Queries

Database design and performance tuning Good database design matters more for long-term performance than fancy queries. When you start from how data will be accessed, you can avoid common bottlenecks. A clear data model helps every later step, from writing queries to scaling the system. In practice, ask how data will be read and updated, and let those patterns guide decisions. Start with the data model and constraints. Normalize to reduce duplication and anomalies, and add primary keys and foreign keys to keep relations sound. Decide between natural keys or surrogate keys based on stability and access needs. Plan for growth with partitioning, archiving, or sharding so hot data stays fast to query and old data can be kept without slowing down writes. ...

Performance Tuning for Databases Databases run best when you balance speed with reliability. Tuning is not a single trick but a process: measure, identify bottlenecks, and apply targeted changes. The goal is faster queries, steady capacity, and lower downtime. Assess the workload Knowing your workload helps you prioritize. OLTP systems benefit from fast reads and short transactions, while OLAP workloads focus on long, complex queries. Track metrics like average query latency, cache hit rate, I/O throughput, CPU waits, and lock times. A simple rule: if most queries are reads and you see frequent table scans, focus on indexes and query structure. ...

Time Series Databases for Real-World Monitoring Time series data comes from devices, apps, and services. A time series database (TSDB) stores data with timestamps in a compact, efficient layout. For real-world monitoring, you need fast writes, durable storage, and quick queries across recent time windows. When choosing a TSDB, look at ingestion rate, memory and disk use, scalability, and how easy it is to set retention and downsampling. High cardinality (many unique series) can hurt performance, so test your workload. Decide on a data model: do you prefer labels and tags, or a SQL table with time context? ...

Performance Tuning for Databases Performance tuning for databases helps keep apps fast as data grows. Start with clear goals: lower end-to-end latency, higher throughput, and predictable response times during peak load. Measure first. Collect baseline metrics: average query latency, 95th percentile, queries per second, CPU and memory use, and I/O wait. Use dashboards and logs to watch trends. With solid data, you can see what improves performance and what does not. ...

Databases 101: From Storage to Query Optimization Databases are more than files on a disk. They organize data so apps can read and write quickly, safely, and predictably. This guide walks from how data is stored to how a database chooses a fast plan to answer a query. Storage and data models. In a relational system, data sits in tables with rows and columns. The storage engine decides how these rows are kept on disk—row-oriented pages for fast single-row access, or columnar blocks for analytics. Other models, like key-value or document stores, use different layouts but still rely on fast lookups. ...

Databases Demystified: From SQL to NoSQL Databases come in two broad families. SQL databases rely on a structured schema and use tables, rows, and columns. NoSQL databases offer more flexible models, often without fixed schemas, to handle diverse data and bigger scale. Both aim to store information, but they optimize for different tasks and teams. Relational databases (SQL) Fixed schemas and powerful SQL queries. ACID transactions give strong correctness guarantees. Great for complex joins, clear relationships, and well-defined data. NoSQL and non-relational options ...

Databases 101: Models, Queries, and Performance Databases store data for apps. They organize information so programs can read and update it quickly. There are many models, and each has its own strengths. This article covers three core ideas: data models, how queries fetch information, and what affects speed and reliability. The goal is to help you choose the right approach for common tasks and to read performance hints without getting overwhelmed. ...

Scaling Databases for High Traffic Apps In modern apps, data access drives user experience. When traffic grows, a single database can slow things down, causing longer page loads and frustrated users. The goal is to keep data safe while serving more customers. Here are practical steps teams use in production. Assess your workload A clear picture starts with measurement. Track reads versus writes per second, peak hours, and typical query latency. Look for hot queries and heavy tables. Simple dashboards and slow query logs help you see where to start. Consider seasonality; plan for spikes like sales events or product launches. ...

Performance Tuning for Web Applications Performance tuning helps every user have a fast, reliable web experience. It starts with a plan, not a single magic setting. Think of speed as a product feature: it matters for engagement and trust. In practice, you look for bottlenecks from server to browser and fix them in a careful sequence. Measure first. To tune well, collect data on how pages load and how users feel when they interact with your site. Use browser DevTools, Lighthouse, and server logs. Track Time to First Byte, First Contentful Paint, Largest Contentful Paint, and Time to Interactive. Note page weight, number of requests, and third‑party impact. Start with a baseline and compare every change. ...

Graph Databases: Beyond SQL Graph databases store data as nodes and the relationships between them. This makes connections an explicit part of the model, not something added later with joins. When data is highly interconnected—social networks, supply chains, or fraud rings—a graph view often feels natural and fast. SQL databases can do these patterns with multiple joins, but the cost grows with data size and path length. Graph databases optimize traversals: stepping from one node to its neighbors is built into the engine, so deep or complex traversals stay responsive. ...