Nowadays it is not straightforward to integrate Alluxio with popular query engines like Presto on existing Hive data. Solutions proposed by the community like Alluxio Catalog Service or Transparent URI brings unnecessary pressure on Alluxio masters when querying files should not be cached.
Tags: alluxio day, cache layer, hive, presto, tiktok
This whitepaper details how to leverage a public cloud, such as Amazon AWS, Google GCP, or Microsoft Azure to scale analytic workloads directly on data on-premises without copying and synchronizing the data into the cloud. We will show an example of what it might look like to run on-demand Presto and Hive with Alluxio in the public cloud using on-prem HDFS. We will also show how to set up and execute performance benchmarks in two geographically dispersed Amazon EMR clusters along with a summary of our findings.
Tags: aws, azure, data analytics, emr, gcp, hdfs, hive, hybrid cloud, presto, public cloud, zero copy
Are you using SQL engines, such as Presto, to query existing Hive data warehouse and experiencing challenges including overloaded Hive Metastore with slow and unpredictable access, unoptimized data formats and layouts such as too many small files, or lack of influence over the existing Hive system and other Hive applications?
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.
Tags: alluxio engineering, catalog service, data orchestration, hive, office hour, performance, presto, structured data services
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.
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.
This tutorial guides users to set up a stack of Presto, Alluxio and Hive Metastore on your local server, and it demonstrates how to use Alluxio as the caching layer for Presto queries.
This tutorial describes steps to set up an EMR cluster with Alluxio as a distributed caching layer for Hive, and run sample queries to access data in S3 through Alluxio.
Learn more about Bazaarvoice’s use case leveraging Apache Spark, Hive, and Alluxio on S3. Along with how to set up Hive with Alluxio so that Hive jobs can seamlessly read from/write to S3.
Tags: alluxio engineering, aws s3, compute storage separation, hdfs, hive, office hour, spark