Archiving Old Database Data: Strategy and Implementation

The events table with 500 million rows and growing by 2 million per day is a problem that gets worse every day. SELECT slows down, VACUUM can't keep up, indexes take gigabytes. Without archiving old database data, the database bloats, locks interfere with operations, and SSD storage costs hit the budget. We solve such tasks end-to-end — implement an archiving system that separates hot from cold data without downtime or performance loss. Our experience shows that a proper data archiving strategy reduces database load by 70% and cuts storage costs by factor of 2–3, saving roughly $500 per month on a 1TB dataset.

What problems does archiving old data solve?

The main pain is performance degradation. When a table grows, even simple SELECT with an index slows due to B-tree depth and fragmentation. VACUUM can't clean dead rows, and autovacuum lags behind. Rising storage costs — expensive SSD for data accessed once a year. Lock contention during bulk deletes — a DELETE without batching locks the table for minutes. We solve this with batch archiving using SKIP LOCKED — parallel workers don't conflict. Understanding MVCC (Multi-Version Concurrency Control) and WAL (Write-Ahead Log) overhead helps tune autovacuum thresholds and prevent bloat.

After archiving, disk load drops by 70%, SELECT time reduces by 3–5 times, and database size shrinks by factor of 2.

Data Archiving Strategy Overview

Choosing the right method for archiving old database data is critical. Below we compare common strategies.

Archiving strategies: comparison of methods

How to choose the right method?

How we implement archiving: step-by-step instructions

Here is a concise outline of the steps involved in archiving old database data:

1. Analyze structure and load
2. Choose strategy (e.g., batch archiving with SKIP LOCKED)
3. Write archiving function with SKIP LOCKED
4. Configure scheduler (e.g., Laravel command)
5. Monitor and VACUUM
6. Restore from archive
7. Verify integrity

-- Archive table (can be in a separate schema or database)
CREATE TABLE archive.events (
    LIKE public.events INCLUDING ALL
);

-- Archiving function with batches
CREATE OR REPLACE FUNCTION archive_old_events(
    p_before_date  TIMESTAMPTZ,
    p_batch_size   INTEGER DEFAULT 10000
) RETURNS TABLE(batches_processed INTEGER, rows_archived BIGINT)
LANGUAGE plpgsql AS $$
DECLARE
    v_batches  INTEGER := 0;
    v_total    BIGINT  := 0;
    v_moved    INTEGER;
BEGIN
    LOOP
        -- Move one batch to archive
        WITH moved AS (
            DELETE FROM public.events
            WHERE id IN (
                SELECT id FROM public.events
                WHERE created_at < p_before_date
                LIMIT p_batch_size
                FOR UPDATE SKIP LOCKED  -- skip locked rows
            )
            RETURNING *
        )
        INSERT INTO archive.events SELECT * FROM moved;

        GET DIAGNOSTICS v_moved = ROW_COUNT;
        EXIT WHEN v_moved = 0;

        v_batches := v_batches + 1;
        v_total   := v_total + v_moved;

        -- Pause between batches — don't overload the disk
        PERFORM pg_sleep(0.1);

        -- Progress
        RAISE NOTICE 'Batch %: % rows archived (total: %)', v_batches, v_moved, v_total;
    END LOOP;

    RETURN QUERY SELECT v_batches, v_total;
END $$;

SELECT * FROM archive_old_events('old_date'::timestamptz, 10000);

// app/Console/Commands/ArchiveOldData.php
class ArchiveOldData extends Command
{
    protected $signature   = 'db:archive {--days=365 : Archive data older than N days}';
    protected $description = 'Archive old records to archive tables';

    public function handle(): int
    {
        $beforeDate = now()->subDays($this->option('days'))->toDateTimeString();

        $this->info("Archiving events before {$beforeDate}...");

        $result = DB::selectOne(
            'SELECT * FROM archive_old_events(?::timestamptz, 5000)',
            [$beforeDate]
        );

        $this->info("Done: {$result->batches_processed} batches, {$result->rows_archived} rows");

        // VACUUM after mass deletion
        DB::statement('VACUUM ANALYZE events');

        return self::SUCCESS;
    }
}

// app/Console/Kernel.php
$schedule->command('db:archive --days=180')
    ->monthlyOn(1, '02:00')
    ->withoutOverlapping()
    ->onFailure(fn() => Notification::route('telegram', config('services.telegram.ops_chat'))
        ->notify(new ArchivingFailedNotification()));

# Restore data from CSV archive back to database
gunzip -c /mnt/archive/events/2024-01/events_2024-01.csv.gz | \
  psql -d mydb -c "COPY events FROM STDIN CSV HEADER"

What's included in the work

• Analysis of table structure and load
• Designing archive schema (partitioning, separate DB, or files)
• Writing archiving functions/scripts
• Configuring scheduler and monitoring
• Retention policy with rotation
• Restore scenario from archive
• Documentation and team training

Implementation timeline

Typical mistakes

• Forgetting VACUUM after mass deletion — table bloat.
• Using one transaction for the entire volume — risk of hours-long rollback.
• Not verifying archive before deletion — data loss.
• Ignoring SKIP LOCKED — parallel processes block each other.

Why trust us with archiving

We have over 10 years of experience in PostgreSQL and MySQL administration. We have implemented archiving systems for projects with petabytes of data. We guarantee that the process will not affect core business logic and will be fully automated. Contact us — we will evaluate your project and offer the optimal solution. Get a free database performance engineer consultation.

For more information refer to the official PostgreSQL documentation at https://www.postgresql.org/docs/current/sql-createtable.html. Retention policy is agreed upon with business and regulatory requirements (e.g., GDPR).

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.