How to Fix Common scaling challenges in distributed systems?

Building a microservice architecture is a crucial task and involves scaling challenges in distributed systems.

Introduction

Date : 21th April 2025 Original Source:(https://medium.com/@mukesh.ram/how-to-fix-common-scaling-challenges-in-distributed-systems-0ec81e673b00) In any ideal software product engineering service, managing a distributed system efficiently is a milestone because it comes with significant challenges. As time flows, data volumes increase within a system, making static processing with a limited tech stack more difficult.

Common scalability issues in software development are thus born out of such situations. If not controlled, it can become worse for your system by impacting its performance, reliability, and consistency. This blog will cover similar aspects and will uncover the solutions to fix them!

What is a Distributed System?

A distributed system is a collective set of independent servers and digital devices. Together, they intercommunicate with each other through message communication to complete a task.

All these servers collaborate as a single system, managing the tasks independently. Common tasks could be managing a complex system or availing a web service, etc.

Working of a Distributed System

• Each node (system/server) can be located at any point globally and performs a specific set of tasks.
• Nodes connected via different hardware configurations and operating systems can communicate with other nodes to share every piece of information, promoting system flexibility.

Though distributed systems are easier to scale, as they process large data volumes in minimal time, their challenges vary and may affect data processing if not addressed on time.

What is Product Scalability in a Distributed System?

Product scalability in a distributed system is defined by developing a system to handle larger data volumes. Any system in its initial stage needs to expand to accommodate more user demand and data processing needs. Mainly, there are two distinct ways to scale a distributed system�"

• *Vertical Scaling�"*This is mostly utilized by monoliths or systems in their early stage when they are not as complex and when they grow big.
• *Horizontal Scaling�"*Widely adopted by businesses implementing microservice architecture scalability within their system.

�'� Vertical scaling involves techniques used to make a single server bigger by adding resources, which make the server grow bigger and more complex with every addition.

�'� Horizontal scaling works by adding more servers that share the workload and perform the tasks independently.

Importance of Scalability in Distributed Systems

Scalability is an important feature and, infact, critical for every system. It depends on a business's goals and how to choose the right technology stack for scalability. With scalability, businesses can find more growth potential and efficient resourcing to serve more users by handling complex tasks hassle-free!

More details are here to prove the importance of scalability to serve scaling challenges in distributed systems�"

For Handling a Larger User Base:

Distributed systems need to scale better as they grow to handle more users without crashing or slowing down. A scalable system architecture can handle growing use demands with its robust design and data replication functionalities across multiple nodes.

Speeding up System Processing:

Many case studies on successful product scaling by startups approve of a faster system processing for achieving a better customer base. When millions of users utilize your system, its scalable architecture keeps it ready to face the immense workload pressure and deliver the results fast.

Promotes Business Growth:

With scalability, a distributed system holds immense potential to expand and get visible over the digital platform. It allows the system to manage more data volumes without rebuilding its structure from scratch or doing any major changes in its source infrastructure.

Increases System Reliability:

From addressing a small data processing error in a small system to managing top-rated cloud-native platforms for large enterprises, scalability can do it all!

If the system undergoes a traffic spike, then with a scalable solution, the business can easily afford to handle it. You can accomplish this without experiencing any system crashing, downtime, fault tolerance, or interruption!

Scaling challenges in Distributed systems (and how to fix them)

With inappropriate scalability measures, there arise more chances of scaling challenges in distributed systems. Here are some of them to note down�"

1️⃣ Load Imbalancing Across Multiple Nodes

One of the main scaling challenges in distributed systems involves load imbalancing within the nodes. Some nodes function more than other nodes across the whole system. This further leads to bottlenecks, and resources remain underutilized, especially in the absence of scalability.

❌ The Problem: Overburned nodes at one end and idle nodes at the other

How to fix this?

• Implement load-balancing techniques with tools like NGINX, HAProxy, or cloud-native options like AWS ELB to distribute workloads evenly across nodes.
• Perform consistent hashing to avoid data reshuffling.
• Consul, Eureka, or Kubernetes DNS for traffic rerouting and balancing.
• Auto-scaling policies must be added accordingly to increase the system's capacity.

2️⃣ Increased Inter-Server Dependencies

If a single server becomes dependent on too many other servers, then the system will become prone to a fragile chain of failures and downtime issues. Tightly coupled servers aren't the solution for achieving greater system scalability.

❌ The Problem: Chain reaction of failure in one service because of its cross-dependency.

How to Fix?

• Implement API gateways and service mesh-based tools like Istio/Linkerd.
• Adopt RabbitMQ and Kafka for asynchronous communication via message queues.
• Microservice-based domain-driven design (DDD) for loosely coupled service architecture.
• Introduce Resilience4j, Hystrix-like circuit breakers, and fallbacks.
• Apply bulkheads, timeout, retry, and isolation-based system scalability techniques.

3️⃣ Data Inconsistency

These scaling challenges in distributed systems arise when different parts of the system start serving conflicting/stale data reads/writes while utilizing eventual consistency. It happens because of data replication delays within the system.

❌ The Problem: Data replication delay and data inconsistency issues

How to fix this?

• Implement Cassandra and DynamoDB-like quorum-based reads/writes models.
• Apply conflict resolution policies like last-write-wins, version vectors, or app-level reconciliation in distributed systems.
• Tune in applicable data replication settings by adjusting write acknowledgments and sync intervals.
• Adopt change data capture (CDC) for precise data synchronization.

4️⃣ Unpredictable Network Latency and System Failure

Most of the scaling challenges in distributed systems arise due to network latency because they depend upon it. Hence, issues like latency spikes cause system downtime and slower responses to user requests.

❌ The Problem: System network latency, packet loss, cascading failures, and intermittent outages.

How to fix it?

• Monitoring system latency with APM tools like Datadog, New Relic, or OpenTelemetry.
• Prevent system-wide collapse with circuit breakers like Hystrix or Resilience4j.
• Perform geo-distributed CDN and edge node deployments for content distribution based on the user's nearest region.
• Utilize exponential backoff and timeout strategies.

5️⃣ Database Scaling Challenges

When any software product engineering company claims to scale your business product, they mean that databases and service architecture both scale on different levels. It's not that databases scale easily as the stateless services; hence, they emerge as a system bottleneck while experiencing traffic spikes.

❌ The Problem: Databases aren't stateless services.

How to fix it?

• Apply read replicas to read offload traffic operations and separate replicas.
• Implement data sharding across multiple databases/nodes and split the data.
• Utilize NoSQL/distributed databases like MongoDB, DynamoDB, or CockroachDB.
• Implement Redis or Memcached-like caching techniques to reduce query load.

6️⃣ Difficulty in System Coordination

One of the best benefits of scalable software architecture is that it doesn't undergo difficulty over sharing states across distributed services. It becomes difficult to figure out the system issue without configuring each server.

❌ The Problem: System becomes difficult and error-prone.

How to fix this?

• Adopt microservice system architecture-based stateless service to avoid shared state.
• External state managers like Zookeeper or Redis for inter-service coordination.
• Adopt real-time state sync at the first case; if it's not feasible, then accept delayed state syncs.

7️⃣ Monitoring and Observability Gaps

For maintaining a continuously successful scaling, it's necessary to monitor the system regularly. Not adopting advanced monitoring techniques gives rise to system errors related to security bugs, data processing, and mismanagement. It's one of the most crucial scaling challenges in distributed systems.

❌ The Problem: Non-regular system health checking and monitoring metrics.

How to fix it?

• Implement centralized logging techniques with the ELK (Elasticsearch, Logstash, Kibana) stack, Loki, or Fluentd + Grafana.
• Utilize distribution tracing tools like penTelemetry, Jaeger, or Zipkin.
• Implement real-time monitoring and system health tracking via Prometheus + Grafana, New Relic, or Datadog.

8️⃣ Manual deployments and scaling complexities

As the system grows bigger, so do the scaling complexities. Manual scaling metrics of managing deployments and updates may be helpful at the initial stage, but with bigger systems, automated scalability metrics are a must. Not implementing automation will make your system fragile and human error-prone, leading to system downtime.

❌ The Problem: Not adopting automation-based scalability metrics within the system architecture.

How to fix it?

• Automate the whole system by using Terraform, Pulumi, CloudFormation, etc., like advanced scalability tools based on Infrastructure as Code.
• Implement CI/CD pipelines, GitHub Actions, GitLab CI, or Jenkins for conducting automated tests and rollouts.
• For automated container orchestration, adopt Kubernetes/Docker Swarm.

9️⃣ Memory leaks and system failures

Inefficient scalability within the system causes its performance degradation and exposes hidden memory leaks and CPU hogs. Furthermore, if critical aspects like message brokers, load balancers, etc. fail, then it causes the whole system to fail as well.

❌ The Problem: Inefficient scaling metrics for memory management and system failures

How to fix this?

• Adopt regular load testing and profiling with tools like Locust, JMeter, and k6.
• Identify Single Points of Failure (SPOFs) and implement data redundancy techniques for each component.
• Thoroughly monitor memory/CPU usage and set up system alerts.
• Ensure high availability (HA) setups and test with chaos engineering.
• Implement Docker for resource limits and auto-restart policies.

�"� Uncontrolled cloud costs

Some businesses neglect pre-planning with scaling techniques out of excitement. But doing this can lead to escalated cloud costs�"often without any business value. Businesses must not underestimate the efficiency of regulated scaling rather than frequent and abrupt scaling.

❌ The Problem: Unplanned scaling and unoptimized workloads.

How to fix this?

• Audit the right-fit resources and downgrade underused ones.
• Implement smart auto-scaling with appropriate upper/lower limits.
• Use advanced cloud billing and budget alert tools like AWS Budgets.
• Utilize Spot/Reserved Instances, like AWS Spot Instances or Azure Reserved, to save on costs.

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Conclusion

Designing a distributed system requires many careful considerations. Scaling it ensures seamless system processing without breaking down or facing challenges like latency, security threats, etc. In a distributed system, horizontal scaling works better, is easier, and features more reliability. But to address the scaling challenges in distributed system, you must adhere to more scalability depths.

If you also want to scale smart and make your system ready for experiencing business growth, then you must consult top software experts for agile software product engineering services.

Remember to fix everything that slows your system before implementing growth measures!