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Industry Panel on Defining Industry Standards for Benchmarking Artificial Intelligence Read chapter Read first chapter

2019 | OriginalPaper | Chapter

A Comparison of Two Cache Augmented SQL Architectures

Authors : Shahram Ghandeharizadeh, Hieu Nguyen

Published in: Performance Evaluation and Benchmarking for the Era of Artificial Intelligence

Publisher: Springer International Publishing

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Abstract

Cloud service providers augment a SQL database management system with a cache to enhance system performance for workloads that exhibit a high read to write ratio. These in-memory caches provide a simple programming interface such as get, put, and delete. Using their software architecture, different caching frameworks can be categorized into Client-Server (CS) and Shared Address Space (SAS) systems. Example CS caches are memcached and Redis. Example SAS caches are Java Cache standard and its Google Guava implementation, Terracotta BigMemory and KOSAR. How do CS and SAS architectures compare with one another and what are their tradeoffs? This study quantifies an answer using BG, a benchmark for interactive social networking actions. In general, obtained results show SAS provides a higher performance with write policies playing an important role.

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Footnotes
1
A key difference is that the write is manipulating key-value pair instead of a disk/SSD block.
 
2
A physical server may host both the Application and memcached processes. We use such a deployment to evaluate CS architecture in Sect. 5.
 
3
While memcached is multithreaded, launching multiple instances reduces contention for its synchronization primitive on its LRU queue. In our experiments with a 16 core processor, we have observed a 20% enhancement in throughput when launching eight instances instead of one.
 
4
Evaluation and comparison of a hybrid with CS and SAS is deferred to future work, see Sect. 6.
 
5
At the time of this writing, Google Guava is not distributed and could not be used for this evaluation. Apache Ignite is a candidate for future analysis, see Sect. 6.
 
6
Redis is the other obvious alternative that we intend to include in our future studies, see Sect. 6.
 
7
The version of BG described in [7] partitions a social graph into N logical subgraphs and assigns each to a BGClient for request generation. It fails to evaluate the SAS architecture objectively because its request generation results in no read-write and write-write conflicts between different BGClients, i.e., emulated application servers of Fig. 1.
 
8
Release and notify are performed with 1 message.
 
9
With 8 nodes, the average application server utilization is lower than 30%.
 
10
Similar results is reported by other systems that partition data [16, 23].
 
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Metadata
Title
A Comparison of Two Cache Augmented SQL Architectures
Authors
Shahram Ghandeharizadeh
Hieu Nguyen
Copyright Year
2019
Book
Performance Evaluation and Benchmarking for the Era of Artificial Intelligence

Print ISBN: 978-3-030-11403-9

Electronic ISBN: 978-3-030-11404-6

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-11404-6_8