While looking for ways to streamline our data pipeline, we learned about Alluxio, an open source, memory speed, virtual distributed file system. We deployed Alluxio as the shared data layer for all of the intermediate stages in the data pipeline. By reading and writing data in Alluxio, the data can be read concurrently and stay in memory for the next stage of the pipeline. This increased the performance by speeding up the entire pipeline, and increased overall throughput of the pipeline allowing us to provide interactive response to our app users.
Tencent is one of the largest technology companies in the world and a leader in multiple sectors such as social networking, gaming, e-commerce, mobile and web portal. Tencent News, one of Tencent’s many offerings, strives to create a rich, timely news application to provide users with an efficient, high-quality reading experience. To provide the best experience to more than 100 million monthly active users of Tencent News, we leverage Alluxio with Apache Spark to create a scalable, robust, and performant architecture.
Alluxio clusters act as a data access accelerator for remote data in connected storage systems. Temporarily storing data in memory, or other media near compute, accelerates access and provides local performance from remote storage. This capability is even more critical with the movement of compute applications to the cloud and data being located in object stores separate from compute. Caching is transparent to users, using read/write buffering to maintain continuity with persistent storage. Intelligent cache management utilizes configurable policies for efficient data placement and supports tiered storage for both memory and disk (SSD/HDD).
Lenovo is an Alluxio customer with a common problem and use case in the world of data analytics. They have petabytes of data in multiple data centers in different geographic locations. Analyzing it requires an ETL process to get all of the data in the right place. This is both slow, because data has to be transferred across the network, and costly because multiple copies of the data need to be stored. Freshness and quality of the data can also suffer as the data is also potentially out of date and incomplete because regulatory issues prevent certain data from being transferred.
Alluxio helps organizations handle their big data by providing a unified view of all of the data in your enterprise – on premise, in the cloud, or hybrid. Applications access data using a standard interface to a global virtual namespace. Alluxio also employs a memory-centric architecture to enable data access at memory speed. With the combined unification and performance benefits, Alluxio can effectively provide big data federation for organizations by acting as a virtual data lake.
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.
Using Alluxio, data can be shared between pipeline stages at memory speed. By reading and writing data in Alluxio, the data can stay in memory for the next stage of the pipeline, and this can greatly increase the performance. Alluxio Enterprise Edition (AEE) introduces Fast Durable Writes, a feature which enables low latency and fault-tolerant writes. In this article, we describe the Fast Durable Writes feature, and explore how Alluxio can be deployed and used with a data pipeline.
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.
In the age of growing datasets and increased computing power, deep learning has become a popular technique for AI. Deep learning models continue to improve their performance across a variety of domains, with access to more and more data, and the processing power to train larger neural networks. This rise of deep learning advances the state-of-the-art for AI, but also exposes some challenges for the access to data and storage systems. In this article, we further describe the storage challenges for deep learning workloads and how Alluxio can help to solve them.