No abstract

Most distributed storage systems provide limited abilities for querying data by attributes other than their primary keys. Supporting efficient search on secondary attributes is challenging as applications pose varying requirements to query processing systems, and no single system design can be suitable for all needs. In this paper, we show how to overcome these challenges in order to extend distributed data stores to support queries on secondary attributes. We propose a modular architecture that is flexible and allows query processing systems to make trade-offs according to different use case requirements. We describe adaptive mechanisms that make use of this flexibility to enable query processing systems to dynamically adjust to query and write operation workloads.

Building scalable and highly available geo-replicated file systems is hard. These systems need to resolve conflicts that emerge in concurrent operations in a way that maintains file system invariants, is meaningful to the user, and does not depart from the traditional file system interface. Conflict resolution in existing systems often leads to unexpected or inconsistent results. This paper introduces ElmerFS, a geo-replicated, truly concurrent file system designed with the aim of addressing these challenges. ElmerFS is based on two key ideas: (1) the use of Conflict-Free Replicated Data Types (CRDTs) for representing file system structures, which ensures that replicas converge to a correct state, and (2) conflict resolution rules, which are determined by the choice of CRDT types and their composition, designed with the principle of being intuitive to the user. We argue that if the state of the file system after resolving a conflict conveys to the user the resolved conflict in an intuitive way, the user can complement or reverse it using traditional file system operations. We discuss the challenges in the design of geo-replicated weakly consistent file systems, and present the design of ElmerFS.

No abstract

In the age of big data, more and more applications need to query and analyse large volumes of continuously updated data in real-time. In response, cloud-scale storage systems can extend their interface that allows fast lookups on the primary key with the ability to retrieve data based on non-primary attributes. However, the need to ingest content rapidly and make it searchable immediately while supporting low-latency, high-throughput query evaluation, as well as the geo-distributed nature and weak consistency guarantees of modern storage systems pose several challenges to the implementation of indexing and search systems. We present our early-stage work on the design and implementation of an indexing and query processing system that enables realtime queries on secondary attributes of data stored in geo-distributed, weakly consistent storage systems.