Running DataHub on Cumulocity IoT Edge

DataHub Edge overview

Documentation overview

The following sections will walk you through all the functionalities of Cumulocity IoT DataHub Edge in detail.

For your convenience, here is an overview of the contents:

DataHub Edge at a glance

Cumulocity IoT Edge is an onsite, single-server variant of the Cumulocity IoT Core platform. It is delivered as a software appliance designed to run on industrial PCs or local servers. DataHub is available as an add-on to Cumulocity IoT Edge.

DataHub Edge offers the same functionality as the cloud-variant of DataHub, but stores the data locally. You can define offloading pipelines, which regularly move data from the Operational Store of Cumulocity IoT into a data lake. In an Edge setup, a NAS is used as data lake. Dremio, the internal engine of DataHub, can access the data lake and run analytical queries against its contents, using SQL as the query interface.

DataHub Edge consists of the following building blocks:

• The DataHub UI is deployed as a web application in the Cumulocity IoT core. It provides the frontend for defining and managing offloading pipelines.
• The DataHub backend manages offloading pipelines and their scheduled execution. The backend and its associated database run within one Docker container in the Docker daemon. Its internal state, e.g. the defined offloading configurations, is persisted on the central data disk.
• The query processing is based on a Dremio master and a Dremio executor as well as on a Zookeeper instance. Dremio master and Zookeeper run in one Docker container and the Dremio executor runs in another one. Both containers are run by the Docker daemon. The internal state of the containers, e.g. the query job history, is persisted on the central data disk.
• The data lake is located on the central data disk.

DataHub Edge versus DataHub cloud deployments

DataHub Edge uses the same software as DataHub, though in the following aspects these two variants differ:

Setting up DataHub Edge

Prerequisites

Before setting up DataHub Edge, you have to check the following prerequisites:

Hardware requirements

The hardware requirements for running a bare Cumulocity IoT Edge instance are described in section Requirements. When DataHub Edge is additionally running, the hardware requirements of the virtual machine are as follows:

• 100 GB of free disk space plus sufficient free disk space for the data lake contents
• Intel x86 CPU
• Recommended: 12 GB RAM, minimum: 8 GB RAM
• Recommended: 4 logical CPU cores, minimum: 2 logical CPU cores
• One NIC

Hardware requirements for the host OS are excluded.

Setting up DataHub Edge

Copy the DataHub Edge archive to the Cumulocity IoT Edge.

scp datahub-<version>.tgz admin@<edge_ip_address>:/tmp

Log in as admin into Cumulocity IoT Edge.

ssh admin@<edge_ip_address>

Run the install script.

sudo /opt/c8y/utilities/install_signed_package.sh /tmp/datahub-<version>.tar

It takes a few minutes to complete the installation. After completion you can delete the DataHub Edge archive.

The install script runs the following basic steps:

• Deploy the DataHub Edge UI as a web application to Cumulocity IoT Core
• Start a Docker container with the DataHub Edge backend and the database system for managing the backend state
• Start a Docker container with the Dremio master and a ZooKeeper instance
• Start a Docker container with the Dremio executor
• Configure corresponding roles and permissions in Cumulocity IoT Core

The Docker containers will be restarted automatically if the container itself fails or the applications within are no longer reachable.

The containers are configured to store their application state on the data disk under /opt/mongodb:

• /cdh-server/data: the state of the Dremio master
• /cdh-executor/data: the state of the Dremio executor
• /cdh-console/db: the state of the DataHub Edge backend
• /cdh-server/datalake: the data lake folder

Warning: You must not modify the contents of these folders as this may corrupt your installation.

Accessing DataHub Edge

The different DataHub Edge interfaces can be accessed in the same way as in a cloud deployment of DataHub.

Info: For JDBC/ODBC you have to configure Cumulocity IoT Edge so that port 31010 can be accessed from the host system. For instructions on port forwarding see section “Setting up port forwarding” under Setting up the environment.

Defining DataHub permissions and roles

The definition and assignment of permissions and roles is done in the same way as in a cloud deployment. See section Defining DataHub permissions and roles for details.

Setting up Dremio account and data lake

The setup of the Dremio account and the data lake is done in the same way as in a cloud deployment. See section Setting up Dremio account and data lake for details.

DataHub Edge is configured to use a NAS as data lake. When configuring the NAS use as mount path /datalake. This path is mounted to /opt/mongodb/cdh-master/datalake.

Working with DataHub Edge

DataHub Edge offers the same set of functionality as the cloud variant. See section Working with DataHub for details on configuring and monitoring offloading jobs, querying offloaded Cumulocity IoT data, and refining offloaded Cumulocity IoT data.

Operating DataHub Edge

Similar to the cloud variant, DataHub Edge UI allows you to check system information and view audit logs. See the section on Operating DataHub for details.

When managing DataHub Edge, the following standard tasks are additionally relevant.

Troubleshooting the system

If problems occur, you should follow these steps:

• Perform a health check, see the section on Health check
• Check the log files, see the section on Log files
• Monitor the system processes, see the section on Monitoring

If you still need to contact product support, include the output of the diagnostics script. See the section on Diagnostics for details of how to run it.

Health check

Check DataHub Edge backend status

You can check the status of the backend in the Administration page of the DataHub UI. Alternatively you can query the isalive endpoint, which should produce an output similar to:

curl --user admin:your_password https://edge_domain_name/service/datahub/isalive

{
  "timestamp" : 1582204706844,
  "version" : {
    "versionId" : "10.6.0.0.337",
    "build" : "202002200050",
    "scmRevision" : "4ddbb70bf96eb82a2f6c5e3f32c20ff206907f43"
  }
}

If the backend cannot be reached, you will get an error response.

Check Dremio backend status

You can check the status of Dremio using the server_status endpoint:

curl http://datahub.edge_domain_name/apiv2/server_status
"OK"

Dremio is running if OK is returned. No response will be returned if it is not running or inaccessible.

Log files

The installation log file is stored at /var/log/cdh.

In order to access the logs of the DataHub and Dremio containers, you have to use the Docker logs command. To follow the logs of cdh-master you have to run:

docker logs -f cdh-master

To follow the logs of cdh-executor you have to run:

docker logs -f cdh-executor

The containers are configured to rotate log files with rotation settings of two days and a maximum file size of 10 MB.

Monitoring

Cumulocity IoT Edge uses Monit for management and monitoring of relevant processes. See the section on Monitoring for details. The DataHub Edge processes, namely the DataHub backend and the Dremio nodes, are also monitored by Monit.

Data disk management and monitoring

The data disk is used for storing the state of DataHub and Dremio and serves as data lake. In order to ensure that the system can work properly, the disk must not run out of space. The main factors for the disk space allocation of DataHub Edge are the Dremio job profiles and the data lake contents.

Cleanup of Dremio job history

Dremio maintains a history of job details and profiles, which can be inspected in Dremio’s job log, i.e. the “Jobs” page of the Dremio UI. This job history must be cleaned up regularly to free the resources necessary for storing it.

Dremio is configured to perform the cleanup of job results automatically without downtime. The default value for the maximum age of stored job results is seven days. To change that value, a Dremio administrator has to modify the support property jobs.max.age_in_days. The changes become effective within 24 hours or after restarting Dremio.

Cleanup of data lake contents

The data lake contents are not automatically purged, as the main purpose of DataHub is to maintain a history of data. However, if disk space is critical and cannot be freed otherwise, parts of the data lake contents need to be deleted.

Browse to the data lake folder /opt/mongodb/cdh-server/datalake. The data within the data lake is organized hierarchically. Delete the temporal folders you deem adequate to be deleted. After that you need to run the following query in Dremio:

ALTER PDS <deleted_folder_path> REFRESH METADATA FORCE UPDATE

Warning: Data being deleted from the data lake cannot be recovered anymore.

Backup and Restore

DataHub’s runtime state as well as the data lake containing offloaded data reside in the Cumulocity IoT Edge server VM. In order to back up and restore DataHub, its runtime state, and its data we recommend you to back up and recover the Cumulocity IoT Edge server VM as described in section Backup and restore.