Azure Marketplace

Overview

This page describes how to launch an Unravel server in Azure from the Azure Marketplace and have your Azure Databricks workloads monitored by Unravel.

Unravel for Azure Databricks provides Application Performance Monitoring and Operational Intelligence for Azure Databricks. It is complete monitoring, tuning, and troubleshooting tool for Spark Applications running on Azure Databricks. Currently, Unravel only supports monitoring Automated (Job) Clusters.

Important

For best results, follow the instructions below.

This topic helps you set up the below configuration.

We walk you through the following steps to get Unravel for Azure Databricks up and running via the Azure Marketplace.

Background

Unravel for Azure can monitor your Azure VM Databricks Automated clusters with the following VNET peering options. Follow the options based on your specific setup.

Databricks Workspace	Unravel VM	Region	VNET peering option
Deployed with VNET Injection	Deployed in the same VNET as Azure Workspace.	-	-
Deployed with VNET Injection	Deployed in different VNET from Azure Workspace.	Any Azure region	Create VNET Peering between the two VNETs
Deployed with Default VNET		Any Azure region	Create VNET Peering between the two VNETs

You must have the following ports open:

Important

Both ports are initially open to the public; you can restrict access as needed.

Port 4043: Open to receive traffic from the workspaces so Unravel can monitor the Automated (Job) Clusters in your workspaces,
Port 3000: Open for HTTP access to access the Unravel UI.

Step 1: Launching and setting up Unravel Server

Search Unravel for Azure Databricks in the Azure Marketplace.
1. In the Create this app in Azure modal, click Continue. You are directed to the Azure portal.
2. In the portal, click on Create to begin the Unravel Server setup.
In Home > Virtual Machine > Create step through the tabs completing the information. Keep your requirements and the background criteria in mind when completing the information.
1. In the Basics tab (default) enter the following.
  Project Details
  - Subscription: Choose the applicable subscription.
  - Resource group: Create a new group or choose an existing one.
  Instance Details
  - Region: Select the Azure region.
2. Click Next: Database > and complete the Database information. Click Next: VM Parameters >.
3. In the VM Parameters tab, enter the following information.
  - Size: Click Change Size to select your desired size.
  - Admin Username: VM admin username.
  - Password: VM admin password.
  - Data Disk Size GB: Enter your disk size.
4. Click Next: Networking >. In the Networking tab, enter the following information.
  - Public IP Address: Create an address or choose an existing one.
    Attach Public IP to Unravel VM: Set to true.
    Important
    If you set it to false, ensure you have a valid way to connect to Unravel UI on port 3000.
Click Next: Review + Create. After you have reviewed and verified your entries, click Create for your deployment to be created. Note your resource group.
Click Go to Resource. Find the details of the Unravel VM. The name of this VM should start with unravel-vm-databricks-.
Click on the VM name to bring it up in the Azure portal.
Notice
We assume you have attached a public IP address to the Unravel VM for the remainder of the configuration. If that is not the case, you can modify these instructions to suit your deployment.
Make a note of the Public and Private IP address.
Log into the Unravel server via a web browser. Enter either http:// hostname or IP of the VM:3000. Login using the credentials provided in the Message of the Day (MOTD). During the first login, data is not displayed because you have yet to configure Unravel.

Step 2: Configure Unravel Log Receiver

Stop Unravel.

<unravel_installation_directory>/unravel/manager stop

Review and update Unravel Log Receiver (LR) endpoint. By default, this is set to local FQDN, only visible to workspaces within the same network. If this is not the case, run the following to set the LR endpoint:
```
<unravel_installation_directory>/unravel/manager config databricks set-lr-endpoint <hostname> ''
```
For example: /opt/unravel/manager config databricks set-lr-endpoint <hostname> ''
After you run this command, you are prompted to specify the port number. Ensure to press ENTER and leave it empty.

Apply the changes.

<Unravel installation directory>/unravel/manager config apply
<Unravel installation directory>/unravel/manager refresh data bricks

Start all the services.

<unravel_installation_directory>/unravel/manager start

Step 3: Connect Databricks cluster to Unravel

Run the following steps to connect the Databricks cluster to Unravel.

Click Add Workspace and enter the following details.

Field	Description
Workspace Id	Databricks workspace ID, which can be found in the Databricks URL. The random numbers shown after o= in the Databricks URL become the workspace ID. For example, in this URL:https://<databricks-instance>/?o=987654321123456, the Databricks workspace ID is the random number after o=, which is 987654321123456.
Workspace Name	Databricks workspace name. A human-readable name for the workspace. For example, `ACME-Workspace`
Instance (Region) URL	Regional URL where the Databricks workspace is deployed. Specify the complete URL. For example: https://dbc-1dbx661f-a33e.cloud.databricks.com
Tier	Select a subscription option: Standard or Premium. For Databricks Azure, you can get the pricing information from the Azure portal. For Databricks AWS you can get detailed information about pricing tiers from Databricks AWS pricing.
Token	Use the personal access token to secure authentication to the Databricks REST APIs instead of passwords. You can generate the token from the workspace URL (Go to Settings > User Settings > Access Token > Generate New Token) See Authentication using Databricks personal access tokens to create personal access tokens. Note Users with admin or non-admin roles can create personal access tokens.

Note

After you click Add, it takes around 2-3 minutes to register the Databricks Workspace with Unravel.

Add Unravel configuration to Databricks clusters using any of the following options:

Global init script

Global init script applies the Unravel configurations to all clusters in a workspace. Do the following to set up Unravel configuration as Global init scripts:

On Databricks, go to Workspace > Settings > Admin Console > Global init scripts.
If you are upgrading from a previous version of Unravel, delete all the existing init scripts; unravel_cluster_init.sh, unravel_spark_init.sh.

Click +Add and set the following:

Item	Settings
Name	Enter the name as unravel_init.
Script	Copy and paste the following content in the Script box: #!/bin/bash # # Runs Unravel Init scripts COUNTER=1 while [ ! -d "/dbfs" ] && [ $COUNTER -le 20 ]; do echo "$(date) Waiting for dbfs mount: RetryCount = ${COUNTER} ....." ((COUNTER++)) sleep 0.1 done UD_ROOT=/dbfs/databricks/unravel CLUSTER_INIT=${UD_ROOT}/unravel-db-sensor-archive/dbin/unravel_cluster_init.sh SPARK_INIT=${UD_ROOT}/unravel-db-sensor-archive/dbin/unravel_spark_init.sh if [ ! -f "${CLUSTER_INIT}" ]; then echo "Unravel Cluster Init ${CLUSTER_INIT} doesn't exist!" exit 0 else cp ${CLUSTER_INIT} /tmp/ chmod a+x /tmp/unravel_cluster_init.sh /tmp/unravel_cluster_init.sh fi if [ ! -f "${SPARK_INIT}" ]; then echo "Unravel Spark Init ${SPARK_INIT} doesn't exist!" exit 0 else cp ${SPARK_INIT} /tmp/ chmod a+x /tmp/unravel_spark_init.sh /tmp/unravel_spark_init.sh fi
Enabled	Turn on the Enable toggle.

Item

Settings

Name

Enter the name as unravel_init.

Script

Copy and paste the following content in the Script box:

#!/bin/bash
#
# Runs Unravel Init scripts

COUNTER=1
while [ ! -d "/dbfs" ] && [ $COUNTER -le 20 ]; do
 echo "$(date) Waiting for dbfs mount: RetryCount = ${COUNTER} ....."
 ((COUNTER++))
 sleep 0.1
done

UD_ROOT=/dbfs/databricks/unravel
CLUSTER_INIT=${UD_ROOT}/unravel-db-sensor-archive/dbin/unravel_cluster_init.sh
SPARK_INIT=${UD_ROOT}/unravel-db-sensor-archive/dbin/unravel_spark_init.sh

if [ ! -f "${CLUSTER_INIT}" ]; then
 echo "Unravel Cluster Init ${CLUSTER_INIT} doesn't exist!"
 exit 0
else
 cp ${CLUSTER_INIT} /tmp/
 chmod a+x /tmp/unravel_cluster_init.sh
 /tmp/unravel_cluster_init.sh
fi

if [ ! -f "${SPARK_INIT}" ]; then
 echo "Unravel Spark Init ${SPARK_INIT} doesn't exist!"
 exit 0
else
 cp ${SPARK_INIT} /tmp/
 chmod a+x /tmp/unravel_spark_init.sh
 /tmp/unravel_spark_init.sh
fi

Enabled

Turn on the Enable toggle.

Click Add to save the settings.

Note

Cluster logging should be enabled at the cluster level. See Logging in Cluster init script for instructions.

Important

When you upgrade from an Unravel version below v4.7.5.0, you must disable or remove all the previously set up global init scripts (unravel_cluster_init, unravel_spark_init).

Cluster init script
Cluster init script applies the Unravel configurations at the cluster level. To setup cluster init scripts from the cluster UI, do the following:
1. Go to Unravel UI, click Manage > Workspaces > Cluster configuration to get the configuration details.
2. Follow the instructions and update each cluster (Automated /Interactive) that you want to monitor with Unravel.
  Add Unravel configuration to Databricks clusters. Go to Unravel UI and from the upper right click Manage > Workspaces > Cluster configuration to get the configuration details. Follow the instructions and update every cluster (Automated /Interactive) in your workspace.
  Tip
  By default, the Ganglia metrics are enabled with Dcom.unraveldata.agent.metrics.ganglia_enabled property set to true.
  Note
  To add Unravel configurations to job clusters via API, refer How to setup cluster init scripts via cluster API.

Set additional configurations if required.
Configure the Workspace for Data page.
Ensure that at least one of the workspaces is populated before you configure a workspace for the Data page.
To configure the Databricks for Data page, do the following:
1. Stop Unravel
```
<Unravel installation directory>/unravel/manager stop
```
2. Set the following property.
```
<Unravel installation directory>/unravel/manager config properties set hive.metastore.<X>.workspace.ids <Comma-separated list of Databricks workspaces>
```
  Replace <X> with the metastore variables listed in the com.unraveldata.hive.metastore.list property. Refer here for more details about this property.
3. Apply the changes.
```
<Unravel installation directory>/unravel/manager config apply
```
4. Start Unravel
```
<Unravel installation directory>/unravel/manager start
```
Optionally, you can run healthcheck, at this point, to verify that all the configurations and services are running successfully.
```
<unravel_installation_directory>/unravel/manager healthcheck
```
Healthcheck is run automatically on an hourly basis in the backend. You can set the healthcheck intervals and email alerts to receive the healthcheck reports.

Tip

The workspace setup can be done anytime and does not impact the running clusters or jobs.

Step 4: Configure Unravel to monitor Azure Databricks workspaces

Install DBFS CLI.

ssh to the Unravel VM and execute the following commands.

yum install https://dl.fedoraproject.org/pub/epel/epel-release-latest-7.noarch.rpm
sudo yum install python-pip
sudo pip install databricks

Configure Unravel for Azure Databricks workspaces so Unravel can monitor your Automated (Job) clusters. You can configure multiple workspaces.
1. Navigate to Workspace > User Settings > Access Tokens and click Generate New Token. Leave the token lifespan as unspecified, and then the token lives indefinitely. Make note of the token's value to use in the next step.
2. Save the Unravel agent binaries, etc. in the Workspace and update the Unravel server’s configuration with your workspace details. ssh to the Unravel VM and run the following commands.
```
/usr/local/unravel/bin/databricks_setup.sh --add-workspace -i Workspace id -n Workspace name -t Workspace token -s https:// Workspace location.azuredatabricks.net -u Unravel VM Private IP address:4043
```
Restart Unravel.
```
service unravel_all.sh restart
```

Step 5: Configure Azure Databricks Automated (Job) clusters with Unravel

Unravel supports all Databricks task types that can be run via Automated (Job) clusters, specifically.

spark-submit
Notebook
Jar
Python

You must configure Azure Databricks for each task type you want Unravel to monitor.

Important

You must complete steps 1 and 2 for all task types. The remaining steps need to complete for the specific task types.

Specify the log path, dbfs:/cluster-logs.
- UI
  In the Azure portal, navigate to Job > Configure Cluster. Under Advanced Options, click Logging. Enter the following information.
  - Destination: Select DBFS.
  - Cluster Log Path: Enter dbfs:/cluster-logs.
- API
  Using the /api/2.0/jobs/create, specify the following in the request body.
```
{
    "new_cluster": {
        "cluster_log_conf": {
                "dbfs": {
                    "destination": "dbfs:/cluster-logs"
                }
         }
    }
}
```
Specify cluster init script.
- UI
  Under Advanced Options, click Init Scripts and enter the following information.
  - Type: Select DBFS.
  - Cluster Log Path: Enter dbfs:/databricks/unravel/unravel-db-sensor-archive/dbin/install-unravel.sh.
- API
  Using the /api/2.0/jobs/create specify the following in the request body.
```
{
    "new_cluster": {
        "init_script": [
             {
                "dbfs": {
                    "destination": ""dbfs:/databricks/unravel/unravel-db-sensor-archive/dbin/install-unravel.sh"
                 }
             }
         ]
    }
}
```

For Notebook and Jar task types, add the following Spark Configuration.

Under Advanced Options click Spark , then Spark Config and enter the following:

spark.executor.extraJavaOptions -Dcom.unraveldata.client.rest.request.timeout.ms=1000 -Dcom.unraveldata.client.rest.conn.timeout.ms=1000 -javaagent:/dbfs/databricks/unravel/unravel-agent-pack-bin/btrace-agent.jar=config=executor,libs=spark-2.3
spark.driver.extraJavaOptions -Dcom.unraveldata.client.rest.request.timeout.ms=1000 -Dcom.unraveldata.client.rest.conn.timeout.ms=1000 -javaagent:/dbfs/databricks/unravel/unravel-agent-pack-bin/btrace-agent.jar=config=driver,script=StreamingProbe.btclass,libs=spark-2.3
spark.eventLog.enabled true
spark.eventLog.dir dbfs:/databricks/unravel/eventLogs/
spark.unravel.server.hostport Unravel VM Private IP Address:4043
spark.unravel.shutdown.delay.ms 300

API

Using /api/2.0/jobs/create and specify the following in the request body.

{
    "new_cluster": {
         "spark_conf": {         
            "spark.executor.extraJavaOptions": 
              "-Dcom.unraveldata.client.rest.request.timeout.ms=1000 -Dcom.unraveldata.client.rest.conn.timeout.ms=1000
               -javaagent:/dbfs/databricks/unravel/unravel-agent-pack-bin/btrace-agent.jar=config=executor,libs=spark-2.3",
           "spark.eventLog.enabled": "true",
           "spark.databricks.delta.preview.enabled": "true",
           "spark.unravel.server.hostport": "Unravel VM Private IP Address>:4043",
           "spark.driver.extraJavaOptions": 
             "-Dcom.unraveldata.client.rest.request.timeout.ms=1000
              -Dcom.unraveldata.client.rest.conn.timeout.ms=1000 
             -javaagent:/dbfs/databricks/unravel/unravel-agent-pack-bin/btrace-agent.jar=config=driver,script=StreamingProbe.btclass,libs=spark-2.3",
           "spark.eventLog.dir": "dbfs:/databricks/unravel/eventLogs/",
           "spark.unravel.shutdown.delay.ms": "300"
           },
     }
}

For spark-submit task types, add the following Spark Configuration.

Under Advanced Options , click Spark then Spark Config Configure spark-submit. Add the following along with the rest of the task parameters.

"--conf", "spark.executor.extraJavaOptions=-Dcom.unraveldata.client.rest.ssl.enabled=true -Dcom.unraveldata.ssl.insecure=true -Dcom.unraveldata.client.rest.request.timeout.ms=1000 -Dcom.unraveldata.client.rest.conn.timeout.ms=1000 -javaagent:/dbfs/databricks/unravel/unravel-agent-pack-bin/btrace-agent.jar=config=executor,libs=spark-2.3",
"--conf", "spark.eventLog.enabled=true",
"--conf", "spark.unravel.server.hostport=Unravel VM Private IP Address:4043",
"--conf", "spark.driver.extraJavaOptions=-Dcom.unraveldata.client.rest.ssl.enabled=true -Dcom.unraveldata.ssl.insecure=true -Dcom.unraveldata.client.rest.request.timeout.ms=1000 -Dcom.unraveldata.client.rest.conn.timeout.ms=1000 -javaagent:/dbfs/databricks/unravel/unravel-agent-pack-bin/btrace-agent.jar=config=driver,libs=spark-2.3",
"--conf", "spark.eventLog.dir=dbfs:/databricks/unravel/eventLogs/",
"--conf", "spark.unravel.shutdown.delay.ms=300"

API

Using /api/2.0/jobs/create and specify the following in the request body.

{
     "spark_submit_task": {
        "parameters": [
            "--conf", 
               "spark.executor.extraJavaOptions=
                -Dcom.unraveldata.client.rest.ssl.enabled=true 
                -Dcom.unraveldata.ssl.insecure=true -Dcom.unraveldata.client.rest.request.timeout.ms=1000 
                -Dcom.unraveldata.client.rest.conn.timeout.ms=1000 
                -javaagent:/dbfs/databricks/unravel/unravel-agent-pack-bin/btrace-agent.jar=config=executor,libs=spark-2.3",
            "--conf", "spark.eventLog.enabled=true",
            "--conf", "spark.unravel.server.hostport=hostname or IP of the VM:4043",
            "--conf", "spark.driver.extraJavaOptions=-Dcom.unraveldata.client.rest.ssl.enabled=true
                -Dcom.unraveldata.ssl.insecure=true -Dcom.unraveldata.client.rest.request.timeout.ms=1000
                -Dcom.unraveldata.client.rest.conn.timeout.ms=1000 -javaagent:/dbfs/databricks/unravel/unravel-agent-pack-bin/btrace-agent.jar=config=driver,libs=spark-2.3",
            "--conf", "spark.eventLog.dir=dbfs:/databricks/unravel/eventLogs/",
            "--conf", "spark.unravel.shutdown.delay.ms=300",
            ]
      }
}

Step 6: View Unravel UI with the Automated (Job) clusters

After you have configured a job with Unravel (Step 5: Configure Azure Databricks Automated (Job) clusters with Unravel), the corresponding job run is listed in the Unravel UI. Sign in to Unravel server with a web browser. Enter either http:// hostname or IP of the VM:3000. Enter the credentials provided in MOTD.

In this section:

Home

Azure Marketplace

Overview

Important

Background

Important

Step 1: Launching and setting up Unravel Server

Important

Notice

Step 2: Configure Unravel Log Receiver

Step 3: Connect Databricks cluster to Unravel

Note

Note

Note

Important

Tip

Note

Tip

Step 4: Configure Unravel to monitor Azure Databricks workspaces

Step 5: Configure Azure Databricks Automated (Job) clusters with Unravel

Important

Step 6: View Unravel UI with the Automated (Job) clusters

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