Adit Madan

Software Engineer, Alluxio

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Getting Started with Spark Caching using Alluxio in 5 Minutes

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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.

Announcing Alluxio 2.0 Preview – enabling hyper-scale data workloads in the cloud

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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.

Asynchronous Caching in Alluxio – High Performance for Partial Read Caching for Presto and Spark

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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.

Enabling Decoupled Compute and Storage with Alluxio

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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.

Announcing the Release of Alluxio Enterprise Edition and Community Edition v1.7.0

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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.

Open Source Alluxio 1.5.0 Release Highlights

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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.

What’s new in Alluxio 1.4.0

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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

Accelerating Data Analytics on Ceph Object Storage with Alluxio

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This is an excerpt from the Accelerating Data Analytics on Ceph Object Storage with Alluxio whitepaper.
As the volume of data collected by enterprises has grown, there is a continual need to find efficient storage solutions. Owing to its simplicity, scalability and cost-efficiency object storage, including Ceph, has increasingly become a popular alternative to traditional file systems. In most cases the object storage system, on-premise or in the cloud, is decoupled from compute nodes where analytics is run. There are several benefits of this separation.

Using Alluxio to Improve the Performance and Consistency of HDFS Clusters

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Alluxio is the world’s first memory-speed virtual distributed storage system that bridges applications and underlying storage systems, providing unified data access orders of magnitudes faster than existing solutions. The Hadoop Distributed File System (HDFS) is a distributed file system for storing large volumes of data. HDFS popularized the paradigm of bringing computation to data and the co-located compute and storage architecture.
In this blog, we highlight two key benefits Alluxio brings to a compute cluster co-located with HDFS.