Alluxio is an open-source data orchestration system widely used to speed up data-intensive workloads in the cloud. Alluxio v2.0 introduced Replicated Async Write to allow users to complete writes to Alluxio file system and return quickly with high application performance, while still providing users with peace of mind that data will be persisted to the chosen under storage like S3 in the background.
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.
Over the years of working in the big data and machine learning space, we frequently hear from data engineers that the biggest obstacle to extracting value from data is being able to access the data efficiently. Data silos, isolated islands of data, are often viewed by data engineers as the key culprit or public enemy №1. There have been many attempts to do away with data silos, but those attempts themselves have resulted in yet another data silo, with data lakes being one such example. Rather than attempting to eliminate data silos, we believe the right approach is to embrace them.
Notice anything new about our websites? That’s right – we are super excited to launch our new website – Alluxio.io!
As we continue our focus on our open source community, one important item on our mind was to rebuild our website to provide better user experience for our community. To that end, you’ll see lots of changes in the Alluxio web experience.
The Apache Spark + Alluxio stack is getting quite popular particularly for the unification of data access across S3 and HDFS. In addition, compute and storage are increasingly being separated causing larger latencies for queries. Alluxio is leveraged as compute-side virtual storage to improve performance. But to get the best performance, like any technology stack, you need to follow the best practices. This article provides the top 10 tips for performance tuning for real-world workloads when running Spark on Alluxio with data locality giving the most bang for the buck.
As the amount of data being collected and analyzed by Enterprises continues to grow unabated, more attention is being placed on managing the cost of storing the data relative to performance. Hadoop provides a scalable and fast way of storing and analyzing data, however, the cost of storing data in Hadoop is typically higher compared to alternative technologies like Object Stores.
From time to time, a question pops up on the user mailing list referencing job failures with the error message “java.lang.ClassNotFoundException: Class alluxio.hadoop.FileSystem not found”. This post explains the reason for the failure and the solution to the issue when it occurs.
This error indicates the Alluxio client is not available at runtime. This causes an exception when the job tries to access the Alluxio filesystem but fails to find the implementation of Alluxio client to connect to the service.
We are excited to announce Alluxio Enterprise Edition (AEE) 1.6.0 and Alluxio Community Edition (ACE) 1.6.0 releases. The AEE release brings a new embedded journal as well as enhancements in the areas of Security and Fast Durable Write. In addition, both the AEE and the ACE releases bring new clients support (Amazon S3 API and Python Client), major usability improvements as well as enhanced integrations with the ecosystem.
Open source Alluxio 1.5.0 has been released with a large number of new features and improvements. Alluxio allows any application to access data from any storage system transparently and at memory speed. Interoperability with other technologies in the ecosystem is an important step for enabling this, and in the 1.5.0 release, we have improved the accessibility of Alluxio in several key ways.