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 What is NEW! |
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| Oct 1st, 2020. Fluid v0.3.0 is RELEASED! It provides various features and bugfix, such as Data Access Acceleration For Persistent Volume and Hostpath mode in K8s, please check the CHANGELOG for details. |
Fluid is an open source Kubernetes-native Distributed Dataset Orchestrator and Accelerator for data-intesive applications, such as big data and AI applications.
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Native Support for DataSet Abstraction
Make the abilities needed by data-intensive applictions as navtive-supported functions, to achieve efficient data access and reduce the cost of multidimensional management.
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Cloud Data Warming up and Accessing Acceleration
Fluid empowers Distributed Cache Capaicty(Alluixo inside) in Kubernetes with Observability, Portability, Horizontal Scalability
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Co-Orchestration for Data and Application
During application scheduling and data placement on cloud, taking both the app's characteristics and data location into consideration, to improve the performance.
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Support Multiple Namespaces Management
User can create and manage datasets in multiple namespaces
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Support Heterogeneous Data Source Management
Unify the Data access for OSS, HDFS, CEPH and Other underlayer storages
Dataset: A set of logically related data that will be used by a computing engine, such as Spark for big data and TensorFlow for AI scenarios. The management of dataset has many metrics, has multiple dimensions, such as security, version management and data acceleration. And we hope to start with data acceleration and provide support for the management of data sets.
Runtime: Security, version management and data acceleration, and defines a series of life cycle interfaces. You can implement them.
AlluxioRuntime: From Alluixo, Fluid manages and schedules Alluxio Runtime to achieve dataset visibility, elastic scaling, and data migration. It is an engine which supports data management and caching of datasets.
- Kubernetes version > 1.14, and support CSI
- Golang 1.12+
- Helm 3
You can follow our Get Started guide to quickly start a testing Kubernetes cluster.
You can see our documentation at docs for more in-depth installation and instructions for production:
Demo 1: Accelerate Remote File Accessing with Fluid
Demo 2: Machine Learning with Fluid
Demo 3: Accelerate PVC with Fluid
Feel free to reach out if you have any questions. The maintainers of this project are reachable via:
DingTalk:
Contributions are welcome and greatly appreciated. See CONTRIBUTING.md for details on submitting patches and the contribution workflow.
Fluid is under the Apache 2.0 license. See the LICENSE file for details.