Feasibility analysis of AsterixDB and Spark streaming with Cassandra for stream-based processing

Pekka Pääkkönen (Corresponding Author)

Research output: Contribution to journalArticleScientificpeer-review

4 Citations (Scopus)

Abstract

For getting up-to-date insight into online services, extracted data has to be processed in near real time. For example, major big data companies (Facebook, LinkedIn, Twitter) analyse streaming data for development of new services. Several technologies have been developed, which could be selected for implementation of stream processing functionalities. The contribution of this paper is feasibility analysis of technologies for stream-based processing of semi-structured data. Particularly, feasibility of a Big Data management system for semi-structured data (AsterixDB) will be compared to Spark streaming, which has been integrated with Cassandra NoSQL database for persistence. The study focuses on stream processing in a simulated social media use case (tweet analysis), which has been implemented to Eucalyptus cloud computing environment on a distributed shared memory multiprocessor platform. The results indicate that AsterixDB is able to provide significantly better performance both in terms of throughput and latency, when data feed functionality of AsterixDB is used, and stream processing has been implemented with Java. AsterixDB also scaled on the same level or better, when the amount of nodes on the cloud platform was increased. However, stream processing in AsterixDB was delayed by batching of data, when tweets were streamed into the database with data feeds.
Original languageEnglish
Number of pages25
JournalJournal of Big Data
Volume3
Issue number6
DOIs
Publication statusPublished - 2016
MoE publication typeA1 Journal article-refereed

Fingerprint

Electric sparks
Processing
Cloud computing
Information management
Throughput
Feasibility analysis
Data storage equipment
Industry
Big data
Semistructured data
Data base
Functionality

Keywords

  • sentiment
  • tweet
  • word count
  • AsterixDB
  • Spark
  • performance
  • Eucalyptus
  • Cassandra

Cite this

@article{c5606f0163b14714a30c3830a5819277,
title = "Feasibility analysis of AsterixDB and Spark streaming with Cassandra for stream-based processing",
abstract = "For getting up-to-date insight into online services, extracted data has to be processed in near real time. For example, major big data companies (Facebook, LinkedIn, Twitter) analyse streaming data for development of new services. Several technologies have been developed, which could be selected for implementation of stream processing functionalities. The contribution of this paper is feasibility analysis of technologies for stream-based processing of semi-structured data. Particularly, feasibility of a Big Data management system for semi-structured data (AsterixDB) will be compared to Spark streaming, which has been integrated with Cassandra NoSQL database for persistence. The study focuses on stream processing in a simulated social media use case (tweet analysis), which has been implemented to Eucalyptus cloud computing environment on a distributed shared memory multiprocessor platform. The results indicate that AsterixDB is able to provide significantly better performance both in terms of throughput and latency, when data feed functionality of AsterixDB is used, and stream processing has been implemented with Java. AsterixDB also scaled on the same level or better, when the amount of nodes on the cloud platform was increased. However, stream processing in AsterixDB was delayed by batching of data, when tweets were streamed into the database with data feeds.",
keywords = "sentiment, tweet, word count, AsterixDB, Spark, performance, Eucalyptus, Cassandra",
author = "Pekka P{\"a}{\"a}kk{\"o}nen",
note = "Project code: 101215",
year = "2016",
doi = "10.1186/s40537-016-0041-8",
language = "English",
volume = "3",
journal = "Journal of Big Data",
issn = "2196-1115",
publisher = "Springer",
number = "6",

}

Feasibility analysis of AsterixDB and Spark streaming with Cassandra for stream-based processing. / Pääkkönen, Pekka (Corresponding Author).

In: Journal of Big Data, Vol. 3, No. 6, 2016.

Research output: Contribution to journalArticleScientificpeer-review

TY - JOUR

T1 - Feasibility analysis of AsterixDB and Spark streaming with Cassandra for stream-based processing

AU - Pääkkönen, Pekka

N1 - Project code: 101215

PY - 2016

Y1 - 2016

N2 - For getting up-to-date insight into online services, extracted data has to be processed in near real time. For example, major big data companies (Facebook, LinkedIn, Twitter) analyse streaming data for development of new services. Several technologies have been developed, which could be selected for implementation of stream processing functionalities. The contribution of this paper is feasibility analysis of technologies for stream-based processing of semi-structured data. Particularly, feasibility of a Big Data management system for semi-structured data (AsterixDB) will be compared to Spark streaming, which has been integrated with Cassandra NoSQL database for persistence. The study focuses on stream processing in a simulated social media use case (tweet analysis), which has been implemented to Eucalyptus cloud computing environment on a distributed shared memory multiprocessor platform. The results indicate that AsterixDB is able to provide significantly better performance both in terms of throughput and latency, when data feed functionality of AsterixDB is used, and stream processing has been implemented with Java. AsterixDB also scaled on the same level or better, when the amount of nodes on the cloud platform was increased. However, stream processing in AsterixDB was delayed by batching of data, when tweets were streamed into the database with data feeds.

AB - For getting up-to-date insight into online services, extracted data has to be processed in near real time. For example, major big data companies (Facebook, LinkedIn, Twitter) analyse streaming data for development of new services. Several technologies have been developed, which could be selected for implementation of stream processing functionalities. The contribution of this paper is feasibility analysis of technologies for stream-based processing of semi-structured data. Particularly, feasibility of a Big Data management system for semi-structured data (AsterixDB) will be compared to Spark streaming, which has been integrated with Cassandra NoSQL database for persistence. The study focuses on stream processing in a simulated social media use case (tweet analysis), which has been implemented to Eucalyptus cloud computing environment on a distributed shared memory multiprocessor platform. The results indicate that AsterixDB is able to provide significantly better performance both in terms of throughput and latency, when data feed functionality of AsterixDB is used, and stream processing has been implemented with Java. AsterixDB also scaled on the same level or better, when the amount of nodes on the cloud platform was increased. However, stream processing in AsterixDB was delayed by batching of data, when tweets were streamed into the database with data feeds.

KW - sentiment

KW - tweet

KW - word count

KW - AsterixDB

KW - Spark

KW - performance

KW - Eucalyptus

KW - Cassandra

U2 - 10.1186/s40537-016-0041-8

DO - 10.1186/s40537-016-0041-8

M3 - Article

VL - 3

JO - Journal of Big Data

JF - Journal of Big Data

SN - 2196-1115

IS - 6

ER -