Abstract

Abstract
There is a growing demand for persistent data in IoT, edge and similar resource-constrained devices. However, standard FLASH memory-based solutions present performance, energy, and reliability limitations in these applications. We propose MRAM persistent memory as an alternative to FLASH based storage. Preliminary experimental results show that its performance, power consumption, and reliability in typical database workloads is competitive for resource-constrained devices. This opens up new opportunities, as well as challenges, for small-scale database systems. MRAM is tested for its raw performance and applicability to key-value and relational database systems on resource-constrained devices. Improvements of as much as three orders of magnitude in write performance for key-value systems were observed in comparison to an alternative NAND FLASH based device. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

Abstract
Cloud native database systems provide highly available and scalable services as part of cloud platforms by transparently replicating and partitioning data across automatically managed resources. Some systems, such as Google Spanner, are designed and implemented from scratch. Others, such as Amazon Aurora, derive from traditional database systems for better compatibility but disaggregate storage to cloud services. Unfortunately, because they follow an open-box approach and fork the original code base, they are difficult to implement and maintain. We address this problem with Loom, a replicated and partitioned database system built on top of PostgreSQL that delegates durable storage to a distributed log native to the cloud. Unlike previous disaggregation proposals, Loom is a closed-box approach that uses the original server through existing interfaces to simplify implementation and improve robustness and maintainability. Experimental evaluation achieves 6x higher throughput and 5x lower response time than standard replication and competes with the state of the art in cloud and HPC hardware.

Abstract
We present LazyFS, a new fault injection tool that simplifies the debugging and reproduction of complex data durability bugs experienced by databases, key-value stores, and other data-centric systems in crashes. Our tool simulates persistence properties of POSIX file systems (e.g., operations ordering and atomicity) and enables users to inject lost and torn write faults with a precise and controlled approach. Further, it provides profiling information about the system’s operations flow and persisted data, enabling users to better understand the root cause of errors. Weuse LazyFS to study seven important systems: PostgreSQL, etcd, Zookeeper, Redis, LevelDB, PebblesDB, and Lightning Network. Our fault injection campaign shows that LazyFS automates and facilitates the reproduction of five known bug reports containing manual and complex reproducibility steps. Further, it aids in understanding and reproducing seven ambiguous bugs reported by users. Finally, LazyFS is used to find eight new bugs, which lead to data loss, corruption, and unavailability.