Cloud, Data, & Orchestration – Austin Meetup
At Bazaarvoice, a software-as-a-service digital marketing company, the data engineering team is tasked to handle data at massive Internet-scale to serve over 1,900 of the biggest internet retailers and brands.
We built our data pipelines all in the cloud using Apache Spark and Hive on AWS EC2 accessing data in S3. AWS enables us to scale “out” the infrastructure capacity effortlessly to keep up with the Internet-scale data and web traffic, but scaling out also exposes certain limitations like the ability to further scale “up”. While this cloud native stack is scalable and elastic we experience performance limitations, because data access is limited by the network bandwidth, and this is exacerbated for workloads that involve repeated queries.
To address the data access challenges, we leverage Alluxio, an open source data orchestration system for analytics in the cloud. Alluxio serves as a transparent caching layer for hot and warm data, such that Hive and Spark jobs are able to access all data transparently in S3. We have seen 10x performance acceleration of Spark and Hive jobs on S3 with Alluxio.
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