Goals and guiding principles

– Primary goals:
– reduce latency,
– increase throughput,
– ensure predictable scalability,
– minimise resource cost,
– improve reliability under failure.

– Principles:
– measure first,
– optimise the critical path,
– prefer simple, observable solutions,
– trade correctness/consistency vs latency only where acceptable,
– automate validation and rollback,
– treat performance as a non‑functional requirement with clear SLOs.

Core strategies and techniques

Architecture and scaling

– Horizontal scaling: add more instances/replicas to distribute load; design stateless services where possible to allow easy scale‑out.
– Vertical scaling: increase CPU/memory for single nodes when horizontal scaling is infeasible; use for single‑threaded or legacy components.
– Partitioning and sharding: split data and workloads by key, tenant, or function to reduce contention and allow parallelism.
– Clustering and high availability: group nodes into clusters with leader election, replication, and failover to improve throughput and resilience.
– Autoscaling policies: reactive and predictive scaling based on CPU, queue depth, custom metrics, or predictive models to balance cost and capacity.

Resource allocation and scheduling

– Right‑sizing: profile typical workloads and provision instance types or containers with appropriate CPU, memory, and I/O characteristics.
– Resource quotas and QoS: use namespaces, cgroups, or orchestrator QoS classes to prevent noisy neighbours and ensure critical services get resources.
– Bin packing and placement: optimize scheduler placement to reduce cross‑node network cost and balance utilization.
– Priority and preemption: ensure high‑priority workloads can get resources under contention, with graceful preemption for lower‑priority jobs.

Code, algorithms, and concurrency

– Profile‑driven optimization: use profilers and flamegraphs to focus work on hot code paths instead of premature micro‑optimisations.
– Efficient algorithms and data structures: replace O(n) scans with indexed lookups, caches, or precomputed aggregates where needed.
– Concurrency and async patterns: use non‑blocking I/O, worker pools, backpressure, and carefully controlled parallelism to increase throughput without contention.
– Idempotency and batching: aggregate small operations into batches to reduce overhead and round trips.

Caching and data access

– Multi‑level caching: client, edge/CDN, in‑memory (Redis, memcached), and DB query caches to reduce latency and DB load.
– Cache invalidation strategy: TTLs, versioned keys, and event‑driven invalidation to maintain correctness.
– Optimise database access: proper indexing, denormalization where appropriate, read replicas, connection pooling, and prepared statements.

I/O, storage, and network

– Asynchronous offloading: move long‑running or non‑critical work to background jobs and message queues.
– Storage tiering: use SSDs, NVMe, or in‑memory stores for hot data and cheaper storage for cold data.
– Network optimisations: compress payloads, use HTTP/2 or gRPC, co‑locate services with heavy communication, and tune TCP parameters where needed.

Observability and defensive patterns

– SLOs and SLIs: set latency, error rate, and throughput targets; use error budgets to guide risk for aggressive releases.
– Circuit breakers and timeouts: fail fast and degrade gracefully to prevent cascading failures.
– Backpressure and throttling: reject or queue excess requests upstream to protect downstream systems.
– Bulkheads: isolate critical resources so failures don’t cascade across services.

Measurement, testing, and validation

– Profiling: CPU, memory, lock contention, heap/GC, and I/O profiling in representative environments.
– Load and performance testing: baseline, scaling, soak, spike, and stress tests using realistic traffic patterns and data sets.
– Chaos and failure injection: validate resilience, failover times, and recovery behaviour under node/network/storage failures.
– Synthetic and real‑user monitoring: combine synthetic checks with real request tracing to detect regressions and hotspots.
– Benchmarking and regressions: automate benchmarks in CI and block merges that degrade critical SLIs beyond thresholds.

Process, governance, and tooling

– Measure before change: collect baseline metrics and traces; define acceptance criteria for any optimisation.
– Small, reversible changes: use canary releases, feature flags, and gradual rollout to validate improvements safely.
– Document decisions and trade‑offs: record why changes were made, expected impact, and monitoring to watch.
– Cross‑team ownership: developers, SRE, platform, and product share responsibility for performance SLOs.
– Common tooling: profilers (perf, async-profiler), APM/tracing (Jaeger, Zipkin, OpenTelemetry), load test frameworks (k6, JMeter), orchestration metrics (Prometheus), and visualization (Grafana).

KPIs, common risks, and mitigations

– Key KPIs: p50/p95/p99 latency; throughput (requests/sec); error rate; saturation metrics (CPU, memory, I/O); queue depths; mean time to detect (MTTD) and mean time to recover (MTTR); cost per request.
– Common risks: optimizing the wrong hotspot; phantom wins from micro‑optimisation; cache consistency bugs; increased complexity harming maintainability; regressions introduced by deployment changes.
– Mitigations: always measure impact end‑to‑end; roll out gradually; maintain observable dashboards and alerts; include performance tests in CI; retire brittle or over‑engineered optimizations when they no longer pay off.

Practical checklist (starter items)

– Define SLOs and instrument SLIs for each service.
– Profile production‑like workloads to identify hot paths.
– Add end‑to‑end tracing and p99 latency dashboards.
– Introduce multi‑level caching and set clear invalidation rules.
– Implement autoscaling and test scaling behaviour with load tests.
– Harden timeouts, circuit breakers, and backpressure across service boundaries.
– Run automated performance tests in CI and block regressions on critical SLIs.
– Use canaries/feature flags for rollout and monitor error budget consumption.