V. Srinivasan, Andrew Gooding, Sunil Sayyaparaju, Thomas Lopatic, Kevin Porter, Ashish Shinde, B. Narendran
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We describe how using efficient algorithms to optimize every area of the DBMS helps the system achieve these stringent requirements. Specifically, we describe, effective ways to shard, place and locate data across a set of nodes, efficient identification of cluster membership and cluster changes, efficiencies generated by using a 'smart' client, how to effectively use replications with two copies replication instead of three-copy, how to reduce the cost of the realtime data footprint by combining the use of memory with flash storage, self-managing clusters for ease of operation including elastic scaling, networking and CPU optimizations including NUMA pinning with multi-threading. 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Techniques and Efficiencies from Building a Real-Time DBMS
This paper describes a variety of techniques from over a decade of developing Aerospike (formerly Citrusleaf), a real-time DBMS that is being used in some of the world's largest mission-critical systems that require the highest levels of performance and availability. Such mission-critical systems have many requirements including the ability to make decisions within a strict real-time SLA (milliseconds) with no downtime, predictable performance so that the first and billionth customer gets the same experience, ability to scale up 10X (or even 100X) with no downtime, support strong consistency for applications that need it, synchronous and asynchronous replication with global transactional capabilities, and the ability to deploy in any public and private cloud environments. We describe how using efficient algorithms to optimize every area of the DBMS helps the system achieve these stringent requirements. Specifically, we describe, effective ways to shard, place and locate data across a set of nodes, efficient identification of cluster membership and cluster changes, efficiencies generated by using a 'smart' client, how to effectively use replications with two copies replication instead of three-copy, how to reduce the cost of the realtime data footprint by combining the use of memory with flash storage, self-managing clusters for ease of operation including elastic scaling, networking and CPU optimizations including NUMA pinning with multi-threading. The techniques and efficiencies described here have enabled hundreds of deployments to grow by many orders of magnitude with near complete uptime.
期刊介绍:
The Proceedings of the VLDB (PVLDB) welcomes original research papers on a broad range of research topics related to all aspects of data management, where systems issues play a significant role, such as data management system technology and information management infrastructures, including their very large scale of experimentation, novel architectures, and demanding applications as well as their underpinning theory. The scope of a submission for PVLDB is also described by the subject areas given below. Moreover, the scope of PVLDB is restricted to scientific areas that are covered by the combined expertise on the submission’s topic of the journal’s editorial board. Finally, the submission’s contributions should build on work already published in data management outlets, e.g., PVLDB, VLDBJ, ACM SIGMOD, IEEE ICDE, EDBT, ACM TODS, IEEE TKDE, and go beyond a syntactic citation.