Concurrency Basics
Interview Questions

Concurrency refers to the execution of multiple instruction sequences at the same time. These sequences can be executed in overlapping periods, rather than simultaneously, using shared resources. Parallelism is a subset where tasks are literally executed at the same time, often requiring parallelizable hardware. Concurrency focuses on managing shared resources efficiently to ensure progress, while parallelism aims at enhancing speed through simultaneous execution.

A race condition occurs when the behavior of a software system depends on the sequence or timing of uncontrollable events, such as scheduling of threads leading to conflicting operations on shared resources. Prevention methods include using locks to ensure mutual exclusion, employing atomic operations to avoid shared state corruption, and adopting Thread-safe or functional programming paradigms where possible.

A deadlock is a state where a set of processes are unable to proceed because each process is waiting for a resource held by another, creating a cycle of dependencies. Necessary conditions for deadlock include mutual exclusion, hold and wait, no preemption, and circular wait. Prevention methods include resource hierarchy ordering, imposing timeouts, and employing deadlock detection algorithms.

Let's consider a banking system where multiple transactions occur on a single account. Synchronization is necessary to ensure transactions like withdrawals and deposits do not result in data inconsistency. I would use mutex locks to protect the account balance variable, ensuring that only one transaction can modify it at a time, thereby preventing race conditions.

A producer-consumer model can be implemented using a shared queue. Producers will add tasks to the queue while consumers remove tasks. Synchronization can be implemented with a semaphore or a condition variable to manage access. For example, a semaphore can be used to count available items and another to track space in the buffer, ensuring producers wait when the buffer is full and consumers wait when empty.

Thread pools are beneficial as they allow task execution without the overhead of thread creation and destruction, improving resource management by reusing threads. They provide control over the maximum number of concurrent threads, enhancing application stability. However, they can be complex to implement and fine-tune; idle threads could also waste resources if not managed properly. Without careful balance, they may also lead to bottlenecks.

A barrier is a synchronization method that blocks a set of threads until they all reach a certain execution point, ensuring collective progression. For example, in a simulation split into phases, all threads might need to complete one phase before starting the next. Using a barrier, each thread signals its completion and waits until all others reach this point, ensuring phase transition synchronization.

An example is a web server handling multiple client requests. Concurrent programming enables handling of simultaneous connections, improving throughput and response times. This can be implemented using a thread-per-request model or asynchronous I/O operations, keeping server resources optimized. Trade-offs include increased complexity in code logic and potential for deadlock or race condition issues, which require careful synchronization management.

Preventing deadlocks can involve using a deadlock avoidance algorithm like 'Banker's algorithm,' implementing a strict resource hierarchy to prevent circular wait conditions, or employing lock timeouts to retry operations if a required resource isn't acquired within a certain time frame. Monitoring and detecting potential deadlocks during runtime can also help in taking corrective actions dynamically.

Designing a concurrent priority queue requires ensuring that both enqueue and dequeue operations respect priority and maintain thread safety. One approach is using a skip list, where insertion and removal operations are lock-free. Alternatively, mutex locks can be used, but segmented or fine-grained locking allows for more efficient thread handling, reducing contention. Careful testing for race conditions and higher-level synchronization logic is key for implementation success.

In real-time systems processing data from multiple sensors, concurrency enables simultaneous data interception, reducing latency significantly. Each sensor can have a dedicated thread, hence processing its data without waiting. Synchronization mechanisms ensure that actions dependent on multiple sensors' data don't suffer from data inconsistency. Consider using event-driven architecture for scalable alert triggering without resource wastage. Potential drawbacks like synchronization overhead must be managed with efficient lock mechanisms.