Modern storage engines and key-value stores have come to rely on the log-structured
merge-tree (LSM-tree) as their core data structure. LSM-tree operates by gradually merge …

Log-structured merge (LSM) trees have emerged as one of the most commonly used storage-
based data structures in modern data systems as they offer high throughput for writes and …

Log-structured merge (LSM) trees have emerged as one of the most commonly used disk-
based data structures in modern data systems. LSM-trees employ out-of-place ingestion to …

Abstract Computational Storage Devices (CSDs), also known as In-Storage Compute or In-
Suit Processing, offer higher computing power than traditional storage devices (SD). The …

LSM-trees are widely adopted as the storage backend of key-value stores. However,
optimizing the system performance under dynamic workloads has not been sufficiently …

We use machine learning to optimize LSM-tree structure, aiming to reduce the cost of
processing various read/write operations. We introduce a new approach CAMAL, which …

Mainstream LSM-tree-based key-value stores face challenges in optimizing performance for
point lookup, range lookup, and update operations concurrently due to their constrained …

Persistent key-value (KV) stores are widely used by cloud services at ByteDance as local
storage engines, and RocksDB used to be the de facto implementation since it can be …

The Log Structured Merge Trees (LSM-tree) based key-value stores are widely used in
many storage systems to support a variety of operations such as updates, point reads, and …

Indexing in modern data systems facilitates efficient query processing when the selection
predicate is on an indexed key. As new data is ingested, indexes are gradually populated …