474e4b8917
Currently, README has invalid markup which will not be rendered on PyPI. This patch updates README to be rendered on PyPI and to be more readable. Change-Id: Ic081c0729e44f4e09ac21c47a5dbbd0da6deb77f
106 lines
4.3 KiB
Markdown
106 lines
4.3 KiB
Markdown
Team and repository tags
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========================
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[](https://governance.openstack.org/tc/reference/tags/index.html)
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<!-- Change things from this point on -->
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# MoNanas - Monasca Analytics Framework
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## Overview
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Monasca Analytics (MoNanas) is a statistical/machine-learning
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([SML](doc/design.md#sml)) [flow](doc/design.md#flow) composition
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engine. Users can compose a sequence of algorithms to be executed by just
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providing a description as an input to MoNanas. The data flow is automatically
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handled by the framework.
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Easy [flow](doc/design.md#flow) composition and reusability means that
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we can speed up the extraction of actionable infrastructure insight.
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### Advantages
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:thumbsup: Decouple algorithm design from execution.
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:thumbsup: Reusable specification of the desired [flow](doc/design.md#flow).
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:thumbsup: Language independent [flow](doc/design.md#flow) definition.
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:thumbsup: Data source and format independent.
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:thumbsup: Easy to add new [SML](doc/design.md#sml) algorithms and # combine them with pre-existing ones in the [flow](doc/design.md#flow).
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:thumbsup: Transparently exploit data parallelism.
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### Example Use Cases
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* [MoNanas/UseCases](doc/use_cases.md): Use cases that are relevant to OpenStack
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### Documentation
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* [MoNanas/GettingStarted](doc/getting_started.md): A starting point for users
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and developers of MoNanas.
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### Repositories
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Core: https://github.com/openstack/monasca-analytics.git
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## MoNanas Design
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See: [MoNanas/Design](doc/design.md) for details on MoNanas's architecture,
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its functional requirements and core concepts.
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## Technologies
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MoNanas uses a number of third-party technologies:
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* Apache Spark (https://spark.apache.org/): Apache Spark is a fast and general
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engine for large-scale data processing.
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* Apache Kafka (https://kafka.apache.org/): Used by Monasca and MoNanas's Kafka
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`source` and `sink`.
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* Apache ZooKeeper (https://zookeeper.apache.org/): Used by Kafka.
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## Feature Release Schedule
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- [x] Basic SML flow.
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- [x] New algorithm "add-on" ability.
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- [x] Example datasets and SML flows.
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- [ ] Support end-to-end learning + data processing flows (currently, the latter part does not get updated due to Spark's immutability.)
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- [ ] Refactor codes to be consistent with terms used in the documentation.
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- [ ] Add a source, ingestor and transformer for Monasca.
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- [ ] Model connections as objects rather than references and have driver specifics in one place.
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- [ ] Expanded orchestration abilities/expressiveness.
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- [ ] Container-enabled testing/deployment for non-production environments.
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- [ ] Add Vitrage Sink.
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- [ ] Add a ready-to-use virtual machine image (get rid of the fetch-deps.sh).
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## Contributing
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There are multiple ways to contribute to the project. All are equally important
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to us!
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* You can have a look at the
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[Monasca launchpad](https://launchpad.net/monasca) for problems that
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needs to be solved (bugs/issues), and blueprints.
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* You can also help us to add
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[new learning algorithms](doc/dev_guide.md#add_new_algorithms).
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* Finally, we are very interested in having more data sources to experiment
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with. The source can either be from an existing data provider or randomly
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generated. The more, the better! :) If you are interested to work on that
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aspect, [you are welcome as well](doc/dev_guide.md#add_new_sources).
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For more information on setting up your development environment, see
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[MoNanas/DevGuide](doc/dev_guide.md).
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For more information about Monanas, please visit the wiki page:
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[Monanas wiki](https://wiki.openstack.org/wiki/Monasca/Analytics).
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And for more information about Monasca, please visit the wiki page:
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[Monasca wiki](https://wiki.openstack.org/wiki/Monasca).
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## License
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Copyright (c) 2016 Hewlett Packard Enterprise Development Company, L.P.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not
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used this file except in compliance with the License. You may obtain a copy of
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the License at:
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```
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http://www.apache.org/licenses/LICENSE-2.0
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```
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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License for the specific language governing permissions and limitations under
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the License.
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