A Performance Evaluation of Apache Kafka in Support of Big Data Streaming Applications

被引：0

作者：

Le Noac'h, Paul ^{[1
]}

Costan, Alexandru ^{[2
]}

Bouge, Luc ^{[3
]}

机构：

[1] INSA Rennes, Rennes, France

[2] INSA Rennes, INRIA, Rennes, France

[3] ENS Rennes, Rennes, France

来源：

2017 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA) | 2017年

关键词：

Stream computing; Apache Kafka; Big Data;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Stream computing is becoming a more and more popular paradigm as it enables the real-time promise of data analytics. Apache Kafka is currently the most popular framework used to ingest the data streams into the processing platforms. However, how to tune Kafka and how much resources to allocate for it remains a challenge for most users, who now rely mainly on empirical approaches to determine the best parameter settings for their deployments. In this poster, we make a through evaluation of several configurations and performance metrics of Kafka in order to allow users avoid bottlenecks, reach its full potential and avoid bottlenecks and eventually leverage some good practice for efficient stream processing.

引用

页码：4803 / 4806

页数：4