Redis

In Memory Databases: Speed at Your Fingertips In memory databases store data in RAM rather than on disk. This design bypasses much of the slow disk I/O, so reads and writes happen in microseconds. The speed boost makes these systems a good fit for tasks that need immediate results. Yet RAM is volatile and memory capacity is limited, so you should plan for durability and growth. Common use cases Real-time analytics and dashboards Session stores and fast caching Leaderboards and live gaming state Price ticks and monitoring data How they work Most in-memory databases keep hot data in memory and offer fast data structures for quick access. They can run with different interfaces, from simple key-value styles to SQL-like queries. Some systems run mostly in memory but periodically write changes to disk, forming a hybrid model that balances speed with durability. ...

NoSQL Considerations: When to Use Document, Key-Value, or Column Stores NoSQL databases come in several flavors. Three common families are document stores, key-value stores, and column stores. Each one has a different data model, a set of strengths, and typical use cases. Understanding these helps avoid overengineering a simple app or underinvesting in a scalable solution. Document stores store data as documents, often JSON or similar. They handle nested structures well and let you evolve schemas gradually. Use a document store when your data looks like objects with fields, and you need powerful queries on those fields. Example: a user profile with name, address, preferences, and a history of actions. ...

Database Performance Tuning: Indexes, Partitions, and Caching Tuning database performance means making careful, small changes and watching their effect. Start with a clear goal: faster queries, steadier response times, or lower load on the database server. Then use measurement before and after each change. This article focuses on three powerful tools: indexes, partitions, and caching. Indexes Indexes speed up data access, but they also add write cost. Use them where queries are common and selective. ...

NoSQL Data Models: Document, Key-Value, Columnar NoSQL databases use different data models to fit varied tasks. The three common ones are document, key-value, and columnar. Each model has strengths for specific access patterns, so picking the right one can simplify development and speed up queries. In a document database, data is stored as documents. Documents use formats like JSON or a binary variant, and they can nest objects and arrays. A single document can hold a user profile with fields such as name and email, plus a list of orders. This flexible schema helps you evolve data without frequent migrations, while keeping related information together. ...

Databases Explained: From Relational to NoSQL Databases come in two big families. Relational databases organize information into tables with a fixed schema. They use SQL to read data, enforce relationships with keys, and keep transactions reliable. NoSQL databases cover several models that let data stay flexible, scale out easily, and handle large volumes. The best choice often depends on how you plan to query data and how you expect it to grow. ...