ALLUXIO COMMUNITY OFFICE HOUR
Kubernetes is widely used to orchestrate computation with improved flexibility and portability for computation in public or hybrid cloud environments across infrastructure providers. However, running data-intensive workloads introduces challenges such as efficiently moving data to compute frameworks, accessing data from multiple or remote clouds, and co-locating data with compute.
Alluxio solves these problems as a new data orchestration layer bridging the gap between data locality with improved performance and data accessibility for analytics workloads in Kubernetes, and enables portability across storage providers.
In this Office Hour:
- Overview of Alluxio and the cloud use case with Spark in Kubernetes
- How to set up Alluxio and Spark to run in Kubernetes
- Open Session for discussion on any topics such as solving the separation of compute and storage problem, and more
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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