Databases are essential for storing, retrieving, and manipulating data in web applications. However, managing databases can be challenging, especially when using different back-end development languages. In this article, you will learn about some of the best methods for managing databases in various back-end languages, such as PHP, Python, Ruby, and Node.js.
The first step in managing databases is choosing the right type of database for your web application. There are two main types of databases: relational and non-relational. Relational databases store data in tables with predefined schemas and use SQL (Structured Query Language) to query and manipulate data. Non-relational databases store data in collections of documents, key-value pairs, graphs, or other formats, and use different query languages or APIs to access and manipulate data. Relational databases are more suitable for applications that require complex queries, data integrity, and consistency. Non-relational databases are more suitable for applications that need scalability, flexibility, and performance.
ORMs (Object-Relational Mapping): Use libraries like Sequelize, Hibernate, or Entity Framework for simplified database interactions. SQL and Query Builders: Directly use SQL queries or query builders (e.g., Knex.js) for fine-grained control over database operations. Connection Pooling: Implement connection pooling (e.g., with HikariCP) to optimize database connections and improve scalability. Caching: Utilize caching mechanisms (e.g., Redis, Memcached) to store frequently accessed data in memory, reducing database queries. Indexing Database Migration Tools Transaction Management Connection String Configuration Database Security Best Practices Backup and Recovery Strategies
Managing databases in backend development involves interacting with a database system, storing and retrieving data efficiently, and ensuring data integrity. The methods for managing databases can vary based on the backend programming language you are using.
if the data is very relational, like dcotor patient relation, teacher student relation, and financial data are stored and read write performance is not much required, relational databases is the choice like mysql/postgresql, i personally prefer postgresql due to more features! use mysql for faster read operations as compared to postegres And if the data is not relational much, like you have unstructured/partial or sparse data, then mongodb is the perfect choice. use redis for storing key values pairs, caching and faster performance but make sure hits and memory arent making cost expensive!
In managing databases, consider also the importance of: - Regular Data Audits: Periodic checks to ensure data remains clean, relevant, and useful. - Automated Backups: Implementing routine backups to prevent data loss. - Scalability Planning: Designing your database with future growth in mind to handle increased loads smoothly.
- **SQL (Relational):** - Structured data, complex queries. - ACID compliance, data consistency. - Ideal for financial apps, structured data. - **NoSQL (Non-Relational):** - Flexible schema, scalability. - Eventual consistency, better for large datasets. - Suited for content management, real-time big data. Consider project needs, query complexity, scalability, and budget. SQL: PostgreSQL, MySQL. NoSQL: MongoDB, Redis.
The next step in managing databases is connecting to the database from your back-end language. Depending on the language and the database, you may need to install a driver, a library, or a framework that provides an interface for communicating with the database. For example, to connect to a MySQL database from PHP, you need to install the mysqli extension or use the PDO (PHP Data Objects) class. To connect to a MongoDB database from Python, you need to install the PyMongo library or use the Django framework. To connect to a PostgreSQL database from Ruby, you need to install the pg gem or use the Rails framework. To connect to a Firebase database from Node.js, you need to install the firebase-admin or firebase package or use the Express framework.
The summary of the steps in connecting to a database from various back-end languages: - Select the Appropriate Driver or Library for the Database and Language - Install the Necessary Driver or Library - Configure the Application with Database Connection Parameters - Establish a Connection to the Database - Perform Database Operations (Querying, Inserting, Updating, Deleting) - Handle Errors and Properly Close the Database Connection
Fortunately, almost all languages have a support library to interact with most of the databases. Choosing the right library for the right database could take into consideration if an additional layer is required or considered to manage data, for example, an ODM or ORM, or instead is preferable for performance reasons for example use a lower level of abstraction
When connecting to a database in web development, it's crucial to prioritize security, efficiency, and maintainability. Always use secure connection methods, such as encrypted connections, to protect sensitive data. Implement proper authentication and authorization mechanisms to control access to the database and safeguard against potential security threats. Consider utilizing connection pooling to enhance performance by efficiently managing and reusing database connections. Additionally, ensure that your database connection code is modular and follows best practices, allowing for easy maintenance and scalability. Regularly update credentials and connection parameters to align with any changes in the database or application requirements.
Saving development i think is the key of all project, so you could use a framework that manage database connections and configurations. Almost all frameworks manage the connections with ease to use, so you save much amount of development time.
The third step in managing databases is creating and modifying the tables or collections that store your data. Depending on the type of database, you may need to define the schema, the indexes, the constraints, and the relationships between the tables or collections. For example, to create a table in a MySQL database from PHP, you need to use the CREATE TABLE statement with the column names, types, and constraints. To create a collection in a MongoDB database from Python, you need to use the db.create_collection method with the collection name and the options. To modify a table in a PostgreSQL database from Ruby, you need to use the ALTER TABLE statement with the changes. To modify a collection in a Firebase database from Node.js, you need to use the update method with the document ID and the data.
Clearly define the purpose of each table or collection & design the schema to reflect the nature of the data. Take into account the potential for future scalability & changes in requirements, ensuring the structure can accommodate evolving needs. Establish proper indexing to optimize query performance & implement data validation to maintain data integrity. Regularly review & update the schema based on evolving project requirements & consider tools or frameworks that facilitate schema migrations to streamline the modification process. Thoughtful planning & documentation during the initial creation & subsequent modifications of tables or collections contribute to a robust & adaptable database architecture for web development projects.
As your application grows, decoupling the responsibility of “managing the database schema” from “the application itself” often makes sense. “Database migrations” is a technique often employed, whereby changes to the schema (“imperative” migrations) or an up-to-date description of the schema (“declarative” migrations) is maintained alongside (but not internal to) the application. Existing, automated tools can handle the heavy-lifting of applying the required changes to a target database, given these migrations for your intended application release. Migrations, I would suggest, sit somewhere between infrastructure-as-code and your software itself.
Use ORM migration tools to keep track of schema changes, like a time machine for your database layout. For relational DBs, I'd suggest take a look at knexjs and prisma.
Use a data layer and make sure it only deals with the database directly and any other code has to go through the layer. Also consider defining your questions to only give the data you need. DON'T select * from products; DO select productid,title,image as that may be all the data you need. Insert many records at once if possible. DON'T for(...) db.insert(xx); DO Create many insert statements and then DO db.insert(allxxx).
The fourth step in managing databases is inserting and retrieving data from the tables or collections. Depending on the type of database, you may need to use different syntaxes, methods, or functions to insert and retrieve data. For example, to insert data into a table in a MySQL database from PHP, you need to use the INSERT INTO statement with the column names and values. To insert data into a collection in a MongoDB database from Python, you need to use the insert_one or insert_many method with the document or documents. To retrieve data from a table in a PostgreSQL database from Ruby, you need to use the SELECT statement with the column names and conditions. To retrieve data from a collection in a Firebase database from Node.js, you need to use the get method with the query or the document ID.
While inserting data, consider batching multiple inserts together to reduce the number of round trips to the database & utilize bulk insert operations when possible. Ensure that data validation & sanitization are implemented to prevent security vulnerabilities such as SQL injection. When retrieving data, carefully design queries to fetch only the necessary information, avoiding unnecessary data transfer & processing. Leverage indexing appropriately to speed up retrieval operations & consider caching strategies to reduce database load. Additionally, implement error handling mechanisms to address potential issues during data insertion & retrieval, ensuring a robust & reliable web application.
One thing to always to keep in mind as a backend developer most especially when writing business logic for a GET endpoint. Pay attention to over population. Over population is simply an act of sending data that are not useful to the client for consumption. At all times always ensure you send only the data the client require. By doing this, it will help reduce the workload of your server and increase performance
In database management, different programming languages with various databases have unique methods. - PHP with MySQL requires precise column details using `INSERT INTO`, reflecting MySQL's structured nature. -Python with MongoDB offers more flexibility, using `insert_one` or `insert_many` without strict schema adherence. -Ruby with PostgreSQL employs the `SELECT` statement, integrating smoothly with Ruby's features - Node.js with Firebase uses the `get` method, catering to real-time data updates. Each pairing caters to specific needs, ranging from structured data organization to flexible, real-time data handling, highlighting the importance of choosing the right combination for specific application requirements.
The fifth step in managing databases is updating and deleting data from the tables or collections. Depending on the type of database, you may need to use different syntaxes, methods, or functions to update and delete data. For example, to update data in a table in a MySQL database from PHP, you need to use the UPDATE statement with the column names, values, and conditions. To update data in a collection in a MongoDB database from Python, you need to use the update_one or update_many method with the filter and the update. To delete data from a table in a PostgreSQL database from Ruby, you need to use the DELETE statement with the conditions. To delete data from a collection in a Firebase database from Node.js, you need to use the delete method with the document ID.
Prioritize the use of transactions to ensure atomicity & consistency, preventing partial updates or deletions that could compromise data accuracy. Implement proper authorization & authentication mechanisms to control access to these operations, mitigating the risk of unauthorized changes. Additionally, establish clear error-handling procedures to manage unexpected issues during updates or deletions gracefully. Regularly back up the database before performing significant changes, providing a safety net in case of accidental data loss. Finally, thoroughly test update & delete operations, particularly in scenarios with concurrent access, to identify & address any potential conflicts or race conditions.
I always advocate using a deletion flag when removing rows or documents. This flag serves as an indicator of whether the row/document has been deleted. By filtering based on this flag, I can easily display non-deleted data from the database. To efficiently manage and clean up this redundant data over time, I recommend implementing a cron job that regularly checks the deletion status and performs the necessary cleanup. When executing multiple updates or deletions, it's always better to employ a transaction strategy. This ensures a more robust approach, allowing for a seamless rollback to the previous state in case something goes wrong midway through the execution.
I would advise considering “soft deletion” and/or copy-on-write (system versioning / temporal tables) as a sane decision when building your schema - regardless of whether you foresee needing them in the future. Also, put engine-managed insert and update timestamps on every table. These rarely cost you performance, but the historical data is gone once deleted or overwritten otherwise. Your “perfect software” doesn’t live in isolation. Mistakes happen - and you will discover bugs. Make your future life (and data warehousing, etc) easier up-front by never deleting records (rather: mark them expired, and/or use system versioning) and having engine-managed indicators of what has changed and when. You’ll thank yourself later.
- Prefer soft-delete (mark as inactive, don't actually remove). It's like sending data to a vacation instead of a black hole. - Auto-update 'updatedAt' and 'updatedBy' columns on every change. It's like having a mini time machine showing you the last visit date of your data. - Log every update and delete action on business critical tables. It's like keeping a diary for your data, so you always know its life story. - Before major update/delete operations, take a backup. It’s like having insurance before going skydiving.
The sixth step in managing databases is securing and optimizing data in the tables or collections. Depending on the type of database, you may need to use different techniques, tools, or best practices to secure and optimize data. For example, to secure data in a MySQL database from PHP, you need to use prepared statements, encryption, authentication, and authorization. To secure data in a MongoDB database from Python, you need to use SSL/TLS, encryption, authentication, and authorization. To optimize data in a PostgreSQL database from Ruby, you need to use indexes, normalization, caching, and performance tuning. To optimize data in a Firebase database from Node.js, you need to use indexes, denormalization, caching, and performance monitoring.
To optimize backend development databases, consider indexing, normalization, and caching for improved performance. Use indexes wisely, organize data into normalized tables, and implement caching mechanisms. For security, focus on parameterized queries, encryption, access controls, and regular audits. Avoid common pitfalls like hard-coding credentials, excessive permissions, and neglecting error handling or logging. Regular backups and keeping software updated are crucial elements for both optimization and security.
- In a MySQL database accessed through PHP, security is achieved through prepared statements, encryption, and proper authentication and authorization mechanisms. - For MongoDB with Python, SSL/TLS encryption and robust authentication are crucial for safeguarding data. - PostgreSQL databases, when used with Ruby, benefit from indexes, normalization, caching, and performance tuning to enhance efficiency. - For Firebase databases accessed via Node.js, applying indexes, denormalization, caching, and performance monitoring are key strategies for optimization.
I would encourage anyone building software applications of “moderate or greater size” to investigate the “Repository Pattern with Unit of Work”, which accompanies domain-driven-design. Martin Fowler has written superb introductions on the subject, as have Microsoft and the guys behind “Cosmic Python”. The principle is to “forget the database exists” in your application. Details of persisting and retrieving data is a complexity on its own, and can muddy your business logic (and make refactoring code considerably harder). Your DB and its schema can be freely optimised for its own concerns (performance, warehousing, etc). The up-front cost can be steep, but readily repays for itself (within months, certainly a year, on a busy project).
- Use ORM/ODM like Sequelize (SQL) or Mongoose (MongoDB). - Employ raw SQL queries or connection pooling (e.g., `pg-pool`, `mysql2`). - Leverage middleware for cleaner code and transaction handling. - Implement migration tools (`knex` for SQL, `migrate-mongo` for MongoDB). - Integrate caching (Redis) for improved performance. - Prioritize robust error handling and security measures. - Store credentials securely using environment variables. - Write unit and integration tests for reliable database interactions. - Choose methods based on project requirements and team expertise.
Rewrite a small application that uses an SQL database to instead use a NoSQL database, or the other way around. This is an excellent mental workout for understanding how to adapt data to either a relational or non-relational model, allowing you to learn practically about all the trade-offs involved.