How To Plan For Software Scalability in 2025?

Businesses must focus on how to plan for software scalability and its top measures for handling their product well. The case studies will help the best way

Step-by-Step Approach by Taskly to Fix System Bottlenecks

1. Configuring System Pain Points

Taskly opted in for a wide range of advanced tech stacks for its system profiling and observability. It utilized Laravel Telescope, Blackfire.io, MySQL, AWS CloudWatch, and Grafana for tracking and measuring in various system metrics.

2. Full-scale Database Optimization

Once figuring out the ideal pain points, Taskly started with goals for database optimization and query refactoring techniques. They indexed all foreign key relationships, like (user_id, project_id, task_id), and broke the single complex report into multiple pre-aggregated tables.

Result: 

The system query performance improved by 60–70%.

3. Implementing Auto Scaling Techniques

To isolate job queuing, Taskly moved all background jobs and launched a dedicated set of queue processors, each on a separate EC2. It also implemented automation-based retry logic and system alerts on every failed job.

Result:

• Job processing time was reduced by 80%.
• Queue system started scaling independently.
• System managed zero silent failures during large-scale operations.

4. Load Balancing, Caching, CDN & Frontend Optimization

Taskly further adopted high-end load balancing and caching techniques. For example, its successful utilization of Laravel Response Cache for guest-accessible pages. A wide range of tech stacks involving Cloudflare CDN, NGINX, and configuring AWS Auto Scaling Groups led to a more static traffic management.

It cached API responses and made its Laravel app fully stateless by deploying three auto-scaling-based web servers behind a load balancer.

Result:

• Taskly successfully handled 10x concurrent traffic during its testing phase.
• Under peak spikes, it maintained an average page load time of <1.3s.
• Backend server load got reduced by 40%.

5. Active Stress Testing

Taskly tested its application for optimal performance and managed heavy scenarios of handling 10,000+ concurrent users, bulk task imports, and up to 2,000+ job queue operations/min. It adopted tools like k6, Artillery.io, etc., for stress performance testing and simulating concurrent user loads and monitored real-time commenting and file sharing via CloudWatch.

Outcome:

• System passed all SLAs with 99.98% uptime.
• CPU utilization remained up to 60% at maximum.

Case Study 2—HireLoop

Company Overview:

Industry: HR Tech/Recruitment SaaS

Initially, HireLoop prepared to launch a new update including automated interview scheduling and video screening. But after running the simulation, the team witnessed some big red flags.

Challenge(s):

To efficiently scale the system faster by identifying the performance cracks without compromising its stability and cost overruns.

Major Pain Points—

• Higher API response time under moderate concurrent usage.
• Delays in data scheduling/resume parsing due to Redis queue overflow.
• System crashed after ~2,000 concurrent users, with 97% CPU spiking.

Goals to be Implemented—

• Introducing a more modular system architecture to support 10,000+ concurrent users during peak time.
• Eliminate over-positioning and make system infrastructure more cost-effective.
• Achieving 99.99% uptime for enterprise SLAs.
• Reducing API response time.

Step-by-Step Approach by HireLoop to Fix System Bottlenecks

1. Identifying Bottlenecks and Database Optimization

Starting with system observability & root cause identification, HireLoop adopted some highly advanced tech stacks involving New Relic (APM), PostgreSQL, Elastic Stack (ELK), etc., to find out the system pain points. It introduced read replicas and migrated its database session storage and job logs to Redis.

Result:

• Adopting data partitioning techniques stabilized the database CPU for 55%.
• Adding compound indexes for common query patterns dropped latency from 1.7s to 400ms, and dashboards became 4x times faster.

2. Embracing Asynchronous Processing

HireLoop actively migrated its resume parsing, calendar, and video analysis syncing to AWS SQS queues. It implemented highly asynchronous data processing, retry policies, metric tracking, and DLQs. It also deployed data containerization-based nodes for quicker data optimization.

Result:

• Incremented system resiliency.
• Main API released from long execution blocks.
• Background job logs reliability increased by 99.97%.

3. Implementing Auto-Scaling Techniques

Next, HireLoop implemented amid application layer decoupling and auto-scaling-based techniques for application containerization (via Docker + ECS Fargate). It embraced decoupling core services like Auth, Scheduling, Screening, and Notifications and introduced CPU/memory-based auto-scaling policies.

Result:

• System handled 12,000 concurrent users without crashing.
• 100% system availability during new feature rollouts.
• System attained seamless scaling after Redis adoption for high-availability queues.

FAQs

1. How to handle increased user load in applications?

There are many effective ways to handle an increased workload within a system architecture. You may consult a software expert from a leading software product engineering company to know more in detail. Here’s a glimpse of the important elements falling under this topic—

• Implementing load-balancing and content delivery networks
• Adopting a microservice system architecture
• Utilizing advanced auto-scaling techniques
• Monitoring and troubleshooting system performance

© 2024 Crivva - Business Promotion. All rights reserved.