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What is Alluxio

Alluxio (formerly known as Tachyon) is a virtual distributed storage system. It bridges the gap between computation frameworks and storage systems, enabling computation applications to connect to numerous storage systems through a common interface. Read more about Alluxio Overview.

The Alluxio project originated from a research project called Tachyon at AMPLab, UC Berkeley, which was the data layer of the Berkeley Data Analytics Stack (BDAS). For more details, please refer to Haoyuan Li's PhD dissertation Alluxio: A Virtual Distributed File System.

Who Uses Alluxio

Alluxio is used in production to manage Petabytes of data in many leading companies, with the largest deployment exceeding 3,000 nodes. You can find more use cases at Powered by Alluxio or visit our first community conference (Data Orchestration Summit) to learn from other community members!

Who Owns and Manages Alluxio Project

Alluxio Open Source Foundation is the owner of Alluxio project. Project operation is done by Alluxio Project Management Committee (PMC). You can checkout more details in its structure and how to join Alluxio PMC here.

Community and Events

Please use the following to reach members of the community:

Download Alluxio

Binary download

Prebuilt binaries are available to download at https://www.alluxio.io/download .

Docker

Download and start an Alluxio master and a worker. More details can be found in documentation.

# Create a network for connecting Alluxio containers
$ docker network create alluxio_nw
# Create a volume for storing ufs data
$ docker volume create ufs
# Launch the Alluxio master
$ docker run -d --net=alluxio_nw \
    -p 19999:19999 \
    --name=alluxio-master \
    -v ufs:/opt/alluxio/underFSStorage \
    alluxio/alluxio master
# Launch the Alluxio worker
$ export ALLUXIO_WORKER_RAMDISK_SIZE=1G
$ docker run -d --net=alluxio_nw \
    --shm-size=${ALLUXIO_WORKER_RAMDISK_SIZE} \
    --name=alluxio-worker \
    -v ufs:/opt/alluxio/underFSStorage \
    -e ALLUXIO_JAVA_OPTS="-Dalluxio.worker.ramdisk.size=${ALLUXIO_WORKER_RAMDISK_SIZE} -Dalluxio.master.hostname=alluxio-master" \
    alluxio/alluxio worker

MacOS Homebrew

$ brew install alluxio

Quick Start

Please follow the Guide to Get Started to run a simple example with Alluxio.

Report a Bug

To report bugs, suggest improvements, or create new feature requests, please open a Github Issue. If you are not sure whether you run into bugs or simply have general questions with respect to Alluxio, post your questions on Alluxio Slack channel.

Depend on Alluxio

Alluxio project provides several different client artifacts for external projects to depend on Alluxio client:

  • Artifact alluxio-shaded-client is recommended generally for a project to use Alluxio client. The jar of this artifact is self-contained (including all dependencies in a shaded form to prevent dependency conflicts), and thus larger than the following two artifacts.
  • Artifact alluxio-core-client-fs provides Alluxio Java file system API) to access all Alluxio-specific functionalities. This artifact is included in alluxio-shaded-client.
  • Artifact alluxio-core-client-hdfs provides HDFS-Compatible file system API. This artifact is included in alluxio-shaded-client.

Here are examples to declare the dependencies on alluxio-shaded-client using Maven:

<dependency>
  <groupId>org.alluxio</groupId>
  <artifactId>alluxio-shaded-client</artifactId>
  <version>2.6.0</version>
</dependency>

Contributing

Contributions via GitHub pull requests are gladly accepted from their original author. Along with any pull requests, please state that the contribution is your original work and that you license the work to the project under the project's open source license. Whether or not you state this explicitly, by submitting any copyrighted material via pull request, email, or other means you agree to license the material under the project's open source license and warrant that you have the legal authority to do so. For a more detailed step-by-step guide, please read how to contribute to Alluxio. For new contributor, please take two new contributor tasks.

For advanced feature requests and contributions, Alluxio core team is hosting regular online meetings with community users and developers to iterate the project in two special interest groups:

  • Alluxio and AI workloads: e.g., running Tensorflow, Pytorch on Alluxio through the POSIX API. Checkout the meeting notes
  • Alluxio and Presto workloads: e.g., running Presto on Alluxio. Checkout the meeting notes

Subscribe our public calendar to join us.

Useful Links

k8s-operator's People

Contributors

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k8s-operator's Issues

Configurable csi images

Pulling images from dockerhub can be slow and thus not the default place to store the images. Making them configurable can be beneficial to users from other regions.

consider using a single CSI to provide services for all alluxio clusters

In order to facilitate management, we are currently building a set of alluxio for a dataset. When trying the operator, we found that each alluxio cluster has an independent CSI; Although the resource overhead of a single CSI is not high, considering a large-scale training cluster, it is very common to use hundreds of datasets at the same time. Since node-plugin needs to be deployed on every machine, the cumulative resource consumption is terrible. Therefore, we can consider using a single CSI to provide services for all alluxio clusters;

[feature]Whether to consider supporting OLM management operators

Through our investigation of the k8s package management scheme, we conclude that in addition to the simple installation management of k8s by helm, it is more convenient and flexible to develop operator by using the open source OLM specification based on RedHat, or Juju based on Ubuntu. Wondering if the community has any plans to consider supporting it, I would love to have more flexible options on our stack.

When developing an operator based on OLM, you can use the operator-sdk development kit to quickly write operator business logic. kubebuilder encapsulates the client-go logic and provides a large number of flexible calling methods, making the development simple and easy.

Developed operators can publish to the operatorhub to host operator index information, or they can publish their own catalogsource installed locally as they are used.

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