Xavier R. Guerin

HydraDB: a resilient RDMA-driven key-value middleware for in-memory cluster computing

In this paper, we describe our experiences and lessons learned from building a general-purpose in-memory key-value middleware, called HydraDB. HydraDB synthesizes a collection of state-of-the-art techniques, including continuous fault-tolerance, Remote Direct Memory Access (RDMA), as well as awareness for multicore systems, etc, to deliver a high-throughput, low-latency access service in a reliable manner for cluster computing applications. The uniqueness of HydraDB mainly lies in its design commitment to fully exploit the RDMA protocol to comprehensively optimize various aspects of a general-purpose key-value store, including latency-critical operations, read enhancement, and data replications for high-availability service, etc. At the same time, HydraDB strives to efficiently utilize multicore systems to prevent data manipulation on the servers from curbing the potential of RDMA. Many teams in our organization have adopted HydraDB to improve the execution of their cluster computing frameworks, including Hadoop, Spark, Sensemaking analytics, and Call Record Processing. In addition, our performance evaluation with a variety of YCSB workloads also shows that HydraDB can substantially outperform several existing in-memory key-value stores by an order of magnitude. Our detailed performance evaluation further corroborates our design choices.

Toward a scale-out data-management middleware for low-latency enterprise computing

Emerging transactional workloads from Internet and mobile commerce require low-latency, massive-scale, and integrated data analytics to enhance user experience and to improve up-selling opportunities. These analytics require new application platforms that must be able to absorb large volumes of data, provide low-latency access to the data, and cache data objects to improve access times in distributed environments. This paper reports on recent technologies built at IBM Research to address challenges in data access latency, data ingestion, and caching in the exemplary context of an online product recommendation application. We describe three technologies related to the issues and optimizations of key-value data object store and access. First, we describe the architecture of a global secondary index to greatly improve data access latency of Hadoop™ Database (HBase™), an open-source key-value distributed data store. Second, we present an in-memory write-ahead log feature on HBase that significantly improves write operations for high-volume data ingestion. Third, we detail an innovative distributed caching system that exploits low-latency interconnects to use hash maps of data keys on each server for local lookup, while data resides and are accessed across clustered systems. The distributed cache can achieve a 100-to 1,000-fold performance gain over many caching methods. These technologies together form some necessary building blocks for a next-generation data-centric middleware for integrated transaction and analytic workloads.

Evaluation of Multi-core Scalability Bottlenecks in Enterprise Java Workloads

The increasing number of cores integrated into modern processors is blurring the line between supercomputers and enterprise-grade servers. Therefore, the same attention to lock contention bottlenecks must be given to Java-based business workloads as it is given to massively parallel, high-performance computing applications, especially when it comes to characterizing global trends that would ease the transition of todays code base to tomorrows parallel configurations. This paper first presents the characteristics of a typical Java-based business application software stack and examines the locking contentions that can appear at each level of that stack. Second, it presents scalability evaluation of three enterprise-grade, Java-based workloads and details the lock contention founds. Third, it summarizes the results of our findings, emphasizing the need for a streamlined methodology for lock-contention analysis of enterprise Java workloads.

Scalability analysis of enterprise javaworkloads on a multi-core system

This paper shows that not only lock contentions, but also adherence issues between two layers of a software stack impact scalability of Java enterprise workloads.