Exception Handling and Retries

When transactions are canceled because of unresolvable conflicts or lock-wait timeouts, Amazon Neptune responds with a ConcurrentModificationException. For more information, see Engine Error Codes. As a best practice, clients should always catch and handle these exceptions.

In many cases, when the number of ConcurrentModificationException instances is low, an exponential backoff-based retry mechanism works well as a way to handle them. In such a retry approach, the maximum number of retries and waiting time generally depends on the maximum size and duration of the transactions.

However, if your application has highly concurrent update workloads, and you observe a large number of ConcurrentModificationException events, you might be able to modify your application to reduce the number of conflicting concurrent modifications.

For example, consider an application that makes frequent updates to a set of vertices and uses multiple concurrent threads for these updates to optimize the write throughput. If each thread continuously executes queries that update one or more node properties, concurrent updates of the same node can produce ConcurrentModificationExceptions. This in turn can degrade write performance.

You can greatly reduce the likelihood of such collisions if you can serialize updates that are likely to conflict with each other. For example, if you can ensure that all update queries for a given node are made on the same thread (maybe using a hash-based assignment), you can be sure that they will be executed one after another rather than concurrently. Although it is still possible that a range lock taken on a neighboring node can cause a ConcurrentModificationException, you eliminate concurrent updates to the same node.