Indexes

SQL Performance Tuning: Indexes, Joins, and Query Plans SQL performance tuning helps data apps feel fast. The fastest query is often the one that scans the fewest rows. A good index strategy and careful join choices let the database work with small, predictable data sets. Indexes form the foundation. Create indexes on columns that appear in WHERE, JOIN, ORDER BY, or GROUP BY clauses. Use B-tree indexes for most needs. When several columns are used together in a predicate, a composite index on (colA, colB) can be powerful, but the order matters: place the most selective column first. A covering index, which includes all columns the query reads, avoids extra lookups. But too many indexes slow writes and consume space, so choose thoughtfully. For example, if you filter by status and created_at, an index on (status, created_at) often helps dozens of similar queries. Keep in mind that update-heavy workloads may favor fewer, well-placed indexes. ...

SQL Performance Tuning for High-Scale Apps High-scale applications face a constant trade-off: feature speed versus database latency. Good SQL performance comes from clear queries, steady measurement, and targeted tuning. This guide offers practical steps you can apply today and wins you can verify quickly. Start with data and plans. Track latency, throughput, and the share of slow queries. Look for patterns like scans on large tables, missing indexes, or functions on filtered columns. Use the execution plan to see where the time goes. Run EXPLAIN (ANALYZE, BUFFERS) on representative queries to learn the real costs. ...

Designing Robust Databases for Growth Designing a database that can grow with a business is more than choosing a single technology. It means planning for larger data volumes, more users, and changing needs. The goal is steady performance and reliability, with less downtime and fewer surprises as traffic rises. Begin with your most common queries. Map how data will be read and written, not just how it is stored. Use clear keys and stable identifiers. Surrogate keys (like UUIDs) simplify merges and sharding later, while meaningful natural keys help at the edges of your system. A simple normalization plan keeps data consistent and reduces duplication, but be ready to denormalize when read speed matters. ...

Database Performance Tuning for Scalable Apps Databases are the backbone of scalable apps. To keep performance steady as traffic grows, tuning must be practical and measurable. Start with observability: collect latency distributions, query counts, cache hit rate, and resource use such as CPU, memory, and disk I/O. A clear baseline helps you see if a change truly helps. Understanding bottlenecks is the first step. Common culprits include slow queries, missing or poor indexes, and operations that scan many rows. Lock contention, high I/O waits, and heavy joins can also bite. Use production or staging data to identify where users feel slower. ...

SQL Performance Optimization: Practical Tips Fine-tuning SQL performance starts with watching what the database does, not guessing. Before changing code or server settings, measure with a realistic workload and a recent dataset. Small gains from careful indexing and query structure add up quickly. In this guide you’ll find practical steps you can apply today, with ideas that work in MySQL, PostgreSQL, or MariaDB. Start by writing clean queries and selecting only what you need. Avoid SELECT * and return only the columns you display. This reduces I/O and makes it easier for the planner to choose efficient plans. Next, invest in the right indexes. A good index supports common lookups and range queries, and a composite index can cover a whole WHERE and ORDER BY clause, letting the database read from the index instead of the table. Ensure columns used in WHERE predicates are mapped to the index, and avoid applying functions to indexed columns in the predicate, which disables usage. ...

Database Design Principles for Scalable Apps As apps grow, data stores must keep up with more users, more queries, and more services. Good database design is the quiet engine behind reliability and speed. This guide outlines practical principles to design scalable data systems without overengineering. Start with clear access patterns. List the common queries, such as finding a user by id, listing recent orders for a customer, or computing totals by day. Design the schema around these paths rather than only storing data. Simple, predictable queries speed up development and reduce surprises in production. ...

Databases Unveiled: From Relational to NoSQL Databases come in different shapes. Relational databases use tables, rows, and fixed schemas. They rely on SQL for queries and support strong consistency through ACID transactions. NoSQL databases offer flexible models for modern apps, large data volumes, and varied access patterns. They trade some consistency for scalability and speed, often using horizontal scaling across many servers. Relational databases Structured data with clear relationships Strong consistency and complex queries Useful joins to connect related records NoSQL families NoSQL databases come in several families. Document stores like MongoDB store JSON-like documents you can evolve over time. Key-value stores focus on simple, fast lookups. Column-family stores such as Cassandra handle large write loads and wide rows. Graph databases like Neo4j model relationships directly and help with network queries. In practice, many teams use a mix, keeping core transactions in SQL and freeing unstructured data to NoSQL. Some teams use hybrid architectures, combining relational stores for core transactions with NoSQL for logs, sessions, and analytics. Many NoSQL systems offer eventual consistency, trading strictness for faster writes and global availability. ...