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Approximate computing aims for efficient execution of workflows where anapproximate output is sufficient instead of the exact output. The idea behindapproximate computing is to compute over a representative sample instead of theentire input dataset. Thus, approximate computing - based on the chosen samplesize - can make a systematic trade-off between the output accuracy andcomputation efficiency.  Unfortunately, the state-of-the-art systems for approximate computingprimarily target batch analytics, where the input data remains unchanged duringthe course of sampling. Thus, they are not well-suited for stream analytics.This motivated the design of StreamApprox - a stream analytics system forapproximate computing. To realize this idea, we designed an online stratifiedreservoir sampling algorithm to produce approximate output with rigorous errorbounds. Importantly, our proposed algorithm is generic and can be applied totwo prominent types of stream processing systems: (1) batched stream processingsuch as Apache Spark Streaming, and (2) pipelined stream processing such asApache Flink.  We evaluated StreamApprox using a set of microbenchmarks and real-world casestudies. Our results show that Spark- and Flink-based StreamApprox systemsachieve a speedup of $1.15\times$-$3\times$ compared to the respective nativeSpark Streaming and Flink executions, with varying sampling fraction of $80\%$to $10\%$. Furthermore, we have also implemented an improved baseline inaddition to the native execution baseline - a Spark-based approximate computingsystem leveraging the existing sampling modules in Apache Spark. Compared tothe improved baseline, our results show that StreamApprox achieves a speedup$1.1\times$-$2.4\times$ while maintaining the same accuracy level.

Approximate Stream Analytics in Apache Flink and Apache Spark Streaming