The big data stack has evolved over the past few years with an explosion of data frameworks, starting with MapReduce and expanding to Apache Spark and Presto. The approach to managing and storing data has evolved as well, starting from using primarily Hadoop distributed file system (HDFS) to newer, cheaper, and easier technologies like object stores. But the design of most object stores inhibits real-time big data and AI workloads running directly on them.
Vitaliy Baklikov and Dipti Borkar explore a different architecture for analytic workloads, particularly those deployed in cloud environment. Alluxio, an open-source virtual distributed file system, provides a unified data access layer for hybrid and multicloud deployments. Alluxio enables distributed compute engines like Spark or Presto or machine learning frameworks like TensorFlow to transparently access different persistent storage systems (including HDFS, S3, Azure, etc.) while actively leveraging in-memory cache to accelerate data access.
Vitaliy and Dipti dive into how DBS Bank built a modern big data analytics stack, leveraging an object store as persistent storage even for data-intensive workloads, and how it uses Alluxio to orchestrate data locality and data access for Spark workloads. In addition, deploying Alluxio to access data solves many challenges that cloud deployments bring with separated compute and storage.
Complete the form below to access the full overview:
.png)
Presentations
Use Alluxio to Unify Storage Systems in Suning
Suning is one of the leading commercial enterprises in China with two public companies in China and Japan respectively. It uses Alluxio to unify storage systems and manage multiple HDFS clusters.
STRATA DATA CONFERENCE LONDON 2018
JD.com is China’s largest online retailer and its biggest overall retailer, as well as the country’s biggest internet company by revenue. Currently, JD.com’s BDP platform runs more than 400,000 jobs (15+ PB) daily, on a system with more than 15,000 cluster nodes and a total capacity of 210 PB.
Alluxio, formerly Tachyon, is the world’s first system that unifies disparate storage systems at memory speed. In the big data ecosystem, Alluxio lies between computation frameworks or jobs and various kinds of storage systems. Additionally, Alluxio’s memory-centric architecture enables data access orders of magnitude faster than existing solutions.
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 HDFSURLs 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.
Alluxio in MOMO: Accelerating Ad Hoc Analysis
From our friends at MOMO
MOMO, a leading pan-entertainment social platform in China, has deployed Alluxio to accelerate ad-hoc query analytics. In the course of evaluating the best fit for Alluxio in their infrastructure they conducted several performance tests to understand how ad-hoc query analytics behaved in several scenarios. These tests give real-world insight to the performance benefits Alluxio provides. The MOMO findings include:
- With Alluxio, performance was improved 3-5x over the current mode
- Even when initially reading ‘cold’ data Alluxio delivered superior performance in most cases
- Alluxio can effectively scale-out to improve performance as requirements grow