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Andrew Gooding

dblp:185/4303 · DBLP profile ↗
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2ranked-venue papers
0as first author
1since 2021 · last 2023
0009-0009-5848-8356ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 2 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
2 papers
Distributed and cloud data management · 58% Database system architecture and tuning · 42%
Computer architecture, parallel and distributed computing, and storage systems
2 papers
Distributed systems · 55% Storage systems · 23% Cloud and datacenter computing · 16%

Topics — the 7 heaviest of 7, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Distributed and cloud data management
data replication
0.712023
Techniques and Efficiencies from Building a Real-Time DBMS · Proc. VLDB Endow. 2023
Database system architecture and tuning
real-time database
0.712023
Techniques and Efficiencies from Building a Real-Time DBMS · Proc. VLDB Endow. 2023
Distributed systems
consensus
0.712023
Techniques and Efficiencies from Building a Real-Time DBMS · Proc. VLDB Endow. 2023
Storage systems
flash and SSD
0.322023
Techniques and Efficiencies from Building a Real-Time DBMS · Proc. VLDB Endow. 2023
Aerospike: Architecture of a Real-Time Operational DBMS · Proc. VLDB Endow. 2016
Distributed and cloud data management
data partitioning
0.212016
Aerospike: Architecture of a Real-Time Operational DBMS · Proc. VLDB Endow. 2016
Cloud and datacenter computing
cloud deployment
0.212023
Techniques and Efficiencies from Building a Real-Time DBMS · Proc. VLDB Endow. 2023
Memory systems
DRAM
0.112016
Aerospike: Architecture of a Real-Time Operational DBMS · Proc. VLDB Endow. 2016
YearPublicationVenuePosition
2023 Techniques and Efficiencies from Building a Real-Time DBMS
abstract
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.
Andrew Gooding, Sunil Sayyaparaju, Thomas Lopatic, Kevin Porter, Ashish Shinde, B. Narendran
Proc. VLDB Endow.2
2016 Aerospike: Architecture of a Real-Time Operational DBMS
abstract
In this paper, we describe the solutions developed to address key technical challenges encountered while building a distributed database system that can smoothly handle demanding real-time workloads and provide a high level of fault tolerance. Specifically, we describe schemes for the efficient clustering and data partitioning for the automatic scale out of processing across multiple nodes and for optimizing the usage of CPUs, DRAM, SSDs and networks to efficiently scale up performance on one node. The techniques described here were used to develop Aerospike (formerly Citrusleaf), a high performance distributed database system built to handle the needs of today's interactive online services. Most real-time decision systems that use Aerospike require very high scale and need to make decisions within a strict SLA by reading from, and writing to, a database containing billions of data items at a rate of millions of operations per second with sub-millisecond latency. For over five years, Aerospike has been continuously used in over a hundred successful production deployments, as many enterprises have discovered that it can substantially enhance their user experience.
Brian Bulkowski, Wei-Ling Chu, Sunil Sayyaparaju, Andrew Gooding, Rajkumar Iyer, Ashish Shinde, Thomas Lopatic
Proc. VLDB Endow.5