After the introduction of Persistent Memory in the form of Intel’s Optane DC Persistent Memory on the market in 2019, it has found its way into manifold applications and systems. As Google and other cloud infrastructure providers are starting to incorporate Persistent Memory into their portfolio, it is only logical that cloud applications have to exploit its inherent properties. Persistent Memory can serve as a DRAM substitute, but guarantees persistence at the cost of compromised read/write performance compared to standard DRAM. These properties particularly affect the performance of index structures, since they are subject to frequent updates and queries. However, adapting each and every index structure to exploit the properties of Persistent Memory is tedious. Hence, we require a general technique that hides this access gap, e.g., by using DRAM caching strategies. To exploit Persistent Memory properties for analytical index structures, we propose selective caching. It is based on a mixture of dynamic and static caching of tree nodes in DRAM to reach near-DRAM access speeds for index structures. In this paper, we evaluate selective caching on the OLAP-optimized main-memory index structure Elf, because its memory layout allows for an easy caching. Our experiments show that if configured well, selective caching with a suitable replacement strategy can keep pace with pure DRAM storage of Elf while guaranteeing persistence. These results are also reflected when selective caching is used for parallel workloads.

在2019年以英特尔的Optane DC永久存储器的形式推出永久存储器后，它已进入多种应用程序和系统。随着Google和其他云基础架构提供商开始将持久性内存整合到他们的产品组合中，云应用程序必须利用其固有属性是合乎逻辑的。持久性存储器可以用作DRAM的替代品，但与标准DRAM相比，以牺牲读写性能为代价来保证持久性。这些属性尤其会影响索引结构的性能，因为它们经常进行更新和查询。但是，调整每个索引结构以利用持久性存储器的属性是很繁琐的。因此，我们需要一种隐藏此访问间隙的通用技术，例如，通过使用DRAM缓存策略。为了利用持久内存属性来分析索引结构，我们提出选择性缓存。它基于DRAM中树节点的动态和静态缓存的混合，以达到索引结构的接近DRAM的访问速度。在本文中，我们评估对OLAP优化的主内存索引结构Elf的选择性缓存，因为它的内存布局允许轻松缓存。我们的实验表明，如果配置正确，则使用合适的替换策略进行选择性缓存可以与Elf的纯DRAM存储保持同步，同时保证持久性。当选择性缓存被用于并行工作负荷这些结果也反映。