Related papers: RESYSTANCE: Unleashing Hidden Performance of Compa…

Scan-based operations, such as backstage compaction and value filtering, have emerged as the main bottleneck for LSM-Trees in supporting contemporary data-intensive applications. For slower external storage devices, such as HDD and SATA…

The overhead of the kernel storage path accounts for half of the access latency for new NVMe storage devices. We explore using BPF to reduce this overhead, by injecting user-defined functions deep in the kernel's I/O processing stack. When…

The log-structured merge tree (LSM-tree) is widely employed to build key-value (KV) stores. LSM-tree organizes multiple levels in memory and on disk. The compaction of LSM-tree, which is used to redeploy KV pairs between on-disk levels in…

Compaction is a necessary, but often costly background process in write-optimized data structures like LSM-trees that reorganizes incoming data that is sequentially appended to logs. In this paper, we introduce Transformation-Embedded…

LSM-tree has been widely used in cloud computing systems by Google, Facebook, and Amazon, to achieve high performance for write-intensive workloads. However, in LSM-tree, random key-value queries can experience long latency and low…

Log-structured merge (LSM) trees offer efficient ingestion by appending incoming data, and thus, are widely used as the storage layer of production NoSQL data stores. To enable competitive read performance, LSM-trees periodically…

Modern key-value storage engines built on Log-Structured Merge-trees (LSM-trees), such as RocksDB and LevelDB, rely heavily on the performance of their compaction operations, which are impacted by a complex set of interdependent…

Log-Structured Merge trees (LSM trees) are increasingly used as the storage engines behind several data systems, frequently deployed in the cloud. Similar to other database architectures, LSM trees take into account information about the…

The Log-Structured Merge-Tree (LSM-tree) has been widely adopted for use in modern NoSQL systems for its superior write performance. Despite the popularity of LSM-trees, they have been criticized for suffering from write stalls and large…

Storage disaggregation, wherein storage is accessed over the network, is popular because it allows applications to independently scale storage capacity and bandwidth based on dynamic application demand. However, the added network processing…

Modern mainstream persistent key-value storage engines utilize Log-Structured Merge tree (LSM-tree) based designs, optimizing read/write performance by leveraging sequential disk I/O. However, the advent of SSDs, with their significant…

LSM-based key-value (KV) stores are an important component in modern data infrastructures. However, they suffer from high tail latency, in the order of several seconds, making them less attractive for user-facing applications. In this…

Log-Structured Merge trees (LSM trees) are increasingly used as part of the storage engine behind several data systems, and are frequently deployed in the cloud. As the number of applications relying on LSM-based storage backends increases,…

In recent years, emerging storage hardware technologies have focused on divergent goals: better performance or lower cost-per-bit. Correspondingly, data systems that employ these technologies are typically optimized either to be fast (but…

Document database systems store self-describing semi-structured records, such as JSON, "as-is" without requiring the users to pre-define a schema. This provides users with the flexibility to change the structure of incoming records without…

In kernel-centric operations, the uprobe component of eBPF frequently encounters performance bottlenecks, largely attributed to the overheads borne by context switches. Transitioning eBPF operations to user space bypasses these hindrances,…

Memory performance is often the main bottleneck in modern computing systems. In recent years, researchers have attempted to scale the memory wall by leveraging new technology such as CXL, HBM, and in- and near-memory processing. Developers…

In-memory key-value stores (IMKVSes) serve many online applications because of their efficiency. To support data backup, popular industrial IMKVSes periodically take a point-in-time snapshot of the in-memory data with the system call fork.…

This paper studies the design of B-tree that can take full advantage of modern storage hardware with built-in transparent compression. Recent years have witnessed significant interest in applying log-structured merge tree (LSM-tree) as an…

Disaggregated storage systems improve resource utilization and enable independent scaling of storage and compute resources by separating storage resources from computing resources in data centers. NVMe over fabrics (NVMeoF) is a key…