Developing an ERP Web Interface: Stack, Virtualization, Performance

Note: When a warehouse report in an ERP system freezes for 10 seconds while loading 50,000 rows, users start using Excel. Developing an ERP web interface isn't about React out of the box—it's about fine-tuning virtualization, state management, and resolving access conflicts. In a multi-user environment, record locking during simultaneous editing is critical. Without proper optimistic concurrency control, users overwrite each other's data. Typical pain points: slow table loading, permission confusion, data loss offline. We solve these with SPA, virtualization, optimistic updates, and a Permission Guard for ERP. We develop interfaces from an MVP with four modules to a full system with 15+ modules. This material covers key architectural decisions and practical techniques to avoid common mistakes and build a scalable interface.

Choosing the Stack for an ERP Interface

The choice of architecture and stack is the foundation that determines performance and maintenance costs. Let's consider two common approaches: SPA and SSR.

For ERP, the choice is clear—SPA. Exception: if SEO is needed for public parts (e.g., product catalog).

Frontend stack: React 18+ with Concurrent Features, TypeScript (strict), TanStack Table for tables, TanStack Query for data, React Hook Form + Zod for forms, Zustand for UI state. Component library—Radix UI + Tailwind (flexibility) or Ant Design (speed).

How to Work with Large Tables?

A table with 50,000 rows is a typical task for warehouse accounting or reporting. Without virtualization, the browser freezes. With virtualization, render time drops to 100 ms even with 100,000 rows—that's 100x faster.

import {
  useReactTable,
  getCoreRowModel,
  flexRender,
  type ColumnDef,
} from '@tanstack/react-table';
import { useVirtualizer } from '@tanstack/react-virtual';
import { useRef } from 'react';

interface VirtualizedTableProps<T> {
  data: T[];
  columns: ColumnDef<T>[];
  rowHeight?: number;
}

export function VirtualizedTable<T>({
  data,
  columns,
  rowHeight = 40,
}: VirtualizedTableProps<T>) {
  const parentRef = useRef<HTMLDivElement>(null);

  const table = useReactTable({
    data,
    columns,
    getCoreRowModel: getCoreRowModel(),
  });

  const { rows } = table.getRowModel();

  const virtualizer = useVirtualizer({
    count: rows.length,
    getScrollElement: () => parentRef.current,
    estimateSize: () => rowHeight,
    overscan: 20,
  });

  const virtualItems = virtualizer.getVirtualItems();
  const totalSize = virtualizer.getTotalSize();

  return (
    <div ref={parentRef} className="overflow-auto h-full">
      <table className="w-full border-collapse">
        <thead className="sticky top-0 bg-white z-10 shadow-sm">
          {table.getHeaderGroups().map(headerGroup => (
            <tr key={headerGroup.id}>
              {headerGroup.headers.map(header => (
                <th
                  key={header.id}
                  style={{ width: header.getSize() }}
                  className="text-left px-3 py-2 text-xs font-semibold text-gray-600 border-b"
                >
                  {flexRender(header.column.columnDef.header, header.getContext())}
                </th>
              ))}
            </tr>
          ))}
        </thead>
        <tbody>
          {virtualItems.length > 0 && (
            <tr style={{ height: virtualItems[0].start }}>
              <td colSpan={columns.length} />
            </tr>
          )}
          {virtualItems.map(virtualRow => {
            const row = rows[virtualRow.index];
            return (
              <tr
                key={row.id}
                className="hover:bg-gray-50 border-b border-gray-100"
                style={{ height: rowHeight }}
              >
                {row.getVisibleCells().map(cell => (
                  <td key={cell.id} className="px-3 py-2 text-sm">
                    {flexRender(cell.column.columnDef.header, cell.getContext())}
                  </td>
                ))}
              </tr>
            );
          })}
          {virtualItems.length > 0 && (
            <tr style={{ height: totalSize - virtualItems[virtualItems.length - 1].end }}>
              <td colSpan={columns.length} />
            </tr>
          )}
        </tbody>
      </table>
    </div>
  );
}

How to Speed Up Interface Response?

Optimistic updates—user changes order status, interface reacts immediately without waiting for server response. On error, a rollback occurs. This cuts perceived response time by 200–500 ms, which is 5x faster than synchronous requests.

const queryClient = useQueryClient();

const updateStatus = useMutation({
  mutationFn: (data: { orderId: string; status: OrderStatus }) =>
    api.patch(`/orders/${data.orderId}/status`, { status: data.status }),

  onMutate: async ({ orderId, status }) => {
    await queryClient.cancelQueries({ queryKey: ['orders', orderId] });
    const prev = queryClient.getQueryData(['orders', orderId]);
    queryClient.setQueryData(['orders', orderId], (old: Order) => ({
      ...old, status,
    }));
    return { prev };
  },

  onError: (_err, { orderId }, context) => {
    queryClient.setQueryData(['orders', orderId], context?.prev);
    toast.error('Failed to change status');
  },

  onSettled: (_, __, { orderId }) => {
    queryClient.invalidateQueries({ queryKey: ['orders', orderId] });
  },
});

Resolving Data Conflicts in Multi-User Access

We use a last-write-wins strategy with entity versioning. Each record has a version field (integer) that increments on change. The client sends the current version, the server checks for a match. If the version is outdated, it returns 409 Conflict; the client fetches current data and prompts the user to resolve the conflict. This is implemented on top of TanStack Query through optimistic updates + rollback.

Access Control with Permission Guard

We create a PermissionGuard component that checks whether the user has a specific permission. If not, you can show a placeholder or nothing. Roles and permissions are stored on the server; the client receives them upon authentication.

type Permission = 'orders:read' | 'orders:write' | 'stock:read';

function PermissionGuard({ permission, children, fallback = null }: {
  permission: Permission;
  children: ReactNode;
  fallback?: ReactNode;
}) {
  const user = useUser();
  if (user.permissions.includes(permission)) {
    return <>{children}</>;
  }
  return <>{fallback}</>;
}

Performance: Additional Optimizations

• Code splitting by module—warehouse user doesn't load HR module, speeding up load by 40%.
• Debounce for search and filters—send request at most once every 300 ms, reducing server load by 3x.
• Memoization of heavy computations—browser-based reports with aggregation via useMemo.

We monitor Core Web Vitals—key performance metrics that directly affect UX. MDN Web Docs recommends targeting LCP < 2.5 s, FID < 100 ms, CLS < 0.1. Our team of 10+ years of ERP development experience has delivered over 50 projects in the last 5 years.

Process of Work

Stages of ERP interface development:

1. Analytics—study business processes, define modules and priorities.
2. Design—create architecture, choose stack, prototype key screens.
3. Implementation—iterative development with demos every two weeks.
4. Testing—unit, integration, e2e tests, load testing.
5. Deployment—set up CI/CD, data migration, user training.

At each stage we prepare documentation: technical specification, architectural decisions, API docs, user manuals.

Timelines

• MVP with four to five modules: 6–8 months for a team of three to four developers.
• Full system (15+ modules): 1.5–2 years with the same team.

An iterative approach—launch minimal set, get user feedback, gradually expand. Trying to do everything at once leads to failure.

What Is Included in the Work

• Technical specification and architectural documentation.
• Development and CI/CD setup.
• Code coverage with tests (unit, integration).
• API documentation and user documentation.
• Access to code repository and deployment pipeline.
• Training for administrators and users.
• Warranty support for 6 months after launch.
• Performance benchmarks and load test report.

Contact us for an estimate of your project. Get a consultation on architecture and timelines.

Backend Development Services: Laravel, Node.js, Go, Django, PostgreSQL

On a production server at 3:14 AM, the Laravel Jobs queue stopped processing. 40,000 unprocessed jobs in Redis. Cause: worker crashed due to a memory leak in one of the Jobs (leak via a static variable in an Eloquent observer), supervisor didn't restart it because of misconfigured stopwaitsecs. This is not a hypothetical scenario — it's Tuesday. We analyzed such an incident on a project with 500 RPS load: diagnosis took 4 hours, fix — 20 minutes. So you don't lose money on downtime, we offer backend development services with a focus on production-grade reliability. We'll assess your project in 2 days.

Backend is what works when no one is watching. Or doesn't work. We guarantee you'll have the first option.

How do we ensure production-grade reliability from day one?

What we do correctly from day one

Service Layer over Fat Controllers. Controller receives HTTP request, validates it via Form Request, passes data to Service, returns response. Business logic in Service, not Controller. This sounds trivial, but most legacy projects have controllers with 500 lines and SQL queries inside.

Repository Pattern we use cautiously. If you just wrap Model::where(...) in a repository method — that's boilerplate without benefit. Repository is justified when: you need to abstract from the data source (DB + cache + external API) or when query logic is complex enough to isolate.

Jobs, Events, Listeners. Everything that can be async — make async. Sending email, PDF generation, external API sync, aggregate recalculation — into Queue. Laravel Horizon for queue monitoring in Redis: see throughput, failed jobs, processing time per queue.

How Octane handles high load

Laravel Octane with RoadRunner or Swoole keeps the app in memory between requests — removes bootstrap overhead (config loading, class autoloading) on each HTTP request. Gain: 3–8x on synthetic benchmarks, 2–4x on real applications. Important: no state between requests in static variables — that leads to exactly the incidents from the beginning. We use this in projects with >1000 RPS.

What to do about N+1 queries

N+1 is the most common cause of slow pages in Laravel apps. Standard story: page worked fine on dev with 10 records, on production with 10,000 — 8-second load.

Laravel Debugbar in dev environment shows the number of queries per page. More than 20 queries per page — signal for audit.

Model::preventLazyLoading(! app()->isProduction());

Telescope for profiling in staging: logs all queries, jobs, mail, notifications with time detail. Numbers: after implementing eager loading, page load time drops from 8s to 0.3s — 27 times faster.

PostgreSQL: indexes that are actually needed

PostgreSQL 14+ is the primary DB on all projects. We use PgBouncer + PostgreSQL combination. 10+ years experience, more than 50 backend projects, 5 years on the market.

How PostgreSQL helps avoid slow queries

Composite indexes for frequent WHERE + ORDER BY. If you have WHERE user_id = ? AND status = ? ORDER BY created_at DESC — you need (user_id, status, created_at DESC). A separate index on (user_id) doesn't help much with sorting.

Partial indexes. If 95% of queries go with WHERE status = 'active':

CREATE INDEX idx_orders_active ON orders (created_at DESC)
WHERE status = 'active';

The index is small, fast, covers the main load.

GIN indexes for JSONB and arrays. @> operator without GIN index — seq scan. With index — fast even on millions of rows.

GIN for full-text search. to_tsvector + GIN instead of LIKE '%query%'. LIKE without index is always seq scan. With pg_trgm extension and gin_trgm_ops — supports LIKE with index, useful for CRM search by partial match.

Connection pooling: why it's more important than it seems

Rails, Laravel, Django open a new connection to PostgreSQL for each PHP/Python process. With 100 workers — 100 connections. PostgreSQL starts degrading from 200–300 active connections — overhead on connection management becomes significant.

PgBouncer — connection pooler in front of PostgreSQL. Transaction pooling mode: connection to PostgreSQL is occupied only during a transaction, returned to pool between requests. 1000 application workers → 20–50 actual connections to PostgreSQL. This reduces latency by 40% and hosting costs by 30%.

Node.js with Fastify: when it's better than Laravel

Node.js is justified for:

• Realtime: WebSocket servers, Server-Sent Events, chat, live updates
• Streaming: large files, video, streaming data
• High I/O concurrency: many parallel requests to external APIs without heavy business logic
• Serverless: Lambda/Cloud Functions — Node.js starts faster than PHP

Fastify over Express: 2–3 times faster on benchmarks, built-in JSON Schema validation, better TypeScript support, plugin architecture.

Typical realtime architecture: Laravel — core business logic and REST API. Node.js + Socket.io or ws — WebSocket server. Laravel publishes events to Redis Pub/Sub, Node.js subscribes and broadcasts to clients. This separation allows scaling the WebSocket server independently of the main app.

Go: microservices and high load

Go we use for:

• High-load microservices (>10,000 RPS)
• Background workers with strict latency requirements
• DevOps tools and CLI
• gRPC services in microservice architecture

Goroutines — thousands of times cheaper than OS threads. 10,000 concurrent connections on Go is normal on one server.

But Go is not a silver bullet. Development is slower than Laravel: more boilerplate, no ORM at Eloquent level, error handling with if err != nil everywhere. Justified only when performance is a real requirement, not an assumption.

Django and Python backend

Django with DRF (Django REST Framework) — for tasks where Python is needed: ML pipelines, data processing, integrations with AI tools.

Celery for background tasks — similar to Laravel Queue but more complex to configure. Celery Beat for cron tasks.

Django ORM vs raw SQL: ORM is convenient for CRUD. For analytical queries with multiple JOINs, window functions, and CTEs — connection.execute() with raw SQL is more readable and predictable.

Redis: not just cache

Redis in our projects plays multiple roles:

Redis Cluster for horizontal scaling. Sentinel for automatic failover on standalone setups.

Deployment and infrastructure

Docker + docker-compose — standard for local development and production. Each service in a container: PHP-FPM/Octane, Nginx, PostgreSQL, Redis, Queue Worker, Scheduler.

CI/CD via GitHub Actions:

1. Run tests (PHPUnit / Pest, Vitest, Playwright)
2. Build Docker image
3. Push to Container Registry
4. Deploy: docker pull → docker-compose up -d on server, or Kubernetes rolling update

Zero-downtime deploy for Laravel: php artisan down --secret=TOKEN is not needed with proper configuration. Strategy: new container starts next to the old one, Nginx switches traffic after health check, old container stops.

Monitoring: Sentry for exception tracking with alerting in Slack/Telegram. Grafana + Prometheus (or Grafana Cloud) for metrics: CPU, memory, request rate, queue depth, database connection count. Alerts on: error rate > 1%, p99 latency > 2s, queue depth > 1000 jobs.

What's included in turnkey work

• Architecture design (API documentation, DB schema, service diagram)
• Implementation according to agreed specification with code review
• CI/CD, monitoring, alerting setup
• Load testing (k6, wrk) with report
• Handover of source code, access, deployment instructions
• Training of customer's team (2-3 sessions)
• Warranty support for 1 month after delivery

Timeline benchmarks

Pricing is calculated individually after analyzing load, integrations, and business logic. Contact us for a free audit of your current backend — get an optimization plan in 2 days. Request a consultation.