内容简介：HSEis an embeddable key-value store designed for SSDs based on NAND flash or persistent memory. HSE optimizes performance and endurance by orchestrating data placement across DRAM and multiple classes of SSDs or other solid-state storage.HSE is ideal for p
HSE: Heterogeneous-Memory Storage Engine
HSEis an embeddable key-value store designed for SSDs based on NAND flash or persistent memory. HSE optimizes performance and endurance by orchestrating data placement across DRAM and multiple classes of SSDs or other solid-state storage.
HSE is ideal for powering NoSQL, Software-Defined Storage (SDS), High-Performance Computing (HPC), Big Data, Internet of Things (IoT), and Artificial Intelligence (AI) solutions.
Key Features
- Standard and advanced key-value operators
- Full transactions with snapshot-isolation spanning multiple independent key-value collections
- Cursors for iterating over snapshot views
- Data model for optimizing mixed use-case workloads in a single data store
- Flexible durability controls
- Configurable data orchestration schemes
- C API library that can be embedded in any application
Benefits
- Scales to terabytes of data and hundreds of billions of keys per store
- Efficiently handles thousands of concurrent operations
- Dramatically improves throughput, latency, write-amplification, and read-amplification versus common alternatives for many workloads
- Optionally combines multiple classes of solid-state storage to optimize performance and endurance
Getting Started
The HSE Wiki contains all the information you need to get started with HSE.
YCSB Performance Results
YCSB (Yahoo!® Cloud Serving Benchmark) is an industry-standard benchmark for databases and storage engines supporting key-value workloads. The following table summarizes several YCSB workload mixes, with application examples taken from the YCSB documentation.
| YCSB Workload | Operations | Application Example |
|---|---|---|
| A | 50% Read; 50% Update | Session store recording user-session activity |
| B | 95% Read; 5% Update | Photo tagging |
| C | 100% Read | User profile cache |
| D | 95% Read; 5% Insert | User status updates |
We integrated HSE with YCSB to make it easy to compare its performance and scalability to that of other storage engines for YCSB workloads. Below are throughput results from running YCSB with HSE.
For comparison, we include results from RocksDB , a popular and widely-deployed key-value store. For these YCSB workloads, HSE delivered up to nearly 6x more throughput than RocksDB.
System configuration details and additional performance results can be found in the YCSB section of the HSE Wiki.
We also integrated HSE with MongoDB® , a popular NoSQL database, to validate its benefits within a real-world storage application. Below are throughput results from running YCSB with MongoDB using HSE (MongoDB/HSE).
For comparison, we include results from MongoDB using the default WiredTiger storage engine (MongoDB/WiredTiger). For these YCSB workloads, MongoDB/HSE delivered up to nearly 8x more throughput than MongoDB/WiredTiger.
System configuration details and additional performance results can be found in the MongoDB section of the HSE Wiki.
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计算机程序设计艺术(第3卷)-排序和查找(英文影印版)
（美）Donald E.Knuth / 清华大学出版社 / 2002-9 / 85.00元
《计算机程序设计艺术排序和查找(第3卷)(第2版)》内容简介：这是对第3卷的头一次修订，不仅是对经典计算机排序和查找技术的最全面介绍，而且还对第1卷中的数据结构处理技术作了进一步的扩充，通盘考虑了将大小型数据库和内外存储器。它遴选了一些经过反复检验的计算机方法，并对其效率做了定量分析。第3卷的突出特点是对“最优排序”一节作了修订，对排列论原理与通用散列法作了全新讨论。一起来看看 《计算机程序设计艺术(第3卷)-排序和查找(英文影印版)》 这本书的介绍吧!
