Abstract: The stream processing engine uses the Actor programming paradigm for defining the application in terms of a graph built with processing elements (PEs) that use a hash based partitioning of data, where events (key, value) are pushed towards the next element in the operator, and in case of an overloaded PE the method changes to a Producer/Consumer Model where new workers pull events from a buffer queue in order to release the amount of traffic in the overloaded PE. The programmer defines a sequential version of the PE and other parallel version that recovers the events from a buffer and, if the operator is stateless sends the result to the next PE, or if the operator is stateful sends the result to an aggregator PE before moving to the next stage of the pipeline process. Strategies for triggering changes in the graph are defined in an administrator module to provide the right amount of elasticity and load balance in the distributed stream processing engine using queues analysis of the monitoring module.