Common Problems in Distributed Systems and their Solutions

Common Problems to solve in Distributed Systems

3 min readFeb 1, 2023

Data Transmission Issues:

High Latency: Network latency can slow down a distributed system, and the overall system throughput can be limited by the slowest node.
Inconsistent Data: In a distributed system there can be inconsistencies when nodes have different versions of the same data. Maintaining consistent data across multiple nodes in a distributed system is critical in most cases.

System Failures:

Node Failures: Nodes can fail. Detecting and recovering it should not be complex and time-consuming.
Network Failures :When a system is divided into two separate parts, communication between parts can sometimes become unreliable or unavailable.

Suboptimal Resource utilisation

Distributing workload fairly across nodes in a system is challenging, especially when nodes have different processing capabilities or network speeds.

Protecting sensitive data :

Ensuring secure communication and protecting sensitive data in a distributed system is critical for some use cases.

Introduce Redundancy: Creating redundant communication paths between nodes can ensure that data can still be transmitted even if one or more communication paths become unavailable.
Add Timeout and Fallback Mechanisms: Implementing timeout and fallback mechanisms, such as automatic reconnection or backup communication paths, can help ensure that communication can continue even if a network connection becomes unavailable.
Setup Network Monitoring to help detect potential issues early and prevent outages.

Using Consensus algorithms: Consensus algorithms are used to ensure that all nodes in a distributed system agree on the state of shared data, despite network failures or node failures. Examples of consensus algorithms include Paxos, Raft, Two-Phase Commit, Three-Phase Commit
Enable Data Versioning: When a node tries to update data, it checks the version number of the data it has against the version number on the central repository. If the version numbers do not match, the node knows that the data has been updated by another node.

Replication algorithms are used to ensure that data is replicated across multiple nodes in a distributed system, providing fault tolerance and improved performance. Examples of replication algorithms include Primary-Backup, Active-Passive, and Active-Active.
Fault tolerance algorithms: Fault tolerance algorithms are used to ensure that the system continues to operate despite failures or faults. Examples of fault tolerance algorithms include Checkpointing, Rollback Recovery, and Backup Algorithms.

Compress Data: Compressing data before transmitting reduces the amount of data that needs to be transmitted
Use faster network protocols like protobuf,avro..
Use Caching: Caching data locally reduces the need for frequent data transfers over the network.

Use Deadlock detection algorithms: Used to detect and resolve deadlocks in distributed systems, where multiple nodes are waiting for each other to release resources. Examples include Resource Allocation Graph Algorithm, and Wait-for Graph Algorithm.
Introduce Load balancing systems to ensure that resources are used efficiently, reducing the risk of overloading any one node.

Encrypt Data: Encrypting sensitive data before transmitting it over the network or storing it on disk can protect it from unauthorized access.
Implement Access Control: Implementing access control mechanisms, such as authentication and authorization, can ensure that only authorized users are able to access sensitive data.
Use Virtual Private Networks (VPNs) and Firewalls: Using firewalls to limit access to sensitive data from external networks can prevent unauthorized access.
Add Monitoring and Auditing: Monitoring and auditing access to sensitive data can help detect and prevent unauthorized access or breaches.