Dataflow systems are widely used today for building and running continuous data-intensive applications. However, the unavoidable semantic gap between the host languages of dataflow system libraries and the dataflow model creates programmability limitations that hinder performance, safety, and ease of use. We propose AquaLang, a new language designed for dataflow systems. Programs in AquaLang blend strongly typed relational and functional syntax and are verified using an effect system that prevents undefined behaviour that can occur when introducing user-defined logic that violates dataflow semantics. Unverified external code is also feasible in AquaLang through the novel use of sandboxing. Furthermore, on top of standard dataflow optimisations employed by current systems, AquaLang's ability to analyze algebraic properties of user-defined functions further unlocks the potential of deeper dataflow program re-writing. In our evaluation, we measure up to one order of magnitude speedup for Nexmark queries against hand-written Flink programs attributed to pushdown and window incrementalisation techniques.

In big data analytics, there is currently a large number of data programming models and their respective frontends such as relational tables, graphs, tensors, and streams. This has lead to a plethora of runtimes that typically focus on the efficient execution of just a single frontend. This fragmentation manifests itself today by highly complex pipelines that bundle multiple runtimes to support the necessary models. Hence, joint optimization and execution of such pipelines across these frontend-bound runtimes is infeasible. We propose Arc as the first unified Intermediate Representation (IR) for data analytics that incorporates stream semantics based on a modern specification of streams, windows and stream aggregation, to combine batch and stream computation models. Arc extends Weld, an IR for batch computation and adds support for partitioned, out-of-order stream and window operators which are the most fundamental building blocks in contemporary data streaming.

Contemporary end-to-end data pipelines need to combine many diverse workloads such as machine learning, relational operations, stream dataflows, tensor transformations, and graphs. For each of these workload types, there exists several frontends (e.g., SQL, Beam, Keras) based on different programming languages as well as different runtimes (e.g., Spark, Flink, Tensorflow) that optimize for a particular frontend and possibly a hardware architecture (e.g., GPUs). The resulting pipelines suffer in terms of complexity and performance due to excessive type conversions, materialization of intermediate results, and lack of cross-framework optimizations. Arcon aims to provide a unified approach to declare and execute tasks across frontend-boundaries as well as enabling their seamless integration with event-driven services at scale. In this demonstration, we present Arcon and through a series of use-case scenarios demonstrate that its execution model is powerful enough to cover existing as well as upcoming real-time computations for analytics and application-specific needs.