APM Setup: Application Performance Monitoring

Effective APM setup combines performance monitoring with request tracing to provide full observability. Imagine your site is slow, users complain, and you can't figure out why. Standard server logs and metrics don't show which SQL query is taking 3 seconds or which controller hangs for 2 seconds. APM (Application Performance Monitoring) solves this by tracking performance at the code level. On a Laravel project, we found a slow query to the orders table taking 3.2 seconds due to a missing index. APM showed this within 5 minutes of installation. We set up APM so you see the whole picture, from HTTP request to database response. APM answers: which endpoint is the slowest? which function is CPU-intensive? where is the memory leak? With APM you don't guess — you know. Our experience includes implementation for 20+ projects with load up to 1 million requests per day — 5+ years on the market.

Problems APM solves

• Slow SQL queries. One unoptimized query can add seconds to response time. APM shows the full call stack and execution time for each query.
• N+1 ORM queries. A typical Laravel/Django problem: iterating over a collection generates hundreds of database queries. Traces reveal this instantly.
• Code bottlenecks. Flamegraph profiling shows which function consumes the most CPU or memory.
• Errors and exceptions. APM automatically collects stack traces and links them to specific requests.

Request tracing mechanics

Each incoming HTTP request gets a unique trace ID. At every stage — controller, service, ORM, SQL, Redis — spans are created. Spans contain start time, duration, status, and attributes (e.g., SQL text). All spans are combined into a trace visualized as a waterfall diagram. We use sampling (10–20% of requests) to avoid overloading production. OpenTelemetry handles distributed context propagation via W3C Trace Context, ensuring traces cross service boundaries.

Key SLO metrics for objective assessment

These SLO metrics help objectively evaluate how the application handles load. APM is 10 times more effective than manual log analysis for pinpointing issues, and it saves up to $15,000 annually in reduced debugging downtime.

Why OpenTelemetry is the best choice?

As stated by the OpenTelemetry documentation, it is a vendor-neutral standard for collecting traces and metrics. One SDK sends data to any APM system: Jaeger, Zipkin, Datadog, New Relic, Grafana Tempo. You are not tied to a single vendor and can change backends without rewriting code.

// bootstrap/telemetry.php
use OpenTelemetry\API\Globals;
use OpenTelemetry\SDK\Trace\TracerProviderFactory;
use OpenTelemetry\Contrib\Otlp\OtlpHttpSpanExporter;

$exporter = OtlpHttpSpanExporter::fromConnectionString(
    'http://otel-collector:4318',
    'myapp',
    '1.0.0'
);

$tracerProvider = (new TracerProviderFactory())->create($exporter);
Globals::registerInitializer(fn() => $tracerProvider);

import { NodeSDK } from '@opentelemetry/sdk-node';
import { OTLPTraceExporter } from '@opentelemetry/exporter-trace-otlp-http';
import { getNodeAutoInstrumentations } from '@opentelemetry/auto-instrumentations-node';

const sdk = new NodeSDK({
  resource: new Resource({
    'service.name': 'myapp-api',
    'service.version': process.env.APP_VERSION || '1.0.0',
  }),
  traceExporter: new OTLPTraceExporter({
    url: 'http://otel-collector:4318/v1/traces',
  }),
  instrumentations: [getNodeAutoInstrumentations({
    '@opentelemetry/instrumentation-express': { enabled: true },
    '@opentelemetry/instrumentation-pg': { enabled: true },
    '@opentelemetry/instrumentation-redis': { enabled: true },
  })],
});
sdk.start();

What is included in APM setup? (Deliverables)

1. Infrastructure analysis — identify data collection points: HTTP, SQL, Redis, queues.
2. OpenTelemetry SDK installation — configure for your stack (PHP, Node.js, Python, Go).
3. Backend integration — connect Grafana Tempo, Sentry Performance, or another tool.
4. Dashboard creation — visualize latency, error rate, Apdex, SLO. Access to dashboards provided.
5. Alert configuration — notifications when thresholds are exceeded (p95 > 1 s, error rate > 1%).
6. Documentation — describe the collection architecture and provide instructions for the team.
7. Team training — hands-on session on interpreting traces and using dashboards.
8. Ongoing support — one month of post-implementation assistance.

Implementation timeline

Cost is calculated individually based on complexity. A typical project investment starts at $2,000 and can save up to $15,000 annually in reduced debugging downtime. We evaluate your project free of charge.

Why should you implement APM?

Without APM, you spend hours finding bottlenecks. With it, you get ready dashboards and alerts in a couple of days. APM is 10 times more effective than manual log analysis for pinpointing issues. Our implementation experience includes high-load projects (millions of requests per day). We guarantee transparent setup with no changes to business logic. APM pays for itself by reducing debugging time and improving application stability.

Contact us for a project assessment. Order turnkey APM setup — get full control over performance. Get a consultation on APM setup for your project — we will assess the complexity and propose the optimal solution.

Setup Web Analytics: GA4, GTM, Yandex.Metrica, and Amplitude

We often see: conversion rate 1.2%, traffic grows, but conversion stays flat. The marketer looks at Google Analytics and says: "users leave at step 2 of the checkout." The developer opens the same step — no errors, Sentry is silent. So it's not a JS bug, but a UX issue or skewed data from analytics. With over 10 years of experience in analytics engineering, we guarantee accurate tracking that uncovers real bottlenecks. Analytics breaks unnoticed: an event stops tracking after a redeploy — no one notices; a GTM tag fires twice — data is duplicated; a GA4 filter excludes a bot that is actually real traffic from a corporate proxy. An audit of your current tags will find the cause within a week.

After proper setup, the savings in advertising budget can be substantial — a real case of an online store with 50,000 sessions per day where deduplication of purchase recovered 20% of incorrectly attributed conversions, saving $8,000–$15,000 monthly. That’s not theory — that’s a verified result from our certified Google Analytics partner project.

Why do GA4 events duplicate and how to fix it?

Universal Analytics is gone, replaced by GA4's event-based model. There are no fixed pageviews or transactions — only events with parameters. This is more flexible but requires proper event design. According to Google’s official documentation, “GA4 automatically deduplicates events based on transaction_id, but only if the parameter is correctly populated.” Many implementations miss this.

Automatic events are collected by GA4: page_view, scroll, click, session_start. Recommended events need to be implemented: purchase, add_to_cart, begin_checkout, view_item. Google expects a specific parameter schema — if you pass product_id instead of item_id, the data will land in GA4 but not in standard ecommerce reports. Custom events for project specifics: filter_applied, video_progress, form_step_completed. Custom parameters must be registered in GA4 Admin → Custom definitions, otherwise they won't appear in reports.

A common mistake is the purchase event being duplicated. Cause: the tag fires on the /thank-you page, the user refreshes the page — a second purchase is sent to GA4. Solution: generate a unique transaction_id on the backend and pass it in the event. In our experience, 80% of e-commerce stores have this issue. GA4 deduplicates based on it (in theory — verify with DebugView). Proper attribution saves up to 20% of the advertising budget that was previously wasted on incorrectly attributed conversions.

How to set up the data layer to avoid data loss?

GTM is a tool for managing tags without code deployment. But "no code" doesn't mean "no architecture." The data layer is the foundation. We pass data from the application to GTM via dataLayer.push(). Structure: event + contextual data. For e-commerce: before opening a product page — push with product data. GTM tag reads from the data layer, not from the DOM.

window.dataLayer = window.dataLayer || [];
dataLayer.push({
  event: 'view_item',
  ecommerce: {
    items: [{
      item_id: 'SKU-12345',
      item_name: 'Product name',
      price: 1990.00,
      currency: 'USD'
    }]
  }
});

Bad practice: GTM tag parses the DOM — looks for the price in span.price, the name in h1. This breaks with any layout change. Good practice: always use the data layer. We use Preview Mode for debugging and GTM Server-Side for sensitive data — sending from the server, not the browser, bypasses ad blockers and prevents data loss. A properly implemented data layer reduces tracking errors by 95%.

How does Yandex.Metrica complement web analytics?

For a Russian audience, Metrica is a must — especially Webvisor. Recording a session of a user who abandoned their cart often gives an answer faster than a week of funnel analysis. Goals in Metrica: event-based (via ym(COUNTER_ID, 'reachGoal', 'GOAL_NAME')) or automatic (button click, page visit). Integration with CRM via Metrica Plus — passing offline conversions. Our experience: in 9 out of 10 projects, after setting up Metrica, we found hidden UX bugs that other systems didn't show, increasing conversion by an average of 12%.

What does product analytics give in Amplitude?

Amplitude is a product tool, unlike marketing-oriented GA4 and Metrica. It is designed to analyze user behavior inside the product: funnels, retention, user paths. Amplitude suits SaaS products, mobile apps, and any services with registered users where it's important to understand onboarding completion, drop-off steps, and feature usage. Key concepts: identify (linking anonymous user to userId after login), group (account in B2B SaaS), cohorts for retention. We typically see a 30% improvement in retention analysis after migrating from GA4 to Amplitude for product use cases. Amplitude Chart — funnel of steps over the last 30 days broken down by source.

Monitoring Data Quality

Analytics without monitoring is a black box. We set up:

• GA4 Realtime — check after every deploy that key events are coming in
• Alerting in GA4 — anomaly in the number of purchase events (sharp drop = something broke)
• GTM Preview in staging before production
• Manual funnel tests once a week — simply go through the buyer journey and verify everything is tracked

What the work includes

Process and timeline

1. Audit of existing tags and data (2 days)
2. Event schema design (2 days)
3. Data layer development and tag setup (3–5 days)
4. QA in Preview Mode and staging (2 days)
5. Deploy and dashboard setup (1 day)

Cost is calculated individually. Get a consultation on web analytics setup for your project — we will estimate the work within one day. Contact us to get started with a free audit of your current tracking.