Major Scaling Challenges In Distributed Systems & How To Avoid

Introduction

There are various scaling challenges in distributed systems while deploying or maintaining. It is important to be aware of common scalability mistakes frequently made during scaling. People often make things while scaling. In this blog, we will update you on the major scalability-related mistakes and how to overcome them.

Why is system scalability important?

Mainly, software engineering services provide high-end system scalability so that your business can handle these aspects—

• Handling high traffic and large data volumes
• Handling scaling challenges in distributed systems like latency and consistency
• Handling system failure and overall cost management
• Handling performance-impacting system scalability mistakes

System scaling challenges in distributed systems

Increasing scalability also increases network latency.

As the business’s product, application, or system scales globally across multiple regions, data travels through multiple data centers. This process may make the system response time slower, and the problem of network latency increases.

This mainly happens because system data servers are at a vast distance, and cross-region API calls invite response delays also. So increased inter-service communication makes the process slower gradually with the increasing scalability.

To manage scalability effectively while keeping costs in check, businesses often Hire Remote Developers who specialize in optimizing distributed systems and reducing network latency challenges.

Data Consistency vs. Availability Trade-offs

Technically, achieving strong data consistency and availability simultaneously is an impossible-to-overcome scaling challenge in distributed systems. This happens because strong consistency models slow down system performance, and eventually it leads to outdated data formats at some replicas.

Database Scalability Bottlenecks

One of the main bottlenecks of database scalability is the struggling monolithic databases under high read/write loads, leading to system downtime and slow queries. For example, in the case of inefficient indexes, queries instantly slow down. Unoptimized read/write scaling patterns overload the primary database and turn single-node databases into a significant bottleneck.

Increased System Complexity

System management becomes more complex as it grows. Maintaining APIs and overall system architecture involves all these aspects stated under—

• Managing system failures and recovery is crucial, as system growth leads to an increase in dependencies and failure points.
• Issues related to observation, security, and data protection, along with challenges in debugging and tracing, will arise in distributed logs.
• Unmanageable manual scaling strategies, monitoring issues, and costs.

Why Do Scaling Challenges In Distributed Systems Occur?

Long answer short: Scaling challenges in distributed systems occur because of numerous mistakes businesses usually make while trying to keep up well with their product scalability measures.

A pre-planned approach is required to remain ahead of the growing business expectations and needs of the system. Without a strategy, you may land in poor product performance and over/underestimated resources.

Major Scaling Mistakes In Distributed Systems

System Hardware Limitations

Hardware plays a crucial role in business scaling, but limitations in system components like CPU, memory, disk storage, network capacity, and power supply can hinder performance. Insufficient resources may lead to slowdowns, errors, or even system crashes, ultimately affecting system reliability and scalability.

To overcome these challenges, businesses must adopt infrastructure scaling and monitoring tools to optimize performance and ensure seamless scaling. Implementing the right tools for infrastructure scaling and monitoring helps in proactive issue detection, resource allocation, and maintaining system stability.

How to avoid hardware limitations—

• Regular monitoring of the system is of utmost necessity. It will help to identify the system bottlenecks at its earliest stage.
• You may need to upgrade, change, replace, or modify the systems as needed.
• System configuration optimization must be proper by applying patches.
• You can reduce or eliminate unnecessary processes and adjust/modify settings based on system priorities.

Software Design Issues

Likewise, hardware limitations—if the software architecture isn’t advanced—will also give birth to major scalability-related issues.

For example, poor software coding will not support business scaling measures. In the absence of optimal/compatible system code optimization, it will still affect your system’s performance—even if you possess enough system resources and configuration. Issues in software design invite system functionality, usability, and security bottlenecks.

To ensure a robust and scalable system, it’s crucial to have the right development team in place. Learn more about how to find skilled professionals in our guide on How to Hire Remote Developers for Your Tech Startup.

How to Avoid?

• Firstly, analyze the system requirements and review the code quality.
• Test the system performance to identify and fix any errors.
• Optimize the coding as needed for debugging, refactoring, or updating.
• Install and implement new software tools, frameworks, APIs, and libraries as required.
• Utilize appropriate data structures and algorithms.
• Minimizing network I/O, disk I/O, caching, and compressing data.

Data Management and Product Scaling

Data management issues occur when there arises a glitch with system data storage, processing, and transmission. If these data aspects are not efficient/scalable, it impacts the overall integrity and data accuracy.

Also, overlooking dependency management and shared resources will promote issues like data fragmentation and synchronization. You must plan and manage shared resources effectively to prevent database cracks under pressure.

What to do to avoid data management issues?

• Firstly, evaluate the data volume, velocity, and variety.
• Choose the appropriate data structures, formats, and models.
• Maintain highly scalable control over system dependencies.
• Effective management of shared resources.

Both horizontal scaling and vertical scaling hold their own positives and negatives. You just need to choose the right strategy. One of the most common scaling challenges in distributed systems happens because of not implementing the right scaling strategy. For example, horizontal scaling improves load balancing but creates synchronization issues. Whereas vertical scaling improves system performance, it can push you to your financial limits.

Tips to choose the right scaling strategy-

• Evaluate and analyze the pros and cons of both scaling types, viz. Horizontal vs. vertical.
• Implement accurate data strategies that enhance scalability. These are
• Data compression, data encryption, data partitioning (data shading), data replication, and caching

5. Network Connectivity Issues

Network connectivity is one of the major scaling challenges in distributed systems, affecting system speed, security, and reliability. Whenever the user traffic increases, the system needs to handle large volumes of data, which can be tough at times, especially without network scalability measures. The primary issue is the lack of sufficient network infrastructure, protocol, and bandwidth. Plus, it will also bring system insecurities, other threats, and cyber attack possibilities.

If you don’t take up system security measures, then the system may become more vulnerable to hacking, malware, bugs, and other cyber attacks. This situation can further damage your system’s availability and data confidentiality.

How to avoid?

• Identify prime network constraints like firewalls, proxies, routers, etc.
• Constantly measure and monitor network traffic and latency.
• Implement advanced, robust security aspects, including data authentication and encryption.
• Thoroughly optimize network configurations by resolving conflicts and adjusting parameters accordingly.
• Implement regular patch updates.
• Implement security policies like logging activities, anomaly detection, and monitoring, etc.

6. Not Implementing Automatic Scaling

Maintaining adequate data consistency and reliability becomes challenging when traffic increases abruptly. Not every traditional database will be able to make it through the massive influx of data. Hence, implementing automation-based scalability strategies becomes important. As manual scaling can be time-consuming and not an ultimate solution to increased mass demands, automated scaling, on the other hand, doesn’t require human intervention at all.

Businesses occasionally do not switch to automatic scaling to prevent costs but end up investing more when the system crashes or bugs. In reality, automated scaling can result in significant cost and resource savings for businesses.

How to overcome?

• Automating the system scaling process
• Thoroughly analyze cloud-native vs. traditional scaling infrastructure.
• Implementing cloud services, APIs, scripts

7. User demand and scalability testing

When a business becomes unable to predict or manage user behavior, expectations, and feedback due to lack of planning, ultimately the brand gets adversely affected. User demand issues and not anticipating user growth and behavior lead to poor load quality and system satisfaction.

Inadequate scaling testing methods, on the other hand, mean the non-effectively implemented system testing metrics. Without proper scalability testing, businesses will be unable to detect, diagnose, and resolve potential system issues. All these events severely impede the overall scalability of the system.

How to avoid?

• Deep understanding of user segmentation like user profiles, patterns, preferences, peaks, growth, and churn.
• Adopting user-centric approaches, like user personalization, localization, or communication.
• Pre-define testing scopes, objectives, and scenarios accordingly; select the right testing tools and techniques.
• Monitor and report the testing results.
• Measure the testing recommendations and implement the ones that can improve system scalability.
• Implement high-end scalability testing tools and metrics for early issue detection and resolution.

The Knockout Tip

Business scalability is an everyday process; you need to constantly work on scalability challenges for distributed systems to get better. An ideal scalability measure in a distributed system requires cost-efficient techniques, balanced performance, and system availability.

The seed of success relies on the early identification and address of these elements. And for more effective management of business scalability, the option to hire a team for software product engineering is evergreen!