DataLake

This is a potential draft of a project proposal template.  It is not final or to be used until the TSC approves it.

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Project Name

• Proposed name for the project: DataLake
• Proposed name for the repository: datalake
  

Project Goal

Build permanent storage to persist the data that flows through ONAP, and build data analytics tools on it.

Project Description

DMaaP data is read and processed by many ONAP components. DMaaP is backed by Kafka, which is a system for Publish-Subscribe, and is not suitable for data query and data analytics. Additionally, data in Kafka is not meant to be permanent and gets deleted after certain retention period. Thus it is useful to persist the data that flows through DMaaP to databases, with the following benefits:

1. Data is stored in a permanent storage for history record. DMaaP is free to set its message retention period without taking history record as a concern.

2. With database table’s schema, it is convenient to query and retrieve data.

3. For data analytics and report, accessing data from a database is easier than from DMaaP/Kafka.

In this project, we provide a systematic way to real-time ingest DMaaP data to permanent storage and provide analytics tools and applications built on the data. DataLake's goals are:

1. Provide a systematic way to real-time ingest DMaaP data to Couchbase, a distributed document-oriented database, and Druid, a data store designed for low-latency OLAP analytics.
2. Serve as a common document storage for other ONAP components as well, with easy access.
3. Provide data-access APIs and ways for ONAP components and external systems (e.g. OSS/BSS) to consume the data.
4. Provide sophisticated and ready-to-use interactive analytics GUI tools that are built on the data. Custom analytics applications are also built on the data, whose results are exposed via REST API.

Architecture

The data storage and associated tools are external infrastructures to ONAP, to be installed only once initially, or making use of existing infrastructures. Since costume setting and applications will be deployed to them, they are really integrated parts of DataLake. 

Scope

Data Sources

• Monitor all or selected Data topics, real-time read the data, and persist it.

• Other ONAP components can use DataLake as a storage to save application specific data, through DMaaP or DataLake REST APIs.

• Other data sources will be supported if needed.

Dispatcher

• Provide admin REST API for configurations and topic management. A topic can be configured to be exported to which data stores, with Couchbase and Druid supported initially, and TTL (Time To Live) in the stores. We will support more distributed databases in the future if needed.

• Provide Admin GUI to manage the dispatcher, making use of the above admin REST API. It also manages the analytics tools and applications.

Document Store

• Monitor selected topics, real-time pull the data and insert it into Couchbase, one table for each topic, with the same table name as the topic name.

• Data types JSON, XML, and YAML are auto converted into native store  schema. We may support additional formats. Data not in these formats is stored as a single string. 

• Provide REST API for data query, while applications can access the data through native API as well.

• Couchbase supports Spark direct running on it, which allow complicate analytics tools to be built. We will develop Spark analytics applications if needed.

• Other ONAP components can take advantage this to store their operational data. If we need to run heavy analytics jobs on historical data, we should separate the operational data from historical data. Otherwise we have the option to have both to coexist, due to Couchbase's scalability.

OLAP Store

• Monitor selected topics, real-time pull the data and insert it into Druid, one datasource for each topic, with the same datasource name as the topic name.

• Extracts the dimensions and metrics from JSON files, and pre-configure Druid settings for each datasource, which is customizable through a web interface.

• Integrate Apache Superset for data exploration and visualization, and provide pre-builds interactive dashboards. 

• Integrate Grafana for time series analytics.

Other Stores

• Based on future requirements, other storage may be supported. For example, if in the future we need to support unstructured data, we will consider including search engine technologies like Elastic Stack (ELK) for it.

Architecture Alignment

• How does this project fit into the rest of the ONAP Architecture?
  DataLake provides both API and UI interfaces. UI is for analyst to analysis the data, while API is for other ONAP (and external) components to query the data. For example, UUI can use the API to retrieve historical events. Some of DCAE service applications may also make use of the APIs.
  
  • What other ONAP projects does this project depend on?
    DataLake depends on DMaaP for data ingestion, also depends on some other common services: OOM, SDC, MSB.
    
    
• In Relation to Other ONAP Components
  • DCAE focuses on being a part of automated closed control loop on VNFs, storing collected data for archiving has not been covered by DCAE scope. (see ONAP wiki forum). Envision that some DCAE analytics applications may use the data in DataLake.
  • PNDA is an infrastructure that bundles a wide variety of big data technologies for data processing. Applications are to be developed on the technologies provided by PNDA. The goal of DataLake is to store DMaaP and other data, and build ready-to-use applications around the data, making use of suitable technologies, whether they are provided by PNDA. Currently Couchbase, Druid and Superset are not included in PNDA.
  • Logging project‘s data source is logs, which are unstructured, and content is at the mercy of developer who usually output only portion of the information. Besides, applications need to follow ONAP Application Logging Specification v1.2 (Casablanca).  On the other hand, DataLake's data source is from DMaaP. Since the data from DMaaP is meant to be consumed by ONAP components, as far as I know, they are all structured. Since DataLake is unintrusive, other components do not need to make any change to harvest the benefit from DataLake.
  • ONAPARC-233 , "Platform data management layer (consolidate multiple DBs)", sounds like having some overlap with DataLake's scope. But there is no detail in that proposal. Will comment when more details are revealed.
    
• How does this align with external standards/specifications?
  • APIs/Interfaces  - REST, JSON, XML, YAML
  • Information/data models - Swagger JSON
• Are there dependencies with other open source projects?
  • Couchbase
    
  • Apache Druid
  • Apache Superset
  • Grafana
  • Apache Spark
  All use Apache 2.0 License.
  

Other Information

• link to seed code (if applicable)
• Vendor Neutral
  • Yes
• Meets Board policy (including IPR)
• This proposal was presented at 2018-10-29 Dublin Architecture Planning F2F Meeting, and a JIRA ticket is created.

Use the above information to create a key project facts section on your project page

Key Project Facts

Release Components Name

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Resources committed to the Release

Note 1: No more than 5 committers per project. Balance the committers list and avoid members representing only one company. Ensure there is at least 3 companies supporting your proposal.

Note 2: It is critical to complete all the information requested, that will help to fast forward the onboarding process.