Persistent Key-value Stores Research Articles

BonsaiKV: Towards Fast, Scalable, and Persistent Key-Value Stores with Tiered, Heterogeneous Memory System

Emerging NUMA/CXL-based tiered memory systems with heterogeneous memory devices such as DRAM and NVMM deliver ultrafast speed, large capacity, and data persistence all at once, offering great promise to high-performance in-memory key-value stores. To fully unleash the performance potential of such memory systems, this paper presents BonsaiKV, a key-value store that makes the best use of different components in a tiered memory system. The core of BonsaiKV is a tri-layer hierarchical storage architecture that separates data indexing, persistence, and scalability from each other and realizes each of them within a specialized software-hardware layer. We design BonsaiKV with a set of novel techniques, including collaborative tiered indexing, NVMM congestion control mechanisms, fine-grained data striping, and NUMA-aware data management, to leverage hardware strengths and tackle device deficiencies. We compare BonsaiKV with state-of-the-art NVMM-optimized key-value stores and persistent index structures using a variety of YCSB workloads. Evaluation results demonstrate that BonsaiKV outperforms others by up to 7.69×, 19.59×, and 12.86× in read-, write- and scan-intensive scenarios, respectively.

K2vTune: A workload-aware configuration tuning for RocksDB

The explosive growth in data has triggered the emergence of NoSQL databases, which can be used to efficiently deal with unstructured and semi-structured data. RocksDB is a widely used persistent key–value store developed by Facebook to support high-throughput storage environments. It provides flexible configuration knobs that can be tuned to optimize performance. However, optimizing the RocksDB performance for specific workloads is challenging because the knobs have complicated inner relationships and different impacts according to workload type. In addition, RocksDB performance is measured using multiple metrics, such as latency and space amplification, which should be simultaneously considered. To address these issues, we designed a workload-aware configuration tuning framework called K2vTune, which can recognize the configuration knobs of RocksDB according to the workload type and effectively consider multiple performance metrics using our knob2vec method. The knob2vec method learns feature vectors for knobs that can capture both the latent relationships between knobs and workload-specific features to develop an attention neural network for accurate configuration-based performance prediction. K2vTune converts multiple metrics into a single problem and optimizes the problem using a genetic algorithm. We evaluated the proposed method using six different RocksDB workloads and confirmed that K2vTune achieved significant tuning performance improvement compared to baselines, showing 8.83% improvements over the state-of-the-art baseline.

HybridKV: An Efficient Key-Value Store with HybridTree Index Structure Based on Non-Volatile Memory

Non-Volatile Memory (NVM) is a new type of storage media with non-volatile data, higher storage density, better performance and concurrency. Persistent key-value stores designed for earlier storage devices, using Log-Structured Merge Tree (LSM-Tree), have serious read-write amplification problem and do not take full advantage of these new devices. Existing works on NVM index structure are mostly based on Radix-Tree or B+-Tree, index structure based on Radix-Tree has better performance but takes up more space. In this paper, we present a new index structure named HybridTree on NVM. HybridTree combines the characteristics of Radix-Tree and B+-Tree. The upper layer is composed of prefix index nodes similar to Radix-Tree, which is indexed by the key prefix speed up data locating, and providing multi-thread support. The lower layer consists of variable-length adaptive B+-Tree nodes organizing key-value data to reduce space waste caused by node sparseness. We evaluate HybridTree on a real NVM devices (Inter Optane DC Persistent Memory). Evaluation results show that HybridTree’s random write performance is 1.2x to 1.62x compared to Fast & Fair and 1.11x to 1.52x compared to NV-Tree, with 54% space utilization reduced compared to WORT. We further integrate HybridTree into LevelDB to build a high performance key-value store HybridKV. By storing HybridTree directly on NVM, the problem of read and write amplification of LSM-Tree is avoided. We evaluate HybridKV on a hybrid DRAM/NVM systems, according to the results, HybridKV can improve random write performance by 7.5x compared to LevelDB and 3.23x compared to RocksDB. In addition, the random read performance of HybridKV is 7x compared to NoveLSM.

Kreon

Persistent key-value stores have emerged as a main component in the data access path of modern data processing systems. However, they exhibit high CPU and I/O overhead. Nowadays, due to power limitations, it is important to reduce CPU overheads for data processing. In this article, we propose Kreon , a key-value store that targets servers with flash-based storage, where CPU overhead and I/O amplification are more significant bottlenecks compared to I/O randomness. We first observe that two significant sources of overhead in key-value stores are: (a) The use of compaction in Log-Structured Merge-Trees (LSM-Tree) that constantly perform merging and sorting of large data segments and (b) the use of an I/O cache to access devices, which incurs overhead even for data that reside in memory. To avoid these, Kreon performs data movement from level to level by using partial reorganization instead of full data reorganization via the use of a full index per-level. Kreon uses memory-mapped I/O via a custom kernel path to avoid a user-space cache. For a large dataset, Kreon reduces CPU cycles/op by up to 5.8×, reduces I/O amplification for inserts by up to 4.61×, and increases insert ops/s by up to 5.3×, compared to RocksDB.

SFM: Mitigating Read/Write Amplification Problem of LSM-Tree-Based Key-Value Stores

Persistent key-value stores have been widely adopted as storage engines for modern IT infrastructures because they provide high performance with simple design principles. Moreover, many key-value stores commonly employ LSM-tree as their index structure due to its attractive features such as high write throughput and storage space efficiency. Unfortunately, LSM-tree has critical drawbacks in that it leads to write/read amplification problem. One of the prevalent solutions for remedying the write amplification problem is the tiering merge policy that reduces the number of rewrites by delaying merge operations. However, in spite of this advantage, the tiering merge policy may lead to a side-effect that induces high read amplification, increasing search/scan cost for upcoming read operations. In this paper, we concentrate on mitigating the high read amplification problem of the tiering merge policy, while maintaining its low write amplification. To achieve this, we propose a novel LSM-tree scheme, called Spatially Fragmented LSM-tree (SFM), which delays merge operations only for the non-read-intensive key-spaces. For this, SFM identifies the read intensity of each key-spaces by dynamically estimating their read/write hotness. We have implemented SFM based on PebblesDB and evaluated the performance benefits of our scheme under real-world workloads of Facebook. Experimental results clearly show that our scheme improves throughput by up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.67\times $ </tex-math></inline-formula> compared with the conventional schemes while maintaining low write amplification, and also indicate that its latency is lowered by up to 41.41% on average by mitigating the read amplification problem of the existing schemes, by up to 43.68%.

Implementing efficient data compression and encryption in a persistent key-value store for HPC

Recently, persistent data structures, like key-value stores (KVSs), which are stored in a high-performance computing (HPC) system’s nonvolatile memory, provide an attractive solution for a number of emerging challenges like limited I/O performance. Data compression and encryption are two well-known techniques for improving several properties of such data-oriented systems. This article investigates how to efficiently integrate data compression and encryption into persistent KVSs for HPC with the ultimate goal of hiding their costs and complexity in terms of performance and ease of use. Our compression technique exploits deep memory hierarchy in an HPC system to achieve both storage reduction and performance improvement. Our encryption technique provides a practical level of security and enables sharing of sensitive data securely in complex scientific workflows with nearly imperceptible cost. We implement the proposed techniques on top of a distributed embedded KVS to evaluate the benefits and costs of incorporating these capabilities along different points in the dataflow path, illustrating differences in effective bandwidth, latency, and additional computational expense on Swiss National Supercomputing Centre’s Grand Tavé and National Energy Research Scientific Computing Center’s Cori.

Prospect of Introducing a New Solution - MongoDB

There are many awesome features of MongoDB that have made MongoDB so perspective. Document-oriented databases share a lot in common with relational databases. Therefore, rather than tiptoeing around it, lets simply state that MongoDB should be seen as a direct alternative to relational databases. Where one might see Lucene as enhancing a relational database with full text indexing, or Redis as a persistent key-value store, MongoDB is a central repository for our data. I didnt call MongoDB a replacement for relational databases, but rather an alternative. Its a tool that can do what a lot of other tools can do. Some of it MongoDB does better, some of it MongoDB does worse. MongoDB might fit into your existing system. There are enough new and competing storage technologies that its easy to get overwhelmed by all of the choices.

Zing Database: high-performance key-value store for large-scale storage service

Nowadays, key-value stores play an important role in cloud storage services and large-scale high-performance applications. This paper proposes a new approach in design key-value store and presents Zing Database (ZDB) which is a high-performance persistent key-value store designed for optimizing reading and writing operations. This key-value store supports sequential write, single disk seek random write and single disk seek for read operations. Key contributions of this paper are the principles in architecture, design and implementation of a high-performance persistent key-value store. This is achieved using a data file structure organized as commit log storage where every new data are appended to the end of the data file. An in-memory index is used for supporting random reading in the commit log. ZDB architecture optimizes the index of key-value store for auto-incremental integer keys which can be applied in storing many real-life data efficiently with minimize memory overhead and reduce the complexity for partitioning data.

Mucura: your personal file repository in the cloud

Large-scale distributed data processing platforms for scientific research such as the LHC computing grid include services for transporting, storing and processing massive amounts of data. They often address the data processing needs of a virtual organization but lack the convenience and flexibility required by individual users for their personal data storage needs. This paper presents the motivation, design and implementation status of Mucura, an open source software system for operating multi-tenant cloud-based storage services. The system is specifically intended for building file repositories for individual users, such as those of the scientific research communities, who use distributed computing infrastructures for processing and sharing data. It exposes the Amazon S3-compatible interface, supports both interactive and batch usage and is compatible with the X509 certificates-based authentication mechanism used by grid infrastructures. The system builds on top of distributed persistent key-value stores for storing user's data.

Exabyte Scale Storage at CERN

The future of data management for LHC at CERN brings new requirements for scalability and a change of scheduling and data handling compared to the HSM mass storage system in use today. A forecast for disk based storage volume at CERN in 2015 is on the Exabyte scale with hundreds of millions of files.A new CERN storage architecture is represented as a storage cluster with an analysis, archive and tape pool with container based data movements and decoupled namespaces.Main assets of a new system is high-availability and life cycle management for large storage installations. Today this is one of the major issues at the CERN computer centre with more than 1,000 disk servers and continuous hardware replacement. Another key point is distributed meta data handling with in-memory caching and persistent key-value stores to reduce latencies and operational complexity.Focus of this paper will be on the analysis pool implementation providing low-latency, nonsequential file access and a hierarchical namespace. A summary of performance indicators and first operational experiences will be reported.

FlashStore

We present FlashStore, a high throughput persistent key-value store, that uses flash memory as a non-volatile cache between RAM and hard disk. FlashStore is designed to store the working set of key-value pairs on flash and use one flash read per key lookup. As the working set changes over time, space is made for the current working set by destaging recently unused key-value pairs to hard disk and recycling pages in the flash store. FlashStore organizes key-value pairs in a log-structure on flash to exploit faster sequential write performance. It uses an in-memory hash table to index them, with hash collisions resolved by a variant of cuckoo hashing. The in-memory hash table stores compact key signatures instead of full keys so as to strike tradeoffs between RAM usage and false flash read operations. FlashStore can be used as a high throughput persistent key-value storage layer for a broad range of server class applications. We compare FlashStore with BerkeleyDB, an embedded key-value store application, running on hard disk and flash separately, so as to bring out the performance gain of FlashStore in not only using flash as a cache above hard disk but also in its use of flash aware algorithms. We use real-world data traces from two data center applications, namely, Xbox LIVE Primetime online multi-player game and inline storage deduplication, to drive and evaluate the design of FlashStore on traditional and low power server platforms. FlashStore outperforms BerkeleyDB by up to 60x on throughput (ops/sec), up to 50x on energy efficiency (ops/Joule), and up to 85x on cost efficiency (ops/sec/dollar) on the evaluated datasets.