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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, the problem of performance tuning of LSM trees receives increasing attention. We consider bothnominaltunings—where workload and execution environment are accurately known a priori—androbusttunings—which consideruncertaintyin the workload knowledge. This type of workload uncertainty is common in modern applications, notably in shared infrastructure environments like the public cloud. To address this problem, we introduceEndure, a new paradigm for tuning LSM trees in the presence of workload uncertainty. Specifically, we focus on the impact of the choice of compaction policy, size ratio, and memory allocation on the overall performance.Endureconsiders a robust formulation of the throughput maximization problem and recommends a tuning that offers near-optimal throughput when the executed workload is not the same, instead in aneighborhoodof the expected workload. Additionally, we explore the robustness of flexible LSM designs by proposing a new unified design called K-LSM that encompasses existing designs. We deploy our robust tuning system,Endure, on a state-of-the-art key-value store, RocksDB, and demonstrate throughput improvements of up to 5$$\times $$$\times$in the presence of uncertainty. Our results indicate that the tunings obtained byEndureare more robust than tunings obtained under our expanded LSM design space. This indicates that robustness may not be inherent to a design, instead, it is an outcome of a tuning process that explicitly accounts for uncertainty.

 

A key design decision for data systems is whether they follow the row-store or the column-store paradigm. The former supports transactional workloads, while the latter is better for analytical queries. This decision has a significant impact on the entire data system architecture. The multiple-decadelong journey of these two designs has led to a new family of hybrid transactional/analytical processing (HTAP) architectures. Several efforts have been proposed to reap the benefits of both worlds by proposing systems that maintain multiple copies of data (in different physical layouts) and convert them into the desired layout as required. Due to data duplication, the additional necessary bookkeeping, and the cost of converting data between different layouts, these systems compromise between efficient analytics and data freshness. We depart from existing designs by proposing a radically new approach. We ask the question: “What if we could access any layout and ship only the relevant data through the memory hierarchy by transparently converting rows to (arbitrary groups of) columns?” To achieve this functionality, we capitalize on the reinvigorated trend of hardware specialization (that has been accelerated due to the tapering of Moore's law) to propose Relational Fabric, a near-data vertical partitioner that allows memory or storage components to perform on-the-fly transparent data transformation. By exposing an intuitive API, Relational Fabric pushes vertical partitioning to the hardware, which profoundly impacts the process of designing and building data systems. (A) There is no need for data duplication and layout conversion, making HTAP systems viable using a single layout. (B) It simplifies the memory and storage manager that needs to maintain and update a single data layout. (C) It reduces unnecessary data movement through the memory hierarchy, allowing for better hardware utilization and, ultimately, better performance. In this paper, we present Relational Fabric for both memory and storage. We present our initial results on Relational Fabric for in-memory systems and discuss the challenges of building this hardware and the opportunities it brings for simplicity and innovation in the data system software stack, including physical design, query optimization, query evaluation, and concurrency control. 

This paper outlines the vision for a new type of software-shaped platforms, or SOSH platforms for short, that can be implemented in commercial CPU+FPGA platforms. At the core of the SOSH paradigm is the idea of exposing direct control over the flow of data exchanged between hardware components in embedded Systemon-Chips (SoC). Data flow manipulation primitives are synthesized in reprogrammable hardware and interposed between central processors, memory modules, and I/O devices. A new layer of system software is then introduced to leverage such primitives and to achieve fine-grained control and introspection over the interaction of SoC resources. By turning memory and I/O data flows into manageable entities, a new degree of internal awareness can be achieved in complex systems. We first review recent works that are well aligned with the concept of data flow manipulation primitives that can be deployed in SOSH platforms. Next, we outline future research avenues concerning the use of the SOSH paradigm for workload profiling and prediction, to implement advanced memory models, and to perform security threat identification and mitigation. 

A key design decision for data systems is whether they follow the row-store or the column-store paradigm. The former supports transactional workloads, while the latter is better for analytical queries. This decision has a profound impact on the entire data system architecture. The multiple-decadelong journey of these two designs has led to a new family of hybrid transactional/analytical processing (HTAP) architectures. Several efforts have been proposed to reap the benefits of both worlds by proposing systems that maintain multiple copies of data (in different physical layouts) and convert them into the desired layout as required. Due to data duplication, the additional necessary bookkeeping, and the cost of converting data between different layouts, these systems compromise between efficient analytics and data freshness. We depart from existing designs by proposing a radically new approach. We ask the question: “What if we could access any layout and ship only the relevant data through the memory hierarchy by transparently converting rows to (arbitrary groups of) columns?” To achieve this functionality, we capitalize on the reinvigorated trend of hardware specialization (that has been accelerated due to the tapering of Moore’s law) to propose Relational Fabric, a near-data vertical partitioner that allows memory or storage component to perform on-the-fly transparent data transformation. By exposing an intuitive API, Relational Fabric pushes vertical partitioning to the hardware, which has a profound impact on the process of designing and building data systems. (A) There is no need for data duplication and layout conversion, making HTAP systems viable using a single layout. (B) It simplifies the memory and storage manager that needs to maintain and update a single data layout. (C) It reduces unnecessary data movement through the memory hierarchy allowing for better hardware utilization, and ultimately better performance. In this paper, we present Relational Fabric for both memory and storage. We present our initial results on Relational Fabric for in-memory systems and discuss the challenges of building this hardware, as well as the opportunities it brings for simplicity and innovation in the data system software stack, including physical design, query optimization, query evaluation, and concurrency control. 

Analytical database systems are typically designed to use a column-first data layout to access only the desired fields. On the other hand, storing data row-first works great for accessing, inserting, or updating entire rows. Transforming rows to columns at runtime is expensive, hence, many analytical systems ingest data in row-first form and transform it in the background to columns to facilitate future analytical queries. How will this design change if we can always efficiently access only the desired set of columns? To address this question, we present a radically new approach to data transformation from rows to columns. We build upon recent advancements in embedded platforms with re-programmable logic to design native in-memory access on rows and columns. Our approach, termed Relational Memory (RM), relies on an FPGA-based accelerator that sits between the CPU and main memory and transparently transforms base data to any group of columns with minimal overhead at runtime. This design allows accessing any group of columns as if it already exists in memory. We implement and deploy RM in real hardware, and we show that we can access the desired columns up to 1.63× faster compared to a row-wise layout, while matching the performance of pure columnar access for low projectivity, and outperforming it by up to 2.23× as projectivity (and tuple reconstruction cost) increases. Overall, RM allows the CPU to access the optimal data layout, radically reducing unnecessary data movement without high data transformation costs, thus, simplifying software complexity and physical design, while accelerating query execution. 

Access patterns and cache utilization play a key role in the analyzability of data-intensive applications. In this demo, we re-examine our previous research on software-hardware codesign to push data transformation closer to memory from a real-time perspective. Deployed in modern CPU+FPGA systems, our design enables efficient and cache-friendly access to large data by only moving relevant bytes from the target memory. This (1) compresses the cache footprint and (2) reorganizes complex memory access patterns into sequential and predictable patterns. 

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 consider information about the expected workload (e.g., reads vs. writes, point vs. range queries) to optimize their performance via tuning. However, operating in a shared infrastructure like the cloud comes with workload uncertainty due to the fast-evolving nature of modern applications. Systems with static tuning discount the variability of such hybrid workloads and hence provide an inconsistent and overall suboptimal performance. To address this problem, we introduce Endure - a new paradigm for tuning LSM trees in the presence of workload uncertainty. Specifically, we focus on the impact of the choice of compaction policies, size ratio, and memory allocation on the overall performance. Endure considers a robust formulation of the throughput maximization problem and recommends a tuning that maximizes the worst-case throughput over the neighborhood of each expected workload. Additionally, an uncertainty tuning parameter controls the size of this neighborhood, thereby allowing the output tunings to be conservative or optimistic. Through both model-based and extensive experimental evaluations of Endure in the state-of-the-art LSM-based storage engine, RocksDB, we show that the robust tuning methodology consistently outperforms classical tuning strategies. The robust tunings output by Endure lead up to a 5X improvement in throughput in the presence of uncertainty. On the flip side, Endure tunings have negligible performance loss when the observed workload exactly matches the expected one.