58 docs tagged with "patterns"

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

Reference guide for common architecture patterns including bounded contexts, sagas, outbox pattern, circuit breakers, bulkheads, sidecars, and service mesh.

How to tell architecture decisions from design and implementation, with cues, examples, and a flow.

Fail fast rather than waiting when a condition isn't satisfied, for non-blocking operations with immediate response.

Reduce network and system overhead by batching operations intelligently.

Manage large refactorings and migrations through feature flags and abstraction layers.

Isolate failures and prevent cascading outages using proven reliability patterns.

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

Essential optimization patterns: caching for latency, batching for throughput, queueing for resilience.

Services making excessive network calls for operations that could be done locally or batched.

Reference architectures for typical application patterns

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

Favor object composition over inheritance to achieve flexibility, reduce fragility, and improve code reuse.

Practical distinctions, decision flows, and patterns to combine concurrency, parallelism, and synchronization safely.

Route and filter messages based on content, enabling dynamic message direction without coupling.

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

Safely migrate data between systems using dual writes, backfill, and change data capture.

Isolate and handle messages that fail repeatedly, preventing system-wide failures and data loss.

Break apart monolithic systems along domain boundaries while maintaining consistency.

Services that call each other synchronously in sequence, creating tight coupling and monolith behavior.

Minimize synchronization overhead for already-initialized state by checking conditions before acquiring locks.

Automatically adjust capacity based on demand while maintaining performance.

Store the complete history of changes as immutable events rather than storing only the current state.

Represent deferred values and asynchronous computations for cleaner, more readable concurrent code.

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

Degrade features rather than fail completely; use kill switches to disable features during incidents.

Wait safely for conditions to be met before proceeding using synchronized guard checks and notifications.

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

Optimize critical code paths through strategic caching and computation reuse.

Master advanced coding techniques including sentinel values, dummy nodes, index mapping, coordinate compression, offline processing, and meet-in-the-middle optimization.

Execute database schema changes without downtime using multi-phase rollouts.

Master technical interview strategies including time management, communication, testing approaches, pattern recognition, and complexity analysis.

Wrap legacy systems with facades and anti-corruption layers to reduce their impact.

Collect and analyze logs with structure and correlation IDs to understand system behavior.

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

Compare architectural patterns for separation of concerns and testability

Services too small to be useful, requiring many inter-service calls and creating excessive complexity.

Software entities should be open for extension, closed for modification. Design for change without breaking existing code.

Abstraction patterns for database persistence with trade-offs in coupling and control

Ensure reliable event publishing and delivery with outbox, inbox, and CDC patterns.

Every inter-service communication is synchronous, creating blocking dependencies and reducing resilience.

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

Process messages through a chain of independent processing stages (filters) connected by data flows (pipes).

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

Master systematic problem decomposition, pattern recognition, constraint analysis, and data structure selection for algorithmic problems.

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

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.

Common stacks, checklists, risk registers, and mitigation plans

Implement synchronous request-response semantics over asynchronous messaging channels.

Evolve data schemas safely without breaking clients as systems change over time.

Multiple services accessing the same database directly, creating coupling and preventing independence.

Deep-dive into architecture patterns for real-time systems, streaming, IoT, ML, compliance, gaming, embedded, fintech, e-commerce, and social platforms

Decompose complex messages into parts, recombine them, and restore ordering in distributed processing.

Incrementally replace legacy systems by growing new functionality alongside the old.

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

Coordinate distributed transactions across services using choreography and orchestration patterns.

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