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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsThe reliable way to improve API performance is to measure the complete request, find its dominant bottleneck, and change one variable at a time. Start with p50, p95, and p99 latency, throughput, errors, saturation, payload size, cache behavior, and database timing. Then apply the technique that matches the evidence: reuse connections and negotiate HTTP/2 or HTTP/3 for transport overhead; send fewer, smaller representations; cache only data with clear freshness and security rules; shape database work to the endpoint; and validate every change with production-like load and a rollback plan.
1. Establish a baseline before changing code
An average latency number hides the requests that users experience as slow. Record p50 (the median), p95, and p99 latency for each important route, along with throughput, error rate, resource saturation, response and request sizes, cache hit rate, and database timing. Keep the measurement window and traffic mix consistent so that before-and-after comparisons are meaningful.
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Trace every part of a request
Distributed traces should separate DNS lookup, TCP connection, TLS negotiation, connection queueing, application handling, downstream calls, database work, and response serialization. A high p99 with a short application span points toward connection setup, queueing, or a downstream dependency; a long database span requires a different fix from a large serialization span. Instrument the same boundaries after each change.
Turn an observation into a testable hypothesis
- Observation: p95 rises when the connection pool is exhausted.
- Hypothesis: connection reuse or pool sizing is limiting throughput.
- Change: enable keep-alive and adjust the pool within the client and server limits.
- Success criteria: lower p95 and p99 without increased errors, saturation, or connection churn.
Profilers and application telemetry can expose expensive queries and hot code paths. Measure the impact of a high-performance approach before accepting additional implementation complexity; a technically faster path that is difficult to observe or roll back may be a poor production trade.
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2. Remove transport and connection overhead
Reuse HTTP/1.1 connections
For HTTP/1.1, use persistent connections (keep-alive) and a correctly sized client connection pool. A new TCP and TLS handshake for every request adds latency and consumes sockets and CPU. Most HTTP libraries manage pools, but verify that the client is not creating a new client instance per call, that idle connections are retained for the expected period, and that pool limits are compatible with the server and load balancer.
- Use one long-lived HTTP client per service or process rather than constructing clients inside request handlers.
- Set maximum idle and total connections for the actual concurrency, then watch queue time and open-socket counts.
- Confirm that proxies and load balancers do not close idle connections sooner than the client expects.
Negotiate HTTP/2 or HTTP/3 when the stack supports it
HTTP/2 and HTTP/3 multiplex multiple requests over a connection. This can reduce connection setup and head-of-line blocking compared with opening many HTTP/1.1 connections. Google Cloud generally recommends HTTP/3 or HTTP/2 for better performance, reduced head-of-line blocking, and greater resilience to network failures.
| Protocol choice | Potential benefit | Checks before rollout |
|---|---|---|
| HTTP/1.1 with keep-alive | Connection reuse with broad compatibility | Pool size, idle timeouts, socket and queue metrics |
| HTTP/2 | Multiplexing over one connection and fewer connection-level delays | Client library, reverse proxy, load balancer, and tracing support |
| HTTP/3 | Multiplexing over QUIC and improved behavior on some lossy networks | QUIC/UDP support, proxy path, client fallback, and observability |
Do not assume that an advertised protocol is the one being used. Inspect negotiated protocol telemetry at the edge and in representative clients. Preserve a tested fallback when an intermediary cannot support the newer protocol.
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Enable gzip or another encoding supported by your clients for JSON, XML, CSV, and similar text. Compression reduces bytes on the wire, but it consumes CPU and can add latency for tiny responses. Clients should advertise support with Accept-Encoding: gzip; verify that the response includes the corresponding Content-Encoding and that intermediaries preserve it.
- Compress larger text responses where bandwidth or transfer time dominates.
- Skip compression for already-compressed media and for very small bodies where framing costs exceed the saving.
- Measure compression CPU, response size, and p95/p99 together; a lower payload size is not a win if server saturation increases.
3. Send less data and make request shapes bounded
Return only fields the client needs
Provide field selection or sparse fieldsets for representations that contain more data than every consumer uses. Omitting unused fields reduces transfer, parsing, and client storage work. Treat the field-selection syntax as part of the API contract: document allowed fields, preserve authorization checks for both selected and omitted data, and return a clear validation error for unknown or forbidden fields.
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Keep a safe default representation for clients that do not select fields. Avoid allowing arbitrary database columns to become selectable; map public field names to an explicit allow-list so internal or sensitive attributes cannot be exposed accidentally.
Use PATCH for partial updates
When an update changes only a subset of attributes, PATCH can send just those changes instead of a complete resource. Define whether a missing field means “leave unchanged,” “clear,” or “use a default,” and state whether the operation is merge-style or replacement-style. Validate the resulting resource, not only the submitted fragment.
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Paginate every collection
A collection endpoint must not return an unbounded result set. Large single responses increase database work, serialization time, network traffic, memory use, and tail latency. Require a bounded page size, enforce a server maximum, and return explicit pagination metadata.
- Use a stable sort order so items do not jump between pages.
- Prefer a continuation token for changing datasets; encode the position needed to continue without exposing implementation details.
- Validate client-supplied page sizes and reject or clamp values above the server limit.
- Define how deletions and inserts between requests affect traversal.
Set request and response size limits
Constrain body size, page size, filter complexity, upload parts, and batch counts before expensive processing begins. Streaming endpoints should stop reading when an acceptable limit is exceeded and can return HTTP 413 Payload Too Large for an oversized request. Apply limits at the edge and in the application so a bypassed proxy cannot remove the protection.
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| Control | What it bounds | Failure behavior to document |
|---|---|---|
| Maximum request body | Memory, parsing, and upload work | Reject with a size error before business processing |
| Maximum page size | Query, serialization, and response transfer | Reject or clamp with the effective size visible |
| Maximum batch count | Transaction and downstream fan-out | Return per-item or whole-batch validation errors |
| Filter and sort limits | Query-planning and execution cost | Reject unsupported or excessively complex expressions |
4. Cache deliberately and validate freshness
Choose the cache layer that matches the data
Useful layers include browser or client caches, a CDN or gateway, an application cache, and database caches. A multi-layer strategy can reduce origin work and network latency, and a distributed cache is appropriate when multiple application instances need shared data. Gateway response caching can also serve repeated responses without invoking the application.
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|---|---|---|
| Client or browser | Public or user-safe representations that tolerate local reuse | Stale or sensitive data remaining on a device |
| CDN or gateway | Cacheable responses shared across callers or regions | Correct cache keys, authorization variation, and purge behavior |
| Application/distributed | Expensive computations or shared reference data | Eviction, consistency, and failure when the cache is unavailable |
| Database | Repeated reads supported by the database engine | Memory pressure and invalidation after writes |
Write freshness and invalidation rules first
Cache frequently accessed, rarely changing, or expensive-to-generate data. Do not cache a response merely because it is slow. For each cacheable resource, specify its lifetime, what event invalidates it, whether stale data is acceptable, how authorization affects the key, and whether encryption is required. Frequently changing data needs an explicit invalidation strategy. Client-side caches can expose sensitive responses to other users of the device, so sensitivity belongs in the decision.
- Vary keys by every request attribute that changes the representation, such as locale, tenant, permissions, and selected fields.
- Prevent private responses from entering a shared cache unless the cache is explicitly partitioned and protected.
- Define behavior when the cache is down: fail open to the origin, serve stale data, or fail closed for sensitive resources.
Use validators for unchanged resources
Send an ETag and, where useful, Last-Modified for representations that can be compared over time. A client can send If-None-Match or If-Modified-Since; if the resource has not changed, return 304 Not Modified without retransmitting the body. Ensure validators reflect the representation variant, including selected fields or content encoding, and do not let a validator bypass authorization.
5. Reduce network round trips
Each round trip adds network latency and creates another opportunity for queueing, failure, and retry amplification. When the domain permits, combine related reads, embed a narrowly defined related resource, or expose a batch endpoint. Keep those mechanisms bounded: batch size, authorization, per-item validation, and timeout behavior must be explicit.
Choose between embedding and separate resources
- Embed a small, stable related object when clients nearly always need it and the combined response remains bounded.
- Use separate calls when the related data is large, rarely needed, independently authorized, or independently cacheable.
- Use a batch operation when many identifiers are known in advance, with strict limits and predictable per-item errors.
Measure the total user-visible transaction, not just one endpoint. A faster endpoint that forces clients to make four additional calls may increase page latency overall.
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6. Shape database work to the endpoint
Filter, aggregate, and project in the database
Push filtering, grouping, aggregation, and column selection into the database instead of loading broad rows into application memory. Select only columns needed for the response or update. Inspect query plans for the real predicates, joins, and ordering used by production traffic.
Add indexes that match those predicates and sort orders, but keep them workload-specific. Every index consumes storage and adds write and maintenance work. Recheck plan stability after data volume or distribution changes; an index that helps one endpoint can slow writes or another query.
Eliminate N+1 access patterns
An N+1 pattern loads a collection with one query and then issues one additional query per item. It can look acceptable with a small development dataset and collapse under production cardinality. Inspect generated SQL and trace database calls per request.
- Batch related lookups with an
INquery or a deliberate join. - Use a projection that retrieves exactly the fields needed for the response.
- Load related data explicitly rather than relying on accidental lazy loading.
- Use read-only, no-tracking queries where the ORM supports them and the endpoint does not modify entities.
Compare query count, rows read, database CPU, lock waits, and endpoint tail latency after the change. Do not trade an N+1 pattern for a single join that multiplies rows and memory without checking its plan.
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7. Roll out changes with evidence and a rollback path
Test production-like conditions
Load tests should use representative payload sizes, concurrency, cache-warm and cache-cold states, geographic paths, database cardinality, and dependency failures. A benchmark with tiny objects and an empty cache cannot validate pagination, compression, query plans, or cache behavior.
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Compare the same metrics before and after
Use the same p50, p95, p99, throughput, error, saturation, payload, cache, and database measurements from the baseline. Look for regressions in tail latency even when the mean improves. Record protocol negotiation, compression CPU, connection queues, query counts, and cache misses when those are relevant to the change.
Canary risky changes
Release protocol upgrades, cache policy changes, new indexes, ORM query rewrites, and batch endpoints to a small traffic slice first. Define rollback thresholds before deployment: for example, an error-rate increase, a p99 regression, cache leakage signal, database saturation, or elevated timeout rate. Keep the previous behavior available until the canary is stable.
Use a bottleneck-led sequence
- Instrument the request and establish percentile and trace baselines.
- Fix connection setup or protocol negotiation if transport spans dominate.
- Reduce representation size and enforce bounds when transfer or serialization dominates.
- Add caching or validators only after freshness, sensitivity, and invalidation rules are written.
- Shape queries and remove N+1 work when database spans or query counts dominate.
- Load-test the complete path, canary the change, and retain a measured rollback trigger.
How to choose among competing optimizations
Evaluate each proposal against the bottleneck it addresses, its correctness and freshness risk, CPU and memory cost, implementation complexity, observability requirements, rollback difficulty, and whether its benefit appears at p95 or p99 rather than only in the mean.
| Technique category | Extra decision factors |
|---|---|
| HTTP/2 or HTTP/3 | Proxy, load-balancer, client, fallback, and telemetry compatibility |
| Compression | CPU consumed versus bandwidth and transfer time saved |
| Caching | Invalidation, authorization variation, encryption, and sensitive-data exposure |
| Database indexes and query rewrites | Write overhead, memory use, plan stability, and production cardinality |
| Batching or embedding | Response bounds, authorization, partial failure, and client complexity |
There is no universal percentage improvement for these techniques. The measurable gain depends on payloads, geography, protocol support, workload shape, cache state, and dependency behavior. Treat each optimization as a hypothesis, and keep the instrumentation that proves whether it helped.
Quick Recap
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