The concept of memory disaggregation has recently been gaining traction in research. With memory disaggregation, data center compute nodes can directly access memory on adjacent nodes and are therefore able to overcome local memory restrictions, introducing a new data management paradigm for distributed computing. This paper proposes and demonstrates a memory disaggregated in-memory object store framework for big data applications by leveraging the newly introduced Thymes-isFlow memory disaggregation system. The framework extends the functionality of the pre-existing Apache Arrow Plasma object store framework to distributed systems by enabling clients to easily and efficiently produce and consume data objects across multiple compute nodes. This allows big data applications to increasingly leverage parallel processing at reduced development costs. In addition, the paper includes latency and throughput measurements that indicate only a modest performance penalty is incurred for remote disaggregated memory access as opposed to local (~6.5 vs ~5.75 GiB/s). The results can be used to guide the design of future systems that leverage memory disaggregation as well as the newly presented framework. This work is open-source and publicly accessible at https://doi.org/10.5281/zenodo.6368998.@en

JSON is a popular data interchange format for many web, cloud, and IoT systems due to its simplicity, human readability, and widespread support. However, applications must first parse and convert the data to a native in-memory format before being able to perform useful computations. Many big data applications with high performance requirements convert JSON data to Apache Arrow RecordBatches, the latter being a widely-used columnar in-memory format for large tabular data sets used in data analytics. In this paper, we analyze the performance characteristics of such applications and show that JSON parsing represents a bottleneck in the system. Various strategies are explored to speed up JSON parsing on CPU and GPU as much as possible. Due to performance limitation of the CPU and GPU implementations, we furthermore present an FPGA accelerated implementation. We explain how hardware components that can parse variable-sized and nested structures can be combined to produce JSON parsers for any type of JSON document. Several fully integrated FPGA-accelerated JSON parser implementations are presented using the Intel Arria 10 GX and Xilinx VU37P devices, and compared to the performance of their respective host systems; an Intel Xeon and an IBM POWER9 system. Result show the accelerators achieve an end-to-end throughput close to 7 GB/s with the Arria 10 GX using PCIe, and close to 20 GB/s with the VU37P using OpenCAPI 3. Depending on the complexity of the JSON data to parse, the bandwidth is limited by the host-to-accelerator interface or available FPGA resources. Overall, this provides a throughput increase of up to 6x, compared to the baseline application. Also, we observe a full system energy efficiency improvement of up to 59x more JSON data parsed per joule.@en

The big data revolution has ushered an era with ever increasing volumes and complexity of data requiring ever faster computational analysis. During this very same era, CPU performance growth has been stagnating, pushing the industry to either scale their computation horizontally using multiple nodes in datacenters, or to scale vertically using heterogeneous components to reduce compute time. However, networking and storage continue to provide both higher throughput and lower latency, which allows for leveraging heterogeneous components, deployed in data centers around the world. Still, the integration of big data analytics frameworks with heterogeneous hardware components such as GPGPUs and FPGAs is challenging, because there is an increasing gap in the level of abstraction between analytics solutions developed with big data analytics frameworks, and accelerated kernels developed with heterogeneous components. In this article, we focus on FPGA accelerators that have seen wide-scale deployment in large cloud infrastructures. FPGAs allow the implementation of highly optimized hardware architectures, tailored exactly to an application, and unburdened by the overhead associated with traditional general-purpose computer architectures. FPGAs implementing dataflow-oriented architectures with high levels of (pipeline) parallelism can provide high application throughput, often providing high energy efficiency. Latency-sensitive applications can leverage FPGA accelerators by directly connecting to the physical layer of a network, and perform data transformations without going through the software stacks of the host system. While these advantages of FPGA accelerators hold promise, difficulties associated with programming and integration limit their use. This article explores the existing practices in big data analytics frameworks, discusses the aforementioned gap in development abstractions, and provides some perspectives on how to address these challenges in the future. @en