As an AI language model, I cannot share any personal experience of designing a distributed system. But, I can provide the following guidelines for designing a distributed system and key considerations for managing data consistency, communication protocols, and failure recovery.
Designing a distributed system:
1. Identify the primary goals and objectives of the system.
2. Define the key components and their functionalities for the system.
3. Determine the distribution of components across different nodes.
4. Select the targeting communication system for the system's components.
5. Design the data storage and access policies.
6. Decide on how to manage concurrency and distributed transactions.
7. Define system monitoring, auditing, and administration mechanisms.
Key considerations for managing data consistency:
1. Use a consistent hashing technique for partitioning data across different nodes.
2. Use distributed consensus algorithms like Paxos or Raft for managing data consistency.
3. Employ conflict resolution strategies in the case of diverging copies of data.
4. Use version control techniques like vector clocks or timestamps to synchronize data across different nodes.
5. Implement various locks to make sure only one node can modify data at a time.
Key considerations for communication protocols:
1. Choose a reliable messaging system that ensures message order and delivery.
2. Define the protocols for message exchange and communication between components.
3. Use a secure messaging protocol to encrypt data in transit.
4. Employ an API gateway to manage access to the system's components.
5. Implement a service discovery mechanism to keep track of services and nodes in the system.
Key considerations for failure recovery:
1. Employ a fault tolerance mechanism to handle failures of system components and nodes.
2. Use leader election techniques to allow other nodes to take over the responsibilities of failed nodes.
3. Define a mechanism to detect and recover from network partitions and split-brain scenarios.
4. Use distributed logging and monitoring tools to identify the cause of the failure and its impact on the system.
5. Have a well-defined process for restoring services after a failure or disruption.
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