This paper introduces the Wave Transactional Filesystem (WTF), a novel, transactional, POSIX-compatible filesystem based on a new file slicing API that enables efficient file transformations. WTF provides transactional access to a distributed filesystem, eliminating the possibility of inconsistencies across multiple files. Further, the file slicing API enables applications to construct files from the contents of other files without having to rewrite or relocate data. Combined, these enable a new class of high-performance applications. Experiments show that WTF can qualitatively outperform the industry-standard HDFS distributed filesystem, up to a factor of four in a sorting benchmark, by reducing I O costs. Microbenchmarks indicate that the new features of WTF impose only a modest overhead on top of the POSIX-compatible API.
Facebook's corpus of photos, videos, and other Binary Large OBjects (BLOBs) that need to be reliably stored and quickly accessible is massive and continues to grow. As the footprint of BLOBs increases, storing them in our traditional storage system, Haystack, is becoming increasingly inefficient. To increase our storage efficiency, measured in the effective-replication-factor of BLOBs, we examine the underlying access patterns of BLOBs and identify temperature zones that include hot BLOBs that are accessed frequently and warm BLOBs that are accessed far less often. Our overall BLOB storage system is designed to isolate warm BLOBs and enable us to use a specialized warm BLOB storage system, f4. f4 is a new system that lowers the effective-replication-factor of warm BLOBs while remaining fault tolerant and able to support the lower throughput demands. f4 currently stores over 65PBs of logical BLOBs and reduces their effective-replication-factor from 3.6 to either 2.8 or 2.1. f4 provides low latency; is resilient to disk, host, rack, and datacenter failures; and provides sufficient throughput for warm BLOBs. A significant fraction of data stored in cloud storage is rarely accessed. This data is referred to as cold data; cost-effective storage for cold data has become a challenge for cloud providers. Pelican is a rack-scale hard-disk based storage unit designed as the basic building block for exabyte scale storage for cold data. In Pelican, server, power, cooling and interconnect bandwidth resources are provisioned by design to support cold data workloads; this right-provisioning significantly reduces Pelican's total cost of ownership compared to traditional disk-based storage. Resource right-provisioning in Pelican means only 8 of the drives can be concurrently spinning. This introduces complex resource management to be handled by the Pelican storage stack. Resource restrictions are expressed as constraints over the hard drives. The data layout and IO scheduling ensures that these constraints are not violated. We evaluate the performance of a prototype Pelican, and compare against a traditional resource overprovisioned storage rack using a cross-validated simulator. We show that compared to this overprovisioned storage rack Pelican performs well for cold workloads, providing high throughput with acceptable latency.