Colocating Real-time Storage and Processing: An Analysis of Pull-based versus Push-based Streaming

Real-time Big Data architectures evolved into specialized layers for handling data streams' ingestion, storage, and processing over the past decade. Layered streaming architectures integrate pull-based read and push-based write RPC mechanisms implemented by stream ingestion/storage systems. In addition, stream processing engines expose source/sink interfaces, allowing them to decouple these systems easily. However, open-source streaming engines leverage workflow sources implemented through a pull-based approach, continuously issuing read RPCs towards the stream ingestion/storage, effectively competing with write RPCs. This paper proposes a unified streaming architecture that leverages push-based and/or pull-based source implementations for integrating ingestion/storage and processing engines that can reduce processing latency and increase system read and write throughput while making room for higher ingestion. We implement a novel push-based streaming source by replacing continuous pull-based RPCs with one single RPC and shared memory (storage and processing handle streaming data through pointers to shared objects). To this end, we conduct an experimental analysis of pull-based versus push-based design alternatives of the streaming source reader while considering a set of stream benchmarks and microbenchmarks and discuss the advantages of both approaches.