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 to improve unsatisfied Presto latency.
Slides from our latest talks
In this talk, we will show you how to leverage any public cloud (AWS, Google Cloud Platform, or Microsoft Azure) to scale analytics workloads directly on on-prem data without copying and synchronizing the data into the cloud.
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.
Alluxio (alluxio.io) is an open-source data orchestration system that provides a single namespace federating multiple external distributed storage systems. It is critical for Alluxio to be able to store and serve the metadata of all files and directories from all mounted external storage both at scale and at speed.
This talk shares our design, implementation, and optimization of Alluxio metadata service (master node) to address the scalability challenges.
Ideally, Presto would access data independently from how the data was originally stored or managed. Alluxio, as a data orchestration layer provides the physical data independence, for Presto to interact with the data more efficiently. In addition to caching for IO acceleration, Alluxio also provides a catalog service to abstract the metadata in the Hive Metastore, and transformations to expose the data in compute-optimized way. In this talk, we describe some of the challenges of using Presto with Hive, and introduce Alluxio data orchestration for solving those challenges.
Accessing data to run analytic workloads in Spark across data centers and/or clouds can be challenging. Additionally, network I/O can bottleneck Spark jobs that need to read a large amount of data. A common solution is to deploy an HDFS cluster closer to Spark as a caching layer and manually copy the input data to HDFS first, purging it afterward. But this ETL process can be both time-consuming and also error-prone.
Building distributed systems is no small feat. Software testing is just one of many critical practices that engineers who build these systems need to utilize to ensure the quality and usability of their software. For distributed systems, scaling out testing frameworks to ensure that enterprises who run our in highly distributed environments is a complicated (and expensive task!)
Many organizations are leveraging EMR to run big data analytics on public cloud. However, reading and writing data to S3 directly can result in slow and inconsistent performance. Alluxio is a data orchestration layer for the cloud, and in this use case it caches data for S3, ensuring high and predictable performance as well as reduced network traffic.
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.