Two Ways to Keep Files in Sync Between Alluxio and HDFS

Alluxio provides a distributed data access layer for applications like Spark or Presto to access different underlying file system (or UFS) through a single API in a unified file system namespace. If users only interact with the files in the UFS through Alluxio, since Alluxio has knowledge of any changes the client makes to the UFS, it will keep Alluxio namespace in sync with the UFS namespace.

Unified Namespace and Tiered Storage in Alluxio

Strata+Hadoop World San Jose *

Calvin Jia and Jiri Simsa explain how the current Alluxio tiered storage can be easily configured to use memory, SSDs, and hard drives in different tiers. Alluxio users and administrators do not have to manually migrate the data because data in Alluxio is managed transparently between all the configured tiers, similar to the way the CPU manages L1, L2, and lower-level caches. Meanwhile, Alluxio also provides users fine-grained control of manipulating data to plug in their own data-management strategies; users can also pin files in Alluxio to a specific storage or specify a TTL to files. Calvin and Jiri also describe the interface for managing heterogeneous data sources into the Alluxio namespace, which takes advantage of Alluxio’s ability to interoperate with different underlying storage systems such as HDFS, S3, GlusterFS, or Swift.

Past, Present and Future of Alluxio [Chinese]

Nanjing Big Data Meetup *

The Alluxio project has greatly improved system performance, Scalability and user experience, and added a series of new features, including scalable tiered storage, transparent UFS data reading and writing, unified namespaces, and more. Easy to use with Alluxio. At the same time, the Alluxio ecosystem has expanded to support different storage systems and computing frameworks. Alluxio now supports a variety of storage systems, including Amazon S3, Google Cloud Storage, Gluster, Ceph, HDFS, NFS and OpenStack Swift, as well as big data processing frameworks such as Spark, MapReduce, Flink and more. These integrations allow Alluxio to manage and help with more and more complex data.

Past, Present and Future of Alluxio [Chinese]

Shanghai Meetup *

The Alluxio project has greatly improved system performance, Scalability and user experience, and added a series of new features, including scalable tiered storage, transparent UFS data reading and writing, unified namespaces, and more. Easy to use with Alluxio. At the same time, the Alluxio ecosystem has expanded to support different storage systems and computing frameworks. Alluxio now supports a variety of storage systems, including Amazon S3, Google Cloud Storage, Gluster, Ceph, HDFS, NFS and OpenStack Swift, as well as big data processing frameworks such as Spark, MapReduce, Flink and more. These integrations allow Alluxio to manage and help with more and more complex data.

Alluxio (formerly Tachyon): The journey thus far and the road ahead

Strata+Hadoop World New York *

The goal is to make Alluxio accessible to an even wider set of users through a focus on security, new language bindings, and further increased stability. In addition, the team is working on new APIs to allow applications to access data more efficiently and manage data across different under storage systems.

Alluxio: Unifying APIs, Accelerating ML, & Enabling Cloud Architectures

Bay Area Meetup *

Using intermediate APIs means developers can learn just one framework and still access features offered by different technologies. It means writing job logic only once and being able to test it easily on a new underlying service with no effort. Not only is modularity a win for users but it means creators of execution frameworks and storage systems can focus on performance and capability without having to worry about API maintenance.

How to Use Alluxio to improve Spark and Hadoop HDFS Performance of Data Access and System Reliability [Chinese]

Database Technology Conference China 2017 *

China Unicom is one of the five largest telecom operators in the world. China Unicom’s booming business in 4G and 5G networks has to serve an exploding base of hundreds of millions of smartphone users. This unprecedented growth brought enormous challenges and new requirements to the data processing infrastructure. The previous generation of its data processing system was based on IBM midrange computers, Oracle databases, and EMC storage devices. This architecture could not scale to process the amounts of data generated by the rapidly expanding number of mobile users. Even after deploying Hadoop and Greenplum database, it was still difficult to cover critical business scenarios with their varying massive data processing requirements. The complicated the architecture of its incumbent computing platform created a lot of new challenges to effectively use resources.