The Alluxio-Presto sandbox is a docker application featuring installations of MySQL, Hadoop, Hive, Presto, and Alluxio. The sandbox lets you easily dive into an interactive environment where you can explore Alluxio, run queries with Presto, and see the performance benefits of using Alluxio in a big data software stack.
Tag: <span>aws s3</span>
The purpose of Alluxio is to be an abstraction layer with storage systems underneath it. Alluxio is designed in a way that it assumes that there’s a storage layer underneath, so using it as another storage system does not solve the problem of having storage and compute co-located. Alluxio allows you to have long-running data … Continued
This article aims to provide a different approach to help connect and make distributed files systems like HDFS or cloud storage systems look like a local file system to data processing frameworks: the Alluxio POSIX API. To explain the approach better, we used the TensorFlow + Alluxio + AWS S3 stack as an example.
The AWS EMR service has made it easy for enterprises to bring up a full-featured analytical stack in the cloud that elastically scales based on demand.
The EMR service along with S3 provides a robust yet flexible platform in the cloud with the click of a few buttons, compared to the highly complex and rigid deployment approach required for on-premise Hadoop Data platforms. However, because data on AWS is typically stored in S3, an object store, you lose some of the key benefits of compute frameworks like Apache Spark and Presto that were designed for distributed file systems like HDFS.
In this white paper, we’ll share some of the challenges that arise because of the impedance mismatch between HDFS and S3, the expectations of analytics workloads of the object store, and how Alluxio with EMR addresses them.
Learn how to set up Presto with Alluxio such that Presto jobs can seamlessly read from and write to S3.
Compare the performance between Presto on S3 with Presto and Alluxio on S3.
This talk shares our design, implementation and optimization of Alluxio metadata service to address the scalability challenges, focusing on how to apply and combine techniques including tiered metadata storage (based on off-heap KV store RocksDB), fine-grained file system inode tree locking scheme, embedded state-replicate machine (based on RAFT), exploration and performance tuning in the correct RPC frameworks (thrift vs gRPC) and etc.
Traditionally, if you want to run a single Spark job on EMR, you might follow the steps: launching a cluster, running the job which reads data from storage layer like S3, performing transformations within RDD/Dataframe/Dataset, finally, sending the result back to S3. You end up having something like this.
If we add more Spark jobs across multiple clusters, you could have something like this.
Problem If you have hundreds of external tables defined in Hive, what is the easist way to change those references to point to new locations? That is a fairly normal challenge for those that want to integrate Alluxio into their stack. A typical setup that we will see is that users will have Spark-SQL or … Continued
How do we access AWS S3 data when running Presto in an on-premise environment, how can we do it efficiently to reduce both egress cost and performance runtimes? Alluxio as a local cache for Presto queries against remote AWS S3 data sources As we move toward more and more decoupled environments one of the things … Continued