Hazelcast employs several strategies to handle concurrent access to shared resources, ensuring data consistency and avoiding race conditions.
Let’s delve into some of these techniques :Distributed Locks :
* Hazelcast provides a distributed lock mechanism that allows multiple nodes in a cluster to coordinate access to a shared resource.
* When a node acquires a lock, other nodes are blocked from accessing the same resource until the lock is released.
* This ensures mutual exclusion and prevents concurrent modifications.
Distributed Data Structures :
* Hazelcast offers various distributed data structures like maps, queues, and semaphores.
* These structures are designed to be thread-safe and can be accessed concurrently by multiple nodes.
* For example, a distributed map can be used to store shared data, and Hazelcast ensures proper synchronization.
Eventual Consistency :
* Hazelcast follows the eventual consistency model.
* When data is updated, it is eventually propagated to all nodes in the cluster.
* This approach balances performance and consistency, allowing for high throughput while maintaining data integrity.
In-Memory Computing :
* Hazelcast’s core strength lies in its in-memory computing capabilities.
* By keeping data in memory, Hazelcast avoids disk I/O bottlenecks and provides fast access to shared resources.
* In-memory storage also enhances parallelism and reduces contention.
Partitioning and Replication :
* Hazelcast partitions data across nodes.
* Each partition is owned by a single node, ensuring that concurrent access within a partition is well-defined.
* Replication provides fault tolerance by maintaining backup copies of data on other nodes.
Custom Locking Mechanisms :
* Developers can implement custom locking mechanisms using Hazelcast’s building blocks.
* For more complex scenarios, you can create your own distributed locks or synchronization primitives.
