data: Ideal for storing and analyzing data from sensors and IoT devices over time.
  • Financial Data: Used for storing stock prices, currency exchange rates, and financial transactions.

Pros

  • Efficient Storage and Retrieval: Optimized for the storage, retrieval, and real-time analysis of time-series data.
  • Trend Analysis: Ideal for applications requiring trend analysis over time.

Cons

  • Specialization: Primarily focused on time-series data, which might not be suitable for all types of data.
  • Query Complexity: Queries can become complex, especially when dealing with large datasets and long time ranges.

Best Practices

  • Choose the right granularity for your data points to balance between detail and storage requirements.
  • Use downsampling to reduce data size and maintain performance.
  • Consider data retention policies to manage data growth efficiently.