The design of the buffer manager in database management systems (DBMSs) is influenced by the performance characteristics of volatile memory (i.e., DRAM) and non-volatile storage (e.g., SSD). The key design assumptions have been that the data must be migrated to DRAM for the DBMS to operate on it and that storage is orders of magnitude slower than DRAM. But the arrival of new non-volatile memory (NVM) technologies that are nearly as fast as DRAM invalidates these previous assumptions.Researchers have recently designed Hymem, a novel buffer manager for a three-tier storage hierarchy comprising of DRAM, NVM, and SSD. Hymem supports cache-line-grained loading and an NVM-aware data migration policy. While these optimizations improve its throughput, Hymem suffers from two limitations. First, it is a single-threaded buffer manager. Second, it is evaluated on an NVM emulation platform. These limitations constrain the utility of the insights obtained using Hymem.
In this paper, we present Spitfire, a multi-threaded, three-tier buffer manager that is evaluated on Optane Persistent Memory Modules, an NVM technology that is now being shipped by Intel. We introduce a general framework for reasoning about data migration in a multi-tier storage hierarchy. We illustrate the limitations of the optimizations used in Hymem on Optane and then discuss how Spitfire circumvents them. We demonstrate that the data migration policy has to be tailored based on the characteristics of the devices and the workload. Given this, we present a machine learning technique for automatically adapting the policy for an arbitrary workload and storage hierarchy. Our experiments show that Spitfire works well across different workloads and storage hierarchies.
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Exploration of memory hybridization for RDD caching in Spark
Apache Spark is a popular cluster computing framework for iterative analytics workloads due to its use of Resilient Distributed Datasets (RDDs) to cache data for in-memory processing. We have revealed that the performance of Spark RDD cache can be severely limited if its capacity falls short to the needs of the workloads. In this paper, we have explored different memory hybridization strategies to leverage emergent Non-Volatile Memory (NVM) devices for Spark's RDD cache. We have found that a simple layered hybridization approach does not offer an effective solution. Therefore, we have designed a flat hybridization scheme to leverage NVM for caching RDD blocks, along with several architectural optimizations such as dynamic memory allocation for block unrolling, asynchronous migration with preemption, and opportunistic eviction to disk. We have performed an extensive set of experiments to evaluate the performance of our proposed flat hybridization strategy and found it to be robust in handling different system and NVM characteristics. Our proposed approach uses DRAM for a fraction of the hybrid memory system and yet manages to keep the increase in execution time to be within 10% on average. Moreover, our opportunistic eviction of blocks to disk improves performance by up to 7.5% when utilized alongside the current mechanism.
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- NSF-PAR ID:
- 10162658
- Date Published:
- Journal Name:
- the 2019 ACM SIGPLAN International Symposium on Memory Management
- Page Range / eLocation ID:
- 41 to 52
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3315573.3329988
