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Distributed data processing ecosystems are widespread and their components are highly specialized, such that efficient interoperability is urgent. Recently, Apache Arrow was chosen by the community to serve as a format mediator, providing efficient in-memory data representation. Arrow enables efficient data movement between data processing and storage engines, significantly improving interoperability and overall performance. In this work, we design a new zero-cost data interoperability layer between Apache Spark and Arrow-based data sources through the Arrow Dataset API. Our novel data interface helps separate the computation (Spark) and data (Arrow) layers. This enables practitioners to seamlessly use Spark to access data from all Arrow Dataset API-enabled data sources and frameworks. To benefit our community, we open-source our work and show that consuming data through Apache Arrow is zero-cost: our novel data interface is either on-par or more performant than native Spark.
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This content will become publicly available on December 3, 2025
Thallus: An RDMA-based Columnar Data Transport Protocol
The volume of data generated and stored in contemporary global data centers is experiencing exponential growth. This rapid data growth necessitates efficient processing and anal- ysis to extract valuable business insights. In distributed data processing systems, data undergoes exchanges between the compute servers that contribute significantly to the total data processing duration in adequately large clusters, neces- sitating efficient data transport protocols. Traditionally, data transport frameworks such as JDBC and ODBC have used TCP/IP-over-Ethernet as their under- lying network protocol. Such frameworks require serializing the data into a single contiguous buffer before handing it off to the network card, primarily due to the requirement of contiguous data in TCP/IP. In OLAP use cases, this seri- alization process is costly for columnar data batches as it involves numerous memory copies that hurt data transport duration and overall data processing performance. We study the serialization overhead in the context of a widely-used columnar data format, Apache Arrow, and propose lever- aging RDMA to transport Arrow data over Infiniband in a zero-copy manner. We design and implement Thallus, an RDMA-based columnar data transport protocol for Apache Arrow based on the Thallium framework from the Mochi ecosystem, compare it with a purely Thallium RPC-based implementation, and show substantial performance improve- ments can be achieved by using RDMA for columnar data transport.
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- PAR ID:
- 10585764
- Publisher / Repository:
- Arxiv
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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