Application Performance Optimization

Find the expensive and inefficient workloads, and fix what is worth fixing.

Overview

Slow applications are usually slow for a small number of specific reasons — a query without an index, a call in a loop, a cache that never hits. Nextherrion measures before changing anything, finds where the time and money actually go, and fixes the few things responsible for most of it rather than optimizing broadly on instinct.

Profiling, database and query tuning, caching, load testing and the front-end work that decides what users actually experience.

Performance Profiling

Measuring where time is spent under realistic load, because intuition about bottlenecks is wrong often enough to be unreliable.

Database & Query Optimization

Indexes, query plans and access patterns — the most common source of application slowness and usually the cheapest to fix.

Caching Strategy

Caching at the layers that pay, with invalidation thought through, since a stale cache trades a slow answer for a wrong one.

Performance ProfilingDatabase & Query OptimizationCaching StrategyLoad & Stress Testing

Front-End PerformanceResource EfficiencyConcurrency & Throughput TuningPerformance Regression Testing

WHAT WE DO

Performance Services We Offer

Load & Stress Testing

Establishing where the system actually breaks and how it behaves as it approaches that, before production finds out for you.

Front-End Performance

Payload, rendering and loading behaviour — what users perceive as speed, which server timings alone do not capture.

Resource Efficiency

Reducing the compute and memory a workload needs for the same result, which shows up directly on the bill in cloud.

Concurrency & Throughput Tuning

Pool sizes, queueing and parallelism, where contention rather than raw capacity is the limit.

Performance Regression Testing

Catching regressions in the pipeline, so performance is a property that is maintained rather than periodically rescued.

OUTCOMES

What Performance Work Changes

Performance work pays when it is targeted. Optimizing without measuring usually makes code harder to read and no faster.

Bottlenecks identified, not guessed
Faster response for users
Lower compute cost for the same work
Known behaviour under load
Headroom before scaling spend
Regressions caught in the pipeline

FAQ

Frequently Asked Questions

Can you make our application faster?

Usually, though the first step is measuring rather than changing. What is slow and why it is slow are frequently not what the team expects.

Should we just scale up instead?

Sometimes that is genuinely cheaper than engineering time. But scaling an inefficient workload multiplies its cost, so it is worth knowing which you are doing.

How much faster can it get?

Impossible to say honestly before profiling. Some systems have a single dominant bottleneck; others are uniformly slow and improve gradually.

Will this require rewriting the application?

Rarely. Most gains come from queries, caching and configuration rather than from restructuring code.

Does front-end performance matter if the backend is fast?

Yes. Users perceive total time, and for many applications the front end accounts for most of what they experience.

How do we stop it degrading again?

Performance tests in the pipeline with thresholds, so a regression is caught on the change that caused it rather than in a complaint.

TECHNOLOGY