Event-Sourcing

Data Modeling Techniques for Scalable Databases Designing a database that scales well means more than adding servers. It starts with a thoughtful data model that matches how the application reads and writes data. You will trade some normalization for speed, plan how data will be partitioned, and leave room for growth. The goal is to keep data accurate, fast, and easy to evolve. Core techniques for scale Normalize where consistency and updates are frequent. Clear relationships and stable keys help keep data clean. Denormalize for fast reads. Redundant data can reduce joins and latency when access patterns favor reads. Use surrogate keys and stable identifiers. They prevent churn if real-world keys change. Plan indexing carefully. Covering indexes and multi-column indexes speed up common queries. Cache hot data and use read replicas. Caching lowers load on primary storage and improves user experience. Adapt schema for your store. Relational databases suit strict transactions, while NoSQL can handle flexible, large-scale data. Data partitioning and sharding Partitioning spreads data across machines. Hash-based sharding works well for even access, while range-based can help with time-series data. Keys matter: avoid hotspots by distributing writes evenly and keeping shard keys stable over time. Plan for rebalancing as data grows. ...

Database Design Patterns for Reliability Reliability in a database means you can trust the data and recover from failures quickly. Good design reduces data loss, avoids inconsistent reads, and keeps services available during problems. A practical approach blends patterns for data structure, operations, and recovery. Event logs and event sourcing Store changes as an append-only stream. The current state is rebuilt by replaying events in order. This pattern gives a clear audit trail and makes recovery straightforward. For example, orders move from OrderPlaced to PaymentCompleted, then OrderShipped, all as events with timestamps and IDs. If a crash happens, replaying events brings the system back to the last known state. ...

Database Design Patterns for Reliability and Scale Databases are the backbone of many apps. To be reliable and fast at scale, teams use proven design patterns. The goal is to keep data correct, even when traffic spikes, and to avoid surprises during growth. This guide highlights practical patterns you can apply with common databases. Partitioning for scale helps you spread load across multiple servers. Horizontal partitioning, or sharding, divides data by a partition key that distributes writes evenly. Pick a key that avoids hotspots and plan for rebalancing as data grows. Example: shard by user_id so different users land on different servers. The benefit is faster writes and parallel reads, but cross-shard queries become more complex and migrations take care. ...

Databases for Microservices: Patterns and Pitfalls Microservices split an application into small, independently deployable parts. Each service often owns its own data store. This reduces coupling, but it also creates new data challenges. You must think about consistency, migrations, and how to surface data to clients across services. The right choices depend on your domain, team, and tolerance for latency. Patterns to consider Database per service: Each microservice manages its own database, aligned to its boundaries. This reduces cross-service coupling but requires clear data ownership and disciplined migrations. Shared database with careful governance: A single database can simplify some reads but creates tight coupling and coordination needs between teams. Event-driven integration: Services publish events when data changes. Other services react, update read models, and keep data eventually consistent. Saga pattern: Long-running, multi-service workflows use compensating actions to handle failures, avoiding distributed transactions. CQRS and read models: Separate write and read paths let you scale reads and tailor models to each consumer. Polyglot persistence: Different databases suit different workloads. A service might use a document store for catalogs and a relational store for payments. Pitfalls to watch ...

Database Design Patterns for Scalable Systems Building apps that stay fast as users grow requires careful database design. This article shares practical patterns you can apply, from small services to large platforms. Start simple and add partitions, read/write separation, and smart storage choices as needed. Choosing a data model for scale Partition data by a clear access key (tenant, user, or time) to spread load. Use read replicas to handle queries and keep writes focused. Denormalize where it helps read speed, but own data changes clearly to avoid drift. Partitioning and sharding help distribute work without breaking consistency. A shard key should avoid hotspots, and plans for cross‑ shard queries should exist from the start. ...