With this release comes the General Availability (GA) of Alluxio Structured Data Services (SDS), the subsystem of Alluxio responsible for managing and transforming structured data, such as databases, tables, and partitions.
Founding Engineer, Alluxio
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.
In a recent blog, we discussed the ideation, design and new features in Alluxio 2.0 preview. Today we are thrilled to announce another new revolutionary project that the Alluxio engineering team has been hard at work on for the past year – the Alluxio Virtual Reality (VR) client.
Apache Spark has brought significant innovation to Big Data computing, but its results are even more extraordinary when paired with Alluxio. Alluxio, provides Spark with a reliable data sharing layer, enabling Spark to excel at performing application logic while Alluxio handles storage. Bazaarvoice uses the combination of Spark and Alluxio to provide a real time big data platform that has the ability to not only handle the intake of 1.5 billion page views during peak events like Black Friday, but also provide real time analytics against it (read more). At this scale, the gain in speed is an enabler for new workloads. We’ve established a clean and simple way to integrate Alluxio and Spark.
We are thrilled and excited to announce the availability of Alluxio 2.0 Preview Release – the largest open source release with the most new features and improvements since the creation of the project. It is now available for download.
While Alluxio already enabled data locality and data accessibility for many big data workloads in the cloud, there was still innovation needed in key areas.
An Alluxio cluster caches data from connected storage systems in memory to create a data layer that can be accessed concurrently by multiple application frameworks. This greatly improves performance for many analytics workloads. On-demand caching occurs when clients read blocks of data using a ‘CACHE’ read type from persistent storage systems connected to the Alluxio cluster.
Prior to Alluxio v1.7, on-demand caching was on the critical path of read operations, requiring a full block to be read before the data was available for the application. Workloads which read partial blocks, for example SQL workloads, would be adversely affected on initial reads from connected storage.
The primary appeal of a coupled compute-storage architecture, an architecture where the computation is happening on the machines where the data resides, is the performance possible by bringing the compute engine to the data it requires; however, the costs of maintaining such tight-knit architectures are gradually overtaking the performance benefits. Especially with the popularity of cloud resources, being able to independently scale compute and storage results in large cost savings and cheaper maintenance. In addition, data has become the new oil, and all modern organizations are looking to capture as much data as possible.
We are excited to announce the release of Alluxio Enterprise Edition (AEE) and Community Edition (ACE) v1.7.0. This release brings enhanced caching policies, further ecosystem integrations, and significant usability improvements. One highlight is the Alluxio FUSE API which provides users with the ability to interact with Alluxio through a local filesystem mount. Alluxio FUSE is particularly useful for integrating with deep learning frameworks such as Tensorflow.
Alluxio 1.4.0 has been released with a large number of new features and improvements. This blog highlights some stand out aspects of the Alluxio 1.4.0 open source release: Improved Alluxio Under Storage API, Native File System REST Interface, Packet Streaming