China Unicom is one of the five largest telecom operators in the world. China Unicom’s booming business in 4G and 5G networks has to serve an exploding base of hundreds of millions of smartphone users. This unprecedented growth brought enormous challenges and new requirements to the data processing infrastructure. The previous generation of its data processing system was based on IBM midrange computers, Oracle databases, and EMC storage devices. This architecture could not scale to process the amounts of data generated by the rapidly expanding number of mobile users. Even after deploying Hadoop and Greenplum database, it was still difficult to cover critical business scenarios with their varying massive data processing requirements. The complicated the architecture of its incumbent computing platform created a lot of new challenges to effectively use resources.
Tag: distributed systems
Haoyuan Li and Cheng Chang explain how Alluxio makes Spark more effective in both on-premises and public cloud deployments and share production deployments of Alluxio and Spark working together. Along the way, they discuss best practices for using Alluxio with Spark, including with RDDs and DataFrames.
Using Alluxio, an open-source memory speed virtual distributed storage system, deployed on Mesos enables connecting any compute framework, such as Apache Spark, to storage systems via a unified namespace. Alluxio enables applications to interact with any data at memory speed. Alluxio can eliminate the pains of ETL and data duplication, and enable new workloads across all data. Adit will discuss the architecture of Mesos, Spark and Alluxio to achieve an optimal architecture for enterprises.
Speed is usually a key factor when analyzing large amounts of data. Alluxio enables analytics applications, such as Apache Spark, to retrieve stored data at memory speeds. DC/OS makes it easy to deploy distributed programs (such as Alluxio and Spark) and containers across large clusters.
In this talk, we will first discuss the development of the DC/OS Alluxio package, which deploys Alluxio on top of DC/OS, and then then demo the deployment a complete analytics stack, both with and without Alluxio, in order to see the benefits Alluxio provides.
In this issue, the Drip Technology Salon and the Alluxio community invited the core engineers of Didi Travel, Alluxio, Kyligence, JD.com, and Tencent to revolve around Alluxio’s position and design philosophy in the big data ecosystem, architectural features, latest developments, and well-known The company’s production-level environmental application exploration and practice, as well as the experience in the use of the process and other topics, and in-depth participants to share.
Using Alluxio, a memory speed virtual distributed storage system, deployed on Mesos enables connecting any compute framework, such as Apache Spark, to storage systems via a unified namespace. Alluxio enables applications to interact with any data at memory speed. Alluxio can eliminate the pains of ETL and data duplication, and enable new workloads across all data. Gene will discuss the architecture of Mesos, Spark and Alluxio to achieve an optimal architecture for enterprises.
Many organizations and deployments use Alluxio with Apache Spark, and some of them scale out to over PB’s of data. Alluxio can enable Spark to be even more effective, in both on-premise deployments and public cloud deployments. Alluxio bridges Spark applications with various storage systems and further accelerates data intensive applications. In this talk, we briefly introduce Alluxio, and present different ways how Alluxio can help Spark jobs. We discuss best practices of using Alluxio with Spark, including RDDs and DataFrames, as well as on-premise deployments and public cloud deployments.
In this talk, we discuss how Alluxio can be deployed and used with a Spark data processing pipeline in the cloud. We show how pipeline stages can share data with Alluxio memory for improved performance benefits, and how Alluxio can improves completion times and reduces performance variability for Spark pipelines in the cloud.
Alluxio has run in JD.com’s production environment on 100 nodes for six months. Mao Baolong, Yiran Wu, and Yupeng Fu explain how JD.com uses Alluxio to provide support for ad hoc and real-time stream computing, using Alluxio-compatible HDFS URLs and Alluxio as a pluggable optimization component. To give just one example, one framework, JDPresto, has seen a 10x performance improvement on average. This work has also extended Alluxio and enhanced the syncing between Alluxio and HDFS for consistency.