Raising the Parallel Abstraction Level for Streaming Analytics Applications

In the stream processing domain, applications are represented by graphs of operators arbitrarily connected and filled with their business logic code. The APIs of existing Stream Processing Systems (SPSs) ease the development of transformations that recur in the streaming practice (e.g., filtering, aggregation and joins). In contrast, their
parallelism abstractions
are quite limited since they provide support to stateless operators only, or when the state is organized in a set of key-value pairs. This paper presents how the
parallel patterns
methodology can be revisited for sliding-window streaming analytics. Our vision fosters a design process of the application as
composition
and
nesting
of ready-to-use patterns provided through a C++17
fluent
interface. Our prototype implements the run-time system of the patterns in the FastFlow parallel library expressing
thread-based parallelism
. The experimental analysis shows interesting outcomes. First, our pattern-based approach allows easy prototyping of different versions of the application, and the programmer can leverage nesting of patterns to increase performance (up to 37% in one of the two considered test-bed cases). Second, our FastFlow implementation outperforms (three times faster) the handmade porting of our patterns in popular JVM-based SPSs. Finally, in the concluding part of this paper, we explore the use of a
task-based
run-time system, by deriving interesting insights into how to make our patterns library suitable for multi backends.