An Evaluation of Alternative Physical Graph Data Designs for Processing Interactive Social Networking Actions

Ghandeharizadeh, Shahram; Boghrati, Reihane; Barahmand, Sumita

doi:10.1007/978-3-319-15350-6_2

Cited by 4 publications

(2 citation statements)

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“…We used a social networking benchmark named BG [1,2,6,7] to generate traces of key-value references from a cache augmented database management system [10]. BG emulates members of a social networking site viewing one another's profile, listing their friends, and other interactive actions.…”

Section: Discussionmentioning

confidence: 99%

Camp

Ghandeharizadeh

Irani

Lam

et al. 2014

Proceedings of the 15th International Middleware Conference on - Middleware '14

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Cost Adaptive Multi-queue eviction Policy (CAMP) is an algorithm for a general purpose key-value store (KVS) that manages key-value pairs computed by applications with different access patterns, key-value sizes, and varying costs for each key-value pair. CAMP is an approximation of the Greedy Dual Size (GDS) algorithm in that its eviction policy is as effective as GDS. At the same time, its implementation is as efficient at LRU. Similar to an implementation of LRU using queues, it adapts to changing workload patterns based on the history of requests for different key-value pairs. It is superior to LRU because it considers both the size and cost of key-value pairs to maximize the utility of the available memory across competing applications. We compare CAMP with both LRU and an alternative that requires human intervention to partition memory into pools and assign grouping of key-value pairs to different pools. The results demonstrate CAMP is as fast as LRU while outperforming both LRU and the pooled alternative. We also present results from an implementation of CAMP using Twitter's version of memcached.

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Section: Discussionmentioning

confidence: 99%

Camp

Ghandeharizadeh

Irani

Lam

et al. 2014

Proceedings of the 15th International Middleware Conference on - Middleware '14

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“…We also considered the use of TPC-C/E [8] and an e-commerce benchmark such as RUBiS [6] and RUBBoS [11]. Ultimately, we selected BG because of its emphasis on social networking actions, its ability to quantify both the response time and SoAR of the alternative implementations [3,4,10], and the amount of unpredictable (stale, erroneous, simply wrong) data produced by an implementation. The latter is useful for our future investigation of Hibernate with caches, see Section D.…”

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confidence: 99%

An Evaluation of the Hibernate Object-Relational Mapping for Processing Interactive Social Networking Actions

Ghandeharizadeh

Mutha

2014

Proceedings of the 16th International Conference on Information Integration and Web-Based Applications &Amp; Services

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With object-oriented programming languages, Object Relational Mapping (ORM) frameworks such as Hibernate have gained popularity due to their ease of use and portability to different relational database management systems.Hibernate implements the Java Persistent API, JPA, and frees a developer from authoring software to address the impedance mismatch between objects and relations. In this paper, we evaluate the performance of Hibernate by comparing it with a native JDBC implementation using a benchmark named BG. BG rates the performance of a system for processing interactive social networking actions such as view profile, extend an invitation from one member to another, and other actions. Our key findings are as follows. First, an object-oriented Hibernate implementation of each action issues more SQL queries than its JDBC counterpart. This enables the JDBC implementation to provide response times that are significantly faster. Second, one may use the Hibernate Query Language (HQL) to refine the object-oriented Hibernate implementation to provide performance that approximates the JDBC implementation. A IntroductionHibernate [9] is an object-relational mapping (ORM) framework for the Java programming language. It supports three key features. First, given the persistent class definitions of an application, it generates its relational database schema.Second, it populates instances of a persistent class by issuing SQL queries to the underlying relational database management system (RDBMS) transparently. A persistence class has a primary key and each of its instances correspond to a row of a table in the RDBMS. Third, Hibernate propagates an application's changes 1 to the underlying database * In Proceedings of the 16th ACM International Conference on Information Integration and Web-based Applications and Services, Hanoi, Vietnam, December 2014.1 A change to a persistent instance might be an update to one or more of its property values, deletion or creation of an instance.

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