Cloud logs can be categorized into on-line, off-line, and near-line logs based on the access frequency. Among them, near-line logs are mainly used for debugging, which means they prefer a low query latency for better user experience. Besides, the storage system for near-line logs prefers a low overall cost including the storage cost to store compressed logs, and the computation cost to compress logs and execute queries. These requirements pose challenges to achieving fast and cheap cloud log storage. This article proposes LogGrep, the first log compression and query tool that exploits both static and runtime patterns to properly structurize and organize log data in fine-grained units. The key idea of LogGrep is “vertical partitioning”: it stores each log entry into multiple partitions by first parsing logs into variable vectors according to static patterns and then extracting runtime pattern(s) automatically within each variable vector. Based on such runtime patterns, LogGrep further decomposes the variable vectors into fine-grained units called “Capsules” and stamps each Capsule with a summary of its values. During the query process, LogGrep can avoid decompressing and scanning Capsules that cannot match the keywords, with the help of the extracted runtime patterns and the Capsule stamps. We further show that the interactive debugging can well utilize the advantages of the vertical-partitioning-based method and mitigate its weaknesses as well. To this end, LogGrep integrates incremental locating and partial reconstruction to mitigate the read amplification incurred by vertical-partitioning-based method. We evaluate LogGrep on 37 cloud logs from the production environment of Alibaba Cloud and the public datasets. The results show that LogGrep can reduce the query latency and the overall cost by an order of magnitude compared with state-of-the-art works. Such results have confirmed that it is worthwhile applying a more sophisticated vertical-partitioning-based method to accelerate queries on compressed cloud logs.

In cloud systems, near-line logs are mainly used for debugging, which means they prefer a low query latency for a better user experience, and like any other logs, they also prefer a low overall cost including storage cost to store compressed logs and computation cost to compress logs and execute queries.

Compared with traditional solid-state drives (SSDs), open-channel SSDs (OCSSDs) expose their internal physical layout and provide a host-based flash translation layer (FTL) that allows host-side software to control the internal operations such as garbage collection (GC) and input/output (I/O) scheduling. In this paper, we comprehensively survey research works built on OCSSDs in recent years. We show how they leverage the features of OCSSDs to achieve high throughput, low latency, long lifetime, strong performance isolation, and high resource utilization. We categorize these efforts into five groups based on their optimization methods: adaptive interface customizing, rich FTL co-designing, internal parallelism exploiting, rational I/O scheduling, and efficient GC processing. We discuss the strengths and weaknesses of these efforts and find that almost all these efforts face a dilemma between performance effectiveness and management complexity. We hope that this survey can provide fundamental knowledge to researchers who want to enter this field and further inspire new ideas for the development of OCSSDs.