Over the last few years, organizations have worked towards the separation of storage and compute for a number of benefits in the areas of cost, data duplication and data latency. Cloud resolves most of these issues but comes to the expense of needing a way to query data on remote storages. Alluxio and Presto are a powerful combination to address the compute problem, which is part of the strategy used by Simbiose Ventures to create a product called StorageQuery – A platform to query files in cloud storages with SQL.
In this talk, we will describe how we have solved an issue with large S3 API costs incurred by Presto under several usage concurrency levels by implementing Alluxio as a data orchestration layer between S3 and Presto. Also, we will show the results of an experiment with estimating the per-query S3 API costs using the TPC-DS dataset.
In Alluxio, an Under File System is the plugin to connect to any file systems or object stores, so users can mount different storages like AWS S3 or HDFS into Alluxio namespace. This under filesystem is designed to be modular, in order to enable users to easily extend this framework with their own Under File System implementation and connect to a new or customized storage system.
Today, many people run deep learning applications with training data from separate storage such as object storage or remote data centers. This presentation will demo the Intel Analytics Zoo + Alluxio stack, an architecture that enables high performance while keeping cost and resource efficiency balanced without network being I/O bottlenecked.
For many latency-sensitive SQL workloads, Presto is often bound by retrieving distant data. In this talk, Rohit Jain, James Sun from Facebook and Bin Fan from Alluxio will introduce their teams’ collaboration on adding a local on-SSD Alluxio cache inside Presto workers at Facebook to improve queries with unsatisfied latency.
This talk will overview two projects at Electronic Arts (EA) that address the mismatch by data orchestration: One project automatically generates configurations for all components in a large monitoring system, which reduces the daily average number of alerts from ~1000 to ~20. The other project introduces Alluxio for caching and unifying address space across ETL and analytics workloads, which substantially simplifies architecture, improves performance, and reduces ops overheads.
The rise of compute intensive workloads and the adoption of the cloud has driven organizations to adopt a decoupled architecture for modern workloads – one in which compute scales independently from storage. While this enables scaling elasticity, it introduces new problems – how do you co-locate data with compute, how do you unify data across multiple remote clouds, how do you keep storage and I/O service costs down and many more.
This talk will guide the audience on how Alluxio can greatly simplify the data preparation phase in with remote and possibly multiple data sources. We will share the lessons and benchmark from Bill Zhao an engineer led in Apple when building a Machine Learning platform using Tensorflow, NFS, DC/OS and Alluxio.