A Survey on NoSQL Stores

Davoudian, Ali; Chen, Liu; Liu, Mengchi

doi:10.1145/3158661

Cited by 166 publications

(121 citation statements)

References 138 publications

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“…Recent surveys on NoSQL-based systems [35,49] derive both, the functional and the non-functional NoSQL and NewSQL features and correlated them with distribution mechanisms (such as sharding, replication, storage management and query processing). However, the implications of Cloud resources or the challenges of Big Data applications were not considered.…”

Section: Database Management Systemsmentioning

confidence: 99%

A survey on data storage and placement methodologies for Cloud-Big Data ecosystem

et al. 2019

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Over the time, the type of applications has evolved from batch, compute or memory intensive applications to streaming or even interactive applications. As a result, applications are getting more complex and become long-running. Such applications might require frequent-access to multiple distributed data sources. During application deployment and provisioning, the user can face various issues such as (i) where to effectively place both the data and the computation; (ii) how to achieve required objectives while reducing the overall application running cost. Data could be generated from various sources, including a multitude of devices over IoT environments that can generate a huge amount of data, while the applications are running. An application can further produce a large amount of data. In general, data of such size is usually referred to as Big Data. In general, Big Data is characterised by five properties [1, 2]. These are volume, velocity (means rapid update and propagation of data), variety

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Section: Database Management Systemsmentioning

confidence: 99%

A survey on data storage and placement methodologies for Cloud-Big Data ecosystem

et al. 2019

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“…These systems constitute a specific class of NoSQL databases, that is, databases that do not rely on the relational data model and (consequently) cannot be queried by means of the SQL query language. The interested reader can refer to Reference [26] for a recent survey on the current scenario concerning NoSQL databases.…”

Section: Basic Concepts On Nosql Databases For Json Documentsmentioning

confidence: 99%

Blind Queries Applied to JSON Document Stores

Marrara

Pelucchi²,

Psaila

2019

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Social Media, Web Portals and, in general, information systems offer their own Application Programming Interfaces (APIs), used to provide large data sets concerning every aspect of day-by-day life. APIs usually provide data sets as collections of JSON documents. The heterogeneous structure of JSON documents returned by different APIs constitutes a barrier to effectively query and analyze these data sets. The adoption of NoSQL document stores, such as MongoDB, is useful for gathering these data sets, but does not solve the problem of querying the final heterogeneous repository. The aim of this paper is to provide analysts with a tool, named HammerJDB, that allows for blind querying collections of JSON documents within a NoSQL document database. The idea below is that users may know the application domain but it may be that they are not aware of the real structures of the documents stored in the database—the tool for blind querying tries to bridge the gap, by adopting a query rewriting mechanism. This paper is an evolution of a technique for blind querying Open Data portals and of its implementation within the Hammer framework, presented in some previous work. In this paper, we evolve that approach in order to query a NoSQL document database by evolving the Hammer framework into the HammerJDB framework, which is able to work on MongoDB databases. The effectiveness of the new approach is evaluated on a data set (derived from a real-life one), containing job-vacancy ads collected from European job portals.

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“…The NoSQL data stores overcome this problem by limiting operations and taking advantage of horizontal scaling. Nowadays, more than 200 different NoSQL stores exist [5] and they are mainly categorized into keyvalue, wide column, document, and graph stores. In the Key-Value Store (KVS), data are represented as pairs of key and value, where the key is unique.…”

Section: Relevant Workmentioning

confidence: 99%

The design of a distributed key-value store for petascale hot storage in data acquisition systems

et al. 2019

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Data acquisition systems for high energy physics experiments readout terabytes of data per second from a large number of electronic components. They are thus inherently distributed systems and require fast online data selection, otherwise requirements for permanent storage would be enormous. Still, incoming data need to be buffered while waiting for this selection to happen. Each minute of an experiment can produce hundreds of terabytes that cannot be lost before a selection decision is made. In this context, we present the design of DAQDB (Data Acquisition Database) -a distributed key-value store for high-bandwidth, generic data storage in event-driven systems. DAQDB offers not only high-capacity and low-latency buffer for fast data selection, but also opens a new approach in high-bandwidth data acquisition by decoupling the lifetime of the data analysis processes from the changing event rate due to the duty cycle of the data source. This is achievable by the option to extend its capacity even up to hundreds of petabytes to store hours of an experiment's data. Our initial performance evaluation shows that DAQDB is a promising alternative to generic database solutions for the high luminosity upgrades of the The ATLAS experiment at the LHC implements physical event building in a two-step process: fragments are transferred from the detector to the readout system (ROS) via pointto-point optical links connected to FPGA-based PCIe cards. The data are buffered in the ROS and made available for the High-Level Trigger (HLT). The HLT processing unit collects and processes fragments of a given event. A data collection manager (DCM) dynamically distributes the available resources and orchestrates all the data flow process [9].Another LHC experiment, CMS, implements physical event building in a similar strategy, the event builder assembles the fragments in the RAM, and a central entity supervises the allocation of available building resources to the events. The acquisition of all the data, from the detector to the selection of the interesting events are coupled. The DAQ system can buffer data up to 90 seconds before the HLT selection, and it can store only selected events for few days [10].

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A Survey on NoSQL Stores

Cited by 166 publications

References 138 publications

A survey on data storage and placement methodologies for Cloud-Big Data ecosystem

A survey on data storage and placement methodologies for Cloud-Big Data ecosystem

Blind Queries Applied to JSON Document Stores

The design of a distributed key-value store for petascale hot storage in data acquisition systems

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