SQL for 10k+ Signup Loads: Speed & Reliability

SQL for 10k+ Signup Loads: Speed & Reliability

Technical Blog Post – 400‑500 words

The Problem

In modern SaaS pipelines we hit demo dashboards, integration tests, and load‑testing suites at the same time. A typical scenario: “What if 10 k users sign up within 30 seconds?” The naive approach is to trigger the signup endpoint repeatedly, each hit generating a tiny row in a users table. Not only does that hammer the database, but the sheer volume of inserts can exceed the default transaction log limits, lock the table, and explode latency. On top of that, if you’re generating real‑looking data—email, name, phone—you instantly risk leaking personal information during the test cycle.

Why it matters

GDPR and similar privacy regimes impose strict controls on any personal data. Even in a non‑production environment a single “test” row that contains a user’s real email or phone can be deemed personal data if it can be traced back to an identifiable individual. Organizations that ignore this risk face auditors, fines, and reputational losses. A clean test strategy should, therefore, obey two rules:

1. Data must be synthetic or fully pseudonymised—no real identifiers.
2. Infrastructure must be resilient—the database should stay responsive under burst traffic.

The Solution

Below is a quick‑start guide you can copy‑paste and adapt. It covers data generation, bulk loading, indexing tricks, and a cleanup routine that looks like a chore but is essential for GDPR compliance.

1. Spin up a dedicated test schema

CREATE SCHEMA IF NOT EXISTS test_signup;

Keeping test data isolated makes cleanup trivial and avoids accidental exposure to client‑facing queries.

2. Build a synthetic user generator

Use a tiny Python helper that prefers Faker v12+ and hashes sensitive parts.

import uuid, random
from faker import Faker
fake = Faker()
Faker.seed(0)

def gen_user():
    # Synthetic email: not a real domain
    email = f"{uuid.uuid4().hex[:8]}@example.com"
    # Name looks real but comes from Faker
    name = fake.name()
    # Phone is anonymised to a static pattern
    phone = fake.phone_number()
    # Hash the phone to keep it non‑identifiable
    phone_hash = uuid.uuid5(uuid.NAMESPACE_DNS, phone).hex
    return {
        'user_id': str(uuid.uuid4()),
        'email': email,
        'name': name,
        'phone_hash': phone_hash,
        'created_at': fake.iso8601()
    }

3. Bulk‑insert with INSERT … SELECT and temp tables

Writing 10 k rows in a single transaction avoids lock escalation.

BEGIN;

-- Step 1: Load synthetic rows into a temporary table
CREATE TEMP TABLE tmp_user (
    user_id UUID PRIMARY KEY,
    email TEXT NOT NULL,
    name TEXT NOT NULL,
    phone_hash TEXT NOT NULL,
    created_at TIMESTAMP
);

-- Bulk insert via COPY from STDIN in psql or using the COPY API
COPY tmp_user FROM STDIN WITH (FORMAT csv, DELIMITER ',');  -- supply CSV payload

-- Step 2: Move into the production‑looking table
INSERT INTO test_signup.users(user_id, email, name, phone_hash, created_at)
SELECT user_id, email, name, phone_hash, created_at FROM tmp_user;

COMMIT;

Why COPY? It bypasses row‑level checks, streams directly into the table, and is significantly faster than a series of INSERT … VALUES.

4. Index tuning for write‑heavy operations

CREATE INDEX CONCURRENTLY test_signup_users_email_idx
    ON test_signup.users (email);

CREATE INDEX CONCURRENTLY test_signup_users_created_at_idx
    ON test_signup.users (created_at);

The CONCURRENTLY keyword lets you create indexes without blocking a heavy write load.

5. Verify GDPR compliance once loaded

Run a quick query to confirm no real‑world identifiers leak:

SELECT COUNT(*)
FROM test_signup.users
WHERE email LIKE '%@%gmail.com%'
   OR email LIKE '%@%outlook.com%';

If the count is zero, you’re good. Remember, even a single personal identifier could break compliance.

6. Quick cleanup routine

DELETE FROM test_signup.users
WHERE created_at < NOW() - INTERVAL '30 days';

Run daily to keep the test schema lean. The predicate is deterministic, so you can schedule a cron job safely.

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Wrap‑up

With this approach you’ll hit high‑volume signup rates without tainting your load tests with personal data. By batching inserts, leveraging temporary tables, and streamlining cleanup, your database remains stable and you keep GDPR on track. Happy testing, and may your queries stay swift and your data stay safe!

🛠️ The Tool I Use

    For generating clean test data and disposable emails for these workflows, I personally use [TempoMail USA](https://tempomailusa.com). It’s fast, has an API-like feel, and keeps my production data clean.