Log-Structured Merge (LSM)-based storage engines offer high write throughput by storing the data on storage as immutable files that are opportunistically reorganized through compactions. Immutability of the files and their frequent garbage collection (GC), however, leads to a major performance bottleneck: high write amplification. This is further exacerbated by the device-level GC when LSM engines run on top of ZNS SSDs. While prior research has shown that compaction-aware file placement at the device level can reduce the overall write amplification, we point out that the proposed solutions are tuned exclusively for specific compaction strategies, SSD configurations, and workloads.

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 with incoming entries. In that respect, indexing can be perceived as the process of adding structure to incoming, otherwise unsorted data. Adding structure, however, comes at a cost. Instead of simply appending the incoming entries, we insert them into the index. If the ingestion order matches the indexed attribute order, the ingestion cost is entirely redundant and can be avoided altogether (e.g., via bulk loading in a B+-tree). However, classical tree index designs do not benefit when incoming data comes with an implicit ordering that is close to being sorted, but not fully sorted.In this paper, we study how indexes can exploit near-sortedness. Particularly, we identify sortedness as a resource that can accelerate index ingestion. We propose a new sortedness-aware (SWARE) design paradigm that combines opportunistic bulk loading, index appends, variable node fill and split factors, and an intelligent buffering scheme, to optimize ingestion and read queries in a tree index in the presence of near-sortedness. We apply SWARE to two state-of-the-art search trees (B+-tree and Bϵ-tree), and we demonstrate that their Sortedness-Aware counterparts (SA B+-tree and SA Bϵ-tree) outperform their respective baselines by up to 8.8× (SA B+-tree) and 7.8× (SA Bϵ-tree) for a write-heavy workload in the presence of data sortedness, while offering competitive read performance, leading to overall benefits between 1.3× – 5× for mixed read/write workloads with near-sorted data. Overall, we highlight that SWARE can be applied to other tree-like data structures to accelerate index ingestion and improve their performance in the presence of data sortedness.

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 good utilization of storage space. However, LSM-trees were not originally designed to facilitate efficient reads. Thus, state-of-the-art LSM engines employ numerous optimization techniques to make reads efficient. The goal of this tutorial is to present the fundamental principles of the LSM paradigm along with the various optimization techniques and hybrid designs adopted by LSM engines to accelerate reads.Toward this, we first discuss the basic LSM operations and their access patterns. We then discuss techniques and designs that optimize point and range lookups in LSM-trees: (i) index and (ii) filter data structures, (iii) caching, and (iv) read-friendly data layouts. Next, we present the performance tradeoff between writes and reads, outlining the rich design space of the LSM paradigm and how one can navigate it to improve query performance. We conclude by discussing practical problems and open research challenges. This will be a 1.5-hour tutorial.

Data-intensive applications have fueled the evolution of log-structured merge (LSM) based key-value engines that employ the out-of-place paradigm to support high ingestion rates with low read/write interference. These benefits, however, come at the cost of treating deletes as second-class citizens . A delete operation inserts a tombstone that invalidates older instances of the deleted key. State-of-the-art LSM-engines do not provide guarantees as to how fast a tombstone will propagate to persist the deletion . Further, LSM-engines only support deletion on the sort key. To delete on another attribute (e.g., timestamp), the entire tree is read and re-written, leading to undesired latency spikes and increasing the overall operational cost of a database. Efficient and persistent deletion is key to support: (i) streaming systems operating on a window of data, (ii) privacy with latency guarantees on data deletion, and (iii) en masse cloud deployment of data systems. Further, we document that LSM-based key-value engines perform suboptimally in the presence of deletes in a workload. Tombstone-driven logical deletes, by design, are unable to purge the deleted entries in a timely manner, and retaining the invalidated entries perpetually affects the overall performance of LSM-engines in terms of space amplification, write amplification, and read performance. Moreover, the potentially unbounded latency for persistent deletes brings in critical privacy concerns in light of the data privacy protection regulations, such as the right to be forgotten in EU’s GDPR, the right to delete in California’s CCPA and CPRA, and deletion right in Virginia’s VCDPA. Toward this, we introduce the delete design space for LSM-trees and highlight the performance implications of the different classes of delete operations. To address these challenges, in this article, we build a new key-value storage engine, Lethe + , that uses a very small amount of additional metadata, a set of new delete-aware compaction policies, and a new physical data layout that weaves the sort and the delete key order. We show that Lethe + supports any user-defined threshold for the delete persistence latency offering higher read throughput (1.17× -1.4×) and lower space amplification (2.1× -9.8×), with a modest increase in write amplification (between 4% and 25%) that can be further amortized to less than 1%. In addition, Lethe + supports efficient range deletes on a secondary delete key by dropping entire data pages without sacrificing read performance or employing a costly full tree merge.

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 support high throughput for writes, while their immutable file structure allows for good utilization of disk space. Thus, the log-structured paradigm has been widely adopted in state-of-the-art NoSQL, relational, spatial, and time-series data systems. However, despite their popularity, there is a lack of pedagogical textbook-like material on LSM designs. The goal of this tutorial is to present the fundamental principles of the LSM paradigm along with a digest of optimizations and new designs proposed in recent research and adopted by modern LSM engines. This will serve as introductory material for non-experts, and as a roadmap to cutting-edge LSM results for the LSM-aware researchers and practitioners.

Log-structured merge (LSM) trees are widely used as the storage layer of modern NoSQL data stores, as they offer efficient ingestion performance. To enable competitive read performance and reduce space amplification, LSM-trees re-organize data layout on disk iteratively, through compactions. Compactions are at the heart of every LSM-based storage engine, fundamentally influencing their performance. However, the process of compaction in LSM-engines is often treated as a black-box that is rarely exposed as a tuning knob. In this paper, we demonstrate Compactionary, a dictionary for LSM compactions, that helps to visualize the implications of compactions on performance for different workloads and LSM tunings. Compactionary breaks down the LSM compaction black-box, expressing compactions as an ensemble of four first-order design choices: (i) when to compact, (ii) how to organize the data after compaction, (iii) how much data to compact, and (iv) which data to compact. We configure Compactionary to demonstrate the operational flow of several state-of-the-art LSM compaction strategies and how each strategy affects performance. The participants can (i) customize the workload, (ii) configure the LSM tuning, and (iii) switch between advanced compaction options, to understand individually the impact of the different factors on performance. Further, to engage the interested participants, we extend the demonstration by allowing the participants to create custom hybrid compaction strategies, as well as to configure the settings separately for each strategy in an individual analysis phase. The demo is available at https://disc-projects.bu.edu/compactionary/#interactiveDemo.

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 re-organize data to form a tree with levels of exponentially increasing capacity, through iterative compactions. Compactions fundamentally influence the performance of an LSM-engine in terms of write amplification, write throughput, point and range lookup performance, space amplification, and delete performance. Hence, choosing the appropriate compaction strategy is crucial and, at the same time, hard as the LSM-compaction design space is vast, largely unexplored, and has not been formally defined in the literature. As a result, most LSM-based engines use a fixed compaction strategy, typically hand-picked by an engineer, which decides how and when to compact data. In this paper, we present the design space of LSM-compactions, and evaluate state-of-the-art compaction strategies with respect to key performance metrics. Toward this goal, our first contribution is to introduce a set of four design primitives that can formally define any compaction strategy: (i) the compaction trigger, (ii) the data layout, (iii) the compaction granularity, and (iv) the data movement policy. Together, these primitives can synthesize both existing and completely new compaction strategies. Our second contribution is to experimentally analyze 10 compaction strategies. We present 12 observations and 7 high-level takeaway messages, which show how LSM systems can navigate the compaction design space.

Data-intensive applications fueled the evolution of log structured merge (LSM) based key-value engines that employ the out-of-place paradigm to support high ingestion rates with low read/write interference. These benefits, however, come at the cost of treating deletes as a second-class citizen. A delete inserts a tombstone that invalidates older instances of the deleted key. State-of-the-art LSM engines do not provide guarantees as to how fast a tombstone will propagate to persist the deletion. Further, LSM engines only support deletion on the sort key. To delete on another attribute (e.g., timestamp), the entire tree is read and re-written. We highlight that fast persistent deletion without affecting read performance is key to support: (i) streaming systems operating on a window of data, (ii) privacy with latency guarantees on the right-to-be-forgotten, and (iii) en masse cloud deployment of data systems that makes storage a precious resource. To address these challenges, in this paper, we build a new key-value storage engine, Lethe, that uses a very small amount of additional metadata, a set of new delete-aware compaction policies, and a new physical data layout that weaves the sort and the delete key order. We show that Lethe supports any user-defined threshold for the delete persistence latency offering higher read throughput (1.17-1.4x) and lower space amplification (2.1-9.8x), with a modest increase in write amplification (between 4% and 25%). In addition, Lethe supports efficient range deletes on a secondary delete key by dropping entire data pages without sacrificing read performance nor employing a costly full tree merge.