Event stream applications consist of an acyclic graph of com-
ponents 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 de-
pends on the current input events, the component can be
trivially parallelized by replication. However, if the com-
ponent 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 de-
signed, but complex or user-defined components are nor-
mally 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 addi-
tion, we investigate indulgent ways to allow the user to pro-
vide application knowledge that can improve the amount of
useful speculative work. The current prototype shows con-
siderable gain in throughput even though some speculative
executions must be disregarded.