45 docs tagged with "scalability"

Decouple concurrent execution by encapsulating each object as a standalone thread receiving method calls via message passing.

Reduce network and system overhead by batching operations intelligently.

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Isolate failures and prevent cascading outages using proven reliability patterns.

Write-through, write-behind, cache-aside, and TTL strategies for reducing database load

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Comprehensive guide to cloud-native architectures, compute models, orchestration, networking, and deployment topologies

Scale work distribution with multiple independent consumers competing for work, and broadcast events to multiple subscribers.

VMs, containers, and serverless platforms—trade-offs in isolation, performance, cold start, and operational complexity

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Separate read and write models to optimize for different access patterns and enable flexible data transformation.

Master modern data architecture patterns, storage technologies, and scalable persistence strategies

Scale data storage horizontally by distributing data across multiple databases and replicas.

Master distributed systems architecture, communication patterns, and resilience strategies for building scalable, fault-tolerant systems.

Flexible JSON/BSON schemas for nested data with horizontal scalability and query flexibility

Automatically adjust capacity based on demand while maintaining performance.

Scale APIs to handle large datasets with efficient query parameters

Predict growth, measure resource needs, and provision infrastructure to meet demand with margin.

Ground yourself in core architecture, systems thinking, paradigms, and data basics to make sound design decisions.

Maintain partial functionality and shed load during overload instead of failing completely.

Implement defensive patterns that gracefully handle failures and slowdowns in distributed systems.

Build systems where components relate strongly to their purpose while remaining loosely connected to other components.

Optimize critical code paths through strategic caching and computation reuse.

Ultra-fast simple get/set operations with sub-millisecond latency and distributed caching

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Validate capacity assumptions with load testing before production; detect regressions automatically.

Implement asynchronous communication with point-to-point queues and publish-subscribe topics for decoupled systems.

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Independent, deployable frontend components for large teams

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Scheduling, service discovery, load balancing, and stateful workload management

Design systems that respond quickly and scale horizontally to handle increasing load while maintaining latency budgets.

Define and enforce performance targets that align with user experience and business goals.

Simplify asynchronous I/O by letting the framework manage event notifications and completion handlers.

Decouple work production from consumption using shared buffers for flexible, scalable systems.

Efficient data retrieval patterns for large result sets with filtering and sorting

Decouple producers and consumers using queues to smooth out demand spikes.

Multiplex I/O events across many connections using a single-threaded event-driven architecture.

Design systems where different areas address different aspects of functionality, improving modularity and maintainability.

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Reuse threads efficiently by maintaining a pool of pre-allocated workers to process tasks.

Understand scaling strategies and their trade-offs in distributed systems.