Event stream applications consist of an acyclic graph of components that are traversed by streams of events. Examples of operations in such components are filtering, aggregation, enrichment, and transformation of events and, commonly, applications include a mix of common-use library functions and user-defined functions. When the operation only depends on the current input events, the component can be trivially parallelized by replication. However, if the component keeps state that is used for the computation of the results, the trivial parallelization approach does not work. Parallel versions for common components have being designed, but complex or user-defined components are normally limited by single thread performance. In this work, we use optimistic parallelization approaches to harness the potential of multi-core processors to scale the performance of stateful operators in event stream applications. In addition, we investigate indulgent ways to allow the user to provide application knowledge that can improve the amount of useful speculative work. The current prototype shows considerable gain in throughput even though some speculative executions must be disregarded.