The End of Slow Networks: It's Time for a Redesign

Next generation high-performance RDMA-capable networks will require a fundamental rethinking of the design and architecture of modern distributed DBMSs. These systems are commonly designed and optimized under the assumption that the network is the bottleneck: the network is slow and "thin", and thus needs to be avoided as much as possible. Yet this assumption no longer holds true. With InfiniBand FDR 4x, the bandwidth available to transfer data across network is in the same ballpark as the bandwidth of one memory channel, and it increases even further with the most recent EDR standard. Moreover, with the increasing advances of RDMA, the latency improves similarly fast. In this paper, we first argue that the "old" distributed database design is not capable of taking full advantage of the network. Second, we propose architectural redesigns for OLTP, OLAP and advanced analytical frameworks to take better advantage of the improved bandwidth, latency and RDMA capabilities. Finally, for each of the workload categories, we show that remarkable performance improvements can be achieved

No Provisioned Concurrency: Fast RDMA-codesigned Remote Fork for Serverless Computing

Serverless platforms essentially face a tradeoff between container startup time and provisioned concurrency (i.e., cached instances), which is further exaggerated by the frequent need for remote container initialization. This paper presents MITOSIS, an operating system primitive that provides fast remote fork, which exploits a deep codesign of the OS kernel with RDMA. By leveraging the fast remote read capability of RDMA and partial state transfer across serverless containers, MITOSIS bridges the performance gap between local and remote container initialization. MITOSIS is the first to fork over 10,000 new containers from one instance across multiple machines within a second, while allowing the new containers to efficiently transfer the pre-materialized states of the forked one. We have implemented MITOSIS on Linux and integrated it with FN, a popular serverless platform. Under load spikes in real-world serverless workloads, MITOSIS reduces the function tail latency by 89% with orders of magnitude lower memory usage. For serverless workflow that requires state transfer, MITOSIS improves its execution time by 86%.Comment: To appear in OSDI'2

Towards Scalable OLTP Over Fast Networks

Online Transaction Processing (OLTP) underpins real-time data processing in many mission-critical applications, from banking to e-commerce. These applications typically issue short-duration, latency-sensitive transactions that demand immediate processing. High-volume applications, such as Alibaba's e-commerce platform, achieve peak transaction rates as high as 70 million transactions per second, exceeding the capacity of a single machine. Instead, distributed OLTP database management systems (DBMS) are deployed across multiple powerful machines. Historically, such distributed OLTP DBMSs have been primarily designed to avoid network communication, a paradigm largely unchanged since the 1980s. However, fast networks challenge the conventional belief that network communication is the main bottleneck. In particular, emerging network technologies, like Remote Direct Memory Access (RDMA), radically alter how data can be accessed over a network. RDMA's primitives allow direct access to the memory of a remote machine within an order of magnitude of local memory access. This development invalidates the notion that network communication is the primary bottleneck. Given that traditional distributed database systems have been designed with the premise that the network is slow, they cannot efficiently exploit these fast network primitives, which requires us to reconsider how we design distributed OLTP systems. This thesis focuses on the challenges RDMA presents and its implications on the design of distributed OLTP systems. First, we examine distributed architectures to understand data access patterns and scalability in modern OLTP systems. Drawing on these insights, we advocate a distributed storage engine optimized for high-speed networks. The storage engine serves as the foundation of a database, ensuring efficient data access through three central components: indexes, synchronization primitives, and buffer management (caching). With the introduction of RDMA, the landscape of data access has undergone a significant transformation. This requires a comprehensive redesign of the storage engine components to exploit the potential of RDMA and similar high-speed network technologies. Thus, as the second contribution, we design RDMA-optimized tree-based indexes — especially applicable for disaggregated databases to access remote data efficiently. We then turn our attention to the unique challenges of RDMA. One-sided RDMA, one of the network primitives introduced by RDMA, presents a performance advantage in enabling remote memory access while bypassing the remote CPU and the operating system. This allows the remote CPU to process transactions uninterrupted, with no requirement to be on hand for network communication. However, that way, specialized one-sided RDMA synchronization primitives are required since traditional CPU-driven primitives are bypassed. We found that existing RDMA one-sided synchronization schemes are unscalable or, even worse, fail to synchronize correctly, leading to hard-to-detect data corruption. As our third contribution, we address this issue by offering guidelines to build scalable and correct one-sided RDMA synchronization primitives. Finally, recognizing that maintaining all data in memory becomes economically unattractive, we propose a distributed buffer manager design that efficiently utilizes cost-effective NVMe flash storage. By leveraging low-latency RDMA messages, our buffer manager provides a transparent memory abstraction, accessing the aggregated DRAM and NVMe storage across nodes. Central to our approach is a distributed caching protocol that dynamically caches data. With this approach, our system can outperform RDMA-enabled in-memory distributed databases while managing larger-than-memory datasets efficiently

RDMA mechanisms for columnar data in analytical environments

Dissertação de mestrado integrado em Engenharia InformáticaThe amount of data in information systems is growing constantly and, as a consequence, the complexity of analytical processing is greater. There are several storage solutions to persist this information, with different architectures targeting different use cases. For analytical processing, storage solutions with a column-oriented format are particularly relevant due to the convenient placement of the data in persistent storage and the closer mapping to in-memory processing. The access to the database is typically remote and has overhead associated, mainly when it is necessary to obtain the same data multiple times. Thus, it is desirable to have a cache on the processing side and there are solutions for this. The problem with the existing so lutions is the overhead introduced by network latency and memory-copy between logical layers. Remote Direct Memory Access (RDMA) mechanisms have the potential to help min imize this overhead. Furthermore, this type of mechanism is indicated for large amounts of data because zero-copy has more impact as the data volume increases. One of the problems associated with RDMA mechanisms is the complexity of development. This complexity is induced by its different development paradigm when compared to other network commu nication protocols, for example, TCP. Aiming to improve the efficiency of analytical processing, this dissertation presents a dis tributed cache that takes advantage of RDMA mechanisms to improve analytical processing performance. The cache abstracts the intricacies of RDMA mechanisms and is developed as a middleware making it transparent to take advantage of this technology. Moreover, this technique could be used in other contexts where a distributed cache makes sense, such as a set of replicated web servers that access the same database.A quantidade de informação nos sistemas informáticos tem vindo a aumentar e consequentemente, a complexidade do processamento analítico torna-se maior. Existem diversas soluções para o armazenamento de dados com diferentes arquiteturas e indicadas para determinados casos de uso. Num contexto de processamento analítico, uma solução com o modelo de dados colunar e especialmente relevante devido à disposição conveniente dos dados em disco e a sua proximidade com o mapeamento em memória desses mesmos dados. Muitas vezes, o acesso aos dados é feito remotamente e isso traz algum overhead, principalmente quando é necessário aceder aos mesmos dados mais do que uma vez. Posto isto, é vantajoso fazer caching dos dados e já existem soluções para esse efeito. O overhead introduzido pela latência da rede e cópia de buffers entre camadas lógicas é o principal problema das soluções existentes. Os mecanismos de acesso direto à memória remota (RDMA - Remote Direct Memory Access) tem o potencial de melhorar o desempenho neste cenário. Para além disso, este tipo de tecnologia faz sentido em sistemas com grandes quantidades de dados, nos quais o acesso direto pode ter um impacto ainda maior por ser zero-copy. Um dos problemas associados com mecanismos RDMA é a complexidade de desenvolvimento. Esta complexidade é causada pelo paradigma de desenvolvimento completamente diferente de outros protocolos de comunicação, como por exemplo, TCP. Tendo em vista melhorar a eficiência do processamento analítico, esta dissertação propõe uma solução de cache distribuída que tira partido de mecanismos de acesso direto a memoria remota (RDMA). A cache abstrai as particularidades dos mecanismos RDMA e é disponibilizada como middleware, tornando a utilização desta tecnologia completamente transparente. Esta solução visa os sistemas de processamento analítico, mas poderá ser utilizada noutros contextos em que uma cache distribuída faça sentido, como por exemplo num conjunto de servidores web replicados que acedem a mesma base de dados

Database architectures for modern hardware: report from Dagstuhl Seminar 18251

The requirements of emerging applications on the one hand and the trends in computing hardware and systems on the other hand demand a fundamental rethinking of current data management architectures. Based on the broad consensus that this rethinking requires expertise from different research disciplines, the goal of this seminar was to bring together researchers and practitioners from these areas representing both the software and hardware sides and to foster cross-cutting architectural discussions. The outcome of this seminar was not only an identification of promising hardware technologies and their exploitation in data management systems but also a set of use cases, studies, and experiments for new architectural concepts

DINOMO: An Elastic, Scalable, High-Performance Key-Value Store for Disaggregated Persistent Memory (Extended Version)

We present Dinomo, a novel key-value store for disaggregated persistent memory (DPM). Dinomo is the first key-value store for DPM that simultaneously achieves high common-case performance, scalability, and lightweight online reconfiguration. We observe that previously proposed key-value stores for DPM had architectural limitations that prevent them from achieving all three goals simultaneously. Dinomo uses a novel combination of techniques such as ownership partitioning, disaggregated adaptive caching, selective replication, and lock-free and log-free indexing to achieve these goals. Compared to a state-of-the-art DPM key-value store, Dinomo achieves at least 3.8x better throughput on various workloads at scale and higher scalability, while providing fast reconfiguration.Comment: This is an extended version of the full paper to appear in PVLDB 15.13 (VLDB 2023